Cloud platform and safe charging current determination method

Abstract

The invention discloses a cloud platform and a safe charging current determination method, and relates to the technical field of battery safety management, the cloud platform comprises a data collector and a cloud processor, and the data collector is used for collecting current working condition data and historical working condition data of a target battery from a vehicle-mounted battery management system; the cloud processor is used for determining a first working condition data increment according to the current working condition data and the historical working condition data; inputting the first working condition data increment into a direct-current internal resistance growth model to obtain a growth rate output by the direct-current internal resistance growth model; determining the current negative direct-current internal resistance of the target battery according to the growth rate and the historical negative direct-current internal resistance of the target battery; the safe charging current of the target battery is determined according to the current negative electrode direct-current internal resistance, the technical problem of how to accurately evaluate the charging capacity of the battery in the safe state is solved, and the evaluation accuracy of the safe charging capacity of the battery is improved.

Description

Technical Field

[0001] This application relates to the field of battery safety management technology, and more specifically, to a cloud platform and a method for determining safe charging current. Background Technology

[0002] With the widespread adoption of electric vehicles and energy storage power stations, a large number of lithium-ion batteries have entered the market. Battery performance, especially charging capacity, continuously degrades with usage time, cycle count, and operating conditions. Currently, market participants such as car owners, used car dealers, and maintenance personnel primarily rely on two traditional methods to assess battery health: one is a simple calculation based on full-charge capacity decay, focusing only on capacity retention and failing to accurately reflect the battery's charging acceptance. The other relies on the global DC internal resistance estimate provided by the battery management system, which lacks sensitivity to the negative electrode state, often only showing changes after significant battery aging, thus failing to provide early warning. Furthermore, existing methods are detached from actual user operating conditions and cannot provide personalized assessments and predictions for specific batteries. Therefore, a key technical challenge remains: how to accurately assess a battery's charging capacity under safe conditions.

[0003] Regarding the technical problem of how to accurately assess the charging capability of a battery under safe conditions, no effective solution has yet been proposed. Summary of the Invention

[0004] This application provides a cloud platform and a method for determining safe charging current, to at least solve the technical problem in the related art of how to accurately assess the charging capability of a battery in a safe state.

[0005] According to one embodiment of this application, a cloud platform is provided, including a data collector and a cloud processor. The data collector is communicatively connected to the cloud processor and an on-board battery management system, respectively. The data collector is used to collect current operating condition data and historical operating condition data of a target battery from the on-board battery management system. The cloud processor is used to: determine a first operating condition data increment based on the current operating condition data and the historical operating condition data; input the first operating condition data increment into a DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, wherein the DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions; determine the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery; and determine the safe charging current of the target battery based on the current negative electrode DC internal resistance.

[0006] According to another embodiment of this application, a method for determining a safe charging current is provided, applied to the aforementioned cloud platform, comprising: collecting current operating condition data and historical operating condition data of a target battery from an on-board battery management system; determining a first operating condition data increment based on the current operating condition data and the historical operating condition data; inputting the first operating condition data increment into a DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, wherein the DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions; determining the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery; and determining the safe charging current of the target battery based on the current negative electrode DC internal resistance.

[0007] In this embodiment, a cloud platform is proposed, including a data acquisition unit and a cloud processor. The data acquisition unit is communicatively connected to both the cloud processor and an on-board battery management system. The data acquisition unit is used to collect current and historical operating condition data of a target battery from the on-board battery management system. The cloud processor is used to: determine a first operating condition data increment based on the current and historical operating condition data; input the first operating condition data increment into a DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, wherein the DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions; determine the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery; and determine the safe charging current of the target battery based on the current negative electrode DC internal resistance. This embodiment calculates the current negative electrode DC internal resistance of the battery by combining the constructed battery negative electrode DC internal resistance growth model with the incremental operating data of the target battery, and then determines the safe charging current of the battery. This solves the technical problem of how to accurately evaluate the charging capacity of the battery under safe conditions in related technologies, and can accurately evaluate the battery's safe charging capacity, thereby improving the accuracy of battery safe charging capacity evaluation and measurement reliability. Attached Figure Description

[0008] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0009] 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1This is a flowchart of a method for determining the safe charging current according to an embodiment of this application;

[0011] Figure 2 This is a schematic diagram of a method for determining a safe charging current according to an embodiment of this application;

[0012] Figure 3 This is a schematic diagram illustrating the relationship between the growth of negative electrode DCR and aging time according to an embodiment of this application;

[0013] Figure 4 This is a schematic diagram illustrating the relationship between the growth of the negative electrode DCR and the number of cycles according to an embodiment of this application;

[0014] Figure 5 This is a schematic diagram illustrating the relationship between the growth of the negative electrode DCR and time under the operating conditions according to the embodiments of this application;

[0015] Figure 6 This is a structural block diagram of a cloud platform according to an embodiment of this application. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] However, there may be instances where unnecessary detailed descriptions are omitted. For example, detailed descriptions of well-known matters or repetitive descriptions of essentially the same structure may be omitted. This is to avoid making the following description unnecessarily lengthy and to facilitate understanding by those skilled in the art. Furthermore, the following description is provided to enable those skilled in the art to fully understand this application and is not intended to limit the subject matter of the claims.

[0019] The following appropriately discloses an example of a battery according to this application. The battery in this application is a secondary battery, also known as a rechargeable battery or a storage battery, which refers to a battery that can be used again after being discharged by recharging to activate the active materials.

[0020] Typically, a secondary battery includes an electrode assembly, an electrolyte, and an outer casing. The electrode assembly consists of a positive electrode, a negative electrode, and a separator. The electrode assembly and electrolyte are assembled inside the outer casing. During charging and discharging, active ions (such as lithium ions) move back and forth between the positive and negative electrodes, inserting and extracting. The separator, positioned between the positive and negative electrodes, primarily prevents short circuits while allowing active ions to pass through. The electrolyte, located between the positive and negative electrodes, mainly serves to conduct active ions.

[0021] For the aforementioned rechargeable batteries, this embodiment provides a method for determining the safe charging current, applied to a cloud platform. Figure 1 This is a flowchart of a method for determining the safe charging current according to an embodiment of this application. The process includes the following steps:

[0022] Step S102: Collect current operating condition data and historical operating condition data of the target battery from the vehicle battery management system;

[0023] The operating data includes, but is not limited to, battery storage temperature, cycle temperature, operating temperature, discharge rate, charge rate, resting SOC (State of Charge), resting time, and average operating discharge rate.

[0024] Step S104: Determine the first working condition data increment based on the current working condition data and the historical working condition data;

[0025] Step S106: Input the incremental data of the first operating condition into the DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, wherein the DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions.

[0026] Step S108: Determine the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery;

[0027] Step S110: Determine the safe charging current of the target battery based on the current negative electrode DC internal resistance.

[0028] Through the above steps, a first operating condition data increment is determined based on the current and historical operating condition data of the target battery. This first operating condition data increment is then input into a DC internal resistance growth model to obtain the growth rate output by the model. This model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions. The current negative electrode DC internal resistance of the target battery is determined based on the growth rate and the historical negative electrode DC internal resistance. Finally, the safe charging current of the target battery is determined based on the current negative electrode DC internal resistance. This embodiment, by constructing a battery negative electrode DC internal resistance growth model and combining it with the target battery's operating condition data increment, calculates the current negative electrode DC internal resistance of the battery, thereby determining the safe charging current. This solves the technical problem in related technologies of how to accurately assess the charging capacity of a battery under safe conditions, enabling accurate evaluation of the battery's safe charging capacity and improving the accuracy and reliability of battery safe charging capacity assessment.

