Battery aging experiment parameter determination method and device and computer equipment

Abstract

The invention relates to a battery aging experiment parameter determination method and device and computer equipment. The method comprises the following steps: acquiring initial performance information of a battery, and simulating and acquiring normal aging condition information of the battery; according to a plurality of preset accelerated stress conditions, determining a plurality of pieces of accelerated aging working condition information of the battery under the plurality of accelerated stress conditions; according to the initial performance information, respectively determining normal aging condition information, multiple pieces of accelerated aging condition information, corresponding lithium inventory loss rate information and active material loss rate information; and according to the lithium inventory loss rate information and the active material loss rate information, determining stress combination information of a plurality of acceleration stress conditions, and obtaining aging experiment parameters of the battery. By adopting the method, the consistency of the battery aging experiment can be improved.

Description

Technical Field

[0001] The present application relates to the field of lithium battery technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining battery aging test parameters. Background Art

[0002] The "dual carbon" goals clearly define the high-quality development direction of my country's transportation electrification and energy decarbonization. As a key power and energy storage unit, lithium-ion batteries, with their excellent performance, have been widely used in energy storage systems, electric vehicles, and consumer electronics. Durability is a key issue for power batteries, and it is difficult to accurately assess and predict the service life of lithium batteries through short-term factory testing, which often consumes a lot of time and effort.

[0003] Currently, in battery aging experiments, accelerated aging is often applied to batteries by increasing the charge / discharge rate, raising the temperature, or adjusting the voltage range to expedite the acquisition of inferred battery lifespan data. However, because lithium-ion battery performance degradation has highly nonlinear characteristics and complex internal mechanisms, its aging path is altered by the stress of cycling conditions. Existing research typically only considers the battery's external characteristics, namely its capacity characteristics, without considering the consistency of the battery's internal attenuation path. This leads to deviations between the accelerated aging path and the aging path under normal operating conditions. Therefore, existing technologies cannot ensure that the internal damage mechanism of the battery in accelerated aging experiments remains consistent with the normal aging process, thus affecting the reliability of the accelerated aging test results. Summary of the Invention

[0004] Based on this, it is necessary to provide a battery aging test parameter determination method, device, computer equipment, computer-readable storage medium and computer program product that can improve the consistency of battery aging experiments in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for determining battery aging test parameters, comprising:

[0006] Obtaining initial performance information of the battery, and simulating normal aging condition information of the battery;

[0007] Determining, according to a plurality of preset accelerated stress conditions, a plurality of accelerated aging condition information of the battery under the plurality of accelerated stress conditions;

[0008] Determining, based on the initial performance information, the normal aging condition information and the plurality of accelerated aging condition information, and corresponding lithium inventory loss rate information and active material loss rate information;

[0009] Stress combination information of the multiple accelerated stress conditions is determined based on the lithium inventory loss rate information and the active material loss rate information to obtain aging test parameters of the battery.

[0010] In one embodiment, each of the normal aging condition information and the multiple accelerated aging condition information includes at least operating condition detection information after each preset charge and discharge cycle; the operating condition detection information includes battery capacity information and voltage-time sampling information of constant current charging;

[0011] The determining, based on the initial performance information, the normal aging condition information and the multiple accelerated aging condition information, and the corresponding lithium inventory loss rate information and active material loss rate information, respectively, includes:

[0012] For any operating condition information, determining, based on the initial performance information and the any operating condition information, lithium inventory loss information and active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of the any operating condition information;

[0013] The lithium inventory loss rate information and the active material loss rate information corresponding to any one of the operating condition information are determined according to the lithium inventory loss information and the active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of any one of the operating condition information.

[0014] In one embodiment, for any operating condition information, determining, based on the initial performance information and the any operating condition information, the lithium inventory loss information and the active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of the any operating condition information includes:

[0015] Determine the internal state parameters of the battery for dual-tank model analysis based on the working condition detection information after a preset charge and discharge cycle of any working condition information;

[0016] Using the voltage-time sampling information of the constant current charging as an optimization target, iteratively optimizing to determine target parameter values ​​of the battery internal state parameters;

[0017] In the case that the target parameter value does not conform to the battery capacity information, the step of performing the iterative optimization to determine the target value of the battery internal state parameter is returned until the target parameter value that conforms to the battery capacity information is obtained. Then, based on the target parameter value that conforms to the battery capacity information and the initial performance information, the lithium inventory loss information and active material loss information corresponding to the operating condition detection information after a preset charge and discharge cycle of any operating condition information are determined.

[0018] In one embodiment, the iterative optimization to determine the target parameter value of the battery internal state parameter includes:

[0019] Setting the battery internal state parameters as particle position parameters in a particle swarm optimization model;

[0020] The initial particle position parameters of the particle swarm are used as the current particle position parameters;

[0021] According to the current particle position parameters, based on the double water tank model analysis, the voltage-time prediction information of the constant current charging process corresponding to the current particle position parameters is obtained;

[0022] The root mean square error between the voltage-time prediction information and the voltage-time sampling information of the constant current charging is used as the fitness value of the particle;

[0023] According to the fitness value of the particle, the current particle position parameter of the particle swarm is updated until a preset iteration termination condition is met, and then the target parameter value of the battery internal state parameter is determined according to the particle position parameter of the optimal particle in the particle swarm finally obtained.

[0024] In one embodiment, determining the stress combination information of the multiple accelerated stress conditions based on the lithium inventory loss rate information and the active material loss rate information includes:

[0025] Determining a target ratio of the lithium inventory loss rate to the active material loss rate for the battery aging experiment based on the lithium inventory loss rate information and the active material loss rate information corresponding to the normal aging condition information;

[0026] An optimization model is used to determine candidate stress combination information by adjusting the application ratio of the multiple accelerated stress conditions, and the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio is minimized to obtain the optimized stress combination information.

[0027] In one embodiment, the optimization model is used to adjust the application ratio of the multiple accelerated stress conditions to determine candidate stress combination information, and minimize the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio to obtain the optimized stress combination information, including:

[0028] using the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio as an optimization objective function;

[0029] The initial candidate stress combination information is used as the current candidate stress combination information;

[0030] Determining an objective function value corresponding to the current candidate stress combination information according to the optimization objective function;

[0031] According to the current candidate stress combination information and the objective function value, the current candidate stress combination information is updated until the objective function value satisfies a preset convergence condition, and the current candidate stress combination information that satisfies the preset convergence condition is determined as the optimized stress combination information.

