Charging method and vehicle

Abstract

The application provides a charging method and a vehicle, and relates to the technical field of batteries. The charging method comprises the following steps: when it is detected that the vehicle is in a charging state, acquiring a charging parameter of a battery in the vehicle and a first voltage curve, the first voltage curve being used for indicating a change rate of an end voltage of the battery with respect to a charged electric quantity; obtaining a lithium precipitation risk coefficient of the battery based on parameters of a target peak in the first voltage curve, the lithium precipitation risk coefficient of the battery being used for indicating a risk coefficient of a lithium precipitation phenomenon of the battery when the battery is charged by using the charging parameter; and adjusting the charging parameter of the vehicle based on the lithium precipitation risk coefficient. The charging method can improve the safety of charging of the vehicle when the vehicle is charged.

Description

Technical Field

[0001] This application relates to the field of battery technology, and more specifically, to a charging method and vehicle in the field of battery technology. Background Technology

[0002] With the rapid development of the new energy vehicle industry, power battery charging technology has become a research hotspot. During the charging of lithium-ion batteries, lithium plating may occur (i.e., lithium ions cannot be intercalated into the graphite layers of the negative electrode in time, and instead deposit as metallic lithium on the surface of the negative electrode). Lithium plating leads to irreversible capacity decay and a continuous increase in internal resistance. In severe cases, it may cause internal short circuits and thermal runaway, resulting in safety accidents such as fires and explosions. However, current technologies cannot accurately detect lithium plating, leading to the use of fixed current-limiting charging methods, which results in low charging safety.

[0003] Therefore, improving the safety of vehicle charging is an urgent problem that needs to be solved. Summary of the Invention

[0004] This application provides a charging method and a vehicle that improves the safety of vehicle charging.

[0005] Firstly, a charging method is provided, the charging method comprising: When the vehicle is detected to be in a charging state, the charging parameters and first voltage curve of the battery in the vehicle are obtained. The first voltage curve is used to indicate the rate of change of the battery terminal voltage with the amount of charge. The first voltage curve is obtained based on the battery state parameters, which include the battery voltage parameters and the amount of charge. Based on the parameters of the target peak in the first voltage curve, the lithium plating risk coefficient of the battery is obtained. The lithium plating risk coefficient of the battery is used to indicate the risk coefficient of lithium plating when the battery is charged with charging parameters. Based on the lithium plating risk factor, adjust the vehicle's charging parameters.

[0006] In the embodiments of this application, the charging parameters of the vehicle and a first voltage curve are obtained. Since the parameters of the target peak in the first voltage curve can reflect the changes in the electrochemical state inside the battery, a lithium plating risk coefficient is obtained based on the parameters of the target peak in the first voltage curve. This allows for real-time quantification of the risk of lithium plating during charging, avoiding the inability to accurately determine the lithium plating risk during charging in existing technologies. This prevents the setting of reasonable charging parameters when the lithium plating risk is high, resulting in low vehicle charging safety. Therefore, this solution can determine the lithium plating risk coefficient based on the parameters of the target peak and adjust the charging parameters accordingly, thereby improving the safety of vehicle charging.

[0007] In conjunction with the first aspect, among some possible implementations, the charging method also includes: Obtain a first parameter and a second parameter. The first parameter is used to indicate the voltage recovery speed of the battery after the charging current is interrupted. The second parameter includes the temperature difference of multiple cells in the battery and the heating rate of the first cell among the multiple cells. The first cell is used to represent the cell with the lowest temperature among the multiple cells. Based on the parameters of the target peak in the first voltage curve, the lithium plating risk coefficient of the battery is obtained, including: Based on the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter, the lithium plating risk coefficient of the battery is obtained. Among them, the parameters of the target peak, the first parameter, and the second parameter in the first voltage curve are positively correlated with the lithium plating risk coefficient.

[0008] In the embodiments of this application, compared to the prior art where the charging strategy relies on an offline calibrated static mapping table, typically only using static parameters such as battery temperature to look up current limits to obtain vehicle charging parameters, this approach fails to detect real-time changes in the battery's internal electrochemical state during charging. This results in the charging strategy being unable to accurately determine the lithium plating risk and unable to reasonably adjust the current, leading to low vehicle charging safety. This solution determines the lithium plating risk coefficient based on the parameters of the target peak in the first voltage curve, the battery's voltage recovery speed after a charging current interruption, and the temperature distribution characteristics between battery cells. This allows for accurate determination of the lithium plating risk coefficient from multiple dimensions, enabling subsequent reasonable adjustments to the charging parameters based on the lithium plating risk coefficient, thereby improving vehicle charging safety.

[0009] Combining the first aspect and the above implementation methods, in some possible implementation methods, the lithium plating risk coefficient of the battery is obtained based on the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter, including: The parameters of the target peak, the first parameter, and the second parameter in the first voltage curve are normalized to obtain the normalized parameters of the target peak, the normalized first parameter, and the normalized second parameter in the first voltage curve. The lithium plating risk coefficient is obtained by weighting the parameters of the target peak in the normalized first voltage curve, the normalized first parameter, and the normalized second parameter.

[0010] In the embodiments of this application, compared to existing technologies where charging strategies rely on offline calibrated static mapping tables, typically relying solely on static parameters such as battery temperature to look up current limits for vehicle charging parameters, this approach fails to detect real-time changes in the battery's internal electrochemical state during charging. This results in inaccurate determination of lithium plating risk and an inability to reasonably adjust the current, leading to low vehicle charging safety. This solution determines the lithium plating risk coefficient based on parameters of the target peak in the first voltage curve, the battery's voltage recovery speed after charging current interruption, and the temperature distribution characteristics between battery cells. This allows for accurate determination of the lithium plating risk coefficient from multiple dimensions. Furthermore, by normalizing and weightedly fusing the three types of parameters to obtain the lithium plating risk coefficient, different characteristic parameters can be evaluated on the same scale, effectively fusing lithium plating risk information from various dimensions and further improving the accuracy and reliability of lithium plating risk quantification. Therefore, when subsequent adjustments to charging parameters are made based on this lithium plating risk coefficient, vehicle charging safety is improved.

[0011] In combination with the first aspect and the above implementation methods, in some possible implementations, the charging method also includes: Based on the battery's current information, detect whether there is a current interruption in the battery; When a current interruption is detected, the voltage parameter corresponding to the first preset time after the current interruption is obtained, and the first parameter is obtained based on the voltage parameter corresponding to the first preset time. If no current interruption is detected, the first parameter is set to the preset minimum value.

[0012] In the embodiments of this application, the battery current information is monitored in real time to detect whether a current interruption occurs. When a current interruption is detected, the voltage parameter within a first preset time after the current interruption is obtained, and a first parameter characterizing the voltage recovery speed of the battery after the current interruption is obtained based on this voltage parameter. When no current interruption is detected, the first parameter is determined to be a preset minimum value. Since the voltage recovery process after a current interruption can reflect the dynamic changes in the internal electrochemical state of the battery, and this recovery process is closely related to the risk of lithium plating, by detecting the current interruption and performing voltage recovery analysis when it occurs, information reflecting the risk of lithium plating can be obtained from the voltage recovery characteristics without adding additional hardware, providing a computational basis for the subsequent determination of the lithium plating risk coefficient. Therefore, this solution can improve the reliability of the determination of the lithium plating risk coefficient and further improve the safety of vehicle charging.

[0013] Combining the first aspect and the above implementation methods, in some possible implementation methods, the first parameter is obtained based on the voltage parameter corresponding to the first preset time, including: The voltage parameters corresponding to the first preset time are fitted to obtain the target time constant, which is used to represent the voltage recovery speed of the battery after the current interruption phenomenon. The first parameter is obtained based on the ratio of the target time constant to the preset time constant.

[0014] In the embodiments of this application, since the voltage recovery process of the battery after current interruption follows a specific law, the target time constant obtained by fitting the voltage parameters within the first preset time period can quantify the speed of the voltage recovery process, thereby indirectly reflecting the changes in the electrochemical state inside the battery; by calculating the ratio of the target time constant to the preset time constant, the first parameter is obtained, which can accurately reflect the changing trend of lithium plating risk during the current charging process, providing reliable parameter support for the subsequent assessment of lithium plating risk coefficient, thereby further improving the safety of vehicle charging.

[0015] Combining the first aspect and the above implementation methods, in some possible implementation methods, the second parameter includes a first target parameter, which represents the heating rate of the first cell among multiple cells, and the charging method further includes: Based on the temperature parameters of multiple cells in the battery, the first cell among the multiple cells is obtained; Obtain the target temperature rise rate of the first battery cell within a second preset time period; The average temperature rise rate of multiple battery cells is obtained within a second preset time period; The first target parameter is obtained based on the ratio of the target temperature rise rate to the average temperature rise rate.

[0016] In the embodiments of this application, since the cell with the lowest temperature (the first cell) is the cell with poor lithium-ion diffusion effect in the battery pack and is relatively prone to lithium plating, the deviation of the temperature rise rate of the first cell from the average temperature rise rate can reflect whether the cell has an abnormal temperature rise due to the heat generated by lithium plating. Furthermore, by calculating the ratio of the target temperature rise rate to the average temperature rise rate, the lithium plating risk trend under the temperature dimension can be accurately reflected, providing reliable data support for the comprehensive assessment of the subsequent lithium plating risk coefficient, thereby further improving the safety of vehicle charging.

[0017] Combining the first aspect and the above implementation methods, in some possible implementation methods, the second parameter includes a second target parameter, which represents the temperature difference among multiple cells in the battery. The charging method further includes: Obtain the maximum and minimum temperature values ​​of multiple battery cells; The target temperature difference is obtained based on the maximum and minimum temperature values; The second target parameter is obtained based on the ratio of the target temperature difference to the preset temperature difference.

[0018] In the embodiments of this application, the maximum and minimum temperatures of multiple cells in the battery are obtained, a target temperature difference is obtained based on the maximum and minimum temperatures, and a second target parameter is obtained based on the ratio of the target temperature difference to a preset temperature difference. Since the temperature difference between the cells within the battery reflects the overall temperature difference of the battery, a large temperature difference indicates poor lithium-ion diffusion in the low-temperature region, leading to a higher risk of lithium plating. Therefore, the target temperature difference can serve as an indicator of lithium plating risk. Furthermore, by calculating the ratio of the target temperature difference to the preset temperature difference, the degree of difference between the target temperature difference and the reference value can be determined, thereby accurately reflecting the trend of lithium plating risk under temperature unevenness. Therefore, this solution can improve the accuracy of determining the lithium plating risk coefficient and further enhance the safety of vehicle charging.

[0019] In combination with the first aspect and the above implementation methods, in some possible implementations, the charging method also includes: When the battery is in a preset charge range, the amplitude and width of multiple peaks in the first voltage curve are obtained; When the amplitude of the first peak exceeds the preset amplitude threshold and the width of the first peak is within the preset width range, the first peak is determined as the target peak. The first peak is used to represent any one of the multiple peaks.

[0020] In the embodiments of this application, when the battery is in a preset charge range, the amplitude and width of multiple peaks in the first voltage curve are acquired. When the amplitude of a certain peak exceeds a preset amplitude threshold and the width of the peak is within a preset width range, the peak is identified as the target peak. Since the lithium plating characteristic peaks have specific amplitude and width characteristics in the first voltage curve, by setting the amplitude threshold and width range for judgment, characteristic peaks related to the lithium plating phenomenon can be accurately identified, avoiding the problem of misjudgment of target peaks caused by noise interference or other peaks in the electrochemical process, thus improving the accuracy of lithium plating risk assessment. Furthermore, peak detection within the preset charge range can eliminate the interference of characteristic peaks generated by other electrochemical processes in non-lithium plating sensitive ranges on the detection results, enabling the lithium plating risk coefficient to be calculated based on accurate lithium plating characteristic peak parameters, further improving the safety of vehicle charging.

[0021] Combining the first aspect and the above implementation methods, in some possible implementation methods, the vehicle's charging parameters are adjusted based on the lithium plating risk coefficient, including: When the lithium plating risk coefficient is less than the first preset threshold, the vehicle's charging parameters are adjusted based on the preset current value. When the lithium plating risk coefficient is greater than or equal to the first preset threshold, the vehicle's charging parameters are adjusted based on the lithium plating risk, preset weight, and preset current value.

[0022] In the embodiments of this application, when the lithium plating risk coefficient is less than a first preset threshold, the vehicle's charging parameters are adjusted based on a preset current value so that the vehicle can be charged with a preset maximum charging current when the lithium plating risk is low, thus ensuring charging efficiency; when the lithium plating risk coefficient is greater than or equal to the first preset threshold, the vehicle's charging parameters are adjusted based on the lithium plating risk coefficient, a preset weight, and a preset current value so that the charging current can be dynamically adjusted according to the change of the lithium plating risk coefficient. Compared to existing technologies that rely on offline calibrated static mapping tables for charging strategies, which typically only look up current limits based on static parameters such as battery temperature to obtain vehicle charging parameters, this solution cannot detect changes in the battery's internal electrochemical state in real time during charging. This results in the charging strategy being unable to accurately determine the risk of lithium plating and cannot reasonably adjust the current, leading to low vehicle charging safety. This solution can continuously adjust the charging current based on the real-time determined lithium plating risk coefficient, avoiding the impact of sudden current changes on the circuit and battery, and improving the stability of the charging process. In addition, since the current adjustment range is related to the lithium plating risk coefficient, the charging current can be reduced in time to ensure safety when the risk of lithium plating increases. Therefore, this solution can balance charging efficiency and safety when charging vehicles, improving the stability of the charging process and the user experience.

[0023] Secondly, a charging device is provided, the charging device comprising: The acquisition module is used to acquire the charging parameters and a first voltage curve of the battery in the vehicle when the vehicle is detected to be in a charging state. The first voltage curve is used to indicate the rate of change of the battery terminal voltage with the amount of charge. The first voltage curve is obtained based on the battery state parameters, which include the battery voltage parameters and the amount of charge. The processing module is used to obtain the lithium plating risk coefficient of the battery based on the parameters of the target peak in the first voltage curve. The lithium plating risk coefficient of the battery is used to indicate the risk factor of lithium plating when the battery is charged with charging parameters. Based on the lithium plating risk coefficient, the charging parameters of the vehicle are adjusted.

[0024] It should be understood that the extensions, limitations, explanations and descriptions of the relevant content in the first aspect above also apply to the same content in the second aspect.

[0025] Thirdly, a vehicle is provided, including a memory and a processor; the memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the charging method in the first aspect or any possible implementation thereof.

[0026] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the charging method described in the first aspect or any possible implementation thereof.

