Battery system charging performance evaluation method and device, equipment and storage medium

Abstract

The invention relates to a battery system charging performance evaluation method and device, equipment, a storage medium and a computer program product. The method comprises the steps that structural data of a current battery system are acquired, a system equivalent model of the current battery system is established according to the structural data, and the current battery system comprises at least two single batteries; acquiring a charging mode of the current battery system; establishing a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode; and according to the real-time charging optimization evaluation model, outputting charging performance evaluation results of the current battery system under different battery parameter distributions in a plurality of target dimensions. By adopting the method, the charging performance evaluation accuracy and evaluation efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of battery charging performance evaluation, and particularly to a method, device, equipment, storage medium, and computer program product for evaluating the charging performance of a battery system. Background Art

[0002] With the rapid development of the electric vehicle market, consumers' demands for charging speed and safety are increasing day by day. Traditional charging methods based on experience (such as constant current charging, multi-stage constant current charging, etc.) can no longer meet the requirements, and different charging methods have different performances. Evaluating the charging performance of a battery system is helpful for the development of a fast charging strategy for the system and can also improve the charging experience of users.

[0003] In the prior art, the evaluation of the charging performance of a battery mainly focuses on a single battery cell, without comprehensively considering the different battery parameter distributions among battery systems or battery packs, resulting in low accuracy of the charging performance evaluation; at the same time, it is difficult to evaluate real-time charging optimization scenarios and the evaluation efficiency is not high. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, equipment, storage medium, and computer program product for evaluating the charging performance of a battery system that can consider different battery parameter distributions of the battery system, improve the accuracy and evaluation efficiency of the charging performance evaluation.

[0005] In a first aspect, the present application provides a method for evaluating the charging performance of a battery system, the method comprising:

[0006] Obtaining the structural data of the current battery system, and establishing a system equivalent model of the current battery system according to the structural data, where the current battery system includes at least two single battery cells;

[0007] Obtaining the charging mode of the current battery system;

[0008] Establishing a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0009] Outputting the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions according to the real-time charging optimization evaluation model.

[0010] In one embodiment, the obtaining the structural data of the current battery system and establishing a system equivalent model of the current battery system according to the structural data includes:

[0011] Establishing a single equivalent model for each single battery cell in the current battery system;

[0012] Based on the structural data and the equivalent model of each single battery, establish the equivalent model of the current battery system.

[0013] In one embodiment, according to the real-time charging optimization evaluation model, output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions, including:

[0014] Output the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions through the real-time charging optimization evaluation model;

[0015] Generate and output the charging performance evaluation results of multiple target dimensions corresponding to different battery parameter distributions according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model.

[0016] In one embodiment, the output of the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions through the real-time charging optimization evaluation model includes:

[0017] Obtain the first input current of the current battery system at a preset moment under the current battery parameter distribution;

[0018] According to the first input current at the preset moment, update the second input current at the next moment of the preset moment through an algorithm so that the current battery system reaches the maximum charging speed within the safety boundary;

[0019] Repeat the above update steps until the cut-off charging condition is reached, and the cut-off charging condition includes that the power of the current battery system reaches the charging threshold or the cumulative charging duration reaches the duration threshold;

[0020] Output the optimal real-time charging strategy under the current battery parameter distribution.

[0021] In one embodiment, the generation and output of the charging performance evaluation results of multiple target dimensions corresponding to different battery parameter distributions according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model includes:

[0022] Determine the consistency evaluation index of the current battery system according to the structural data and the optimal real-time charging strategy under the current battery parameter distribution;

[0023] Determine the weight of the consistency evaluation index;

[0024] Determine the consistency evaluation result of the current battery system under the current battery parameter distribution according to the consistency evaluation index and the corresponding weight.

[0025] In one embodiment, the target dimensions include the average charging speed within a first preset time, the average charging speed within a second preset time, the overall average charging speed until the cut-off charging condition is reached, the consistency data of the current battery system within the first preset time, the consistency data of the current battery system within the second preset time, and the consistency data of the current battery system until the cut-off charging condition is reached.