[0029] In an exemplary embodiment, before inputting the first operating condition data increment into the DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, the method further includes: acquiring test data of a test battery, wherein the test battery is of the same model as the target battery; and constructing the DC internal resistance growth model based on the test data.

[0030] Optionally, in the above embodiments, to obtain a high-precision DC internal resistance growth model, the method for determining the safe charging current includes a model building stage before application. This stage requires acquiring test data from a test battery identical to the target battery model. This test data originates from systematic, multi-stress accelerated life tests conducted on the test batteries, aiming to cover various operating conditions that may accelerate battery aging. Through analysis and modeling of a large amount of test data, a DC internal resistance growth model capable of describing the quantitative relationship between operating stress and the rate of increase of negative electrode DC internal resistance is constructed. This ensures that the constructed model has a solid experimental data foundation and is consistent with the target battery to be evaluated in terms of material system and design, thereby guaranteeing the accuracy and reliability of the model predictions.

[0031] In an exemplary embodiment, obtaining test data for a test battery includes: performing an accelerated life test on the test battery based on test variables to obtain the correlation between the negative electrode DC internal resistance of the test battery and the test variables, wherein the accelerated life test is used to test the growth rate of the negative electrode DC internal resistance of the test battery under preset operating conditions; and determining the test data based on the test variables, the negative electrode DC internal resistance of the test battery corresponding to the test variables, and the correlation.

[0032] Optionally, in the above embodiments, the specific process of acquiring test data forms the scientific basis for model construction. This method actively acquires data by designing accelerated life tests, where the test variables are core control parameters, such as temperature, charge / discharge rate, and state of charge (SOC). During testing, these test variables are systematically changed, and the evolution of the negative electrode DC internal resistance of the test battery is monitored, thereby establishing a quantitative correlation between the negative electrode DC internal resistance and each test variable. For example, by fixing the charge / discharge rate and SOC range and only changing the ambient temperature for cyclic testing, a correlation curve between the cyclic temperature and the negative electrode DCR (Direct Current Resistance) growth rate can be obtained; by fixing the temperature and only changing the charge / discharge rate, a correlation curve between the charge / discharge rate and the growth rate can be obtained. Finally, all test variables, the negative electrode DC internal resistance values ​​measured under each variable condition, and the correlation patterns analyzed from them constitute the test dataset used for model construction. Through the above embodiments, using a single-variable controlled scientific experimental method, the impact of each stress factor on battery aging can be clearly and independently isolated and quantified, providing clean and reliable input data for the subsequent construction of complex multi-stress fusion models.

[0033] In an exemplary embodiment, accelerated life testing of the test battery is performed based on a test variable to obtain the correlation between the negative electrode DC internal resistance of the test battery and the test variable. This includes: when the test variable is determined to be the battery storage temperature, obtaining a first test temperature, a second test temperature, and a third test temperature determined from a test temperature range, wherein the first test temperature is lower than the second test temperature, and the second test temperature is lower than the third test temperature; recording a first curve, a second curve, and a third curve showing the increase of the negative electrode DC internal resistance of the test battery over time when the test battery is stored at the first test temperature, the second test temperature, and the third test temperature, respectively; and determining the correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature based on the Arrhenius equation and the first curve, the second curve, and the third curve.

[0034] Optionally, in the above embodiments, at least three representative temperature points are selected from the expected application temperature range (e.g., 25°C to 55°C), such as a first test temperature of 25°C, a second test temperature of 40°C, and a third test temperature of 55°C. Multiple groups of test batteries are subjected to storage aging tests under the above constant temperature conditions, and their negative electrode DC internal resistance is measured periodically, thereby plotting the first, second, and third curves showing the increase of negative electrode DC internal resistance with storage time. These curves visually demonstrate the significant impact of temperature on the aging rate. Subsequently, these three curves are analyzed based on the Arrhenius equation for chemical reactions. This equation shows that the chemical reaction rate (analogous to the DCR growth rate) has an exponential relationship with temperature. By performing Arrhenius fitting on the experimental data, the key parameter characterizing the temperature sensitivity of the battery system during storage aging—activation energy—can be extracted, thereby establishing a precise mathematical correlation between the negative electrode DC internal resistance growth rate and the battery storage temperature. Through the above embodiments, the influence of temperature, a key environmental stress, on the static aging of the battery is quantified and modeled, providing a key parameter for predicting the degradation of battery charging performance during aging.

[0035] In an exemplary embodiment, determining the correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature based on the Arrhenius equation and the first, second, and third curves includes: determining a first growth rate based on the first curve, a second growth rate based on the second curve, and a third growth rate based on the third curve, wherein the first growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when stored at the first test temperature, the second growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when stored at the second test temperature, and the third growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when stored at the third test temperature; substituting the first growth rate, the second growth rate, and the third growth rate into the Arrhenius equation to obtain a first fitting point, a second fitting point, and a third fitting point; performing mathematical formula fitting on the first fitting point, the second fitting point, and the third fitting point to obtain a first mathematical expression; and determining the correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature based on the first mathematical expression.

[0036] Optionally, in the above embodiments, the method of extracting mathematical relationships from storage temperature curves using the Arrhenius equation is further refined. Specifically, firstly, from the first, second, and third curves, the growth rate of the negative electrode DC internal resistance at the first, second, and third test temperatures is quantitatively obtained by calculating the slope of the curves at different time periods or by fitting the curve equation. Then, these three temperature points and their corresponding growth rates are substituted into the linear form of the Arrhenius equation: ln(k) = ln(A) - Ea / (RT), resulting in three data points (i.e., the first, second, and third fitting points). Here, k is the DCR growth rate, A is the pre-exponential factor, Ea is the activation energy, R is the gas constant, and T is the absolute temperature. By using mathematical methods such as linear regression to fit these fitting points, a straight line is obtained. The slope of this line includes the activation energy Ea of the aging reaction, and the intercept is related to the pre-exponential factor A, thus obtaining a specific first mathematical expression describing the relationship between temperature and growth rate, such as k = A. exp(-Ea / (RT)). This expression represents the mathematical representation of the determined correlation. Through the above embodiments, a standardized scientific data processing workflow transforms experimental curves into precise mathematical formulas that can be used for quantitative calculations, enhancing the theoretical rigor and computational feasibility of the model.

[0037] In an exemplary embodiment, accelerated life testing of the test battery is performed based on a test variable to obtain the correlation between the negative electrode DC internal resistance of the test battery and the test variable. This includes: when the test variable is determined to be the battery cycle temperature, obtaining a fourth test temperature, a fifth test temperature, and a sixth test temperature determined from the test temperature range, wherein the fourth test temperature is lower than the fifth test temperature, and the fifth test temperature is lower than the sixth test temperature; recording the fourth curve, the fifth curve, and the sixth curve showing the increase of the negative electrode DC internal resistance of the test battery over time under charge-discharge cycles at the fourth test temperature, the fifth test temperature, and the sixth test temperature, respectively; and determining the correlation between the negative electrode DC internal resistance of the test battery and the battery cycle temperature based on the Arrhenius equation and the fourth curve, the fifth curve, and the sixth curve.