[0032] In a second aspect, the present application further provides a device for determining battery aging test parameters, comprising:

[0033] A normal operating condition acquisition module, used to obtain initial performance information of the battery and simulate the normal aging condition information of the battery;

[0034] an accelerated working condition acquisition module, configured to determine, based on a plurality of preset accelerated stress conditions, a plurality of accelerated aging condition information of the battery under the plurality of accelerated stress conditions;

[0035] a loss rate acquisition module, configured to determine, based on the initial performance information, the normal aging condition information and the plurality of accelerated aging condition information, and the corresponding lithium inventory loss rate information and active material loss rate information;

[0036] The experimental parameter determination module is used to determine stress combination information of the multiple accelerated stress conditions according to the lithium inventory loss rate information and the active material loss rate information, so as to obtain the aging experimental parameters of the battery.

[0037] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Obtaining initial performance information of the battery, and simulating normal aging condition information of the battery;

[0039] Determining, according to a plurality of preset accelerated stress conditions, a plurality of accelerated aging condition information of the battery under the plurality of accelerated stress conditions;

[0040] Determining, based on the initial performance information, the normal aging condition information and the plurality of accelerated aging condition information, and corresponding lithium inventory loss rate information and active material loss rate information;

[0041] Stress combination information of the multiple accelerated stress conditions is determined based on the lithium inventory loss rate information and the active material loss rate information to obtain aging test parameters of the battery.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtaining initial performance information of the battery, and simulating normal aging condition information of the battery;

[0044] Determining, according to a plurality of preset accelerated stress conditions, a plurality of accelerated aging condition information of the battery under the plurality of accelerated stress conditions;

[0045] Determining, based on the initial performance information, the normal aging condition information and the plurality of accelerated aging condition information, and corresponding lithium inventory loss rate information and active material loss rate information;

[0046] Stress combination information of the multiple accelerated stress conditions is determined based on the lithium inventory loss rate information and the active material loss rate information to obtain aging test parameters of the battery.

[0047] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0048] Obtaining initial performance information of the battery, and simulating normal aging condition information of the battery;

[0049] Determining, according to a plurality of preset accelerated stress conditions, a plurality of accelerated aging condition information of the battery under the plurality of accelerated stress conditions;

[0050] Determining, based on the initial performance information, the normal aging condition information and the plurality of accelerated aging condition information, and corresponding lithium inventory loss rate information and active material loss rate information;

[0051] Stress combination information of the multiple accelerated stress conditions is determined based on the lithium inventory loss rate information and the active material loss rate information to obtain aging test parameters of the battery.

[0052] The above-mentioned battery aging experiment parameter determination method, device, computer equipment, computer-readable storage medium and computer program product first obtain the initial performance information of the battery and simulate the normal aging condition information of the battery, which can accurately grasp the health level of the battery in the initial state and the aging evolution law under normal use conditions, and provide a benchmark for the subsequent aging characteristic comparison under accelerated aging conditions, ensuring that the subsequent aging path identification has a unified and comparable reference standard; then, based on the preset multiple accelerated stress conditions, multiple accelerated aging condition information of the battery under multiple accelerated stress conditions is determined, and the aging characteristic performance of the battery under different external stresses is systematically collected to form a diversified accelerated aging data set, thereby providing basic data support for the subsequent stress combination optimization and improving the flexibility and accuracy of the accelerated aging experiment parameter determination; then, based on the Based on the initial performance information, normal aging condition information and multiple accelerated aging condition information are determined, along with the corresponding lithium inventory loss rate information and active material loss rate information, to quantitatively identify the internal aging mechanism of the battery. This not only focuses on the external manifestations of capacity decline, but also deeply distinguishes the contribution of different aging paths to performance degradation, thereby providing a key basis for accurately regulating the accelerated stress combination to match the aging path. Finally, based on the lithium inventory loss rate information and the active material loss rate information, the stress combination information of multiple accelerated stress conditions is determined to obtain the battery aging test parameters. This can ensure that the internal damage mechanism of the battery during the accelerated aging process is consistent with normal aging while ensuring the accelerated aging rate is increased, thereby improving the path consistency of the accelerated aging experiment and ensuring that the experimental results have higher accuracy and reliability in predicting the actual service life of the battery. In the above method, based on the initial performance and normal aging characteristics of the battery, the aging behavior under different accelerated stresses is collected, the lithium inventory loss and active material loss rate characteristics are accurately identified, and the accelerated aging path is consistent with the normal aging path by optimizing the combination of accelerated stress conditions, thereby improving the scientificity, standardization and prediction reliability of the battery accelerated life test. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 Schematic diagram of a flow chart of a method for determining battery aging test parameters in one embodiment;

[0055] Figure 2FIG1 is a flow chart of the steps for determining lithium inventory loss rate information and active material loss rate information in one embodiment;

[0056] Figure 3 1 is a flow chart of the steps of determining stress combination information in one embodiment;

[0057] Figure 4 A schematic flow chart of a method for determining battery aging test parameters in another embodiment;

[0058] Figure 5 This is a flow chart of a battery experiment under normal aging conditions in one embodiment;

[0059] Figure 6 2 is a positive and negative electrode potential curve in a button cell experiment in an embodiment;

[0060] Figure 7 This is a schematic diagram of the principle of a double water tank model in one embodiment;

[0061] Figure 8 A comparison diagram of capacity attenuation between each single stress condition and normal aging condition in one embodiment;

[0062] Figure 9 An aging path diagram of internal characteristics of a dual-water tank model identified under normal aging conditions in one embodiment;

[0063] Figure 10 A structural block diagram of a device for determining battery aging test parameters in one embodiment;

[0064] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0066] In one embodiment, Figure 1 As shown, a method for determining battery aging experiment parameters is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the method includes the following steps:

[0067] Step S101 : obtaining initial performance information of the battery and simulating normal aging condition information of the battery.

[0068] Among them, initial performance information refers to the basic performance data of the battery before aging or in the standard factory state, including but not limited to battery capacity, internal resistance, charge and discharge characteristic curve, open circuit voltage characteristics and other parameters, which are used to characterize the health level of the battery in the standard initial state; normal aging condition information refers to the battery's external performance indicators and internal mechanism change data collected at predetermined intervals during the battery's cyclic use under standard rate charge and discharge conditions, which is used to simulate the battery's aging behavior under natural use conditions.