[0026] In a second aspect, the present application further provides a device for evaluating the charging performance of a battery system. The device includes:

[0027] A first data acquisition module, configured to acquire the structural data of the current battery system, and establish a system equivalent model of the current battery system according to the structural data. The current battery system includes at least two single cells;

[0028] A second data acquisition module, configured to acquire the charging mode of the current battery system;

[0029] A processing module, configured to establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0030] A performance evaluation module, configured to output the charging performance evaluation results of the current battery system in multiple target dimensions under different battery parameter distributions according to the real-time charging optimization evaluation model.

[0031] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Acquire the structural data of the current battery system, and establish a system equivalent model of the current battery system according to the structural data. The current battery system includes at least two single cells;

[0033] Acquire the charging mode of the current battery system;

[0034] Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0035] Output the charging performance evaluation results of the current battery system in multiple target dimensions under different battery parameter distributions according to the real-time charging optimization evaluation model.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Obtain the structural data of the current battery system, and establish a system equivalent model of the current battery system according to the structural data, where the current battery system includes at least two single cells;

[0038] Obtain the charging mode of the current battery system;

[0039] Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0040] According to the real-time charging optimization evaluation model, output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions.

[0041] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0042] Obtain the structural data of the current battery system, and establish a system equivalent model of the current battery system according to the structural data, where the current battery system includes at least two single cells;

[0043] Obtain the charging mode of the current battery system;

[0044] Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0045] According to the real-time charging optimization evaluation model, output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions.

[0046] The embodiments of the present application have the following beneficial effects:

[0047] The battery system charging performance evaluation method, device, computer device, storage medium and computer program product provided by the embodiments of the present application can perform real-time charging strategy simulation on the current battery system, obtain the process of charging the current battery system from the initial state or low state of charge to the charging cut-off condition, so as to obtain the charging performance evaluation results of the current battery system in combination with multiple target dimensions. Users can intuitively compare the influence of different parameter distributions on the fast charging ability, providing data support for battery system design, charging strategy optimization and fault diagnosis. Description of the Drawings

[0048] Figure 1 It is a schematic flowchart of the battery system charging performance evaluation method in an embodiment;

[0049] Figure 2 It is a schematic structural diagram of the battery single cell equivalent model in an embodiment;

[0050] Figure 3 It is a schematic structural diagram of an equivalent model of a battery system in an embodiment;

[0051] Figure 4 It is the charging current curve of a battery system in an embodiment;

[0052] Figure 5 It is a schematic diagram of the charged amount under different parameter distributions of a battery system in an embodiment;

[0053] Figure 6 It is a schematic diagram of the charging speed under different parameter distributions of a battery system in an embodiment;

[0054] Figure 7 It is a schematic diagram of the consistency score under different parameter distributions of a battery system in an embodiment;

[0055] Figure 8 It is a schematic diagram of the evaluation result of the charging performance of a battery system in an embodiment;

[0056] Figure 9 It is a structural block diagram of a device for evaluating the charging performance of a battery system in an embodiment. Detailed implementation manners

[0057] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] The method for evaluating the charging performance of a battery system provided by an embodiment of the present application can be applied to a terminal or a server. Among them, the terminal communicates with the server through a network. The terminal can be, but is not limited to, various in-vehicle terminals, personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0059] In one embodiment, as Figure 1 shown, a method for evaluating the charging performance of a battery system is provided, and the method includes:

[0060] S1. Obtain the structure data of the current battery system, and establish a system equivalent model of the current battery system according to the structure data. The current battery system includes at least two single cells;

[0061] S2. Obtain the charging mode of the current battery system;

[0062] S3. Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0063] S4. According to the real-time charging optimization evaluation model, output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions.

[0064] Specifically, the battery system may include one or more battery packs with the same or different power and energy for providing power in electric vehicles and hybrid electric vehicles. It can be in the form of a battery pack, a battery module, or a battery device. Whether it is a battery pack, a battery module, or a battery device, it includes at least two single cells. Taking the battery pack as an example, the battery pack is at least composed of two single cells connected in parallel and / or in series.