[0038] Optionally, in the above embodiments, similar to storage testing, different cycling temperature conditions are selected, such as a fourth test temperature of 10°C, a fifth test temperature of 25°C, and a sixth test temperature of 45°C. The test battery is subjected to charge-discharge cycles in a specified pattern under these constant temperature environments, and the fourth, fifth, and sixth curves showing the increase in its negative electrode DC internal resistance as a function of the number of cycles or cumulative throughput are recorded. Although cycle aging involves electrochemical stress, temperature, as a fundamental physical factor, still follows the Arrhenius equation in accelerating the reaction rate. Therefore, by analyzing the DCR growth curves at different cycling temperatures, calculating their respective growth rates, and fitting them again using the Arrhenius equation, the correlation between temperature and the growth rate of negative electrode DC internal resistance under dynamic cycling conditions can be determined. Through the above embodiments, the contribution of temperature to the aging process during battery dynamic use can be quantified separately, providing key sub-parameters for constructing a fusion model that closely resembles real-world vehicle usage scenarios.

[0039] It should be noted that the accelerated life test is divided into two parts: storage accelerated test and cycle accelerated test. The above embodiment only specifically describes the storage temperature variable in the storage accelerated test and the cycle temperature variable in the cycle accelerated test. Similarly, by controlling the storage SOC, cycle rate, cycle temperature, cycle SOC range, cumulative fast charge cycles and throughput of the above single variables, the relationship between the battery negative electrode DCR and each variable can be obtained.

[0040] In an exemplary embodiment, constructing the DC internal resistance growth model based on the test data includes: obtaining multiple mathematical functions based on mathematical functions corresponding to different correlations; determining a target mathematical function based on the multiple mathematical functions, wherein the input of the target mathematical function is multiple variable values ​​in the battery operating condition data, and the output of the target mathematical function is the growth rate of the negative electrode DC internal resistance of the battery; and constructing the DC internal resistance growth model based on the target mathematical function.

[0041] Optionally, in the above embodiments, firstly, each correlation obtained in the preceding steps (such as temperature and growth rate, charge / discharge rate and growth rate, etc.) is converted into a corresponding mathematical function. Next, these mathematical functions describing individual effects need to be merged into a unified target mathematical function. This target mathematical function is a multi-input, single-output function, whose input variables cover multiple key variables in battery operating condition data (such as temperature, charge / discharge rate, SOC, etc.), and whose output is the predicted growth rate of the battery negative electrode DC internal resistance. The construction process is not a simple patchwork, but rather a coupling based on electrochemical principles and aging mechanisms. For example, the temperature term usually exists in the form of an Arrhenius factor, while the electrochemical stress (charge / discharge rate, SOC) term may be coupled based on the Butler-Wolmer equation. Finally, based on this comprehensive target mathematical function, a DC internal resistance growth model that can be used for real-time calculation is constructed. Through the above embodiments, an aging prediction model that can comprehensively reflect the coupling effects of multiple stresses is created, enabling the model to cope with the complex and variable battery operating conditions in the real world.

[0042] In an exemplary embodiment, determining the target mathematical function based on the plurality of mathematical functions includes: obtaining the basic aging rate constant and aging state function of the test battery, wherein the basic aging rate constant is used to characterize the inherent aging characteristics of the test battery, and the aging state function is used to characterize the characteristic that the aging rate of the test battery changes with the degree of aging; converting the function expression of each mathematical function into factor terms, and multiplying the plurality of factor terms to obtain a comprehensive factor term; and multiplying the comprehensive factor term, the basic aging rate constant, and the aging state function to obtain the function expression of the target mathematical function.

[0043] Optionally, in the above embodiments, firstly, the expression of each univariate mathematical function is converted into a "factor term," which represents the acceleration factor of different stresses on the aging rate (such as temperature acceleration factor and rate acceleration factor). Multiplying these factor terms yields a comprehensive factor term, representing the total acceleration effect under the combined action of all current operating stresses. Secondly, a "basic aging rate constant" is introduced, obtained through global data fitting, characterizing the intrinsic aging rate of the battery material system under the reference stress. Finally, an "aging state function" is introduced, using the cumulative damage of the battery (such as cumulative throughput) as the independent variable, to describe how the aging rate of the battery is not constant but changes (potentially accelerating or decelerating) as the battery ages. Multiplying the comprehensive factor term, the basic aging rate constant, and the aging state function yields the complete expression of the target mathematical function. For example, the final form of the model can be expressed as: dRa / dt=A exp(-Ea / RT) f(C,SOC) g(Ah_tot). Where A represents the basic aging rate constant, exp(-Ea / RT) represents the temperature acceleration factor (Arrhenius term), f(C,SOC) represents the electrochemical stress acceleration factor, and g(Ah_tot) represents the aging state function. This embodiment, by introducing the basic aging rate constant and the aging state function, enables the model to reflect not only the influence of immediate stress but also the inherent characteristics of the battery itself and the modulating effect of historical accumulated damage on the future, greatly enhancing the physical completeness and long-term prediction accuracy of the model.

[0044] In an exemplary embodiment, acquiring current operating condition data of a target battery and determining a first operating condition data increment based on the current operating condition data and historical operating condition data includes: acquiring first historical operating condition data at a first moment from the historical operating condition data, wherein the first moment is the latest operating condition data recording moment; and determining the first operating condition data increment based on the change between the current operating condition data and the first historical operating condition data.

[0045] Optionally, in the above embodiments, during continuous monitoring, the system records and stores the target battery's operating condition data at preset intervals (e.g., hourly or daily), forming a historical operating condition data sequence. When a new round of evaluation is required, the most recently recorded (i.e., the first moment) historical operating condition data is first retrieved from this sequence. Then, the latest collected current operating condition data is compared with the historical operating condition data from the first moment, and the changes in various stress parameters are calculated. For example, the increase in charging amount, average temperature change, and fast charging count since the last upload are calculated. The set of these changes constitutes the first operating condition data increment. This method achieves discretization and segmented processing of battery usage history, making it very suitable for application scenarios where vehicle terminals periodically upload data. Through the above embodiments, the continuous battery usage process is transformed into discrete data increments, greatly reducing the complexity of real-time data processing and the instantaneous demand on cloud computing resources, while improving the timeliness of evaluating battery charging capabilities.

[0046] In an exemplary embodiment, determining the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery includes: obtaining a first historical negative electrode DC internal resistance corresponding to the first time point from the historical negative electrode DC internal resistance; determining a first negative electrode DC internal resistance increment of the target battery based on the product of the first historical negative electrode DC internal resistance and the growth rate; and determining the current negative electrode DC internal resistance based on the sum of the first negative electrode DC internal resistance increment and the first historical negative electrode DC internal resistance.

[0047] Optionally, in the above embodiments, a method is provided for calculating the current negative electrode DC internal resistance based on a recursive update of the most recent state. First, a first historical negative electrode DC internal resistance value corresponding to a first moment (i.e., the moment of the most recent operating condition data recording) is obtained from the stored historical negative electrode DC internal resistance records. Then, using a DC internal resistance growth model, the growth rate calculated based on the increment of the first operating condition data (representing the average aging rate since the first moment) is combined with the time interval from the first moment to the current moment to calculate the expected increment of the negative electrode DC internal resistance during this period, i.e., the first negative electrode DC internal resistance increment. Finally, this increment is added to the first historical negative electrode DC internal resistance; the sum is the estimated current negative electrode DC internal resistance of the target battery. For example, if the internal resistance was 0.35mΩ at the last update, and the model calculates a growth rate of 0.001mΩ / day based on the usage data of the past 24 hours, then the current internal resistance is estimated to be 0.351mΩ. Its technical advantage is that it provides an efficient and computationally inexpensive real-time state update method. Through iterative recursion, it can continuously track the dynamic changes in battery health state and achieve quasi-continuous state estimation.