[0027] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the charging method described in the first aspect or any possible implementation thereof. Attached Figure Description

[0028] Figure 1 This is a schematic flowchart of a charging method provided in an embodiment of this application; Figure 2 This is a schematic flowchart of another charging method provided in an embodiment of this application; Figure 3 This is a schematic flowchart illustrating another charging method provided in an embodiment of this application; Figure 4 This is a schematic flowchart illustrating another charging method provided in an embodiment of this application; Figure 5 This is a schematic flowchart illustrating another charging method provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of a charging device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0030] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0031] Before introducing the methods of the embodiments of this application, the technical terms that may be involved in the embodiments of this application will be explained first.

[0032] Lithium deposition phenomenon: also known as lithium precipitation, lithium metal deposition, or lithium dendrite growth, is an electrochemical phenomenon in which lithium ions cannot be inserted into the graphite layers of the negative electrode in time during charging and are deposited on the surface of the negative electrode in the form of metallic lithium.

[0033] For example, lithium plating can manifest as grayish-white or silvery-gray metallic lithium deposits on the negative electrode surface. In severe cases, it can form dendritic structures, piercing the separator and causing internal short circuits and thermal runaway.

[0034] Differential voltage curve: also known as voltage differential curve or differential voltage curve, it is used to represent the differential relationship between the battery terminal voltage and the charged amount of electricity, reflecting the stage-by-stage changes in the internal electrochemical reaction of the battery as the charging process progresses.

[0035] For example, the differential voltage curve can be obtained by numerically differentiating the voltage-charge data collected during the charging process. When lithium plating occurs, the curve will show an abnormal characteristic peak in a specific state of charge range. The amplitude and area of ​​the characteristic peak can reflect the severity of lithium plating.

[0036] Relaxation curve: also known as voltage relaxation curve, voltage recovery curve, or open-circuit voltage recovery curve, is used to represent the change curve of the battery terminal voltage gradually recovering to the open-circuit voltage over time after the charging current is interrupted or the current jumps.

[0037] For example, the relaxation curve can be obtained by continuously collecting the battery terminal voltage within a first preset time after the current is interrupted. Under normal circumstances, it exhibits a specific exponential decay pattern. When lithium plating occurs, the metallic lithium is partially re-embedded into the negative electrode during the relaxation period, causing the relaxation curve to show an abnormal plateau or abrupt change in slope.

[0038] Temperature rise rate: also known as temperature rise rate, heating rate, or temperature rise slope, is used to express the amount of temperature change of the battery cell per unit time, reflecting how quickly the battery cell heats up during the charging process.

[0039] For example, the temperature rise rate can be obtained by collecting the surface temperature of the battery cell within a preset time window and calculating the ratio of the temperature change to the time interval. The difference in temperature rise rate between each battery cell can be used to determine whether there is local abnormal heating.

[0040] With the rapid development of the new energy vehicle industry, battery charging technology has become a research hotspot. Within this field, lithium plating is one of the core technological bottlenecks restricting the improvement of fast charging speeds and threatening battery safety. Lithium plating refers to the phenomenon where, during charging, lithium ions cannot be timely embedded between the graphite layers of the negative electrode and instead deposit as metallic lithium on the surface. Lithium plating leads to irreversible degradation of the battery's usable capacity and a continuous increase in internal resistance. In severe cases, lithium dendrites may pierce the separator, causing internal short circuits and thermal runaway, ultimately resulting in safety accidents such as fires and explosions.

[0041] To prevent lithium plating, existing technologies typically employ a static lookup table-based method for limiting charging current. Specifically, during battery development, the maximum allowable charging current value under different temperature combinations is experimentally calibrated to obtain a mapping table. When the vehicle is charging, the battery management system queries this mapping table based on the current battery temperature to obtain the current limit value, and controls the output current of the charging station based on this current limit value.

[0042] However, this static table lookup strategy has the following problems in practical applications: Question 1: Since the mapping table needs to cover all possible battery state combinations, its design must be based on the worst conditions, such as considering the individual cells with the worst consistency. This results in the charging current being excessively limited under most normal conditions, and the charging time being unnecessarily extended.

[0043] For example, when the temperature range is suitable and the state of charge is within the fast charging window (e.g., between 20% and 60%), the charging strategy typically limits the current to 60% to 70% of the battery's theoretical safe limit, failing to fully utilize the battery's charging capacity.

[0044] Question 2: The mapping table is essentially an empirical mapping based on offline experimental data, and it cannot detect in real time whether lithium plating is actually happening or about to happen during the current charging process. In actual charging, the risk of lithium plating in the battery is affected by factors that are difficult to pre-calibrate, such as the thermal history before charging, temperature non-uniformity between cells, and degradation of the battery's microstructure. The static mapping table cannot cover these dynamic changes, which means that under implicit high-risk conditions such as low-temperature start-up, large cell temperature gradients, and late-stage battery aging, the current allowed by the mapping table may exceed the actual safe tolerance of the battery.

[0045] Question 3: When changes in conditions such as temperature cause the lookup interval to switch, the current limiting value will change abruptly. This sudden change will cause power fluctuations at the charging pile end and abrupt changes in the battery voltage end, which will have an adverse effect on battery consistency and the stability of the DC bus of the charging pile.

[0046] Question 4: Existing technologies neglect electrochemical signals during the charging process. The dynamic response of battery terminal voltage and current during charging includes various electrochemical state information. For example, changes in the shape of the differential voltage curve can indicate the occurrence of lithium plating, and the voltage relaxation characteristics after charging can reflect the degree of lithium plating. However, existing strategies ignore these available electrochemical signals, making it impossible to adjust the current limiting strategy according to the real-time lithium plating risk. This results in the problem of excessive current limiting affecting charging speed under low risk and insufficient current limiting affecting battery safety under high risk.

[0047] In view of this, this application provides a charging method and vehicle. Compared with the prior art, where the charging strategy relies on an offline calibrated static mapping table, and typically only uses static parameters such as battery temperature to look up the current limit to obtain the vehicle's charging parameters, this method cannot detect changes in the battery's internal electrochemical state in real time during charging. This results in the charging strategy being unable to accurately determine the risk of lithium plating and being unable to reasonably adjust the current, leading to low vehicle charging safety. This solution obtains the vehicle's charging parameters and battery state parameters, including the battery's voltage and capacity parameters. By analyzing the voltage and capacity parameters, a first voltage curve is constructed that reflects the rate of change of battery terminal voltage with the amount of charge. Since the parameters of the target peak in the first voltage curve can reflect changes in the battery's internal electrochemical state, a lithium plating risk coefficient is obtained based on the parameters of the target peak in the first voltage curve. This allows for real-time quantification of the risk of lithium plating during charging, avoiding the inability to accurately determine the lithium plating risk during charging in the prior art, which leads to the inability to set reasonable charging parameters when the lithium plating risk is high, resulting in low vehicle charging safety. Therefore, this solution can determine the lithium plating risk coefficient based on the parameters of the target peak, and adjust the charging parameters according to the lithium plating risk coefficient, thereby improving the safety of vehicle charging.

[0048] The following is combined Figure 1 A charging method provided in the embodiments of this application will be described in detail.

[0049] Figure 1 This is a schematic flowchart illustrating a charging method provided in an embodiment of this application. Figure 1 As shown, method 100 includes S101 to S103, which are described in detail below.

[0050] For example, Figure 1 The method 100 shown can be executed by the vehicle; or by the vehicle's Battery Management System (BMS); or by the processor in the vehicle; or by a chip in the processor of the vehicle; or by a software platform integrated in the vehicle.

[0051] S101, when the vehicle is detected to be in a charging state, the charging parameters and first voltage curve of the battery in the vehicle are acquired.

[0052] The first voltage curve is used to indicate the rate of change of the battery's terminal voltage with the amount of charge. The first voltage curve is obtained based on the battery state parameters, which include the battery's voltage parameters and charge parameters.

[0053] It should be noted that the first voltage curve can also be called the differential voltage curve, which is used to represent the differential relationship between the battery terminal voltage and the charged capacity.

[0054] It should be understood that battery state parameters are a set of parameters used to represent the current operating state and / or internal physicochemical properties of the battery, and may include at least one of voltage parameters, charge parameters, current parameters and temperature parameters. These parameters can be acquired in real time through the data acquisition module in the battery management system at a preset frequency.

[0055] It should be understood that the vehicle's charging parameters are key electrical parameters used to represent the vehicle's charging behavior during the charging process. These parameters may include target current value, target voltage value, charging mode (e.g., constant current charging mode or constant voltage charging mode), etc. The charging parameters are dynamically determined by the battery management system based on the current battery state and sent to the charging device for execution.

[0056] Optionally, the vehicle's charging parameters can be determined and executed by the battery management system to control the vehicle's charging process.

[0057] For example, whether a vehicle is charging can be identified by detecting the connection signal of the charging device, the wake-up signal provided by the charging device, or the rising trend of the battery terminal voltage of the vehicle.

[0058] In the embodiments of this application, when a physical connection is established between the charging device and the vehicle and a wake-up signal is received from the charging pile, it is determined that the vehicle is in a charging state. Data acquisition is initiated, and the voltage, charge, current, and temperature parameters of the battery are acquired in real time at a preset frequency through a data acquisition module (e.g., voltage sampling circuit, current sensor, temperature sensor, etc.). After acquiring the voltage and charge parameters of the battery, the voltage change and charge change within the preset sampling window (e.g., a window length of 100ms, i.e., the data within the latest 100ms is used as the analysis object) are acquired. The ratio of the two is calculated as the instantaneous differential voltage value. Multiple instantaneous differential voltage values ​​are continuously calculated. With the charged charge as the horizontal axis and the differential voltage value as the vertical axis, a first voltage curve of the battery during this charging process is constructed. The horizontal axis of this curve is the cumulative charged charge, and the vertical axis is the rate of change of the battery terminal voltage with the charged charge. The obtained first voltage curve and charging parameters are used for subsequent calculation of the lithium plating risk coefficient and adjustment of charging parameters.

[0059] For example, upon detecting a charging wake-up signal and determining that the vehicle has entered the charging state, the battery management system then collects parameters such as battery voltage, current, charge, and temperature at a preset frequency (e.g., 10Hz).

[0060] For example, battery state parameters can be acquired at different sampling frequencies. For instance, voltage and current parameters can be acquired at a frequency of 10Hz to ensure that transient changes during charging can be captured; current parameters can be acquired at a frequency of 10Hz; capacity parameters are calculated in real time by integrating the current parameters in ampere-hours; and temperature parameters can be acquired at a frequency of not less than 1Hz.

[0061] Optionally, when acquiring voltage parameters, the individual cell voltage of each cell in the battery pack can be acquired using a voltage acquisition chip.

[0062] Alternatively, current parameters can be acquired using a current sensor (e.g., a Hall sensor or a shunt).

[0063] Optionally, when acquiring temperature parameters, the surface temperature of the cells in each region can be collected by multiple negative temperature coefficient thermistors arranged in the battery pack.

[0064] In one embodiment, parameters such as battery voltage, current, and temperature are collected, and the battery's capacity parameters are calculated based on the collected data.

[0065] For example, during the charging process, assuming a sampling window of 100ms is used to obtain the voltage change ΔV and the charge change ΔQ within the window, dV / dQ = ΔV / ΔQ is calculated; where dV / dQ is used to represent the instantaneous differential voltage value.

[0066] For example, assuming the charging process reaches the 300th second, taking the data within a 100ms window (i.e., from the 299.9th to the 300th second) as an example, the voltage changes from 3.850V to 3.856V (a change of 6mV), and the charge changes from 39.98Ah to 40.02Ah (a change of 0.04Ah). The instantaneous differential voltage value is calculated as dV / dQ = 6mV / 0.04Ah = 150mV / Ah. This data point (40Ah on the x-axis, 150mV / Ah on the y-axis) is recorded as a point on the first voltage curve. As charging continues, the above calculation process is repeated every 100ms, and the resulting continuous data points are connected in chronological order to construct the first voltage curve.

[0067] In one embodiment, after obtaining multiple instantaneous differential voltage values, before constructing the first voltage curve, in order to accurately identify the characteristic peaks related to lithium plating (i.e., the target peaks in S102) during peak detection, the multiple instantaneous differential voltage values ​​can be smoothed and filtered. This can remove noise while preserving the peak shape characteristics of the differential voltage curve, avoiding the attenuation of characteristic peak amplitude or peak position shift due to excessive smoothing.

[0068] Alternatively, filtering methods can be used to smooth multiple instantaneous differential voltage values.

[0069] For example, the window length is set to 21 data points and the polynomial order is set to 3.

[0070] In another implementation, the process of obtaining the first voltage curve in S101 can be replaced by obtaining the target charge curve, with the terminal voltage as the horizontal axis and the derivative of the charge amount with respect to the terminal voltage (i.e., dQ / dV value) as the vertical axis.

[0071] It should be understood that the target charge curve is used to represent the differential relationship between the charged charge and the battery terminal voltage, and is the reciprocal form of the dV / dQ curve.

[0072] In another implementation, a target charge curve can be constructed based on the first voltage curve; the risk of lithium plating can be detected by combining the first voltage curve and the target charge curve.

[0073] It should be noted that for some battery systems, the newly added characteristic peaks on the dV / dQ curve are more significant, while for others, the abnormal decay of existing peaks on the dQ / dV curve is more pronounced. By combining the detection results of both curves, the applicability and robustness of lithium plating risk detection can be improved.

[0074] S102, based on the parameters of the target peak in the first voltage curve, obtain the lithium plating risk coefficient of the battery.

[0075] Among them, the lithium plating risk coefficient of the battery is used to indicate the risk coefficient of lithium plating in the battery when it is charged by charging parameters; the lithium plating risk coefficient is used to characterize the degree of risk of lithium plating in the battery during the current charging process.

[0076] It should be understood that the target peak can also be called the lithium plating characteristic peak, which refers to the characteristic peak in the first voltage curve that is related to the lithium plating phenomenon. The parameters of the target peak include the peak value and the area of ​​the target peak. The appearance and morphological parameters of the target peak (e.g., amplitude and area) can be used as the basis for judging whether the lithium plating phenomenon has occurred and the severity of the lithium plating phenomenon.

[0077] In one implementation, the process of obtaining the first voltage curve in S101 can be replaced by: obtaining a target charge curve, which uses the terminal voltage as the abscissa and the derivative of the charge amount with respect to the terminal voltage (i.e., dQ / dV value) as the ordinate. In this case, S102 can be replaced by: obtaining the lithium plating risk coefficient of the battery based on the parameters of the target peak in the target charge curve.

[0078] For example, after constructing the target charge curve, peak detection is performed on the curve within a preset state of charge range to extract the parameters of the target peak (e.g., the change in peak height, the shift in peak position, or the change in peak area). The lithium plating risk coefficient is determined based on the degree of change of the parameters of the target peak relative to the preset reference value.