[0069] For example, the terminal may first control the battery to undergo three consecutive charge-discharge cycles at a C / 3 rate (i.e., charging and discharging at a current one-third of the battery's nominal capacity) to complete the stabilization process, ensuring that the battery's internal electrochemical interfaces are balanced and its performance is stable. Subsequently, the terminal collects initial performance information for the stabilized battery, including but not limited to the battery's initial capacity, internal resistance, and open-circuit voltage characteristics. Next, the terminal sets the charge-discharge experimental conditions, cycling the battery under normal aging conditions at a standard rate of 0.5C (i.e., charging at a current of half the battery's nominal capacity) and 1C (i.e., discharging at a current equivalent to the battery's nominal capacity). During the cycling process, the terminal performs a reference performance test (RPT) every 40 charge-discharge cycles to obtain information on the battery's capacity, internal resistance, and voltage-time curve during constant-current charging. Based on the collected data, the terminal applies a dual-tank model to analyze changes in the battery's internal state, thereby identifying lithium inventory loss (LLI) and active material loss (LAM). The terminal continues the normal aging simulation until the battery capacity drops to 80% of the initial standard capacity, thereby completing the simulated collection of normal aging condition information.

[0070] Step S102 : determining multiple accelerated aging condition information of the battery under the multiple accelerated stress conditions according to the preset multiple accelerated stress conditions.

[0071] Among them, accelerated stress conditions refer to the operating condition settings applied to the battery to accelerate its aging process by adjusting external operating parameters such as the battery charge and discharge rate, charge and discharge cut-off voltage, and ambient temperature. Common accelerated stress conditions include high-rate charge and discharge, high-temperature environment, or high-voltage charging. Accelerated aging condition information refers to the external performance change data collected during the battery's charge and discharge cycle under the corresponding accelerated stress conditions, including but not limited to battery capacity change data, internal resistance change data, and constant current charging voltage-time curve data, which is used to characterize the performance evolution process of the battery under accelerated aging conditions.

[0072] For example, the terminal controls the battery to perform charge and discharge cycle experiments based on multiple accelerated stress condition combinations set by the system, such as setting the charge rate to 1.5C and the discharge rate to 2C, or setting the ambient temperature to 45°C, or setting the charge cut-off voltage to 4.3V. During the experiment, the terminal performs reference performance tests according to the predetermined cycle period (for example, every 40 cycles), obtaining battery capacity data, internal resistance data, and voltage-time curve data during constant current charging. This generates a collection of accelerated aging condition information under the corresponding accelerated stress conditions, providing raw data support for subsequent analysis of the battery's internal state parameters.

[0073] Step S103 : determining normal aging condition information and multiple accelerated aging condition information, and corresponding lithium inventory loss rate information and active material loss rate information, respectively, based on the initial performance information.

[0074] Among them, the lithium inventory loss rate information refers to the rate at which the total amount of lithium ions inside the battery decreases per unit time or per unit charge and discharge cycle, which is used to reflect the degree of impact of lithium ion loss on battery capacity decay; the active material loss rate information refers to the rate at which the capacity of the active material at the positive or negative electrode of the battery decreases per unit time or per unit charge and discharge cycle, which is used to characterize the contribution of material structure destruction to battery performance degradation.

[0075] For example, based on the collected initial performance information, normal aging condition information, and each accelerated aging condition information, the terminal applies a double-tank model to fit and analyze the constant current charging voltage-time curve of each stage. The terminal adjusts the internal state parameters of the battery, such as the positive electrode capacity, the negative electrode capacity, the negative electrode initial lithium ion content, the positive electrode initial lithium ion content, and the battery internal resistance, to minimize the error between the fitted voltage curve and the actual collected voltage curve. Based on the changes in the initial parameters and the parameters of each cycle node, the terminal calculates the LLI rate information and LAM rate information under normal aging conditions and each accelerated aging condition, establishes the rate characteristic data of lithium inventory loss and active material degradation under different aging paths, and provides a reference basis for the consistency of the mechanism for subsequent stress combination optimization.

[0076] Step S104 : determining stress combination information of multiple accelerated stress conditions based on the lithium inventory loss rate information and the active material loss rate information, and obtaining battery aging test parameters.

[0077] Among them, stress combination information refers to a set of comprehensive aging condition parameters formed by combining the application ratios of multiple different accelerated stress conditions (such as charge rate, discharge rate, charge and discharge cut-off voltage, ambient temperature and other external applied conditions), which is used to simultaneously control the lithium inventory loss rate and active material loss rate of the battery in the accelerated aging experiment, ensuring that the accelerated aging path is consistent with the normal aging path; battery aging experiment parameters refer to the experimental condition parameters such as charge and discharge rate, voltage window, temperature setting, etc. formulated according to the final determined stress combination information, which are used to guide the execution of the actual accelerated aging experiment.

[0078] Exemplarily, the terminal sets the target lithium inventory loss rate and active material loss rate ratio under accelerated aging conditions based on the determined LLI rate information and LAM rate information under normal aging conditions. The terminal establishes a matching model between the accelerated stress conditions and the loss rate change based on the LLI rate information and LAM rate information collected under multiple single accelerated stress conditions. The terminal adopts an optimization method to minimize the rate deviation between the accelerated aging path and the normal aging path by adjusting the application ratio of each accelerated stress condition, and determines the stress combination information that meets the target ratio. Finally, the terminal outputs the battery aging experiment parameters based on the optimized stress combination information, which is used for the specific implementation of the subsequent accelerated aging experiment to ensure the consistency of the internal aging mechanism of the battery during the accelerated aging experiment and the reliability of the experimental results.

[0079] In the above-mentioned method for determining the parameters of the battery aging experiment, first, the initial performance information of the battery is obtained, and the normal aging condition information of the battery is simulated to obtain the information. This can accurately grasp the health level of the battery in the initial state and the aging evolution law under normal use conditions, and provide a benchmark for the comparison of aging characteristics under subsequent accelerated aging conditions, ensuring that the subsequent aging path identification has a unified and comparable reference standard; then, according to the preset multiple accelerated stress conditions, multiple accelerated aging condition information of the battery under multiple accelerated stress conditions is determined, and the aging characteristic performance of the battery under different external stresses is systematically collected to form a diversified accelerated aging data set, thereby providing basic data support for the subsequent stress combination optimization and improving the flexibility and accuracy of the accelerated aging experiment parameter determination; then, according to the initial performance information, the normal aging condition information of the battery is determined respectively. The information of normal aging conditions and multiple accelerated aging conditions, as well as the corresponding information of lithium inventory loss rate and active material loss rate, are used to quantitatively identify the internal aging mechanism of the battery. This not only focuses on the external manifestations of capacity decline, but also deeply distinguishes the contribution of different aging paths to performance degradation, thereby providing a key basis for accurately regulating the accelerated stress combination to match the aging path. Finally, based on the information of lithium inventory loss rate and active material loss rate, the stress combination information of multiple accelerated stress conditions is determined to obtain the aging test parameters of the battery. This can ensure that the internal damage mechanism of the battery during the accelerated aging process is consistent with normal aging while ensuring the increase in the accelerated aging rate, thereby improving the path consistency of the accelerated aging experiment and ensuring that the experimental results have higher accuracy and reliability in predicting the actual service life of the battery. In the above method, based on the initial performance and normal aging characteristics of the battery, the aging behavior under different accelerated stresses is collected, the characteristics of lithium inventory loss and active material loss rate are accurately identified, and the consistency of the accelerated aging path with the normal aging path is achieved by optimizing the combination of accelerated stress conditions, thereby improving the scientificity, standardization and prediction reliability of the battery accelerated life test.