[0048] In one exemplary embodiment, determining the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery further includes: obtaining the initial historical negative electrode DC internal resistance corresponding to the initial time from the historical negative electrode DC internal resistance; determining the second negative electrode DC internal resistance increment of the target battery based on the product of the initial historical negative electrode DC internal resistance and the growth rate; and determining the current negative electrode DC internal resistance based on the sum of the second negative electrode DC internal resistance increment and the initial historical negative electrode DC internal resistance.

[0049] Optionally, in the above embodiments, when the battery is used for the first time after leaving the factory, cumulative calculation is performed starting from the battery's initial state. First, the initial historical negative electrode DC internal resistance, obtained through precise measurement at the initial time of leaving the factory or during initial use, is used as the baseline for cumulative calculation. Then, historical operating data from the initial time to the current time is obtained, and the growth rate from the initial time to the current time is calculated using a DC internal resistance growth model, thereby determining the second negative electrode DC internal resistance increment. Finally, the second increment is added to the initial historical negative electrode DC internal resistance to obtain the current negative electrode DC internal resistance. This method is suitable for scenarios where the battery is used for the first time after leaving the factory.

[0050] In an exemplary embodiment, before obtaining the initial historical negative electrode DC internal resistance corresponding to the initial moment from the historical negative electrode DC internal resistance, the method further includes: obtaining the voltage between the negative electrode of the target battery and the copper wire, wherein the copper wire is disposed between the negative electrode of the target battery and the negative electrode separator; obtaining the test current for testing the negative electrode DC internal resistance of the target battery; and determining the initial negative electrode DC internal resistance based on the ratio of the voltage to the test current.

[0051] Optionally, in the above embodiments, an innovative measurement technique with an embedded reference electrode is employed. Specifically, during the manufacturing stage of the target battery, a fine copper wire is implanted inside the battery as a probe, positioned between the negative electrode and the adjacent separator, and insulated to ensure it serves only as a measurement terminal. After battery formation, the copper wire is electroplated with lithium using a small current, transforming it into a stable lithium metal reference electrode. During standard DC internal resistance testing of the battery, the voltage change ΔU between the copper wire and the negative electrode is monitored simultaneously. According to Ohm's law, this voltage change ΔU is divided by the known test current I used for the DCR test; the resulting ratio ΔU / I is the pure negative electrode DC internal resistance Ra0, excluding the influence of the positive electrode, electrolyte, etc. For example, if the test current is 10A and the measured voltage change is 3.5mV, then the initial negative electrode DCR is 0.35mΩ. The above embodiments provide an engineering method for directly and accurately measuring the internal resistance of a single electrode in a commercial battery, establishing an accurate and reliable zero-point benchmark for the entire evaluation system, and fundamentally improving the accuracy of all subsequent predictions and evaluations.

[0052] In an exemplary embodiment, determining the safe charging current of the target battery based on the current negative electrode DC internal resistance includes: determining the critical current corresponding to the target battery under different negative electrode DC internal resistances, wherein the target battery undergoes negative electrode lithium plating when the charging current is greater than the critical current; obtaining the charging current when the target battery undergoes negative electrode lithium plating under different negative electrode DC internal resistances to obtain different critical currents; establishing a target mapping relationship function based on the correspondence between the different critical currents and the different negative electrode DC internal resistances; determining the current critical current corresponding to the current negative electrode DC internal resistance based on the target mapping relationship function, and determining the current critical current as the safe charging current.

[0053] Optionally, in the above embodiments, the core is to establish a mapping relationship between the negative electrode DC internal resistance and the critical current. The critical current is defined as the charging current that, given the internal resistance and current temperature and SOC conditions, just causes the negative electrode potential to drop to the lithium plating threshold; exceeding this current poses a risk of lithium plating. This mapping relationship can be established through a combination of electrochemical principle derivation and experimental calibration. For example, this mapping relationship function can be represented by the formula Ic_max=f(Ra,T), which is the maximum safe charging current when the negative electrode internal resistance is Ra at temperature T. After determining the current negative electrode DC internal resistance Ra', inputting it into the mapping relationship function will output the corresponding current critical current Ic_max, which is then determined as the safe charging current at this moment. Through the above embodiments, the internal microscopic electrochemical state (internal resistance) is directly transformed into an externally macroscopically controllable charging parameter (current upper limit), providing a clear and scientific safe operating boundary for the battery management system and realizing a closed loop from state perception to safety control.

[0054] In one exemplary embodiment, the method further includes: determining a second operating condition data increment based on preset operating condition data and the current operating condition data, wherein the preset operating condition data represents operating condition data simulated at a preset time, and the preset time is after the current time; inputting the second operating condition data increment into the DC internal resistance growth model to obtain the predicted growth rate output by the DC internal resistance growth model; determining the predicted negative DC internal resistance of the target battery at the preset time based on the predicted growth rate and the current negative DC internal resistance of the target battery; and determining the predicted safe charging current of the target battery at the preset time based on the predicted negative DC internal resistance.

[0055] Optionally, in the above embodiments, the functionality of the method for determining the safe charging current is expanded, enabling it not only to assess the current state but also to predict the charging capacity at a specific future point in time. First, simulated operating condition data for a future point in time (the preset time) is preset based on user-defined or typical scenario data, such as predicting whether the user will maintain their current driving habits or plan to increase fast charging frequency a year from now. Based on the difference between the preset operating condition data and the current operating condition data, a second operating condition data increment is calculated. This increment is input into a DC internal resistance growth model to obtain the predicted growth rate over that future time period. Combined with the currently estimated negative DC internal resistance, the predicted negative DC internal resistance at the preset time can be calculated. Finally, using the same mapping relationship, the predicted safe charging current at the preset time is determined based on the predicted negative DC internal resistance. For example, the system can predict that the maximum fast charging current of the vehicle battery will decrease from the current 150A to 120A three years from now. The technical effect is that it endows the method with forward-looking early warning and planning capabilities, providing crucial data support for user vehicle replacement decisions, used car valuation, battery warranty strategies, and charging infrastructure planning.

[0056] In one exemplary embodiment, the method further includes: establishing backup data for the target battery, wherein the backup data includes the DC internal resistance growth model, the initial negative electrode DC internal resistance of the target battery, and the operating condition data of the target battery, and the backup data is stored in a cloud server; acquiring the latest operating condition data of the target battery according to a preset period, and saving the latest operating condition data to the backup data.

[0057] Optionally, in the above embodiments, an independent digital profile can be created for each target battery on a cloud server as its "backup data." This profile persistently stores three main categories of key information: 1) the DC internal resistance growth model corresponding to the battery model; 2) the initial negative electrode DC internal resistance measurement value unique to each battery; and 3) the time-series record of historical operating condition data throughout the battery's entire life cycle. The vehicle terminal or data acquisition device automatically collects the latest operating condition data of the battery according to a preset cycle (e.g., once a day) and uploads it to the cloud via encryption, updating the corresponding backup data. This architecture achieves centralized data management, persistent storage, and continuous accumulation. Through the above embodiments, complex model calculations and massive historical data storage are transferred from the vehicle to the cloud, reducing the requirements for computing power and storage resources of the vehicle controller; at the same time, cloud storage ensures data security and traceability, facilitating big data analysis and model iterative optimization.