[0079] It should be understood that since the dQ / dV curve is the reciprocal of the dV / dQ curve, lithium plating in the dQ / dV curve is usually not manifested as the appearance of a new peak, but rather as a change in the shape of an existing peak (such as a decrease in peak height, a shift in peak position, or a reduction in peak area). Therefore, the parameters of the target peak can include at least one of the changes in peak height, peak position, and peak area. These parameters are negatively correlated with the lithium plating risk coefficient.

[0080] In another implementation, S101 can further obtain a target charge curve based on the first voltage curve; and combine the first voltage curve and the target charge curve to comprehensively detect the risk of lithium plating.

[0081] It should be understood that although the dV / dQ curve and the dQ / dV curve are reciprocals of each other, the lithium plating characteristics may appear differently on the two curves in different battery systems. Therefore, S102 can be replaced by: obtaining the lithium plating risk coefficient of the battery based on the first peak parameter of the target peak in the first voltage curve and the second peak parameter of the target peak in the target charge curve.

[0082] For example, peak detection is performed on the first voltage curve and the target charge curve respectively. The first parameter (e.g., amplitude and area) of the lithium plating characteristic peak is extracted from the first voltage curve, and the second parameter (e.g., peak height change, peak position shift, and peak area change) of the target peak is extracted from the target charge curve. The first parameter and the second parameter are normalized and then weighted and fused according to a preset weight to obtain the lithium plating risk coefficient.

[0083] For example, if a new characteristic peak (with an amplitude exceeding a preset proportional threshold of the dynamic baseline value) is detected in the first voltage curve and the peak height and peak area of ​​the corresponding position in the target charge curve decrease, the lithium plating risk coefficient can be determined with a high degree of confidence. If an abnormal signal is detected in only one curve, the weight of this dimension in the weighted fusion can be reduced or a re-detection can be triggered to avoid misjudgment due to a single detection method.

[0084] In one implementation, the charging method further includes: When the battery is in a preset charge range, the amplitude and width of multiple peaks in the first voltage curve are obtained; When the amplitude of the first peak exceeds the preset amplitude threshold and the width of the first peak is within the preset width range, the first peak is determined as the target peak. The first peak is used to represent any one of the multiple peaks.

[0085] The preset charge range refers to a pre-defined range of charge states, which is used to represent the charge state region where lithium plating characteristic peaks may appear.

[0086] Amplitude refers to the vertical distance between the peak apex and the dynamic baseline in the first voltage curve, which can reflect the intensity or prominence of the peak.

[0087] The width of a peak refers to the width of the peak in the horizontal direction. In the embodiments of this application, the width of the first peak can be replaced by the half-height width or the full half-height width of the first peak.

[0088] The preset amplitude threshold refers to the pre-set amplitude judgment threshold. Only when the amplitude of a peak exceeds this threshold can it be identified as a target peak.

[0089] The preset width range refers to the pre-defined width determination interval. Only when the width of a peak falls within this interval can it be identified as a target peak.

[0090] In the embodiments of this application, when the battery is in a preset charge range, peak detection is performed on the first voltage curve to identify multiple candidate peaks in the range and obtain the amplitude and half-width at half maximum (WHM) of each candidate peak. When it is detected that the amplitude of a certain peak among the multiple candidate peaks exceeds a preset amplitude threshold and the WHM of the peak is within a preset width range, the peak that satisfies the above two conditions is determined as the target peak, i.e., the lithium plating characteristic peak. Subsequently, the lithium plating risk coefficient is calculated based on the parameters of the target peak.

[0091] For example, assuming the vehicle is currently charging, a first voltage curve is constructed corresponding to the remaining battery level between 50% and 85%. First, a preset amplitude threshold (e.g., 1.61 mV / Ah) and a preset width range (e.g., 0.5 to 2 Ah) are obtained. It is then detected whether there are peaks within this range whose amplitude exceeds the preset amplitude threshold and whose half-width at half-maximum (HWHM) is within the preset width range. Assuming peak A is detected at 75% battery level, with an amplitude of 1.75 mV / Ah (greater than the preset amplitude threshold) and a HWHM of 0.8 Ah (within the preset width range), peak A is identified as the target peak (i.e., the lithium plating characteristic peak). Based on the peak's amplitude and area, a lithium plating risk coefficient is calculated for subsequent charging parameter adjustments.

[0092] Optionally, the aforementioned preset amplitude threshold can be a preset fixed value, or it can be obtained by multiplying the dynamic baseline value of the first voltage curve by a preset scaling factor.

[0093] For example, the dynamic baseline value can be obtained by averaging the differential voltage values ​​of charging intervals other than the preset charging interval (e.g., the interval between 50% and 85% of the remaining charge) (e.g., the interval between 0% and 50% and the interval between 85% and 100% of the remaining charge); for example, the dynamic baseline value corresponding to the remaining charge being 55% is 1.40mV / Ah, and assuming the preset scaling factor is 115%, then the preset amplitude threshold is 1.40×1.15=1.61mV / Ah.

[0094] It should be understood that the preset charge range can be set according to the chemical characteristics of the battery and the range of charge states in which lithium plating characteristic peaks may appear.

[0095] It should be noted that the above-mentioned preset charge range is only an example. The range of occurrence of lithium plating characteristic peaks varies in batteries with different chemical systems (e.g., different negative electrode materials or different electrolyte formulations). It can be adaptively adjusted according to the offline calibration results of the actual battery.

[0096] Optionally, after the target peak is determined, in addition to extracting the peak position, amplitude, and area, other parameters of the target peak can also be extracted as auxiliary features, such as the symmetry of the left and right half-widths of the peak (used to determine whether the lithium plating reaction is superimposed on other side reactions), the sharpness of the peak (i.e., the ratio of amplitude to half-width, used to indicate the concentration of the lithium plating reaction), and the shift trend of the peak position in multiple detections (for example, the shift of the peak position towards the direction of high remaining charge can indicate a high probability of increased lithium plating accumulation).

[0097] Optionally, trend analysis can be performed on the parameters of the target peak. For example, during this charging process, peak detection and target peak determination can be repeated at preset time intervals (e.g., every 10 seconds), and the changing trends of the target peak amplitude and area can be recorded. If the amplitude and area show an increasing trend, it indicates that lithium plating is intensifying, and the current pullback amplitude should be increased (i.e., the maximum charging current should be reduced). If the amplitude and area show a decreasing trend, the current vehicle charging parameters can be maintained, or the current pullback amplitude can be reduced (i.e., the maximum charging current should be increased).

[0098] In one embodiment, the decision criteria for peak detection described above can be replaced by a machine learning-based classifier.

[0099] Specifically, morphological features (e.g., amplitude, half-width at half-maximum, peak area, peak position, symmetry, sharpness, etc.) of each candidate peak in the first voltage curve are extracted, and these features are input into a pre-trained classifier (e.g., support vector machine). The classifier outputs the probability that the candidate peak is a lithium plating feature peak. When the probability exceeds a preset probability threshold, the candidate peak is determined as the target peak.

[0100] In the above implementation, when the battery is in a preset charge range, the amplitude and width of multiple peaks in the first voltage curve are acquired. When the amplitude of a certain peak exceeds a preset amplitude threshold and the width of the peak is within a preset width range, the peak is identified as the target peak. Since the lithium plating characteristic peaks have specific amplitude and width characteristics in the first voltage curve, by setting the amplitude threshold and width range for judgment, characteristic peaks related to lithium plating can be accurately identified, avoiding the problem of misjudgment of target peaks caused by noise interference or other peaks in the electrochemical process, thus improving the accuracy of lithium plating risk assessment. Furthermore, peak detection within the preset charge range can eliminate the interference of characteristic peaks generated by other electrochemical processes in non-lithium plating sensitive ranges on the detection results, enabling the lithium plating risk coefficient to be calculated based on accurate lithium plating characteristic peak parameters, further improving the safety of vehicle charging.

[0101] In one implementation, the charging method further includes: Obtain a first parameter and a second parameter. The first parameter is used to indicate the voltage recovery speed of the battery after the charging current is interrupted. The second parameter includes the temperature difference of multiple cells in the battery and the heating rate of the first cell among the multiple cells. The first cell is used to represent the cell with the lowest temperature among the multiple cells. Based on the parameters of the target peak in the first voltage curve, the lithium plating risk coefficient of the battery is obtained, including: Based on the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter, the lithium plating risk coefficient of the battery is obtained. Among them, the parameters of the target peak, the first parameter, and the second parameter in the first voltage curve are positively correlated with the lithium plating risk coefficient.

[0102] It should be understood that the first parameter can also be called the voltage relaxation anomaly characteristic or relaxation anomaly index. It is used to indicate the degree of change in the voltage recovery rate of the battery after the charging current is interrupted. The first parameter is positively correlated with the lithium plating risk coefficient. The larger the value of the first parameter (i.e. the greater the time constant deviates from the reference value), the slower the concentration polarization recovery inside the battery is, and the higher the lithium plating risk is.

[0103] It should be understood that the second parameter can also be called the temperature distribution parameter or the cell temperature gradient characteristic. It is used to represent the degree of temperature difference between multiple cells in the battery and the degree of abnormal heating of the cell with the lowest temperature. The second parameter includes the temperature difference between multiple cells in the battery and the heating rate of the first cell with the lowest temperature among the multiple cells. The larger the temperature difference, the worse the thermal uniformity of the battery pack and the higher the risk of lithium plating in the low temperature region. The larger the ratio of the heating rate of the first cell to the average heating rate, the more likely the low temperature cell may have abnormal lithium plating exothermic reaction. Both of the above are positively correlated with the lithium plating risk coefficient.

[0104] In the embodiments of this application, after obtaining the parameters of the target peak in the first voltage curve, a first parameter and a second parameter are obtained. The first parameter is used to indicate the voltage recovery speed of the battery after the charging current is interrupted. This parameter reflects the concentration polarization state and lithium-ion diffusion capability inside the battery. The second parameter includes the temperature difference among multiple cells in the battery and the heating rate of the first cell with the lowest temperature among the multiple cells. This parameter reflects the thermal equilibrium state of the battery pack and the heating behavior of the low-temperature cells. The parameters of the target peak in the first voltage curve, the first parameter, and the second parameter are used as three-dimensional inputs to obtain the lithium plating risk coefficient. The parameters of the target peak, the first parameter, and the second parameter are positively correlated with the lithium plating risk coefficient. An increase in any parameter indicates an increase in the lithium plating risk.

[0105] Optionally, before determining the lithium plating risk coefficient based on the target peak parameters, the first parameter, and the second parameter, the availability of the first parameter and the second parameter can be judged; for example, if no current interruption event is detected during the current charging cycle, the first parameter cannot be obtained, and the weight corresponding to the first parameter can be reallocated to other parameters.

[0106] In the aforementioned implementation, compared to existing technologies, the charging strategy relies on an offline calibrated static mapping table. Typically, it only uses static parameters such as battery temperature to look up current limits to obtain vehicle charging parameters. This approach fails to detect real-time changes in the battery's internal electrochemical state during charging, leading to inaccurate determination of lithium plating risk and inability to reasonably adjust the current, resulting in low vehicle charging safety. This solution, however, determines the lithium plating risk coefficient based on the parameters of the target peak in the first voltage curve, the battery's voltage recovery speed after a charging current interruption, and the temperature distribution characteristics between battery cells. This allows for accurate determination of the lithium plating risk coefficient from multiple dimensions. Therefore, subsequent adjustments to the charging parameters based on the lithium plating risk coefficient improve vehicle charging safety.

[0107] In one implementation, the specific process of obtaining the lithium plating risk coefficient of the battery based on the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter may include: The parameters of the target peak, the first parameter, and the second parameter in the first voltage curve are normalized to obtain the normalized parameters of the target peak, the normalized first parameter, and the normalized second parameter in the first voltage curve. The lithium plating risk coefficient is obtained by weighting the parameters of the target peak in the normalized first voltage curve, the normalized first parameter, and the normalized second parameter.

[0108] Normalization is used to represent the mathematical transformation process of mapping parameters with different dimensions and numerical ranges to a unified numerical interval (e.g., between 0 and 1).

[0109] For example, the parameters of the target peak include peak amplitude and peak area, where peak amplitude represents the ratio of the peak value of the lithium plating characteristic peak to the dynamic baseline value, and peak area represents the area enclosed by the lithium plating characteristic peak and the dynamic baseline; the first parameter is a relaxation anomaly index, used to represent the degree to which the voltage recovery rate deviates from the normal value after the charging current is interrupted; the second parameter is a temperature gradient characteristic index, used to represent the temperature difference between multiple cells in the battery and the degree to which the temperature rise rate of the lowest temperature cell deviates from the average temperature rise rate. The battery management system normalizes the parameters of the target peak, the first parameter, and the second parameter respectively.

[0110] Specifically, for the parameters of the target peak, the battery management system determines the normalized value of the target peak parameter based on the ratio of the peak amplitude to the preset peak value confirmation threshold and the ratio of the peak area to the preset area confirmation threshold.

[0111] For example, when the sum of the ratio of peak amplitude to peak value confirmation threshold and the ratio of peak area to area confirmation threshold exceeds 1, the normalized value of the target peak parameter is truncated to 1 to indicate the presence of lithium plating risk in that dimension. For the first parameter (relaxation anomaly index), the battery management system determines the normalized value of the first parameter based on the ratio of this index to a preset relaxation anomaly confirmation threshold, and truncates it to 1 when the ratio exceeds 1. For the second parameter (temperature gradient characteristic index), the battery management system determines the normalized value of the second parameter based on the ratio of this index to a preset temperature gradient confirmation threshold, and truncates it to 1 when the ratio exceeds 1.

[0112] After calculating the normalized values ​​of the three indicators separately, the three normalized values ​​and their corresponding preset weights are weighted and summed to obtain the lithium plating risk coefficient. For example, the three weights can be 0.5, 0.3, and 0.2, respectively, with a sum of 1. The lithium plating risk coefficient obtained through the weighted summation is a continuous value between 0 and 1, used to quantify the overall risk level of lithium plating in the battery during the current charging process; a higher value indicates a higher risk of lithium plating.

[0113] Optionally, the sum of w1, w2 and w3 can be 1. w1 is used to represent the weight corresponding to the normalized value of the target peak parameter, and can be set to 0.5. w2 is used to represent the weight corresponding to the normalized value of the first parameter, and can be set to 0.3. w3 is used to represent the weight corresponding to the normalized value of the second parameter, and can be set to 0.2. The specific values ​​can be set, and this application embodiment does not limit them.

