Quick charging control method and system for lithium battery

Abstract

The invention relates to a lithium battery rapid charging control method and system, and the method comprises the following steps: continuously collecting operation parameters including the terminal voltage, the charging current and the battery surface temperature of a lithium battery, and calculating the current internal resistance value of the lithium battery based on the operation parameters; performing time sequence analysis on the lithium battery based on the battery surface temperature and the current internal resistance value to obtain a dynamic internal resistance curve, and performing slope change analysis on the dynamic internal resistance curve to obtain internal resistance change characteristics; and matching the internal resistance change characteristics through a preset charging strategy mapping table to obtain a target charging stage, determining a corresponding charging control instruction based on the target charging stage, and performing charging control on the lithium battery according to the charging control instruction, thereby solving the problem that the lithium battery cannot be charged rapidly in the process of pursuing rapid charging. The technical problem of how to balance the relationship among the charging speed, the battery life and the safety is solved.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method and system for controlling fast charging of lithium batteries. Background Technology

[0002] With the rapid popularization of new energy vehicles, portable electronic devices, and energy storage systems, lithium batteries, as core energy storage components, have seen their charging performance become a key factor affecting user experience and industrial development. Current market demand for lithium batteries is not only reflected in higher energy density but also in the urgent need for faster charging speeds, especially in the new energy vehicle sector, where users increasingly expect "rapid charging" to meet long-distance travel needs. However, in the pursuit of fast charging, balancing charging speed with battery life and safety has become a pressing problem for the industry. Traditional fast charging methods often lead to violent chemical reactions inside the battery, causing problems such as increased temperature and lithium plating, which not only shortens the battery's cycle life but can also pose safety hazards in severe cases. Summary of the Invention

[0003] The main technical problem addressed in this application is to provide a method and system for controlling fast charging of lithium batteries, which solves the technical problem of how to balance the relationship between charging speed, battery life, and safety in the pursuit of fast charging.

[0004] To solve the above-mentioned technical problems, this application adopts a lithium battery fast charging control method, which includes the following steps: The system continuously collects operating parameters, including the terminal voltage, charging current, and surface temperature of the lithium battery, and calculates the current internal resistance of the lithium battery based on these operating parameters. Based on the battery surface temperature and current internal resistance value, a time-series analysis is performed on the lithium battery to obtain a dynamic internal resistance curve. The slope change of the dynamic internal resistance curve is then analyzed to obtain the internal resistance change characteristics. The internal resistance change characteristics are matched by a preset charging strategy mapping table to obtain the target charging stage, and the corresponding charging control command is determined based on the target charging stage. The lithium battery is then charged according to the charging control command.

[0005] Furthermore, the continuous acquisition of operating parameters including the lithium battery's terminal voltage, charging current, and battery surface temperature includes: Continuous multi-channel synchronous data acquisition is performed on the lithium battery to obtain raw terminal voltage data, raw charging current data, and raw battery surface temperature data; Based on the timestamp corresponding to the acquisition time, the raw terminal voltage data, raw charging current data, and raw battery surface temperature data are aligned to obtain aligned operating parameters. The aligned operating parameters are then filtered to obtain the final operating parameters.

[0006] Furthermore, the calculation of the current internal resistance value of the lithium battery based on the operating parameters includes: Differential calculations are performed on the terminal voltage data and charging current data in the operating parameters to obtain the voltage change and current change, and the basic internal resistance value is calculated based on the ratio of the voltage change and current change. The base internal resistance value is corrected for temperature based on the battery surface temperature to obtain the current internal resistance value.

[0007] Furthermore, the step of performing time-series analysis on the lithium battery based on the battery surface temperature and current internal resistance value to obtain a dynamic internal resistance curve includes: The surface temperature and current internal resistance of the battery are marked according to a time series to obtain time series data points; The time series data points are segmented based on a preset time window to obtain segmented internal resistance data, and the segmented internal resistance data are smoothly connected to obtain a connection curve. Key state points, including internal resistance abrupt change points and temperature jump points, are extracted from the connection curve, and curve fitting is performed on the connection curve based on the key state points to obtain a dynamic internal resistance curve.

[0008] Furthermore, the slope change analysis of the dynamic internal resistance curve to obtain the internal resistance change characteristics includes: The slope of the tangent line is calculated point by point on the dynamic internal resistance curve to obtain a curve slope array. The curve slope array is then classified into intervals according to the magnitude of the slope value to obtain the slope classification result. The slope classification results are used to detect the change points of the dynamic internal resistance curve to obtain slope transformation nodes. Based on the slope transformation nodes, the dynamic internal resistance curve is segmented into trend segments to obtain internal resistance trend segments. The internal resistance change characteristics are obtained by statistically analyzing the segment length and calculating the average slope of the internal resistance trend segment.