[0058] In an optional embodiment, combined with Figure 2 The method for determining the safe charging current in this application is explained, such as... Figure 2 As shown, the method for determining the safe charging current in this application can be divided into three parts:

[0059] I. DCR Model Establishment: Accelerated testing was conducted, using key operating conditions such as temperature, charge / discharge rate, and state of charge as inputs. Systematic aging tests were performed on the test batteries to obtain data on the growth of the negative electrode's DC internal resistance (DCR). Based on this data, a negative electrode DCR growth model was built that can predict the DCR growth rate under different stress combinations. This model serves as the computational foundation for all subsequent online evaluations.

[0060] II. Cloud Data Collection and Processing: First, initial negative electrode internal resistance is collected, specifically the initial negative electrode DCR baseline value Ra0, obtained through precise measurement at the battery's factory. Simultaneously, real-time operating condition data, including but not limited to battery operating temperature, real-time SOC, charge / discharge rate, energy consumption per 100 kilometers, and resting time, is continuously received from the vehicle's Battery Management System (BMS) via the vehicle network. This data includes data such as battery operating temperature, real-time SOC, charge / discharge rate, energy consumption per 100 kilometers, and resting time. The cloud data processing unit integrates these two pieces of information: receiving real-time operating condition data from the BMS and using a pre-deployed negative electrode DCR growth model, it calculates and updates the current cumulative negative electrode DC internal resistance estimate Ra' based on the latest operating condition stress changes. Further, based on the updated Ra' value, the maximum allowable safe charging current Ic_max for the battery under the current state is calculated using a preset electrochemical mapping function.

[0061] III. Full Lifecycle Charging Capability Assessment and Early Warning: The real-time Ic_max value calculated in the cloud is compared with the user's planned or currently used charging current I. Based on the comparison result, a judgment and output are made: if I ≤ Ic_max, it is determined to be risk-free, and the current charging strategy is allowed; if I > Ic_max, it is determined to have a risk of lithium plating, and the system will not only issue an early warning but can also further combine model prediction functions to output the expected time of lithium plating, achieving proactive alarm. All assessment results, early warning information, and historical trends are ultimately presented to end users, maintenance personnel, or third-party platforms through client apps or web push notifications and visualization interfaces, completing the final step from data to decision support.

[0062] In an optional embodiment, the method for determining the safe charging current of this application is described using a lithium iron phosphate / graphite system aluminum-cased square battery as an example.

[0063] 1. Obtaining the baseline value Ra0 of the initial negative DC internal resistance DCR.

[0064] To accurately obtain the negative electrode health baseline of the battery in the Beginning of Life (BOL) state, a measurement method with an embedded reference electrode is used:

[0065] Reference electrode implantation: During the winding or stacking process of the battery cell, a fine copper wire is used as a probe and pre-placed between the negative electrode and its corresponding main separator. To ensure that the copper wire does not short-circuit with the opposite positive electrode, an additional insulating separator is added on the side of the copper wire facing inwards from the battery. Subsequently, the battery cell completes drying, electrolyte injection, formation, and capacity setting steps according to standard process procedures.

[0066] Activating the reference electrode: For the formed cell, a small current (e.g., 5 μA to 0.1 mA) is used to charge the implanted copper wire using an external circuit. This process causes lithium ions to deposit on the surface of the copper wire, forming a stable lithium metal layer, thereby transforming the copper wire into a reliable lithium metal reference electrode.

[0067] Measurement and calculation of initial Ra0: A standard DC internal resistance (DCR) test is performed on the assembled battery. During the test, a known pulse current I0 is applied, and two voltage signals are measured simultaneously: the change in the terminal voltage of the entire battery; and the voltage change ΔU0 between the negative electrode and the aforementioned copper wire (lithium reference electrode). According to Ohm's law, the initial negative electrode DC internal resistance baseline value Ra0 of the battery can be calculated by the following formula: Ra0 = ΔU0 / I0. By repeating this test at different states of charge (SOC), the mapping relationship of the initial negative electrode DCR as a function of SOC (DCR map) can be obtained as shown in Table 1.

[0068] Table 1

[0069]

[0070] Determine the inherent safety parameter Ua_min: Using the same battery, perform slow charging according to its standard charging strategy and continuously monitor the potential Ua of the negative electrode relative to the lithium reference electrode. Record the lowest value of Ua throughout the entire charging SOC range and define it as the inherent safety parameter Ua_min for this battery model (e.g., in this experimental example, this value was measured near 60% SOC, and was 20mV). This parameter represents the safe boundary of the negative electrode potential of the battery in a brand-new state.

[0071] 2. Establishment of the DC internal resistance (DCR) growth model for the negative electrode.

[0072] Based on a battery with a known Ra0, Ra' was obtained after storage and cyclic aging to different health states. Models for the increase in negative electrode internal resistance under both storage and cyclic conditions were established. The relationship between the increase in negative electrode internal resistance under 45℃ storage conditions and aging time is shown below. Figure 3 As shown, the relationship between the increase in negative electrode internal resistance and the number of cycles under 45℃ cycling conditions is as follows: Figure 4 As shown.

[0073] Based on the market-end operating conditions input by the customer, data such as daily operating time of the battery cells, stored SOC, number of charge / discharge cycles, charge / discharge rate, and operating temperature are extracted. Combined with the negative electrode DCR growth rate model under single storage and single cycle conditions, a negative electrode DCR growth model under operating conditions is constructed. The relationship between negative electrode DCR growth and time under operating conditions is as follows: Figure 5As shown in Table 2, the negative electrode DCR map for a given battery condition can be obtained by inputting mileage or operating time into the model. For example, the negative electrode DCR map after 10 years of operation is shown in Table 2 below.

[0074] Table 2

[0075]

[0076] 3. Example of charging capability assessment.

[0077] This section demonstrates how to use established models and parameters to assess the charging safety boundaries of a battery that has been used for 10 years.

[0078] During battery charging, the true potential of the negative electrode is Ua = OCP - I × Ra, where OCP is the open-circuit voltage, I is the charging current, and Ra is the DC internal resistance of the negative electrode. The critical condition for lithium plating is that the negative electrode potential Ua drops to 0V (relative to the lithium metal reference electrode). Therefore, to ensure that the battery does not plating lithium, the predicted minimum negative electrode potential of the aged battery, Ua'_min, must be greater than 0V. This means Ua'_min - Ua - Ua_min > 0, i.e., I' × Ra' - I × Ra - Ua_min > 0V, where I' represents the charging current of the aged battery, and Ra' represents the internal resistance of the negative electrode after aging. After simplification, the formula for calculating the maximum safe charging current of the aged battery is: Ic_max = (I × Ra - Ua_min) / Ra', where Ic_max represents the maximum safe charging current of the aged battery, and Ua_min represents the minimum negative electrode potential in the initial state of the battery. Based on the above formula, the maximum allowable charging current for a battery operating for 10 years is calculated as Ic_max = (0.02 + 100) / Ra'. (0.315 / 1000) / (0.434 / 1000) = 118.6A. This current is higher than the original 100A charging current, therefore there is no risk of lithium plating.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0080] In one embodiment, Figure 6 This is a structural block diagram of a cloud platform according to an embodiment of this application, such as... Figure 6 As shown, the cloud platform 60 includes a data acquisition unit 604 and a cloud processor 602. The data acquisition unit 604 is communicatively connected to the cloud processor 602 and the vehicle battery management system 606. The data acquisition unit 604 is used to collect current operating condition data and historical operating condition data of the target battery from the vehicle battery management system. The cloud processor 602 is used to: determine a first operating condition data increment based on the current operating condition data and the historical operating condition data; input the first operating condition data increment into a DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, wherein the DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions; determine the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery; and determine the safe charging current of the target battery based on the current negative electrode DC internal resistance.