[0114] It should be understood that the aforementioned confirmation thresholds (including but not limited to peak confirmation threshold, area confirmation threshold, relaxation anomaly confirmation threshold, and temperature gradient confirmation threshold) are preset critical values ​​used to determine whether the parameters of the corresponding dimensions have reached or exceeded the critical level indicating lithium plating risk. When the parameter value of a certain dimension reaches or exceeds its corresponding confirmation threshold, lithium plating risk is confirmed, and the normalized value is set to an upper limit (e.g., 1). When the parameter value of a certain dimension is less than the confirmation threshold, the normalized value linearly takes values ​​between 0 and 1, indicating that there is different degrees of lithium plating risk in that dimension. The aforementioned confirmation thresholds can be obtained through offline experimental calibration.

[0115] In the aforementioned implementation, compared to existing technologies, the charging strategy relies on offline calibrated static mapping tables. Typically, it only uses static parameters such as battery temperature to look up current limits to obtain vehicle charging parameters. This approach fails to detect real-time changes in the battery's internal electrochemical state during charging, leading to inaccurate determination of lithium plating risk and inability to reasonably adjust the current, resulting in low vehicle charging safety. This solution determines the lithium plating risk coefficient based on the parameters of the target peak in the first voltage curve, the battery's voltage recovery speed after charging current interruption, and the temperature distribution characteristics between battery cells. This allows for accurate determination of the lithium plating risk coefficient from multiple dimensions. Furthermore, by normalizing and weightedly fusing the three types of parameters to obtain the lithium plating risk coefficient, different characteristic parameters can be evaluated on the same scale, effectively integrating lithium plating risk information from various dimensions and further improving the accuracy and reliability of lithium plating risk quantification. Therefore, when subsequently adjusting charging parameters based on this lithium plating risk coefficient, the safety of vehicle charging is improved.

[0116] In one implementation, the charging method further includes: Based on the battery's current information, detect whether there is a current interruption in the battery; When a current interruption is detected, the voltage parameter corresponding to the first preset time after the current interruption is obtained, and the first parameter is obtained based on the voltage parameter corresponding to the first preset time. If no current interruption is detected, the first parameter is set to the preset minimum value.

[0117] Among them, the current interruption phenomenon is used to indicate that the charging current suddenly drops or briefly returns to zero during the charging process.

[0118] In the embodiments of this application, the battery current information is continuously monitored during charging, and the presence of a current interruption event is detected based on the changing characteristics of the current information. If a current interruption event is detected, the battery management system takes the moment of the current interruption as the starting point and acquires the voltage parameters within a first preset time (e.g., 0.5 seconds to 30 seconds) after the current interruption. By analyzing and processing the voltage parameters within this time period, a first parameter characterizing the battery's voltage recovery speed after the current interruption is obtained. If no current interruption event is detected, this first parameter is then determined to be a preset minimum value (e.g., 0).

[0119] For example, if an event is detected in which the current drop exceeds 10A and lasts for more than 5 seconds (e.g., a naturally occurring charging interruption or a diagnostic brief pause triggered by the battery management system at a specific remaining charge node), relaxation analysis is initiated; the voltage relaxation curve is recorded within 0.5 to 30 seconds after the current interruption; and the first parameter is obtained based on the voltage relaxation curve.

[0120] For example, if no current drop exceeding 10A is detected, or if a current drop exceeding 10A is detected but the duration does not exceed 5 seconds, the first parameter is determined to be a preset minimum value (e.g., 0).

[0121] In one embodiment, the detection of a current interruption event can be based on the real-time change characteristics of current information.

[0122] For example, the charging current value is collected at a preset frequency (e.g., 10Hz). When the current drop exceeds a preset current threshold (e.g., 10A) and the low current state continues for more than a preset time threshold (e.g., 5 seconds), a current interruption event is determined to have occurred and relaxation analysis is initiated.

[0123] In the above implementation, the battery current information is monitored in real time to detect the presence of current interruption. When a current interruption is detected, the voltage parameter within a first preset time after the current interruption is acquired, and a first parameter characterizing the battery's voltage recovery speed after the current interruption is obtained based on this voltage parameter. When no current interruption is detected, the first parameter is set to a preset minimum value. Since the voltage recovery process after a current interruption reflects the dynamic changes in the battery's internal electrochemical state, and this recovery process is closely related to the risk of lithium plating, by detecting the current interruption and performing voltage recovery analysis when it occurs, information reflecting the risk of lithium plating can be obtained from the voltage recovery characteristics without adding additional hardware, providing a computational basis for the subsequent determination of the lithium plating risk coefficient. Therefore, this solution can improve the reliability of the determination of the lithium plating risk coefficient and further improve the safety of vehicle charging.

[0124] In one implementation, the specific process of obtaining the first parameter based on the voltage parameter corresponding to the first preset time may include: The voltage parameters corresponding to the first preset time are fitted to obtain the target time constant, which is used to represent the voltage recovery speed of the battery after the current interruption phenomenon. The first parameter is obtained based on the ratio of the target time constant to the preset time constant.

[0125] In the embodiments of this application, after obtaining the voltage parameter within a first preset time after the current interruption, the voltage parameter is subjected to double exponential fitting processing, and the target time constant (i.e., slow time constant τ2) is extracted from the fitting result. The target time constant is used to represent the voltage recovery speed of the battery after the current interruption. The preset time constant (i.e., the reference slow time constant τ2_reference in the non-lithium plating state) corresponding to the current state of charge and temperature is obtained. The first parameter is calculated based on the ratio of the target time constant to the preset time constant. The first parameter is used to quantitatively characterize the degree to which the current relaxation process deviates from the normal state.

[0126] For example, if an event is detected where the current drop exceeds 10A and lasts for more than 5 seconds (e.g., a naturally occurring charging interruption or a diagnostic brief pause triggered by the battery management system at a specific remaining charge level), relaxation analysis is initiated; the voltage relaxation curve is recorded within 0.5 to 30 seconds after the current interruption; a double exponential fit is performed on the voltage relaxation curve to obtain the corresponding formula, which includes time, battery terminal voltage, the final stable voltage value (i.e., the estimated open-circuit voltage), fast time constant, slow time constant, and amplitude coefficient. The fast time constant reflects the ohmic polarization recovery rate, typically ranging from 0.1 to 1 second, while the slow time constant is the target time constant, reflecting the concentration polarization recovery rate, typically ranging from 5 to 30 seconds. Extract the fast time constant and the slow time constant from the fitted voltage relaxation curve. After obtaining the slow time constant, calculate the first parameter (relaxation anomaly index): RA_index = |τ2_current - τ2_reference| / τ2_reference; where RA_index represents the first parameter, τ2_current represents τ2 calculated from the fitted voltage relaxation curve, i.e., the target time constant currently extracted, and τ2_reference is the preset time constant.

[0127] It should be noted that the above formula calculates the relative deviation of the target time constant from the preset time constant. The above scheme uses a relative ratio to eliminate the influence of operating conditions on τ2, making the first parameter comparable under different operating conditions. A larger RA_index indicates that the current relaxation process deviates further from the normal state, the concentration polarization recovery is slower, and the probability of lithium plating is higher.

[0128] Optionally, the above double-exponential fitting can be solved iteratively using the nonlinear least squares method, with the optimization objective being to minimize the sum of squares of the fitting residuals. When the coefficient of determination R² of the fitting result is greater than a preset threshold (e.g., 0.95), the fitting result is considered reliable and the extracted τ² value is valid; when R² is less than the threshold, it indicates that the quality of the relaxed data is poor and the fitting result should not be used.

[0129] Optionally, the preset time constant (i.e., τ2_reference) can be obtained through offline experimentation. The calibration method may include: during the testing phase before the battery leaves the factory, conducting current interruption tests under safe charging conditions without lithium plating (e.g., 0.3C low-rate charging) at different temperatures and different states of charge combinations, obtaining the slow time constant values ​​under each operating condition, and constructing a two-dimensional reference table indexed by temperature and state of charge.

[0130] For example, suppose that during the charging process, voltage relaxation curves were collected within 0.5 to 30 seconds after the current interruption. After performing a double exponential fit on 300 data points, the following fitting parameters were obtained: V_final = 4.15V, τ1 = 0.25 seconds, τ2 = 12.5 seconds, a1 = 0.15V, a2 = 0.08V. τ2_current = 12.5 seconds was determined as the target time constant. The preset time constants corresponding to the current remaining charge (65%) and temperature (25°C) were obtained, and τ2_reference = 8.2 seconds was found from the reference table. The first parameter (relaxation anomaly index) was calculated: RA_index = |12.5 - 8.2| / 8.2 = 0.524. At this point, RA_index indicates that the current voltage recovery rate deviates from the normal state by 52.4%, indicating that the concentration polarization recovery is significantly slow, and there is a high risk of lithium plating.

[0131] In another implementation, the first parameter can also be calculated using a difference-based approach: RA_index =|τ2_current - τ2_reference|, which directly calculates the absolute difference between the target time constant and the preset time constant.

[0132] In the above implementation, since the voltage recovery process of the battery after current interruption follows a specific law, the target time constant obtained by fitting the voltage parameters within the first preset time period can quantify the speed of the voltage recovery process, thereby indirectly reflecting the changes in the electrochemical state inside the battery. By calculating the ratio of the target time constant to the preset time constant, the first parameter is obtained, which can accurately reflect the changing trend of lithium plating risk during the current charging process, providing reliable parameter support for the subsequent assessment of lithium plating risk coefficient, thereby further improving the safety of vehicle charging.

[0133] In one implementation, the second parameter includes a first target parameter, which represents the heating rate of the first cell among multiple cells. The charging method further includes: Based on the temperature parameters of multiple cells in the battery, the first cell among the multiple cells is obtained; Obtain the target temperature rise rate of the first battery cell within a second preset time period; The average temperature rise rate of multiple battery cells is obtained within a second preset time period; The first target parameter is obtained based on the ratio of the target temperature rise rate to the average temperature rise rate.

[0134] The first cell refers to the cell with the lowest temperature among the multiple cells in the battery pack. This cell has the slowest lithium-ion diffusion rate at low temperatures and is the area in the battery pack most prone to lithium plating.

[0135] The target temperature rise rate refers to the ratio of the temperature change of the first cell within a second preset time period to the time interval, which is used to indicate how quickly the cell heats up during the charging process.

[0136] The average temperature rise rate refers to the average rate of temperature rise of all cells in the battery pack within the same preset time period, and is used to represent the average heat generation level of the battery pack as a whole.

[0137] The first target parameter is an index calculated based on the ratio of the target temperature rise rate to the average temperature rise rate, used to represent the degree of deviation of the temperature rise behavior of the lowest temperature cell from the overall average level.

[0138] In embodiments of this application, based on the temperature parameters of multiple cells in the battery, the cell with the lowest temperature among the multiple cells is determined as the first cell; the target temperature rise rate of the first cell within a second preset time (e.g., 30 seconds) is obtained, and the average temperature rise rate of the multiple cells within the same second preset time is obtained; the ratio of the target temperature rise rate to the average temperature rise rate is calculated, and this ratio is used as a first target parameter, which is used to represent the degree of deviation of the temperature rise behavior of the cell with the lowest temperature from the overall average level.

[0139] In one embodiment, obtaining the target temperature rise rate of the first cell within a second preset time period may include: collecting the temperature value of the first cell at a preset sampling frequency (e.g., 1Hz or 2Hz) within the second preset time period, performing linear regression fitting on the collected temperature-time data points, and using the slope of the fitted straight line as the target temperature rise rate.

[0140] For example, multiple temperature-time data points of the first battery cell are collected at a preset sampling frequency within a second preset time period. Using time as the independent variable and temperature as the dependent variable, linear regression is performed on these data points. The fitting objective is to determine a straight line that minimizes the sum of squared residuals from each sampling point to that line. The slope of this straight line is the target temperature rise rate.

[0141] It should be understood that using linear regression instead of simple first and last difference to calculate the temperature rise rate can effectively suppress the influence of temperature sensor measurement noise on the temperature rise rate calculation results, making the obtained temperature rise rate more stable and reliable.

[0142] For example, obtaining the average temperature rise rate of multiple cells within a second preset time period may include: calculating the temperature rise rate of each cell in the battery pack within the second preset time period (using linear regression); summing the temperature rise rates of all cells and dividing by the total number of cells to obtain the average temperature rise rate.

[0143] It should be noted that when calculating the average temperature rise rate, the same second preset time window and the same sampling frequency as the target temperature rise rate should be used to ensure that the two are comparable.

[0144] For example, the first target parameter is obtained based on the ratio of the target temperature rise rate to the average temperature rise rate: TG_ratio = dT_min / dt / dT_avg / dt, where dT_min / dt is the target temperature rise rate (i.e., the temperature rise rate of the first cell with the lowest temperature), dT_avg / dt is the average temperature rise rate, and TG_ratio is the first target parameter.

[0145] It should be noted that during normal charging, the cell with the lowest temperature typically has a temperature rise rate close to or slightly lower than the average due to its higher internal resistance and slower lithium-ion intercalation kinetics; its TG_ratio is close to 1.0 or slightly less than 1.0. When lithium plating occurs in the cell with the lowest temperature, the overpotential of the plating process itself generates additional Joule heat, which, combined with the heat generated during normal charging, causes an abnormally high temperature rise rate for that cell. That is, the cell that was originally the coldest heats up faster than the average, and its TG_ratio > 1.0. Therefore, the higher the TG_ratio value (the greater the degree exceeding 1.0), the higher the risk of lithium plating.

[0146] For example, assuming a battery pack contains 96 cells, after reading the temperature values ​​of all cells at a certain moment, the temperature of the 32nd cell is determined to be 17°C, the lowest among all cells. Therefore, the 32nd cell is designated as the first cell. A second preset time window is defined as 30 seconds. Within this window, the temperature data of the 32nd cell is collected at a frequency of 1Hz. A target temperature rise rate, dT_min / dt = 0.8°C / min, is obtained through linear regression fitting. The temperature rise rate of each of the 96 cells within the same 30-second window is calculated and averaged to obtain the average temperature rise rate, dT_avg / dt = 0.6°C / min. The ratio of the target temperature rise rate to the average temperature rise rate is calculated: TG_ratio = 0.8 / 0.6 = 1.33. This value is greater than 1.0, indicating that the temperature rise rate of the 32nd cell, which has the lowest temperature, is significantly higher than the overall average (33% higher). This cell may be experiencing abnormal heating due to lithium plating.

[0147] In the above implementation, since the cell with the lowest temperature (the first cell) is the cell with poor lithium-ion diffusion in the battery pack and is relatively prone to lithium plating, the deviation of the temperature rise rate of the first cell from the average temperature rise rate can reflect whether the cell has an abnormal temperature rise due to the heat generated by lithium plating. Furthermore, by calculating the ratio of the target temperature rise rate to the average temperature rise rate, the lithium plating risk trend under the temperature dimension can be accurately reflected, providing reliable data support for the comprehensive assessment of the subsequent lithium plating risk coefficient, thereby further improving the safety of vehicle charging.