[0009] Furthermore, the internal resistance change characteristics are matched using a preset charging strategy mapping table to obtain the target charging stage, including: The internal resistance change characteristics are numerically normalized to obtain normalized feature values, and a feature vector group including the mean slope, the rate of change of slope, and the duration of trend is generated based on the normalized feature values. The feature vector group is hierarchically mapped by a preset threshold range to obtain a stage matching identifier, and the charging strategy mapping table is queried based on the stage matching identifier to obtain the target charging stage.

[0010] Furthermore, based on the target charging stage, a corresponding charging control command is determined, and the lithium battery is charged according to the charging control command, including: Stage parameters are extracted for the target charging stage to obtain key stage parameters; wherein, the key stage parameters include the maximum charging current value, the charging cut-off voltage value, and the charging time limit value, and the key stage parameters are verified for safety parameter range to obtain qualified parameters; Based on the verified parameters, control commands are generated and buffered and smoothed to obtain a transition control sequence. The transition control sequence is then converted into a charging execution signal by the charging controller to perform segmented charging control of the lithium battery.

[0011] The present invention also provides a lithium battery fast charging control device, comprising: The acquisition module is used to continuously acquire operating parameters including the terminal voltage, charging current and surface temperature of the lithium battery, and calculate the current internal resistance value of the lithium battery based on the operating parameters. The analysis module is used to perform time-series analysis on the lithium battery based on the battery surface temperature and the current internal resistance value, obtain a dynamic internal resistance curve, and perform slope change analysis on the dynamic internal resistance curve to obtain the internal resistance change characteristics. The matching module is used to match the internal resistance change characteristics through a preset charging strategy mapping table to obtain the target charging stage, determine the corresponding charging control command based on the target charging stage, and control the charging of the lithium battery according to the charging control command.

[0012] The above scheme continuously collects operating parameters including the lithium battery's terminal voltage, charging current, and battery surface temperature, and calculates the current internal resistance value of the lithium battery based on these parameters. It then performs time-series analysis on the lithium battery based on the battery surface temperature and current internal resistance value to obtain a dynamic internal resistance curve, and analyzes the slope change of the dynamic internal resistance curve to obtain internal resistance change characteristics. By matching these internal resistance change characteristics with a preset charging strategy mapping table, a target charging stage is obtained, and a corresponding charging control command is determined based on the target charging stage. The lithium battery is then charged according to the charging control command. This solves the technical problem of balancing charging speed with battery life and safety in the pursuit of fast charging. Because this method combines battery surface temperature and current internal resistance value analysis, it can capture the corresponding change characteristics through the dynamic internal resistance curve, whether it's the difference in internal resistance between new and aged batteries or the difference in battery reaction efficiency under different temperature environments, and match appropriate charging control commands. For example, when the internal resistance of an aging battery is high, it automatically adjusts to a lower current and gentler charging method to avoid excessive battery wear, thereby adapting to batteries in different states and extending their overall cycle life. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the steps of a lithium battery fast charging control method in one embodiment of the present invention; Figure 2 This is a structural block diagram of a lithium battery fast charging control device according to an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0015] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] Specifically, the lithium battery fast charging control method of this embodiment includes the following steps: like Figure 1 As shown, Figure 1 This invention provides a lithium battery fast charging control method, comprising the following steps: Step S1: Continuously collect operating parameters including the terminal voltage, charging current and surface temperature of the lithium battery, and calculate the current internal resistance of the lithium battery based on the operating parameters.

[0018] Specifically, in implementation, a data acquisition system including voltage, current, and temperature sensors is first built. The voltage sensor is directly connected in parallel across the positive and negative terminals of the lithium battery to continuously capture terminal voltage data. The current sensor is connected in series in the charging circuit to record changes in charging current in real time. The temperature sensor is closely attached to a designated monitoring area on the battery surface to continuously acquire the battery surface temperature. The acquisition frequency of these three types of operating parameters must be consistent, usually set to 10-50 times per second to ensure data continuity. Then, based on Ohm's law and the equivalent circuit model of the lithium battery, the current internal resistance value can be calculated by subtracting the product of the charging current and the battery's equivalent series resistance (not the current internal resistance value) from the terminal voltage at the same acquisition moment, and then dividing by the charging current. For example, if the terminal voltage is 3.8V and the charging current is 2A at a certain moment, and the preset value of the equivalent series resistance is 0.05Ω, the current internal resistance value can be calculated as (3.8 - 2 × 0.05) ÷ 2 = 1.85Ω. The entire process requires a data acquisition card to convert the sensor signals into digital signals and then transmit them to the control chip for real-time calculation.

[0019] Step S2: Perform time-series analysis on the lithium battery based on the battery surface temperature and current internal resistance value to obtain a dynamic internal resistance curve, and perform slope change analysis on the dynamic internal resistance curve to obtain the internal resistance change characteristics.