[0081] Through the aforementioned cloud platform, a first operating condition data increment is determined based on the current and historical operating condition data of the target battery. This first operating condition data increment is then input into a DC internal resistance growth model to obtain the growth rate output by the model. This model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions. The current negative electrode DC internal resistance of the target battery is determined based on the growth rate and the historical negative electrode DC internal resistance. Finally, the safe charging current of the target battery is determined based on the current negative electrode DC internal resistance. This embodiment, by combining the constructed battery negative electrode DC internal resistance growth model with the target battery's operating condition data increment, calculates the current negative electrode DC internal resistance of the battery and then determines the safe charging current. This solves the technical problem of accurately assessing the charging capacity of a battery under safe conditions in related technologies, enabling accurate evaluation of the battery's safe charging capacity and thus improving the accuracy and reliability of battery safe charging capacity assessment.

[0082] In one exemplary embodiment, the cloud processor is further configured to: determine a charging strategy for the target battery based on the safe charging current.

[0083] Optionally, in the above embodiments, for example, when the cloud processor calculates that the safe charging current of a vehicle battery is 145A, if the vehicle plans to use a charging pile with an output capacity of 250A, the cloud processor will generate a current-limiting charging strategy, that is, suggest that the charging pile charge at a current not exceeding 145A, and send instructions to the vehicle BMS to coordinate with the charging pile during the charging process to limit the charging current within a safe range.

[0084] In one exemplary embodiment, the cloud platform further includes a data transmitter, which is configured to: send the charging strategy to a user terminal to instruct the user terminal to display the charging strategy on a display page corresponding to the user terminal after receiving the charging strategy, wherein the user terminal includes a personal terminal or a third-party terminal.

[0085] Optionally, in the above embodiments, for example, if the battery of an electric vehicle driven by a car owner is evaluated by a cloud platform and the current maximum safe charging current is updated to 145A, the data transmitter sends this information and the generated charging strategy to the car owner's smartphone via an app push notification. The car owner can then open the vehicle brand's app on their phone and see the pushed battery health report card on the homepage. For third-party terminals, such as used car appraisal platforms, after obtaining authorization from the car owner, the data transmitter can push the anonymized battery appraisal report (including the current maximum safe charging current, estimated future degradation curve, etc.) to the platform's backend for reference when valuing the vehicle in a transaction.

[0086] In one exemplary embodiment, the cloud platform is communicatively connected to multiple vehicle battery management systems, each corresponding to a different user terminal. After processing the data corresponding to any vehicle battery management system, the cloud platform sends the resulting charging strategy for the battery corresponding to that vehicle battery management system to the user terminal corresponding to that vehicle battery management system.

[0087] Optionally, in a large-scale application scenario, the cloud platform of this application, as a centralized battery management service platform, can simultaneously establish communication connections with the on-board battery management systems of multiple electric vehicles of different brands and models.

[0088] For example, the cloud platform simultaneously serves both owner A's LFP battery vehicle and owner B's NCM ternary battery vehicle. The cloud platform establishes an independent digital profile for each vehicle, storing its model-specific DC internal resistance growth model, initial factory internal resistance value, and operational data records throughout its entire lifecycle. Every morning, the platform's data collector receives operational data uploaded by all online vehicle BMS systems in parallel. The cloud processor initiates distributed computing tasks to process the battery status of each vehicle in parallel, calculating its first operational data increment, current negative electrode DC internal resistance, and maximum safe charging current, and generating personalized charging strategies. After data processing, the data transmitter, based on each vehicle's unique identifier, pushes the corresponding evaluation results and charging strategies to the user terminals bound to the vehicle. For example, owner A's battery evaluation report is pushed to owner A's mobile app, and owner B's battery warning information is pushed to owner B's application mini-program.

[0089] In one exemplary embodiment, the cloud processor is further configured to: acquire test data of a test battery, wherein the test battery is of the same model as the target battery; and construct the DC internal resistance growth model based on the test data.

[0090] In an exemplary embodiment, the cloud processor is further configured to acquire test data of the test battery by: performing an accelerated life test on the test battery according to a test variable to obtain the correlation between the negative electrode DC internal resistance of the test battery and the test variable, wherein the accelerated life test is used to test the growth rate of the negative electrode DC internal resistance of the test battery under preset operating conditions; and determining the test data based on the test variable, the negative electrode DC internal resistance of the test battery corresponding to the test variable, and the correlation.

[0091] In an exemplary embodiment, the cloud processor is further configured to obtain the correlation between the negative electrode DC internal resistance of the test battery and the test variable by: when the test variable is determined to be the battery storage temperature, acquiring a first test temperature, a second test temperature, and a third test temperature determined from the test temperature range, wherein the first test temperature is lower than the second test temperature, and the second test temperature is lower than the third test temperature; recording a first curve, a second curve, and a third curve showing the increase of the negative electrode DC internal resistance of the test battery over time when the test battery is stored at the first test temperature, the second test temperature, and the third test temperature, respectively; and determining the correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature based on the Arrhenius equation and the first curve, the second curve, and the third curve.

[0092] In an exemplary embodiment, the cloud processor is further configured to determine the correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature by: determining a first growth rate based on the first curve, determining a second growth rate based on the second curve, and determining a third growth rate based on the third curve, wherein the first growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when stored at the first test temperature, the second growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when stored at the second test temperature, and the third growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when stored at the third test temperature; substituting the first growth rate, the second growth rate, and the third growth rate into the Arrhenius equation respectively to obtain a first fitting point, a second fitting point, and a third fitting point; performing mathematical formula fitting on the first fitting point, the second fitting point, and the third fitting point to obtain a first mathematical expression; and determining the correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature based on the first mathematical expression.

[0093] In an exemplary embodiment, the cloud processor is further configured to obtain the correlation between the negative electrode DC internal resistance of the test battery and the test variable by: when the test variable is determined to be the battery cycle temperature, obtaining a fourth test temperature, a fifth test temperature, and a sixth test temperature determined from the test temperature range, wherein the fourth test temperature is lower than the fifth test temperature, and the fifth test temperature is lower than the sixth test temperature; recording the fourth curve, the fifth curve, and the sixth curve showing the increase of the negative electrode DC internal resistance of the test battery over time when the test battery is charged and discharged at the fourth test temperature, the fifth test temperature, and the sixth test temperature, respectively; and determining the correlation between the negative electrode DC internal resistance of the test battery and the battery cycle temperature based on the Arrhenius equation and the fourth curve, the fifth curve, and the sixth curve.

[0094] In an exemplary embodiment, the cloud processor is further configured to construct the DC internal resistance growth model by: obtaining multiple mathematical functions based on mathematical functions corresponding to different correlations; determining a target mathematical function based on the multiple mathematical functions, wherein the input of the target mathematical function is multiple variable values ​​in the battery operating condition data, and the output of the target mathematical function is the growth rate of the negative electrode DC internal resistance of the battery; and constructing the DC internal resistance growth model based on the target mathematical function.