[0148] In one implementation, the second parameter includes a second target parameter, which represents the temperature difference among multiple cells in the battery. The charging method further includes: Obtain the maximum and minimum temperature values ​​of multiple battery cells; The target temperature difference is obtained based on the maximum and minimum temperature values; The second target parameter is obtained based on the ratio of the target temperature difference to the preset temperature difference.

[0149] The maximum temperature refers to the highest temperature value of all cells in the battery pack at a given moment. The minimum temperature refers to the lowest temperature value of all cells in the battery pack at a given moment.

[0150] The target temperature difference refers to the difference between the maximum and minimum temperatures (i.e., the maximum temperature difference), which is used to indicate the overall thermal balance of the battery pack.

[0151] The preset temperature difference value refers to the pre-set temperature difference alarm threshold, which serves as a reference value for determining whether the temperature difference is too large.

[0152] The second target parameter is an index calculated based on the ratio of the target temperature difference to the preset temperature difference. It is used to indicate the degree to which the current thermal equilibrium state of the battery pack deviates from the normal range. The larger the temperature difference, the greater the temperature difference between the cells in the battery pack, the worse the lithium-ion diffusion ability of the cells in the low-temperature region, and the higher the risk of lithium plating.

[0153] In the embodiments of this application, the maximum and minimum temperatures of the multiple battery cells are obtained based on the temperature parameters of the multiple battery cells; the difference between the maximum and minimum temperatures is calculated to obtain the target temperature difference (i.e., the maximum temperature difference); the ratio of the target temperature difference to the preset temperature difference is calculated, and this ratio is used as the second target parameter.

[0154] In one embodiment, obtaining the maximum and minimum temperature values ​​of multiple battery cells may include: reading the temperature values ​​of all battery cells at each sampling time (e.g., every 1 second or every 2 seconds), and obtaining the maximum temperature value and its corresponding battery cell number, as well as the minimum temperature value and its corresponding battery cell number, by comparison.

[0155] In one implementation, the maximum and minimum values ​​can be selected from all cell temperature values, and the difference between the two can be calculated.

[0156] It should be noted that the maximum and minimum temperature values ​​can be instantaneous sampled values ​​or smoothed values ​​within a preset time window (e.g., the value after applying a moving average filter to temperature data from the past 5 seconds) to suppress the influence of instantaneous noise from the temperature sensor on the temperature difference calculation.

[0157] For example, the target temperature difference ΔT_max can be obtained by subtracting the maximum temperature value from the minimum temperature value among multiple cells.

[0158] It should be understood that the preset temperature difference ΔT_threshold can be calibrated according to the thermal management design requirements of the battery pack and the chemical characteristics of the cells, and its typical value can be set to 8 to 10°C. When the target temperature difference reaches or exceeds the preset temperature difference, it indicates that the thermal equilibrium state within the battery pack has been severely deteriorated, requiring temperature management intervention or current limiting.

[0159] For example, the second target parameter can be the ratio of the target temperature difference ΔT_max to the preset temperature difference ΔT_threshold.

[0160] For example, assuming a battery pack contains 96 cells, the battery management system reads the temperature values ​​of all cells at a certain moment, obtaining a maximum temperature T_max of 28°C (assuming the corresponding cell number is the 12th cell, located in the center of the battery pack) and a minimum temperature T_min of 17°C (assuming the corresponding cell number is the 32nd cell, located in the edge of the battery pack). The target temperature difference ΔT_max = 28 - 17 = 11°C is calculated. Assuming the preset temperature difference ΔT_threshold is 10°C, the second target parameter ΔT_norm = 11 / 10 = 1.10. This value is greater than 1.0, indicating that the maximum temperature difference of the current battery pack has exceeded the preset alarm threshold, the thermal equilibrium state is poor, and the 32nd cell in the low-temperature region has a high risk of lithium plating. This second target parameter (1.10) and the first target parameter (e.g., TG_ratio = 1.33) together constitute the second parameter, which participates in the subsequent calculation of the lithium plating risk coefficient.

[0161] In the above implementation, the maximum and minimum temperatures of multiple cells in the battery are obtained. A target temperature difference is calculated based on these values, and a second target parameter is obtained based on the ratio of the target temperature difference to a preset temperature difference. Since the temperature difference between cells reflects the overall temperature variation of the battery, a large temperature difference indicates poor lithium-ion diffusion in the low-temperature region, leading to a higher risk of lithium plating. Therefore, the target temperature difference can serve as an indicator of lithium plating risk. Furthermore, by calculating the ratio of the target temperature difference to the preset temperature difference, the degree of difference between the target temperature difference and the reference value can be determined, thus accurately reflecting the lithium plating risk trend under temperature unevenness. Therefore, this solution improves the accuracy of determining the lithium plating risk coefficient and further enhances the safety of vehicle charging.

[0162] S103 adjusts the vehicle's charging parameters based on the lithium plating risk factor.

[0163] The vehicle's charging parameters may include at least one of the maximum allowable current value and the target current value.

[0164] In the embodiments of this application, based on the lithium plating risk coefficient, the maximum allowable current value is determined in a preset manner, and the maximum allowable current value is sent to the charging pile through the communication interface. The charging pile adjusts the output current to charge the vehicle according to the received maximum allowable current value.

[0165] For example, suppose that during charging, a lithium plating characteristic peak is detected at 68% remaining charge based on the first voltage curve (e.g., amplitude of 128% of the dynamic baseline value, area of ​​0.35 Ah·mV / Ah). And when the remaining charge reaches 50%, the battery management system actively triggers a 200ms current interruption. The relaxation curves within 0.5~30 seconds after the current interruption are obtained and subjected to double exponential fitting, yielding a slow time constant τ2_current = 12.5 seconds. Compared to the reference value τ2_reference = 8.2 seconds for the same charge and temperature without lithium plating, the deviation is 52%, resulting in a first parameter of 0.52. In addition, the temperature of each cell was collected by eight NTC temperature sensors arranged in the battery pack. The highest temperature cell was 28°C and the lowest temperature cell was 17°C, with a temperature difference of 11°C. The average temperature rise rate of the lowest temperature cell in the last 30 seconds was 0.8°C / min, and the average temperature rise rate of all cells in the last 30 seconds was 0.6°C / min. The ratio of the two was 1.33, and the second parameter combined with the temperature difference was 0.45. The battery management system weights and fuses the target peak parameter (assuming a normalized value of 0.85), the first parameter (assuming a normalized value of 0.52), and the second parameter (assuming a normalized value of 0.45) with a weight of 0.5:0.3:0.2 to calculate the lithium plating risk coefficient R_Li = 0.85×0.5 + 0.52×0.3 + 0.45×0.2 = 0.671. This coefficient is greater than the first preset threshold, indicating that there is a moderate risk of lithium plating. Subsequently, the charging current will be adjusted accordingly based on this coefficient, for example, the current will be reduced from the nominal value of 250A to about 180A.

[0166] In one implementation, the specific process of adjusting the vehicle's charging parameters based on the lithium plating risk coefficient may include: When the lithium plating risk coefficient is less than the first preset threshold, the vehicle's charging parameters are adjusted based on the preset current value. When the lithium plating risk coefficient is greater than or equal to the first preset threshold, the vehicle's charging parameters are adjusted based on the lithium plating risk, preset weight, and preset current value.

[0167] The first preset threshold is used to represent the critical value of the risk coefficient between the low-risk area and the medium-risk area (and above) of lithium plating phenomenon analysis. When the lithium plating risk coefficient is lower than the threshold, it indicates that the current lithium plating risk is low and no flow restriction is required.

[0168] The preset weight is used to determine the proportional coefficient of the current adjustment range. It can be set according to the battery's sensitivity to lithium plating and the current operating conditions (such as battery temperature, state of charge, etc.). The current pullback range can be calculated by combining the preset weight with the lithium plating risk coefficient.

[0169] In the embodiments of this application, when the lithium plating risk coefficient is less than a first preset threshold, it indicates that the current lithium plating risk is low. The target current value is determined as the preset current value (i.e., the maximum charging current), and the vehicle's charging parameters are adjusted based on the preset current value to allow charging at this current value, so as to make full use of the battery's charging capacity and improve charging efficiency. When the lithium plating risk coefficient is greater than or equal to the first preset threshold, it indicates that there is a certain degree of lithium plating risk. The target current value is determined based on the lithium plating risk coefficient, the preset weight, and the preset current value, and the vehicle's charging parameters are adjusted based on the target current value to achieve a continuous reduction in charging current as the lithium plating risk increases.

[0170] Optionally, the vehicle's charging parameters may include a target current value, which is a requested current value sent to the charging pile after being determined based on the current lithium plating risk coefficient. The charging pile adjusts the actual output current to charge the vehicle based on this value.

[0171] Optionally, the vehicle's charging parameters may include the maximum charging current value.

[0172] Optionally, after obtaining the lithium plating risk coefficient by performing S102, the lithium plating risk coefficient can be smoothed to suppress the fluctuation of the risk coefficient caused by transient noise in the signal or instantaneous fluctuation of parameters.

[0173] In one possible implementation, an exponentially weighted moving average method can be used to smooth the lithium plating risk coefficient. For example, the smoothed value output at the current time is equal to the weighted sum of the original risk coefficient obtained by weighted fusion calculation at the current time and the smoothed value at the previous time.

[0174] The calculated lithium plating risk coefficient is multiplied by a preset smoothing coefficient, and the smoothed value from the previous moment is multiplied by one minus the smoothing coefficient. The sum of the two is the smoothed risk coefficient for the current moment.

[0175] Through the above calculations, the smoothed value at the current moment is determined by the current original value and the smoothed value at the previous moment, so that the smoothed risk coefficient output can filter out the fluctuations caused by transient noise and retain the overall trend of risk coefficient change.

[0176] It should be understood that the smaller the value of the smoothing coefficient α, the stronger the smoothing effect and the more obvious the suppression of transient fluctuations, but the response delay also increases accordingly; the larger the value of the smoothing coefficient α, the faster the response speed, but the smoothing effect weakens.

[0177] Optionally, the smoothing coefficient α can be set to a sliding window of about 2 to 5 seconds (e.g., α = 0.3 corresponds to a time constant of about 3.3 seconds), which can achieve a reasonable balance between effectively suppressing noise jitter and maintaining risk response sensitivity.

[0178] For example, suppose the lithium plating risk coefficient R(t) calculated by weighted fusion in S102 at the current time is 0.805, the smoothed value R_smooth(t-1) at the previous time is 0.620, and the smoothing coefficient α is set to 0.3. The smoothed value at the current time is calculated according to the above formula: R_smooth(t) = 0.3 × 0.805 + 0.7 × 0.620 = 0.6755, that is, the smoothed lithium plating risk coefficient output at the current time is 0.676.

[0179] Alternatively, the above smoothing process can be implemented using other low-pass filtering methods, such as sliding window averaging filtering. For example, sliding window averaging filtering stores historical data in a fixed-length window (e.g., 10 sampling points) and uses the arithmetic mean of all data within the window as the current output value.

[0180] Optionally, the smoothing coefficient α can be dynamically adaptive; for example, the smoothing coefficient α can be dynamically adjusted according to the rate of change of the lithium plating risk coefficient.

[0181] For example, when the risk factor increases rapidly (e.g., the rate of change in adjacent time intervals exceeds a preset rate threshold), the value of α is increased (e.g., from 0.3 to 0.5), allowing the smoothed value to follow the upward trend of the original value more quickly, improving the response speed to increased lithium plating risk, and thus enabling more timely execution of current limiting to ensure safety. When the risk factor changes slowly or decreases, the value of α is decreased (e.g., from 0.3 to 0.2), making the smoothing effect stronger, thereby further suppressing the impact of noise jitter on current adjustment. The above smoothing method can maintain noise suppression while prioritizing response speed when the risk increases rapidly, better balancing charging safety and control stability.

[0182] In the embodiments of this application, the upper limit of the charging current is determined based on a preset piecewise continuous linear mapping relationship according to the smoothed lithium plating risk index, and the charging parameters of the vehicle are adjusted based on the upper limit of the current.

[0183] It should be understood that the upper limit of the current changes continuously with the lithium plating risk index, so that the output power of the charging pile changes smoothly, avoiding the impact of sudden current changes on the DC bus of the charging pile and the battery terminal voltage, and improving the stability of the charging process.

[0184] For example, the upper limit of the current can be determined by mapping the smoothed lithium plating risk index R_Li_smooth into three zones: low-risk, medium-risk, and high-risk. In the low-risk zone (e.g., R_Li_smooth < 0.3), the upper limit of the current is determined to be the nominal maximum charging current I_max_nominal, meaning there is no current limit, allowing charging at the nominal maximum current to fully utilize the battery's charging capacity and improve charging efficiency. In the medium-risk zone (e.g., 0.3 ≤ R_Li_smooth < 0.6), the upper limit of the current is determined according to a first continuous mapping relationship. The upper limit of the current decreases as the lithium plating risk index increases, linearly decreasing from the nominal maximum charging current to a first preset percentage of the nominal maximum charging current (e.g., 60% to 80%). In the high-risk region (e.g., R_Li_smooth ≥ 0.6), the upper limit of the current is determined according to the second continuous mapping relationship. The upper limit of the current continues to decrease from the lowest value in the medium-risk region as the lithium plating risk index increases, decreasing to a second preset percentage of the nominal maximum charging current (e.g., 24% to 48%). When the lithium plating risk index approaches 1.0, the upper limit of the current drops to the lower safety limit (e.g., the current value corresponding to a 0.1C rate).

[0185] Optionally, the first preset percentage can be determined based on the maximum current drawdown ratio k_mid in the medium-risk zone: First preset percentage = (1 - k_mid) × 100%, where the value of k_mid ranges from 0.2 to 0.4, meaning the current drawdown is at most 60% to 80% of the nominal maximum charging current. The second preset percentage can be jointly determined by the maximum current drawdown ratio k_mid in the medium-risk zone and the additional drawdown ratio k_high in the high-risk zone, specifically: Second preset percentage = (1 - k_mid) × (1 - k_high) × 100%, where the value of k_high ranges from 0.4 to 0.6, meaning the current drawdown is further reduced to 24% to 48% of the nominal maximum charging current based on the drawdown in the medium-risk zone. The specific values ​​of k_mid and k_high can be calibrated and adjusted according to the battery's chemical system, temperature, and aging state.