[0020] Specifically, the continuously collected battery surface temperatures and corresponding current internal resistance values ​​are first organized in chronological order, with time as the horizontal axis and current internal resistance value as the vertical axis. The battery surface temperature corresponding to each data point is also labeled. A dynamic internal resistance curve is generated using a data fitting tool. For example, in a 25℃ environment, the current internal resistance values ​​of a certain lithium battery every 5 minutes from 0 to 30 minutes are 1.8Ω, 1.82Ω, 1.85Ω, 1.9Ω, 1.98Ω, and 2.1Ω, respectively. Based on this, a dynamic internal resistance curve that gradually increases over time is plotted. Next, select two adjacent time periods on the curve and calculate the ratio of the change in resistance to the change in time within these two time periods to obtain the slope. For example, if the resistance increases from 1.8Ω to 1.85Ω in 0-10 minutes, the slope is 0.005Ω / minute; if the resistance increases from 1.9Ω to 2.1Ω in 20-30 minutes, the slope is 0.02Ω / minute. By comparing the differences in slopes at different time periods, the characteristics of internal resistance change can be obtained. In this case, the slope increases significantly in the later stage, which indicates an accelerated increase in internal resistance.

[0021] Step S3: Match the internal resistance change characteristics using a preset charging strategy mapping table to obtain the target charging stage, determine the corresponding charging control command based on the target charging stage, and control the charging of the lithium battery according to the charging control command.

[0022] Specifically, the system first retrieves a pre-stored charging strategy mapping table in the control system. This table clearly indicates the correlation between different internal resistance change characteristics and corresponding target charging stages. For example, "internal resistance slope 0.005Ω / min and surface temperature 25℃" corresponds to the "constant current fast charging stage," and "internal resistance slope 0.02Ω / min and surface temperature 32℃" corresponds to the "constant voltage slow charging stage." Next, the previously obtained internal resistance change characteristics are compared one by one with the entries in the table. Once a completely matching entry is found, the target charging stage is determined. If the internal resistance change characteristic is "internal resistance slope 0.02Ω / min and surface temperature 32℃," then it matches the "constant voltage slow charging stage." Subsequently, based on this target charging stage, the corresponding charging control command is retrieved from the system's preset command library, such as "charging voltage 3.6V, current 0.5A." Finally, this command is transmitted to the charging execution module, which adjusts the output parameters to control the charging of the lithium battery.

[0023] In a specific embodiment, the continuous acquisition of operating parameters including the lithium battery's terminal voltage, charging current, and battery surface temperature includes: Continuous multi-channel synchronous data acquisition is performed on the lithium battery to obtain raw terminal voltage data, raw charging current data, and raw battery surface temperature data; Based on the timestamp corresponding to the acquisition time, the raw terminal voltage data, raw charging current data, and raw battery surface temperature data are aligned to obtain aligned operating parameters. The aligned operating parameters are then filtered to obtain the final operating parameters.

[0024] Specifically, when implementing continuous acquisition of operating parameters such as terminal voltage, charging current, and battery surface temperature of lithium batteries, a multi-channel synchronous data acquisition system must first be built. This system needs to include three independent acquisition channels, corresponding to the acquisition of terminal voltage, charging current, and battery surface temperature, respectively. The terminal voltage acquisition channel uses a differential voltage sensor with an accuracy of 0.1%, with its two detection terminals connected to the positive and negative terminals of the lithium battery, respectively. The sensor output signal is connected to the first channel of the data acquisition card. The charging current acquisition channel uses a Hall current sensor, connected in series in the main charging circuit of the lithium battery to ensure that the current signal can completely pass through the sensor detection area. Its output terminal is connected to the second channel of the data acquisition card. The battery surface temperature acquisition channel uses a surface-mount PT100 platinum resistance sensor, which is tightly attached to the center of the lithium battery surface with thermally conductive adhesive. The sensor leads are connected to the third channel of the data acquisition card. Next, set the sampling frequency of the data acquisition card, usually to 50Hz, and simultaneously start the acquisition function of the three channels, so that the three channels can synchronously acquire data under the control of the same clock signal, thereby obtaining the raw terminal voltage data (such as 3.72V, 3.73V, etc.), the raw charging current data (such as 1.98A, 2.01A, etc.), and the raw battery surface temperature data (such as 24.5℃, 24.6℃, etc.) generated every 20ms.

[0025] Next, data alignment is performed based on the timestamps corresponding to the acquisition times. Since the three channels acquire data synchronously, each acquired raw data point carries a unique timestamp (e.g., 2025-12-11 10:00:00.000, 2025-12-11 10:00:00.020, etc.). At this point, an alignment algorithm needs to be written in the data processing module. Using the timestamp as a benchmark, the raw terminal voltage data, raw charging current data, and raw battery surface temperature data corresponding to the same timestamp are grouped together to form a dataset containing three parameters. For example, the dataset corresponding to the timestamp "2025-12-11 10:00:00.020" is "terminal voltage 3.73V, charging current 2.01A, temperature 24.6℃". By matching all raw data one by one according to their timestamps in this way, the data alignment process is completed, and the aligned operating parameters are obtained.