[0095] In an exemplary embodiment, the cloud processor is further configured to determine the target mathematical function by: obtaining the basic aging rate constant and aging state function of the test battery, wherein the basic aging rate constant is used to characterize the inherent aging characteristics of the test battery, and the aging state function is used to characterize the characteristic that the aging rate of the test battery changes with the degree of aging; converting the function expression of each mathematical function into factor terms, and multiplying multiple factor terms to obtain a comprehensive factor term; multiplying the comprehensive factor term, the basic aging rate constant, and the aging state function to obtain the function expression of the target mathematical function.

[0096] In an exemplary embodiment, the cloud processor is further configured to determine the first operating condition data increment by: obtaining first historical operating condition data at a first moment from the historical operating condition data, wherein the first moment is the latest operating condition data recording moment; and determining the first operating condition data increment based on the change between the current operating condition data and the first historical operating condition data.

[0097] In an exemplary embodiment, the cloud processor is further configured to determine the current negative electrode DC internal resistance of the target battery by: obtaining a first historical negative electrode DC internal resistance corresponding to the first time moment from the historical negative electrode DC internal resistance; determining a first negative electrode DC internal resistance increment of the target battery based on the product of the first historical negative electrode DC internal resistance and the growth rate; determining the current negative electrode DC internal resistance based on the sum of the first negative electrode DC internal resistance increment and the first historical negative electrode DC internal resistance; or, obtaining an initial historical negative electrode DC internal resistance corresponding to the initial time moment from the historical negative electrode DC internal resistance; determining a second negative electrode DC internal resistance increment of the target battery based on the product of the initial historical negative electrode DC internal resistance and the growth rate; and determining the current negative electrode DC internal resistance based on the sum of the second negative electrode DC internal resistance increment and the initial historical negative electrode DC internal resistance.

[0098] In an exemplary embodiment, the cloud processor is further configured to: acquire the voltage between the negative electrode of the target battery and the copper wire, wherein the copper wire is disposed between the negative electrode of the target battery and the negative electrode separator; acquire the test current for performing a negative electrode DC internal resistance test on the target battery; and determine the initial negative electrode DC internal resistance based on the ratio of the voltage to the test current.

[0099] In an exemplary embodiment, the cloud processor is further configured to determine the safe charging current of the target battery by: determining the critical current corresponding to the target battery under different negative electrode DC internal resistances, wherein the target battery undergoes negative electrode lithium plating when the charging current is greater than the critical current; obtaining the charging current when the target battery undergoes negative electrode lithium plating under different negative electrode DC internal resistances to obtain different critical currents; establishing a target mapping relationship function based on the correspondence between the different critical currents and the different negative electrode DC internal resistances; determining the current critical current corresponding to the current negative electrode DC internal resistance based on the target mapping relationship function, and determining the current critical current as the safe charging current.

[0100] In an exemplary embodiment, the cloud processor is further configured to: determine a second operating condition data increment based on preset operating condition data and the current operating condition data, wherein the preset operating condition data represents operating condition data simulated at a preset time, the preset time being after the current time; input the second operating condition data increment into the DC internal resistance growth model to obtain the predicted growth rate output by the DC internal resistance growth model; determine the predicted negative DC internal resistance of the target battery at the preset time based on the predicted growth rate and the current negative DC internal resistance of the target battery; and determine the predicted safe charging current of the target battery at the preset time based on the predicted negative DC internal resistance.

[0101] In an exemplary embodiment, the cloud processor is further configured to: establish backup data for the target battery, wherein the backup data includes the DC internal resistance growth model, the initial negative electrode DC internal resistance of the target battery, and the operating condition data of the target battery, and the backup data is stored in a cloud server; acquire the latest operating condition data of the target battery according to a preset period, and save the latest operating condition data to the backup data.

[0102] Embodiments of this application also provide a storage medium including a stored program, wherein the program executes any of the methods described above when it is run.

[0103] Optionally, in this embodiment, the storage medium may be configured to store program code for performing the following steps:

[0104] S1 collects current and historical operating condition data of the target battery from the vehicle battery management system;

[0105] S2, determine the first working condition data increment based on the current working condition data and the historical working condition data;

[0106] S3, input the incremental data of the first operating condition into the DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, wherein the DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions.

[0107] S4, determine the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery;

[0108] S5, determine the safe charging current of the target battery based on the current negative electrode DC internal resistance.

[0109] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0110] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0111] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0112] S1 collects current and historical operating condition data of the target battery from the vehicle battery management system;

[0113] S2, determine the first working condition data increment based on the current working condition data and the historical working condition data;

[0114] S3, input the incremental data of the first operating condition into the DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model, wherein the DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions.

[0115] S4, determine the current negative electrode DC internal resistance of the target battery based on the growth rate and the historical negative electrode DC internal resistance of the target battery;

[0116] S5, determine the safe charging current of the target battery based on the current negative electrode DC internal resistance.

[0117] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0118] Optionally, embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0119] Optionally, embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0120] Optionally, embodiments of this application also provide a computer program that includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in any of the above method embodiments.

[0121] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0122] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuits, or multiple modules or steps can be fabricated as a single integrated circuit. Thus, this application is not limited to any particular hardware and software combination.

[0123] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A cloud platform, characterized in that, It includes a data acquisition unit and a cloud processor, wherein the data acquisition unit is communicatively connected to both the cloud processor and the vehicle battery management system. The data acquisition device is used to collect current operating condition data and historical operating condition data of the target battery from the vehicle battery management system; The cloud processor is used for: The first operating condition data increment is determined based on the current operating condition data and the historical operating condition data; The incremental data of the first operating condition is input into the DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model. The DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions. The current negative electrode DC internal resistance of the target battery is determined based on the growth rate and the historical negative electrode DC internal resistance of the target battery. The safe charging current of the target battery is determined based on the current negative DC internal resistance.

2. The cloud platform according to claim 1, characterized in that, The cloud processor is also used for: The charging strategy for the target battery is determined based on the safe charging current.

3. The cloud platform according to claim 2, characterized in that, It also includes a data transmitter, which is used for: The charging strategy is sent to the user terminal to instruct the user terminal to display the charging strategy on the corresponding display page of the user terminal after receiving the charging strategy, wherein the user terminal includes a personal terminal or a third-party terminal.

4. The cloud platform according to claim 3, characterized in that, The cloud platform is communicatively connected to multiple vehicle battery management systems, each corresponding to a different user terminal. After processing the data corresponding to any vehicle battery management system, the cloud platform sends the resulting charging strategy for the battery corresponding to that vehicle battery management system to the user terminal corresponding to that vehicle battery management system.

5. The cloud platform according to claim 1, characterized in that, The cloud processor is also used for: Obtain test data for the test battery, wherein the test battery is of the same model as the target battery; The DC internal resistance growth model is constructed based on the test data.

6. The cloud platform according to claim 5, characterized in that, The cloud processor is also used to acquire test data of the test battery in the following ways: Accelerated life testing is performed on the test battery based on the test variables to obtain the correlation between the negative electrode DC internal resistance of the test battery and the test variables. The accelerated life test is used to test the growth rate of the negative electrode DC internal resistance of the test battery under preset operating conditions. The test data is determined based on the test variable, the negative DC internal resistance of the test battery corresponding to the test variable, and the correlation.