[0065] The battery pack is an integrated overall battery unit formed by integrating multiple battery modules. When multiple battery modules are connected in series, there may be a situation where the single cells in the battery pack are inconsistent. Among them, the inconsistency of single cells includes the inconsistency of the voltage, internal resistance, temperature, capacity, and state of charge of the battery cells in the battery pack. The inconsistency of single cells may be caused by the difference in the initial performance of single cells before the formation of battery cells, that is, during the manufacturing process, or may be caused by the inconsistent change of the internal performance of the battery due to inconsistent use conditions after the formation of battery cells, that is, during the use process.

[0066] The charging mode may include an offline static charging mode or an online dynamic charging mode. The offline static charging mode may include constant current charging, multi-stage constant current charging, etc.; the online dynamic charging mode may include online super-fast charging, lithium-free fast charging, and multi-objective optimal charging considering safety boundaries such as temperature potential. Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode of the current battery system, and conduct real-time charging optimization evaluation. The traditional battery charging performance evaluation method mainly targets single cells and does not consider the dynamic impact of the parameter distribution difference between single cells inside the battery system on the overall charging performance, resulting in a deviation between the evaluation result and the actual working condition. The charging optimization evaluation model of this embodiment can simulate the real-time charging strategy of the current battery system, obtain the process of the current battery system charging from the initial state or low state of charge to the charging cut-off condition, so as to obtain the charging performance evaluation result of the current battery system combined with multiple target dimensions. Users can intuitively compare the impact of different parameter distributions on the fast charging ability, providing data support for battery system design, charging strategy optimization, and fault diagnosis.

[0067] In one embodiment, the target dimensions include the average charging speed within a first preset time, the average charging speed within a second preset time, the overall average charging speed until the cut-off charging condition is reached, the consistency data of the current battery system within the first preset time, the consistency data of the current battery system within the second preset time, and the consistency data of the current battery system when the cut-off charging condition is reached.

[0068] Exemplarily, the first preset time can be 1 minute, 2 minutes, 3 minutes, 5 minutes, 10 minutes, etc., or it can also be 30 seconds, 90 seconds, 150 seconds, etc.; the second preset time can be 5 minutes, 10 minutes, 20 minutes, 30 minutes, etc., or it can also be 90 seconds, 180 seconds, 400 seconds, etc. The first preset time and the second preset time can be set according to the actual application scenario. An initial value can be preset. For example, the first preset time is 5 minutes and the second preset time is 10 minutes; it can be set according to user customization, or it can be adaptively and dynamically changed according to the preset configuration scenario. The first preset time can be greater than the second preset time, or the first preset time can also be less than the second preset time. Generally, the first preset time is less than the second preset time. It should be noted that the above descriptions of the first preset time and the second preset time are only exemplary explanations, rather than actual limitations. Hereinafter, an example where the first preset time is 5 minutes and the second preset time is 10 minutes will be used for illustration. The cut-off charging condition is used to describe that the state of charge of the battery reaches the charging threshold or the cumulative charging duration reaches the duration threshold. Exemplarily, the target dimensions include the average charging speed in 5 minutes, the average charging speed in 10 minutes, the overall charging speed, the consistency data of the current battery system in 5 minutes, the consistency data of the current battery system in 10 minutes, and the consistency data of the current battery system at the end of the overall charging. Since the charging of the battery system has significant stage characteristics: generally, a large charging rate is required in the initial stage to quickly increase the state of charge (SOC), and overcharging needs to be avoided and the consistency between battery cells needs to be ensured in the final stage. This embodiment can achieve multi-dimensional and multi-stage refined analysis of the charging process through multiple target dimensions (such as the speed in the first 5 minutes, the speed in 10 minutes, the overall speed, and the corresponding consistency data). For example, a certain system may show a high charging speed in the initial stage, but the speed drops suddenly in the middle stage due to excessive temperature rise; or in the final stage, the protection is triggered due to deteriorated consistency, affecting the total charging amount. The multi-stage data can provide a clear direction for strategy optimization, such as adjusting the initial current rise rate, enhancing the heat dissipation in the middle stage, or optimizing the equalization logic in the final stage, etc.

[0069] In one embodiment, S1 includes:

[0070] S11. Establish an equivalent model for each single battery in the current battery system;

[0071] S12. Based on the structural data and the equivalent model of each single battery, establish the equivalent model of the current battery system.