[0080] In an exemplary embodiment, each of the normal aging condition information and the multiple accelerated aging condition information includes at least operating condition detection information after each preset charge and discharge cycle; the operating condition detection information includes battery capacity information and voltage-time sampling information of constant current charging;

[0081] like Figure 2 As shown, the above step S103 determines normal aging condition information and multiple accelerated aging condition information based on the initial performance information, and the corresponding lithium inventory loss rate information and active material loss rate information, which can also be achieved by the following steps:

[0082] Step S201, for any operating condition information, determining lithium inventory loss information and active material loss information corresponding to operating condition detection information after each preset charge and discharge cycle of any operating condition information based on initial performance information and any operating condition information;

[0083] Step S202 , determining lithium inventory loss rate information and active material loss rate information corresponding to any operating condition information based on lithium inventory loss information and active material loss information corresponding to operating condition detection information after each preset charge and discharge cycle of any operating condition information.

[0084] Among them, the operating condition detection information refers to the data set reflecting the changes in battery performance collected after the preset charge and discharge cycle during the battery charge and discharge cycle, specifically including battery capacity information and voltage-time curve information in the constant current charging stage; battery capacity information refers to the actual available capacity of the battery after a complete charge and discharge under standard rate conditions, which is used to evaluate the overall health status of the battery; the voltage-time sampling information of constant current charging refers to a set of discrete data points collected from the terminal voltage change curve during the charging process according to a preset sampling interval rule (for example, a fixed time interval, a fixed SOC interval or a fixed capacity interval) during the constant current charging stage of the battery. Each data point corresponds to a charging time value and a battery terminal voltage value, which is used to characterize the voltage response characteristics of the battery during the constant current charging process, and provide basic data support for subsequent model prediction and fitting of actual voltage behavior.

[0085] For each operating condition, the terminal collects corresponding operating condition detection information after completing a preset charge-discharge cycle (e.g., every 40 cycles). Based on initial performance information (including the battery's initial capacity, internal resistance, and charging characteristic curve), combined with the operating condition detection information collected after each cycle, the terminal applies a dual-tank model to fit the charging voltage-time prediction information, identifies the battery's internal state parameters, and then determines the corresponding lithium inventory loss and active material loss information for each operating condition. Furthermore, based on the lithium inventory loss and active material loss information after all preset charge-discharge cycles under any operating condition, the terminal calculates the lithium inventory loss rate and active material loss rate, respectively, to characterize the rate of change in the battery's internal mechanisms under normal aging conditions and various accelerated aging conditions. Through this process, the terminal can establish quantitative characteristics of changes in the battery's internal aging pathways under different operating conditions, providing accurate basic data support for subsequent accelerated stress combination optimization and aging experiment parameter determination.

[0086] In this embodiment, by identifying and calculating the lithium inventory loss rate information and the active material loss rate information under normal aging conditions and multiple accelerated aging conditions based on the initial performance information and the operating condition detection information after each charge and discharge cycle, the internal mechanism evolution characteristics of the battery under different aging paths can be accurately characterized, thereby providing a scientific basis for the subsequent accelerated stress combination optimization, effectively improving the consistency between the internal aging path of the battery and the normal aging path during the accelerated aging experiment, and improving the accuracy and reliability of the accelerated aging experiment results.

[0087] In an exemplary embodiment, the above-mentioned step S202 determines, for any operating condition information, the lithium inventory loss information and active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of any operating condition information based on the initial performance information and any operating condition information, and also includes: determining the battery internal state parameters used for double-tank model analysis for the operating condition detection information after a preset charge and discharge cycle of any operating condition information; using the voltage-time sampling information of constant current charging as the optimization target, iteratively optimizing to determine the target parameter value of the battery internal state parameter; if the target parameter value does not conform to the battery capacity information, returning to the step of performing iterative optimization to determine the target value of the battery internal state parameter until the target parameter value that conforms to the battery capacity information is obtained, and then determining the lithium inventory loss information and active material loss information corresponding to the operating condition detection information after a preset charge and discharge cycle of any operating condition information based on the target parameter value that conforms to the battery capacity information and the initial performance information.

[0088] Among them, the battery internal state parameters refer to characteristic variables such as the positive electrode capacity, negative electrode capacity, negative electrode initial lithium ion content, positive electrode initial lithium ion content and battery internal resistance used to describe the battery during the charging and discharging process, which are used to characterize the changes in the internal electrochemical state of the battery during the charging process; the target parameter value refers to a set of battery internal state parameter values ​​obtained through optimization calculation, which makes the model predicted voltage curve closest to the actual charging voltage curve and meets the battery capacity information requirements.

[0089] For example, for any operating condition information after a preset charge / discharge cycle, the terminal first determines the initial battery internal state parameters for dual-tank model analysis based on the initial performance information and the operating condition information for that cycle. Subsequently, the terminal uses the voltage-time sampling information from constant current charging as the optimization target and adjusts the battery internal state parameters through an iterative optimization process, searching to minimize the root mean square error (RMSE) between the voltage predicted by the dual-tank model and the actual sampled voltage. If the currently obtained target parameter value has a small fitting voltage error but a large deviation between the corresponding estimated battery capacity and the actual capacity information, the terminal returns to the iterative optimization step and readjusts the battery internal state parameters until the deviation between the estimated battery capacity and the actual capacity information corresponding to the current target parameter value falls below a preset threshold. At this point, a target parameter value that meets both voltage fitting accuracy and capacity consistency requirements is obtained. After obtaining the target parameter value consistent with the battery capacity information, the terminal compares the target parameter value with the initial performance information to determine the lithium inventory loss and active material loss information after the preset charge / discharge cycle, providing basic data support for the subsequent calculation of the lithium inventory loss rate and active material loss rate information.