[0026] Finally, the aligned operating parameters are filtered. Considering the possibility of electromagnetic interference causing abnormal data during the acquisition process (such as a sudden jump in terminal voltage to 4.5V, significantly exceeding the normal range), a moving average filtering algorithm is chosen. The filtering window size is set to 5, meaning that the same parameter is averaged across a given dataset and its two adjacent datasets before and after it. For example, when processing terminal voltage parameters, if the terminal voltage of a dataset is 3.73V, the terminal voltages of the two preceding datasets are 3.72V and 3.71V, and the terminal voltages of the two following datasets are 3.74V and 3.75V, then the filtered terminal voltage is (3.71+3.72+3.73+3.74+3.75)÷5 = 3.73V. If a dataset contains obvious anomalies (such as a terminal voltage of 4.5V), the Laida criterion is first used to remove the abnormal data, and then the average of adjacent normal data is used to fill the gap before performing a moving average calculation. Through this filtering process, operating parameters with reduced interference and more stable data fluctuations are finally obtained.

[0027] In a specific embodiment, calculating the current internal resistance value of the lithium battery based on the operating parameters includes: Differential calculations are performed on the terminal voltage data and charging current data in the operating parameters to obtain the voltage change and current change, and the basic internal resistance value is calculated based on the ratio of the voltage change and current change. The base internal resistance value is corrected for temperature based on the battery surface temperature to obtain the current internal resistance value.

[0028] Specifically, when calculating the current internal resistance of a lithium battery based on operating parameters, the terminal voltage and charging current data at two consecutive adjacent time points are first extracted from the aligned and filtered operating parameters. For example, the terminal voltage U1 = 3.72V and charging current I1 = 1.98A at time t1, and the terminal voltage U2 = 3.75V and charging current I2 = 2.02A at time t2. Then, a difference operation is performed on these two sets of data—the voltage change ΔU is the difference between the terminal voltage at time t2 and the terminal voltage at time t1, calculated as ΔU = 3.75V - 3.72V = 0.03V; the current change ΔI is the difference between the charging current at time t2 and the charging current at time t1, i.e., ΔI = 2.02A - 1.98A = 0.04A. Then, based on the derived relationship of Ohm's law, the voltage change ΔU... Dividing by the current change ΔI yields the base internal resistance value Rbase. Substituting the data, we get Rbase = 0.03V ÷ 0.04A = 0.75Ω. It's important to note that differential calculations require selecting two adjacent sets of data with a fixed time interval. For example, if the previously set sampling interval is 20ms, then the interval between t2 and t1 should also be 20ms to avoid calculation errors due to inconsistent time intervals. If data with a 100ms interval is mistakenly selected, excessive changes in battery state may cause ΔU and ΔI to deviate from the actual range of internal resistance changes, affecting the accuracy of the base internal resistance value.

[0029] After calculating the basic internal resistance, temperature correction is required based on the battery surface temperature from the operating parameters. First, a pre-established lithium battery internal resistance temperature correction model must be invoked. This model obtains the correspondence between temperature and internal resistance correction coefficients by testing the lithium battery internal resistance under different temperature environments (e.g., -20℃, 0℃, 25℃, 45℃, etc.). For example, when the battery surface temperature T=25℃, the correction coefficient K=1.0; when T=45℃, K=0.92; and when T=-10℃, K=1.25. Assuming the current battery surface temperature obtained from the operating parameters is T=35℃, by querying the correction model, the corresponding correction coefficient K=0.96 can be interpolated. Then, multiplying the basic internal resistance value Rbase by this correction coefficient K yields the current internal resistance value Rcurrent, i.e., Rcurrent=0.75Ω×0.96=0.72Ω. Temperature correction is crucial here because the internal resistance of lithium batteries fluctuates significantly with temperature. For example, at low temperatures, the migration rate of electrolyte ions slows down, and the internal resistance increases significantly. If no correction is performed and the baseline internal resistance value is directly used as the current internal resistance value, it may lead to a misjudgment that the battery internal resistance is too high in low-temperature environments, resulting in an incorrect adjustment of the charging strategy. For instance, if correction is ignored at -10℃ and the baseline internal resistance value of 0.75Ω is used to replace the actual internal resistance of 1.25×0.75Ω=0.9375Ω, the charging current will be set too high, increasing the risk of lithium plating in the battery.