[0072] Specifically, referring to Figure 2 , establish a thermoelectrically coupled equivalent model of the battery cell. In this embodiment, a thermoelectrically coupled polarized Rint model is established. The polarized Rint model is a lithium-ion battery model that decouples the positive and negative electrodes of the lithium-ion battery through a reference electrode and equivalent it to an ideal voltage source, a positive electrode internal resistance, and a negative electrode internal resistance. The circuit principle of the model is as follows:

[0073]

[0074]

[0075]

[0076]

[0077] Among them, V ca , V an and U t are the positive terminal voltage, negative terminal voltage, and full battery terminal voltage of the current battery cell respectively, OCV ca , OCV an and U ocv are the positive electrode open circuit voltage, negative electrode open circuit voltage, and full battery open circuit voltage of the current battery cell respectively, R ca and R an are the positive electrode internal resistance and negative electrode internal resistance of the current battery cell respectively, I bat is the current passing through the current battery cell, and it is stipulated that discharge is positive and charge is negative.

[0078] The heat generation and heat dissipation principle of the battery thermoelectrically coupled lumped thermal model is as follows:

[0079]

[0080]

[0081] Among them, is the heat generation per unit time, T bat is the temperature of the current battery cell, is the heat transfer per unit time, h represents the heat transfer coefficient, S represents the surface area of the current battery cell, m represents the mass of the battery, and T amb represents the ambient temperature. Therefore, the change of the battery temperature with time is:

[0082]

[0083] Exemplarily, referring toFigure 3 , based on the monomer model, an equivalent model of the 3P6S battery system (6 series modules, with three parallel monomer batteries in each group, that is, after three monomer batteries in each group are connected in parallel to form a battery module, 6 battery modules are connected in series) can be established, where B m,n represents the nth battery monomer in the mth series module, and R sb and R bp respectively represent the series contact internal resistance and parallel contact internal resistance in the battery. I pack,k represents the current passing through the battery system at time k. In this embodiment, by first establishing a monomer equivalent model and then integrating it into a system-level model based on the system structure (series-parallel topology, contact internal resistance), the model's ability to represent parameter discreteness is significantly improved. The monomer model can accurately capture the dynamic response of the monomer (such as polarization voltage, temperature rise characteristics), and the system model can accurately predict the overall voltage, current, and temperature distribution of the system by superimposing the monomer behavior and considering the influence of connection impedance. This method is not only applicable to battery modules with completely consistent monomer parameters under ideal conditions, but also can simulate complex scenarios such as actual inconsistency differences by introducing assumptions about parameter distribution among monomers (such as normal distribution, Weibull distribution, etc.), parameter anomalies, etc., which is closer to the actual scenario and provides a theoretical basis for the robust design of battery systems.

[0084] In one embodiment, S4 includes:

[0085] S41. Optimize the evaluation model through real-time charging to output the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions;

[0086] S42. Generate and output the charging performance evaluation results of multiple target dimensions corresponding to different battery parameter distributions according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model.

[0087] Specifically, considering the electrical parameter inconsistency among battery monomers, mainly including: inconsistency in capacity, internal resistance, and initial charge, it is assumed that the parameters conform to a normal distribution , where is the mean value, represents the root mean square, and can be specifically expressed as:

[0088]

[0089]

[0090]

[0091] Among them, the superscript 0 represents the initial state, represents the battery model B m,nThe internal resistance change rate, assuming that the internal resistances of the positive and negative electrodes follow the same change law, and its mean value rr 0 is 1. The corresponding initial charge of monomer B i,j varies with different initial states of the system. The larger it is, the greater the deviation of the parameter distribution. The basic assumptions of the distribution of system parameters are determined by testing as shown in Table 1 below:

[0092] Table 1 Assumptions of the parameter distribution of the battery system

[0093]