[0090] By combining a dual-tank model with a particle swarm optimization algorithm for analysis, a novel approach has been developed to accurately invert the internal state parameters of the battery. Because the dual-tank model possesses physical intuition for describing the relationship between electrode reactant migration and voltage response during battery charge and discharge, and the particle swarm optimization algorithm offers superior performance in global search and convergence of complex objective functions, the two combine to form a joint identification framework that balances physical interpretability with parameter optimization capabilities. The target internal state parameters identified through this framework not only significantly improve the fitting accuracy of the charge voltage curve but also enhance the consistency of the predicted capacity degradation trend. This effectively tracks the evolution of LLI and LAM under multiple stress aging conditions, improving the accuracy of aging path consistency modeling. This approach demonstrates significant technical advantages and innovation. Compared to the traditional approach of using a single physical model or optimization algorithm, this approach utilizes the dual-tank model as the fundamental electrochemical state modeling tool in battery aging analysis. Combined with the global search capabilities of the particle swarm optimization algorithm in a nonlinear, multi-parameter space, a multi-objective iterative optimization mechanism is established that balances voltage fitting accuracy with capacity consistency constraints. This mechanism effectively avoids local optimality traps and improves the stability and accuracy of battery aging path feature identification.

[0091] In this embodiment, by combining the operating condition detection information after each preset charge and discharge cycle of each operating condition information with the initial performance information, the internal state parameters of the battery are identified through iterative optimization based on the dual-water tank model, and the voltage fitting accuracy and capacity consistency requirements are considered at the same time. It is possible to accurately infer the lithium inventory loss information and active material loss information of the battery at different aging stages, thereby achieving accurate identification of the lithium inventory loss rate information and the active material loss rate information, improving the accuracy of the quantitative analysis of the evolution law of the internal mechanism of the battery during the accelerated aging experiment, and providing a reliable mechanism data basis for subsequent accelerated stress combination optimization.

[0092] In an exemplary embodiment, the above-mentioned iterative optimization to determine the target parameter value of the battery internal state parameter also includes: setting the battery internal state parameter as the particle position parameter in the particle swarm optimization model; using the initial particle position parameter of the particle swarm as the current particle position parameter; according to the current particle position parameter, based on the double water tank model analysis, obtaining the voltage-time prediction information of the constant current charging process corresponding to the current particle position parameter; using the root mean square error between the voltage-time prediction information and the voltage-time sampling information of the constant current charging as the fitness value of the particle; according to the fitness value of the particle, updating the current particle position parameter of the particle swarm until the preset iterative termination condition is met, and then determining the target parameter value of the battery internal state parameter based on the particle position parameter of the optimal particle in the particle swarm finally obtained.

[0093] Among them, the battery internal state parameters refer to a set of parameters used to describe the internal electrochemical state characteristics of the battery during the charging process, including the positive electrode capacity, the negative electrode capacity, the initial lithium ion content of the negative electrode, the initial lithium ion content of the positive electrode and the internal resistance of the battery; the particle position parameter refers to the state vector of each particle in the search space during the particle swarm optimization process, representing a set of battery internal state parameter values; the voltage-time prediction information of constant current charging refers to the set of predicted voltage values ​​corresponding to each sampling moment calculated based on the current particle position parameters according to the double water tank model; the voltage-time sampling information of constant current charging refers to the set of discrete voltage-time data points collected according to preset rules during the actual charging process; the fitness value refers to an indicator used to evaluate the quality of the current position of the particle. In this embodiment, the RMSE between the voltage-time prediction information and the voltage-time sampling information is used as the fitness function.

[0094] Exemplarily, the terminal first initializes a particle swarm, generating initial position parameters and corresponding initial velocity parameters for multiple particles. During the constant-current charging phase, the terminal collects the battery terminal voltage at a fixed sampling interval (e.g., every 5 seconds or every 1% SOC), generating voltage-time sampling information for constant-current charging. For each set of particle position parameters, the terminal uses a two-tank model to infer the predicted voltage value at each sampling point during the constant-current charging phase, generating voltage-time prediction information. This information is then compared with the actual voltage values ​​corresponding to the sampling information, and the root mean square error (RMSE) of all sampling points is calculated as the particle's fitness. Based on each particle's fitness, the terminal updates the optimal position of each individual particle and the optimal position of the swarm, adjusts particle velocity and position, and performs iterative optimization. When a preset iteration termination condition is met, such as reaching the maximum number of iterations or the fitness converges to a set threshold, the terminal selects the particle position parameters of the particle with the best fitness in the swarm as the final target values ​​for the battery's internal state parameters, which are used for subsequent inference of lithium inventory loss (LLI) and active material loss (LAM) information.

[0095] The battery voltage response modeling (dual water tank) is tightly integrated with particle swarm optimization, and dual constraints on voltage-time RMSE and battery capacity consistency are introduced in the internal state parameter search process. This breaks through the problem of conventional single-objective optimization easily falling into local optimality and significantly improves the recognition accuracy and stability. In addition, by introducing the particle swarm algorithm to automatically search and optimize the parameters of the dual-water tank model, the problems of traditional manual parameter adjustment or gradient optimization being sensitive to initial values ​​and slow convergence are avoided. This makes the model adaptable and transferable under multiple working conditions and has the potential for promotion in engineering practice. Therefore, this combination is not only a superposition of model + algorithm, but also constructs a high-precision and robust joint optimization framework for battery aging parameter inversion. It is of great significance to improving the estimation accuracy of LLI and LAM and improving the consistency control of battery aging paths. It has significant technological advancement and innovation.

[0096] In this embodiment, the particle swarm optimization algorithm is used to iteratively optimize the internal state parameters of the battery based on the voltage-time sampling information of constant current charging. The RMSE between the voltage-time prediction information generated by the double-water-tank model and the actual sampling information is used as the fitness standard. This can achieve high-precision identification of the internal state parameters of the battery, while ensuring the voltage fitting accuracy. It also improves the reliability and consistency of the battery aging path mechanism parameter inference, providing solid data support for subsequent accelerated aging experiment parameter optimization and path consistency control.