[0030] In a specific embodiment, the step of performing time-series analysis on the lithium battery based on the battery surface temperature and current internal resistance value to obtain a dynamic internal resistance curve includes: The surface temperature and current internal resistance of the battery are marked according to a time series to obtain time series data points; The time series data points are segmented based on a preset time window to obtain segmented internal resistance data, and the segmented internal resistance data are smoothly connected to obtain a connection curve. Key state points, including internal resistance abrupt change points and temperature jump points, are extracted from the connection curve, and curve fitting is performed on the connection curve based on the key state points to obtain a dynamic internal resistance curve.

[0031] Specifically, when performing time-series analysis based on battery surface temperature and current internal resistance to obtain the dynamic internal resistance curve, the first step is to extract each set of corresponding data from the processed operating parameters. For example, at 14:00:00, the battery surface temperature is 26℃ and the current internal resistance is 0.72Ω; at 14:00:02, the temperature is 26.1℃ and the internal resistance is 0.73Ω, and so on. The battery surface temperature at each moment is bound to the current internal resistance value, and the data is marked with the acquisition time as the identifier, forming time-series data points such as "14:00:00-26℃-0.72Ω" and "14:00:02-26.1℃-0.73Ω". This ensures that each data point corresponds to a specific time node and avoids time misalignment in subsequent analysis.

[0032] Next, these time-series data points are segmented according to a preset time window. If the preset time window is 10 minutes, all time-series data points from 14:00:00 to 14:09:59 are grouped into the first segment, those from 14:10:00 to 14:19:59 into the second segment, and so on. The time-series data points within each segment together constitute the segmented internal resistance data for that time period. Then, linear interpolation is used to smoothly connect adjacent time-series data points within each segment. For example, between the internal resistance of 0.72Ω at 14:00:00 and 0.73Ω at 14:00:02 in the first segment, a virtual data point with an internal resistance of 0.725Ω at 14:00:01 is inserted. Then, all real and virtual data points are connected in chronological order to form a preliminary connection curve, allowing the curve to continuously reflect the changing trend of internal resistance within that time period and avoiding obvious discontinuities in the curve due to data acquisition intervals.

[0033] Finally, key state points are extracted from the connection curves. Among them, the internal resistance mutation point refers to the change in internal resistance that exceeds the normal fluctuation range in a short period of time. For example, in a certain connection curve, the internal resistance is 0.75Ω at 14:08:00 and suddenly rises to 0.82Ω at 14:08:03, with a difference of 0.07Ω, which is far greater than the previous fluctuation of 0.01Ω per minute. This point is the internal resistance mutation point. The temperature jump point is the node where the temperature changes abnormally. For example, the temperature is 28℃ at 14:15:00 and suddenly rises to 32℃ at 14:15:05. This point is the temperature jump point. After extracting these key state points, time is used as the horizontal axis and the current internal resistance value is used as the vertical axis. The key state points are used as anchor points, and a cubic polynomial fitting algorithm is used to fit the connection curve as a whole. For example, by adjusting the polynomial coefficients, the fitted curve can accurately pass through key state points such as "14:00:00-0.72Ω", "14:08:03-0.82Ω", and "14:15:05-0.83Ω", while smoothly transitioning to other non-key regions. Finally, a dynamic internal resistance curve that can fully reflect the internal resistance change law under different time is obtained.

[0034] In a specific embodiment, the step of analyzing the slope change of the dynamic internal resistance curve to obtain the internal resistance change characteristics includes: The slope of the tangent line is calculated point by point on the dynamic internal resistance curve to obtain a curve slope array. The curve slope array is then classified into intervals according to the magnitude of the slope value to obtain the slope classification result. The slope classification results are used to detect the change points of the dynamic internal resistance curve to obtain slope transformation nodes. Based on the slope transformation nodes, the dynamic internal resistance curve is segmented into trend segments to obtain internal resistance trend segments. The internal resistance change characteristics are obtained by statistically analyzing the segment length and calculating the average slope of the internal resistance trend segment.

[0035] Specifically, when analyzing the slope change of the dynamic internal resistance curve to obtain the characteristics of internal resistance change, the numerical differential method is first used to calculate the tangent slope of each data point on the dynamic internal resistance curve. For example, if the time corresponding to a certain point on the curve is 14:00:00 and the current internal resistance value is 0.72Ω, and the adjacent next point has a time of 14:00:02 and an internal resistance of 0.73Ω, the tangent slope of that point is obtained by calculating (0.73-0.72)÷(2-0)=0.005Ω / second. By traversing all points on the curve in this way, a curve slope array containing dozens or even hundreds of slope values ​​is obtained. The values ​​in the array may cover slopes of different magnitudes such as 0.003Ω / second, 0.005Ω / second, and 0.012Ω / second. Next, the curve slope array is classified into intervals according to the preset slope interval division standard. If "0-0.005Ω / second is the low-speed growth interval, 0.005-0.01Ω / second is the medium-speed growth interval, and above 0.01Ω / second is the high-speed growth interval", then the slopes of 0.003Ω / second and 0.005Ω / second in the array are classified into the low-speed growth interval, 0.006Ω / second and 0.009Ω / second are classified into the medium-speed growth interval, and 0.012Ω / second and 0.015Ω / second are classified into the high-speed growth interval, thus obtaining a clear slope classification result.