[0094] Exemplarily, other initial conditions of the battery system can be set as follows: the initial remaining charge is 0, the initial temperature is 25 degrees Celsius, the initial charging speed of the system is set to 1C (i.e., ), the maximum charging rate is 2.5C (i.e., ), to determine the optimal real-time charging strategy, referring to Figure 4 , which is an optimal fast charging curve for the current battery system under the non-lithium deposition fast charging strategy. To show the differences in the charging performance evaluation results under different parameter settings of the battery system, the standard deviation rr of the battery internal resistance coefficient in Table 1 can be set to 0, 0.05, 0.1, 0.15, 0.2, 0.25, etc. respectively. Similarly, other initial conditions of the battery system can be combined, and the optimal real-time charging strategy curves of the current battery system in the charging mode output under different battery parameter distributions can be obtained. Referring to Figures 5 - 7 , the target dimensions include the average charging speed within 5 minutes, the average charging speed within 10 minutes, the full-course charging speed, the consistency data of the current battery system at 5 minutes, the consistency data of the current battery system at 10 minutes, and the consistency data of the current battery system at the end of the full-course charging. Combining multiple above-mentioned target dimensions, the charging performance evaluation results corresponding to different battery parameter distributions are generated and output according to the optimal real-time charging strategy. By adopting such a technical solution, the optimal charging strategy under different parameter distributions can be generated through a real-time optimization model, and multi-dimensional evaluation results can be synchronously output, including short-term charging speed, long-term charging speed (charging speed for a relatively long time charging compared to short-term charging), full-course charging speed, and the corresponding consistency scoring results, so as to break through the limitations of traditional evaluation and reveal the dynamic influence law of different parameter distributions on charging performance.

[0095] In one embodiment, S41 includes:

[0096] Obtain the first input current of the current battery system at a preset moment under the current battery parameter distribution;

[0097] Update the second input current at the next moment of the preset moment according to the first input current at the preset moment, so that the current battery system reaches the maximum charging speed within the safety boundary;

[0098] Repeat the above update steps until the charging cut-off condition is reached. The charging cut-off condition includes that the power of the current battery system reaches the charging threshold or the cumulative charging duration reaches the duration threshold;

[0099] Output the optimal real-time charging strategy under the current battery parameter distribution.

[0100] Exemplarily, the fast charging current of the battery system can be optimized by the Proportional Integral Derivative (PID) controller algorithm. It should be noted that the above algorithm can include the above PID control algorithm, and can also include the Model Predictive Control (MPC) algorithm, and can also include machine learning algorithms (such as reinforcement learning), etc. It is not limited to the above algorithms. As long as it can implement the technical solution of updating the second input current at the next moment of the preset moment so that the current battery system reaches the maximum charging speed within the safety boundary, it is covered in the embodiments of the present disclosure. The safety boundary can include voltage thresholds, temperature thresholds, etc. The voltage threshold can be described as the maximum allowable limit value corresponding to the voltage of one or more single cells in the current battery system, and the temperature threshold can be described as the maximum or minimum allowable limit value of the overall temperature of the current battery system or the temperature of one or more single cells in the current battery system.

[0101] The input current of the battery system at time k (i.e., the above first input current) can be used Based on the model-estimated minimum negative electrode potential at time k Not exceeding the safety threshold As the goal, optimize the system input current at time k + 1 (i.e., the above second input current) to achieve the maximum charging speed of the battery system within the safety boundary. The input current update steps of the battery system are as follows:

[0102]

[0103]

[0104]

[0105] In the formula, Is the difference between the minimum negative electrode potential at the current moment and the set safety threshold, Is the optimal fast charging current at time k calculated using PID control; k p, k i and k d They are the proportional, integral, and derivative parameters preset by the controller respectively. For different system optimization objectives and control requirements, the magnitudes of the three can be flexibly adjusted to achieve an ideal control effect. By adopting such a technical solution, the input current can be adjusted in real time through the PID controller, enabling the current battery system to reach the maximum charging speed within the safety boundary, outputting the optimal real-time charging strategy of the current battery system under the current battery parameter distribution, simulating the charging until the optimal charging process under the cut-off charging condition is reached, so as to be able to output the optimal real-time charging strategy under different battery parameter distributions, providing a simulation environment for the subsequent output of the charging performance evaluation results of multiple target dimensions under the corresponding battery parameter distribution.