[0097] In an exemplary embodiment, Figure 3As shown, the above step S104 determines stress combination information of multiple accelerated stress conditions based on the lithium inventory loss rate information and the active material loss rate information, which can also be achieved by the following steps:

[0098] Step S301, determining a target ratio of the lithium inventory loss rate to the active material loss rate for a battery aging experiment based on the lithium inventory loss rate information and the active material loss rate information corresponding to the normal aging condition information;

[0099] Step S302 , using an optimization model, by adjusting the application ratio of multiple accelerated stress conditions, determining candidate stress combination information, and minimizing the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio, to obtain the optimized stress combination information.

[0100] Among them, stress combination information refers to a set of aging experiment control parameters formed by combining the application ratios of multiple different external accelerated stress conditions (such as charge rate, discharge rate, charge cut-off voltage, ambient temperature, etc.), which is used to simultaneously regulate the lithium inventory loss rate and active material loss rate of the battery during the accelerated aging process, so that the accelerated aging path and the normal aging path remain consistent in the internal mechanism evolution; the target ratio refers to the proportional relationship between the LLI rate and the LAM rate identified under normal aging conditions, which serves as a comparison benchmark for the consistency of the accelerated aging experiment path.

[0101] The optimization model can be constructed using methods such as particle swarm optimization, genetic algorithm, and weighted least squares optimization. The objective function is used to measure the difference between the ratio of lithium inventory loss rate to active material loss rate corresponding to the candidate stress combination information and the target ratio under the normal aging path. Taking the particle swarm optimization algorithm as an example, each set of stress application ratios can be used as the search vector of the particles, and the optimal stress combination can be determined by minimizing the deviation between the predicted path ratio and the target ratio.

[0102] Exemplarily, the terminal first extracts the corresponding lithium inventory loss rate and active material loss rate information based on the acquired normal aging condition information, and calculates the target ratio of the LLI rate to the LAM rate during normal aging. Then, the terminal uses an optimization model to adjust the stress application ratios for multiple preset accelerated stress conditions, generating multiple candidate stress combination information. For each candidate stress combination, the terminal calculates the corresponding LLI rate to LAM rate ratio based on the weighted superposition of the lithium inventory loss rate and active material loss rate data, and further calculates the deviation between this ratio and the target ratio. With minimizing this deviation as the optimization objective, the terminal uses an optimization algorithm (such as particle swarm optimization or genetic algorithm) to search for the optimal stress combination. If the terminal detects that the ratio deviation corresponding to a candidate stress combination is less than a preset tolerance or reaches the maximum number of iterations, the terminal outputs the current optimal stress combination information as the optimized stress combination information, which is used to guide subsequent accelerated aging experiments on the battery, ensuring that the battery aging path during the experiment is consistent with the normal aging mechanism.

[0103] In this embodiment, by dynamically adjusting the application ratio of multiple accelerated stress conditions based on the proportional relationship between the lithium inventory loss rate and the active material loss rate under the normal aging path, an optimization model is used to achieve consistency between the internal mechanism change path of the battery during the accelerated aging experiment and the normal aging path, effectively improving the scientificity and reliability of the accelerated aging experiment prediction results, and avoiding life prediction errors caused by deviations from the aging path.

[0104] In an exemplary embodiment, the above-mentioned step S302 adopts an optimization model to determine candidate stress combination information by adjusting the application ratio of multiple accelerated stress conditions, and minimizes the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio to obtain the optimized stress combination information. The step also includes: using the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio as the optimization objective function; using the initial candidate stress combination information as the current candidate stress combination information; determining the objective function value corresponding to the current candidate stress combination information according to the optimization objective function; updating the current candidate stress combination information according to the current candidate stress combination information and the objective function value until the objective function value satisfies a preset convergence condition, and then determining the current candidate stress combination information that satisfies the preset convergence condition as the optimized stress combination information.

[0105] The optimization objective function refers to the deviation between the ratio of the LLI rate to the LAM rate corresponding to the candidate stress combination information and the target ratio as the evaluation criterion, and the deviation is minimized through optimization search; the current candidate stress combination information refers to the stress combination configuration currently being evaluated during the optimization iteration process.

[0106] For example, the terminal first sets the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio under normal aging conditions as the optimization objective function. The terminal randomly initializes an initial candidate stress combination information as the current candidate stress combination information. Based on the current candidate stress combination information, the terminal predicts the corresponding LLI rate and LAM rate based on the accelerated stress condition combination and calculates the corresponding objective function value. Subsequently, based on the current candidate stress combination information and its objective function value, the terminal uses an optimization algorithm (such as particle swarm optimization, genetic algorithm, or other heuristic optimization algorithm) to adjust the application ratio of each accelerated stress condition to generate an updated current candidate stress combination information. After each update, the terminal recalculates the new objective function value and determines whether a preset convergence condition is met (e.g., the objective function value falls below a set threshold or the maximum number of iterations is reached). If the convergence condition is not met, the terminal continues to update the candidate stress combination information. If the convergence condition is met, the terminal determines the currently meeting candidate stress combination information as the final stress combination information after optimization, which is used to guide subsequent accelerated aging experiments.

[0107] In this embodiment, by using an optimization objective function based on the ratio of the lithium inventory loss rate to the active material loss rate and dynamically adjusting the application ratio of multiple accelerated stress conditions using an optimization model, the mechanism deviation between the accelerated aging path and the normal aging path can be effectively minimized, thereby improving the consistency of the internal aging mechanism of the battery in the accelerated aging experiment, enhancing the scientific nature and repeatability of the accelerated experiment results, and ensuring the accuracy of the accelerated experiment in predicting the actual service life of the battery.

[0108] In another exemplary embodiment, Figure 4 As shown, the present application provides a method for determining battery aging test parameters, including:

[0109] Normal aging test route: RPT is used to test external characteristics (battery capacity, internal resistance, etc.), and dual-water tank analysis is used to test internal mechanism characteristics (decoupling LLI and LAM).

[0110] Single stress acceleration experimental route: Based on double water tank analysis, the internal mechanism characteristics are tested and then the stress is classified (LLI accelerated stress or LLA accelerated stress).

[0111] Based on the internal mechanism characteristics of the normal aging test circuit and the internal mechanism characteristics of the single stress acceleration test route, the accelerated stress combination information is determined; then, based on the accelerated stress combination information, the external characteristic characterization is adopted by RPT test, and the internal mechanism characteristics are tested based on double water tank analysis; finally, it is verified whether the external characteristic characterization and internal mechanism characteristics of the accelerated stress combination information are consistent with the external characteristic characterization and internal mechanism characteristics of the normal aging test circuit.

[0112] In the specific implementation, the following steps can be taken:

[0113] Step 1: Before testing, cycle the battery three times at C / 3 rate to obtain stable performance, and then test the initial performance of the battery.