[0036] Subsequently, based on the slope classification results, the dynamic internal resistance curve is subjected to change point detection. When the slope interval of adjacent points before and after a certain position on the curve changes, that position is the slope transition node. For example, in a certain curve segment, the slope at 14:08:00 is 0.005Ω / s (low-speed growth interval), and the slope at the next adjacent point 14:08:03 suddenly changes to 0.012Ω / s (high-speed growth interval). Then the curve position corresponding to 14:08:03 is the slope transition node. Using these slope transition nodes as dividing points, the dynamic internal resistance curve is divided into trend segments. The curve segment between two adjacent slope transition nodes is divided into an internal resistance trend segment. If there are two slope transition nodes, 14:08:03 and 14:15:05, the curve can be divided into three internal resistance trend segments: “14:00:00-14:08:03”, “14:08:03-14:15:05”, and “14:15:05-14:30:00”.

[0037] Finally, the segment length and average slope of each internal resistance trend segment are statistically analyzed. For example, the segment length of "14:00:00-14:08:03" is 483 seconds, and the average slope value of all values ​​in this segment is calculated to be 0.0045Ω / second; the segment length of "14:08:03-14:15:05" is 422 seconds, and the average slope value is 0.013Ω / second. The length data of each segment is integrated with the corresponding average slope data to obtain information such as "low-speed growth - 483 seconds - average 0.0045Ω / second, high-speed growth - 422 seconds - average 0.013Ω / second". This information together constitutes the internal resistance change characteristics.

[0038] In a specific embodiment, the internal resistance change characteristics are matched using a preset charging strategy mapping table to obtain the target charging stage, including: The internal resistance change characteristics are numerically normalized to obtain normalized feature values, and a feature vector group including the mean slope, the rate of change of slope, and the duration of trend is generated based on the normalized feature values. The feature vector group is hierarchically mapped by a preset threshold range to obtain a stage matching identifier, and the charging strategy mapping table is queried based on the stage matching identifier to obtain the target charging stage.

[0039] Specifically, when matching the internal resistance change characteristics to obtain the target charging stage using a preset charging strategy mapping table, the data in the internal resistance change characteristics must first be numerically normalized. For example, if the previously obtained internal resistance change characteristics include "mean slope 0.0045Ω / second, slope change rate 0.002Ω / second², trend duration 483 seconds", the normalization range of each parameter must first be determined—mean slope set to 0-0.02Ω / second, slope change rate set to 0-0.005Ω / second², and trend duration set to 0-600 seconds. Then, the min-max normalization formula is used (normalized value = (original value - minimum value) ÷ (maximum value - maximum value)). Calculate the normalized eigenvalues, where the normalized slope mean is (0.0045-0)÷(0.02-0)=0.225, the normalized slope change rate is (0.002-0)÷(0.005-0)=0.4, and the normalized trend duration is (483-0)÷(600-0)=0.805. Combine these three normalized eigenvalues ​​in the order of "slope mean, slope change rate, trend duration" to generate an eigenvector group such as [0.225, 0.4, 0.805].

[0040] Next, the generated feature vector group is mapped hierarchically using a preset threshold range. The preset threshold range sets different level standards for the values ​​of each dimension in the feature vector group. For example, the slope mean dimension is set to "0-0.3 for low slope layer, 0.3-0.7 for medium slope layer, and 0.7-1 for high slope layer", the slope change rate dimension is set to "0-0.4 for low change rate layer, 0.4-0.8 for medium change rate layer, and 0.8-1 for high change rate layer", and the trend duration dimension is set to "0-0.5 for short duration layer, 0.5-0.9 for medium duration layer, and 0.9-1 for long duration layer". The values ​​in the feature vector group [0.225, 0.4, 0.805] are mapped to threshold intervals. The average slope of 0.225 belongs to the low slope layer, the slope change rate of 0.4 just reaches the lower limit of the medium change rate layer (according to the preset rule, if it equals the threshold, it is assigned to the next layer), and the trend duration of 0.805 belongs to the medium duration layer. The layer results of these three dimensions are combined to obtain the stage matching identifier of "low slope - medium change rate - medium duration".

[0041] Finally, based on the obtained stage matching identifier, a pre-defined charging strategy mapping table is queried. This mapping table has a pre-established correspondence between stage matching identifiers and target charging stages. For example, "low slope - low rate of change - short time" corresponds to "pre-charging stage", "low slope - medium rate of change - medium time" corresponds to "constant current fast charging stage", and "medium slope - high rate of change - long time" corresponds to "constant voltage slow charging stage". By looking up the entry corresponding to "low slope - medium rate of change - medium time", the target charging stage matched by this identifier can be determined to be "constant current fast charging stage", completing the entire matching process from internal resistance change characteristics to target charging stage.