[0106] In one embodiment, S42 includes:

[0107] Determine the consistency evaluation index of the current battery system according to the structure data and the optimal real-time charging strategy under the current battery parameter distribution;

[0108] Determine the weight of the consistency evaluation index;

[0109] Determine the consistency evaluation result of the current battery system under the current battery parameter distribution according to the consistency evaluation index and the corresponding weight.

[0110] Exemplarily, in combination with the structure data of the current battery system, the consistency evaluation index of the current battery system can be determined from multiple aspects such as voltage, temperature, internal resistance, capacity, and power reflected in the optimal real-time charging strategy under the current battery parameter distribution. With the evolution during the battery usage process, combined with the durability, consistency, and safety of the battery, the consistency of the battery module can be effectively quantitatively evaluated. Weight allocation can be made for voltage, temperature, internal resistance, capacity, power, and other aspects and weighted summation can be performed to determine the consistency weighted score of the current battery system under the current battery parameter distribution, and the total consistency weighted score is used as the consistency evaluation result. The higher the score of the total consistency weighted score of the battery system, the better its consistency.

[0111] Exemplarily, by comprehensively comparing the charging capacity evaluations of battery systems under different parameter distributions, referring to Figure 8, a score can be comprehensively given from six dimensions: 5-minute fast charging ability, 10-minute fast charging ability, full-process fast charging ability, battery system consistency at the 5th minute, battery system consistency at the 10th minute, and battery system consistency at the end of charging. It can be seen that as the internal resistance distribution among the single cells in the battery system increases, the fast charging speed of the system drops significantly, especially the average charging speed in the first 10 minutes and the first 5 minutes. In addition, the system inconsistency of the battery in the early stage of charging also decreases, but the score of the battery pack at the end of charging remains relatively stable. It should be noted that the charging performance evaluation result can be, for example Figure 8 a radar chart, or other visualization display charts such as bar charts, column charts, line charts or pie charts, etc., and can also be a non-visual data report display result. The charging performance evaluation result is used to describe the charging performance of the current battery system in multiple target dimensions under different battery parameter distributions. Based on the charging performance evaluation result, users can quickly understand the charging situation of the current battery system. The charging performance evaluation result is not limited to or restricted by display forms such as pictures, documents or data, and the above display forms are only for illustrative purposes.

[0112] In this embodiment, real-time charging strategy simulation can be performed on the current battery system to obtain the process of charging the current battery system from the initial state or a lower state of charge to the charging cut-off condition, so as to obtain the charging performance evaluation result of the current battery system combined with multiple target dimensions. Users can intuitively compare the influence of different parameter distributions on the fast charging ability, providing data support for battery system design, charging strategy optimization and fault diagnosis.

[0113] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0114] Based on the same inventive concept, an embodiment of the present application further provides a battery system charging performance evaluation device for implementing the battery system charging performance evaluation method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the battery system charging performance evaluation device provided below can refer to the limitations on the battery system charging performance evaluation method in the above text, and will not be elaborated here.

[0115] In one embodiment, as Figure 9 shown, a battery system charging performance evaluation device is provided, including: a first data acquisition module, configured to acquire the structure data of the current battery system, and establish a system equivalent model of the current battery system according to the structure data, where the current battery system includes at least two single cells; a second data acquisition module, configured to acquire the charging mode of the current battery system; a processing module, configured to establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode; a performance evaluation module, configured to output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions according to the real-time charging optimization evaluation model.

[0116] Further, the first data acquisition module is further configured to establish an equivalent model for each single cell in the current battery system; and to establish the system equivalent model of the current battery system based on the equivalent model of each single cell according to the structure data.

[0117] Further, the performance evaluation module is further configured to output the optimal real-time charging strategy of the current battery system under the charging mode and different battery parameter distributions through the real-time charging optimization evaluation model; and to generate and output the charging performance evaluation results of multiple target dimensions corresponding to different battery parameter distributions according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model.

[0118] Further, the performance evaluation module is further configured to acquire the first input current of the current battery system at a preset moment under the current battery parameter distribution; and to update the second input current at the next moment of the preset moment through an algorithm according to the first input current at the preset moment, so that the current battery system reaches the maximum charging speed within the safety boundary; and to repeat the above update step until the cut-off charging condition is reached, where the cut-off charging condition includes that the battery level of the current battery system reaches the charging threshold or the cumulative charging duration reaches the duration threshold; and to output the optimal real-time charging strategy under the current battery parameter distribution.