7. The cloud platform according to claim 6, characterized in that, The cloud processor is also used to obtain the correlation between the negative DC internal resistance of the test battery and the test variable in the following ways: When the test variable is determined to be the battery storage temperature, a first test temperature, a second test temperature, and a third test temperature determined from the test temperature range are obtained, wherein the first test temperature is less than the second test temperature, and the second test temperature is less than the third test temperature. Record the first curve, the second curve, and the third curve of the increase of the negative electrode DC internal resistance of the test battery over time when the test battery is stored at the first test temperature, the second test temperature, and the third test temperature, respectively. The correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature is determined based on the Arrhenius equation and the first, second, and third curves.

8. The cloud platform according to claim 7, characterized in that, The cloud processor is also used to determine the correlation between the negative DC internal resistance of the test battery and the battery storage temperature in the following ways: A first growth rate is determined based on the first curve, a second growth rate is determined based on the second curve, and a third growth rate is determined based on the third curve. The first growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when it is stored at the first test temperature, the second growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when it is stored at the second test temperature, and the third growth rate represents the growth rate of the negative electrode DC internal resistance of the test battery when it is stored at the third test temperature. Substituting the first growth rate, the second growth rate, and the third growth rate into the Arrhenius equation respectively, we obtain the first fitting point, the second fitting point, and the third fitting point. A mathematical formula is applied to fit the first fitting point, the second fitting point, and the third fitting point to obtain a first mathematical expression; The correlation between the negative electrode DC internal resistance of the test battery and the battery storage temperature is determined based on the first mathematical expression.

9. The cloud platform according to claim 6, characterized in that, The cloud processor is also used to obtain the correlation between the negative DC internal resistance of the test battery and the test variable in the following ways: When the test variable is determined to be the battery cycle temperature, a fourth test temperature, a fifth test temperature, and a sixth test temperature determined from the test temperature range are obtained, wherein the fourth test temperature is less than the fifth test temperature, and the fifth test temperature is less than the sixth test temperature. Record the fourth, fifth, and sixth curves showing the increase of the negative electrode DC internal resistance of the test battery over time under the conditions of charge-discharge cycles at the fourth, fifth, and sixth test temperatures, respectively. The correlation between the negative electrode DC internal resistance of the test battery and the battery cycling temperature is determined based on the Arrhenius equation and the fourth, fifth, and sixth curves.

10. The cloud platform according to claim 6, characterized in that, The cloud processor is also used to construct the DC internal resistance growth model in the following manner: Multiple mathematical functions are obtained based on the mathematical functions corresponding to different correlation relationships; A target mathematical function is determined based on the plurality of mathematical functions, wherein the input of the target mathematical function is a plurality of variable values ​​in the battery operating condition data, and the output of the target mathematical function is the growth rate of the negative electrode DC internal resistance of the battery; The DC internal resistance growth model is constructed based on the objective mathematical function.

11. The cloud platform according to claim 10, characterized in that, The cloud processor is also used to determine the target mathematical function in the following ways: Obtain the basic aging rate constant and aging state function of the test battery, wherein the basic aging rate constant is used to characterize the inherent aging characteristics of the test battery, and the aging state function is used to characterize the characteristic that the aging rate of the test battery changes with the degree of aging. The function expression of each mathematical function is converted into factor terms, and multiple factor terms are multiplied together to obtain a composite factor term; The comprehensive factor term, the basic aging rate constant, and the aging state function are multiplied together to obtain the functional expression of the target mathematical function.

12. The cloud platform according to claim 1, characterized in that, The cloud processor is also used to determine the data increment of the first operating condition in the following ways: Obtain the first historical operating condition data at the first moment from the historical operating condition data, wherein the first moment is the latest operating condition data recording moment; The increment of the first operating condition data is determined based on the change between the current operating condition data and the first historical operating condition data.

13. The cloud platform according to claim 12, characterized in that, The cloud processor is also used to determine the current negative DC internal resistance of the target battery in the following ways: The first historical negative electrode DC internal resistance at the first moment is obtained from the historical negative electrode DC internal resistance. The increment of the first negative electrode DC internal resistance of the target battery is determined by multiplying the first historical negative electrode DC internal resistance by the growth rate. The current negative electrode DC internal resistance is determined based on the sum of the first negative electrode DC internal resistance increment and the first historical negative electrode DC internal resistance. Alternatively, the initial historical negative electrode DC internal resistance at the initial moment can be obtained from the historical negative electrode DC internal resistance. The second negative electrode DC internal resistance increment of the target battery is determined by multiplying the initial historical negative electrode DC internal resistance by the growth rate. The current negative electrode DC internal resistance is determined based on the sum of the second negative electrode DC internal resistance increment and the initial historical negative electrode DC internal resistance.

14. The cloud platform according to claim 13, characterized in that, The cloud processor is also used for: The voltage between the negative electrode of the target battery and the copper wire is obtained, wherein the copper wire is disposed between the negative electrode of the target battery and the negative electrode separator. Obtain the test current for testing the negative electrode DC internal resistance of the target battery; The initial negative DC internal resistance is determined based on the ratio of the voltage to the test current.

15. The cloud platform according to claim 1, characterized in that, The cloud processor is also used to determine the safe charging current of the target battery in the following ways: Determine the critical current corresponding to the target battery under different negative electrode DC internal resistances during charging, wherein the target battery undergoes negative electrode lithium plating when the charging current is greater than the critical current; The charging current at which lithium plating occurs at the negative electrode of the target battery is obtained under different DC internal resistances of the negative electrode, and different critical currents are obtained. Establish a target mapping function based on the correspondence between the different critical currents and the different negative electrode DC internal resistances; The current critical current corresponding to the current negative DC internal resistance is determined according to the target mapping function, and the current critical current is determined as the safe charging current.

16. The cloud platform according to claim 1, characterized in that, The cloud processor is also used for: The second working condition data increment is determined based on the preset working condition data and the current working condition data, wherein the preset working condition data represents the working condition data simulated at a preset time, and the preset time is after the current time; The second operating condition data increment is input into the DC internal resistance growth model to obtain the predicted growth rate output by the DC internal resistance growth model. The predicted negative DC internal resistance of the target battery at the preset time is determined based on the predicted growth rate and the current negative DC internal resistance of the target battery. The predicted safe charging current of the target battery at the preset time is determined based on the predicted negative DC internal resistance.

17. The cloud platform according to claim 1, characterized in that, The cloud processor is also used for: Backup data is established for the target battery, wherein the backup data includes the DC internal resistance growth model, the initial negative electrode DC internal resistance of the target battery, and the operating condition data of the target battery, and the backup data is stored on a cloud server; The latest operating condition data of the target battery is acquired according to a preset cycle, and the latest operating condition data is saved to the backup data.

18. A method for determining a safe charging current, characterized in that, Applied to cloud platforms, including: Collect current and historical operating condition data of the target battery from the vehicle battery management system; The first operating condition data increment is determined based on the current operating condition data and the historical operating condition data; The incremental data of the first operating condition is input into the DC internal resistance growth model to obtain the growth rate output by the DC internal resistance growth model. The DC internal resistance growth model is used to determine the growth rate of the negative electrode DC internal resistance of the battery under different operating conditions. The current negative electrode DC internal resistance of the target battery is determined based on the growth rate and the historical negative electrode DC internal resistance of the target battery. The safe charging current of the target battery is determined based on the current negative DC internal resistance.

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