[0042] In a specific embodiment, a corresponding charging control command is determined based on the target charging stage, and the lithium battery is charged according to the charging control command, including: Stage parameters are extracted for the target charging stage to obtain key stage parameters; wherein, the key stage parameters include the maximum charging current value, the charging cut-off voltage value, and the charging time limit value, and the key stage parameters are verified for safety parameter range to obtain qualified parameters; Based on the verified parameters, control commands are generated and buffered and smoothed to obtain a transition control sequence. The transition control sequence is then converted into a charging execution signal by the charging controller to perform segmented charging control of the lithium battery.

[0043] Specifically, when determining the corresponding charging control command based on the target charging stage and controlling the charging of the lithium battery, the stage parameters of the target charging stage are first extracted from the preset stage parameter library. Assuming that the previously obtained target charging stage is the "constant current fast charging stage", the key parameters corresponding to this stage are extracted from the parameter library. These usually include the maximum charging current value, the charging cut-off voltage value, and the charging time limit value. For example, the extracted parameters are "maximum charging current value 3A, charging cut-off voltage value 4.2V, and charging time limit value 30 minutes". Next, the safety parameter range of these key parameters at each stage is verified. The safety parameter standard library for lithium batteries is called. This library specifies the safety parameter thresholds for different types of lithium batteries at each stage. Taking ternary lithium batteries as an example, the maximum charging current safety threshold for the constant current fast charging stage is 0.1C-1C (0.5A-5A if the battery capacity is 5Ah), the charging cut-off voltage safety threshold is 4.15V-4.25V, and the charging time limit safety threshold is 20 minutes-40 minutes. The extracted "3A, 4.2V, 30 minutes" are compared with the safety thresholds respectively. It is confirmed that 3A is within the range of 0.5A-5A, 4.2V is within the range of 4.15V-4.25V, and 30 minutes is within the range of 20 minutes-40 minutes. All parameters meet the safety standards, thus obtaining the qualified parameters.

[0044] Then, based on the verified parameters, control commands are generated. The commands must explicitly include the core information: "Charge at the maximum charging current of 3A until the battery terminal voltage reaches 4.2V, and the entire charging process shall not exceed 30 minutes." Considering that directly switching charging parameters may cause sudden changes in current or voltage, the control commands need to be buffered and smoothed. A linear transition algorithm is used to generate a transition control sequence. For example, if the current charging current is 1A and the transition to the maximum charging current of 3A is to be made, with a buffer time of 5 seconds, the transition control sequence will include current commands of "1.4A in the first second, 1.8A in the second second, 2.2A in the third second, 2.6A in the fourth second, and 3A in the fifth second." At the same time, the voltage monitoring command will also be adjusted synchronously with the current change to ensure a smooth parameter transition. Finally, the transition control sequence is transmitted to the charging controller. The controller converts the digital instructions in the sequence into analog charging execution signals. For example, it converts a 3A current instruction into a corresponding PWM (pulse width modulation) signal to control the power devices in the charging circuit to output a stable current. The charging parameters are gradually adjusted according to the transition control sequence to achieve segmented charging control of the lithium battery. The battery status is also fed back in real time throughout the process to ensure that the charging always meets the requirements of the calibrated parameters.

[0045] Please see Figure 2 , Figure 2This is a schematic diagram of the framework of an embodiment of the lithium battery fast charging control device of this application. Figure 2 As shown, the lithium battery fast charging control device includes a data acquisition module 1, used to continuously acquire operating parameters including the terminal voltage, charging current, and surface temperature of the lithium battery, and calculate the current internal resistance value of the lithium battery based on the operating parameters; an analysis module 2, used to perform time-series analysis on the lithium battery based on the surface temperature and current internal resistance value to obtain a dynamic internal resistance curve, and to perform slope change analysis on the dynamic internal resistance curve to obtain internal resistance change characteristics; and a matching module 3, used to match the internal resistance change characteristics through a preset charging strategy mapping table to obtain a target charging stage, and to determine the corresponding charging control command based on the target charging stage, and to perform charging control on the lithium battery according to the charging control command.

[0046] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0047] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0048] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0049] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0050] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0051] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0052] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations 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 system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0053] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0054] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0055] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

Claims

1. A method for controlling fast charging of a lithium battery, characterized in that, Includes the following steps: The system continuously collects operating parameters, including the terminal voltage, charging current, and surface temperature of the lithium battery, and calculates the current internal resistance of the lithium battery based on these operating parameters. Based on the battery surface temperature and current internal resistance value, a time-series analysis is performed on the lithium battery to obtain a dynamic internal resistance curve. The slope change of the dynamic internal resistance curve is then analyzed to obtain the internal resistance change characteristics. The internal resistance change characteristics are matched by a preset charging strategy mapping table to obtain the target charging stage, and the corresponding charging control command is determined based on the target charging stage. The lithium battery is then charged according to the charging control command.