[0119] Further, the performance evaluation module is further configured to determine a consistency evaluation index of the current battery system according to the structure data and the optimal real-time charging strategy under the current battery parameter distribution; and to determine the weight of the consistency evaluation index; and further to determine a consistency evaluation result of the current battery system under the current battery parameter distribution according to the consistency evaluation index and the corresponding weight.

[0120] Further, the target dimensions include the average charging speed within a first preset time, the average charging speed within a second preset time, the overall average charging speed until the cut-off charging condition is reached, the consistency data of the current battery system within the first preset time, the consistency data of the current battery system within the second preset time, and the consistency data of the current battery system until the cut-off charging condition is reached.

[0121] Each module in the above battery system charging performance evaluation device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above each module.

[0122] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0123] Obtain the structure data of the current battery system, and establish a system equivalent model of the current battery system according to the structure data. The current battery system includes at least two single cells;

[0124] Obtain the charging mode of the current battery system;

[0125] Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0126] Output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions according to the real-time charging optimization evaluation model.

[0127] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0128] Establish a single-cell equivalent model for each single cell in the current battery system;

[0129] Establish a system equivalent model of the current battery system based on the single-cell equivalent model of each single cell according to the structure data.

[0130] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0131] Output the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions through a real-time charging optimization evaluation model;

[0132] Generate and output the charging performance evaluation results of multiple target dimensions corresponding to different battery parameter distributions according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model.

[0133] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0134] Obtain the first input current of the current battery system at a preset moment under the current battery parameter distribution;

[0135] According to the first input current at the preset moment, update the second input current at the next moment of the preset moment through an algorithm, so that the current battery system reaches the maximum charging speed within the safety boundary;

[0136] Repeat the above update steps until the cut-off charging condition is reached, and the cut-off charging condition includes that the battery level of the current battery system reaches the charging threshold or the cumulative charging duration reaches the duration threshold;

[0137] Output the optimal real-time charging strategy under the current battery parameter distribution.

[0138] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0139] Determine the consistency evaluation index of the current battery system according to the structure data and the optimal real-time charging strategy under the current battery parameter distribution;

[0140] Determine the weight of the consistency evaluation index;

[0141] Determine the consistency evaluation result of the current battery system under the current battery parameter distribution according to the consistency evaluation index and the corresponding weight.

[0142] In one embodiment, the target dimensions include the average charging speed within the first preset time, the average charging speed within the second preset time, the overall average charging speed until the cut-off charging condition is reached, the consistency data of the current battery system within the first preset time, the consistency data of the current battery system within the second preset time, and the consistency data of the current battery system until the cut-off charging condition is reached.

[0143] 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 following steps are implemented:

[0144] Obtain the structural data of the current battery system, and establish a system equivalent model of the current battery system according to the structural data, where the current battery system includes at least two single cells;

[0145] Obtain the charging mode of the current battery system;

[0146] Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0147] Output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions according to the real-time charging optimization evaluation model.

[0148] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0149] Establish a single-cell equivalent model for each single cell in the current battery system;

[0150] Establish a system equivalent model of the current battery system based on the single-cell equivalent model of each single cell according to the structural data.

[0151] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0152] Output the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions through the real-time charging optimization evaluation model;

[0153] Generate and output the charging performance evaluation results of multiple target dimensions corresponding to the battery parameter distribution according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model.

[0154] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0155] Obtain the first input current at a preset moment under the current battery parameter distribution of the current battery system;

[0156] Update the second input current at the next moment of the preset moment according to the first input current at the preset moment, so that the current battery system reaches the maximum charging speed within the safety boundary;

[0157] Repeat the above update steps until the cut-off charging condition is reached, where the cut-off charging condition includes that the battery level of the current battery system reaches the charging threshold or the cumulative charging duration reaches the duration threshold;

[0158] Output the optimal real-time charging strategy under the current battery parameter distribution.