[0186] For example, taking a nominal maximum charging current I_max_nominal of 250A, k_mid of 0.3, and k_high of 0.5 as an example, when R_Li_smooth is 0.2 (low-risk zone), the upper limit of the current is 250A (no current limit); when R_Li_smooth is 0.45 (medium-risk zone), according to the first mapping relationship, the upper limit of the current = 250 × (1 - 0.3 × (0.45-0.3) / 0.3) = 250 × (1 - 0.3 × 0.5) = 212.5A; when R_Li_smooth is 0.8 (high-risk zone), according to the second mapping relationship, the upper limit of the current = 250 × 0.7 × (1 - 0.5 × (0.8-0.6) / 0.4) = 250 × 0.7 × (1 - 0.5 × 0.5) = 131.25A. The above mapping relationship is a continuous linear mapping with no abrupt transition points, ensuring the smoothness of the charging current adjustment process.

[0187] It should be understood that the first mapping relationship can be: I_max_allow = I_max_nominal × (1 - k_mid × (R_Li_smooth - 0.3) / 0.3), where I_max_allow is the maximum allowable charging current value (or target current value) calculated by I_max_allow, I_max_nominal is the nominal maximum current value, k_mid is the maximum current pullback ratio in the medium-risk zone, and R_Li_smooth is the lithium plating risk coefficient obtained after smoothing. The second mapping relationship can be: I_max_allow = I_max_nominal × (1 - k_mid) × (1 - k_high × (R_Li_smooth - 0.6) / 0.4), where I_max_allow is the maximum allowable charging current value (or target current value) calculated by I_max_allow, I_max_nominal is the nominal maximum current value, k_mid is the maximum current pullback ratio in the medium-risk area, k_high is the additional pullback ratio in the high-risk area, and R_Li_smooth is the lithium plating risk coefficient obtained after smoothing.

[0188] It should be understood that the three intervals of R_Li_smooth and their corresponding mapping relationships mentioned above are only illustrative examples. The specific thresholds for the low-risk, medium-risk, and high-risk zones, as well as the specific values ​​of the first and second preset percentages, can be adaptively adjusted according to the battery's chemical system, temperature, and state of charge to adapt to the lithium plating risk characteristics under different operating conditions.

[0189] In another embodiment, to prevent the lithium plating risk index after smoothing from fluctuating frequently around a preset threshold, causing the upper limit of the current to be repeatedly adjusted and affecting the stability of the charging process, a hysteresis anti-shake mechanism can be introduced.

[0190] Specifically, the hysteresis stabilization mechanism may include: setting the rising threshold for entering the medium-risk zone to a first preset threshold (e.g., 0.3), and the falling threshold for returning to the low-risk zone to the first preset threshold minus a preset hysteresis bandwidth (e.g., 0.3 - 0.05 = 0.25); setting the rising threshold for entering the high-risk zone to a second preset threshold (e.g., 0.6), and the falling threshold for returning to the medium-risk zone to the second preset threshold minus a preset hysteresis bandwidth (e.g., 0.6 - 0.05 = 0.55).

[0191] It should be understood that by separating the rising threshold from the falling threshold, when the risk index fluctuates slightly around the threshold (for example, fluctuating around the critical value of 0.6 between the medium-risk and high-risk zones), the upper limit of the current will not change frequently, thus avoiding the impact of repeated current adjustments on the DC bus of the charging pile and the battery terminal voltage.

[0192] Optionally, the specific value of the hysteresis bandwidth (e.g., 0.05) can be adaptively adjusted according to the response speed of the current adjustment and the amplitude of the risk index fluctuation. When the risk index fluctuates significantly, the hysteresis bandwidth can be appropriately increased (e.g., 0.08) to further reduce the sensitivity of the current adjustment; when a high response speed to lithium plating risk is required, the hysteresis bandwidth can be appropriately decreased (e.g., 0.03) to respond more quickly to actual changes in the risk index.

[0193] For example, suppose the smoothed lithium plating risk index R_smooth(t) continuously rises from 0.55 to 0.62. During this rise, when R_smooth(t) reaches the high-risk zone threshold of 0.6, the upper limit of the current is switched from the mapping of the medium-risk zone to the mapping of the high-risk zone, and the current begins to decrease further. Subsequently, if R_smooth(t) falls back from 0.62 to 0.58, since this value is still higher than the high-risk zone threshold of 0.55, the high-risk zone mapping is maintained when calculating charging parameters, and the current value does not immediately revert to the medium-risk zone. When it is detected that R_smooth(t) continues to fall below 0.55, the mapping is restored to the medium-risk zone.

[0194] Optionally, a current change rate limiting mechanism can be introduced to further prevent excessively rapid current changes from impacting the charging station and battery.

[0195] Specifically, the rate of increase of the upper limit current value I_max_allow shall not exceed a preset first percentage per second (e.g., 5% / second) of the nominal maximum charging current I_max_nominal, and the rate of decrease of the upper limit current value shall not exceed a preset second percentage per second (e.g., 20% / second, i.e., rapid current reduction to ensure safety).

[0196] In the above implementation, when the lithium plating risk coefficient is less than the first preset threshold, the vehicle's charging parameters are adjusted based on the preset current value so that the vehicle can be charged with the preset maximum charging current when the lithium plating risk is low, thus ensuring charging efficiency; when the lithium plating risk coefficient is greater than or equal to the first preset threshold, the vehicle's charging parameters are adjusted based on the lithium plating risk coefficient, the preset weight, and the preset current value so that the charging current can be dynamically adjusted according to the change of the lithium plating risk coefficient. Compared to existing technologies that rely on offline calibrated static mapping tables for charging strategies, which typically only look up current limits based on static parameters such as battery temperature to obtain vehicle charging parameters, this solution cannot detect changes in the battery's internal electrochemical state in real time during charging. This results in the charging strategy being unable to accurately determine the risk of lithium plating and cannot reasonably adjust the current, leading to low vehicle charging safety. This solution can continuously adjust the charging current based on the real-time determined lithium plating risk coefficient, avoiding the impact of sudden current changes on the circuit and battery, and improving the stability of the charging process. In addition, since the current adjustment range is related to the lithium plating risk coefficient, the charging current can be reduced in time to ensure safety when the risk of lithium plating increases. Therefore, this solution can balance charging efficiency and safety when charging vehicles, improving the stability of the charging process and the user experience.

[0197] In the above embodiments, vehicle charging parameters and battery state parameters are obtained. The battery state parameters include battery voltage parameters and capacity parameters. By analyzing the voltage and capacity parameters, a first voltage curve reflecting the rate of change of battery terminal voltage with the amount of charge is constructed. Since the parameters of the target peak in the first voltage curve can reflect the changes in the internal electrochemical state of the battery, a lithium plating risk coefficient is obtained based on the parameters of the target peak in the first voltage curve. This allows for real-time quantification of the risk of lithium plating during charging, avoiding the inability to accurately determine the lithium plating risk during charging in existing technologies. This prevents the setting of reasonable charging parameters when the lithium plating risk is high, resulting in low vehicle charging safety. Therefore, this solution can determine the lithium plating risk coefficient based on the parameters of the target peak and adjust the charging parameters according to the lithium plating risk coefficient, thereby improving the safety of vehicle charging.

[0198] The following is combined Figure 2 Another charging method provided in the embodiments of this application will be described in detail.

[0199] S201, The vehicle is detected to be charging.

[0200] For example, the system detects the level of the physical connection signal between the charging gun and the vehicle's charging socket, as well as the duty cycle of the wake-up signal (i.e., the CP signal) provided by the charging pile. When the level changes from low to high and the duty cycle of the CP signal is within a preset range (e.g., 5% to 85%), it is determined that the vehicle has entered the charging state.

[0201] S202 collects the battery's voltage and current parameters, and calculates the battery's capacity parameters.

[0202] For example, the voltage of each cell is acquired at a frequency of 10Hz by a voltage acquisition chip (voltage resolution of 1mV), the charging current is acquired by a Hall current sensor (current accuracy of ±0.5%), the surface temperature of each cell is acquired at a frequency of 1Hz by multiple NTC thermistors arranged in the battery pack, and the cumulative charging capacity (i.e., the capacity parameter) is calculated in real time by integrating the charging current in ampere-hours.

[0203] S203 constructs a differential voltage curve based on voltage and electrical parameters.

[0204] For example, with a sampling window of 100ms, the voltage change ΔV and the charge change ΔQ within the window are obtained. The instantaneous differential voltage value dV / dQ = ΔV / ΔQ is calculated, and the instantaneous differential voltage value sequence is smoothed by Savitzky-Golay filtering. With the charged charge (or state of charge) as the horizontal axis and the differential voltage value as the vertical axis, a differential voltage curve (i.e., the first voltage curve) is constructed.

[0205] S204, determine whether there is a lithium plating characteristic peak in the preset charging range of the detection differential voltage curve; if yes, proceed to S206; if no, proceed to S205.

[0206] For example, within a preset charge range (e.g., 50% to 85% of remaining charge), a peak detection algorithm is used to identify candidate peaks in the differential voltage curve, and the amplitude and half-width at half-maximum (WHM) of each candidate peak are obtained. When a candidate peak is detected to have an amplitude exceeding a preset amplitude threshold (e.g., 115% of the dynamic baseline value) and its WHM is within a preset width range (e.g., 0.5 to 2 Ah), a lithium plating characteristic peak is determined to exist; otherwise, a lithium plating characteristic peak is determined not to exist.

[0207] S205, the amplitude and area of ​​the lithium plating characteristic peak are set to a preset minimum value.

[0208] For example, when no candidate peak satisfying the amplitude condition and the half-width at half-maximum condition is detected within a preset charge range, the battery management system determines the amplitude A_peak and area S_peak of the lithium plating characteristic peak to a preset minimum value (e.g., 0) to indicate that no lithium plating risk signal was detected in that dimension and that it does not contribute to the lithium plating risk coefficient.

[0209] S206, obtain the amplitude and area of ​​the lithium plating characteristic peak.

[0210] For example, when a candidate peak that meets the amplitude condition and the half-width at half-maximum condition is detected within a preset charge range, the amplitude A_peak (the ratio of the amplitude to the dynamic baseline value) and the peak area S_peak (the area enclosed by the peak and the dynamic baseline) of the peak are extracted.

[0211] S207, based on the amplitude and area of ​​the lithium plating characteristic peak, yields the lithium plating risk coefficient.

[0212] For example, the normalized value of the target peak parameter is determined based on the ratio of the peak amplitude to the preset peak confirmation threshold and the ratio of the peak area to the preset area confirmation threshold, thereby obtaining the corresponding lithium plating risk coefficient.

[0213] S208 smooths out the lithium plating risk factor.

[0214] For example, the lithium plating risk coefficient can be smoothed using an exponentially weighted moving average method. For instance, the smoothed value output at the current time is equal to the weighted sum of the original risk coefficient obtained by weighted fusion calculation at the current time and the smoothed value at the previous time.

[0215] S209 adjusts the upper limit of the charging current based on the lithium plating risk coefficient after smoothing.

[0216] For example, the target current value is determined based on the smoothed lithium plating risk coefficient R_smooth. When R_smooth is less than a first preset threshold (e.g., 0.3), the target current value is the nominal maximum charging current (without current limiting); when R_smooth is greater than or equal to the first preset threshold and less than a second preset threshold (e.g., 0.6), the target current value is continuously reduced from the nominal maximum charging current to 60% to 80% of the nominal maximum charging current according to a first continuous mapping relationship; when R_smooth is greater than or equal to the second preset threshold, the target current value is continuously reduced from 60% to 80% of the nominal maximum charging current to 24% to 48% of the nominal maximum charging current according to a second continuous mapping relationship.

[0217] It should be understood that Figure 2Using the amplitude and area of ​​lithium plating characteristic peaks as the basis for judgment, this method is applicable to scenarios where current interruption events occur and voltage relaxation information is used to assess the risk of lithium plating characteristics. It is suitable for scenarios where risk assessment is based on electrochemical characteristics.

[0218] The following is combined Figure 3 Another charging method provided in the embodiments of this application will be described in detail.

[0219] S301, the vehicle is detected to be charging, and the battery current parameters are collected.

[0220] For example, the system detects the level of the physical connection signal between the charging gun and the vehicle's charging socket, as well as the duty cycle of the wake-up signal (i.e., the CP signal) provided by the charging pile. When the level changes from low to high and the duty cycle of the CP signal is within a preset range (e.g., 5% to 85%), it is determined that the vehicle has entered the charging state.

[0221] S302, determine if there is a current interruption event; if yes, execute S303; if no, execute S302.

[0222] For example, the charging current is monitored at a frequency of 10Hz. When a current drop exceeding 10A is detected and the low current state lasts for more than 5 seconds, it is determined that a valid current interruption event has occurred (including natural interruption caused by power adjustment on the charging pile side, or diagnostic pause actively triggered by the battery management system). If no interruption is detected, monitoring continues, and S302 is executed.

[0223] S303, after detecting a current interruption event, records the voltage relaxation curve within the first preset time after the current interruption.

[0224] For example, starting from the moment of current interruption, the battery terminal voltage data is obtained from 0.5 seconds to 30 seconds after the current interruption to obtain the voltage relaxation curve.

[0225] S304 performs a double exponential fit on the voltage relaxation curve to extract the slow time constant.

[0226] For example, a double exponential fit is performed on the voltage relaxation curve, and the slow time constant is extracted from the formula of the fitted voltage duration curve.

[0227] S305, calculate the ratio of the slow time constant to the preset time constant to obtain the relaxation anomaly index.

[0228] For example, a preset time constant corresponding to the current state of charge and temperature (i.e., the reference slow time constant τ2_reference in the absence of lithium plating) is obtained, and the relaxation anomaly index RA_index = |τ2_current - τ2_reference| / τ2_reference is calculated. This index is used to quantify the degree to which the current voltage recovery rate deviates from the normal state. The larger the RA_index, the slower the concentration polarization recovery and the higher the risk of lithium plating. If no current interruption event occurs during this charge, preventing the calculation of the relaxation anomaly index, then the index is set to a preset minimum value of 0.

[0229] S306, based on the relaxation anomaly index, yields the lithium plating risk coefficient.

[0230] For example, the normalized value of the first parameter is determined based on the ratio of the indicator to a preset relaxation anomaly confirmation threshold, and the ratio is truncated to 1 when it exceeds 1.

[0231] S307 smooths out the lithium plating risk factor.

[0232] For example, the lithium plating risk coefficient can be smoothed using an exponentially weighted moving average method. For instance, the smoothed value output at the current time is equal to the weighted sum of the original risk coefficient obtained by weighted fusion calculation at the current time and the smoothed value at the previous time.

[0233] S308 adjusts the upper limit of charging current based on the lithium plating risk coefficient after smoothing.

[0234] For example, the target current value is determined based on the smoothed lithium plating risk coefficient R_smooth. When R_smooth is less than a first preset threshold (e.g., 0.3), the target current value is the nominal maximum charging current (without current limiting); when R_smooth is greater than or equal to the first preset threshold and less than a second preset threshold (e.g., 0.6), the target current value is continuously reduced from the nominal maximum charging current to 60% to 80% of the nominal maximum charging current according to a first continuous mapping relationship; when R_smooth is greater than or equal to the second preset threshold, the target current value is continuously reduced from 60% to 80% of the nominal maximum charging current to 24% to 48% of the nominal maximum charging current according to a second continuous mapping relationship.