[0114] Step 2: Perform a charge-discharge cycle experiment at a rate of 0.5C / 1C to simulate the normal aging condition of the battery. During the experiment, the battery is taken out every 40 cycles to perform RPT and double-tank analysis to characterize the external health status of the battery and the LLI and LAM loss of internal characteristics respectively. The battery capacity is cycled to 80% of the initial standard capacity. Figure 5 As shown. Among them, Figure 5 The power-off test is used to determine the Figure 6 The positive and negative electrode potential curves under the button battery experiment are shown. Figure 6 OCV is the open circuit voltage; SOC is the proportion of the current remaining battery power (State of Charge); DOD is the proportion of the battery power that has been discharged (Depth of Discharge). Figure 7 Schematic diagram of the double water tank model.

[0115] The formula of the double water tank analysis model is as follows:

[0116]

[0117] Where y is the current occupancy ratio of positive electrode lithium ions, x is the current occupancy ratio of negative electrode lithium ions; y0 is the occupancy ratio of positive electrode lithium ions at the start of charging, x0 is the occupancy ratio of negative electrode lithium ions at the start of charging; Q is the amount of charge charged; c p is the current active capacity of the positive electrode, c n is the current active capacity of the negative electrode; V(t) is the terminal voltage of the battery at time t; U p (y) is the open circuit voltage (OCV) of the positive electrode at the lithium ion occupancy ratio y, U n (x) is the open circuit voltage of the negative electrode at the lithium ion occupancy ratio x. The two parameters can be based on Figure 6The curve graph shown confirms that the change in SOC is approximately equal to the change in lithium occupancy within the material; I is the charging current and R is the internal resistance of the battery.

[0118] Step 3: Design a short-cycle single stress accelerated aging comparison experiment for the battery, conduct orthogonal experiments at different levels for charge and discharge rate, charge and discharge cut-off voltage, ambient temperature, etc., and perform double-water tank analysis on the charging curves at different stages to obtain the LLI and LAM under different stresses. The results are as follows: Figure 8 shown.

[0119] Among them, the parameters used for fitting by particle swarm algorithm are (c p , c n , x0, y0, R).

[0120] The root mean square error (RMSE) calculation formula is:

[0121]

[0122] Step 4: By analyzing the results of short-cycle single stress experiments, specific stress combinations are classified into LLI accelerated stress and LAM accelerated stress to achieve internal mechanism decoupling of battery aging.

[0123] Step 5: Based on the LLI and LAM loss rates under normal aging cycles, conduct ratio tests on various accelerated stresses, analyze the aging path errors, and ultimately obtain a reasonable accelerated aging test method. Figure 9 As shown in Figure 1, the internal characteristic aging path diagram of the double-tank model identification under normal aging conditions, where LLI-real / LAM-real are the actual sampling points, and LLI-fit / LAM-fit are the fitting curves determined based on the actual sampling points.

[0124] Among them, the mathematical expression of the ratio model is:

[0125]

[0126] in, The decay ratio of LLI and LAM of a normally aged battery is is the decay ratio of LLI and LAM of the accelerated aged battery.

[0127] In this example, a dual-tank model is used to analyze the LLI and LAM loss rates within normally aged batteries. This decouples the LLI and LAM loss rates during battery aging using a combined stress model. This method then uses a combination of ratios to create equivalent accelerated aging test methods with different acceleration ratios. This mathematically defined equivalent aging method standardizes equivalent accelerated aging test design and addresses the issue of internal mechanistic equivalence in energy storage battery accelerated aging experiments.

[0128] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0129] Based on the same inventive concept, embodiments of the present application further provide a device for determining battery aging test parameters for implementing the aforementioned method for determining battery aging test parameters. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more embodiments of the device for determining battery aging test parameters provided below can be found in the aforementioned method for determining battery aging test parameters, and will not be further elaborated here.

[0130] In an exemplary embodiment, Figure 10 As shown, a device for determining battery aging experiment parameters is provided, including: a normal operating condition acquisition module 1001, an accelerated operating condition acquisition module 1002, a loss rate acquisition module 1003 and an experimental parameter determination module 1004, wherein:

[0131] Normal operating condition acquisition module 1001, used to obtain initial performance information of the battery and simulate and obtain normal aging condition information of the battery;

[0132] The accelerated working condition acquisition module 1002 is used to determine multiple accelerated aging condition information of the battery under multiple accelerated stress conditions according to multiple preset accelerated stress conditions;

[0133] A loss rate acquisition module 1003 is configured to determine normal aging condition information and multiple accelerated aging condition information, and corresponding lithium inventory loss rate information and active material loss rate information based on the initial performance information;

[0134] The experimental parameter determination module 1004 is used to determine stress combination information of multiple accelerated stress conditions based on the lithium inventory loss rate information and the active material loss rate information to obtain battery aging experimental parameters.

[0135] In one embodiment, each operating condition information in the normal aging condition information and the multiple accelerated aging condition information includes at least operating condition detection information after each preset charge and discharge cycle; the operating condition detection information includes battery capacity information and voltage-time sampling information of constant current charging; the above-mentioned loss rate acquisition module 1003 is further used to determine, for any operating condition information, based on the initial performance information and any operating condition information, the lithium inventory loss information and active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of any operating condition information; and determine the lithium inventory loss rate information and active material loss rate information corresponding to any operating condition information based on the lithium inventory loss information and active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of any operating condition information.

[0136] In one embodiment, the above-mentioned loss rate acquisition module 1003 is also used to determine the battery internal state parameters used for dual-tank model analysis based on the operating condition detection information after a preset charge and discharge cycle of any operating condition information; the voltage-time sampling information of constant current charging is used as the optimization target, and the target parameter value of the battery internal state parameter is determined by iterative optimization; if the target parameter value does not conform to the battery capacity information, the step of performing iterative optimization to determine the target value of the battery internal state parameter is returned until the target parameter value that conforms to the battery capacity information is obtained, and then the lithium inventory loss information and active material loss information corresponding to the operating condition detection information after a preset charge and discharge cycle of any operating condition information are determined based on the target parameter value that conforms to the battery capacity information and the initial performance information.