2. The lithium battery fast charging control method according to claim 1, characterized in that, The continuous acquisition includes operating parameters such as the lithium battery's terminal voltage, charging current, and battery surface temperature, including: Continuous multi-channel synchronous data acquisition is performed on the lithium battery to obtain raw terminal voltage data, raw charging current data, and raw battery surface temperature data; Based on the timestamp corresponding to the acquisition time, the raw terminal voltage data, raw charging current data, and raw battery surface temperature data are aligned to obtain aligned operating parameters. The aligned operating parameters are then filtered to obtain the final operating parameters.

3. The lithium battery fast charging control method according to claim 1, characterized in that, The calculation of the current internal resistance value of the lithium battery based on the operating parameters includes: Differential calculations are performed on the terminal voltage data and charging current data in the operating parameters to obtain the voltage change and current change, and the basic internal resistance value is calculated based on the ratio of the voltage change and current change. The base internal resistance value is corrected for temperature based on the battery surface temperature to obtain the current internal resistance value.

4. The lithium battery fast charging control method according to claim 1, characterized in that, The step of performing time-series analysis on the lithium battery based on the battery surface temperature and current internal resistance value to obtain a dynamic internal resistance curve includes: The surface temperature and current internal resistance of the battery are marked according to a time series to obtain time series data points; The time series data points are segmented based on a preset time window to obtain segmented internal resistance data, and the segmented internal resistance data are smoothly connected to obtain a connection curve. Key state points, including internal resistance abrupt change points and temperature jump points, are extracted from the connection curve, and curve fitting is performed on the connection curve based on the key state points to obtain a dynamic internal resistance curve.

5. The lithium battery fast charging control method according to claim 1, characterized in that, The slope change analysis of the dynamic internal resistance curve yields the internal resistance change characteristics, including: The slope of the tangent line is calculated point by point on the dynamic internal resistance curve to obtain a curve slope array. The curve slope array is then classified into intervals according to the magnitude of the slope value to obtain the slope classification result. The slope classification results are used to detect the change points of the dynamic internal resistance curve to obtain slope transformation nodes. Based on the slope transformation nodes, the dynamic internal resistance curve is segmented into trend segments to obtain internal resistance trend segments. The internal resistance change characteristics are obtained by statistically analyzing the segment length and calculating the average slope of the internal resistance trend segment.

6. The lithium battery fast charging control method according to claim 1, characterized in that, The target charging stage is obtained by matching the internal resistance change characteristics using a preset charging strategy mapping table, including: The internal resistance change characteristics are numerically normalized to obtain normalized feature values, and a feature vector group including the mean slope, the rate of change of slope, and the duration of trend is generated based on the normalized feature values. The feature vector group is hierarchically mapped by a preset threshold range to obtain a stage matching identifier, and the charging strategy mapping table is queried based on the stage matching identifier to obtain the target charging stage.

7. The lithium battery fast charging control method according to claim 1, characterized in that, Based on the target charging stage, a corresponding charging control command is determined, and the lithium battery is charged according to the charging control command, including: Stage parameters are extracted for the target charging stage to obtain key stage parameters; wherein, the key stage parameters include the maximum charging current value, the charging cut-off voltage value, and the charging time limit value, and the key stage parameters are verified for safety parameter range to obtain qualified parameters; Based on the verified parameters, control commands are generated and buffered and smoothed to obtain a transition control sequence. The transition control sequence is then converted into a charging execution signal by the charging controller to perform segmented charging control of the lithium battery.

8. A lithium battery fast charging control device, characterized in that, include: The acquisition module is used to continuously acquire operating parameters including the terminal voltage, charging current and surface temperature of the lithium battery, and calculate the current internal resistance value of the lithium battery based on the operating parameters. The analysis module is used to perform time-series analysis on the lithium battery based on the battery surface temperature and the current internal resistance value, obtain a dynamic internal resistance curve, and perform slope change analysis on the dynamic internal resistance curve to obtain the internal resistance change characteristics. The matching module is used to match the internal resistance change characteristics through a preset charging strategy mapping table to obtain the target charging stage, determine the corresponding charging control command based on the target charging stage, and control the charging of the lithium battery according to the charging control command.

9. A computer device, characterized in that, The method includes a memory and a processor coupled to each other, wherein the memory stores program instructions and the processor executes the program instructions to implement the lithium battery fast charging control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The device stores program instructions that can be executed by a processor, the program instructions being used to implement the lithium battery fast charging control method according to any one of claims 1 to 7.

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