[0235] It should be understood that Figure 3 Using relaxation anomaly indicators as a criterion, this approach is applied to scenarios where current interruption events occur and voltage relaxation information is used to assess the risk of lithium plating characteristics.

[0236] It should be noted that, Figure 2 The method 200 shown can be used with Figure 3The method 300 shown is used in combination. For example, when both a lithium plating characteristic peak and a current interruption event are detected during charging, the amplitude and area of ​​the lithium plating characteristic peak can be obtained based on method 200, and the relaxation anomaly index based on method 300 can be normalized. The amplitude and area of ​​the lithium plating characteristic peak obtained by method 200 and the relaxation anomaly index obtained by method 300 are then normalized. The amplitude and area of ​​the normalized lithium plating characteristic peak and the normalized relaxation anomaly index are then weighted and fused to obtain a lithium plating risk coefficient. The lithium plating risk coefficient is then smoothed and the upper limit of the charging current is adjusted.

[0237] The following is combined Figure 4 Another charging method provided in the embodiments of this application will be described in detail.

[0238] S401, the vehicle is detected to be charging, and the battery temperature parameters are collected.

[0239] For example, the system detects the level of the physical connection signal between the charging gun and the vehicle's charging socket, as well as the duty cycle of the wake-up signal (i.e., the CP signal) provided by the charging pile. When the level changes from low to high and the duty cycle of the CP signal is within a preset range (e.g., 5%~85%), the system determines that the vehicle has entered the charging state. The system then collects the surface temperature of the battery cells in each area at a frequency of 1Hz using multiple NTC thermistors arranged within the battery pack, and calculates the cumulative charging capacity (i.e., the capacity parameter) in real time by integrating the charging current in ampere-hours.

[0240] S402 calculates the maximum temperature difference based on the temperature of each cell inside the battery.

[0241] For example, the temperature values ​​of all cells in the battery pack are read to obtain the maximum temperature T_max and the minimum temperature T_min, and the maximum temperature difference ΔT_ma is calculated. This maximum temperature difference is used to characterize the overall thermal balance of the battery pack. The larger the temperature difference, the higher the risk of lithium plating in the cells in the low-temperature region.

[0242] S403 calculates the temperature rise ratio between the temperature rise rate of the lowest temperature cell and the average temperature rise rate of all cells.

[0243] For example, the lowest-temperature cell among multiple cells is identified. The target temperature rise rate (dT_min / dt) of this cell is calculated using a 30-second sliding window. Simultaneously, the average temperature rise rate (dT_avg / dt) of all cells within the same window is calculated. The temperature rise ratio (TG_ratio) is then calculated as dT_min / dt / dT_avg / dt. During normal charging, TG_ratio is close to 1.0. When lithium plating occurs in the lowest-temperature cell, the heat released during lithium plating, combined with the heat generated during normal charging, causes TG_ratio to exceed 1.0.

[0244] S404, the temperature index is obtained based on the ratio of the maximum temperature difference to the temperature rise.

[0245] For example, the temperature index TG_index is calculated as: TG_index = max(0, TG_ratio - 1.0) + ΔT_max / ΔT_threshold, based on the ratio of the maximum temperature difference ΔT_max to the temperature rise TG_ratio, where ΔT_threshold is the preset temperature difference alarm threshold (typically 8~10°C). The larger the TG_index, the more severe the abnormal temperature gradient and the higher the risk of lithium plating.

[0246] S405, based on the temperature index, yields the lithium plating risk coefficient.

[0247] For example, the normalized value of the first parameter is determined based on the ratio of the index to the preset temperature difference ΔT_threshold, and the ratio is truncated to 1 when it exceeds 1.

[0248] S406 smooths out the lithium plating risk factor.

[0249] For example, the lithium plating risk coefficient can be smoothed using an exponentially weighted moving average method. For instance, the smoothed value output at the current time is equal to the weighted sum of the original risk coefficient obtained by weighted fusion calculation at the current time and the smoothed value at the previous time.

[0250] S407 adjusts the upper limit of charging current based on the lithium plating risk coefficient after smoothing.

[0251] For example, the target current value is determined based on the smoothed lithium plating risk coefficient R_smooth. When R_smooth is less than a first preset threshold (e.g., 0.3), the target current value is the nominal maximum charging current (without current limiting); when R_smooth is greater than or equal to the first preset threshold and less than a second preset threshold (e.g., 0.6), the target current value is continuously reduced from the nominal maximum charging current to 60% to 80% of the nominal maximum charging current according to a first continuous mapping relationship; when R_smooth is greater than or equal to the second preset threshold, the target current value is continuously reduced from 60% to 80% of the nominal maximum charging current to 24% to 48% of the nominal maximum charging current according to a second continuous mapping relationship.

[0252] It should be understood that Figure 4 Using temperature distribution characteristics as a criterion is suitable for scenarios where temperature gradients change significantly and risks need to be assessed from a thermal state perspective.

[0253] It should be noted that, Figure 4 The method 400 shown can be used with Figure 2The method 200 shown is used in combination. For example, when a lithium plating characteristic peak is detected during charging and temperature parameters are obtained, the amplitude and area of ​​the lithium plating characteristic peak can be obtained based on method 200, and the temperature index can be obtained based on method 400. The amplitude and area of ​​the lithium plating characteristic peak obtained by method 200 are normalized, and the temperature parameters obtained by method 400 are normalized. The amplitude and area of ​​the normalized lithium plating characteristic peak and the normalized temperature parameters are weighted and fused to obtain a lithium plating risk coefficient. The lithium plating risk coefficient is then smoothed and the upper limit of the charging current is adjusted.

[0254] It should be understood that Figures 2 to 4 Any combination of the methods shown is within the scope of protection of this application.

[0255] The following is combined Figure 5 Another charging method provided in the embodiments of this application will be described in detail.

[0256] Figure 5 This is a schematic flowchart illustrating another charging method provided in an embodiment of this application. Figure 5 As shown, method 500 includes S501 to S516, which are described in detail below.

[0257] For example, Figure 5 The method 500 shown can be executed by the vehicle; or by the vehicle's battery management system; or by the processor in the vehicle; or by a chip in the processor of the vehicle; or by a software platform integrated in the vehicle.

[0258] S501, the vehicle is detected to be charging.

[0259] For example, the system detects the level of the physical connection signal between the charging gun and the vehicle's charging socket, as well as the duty cycle of the wake-up signal (i.e., the CP signal) provided by the charging pile. When the level changes from low to high and the duty cycle of the CP signal is within a preset range (e.g., 5% to 85%), it is determined that the vehicle has entered the charging state.

[0260] Alternatively, the implementation of S501 can be found in [reference needed]. Figure 1 The relevant descriptions in S101 will not be repeated here.

[0261] The S502 collects battery voltage, current, and temperature parameters, and calculates battery capacity parameters.

[0262] For example, the voltage of each cell is acquired at a frequency of 10Hz by a voltage acquisition chip (voltage resolution of 1mV), the charging current is acquired by a Hall current sensor (current accuracy of ±0.5%), the surface temperature of each cell is acquired at a frequency of 1Hz by multiple NTC thermistors arranged in the battery pack, and the cumulative charging capacity (i.e., the capacity parameter) is calculated in real time by integrating the charging current in ampere-hours.

[0263] Alternatively, the implementation of S502 can be found in [reference needed]. Figure 1 The relevant descriptions in S101 will not be repeated here.

[0264] S503 constructs a differential voltage curve based on voltage and electrical parameters.

[0265] For example, with a sampling window of 100ms, the voltage change ΔV and the charge change ΔQ within the window are obtained. The instantaneous differential voltage value dV / dQ = ΔV / ΔQ is calculated, and the instantaneous differential voltage value sequence is smoothed by Savitzky-Golay filtering. With the charged charge (or state of charge) as the horizontal axis and the differential voltage value as the vertical axis, a differential voltage curve (i.e., the first voltage curve) is constructed.

[0266] Alternatively, the implementation of S503 can be found in [reference needed]. Figure 1 The relevant descriptions in S101 will not be repeated here.

[0267] S504, determine whether there is a lithium plating characteristic peak within the preset charging range of the detection differential voltage curve; if yes, proceed to S506; if no, proceed to S505.

[0268] For example, within a preset charge range (e.g., 50% to 85% of remaining charge), a peak detection algorithm is used to identify candidate peaks in the differential voltage curve, and the amplitude and half-width at half-maximum (WHM) of each candidate peak are obtained. When a candidate peak is detected to have an amplitude exceeding a preset amplitude threshold (e.g., 115% of the dynamic baseline value) and its WHM is within a preset width range (e.g., 0.5 to 2 Ah), a lithium plating characteristic peak is determined to exist; otherwise, a lithium plating characteristic peak is determined not to exist.

[0269] Alternatively, the implementation of S503 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0270] S505 sets the amplitude and area of ​​the lithium plating characteristic peak to a preset minimum value.

[0271] For example, when no candidate peak satisfying the amplitude condition and the half-width at half-maximum condition is detected within a preset charge range, the battery management system determines the amplitude A_peak and area S_peak of the lithium plating characteristic peak to a preset minimum value (e.g., 0) to indicate that no lithium plating risk signal was detected in that dimension and that it does not contribute to the lithium plating risk coefficient.

[0272] Alternatively, the implementation of S505 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0273] S506, obtain the amplitude and area of ​​the lithium plating characteristic peak.

[0274] For example, when a candidate peak that meets the amplitude condition and the half-width at half-maximum condition is detected within a preset charge range, the battery management system extracts the amplitude A_peak (the ratio of the amplitude to the dynamic baseline value) and the peak area S_peak (the area enclosed by the peak and the dynamic baseline).

[0275] Alternatively, the implementation of S506 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0276] S507, after detecting a current interruption event, records the voltage relaxation curve within the first preset time after the current interruption.

[0277] For example, the charging current is monitored at a frequency of 10Hz. When a current drop exceeding 10A is detected and this low current state lasts for more than 5 seconds, it is determined that a valid current interruption event has occurred (including natural interruption caused by power adjustment on the charging pile side, or diagnostic pause actively triggered by the battery management system). Then, starting from the moment of current interruption, the battery terminal voltage data within 0.5 seconds to 30 seconds after the current interruption is obtained to obtain the voltage relaxation curve.

[0278] Alternatively, the implementation of S507 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0279] The S508 performs a double exponential fit on the voltage relaxation curve and extracts the slow time constant.

[0280] For example, a double-exponential fitting is performed on the voltage relaxation curve, and the slow time constant τ2 is extracted from the fitting result as the basis for subsequent calculation of the relaxation anomaly index.

[0281] Alternatively, the implementation of S508 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0282] S509, calculate the ratio of the slow time constant to the preset time constant to obtain the relaxation anomaly index.

[0283] For example, a preset time constant corresponding to the current state of charge and temperature (i.e., the reference slow time constant τ2_reference in the absence of lithium plating) is obtained, and the relaxation anomaly index RA_index = |τ2_current - τ2_reference| / τ2_reference is calculated. This index is used to quantify the degree to which the current voltage recovery rate deviates from the normal state. The larger the RA_index, the slower the concentration polarization recovery and the higher the risk of lithium plating. If no current interruption event occurs during this charge, preventing the calculation of the relaxation anomaly index, then the index is set to a preset minimum value of 0.

[0284] Alternatively, the implementation of S509 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0285] S510 calculates the maximum temperature difference based on the temperature of each cell inside the battery.

[0286] For example, the temperature values ​​of all cells in the battery pack are read to obtain the maximum temperature T_max and the minimum temperature T_min, and the maximum temperature difference ΔT_max is calculated. This maximum temperature difference is used to characterize the overall thermal balance of the battery pack. The larger the temperature difference, the higher the risk of lithium plating in the cells in the low-temperature region.

[0287] Alternatively, the implementation of S510 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0288] S511 calculates the temperature rise ratio between the temperature rise rate of the lowest temperature cell and the average temperature rise rate of all cells.

[0289] For example, the lowest-temperature cell among multiple cells is identified. The target temperature rise rate (dT_min / dt) of this cell is calculated using a 30-second sliding window. Simultaneously, the average temperature rise rate (dT_avg / dt) of all cells within the same window is calculated. The temperature rise ratio (TG_ratio) is then calculated as dT_min / dt / dT_avg / dt. During normal charging, TG_ratio is close to 1.0. When lithium plating occurs in the lowest-temperature cell, the heat released during lithium plating, combined with the heat generated during normal charging, causes TG_ratio to exceed 1.0.

[0290] Alternatively, the implementation of S511 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0291] S512, the temperature index is obtained based on the ratio of the maximum temperature difference to the temperature rise.

[0292] For example, the temperature index TG_index is calculated as: TG_index = max(0, TG_ratio - 1.0) + ΔT_max / ΔT_threshold, based on the ratio of the maximum temperature difference ΔT_max to the temperature rise TG_ratio, where ΔT_threshold is the preset temperature difference alarm threshold (typically 8~10°C). The larger the TG_index, the more severe the abnormal temperature gradient and the higher the risk of lithium plating.

[0293] Alternatively, the implementation of S512 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0294] S513 normalizes the amplitude and area of ​​the lithium plating characteristic peak, and normalizes the relaxation anomaly index and temperature index respectively to obtain the normalized index.

[0295] For example, the three types of parameters are normalized respectively, mapping the parameters with different dimensions to a unified range of 0 to 1, and thus obtaining the normalized indicators.

[0296] Alternatively, the implementation of S513 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0297] S514 calculates the lithium plating risk coefficient by weighting the normalized indicators.

[0298] For example, suppose that during charging, a lithium plating characteristic peak is detected at 68% remaining charge based on the first voltage curve (e.g., amplitude of 128% of the dynamic baseline value, area of ​​0.35 Ah·mV / Ah). And when the remaining charge reaches 50%, the battery management system actively triggers a 200ms current interruption. The relaxation curves within 0.5~30 seconds after the current interruption are obtained and subjected to double exponential fitting, yielding a slow time constant τ2_current = 12.5 seconds. Compared to the reference value τ2_reference = 8.2 seconds for the same charge and temperature without lithium plating, the deviation is 52%, resulting in a first parameter of 0.52. In addition, the temperature of each cell was collected by eight NTC temperature sensors arranged in the battery pack. The highest temperature cell was 28°C and the lowest temperature cell was 17°C, with a temperature difference of 11°C. The average temperature rise rate of the lowest temperature cell in the last 30 seconds was 0.8°C / min, and the average temperature rise rate of all cells in the last 30 seconds was 0.6°C / min. The ratio of the two was 1.33, and the second parameter combined with the temperature difference was 0.45. The battery management system weights and fuses the target peak parameter (assuming a normalized value of 0.85), the first parameter (assuming a normalized value of 0.52), and the second parameter (assuming a normalized value of 0.45) with a weight of 0.5:0.3:0.2 to calculate the lithium plating risk coefficient R_Li = 0.85×0.5 + 0.52×0.3 + 0.45×0.2 = 0.671. This coefficient is greater than the first preset threshold, indicating that there is a moderate risk of lithium plating. Subsequently, the charging current will be adjusted accordingly based on this coefficient, for example, the current will be reduced from the nominal value of 250A to about 180A.