[0137] In one embodiment, the loss rate acquisition module 1003 is further used to set the internal state parameters of the battery as the particle position parameters in the particle swarm optimization model; use the initial particle position parameters of the particle swarm as the current particle position parameters; obtain the voltage-time prediction information of the constant current charging process corresponding to the current particle position parameters based on the double-water tank model analysis according to the current particle position parameters; use the root mean square error between the voltage-time prediction information and the voltage-time sampling information of the constant current charging as the fitness value of the particle; update the current particle position parameters of the particle swarm according to the fitness value of the particle until the preset iteration termination condition is met, and then determine the target parameter value of the internal state parameter of the battery according to the particle position parameters of the optimal particle in the particle swarm finally obtained.

[0138] In one embodiment, the experimental parameter determination module 1004 is further configured to determine a target ratio of the lithium inventory loss rate to the active material loss rate in the battery aging experiment based on the lithium inventory loss rate information and the active material loss rate information corresponding to the normal aging condition information; and to determine candidate stress combination information by adjusting the application ratio of multiple accelerated stress conditions using an optimization model, and to minimize the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio, thereby obtaining the optimized stress combination information.

[0139] In one embodiment, the experimental parameter determination module 1004 is further configured to use the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio as an optimization objective function; use the initial candidate stress combination information as the current candidate stress combination information; determine the objective function value corresponding to the current candidate stress combination information based on the optimization objective function; update the current candidate stress combination information based on the current candidate stress combination information and the objective function value until the objective function value satisfies a preset convergence condition, and then determine the current candidate stress combination information that satisfies the preset convergence condition as the optimized stress combination information.

[0140] Each module in the above-mentioned battery aging test parameter determination device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0141] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 11As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for determining battery aging test parameters. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0142] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0143] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0145] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0147] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0148] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0149] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining battery aging test parameters, characterized in that: The method comprises: Obtaining initial performance information of the battery, and simulating normal aging condition information of the battery; Determining, according to a plurality of preset accelerated stress conditions, a plurality of accelerated aging condition information of the battery under the plurality of accelerated stress conditions; Determining, based on the initial performance information, the normal aging condition information and the plurality of accelerated aging condition information, and corresponding lithium inventory loss rate information and active material loss rate information; Stress combination information of the multiple accelerated stress conditions is determined based on the lithium inventory loss rate information and the active material loss rate information to obtain aging test parameters of the battery.

2. The method according to claim 1, characterized in that Each of the normal aging condition information and the multiple accelerated aging condition information includes at least operating condition detection information after each preset charge and discharge cycle; the operating condition detection information includes battery capacity information and voltage-time sampling information of constant current charging; The determining, based on the initial performance information, the normal aging condition information and the multiple accelerated aging condition information, and the corresponding lithium inventory loss rate information and active material loss rate information, respectively, includes: For any operating condition information, determining, based on the initial performance information and the any operating condition information, lithium inventory loss information and active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of the any operating condition information; The lithium inventory loss rate information and the active material loss rate information corresponding to any one of the operating condition information are determined according to the lithium inventory loss information and the active material loss information corresponding to the operating condition detection information after each preset charge and discharge cycle of any one of the operating condition information.

3. The method according to claim 2, characterized in that The determining, for any operating condition information, based on the initial performance information and the any operating condition information, of lithium inventory loss information and active material loss information corresponding to operating condition detection information after each preset charge and discharge cycle of the any operating condition information includes: Determine the internal state parameters of the battery for dual-tank model analysis based on the working condition detection information after a preset charge and discharge cycle of any working condition information; Using the voltage-time sampling information of the constant current charging as an optimization target, iteratively optimizing to determine target parameter values ​​of the battery internal state parameters; In the case that the target parameter value does not conform to the battery capacity information, the step of performing the iterative optimization to determine the target value of the battery internal state parameter is returned until the target parameter value that conforms to the battery capacity information is obtained. Then, based on the target parameter value that conforms to the battery capacity information and the initial performance information, the lithium inventory loss information and active material loss information corresponding to the operating condition detection information after a preset charge and discharge cycle of any operating condition information are determined.

4. The method according to claim 3, characterized in that The iterative optimization to determine the target parameter value of the battery internal state parameter includes: Setting the battery internal state parameters as particle position parameters in a particle swarm optimization model; The initial particle position parameters of the particle swarm are used as the current particle position parameters; According to the current particle position parameters, based on the double water tank model analysis, the voltage-time prediction information of the constant current charging process corresponding to the current particle position parameters is obtained; The root mean square error between the voltage-time prediction information and the voltage-time sampling information of the constant current charging is used as the fitness value of the particle; According to the fitness value of the particle, the current particle position parameter of the particle swarm is updated until a preset iteration termination condition is met, and then the target parameter value of the battery internal state parameter is determined according to the particle position parameter of the optimal particle in the particle swarm finally obtained.

5. The method according to claim 1, wherein Determining stress combination information of the plurality of accelerated stress conditions according to the lithium inventory loss rate information and the active material loss rate information includes: Determining a target ratio of the lithium inventory loss rate to the active material loss rate for the battery aging experiment based on the lithium inventory loss rate information and the active material loss rate information corresponding to the normal aging condition information; An optimization model is used to determine candidate stress combination information by adjusting the application ratio of the multiple accelerated stress conditions, and the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio is minimized to obtain the optimized stress combination information.

6. The method according to claim 5, characterized in that The optimization model is used to adjust the application ratio of the multiple accelerated stress conditions to determine candidate stress combination information, and minimize the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio to obtain the optimized stress combination information, including: using the deviation between the ratio of the lithium inventory loss rate to the active material loss rate corresponding to the candidate stress combination information and the target ratio as an optimization objective function; The initial candidate stress combination information is used as the current candidate stress combination information; Determining an objective function value corresponding to the current candidate stress combination information according to the optimization objective function; According to the current candidate stress combination information and the objective function value, the current candidate stress combination information is updated until the objective function value satisfies a preset convergence condition, and the current candidate stress combination information that satisfies the preset convergence condition is determined as the optimized stress combination information.

7. A device for determining battery aging test parameters, characterized in that: The device comprises: A normal operating condition acquisition module, used to obtain initial performance information of the battery and simulate the normal aging condition information of the battery; an accelerated working condition acquisition module, configured to determine, based on a plurality of preset accelerated stress conditions, a plurality of accelerated aging condition information of the battery under the plurality of accelerated stress conditions; a loss rate acquisition module, configured to determine, based on the initial performance information, the normal aging condition information and the plurality of accelerated aging condition information, and the corresponding lithium inventory loss rate information and active material loss rate information; The experimental parameter determination module is used to determine stress combination information of the multiple accelerated stress conditions according to the lithium inventory loss rate information and the active material loss rate information, so as to obtain the aging experimental parameters of the battery.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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