[0299] Alternatively, the implementation of S514 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0300] S515 smooths out the lithium plating risk factor.

[0301] For example, the lithium plating risk coefficient can be smoothed using an exponentially weighted moving average method. For instance, the smoothed value output at the current time is equal to the weighted sum of the original risk coefficient obtained by weighted fusion calculation at the current time and the smoothed value at the previous time.

[0302] Alternatively, the implementation of S515 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0303] S516 adjusts the upper limit of charging current based on the lithium plating risk coefficient after smoothing.

[0304] For example, the target current value is determined based on the smoothed lithium plating risk coefficient R_smooth. When R_smooth is less than a first preset threshold (e.g., 0.3), the target current value is the nominal maximum charging current (without current limiting); when R_smooth is greater than or equal to the first preset threshold and less than a second preset threshold (e.g., 0.6), the target current value is continuously reduced from the nominal maximum charging current to 60% to 80% of the nominal maximum charging current according to a first continuous mapping relationship; when R_smooth is greater than or equal to the second preset threshold, the target current value is continuously reduced from 60% to 80% of the nominal maximum charging current to 24% to 48% of the nominal maximum charging current according to a second continuous mapping relationship.

[0305] Optionally, the target current value can be sent to the charging device via a communication protocol.

[0306] Optionally, a hysteresis anti-jitter mechanism (separation of rising / falling thresholds) and an asymmetric rate limit (rising rate not exceeding 5% / second of the nominal maximum charging current and falling rate not exceeding 20% / second of the nominal maximum charging current) are also introduced during the current adjustment process.

[0307] Alternatively, the implementation of S516 can be found in [reference needed]. Figure 1 The relevant descriptions in S103 will not be repeated here.

[0308] In the above embodiments, vehicle charging parameters and battery state parameters are obtained. The battery state parameters include battery voltage parameters and capacity parameters. By analyzing the voltage and capacity parameters, a first voltage curve reflecting the rate of change of battery terminal voltage with the amount of charge is constructed. Since the parameters of the target peak in the first voltage curve can reflect the changes in the internal electrochemical state of the battery, a lithium plating risk coefficient is obtained based on the parameters of the target peak in the first voltage curve. This allows for real-time quantification of the risk of lithium plating during charging, avoiding the inability to accurately determine the lithium plating risk during charging in existing technologies. This prevents the setting of reasonable charging parameters when the lithium plating risk is high, resulting in low vehicle charging safety. Therefore, this solution can determine the lithium plating risk coefficient based on the parameters of the target peak and adjust the charging parameters according to the lithium plating risk coefficient, thereby improving the safety of vehicle charging.

[0309] The above text combined Figure 1 and Figure 2 This application provides a detailed description of a charging method based on its embodiments; the following will be combined with... Figure 6 and Figure 7 The apparatus embodiments of this application are described in detail below. It should be understood that the apparatus in the embodiments of this application can perform the various methods described in the foregoing embodiments of this application, that is, the specific working processes of the various products described below can be referred to the corresponding processes in the foregoing method embodiments.

[0310] Figure 6 This is a schematic diagram of a charging device provided in an embodiment of this application. The charging device 600 includes an acquisition module 601 and a processing module 602.

[0311] The acquisition module 601 is used to acquire the charging parameters and a first voltage curve of the battery in the vehicle when the vehicle is detected to be in a charging state. The first voltage curve is used to indicate the rate of change of the battery terminal voltage with the amount of charge. The first voltage curve is obtained based on the battery state parameters, which include the battery voltage parameters and the amount of charge. The processing module 602 is used to obtain the lithium plating risk coefficient of the battery based on the parameters of the target peak in the first voltage curve. The lithium plating risk coefficient of the battery is used to indicate the risk coefficient of lithium plating when the battery is charged with the charging parameters. Based on the lithium plating risk coefficient, the charging parameters of the vehicle are adjusted.

[0312] Optionally, as an embodiment, the processing module 602 is further configured to: obtain a first parameter and a second parameter, wherein the first parameter indicates the voltage recovery speed of the battery after the charging current is interrupted, and the second parameter includes the temperature difference between multiple cells in the battery and the heating rate of the first cell among the multiple cells, wherein the first cell represents the cell with the lowest temperature among the multiple cells; and obtain the lithium plating risk coefficient of the battery based on the parameters of the target peak in the first voltage curve, including: obtaining the lithium plating risk coefficient of the battery based on the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter; wherein the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter are positively correlated with the lithium plating risk coefficient.

[0313] Optionally, as an embodiment, the processing module 602 is specifically used to: normalize the parameters of the target peak, the first parameter, and the second parameter in the first voltage curve to obtain the parameters of the target peak, the first parameter, and the second parameter in the normalized first voltage curve; and perform a weighted calculation based on the parameters of the target peak, the first parameter, and the second parameter in the normalized first voltage curve to obtain the lithium plating risk coefficient.

[0314] Optionally, as an embodiment, the processing module 602 is further configured to: detect whether there is a current interruption in the battery based on the battery's current information; if a current interruption is detected, obtain the voltage parameter corresponding to a first preset time after the current interruption, and obtain a first parameter based on the voltage parameter corresponding to the first preset time; if no current interruption is detected, determine the first parameter as a preset minimum value.

[0315] Optionally, as an embodiment, the processing module 602 is specifically used to: perform fitting processing on the voltage parameters corresponding to the first preset time to obtain a target time constant, the target time constant being used to represent the voltage recovery speed of the battery after the current interruption phenomenon; and obtain a first parameter based on the ratio of the target time constant to the preset time constant.

[0316] Optionally, as an embodiment, the processing module 602 is further configured to: obtain a first cell among the multiple cells based on the temperature parameters of the multiple cells in the battery; obtain a target temperature rise rate of the first cell within a second preset time period; obtain an average temperature rise rate of the multiple cells within the second preset time period; and obtain a first target parameter based on the ratio of the target temperature rise rate to the average temperature rise rate.

[0317] Optionally, as an embodiment, the processing module 602 is further configured to: obtain the maximum and minimum temperature values ​​of multiple battery cells; obtain a target temperature difference based on the maximum and minimum temperature values; and obtain a second target parameter based on the ratio of the target temperature difference to a preset temperature difference.

[0318] Optionally, as an embodiment, the processing module 602 is further configured to: when the battery is in a preset charge range, acquire the amplitude and width of multiple peaks in the first voltage curve; when the amplitude of the first peak is detected to exceed a preset amplitude threshold and the width of the first peak is within a preset width range, determine the first peak as the target peak, and the first peak is used to represent any one of the multiple peaks.

[0319] Optionally, as an embodiment, the processing module 602 is specifically used to: adjust the vehicle's charging parameters based on a preset current value when the lithium plating risk coefficient is less than a first preset threshold; and adjust the vehicle's charging parameters based on the lithium plating risk, a preset weight, and a preset current value when the lithium plating risk coefficient is greater than or equal to the first preset threshold.

[0320] It should be noted that the charging device 600 described above is embodied in the form of a functional unit. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0321] For example, a "module" can be a software program, hardware circuit, or a combination of both that implements the above functions. Hardware circuits may include application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functions.

[0322] Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0323] Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0324] For example, vehicle 700 includes: processor 701, memory 702 and executable program code 706.

[0325] For example, vehicle 700 includes one or more processors 701 that can support the charging method in the method embodiments of vehicle 700. Processor 701 can be a general-purpose processor or a special-purpose processor. For example, processor 701 can be a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0326] For example, the processor 701 can be used to control the vehicle 700, execute software programs, and process data from the software programs. The vehicle 700 may also include a communication unit for receiving and transmitting signals.

[0327] For example, the vehicle 700 may include one or more memories 702 storing executable program code 706. The executable program code 706 can be run by the processor 701 to generate instructions, causing the processor 701 to execute the charging method described in the above method embodiments according to the instructions. For example, the processor 701 executes the following according to the instructions: when the vehicle is detected to be in a charging state, it acquires the charging parameters and a first voltage curve of the battery in the vehicle. The first voltage curve is used to indicate the rate of change of the battery's terminal voltage with the amount of charge. The first voltage curve is obtained based on battery state parameters, which include the battery's voltage parameters and charge parameters; based on the parameters of the target peak in the first voltage curve, it obtains the lithium plating risk coefficient of the battery. The lithium plating risk coefficient of the battery is used to indicate the risk coefficient of lithium plating when the battery is charged according to the charging parameters; based on the lithium plating risk coefficient, it adjusts the charging parameters of the vehicle.

[0328] Optionally, the memory 702 may also store data. Optionally, the processor 701 may also read the data stored in the memory 702, which may be stored at the same memory address as the executable program code 706, or the data may be stored at a different memory address than the executable program code 706.

[0329] For example, the processor 701 and memory 702 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0330] For example, the memory 702 can be used to store related programs of the charging method provided in the embodiments of this application, and the processor 701 can be used to call the executable program code 706 stored in the memory 702 when controlling the vehicle to execute the charging method of the embodiments of this application.

[0331] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the charging method of any of the foregoing embodiments.

[0332] The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, Digital Video Discs (DVDs), Compact Disc Read-Only Memory (CD-ROMs), microdrives, and magneto-optical disks, read-only memory (ROMs), random access memory (RAMs), erasable programmable read-only memory (EPROMs), electrically erasable programmable read-only memory (EEPROMs), dynamic random access memory (DRAMs), video random access memory (VRAMs), flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0333] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the charging method in the above embodiments.

[0334] In addition, the electronic device provided in the embodiments of this application may specifically be a chip, component or module. The electronic device may include a connected processor and a memory. The memory is used to store instructions. When the electronic device is running, the processor may call and execute the instructions to make the chip perform the charging method in the above embodiments.

[0335] The vehicle, computer-readable storage medium, computer program product or chip provided in this application are all used to execute the corresponding charging method provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding charging method provided above, and will not be repeated here.

[0336] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0337] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0338] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A charging method, characterized in that, The charging method includes: When the vehicle is detected to be in a charging state, the charging parameters of the battery in the vehicle and a first voltage curve are obtained. The first voltage curve is used to indicate the rate of change of the battery terminal voltage with the amount of charge. Based on the parameters of the target peak in the first voltage curve, the lithium plating risk coefficient of the battery is obtained. The lithium plating risk coefficient of the battery is used to indicate the risk coefficient of lithium plating when the battery is charged with the charging parameters. The charging parameters of the vehicle are adjusted based on the lithium plating risk coefficient.

2. The charging method according to claim 1, characterized in that, The charging method further includes: Obtain a first parameter and a second parameter. The first parameter is used to indicate the voltage recovery speed of the battery after the charging current is interrupted. The second parameter includes the temperature difference of multiple cells in the battery and the heating rate of the first cell among the multiple cells. The first cell is used to represent the cell with the lowest temperature among the multiple cells. The method of obtaining the lithium plating risk coefficient of the battery based on the parameters of the target peak in the first voltage curve includes: Based on the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter, the lithium plating risk coefficient of the battery is obtained. Among them, the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter are positively correlated with the lithium plating risk coefficient.

3. The charging method according to claim 2, characterized in that, The method of obtaining the lithium plating risk coefficient of the battery based on the parameters of the target peak in the first voltage curve, the first parameter, and the second parameter includes: The parameters of the target peak in the first voltage curve, the first parameter, and the second parameter are normalized respectively to obtain the parameters of the target peak in the first voltage curve after normalization, the first parameter after normalization, and the second parameter after normalization. The lithium plating risk coefficient is obtained by weighting the parameters of the target peak in the normalized first voltage curve, the normalized first parameter, and the normalized second parameter.

4. The charging method according to claim 2, characterized in that, The charging method further includes: Based on the current information of the battery, detect whether there is a current interruption in the battery; When the current interruption is detected, the voltage parameter corresponding to the first preset time after the current interruption is obtained, and the first parameter is obtained based on the voltage parameter corresponding to the first preset time. If no current interruption is detected, the first parameter is set to a preset minimum value.

5. The charging method according to claim 4, characterized in that, The process of obtaining the first parameter based on the voltage parameter corresponding to the first preset time includes: The voltage parameters corresponding to the first preset time are fitted to obtain a target time constant, which is used to represent the voltage recovery speed of the battery after the current interruption phenomenon. The first parameter is obtained based on the ratio of the target time constant to the preset time constant.

6. The charging method according to claim 2, characterized in that, The second parameter includes a first target parameter, which represents the heating rate of the first cell among the plurality of cells. The charging method further includes: Based on the temperature parameters of the plurality of cells in the battery, the first cell among the plurality of cells is obtained; Obtain the target temperature rise rate of the first battery cell within a second preset time period; Obtain the average temperature rise rate of the plurality of battery cells within the second preset time period; The first target parameter is obtained based on the ratio of the target temperature rise rate to the average temperature rise rate.

7. The charging method according to claim 2, characterized in that, The second parameter includes a second target parameter, which represents the temperature difference among multiple cells in the battery. The charging method further includes: Obtain the maximum and minimum temperature values ​​of the multiple battery cells; The target temperature difference is obtained based on the maximum and minimum temperature values. The second target parameter is obtained based on the ratio of the target temperature difference to the preset temperature difference.

8. The charging method according to claim 1, characterized in that, The charging method further includes: When the battery is in a preset charge range, the amplitude and width of multiple peaks in the first voltage curve are obtained; When the amplitude of the first peak exceeds a preset amplitude threshold and the width of the first peak is within a preset width range, the first peak is determined as the target peak, and the first peak is used to represent any one of the plurality of peaks.

9. The charging method according to any one of claims 1 to 8, characterized in that, Adjusting the vehicle's charging parameters based on the lithium plating risk coefficient includes: When the lithium plating risk coefficient is less than a first preset threshold, the charging parameters of the vehicle are adjusted based on a preset current value. When the lithium plating risk coefficient is greater than or equal to the first preset threshold, the charging parameters of the vehicle are adjusted based on the lithium plating risk, the preset weight, and the preset current value.

10. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor is configured to call and run the executable program code from the memory, causing the vehicle to perform the charging method as described in any one of claims 1 to 9.

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