[0159] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0160] Determine the consistency evaluation index of the current battery system according to the structure data and the optimal real-time charging strategy under the current battery parameter distribution;

[0161] Determine the weight of the consistency evaluation index;

[0162] Determine the consistency evaluation result of the current battery system under the current battery parameter distribution according to the consistency evaluation index and the corresponding weight.

[0163] In one embodiment, the target dimensions include the average charging speed within a first preset time, the average charging speed within a second preset time, the overall average charging speed until the cut-off charging condition is reached, the consistency data of the current battery system within the first preset time, the consistency data of the current battery system within the second preset time, and the consistency data of the current battery system until the cut-off charging condition is reached.

[0164] In one embodiment, a computer program product is provided, including a computer program, which when executed by a processor, implements the following steps:

[0165] Obtain the structure data of the current battery system, and establish a system equivalent model of the current battery system according to the structure data, where the current battery system includes at least two single cells;

[0166] Obtain the charging mode of the current battery system;

[0167] Establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode;

[0168] According to the real-time charging optimization evaluation model, output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions.

[0169] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0170] Establish a single-cell equivalent model for each single cell in the current battery system;

[0171] Establish a system equivalent model of the current battery system based on the single-cell equivalent model of each single cell according to the structure data.

[0172] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0173] Optimize the evaluation model through real-time charging to output the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions;

[0174] Generate and output the charging performance evaluation results of multiple target dimensions under the corresponding battery parameter distributions according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model.

[0175] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0176] Obtain the first input current of the current battery system at a preset moment under the current battery parameter distribution;

[0177] According to the first input current at the preset moment, update the second input current at the next moment of the preset moment through an algorithm so that the current battery system reaches the maximum charging speed within the safety margin;

[0178] Repeat the above update steps until the cut-off charging condition is reached, and the cut-off charging condition includes that the battery level of the current battery system reaches the charging threshold or the cumulative charging duration reaches the duration threshold;

[0179] Output the optimal real-time charging strategy under the current battery parameter distribution.

[0180] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0181] Determine the consistency evaluation index of the current battery system according to the structural data and the optimal real-time charging strategy under the current battery parameter distribution;

[0182] Determine the weight of the consistency evaluation index;

[0183] Determine the consistency evaluation result of the current battery system under the current battery parameter distribution according to the consistency evaluation index and the corresponding weight.

[0184] In one embodiment, the target dimensions include the average charging speed within the first preset time, the average charging speed within the second preset time, the overall average charging speed until the cut-off charging condition is reached, the consistency data of the current battery system within the first preset time, the consistency data of the current battery system within the second preset time, and the consistency data of the current battery system until the cut-off charging condition is reached.

[0185] 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 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.

[0186] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0187] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in this specification.

[0188] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for evaluating the charging performance of a battery system, characterized in that, The method includes: Obtaining the structure data of the current battery system, and establishing a system equivalent model of the current battery system according to the structure data, where the current battery system includes at least two single cells; Obtaining the charging mode of the current battery system; Establishing a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode; Outputting the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions according to the real-time charging optimization evaluation model.

2. The method according to claim 1, characterized in that, The obtaining the structure data of the current battery system and establishing a system equivalent model of the current battery system according to the structure data includes: Establishing a single-cell equivalent model for each single cell in the current battery system; Establishing a system equivalent model of the current battery system based on the single-cell equivalent models of each single cell according to the structure data.

3. The method according to claim 1, characterized in that The outputting the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions according to the real-time charging optimization evaluation model includes: Outputting the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions through the real-time charging optimization evaluation model; Generating and outputting the charging performance evaluation results of multiple target dimensions corresponding to the battery parameter distribution according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model.

4. The method according to claim 3, wherein The outputting the optimal real-time charging strategy of the current battery system in the charging mode and under different battery parameter distributions through the real-time charging optimization evaluation model includes: Obtaining the first input current at a preset moment under the current battery parameter distribution of the current battery system; Updating the second input current at the next moment of the preset moment according to the first input current at the preset moment through an algorithm, so that the current battery system reaches the maximum charging speed within the safety boundary; Repeating the above update steps until the cut-off charging condition is reached, where the cut-off charging condition includes that the battery power of the current battery system reaches the charging threshold or the cumulative charging duration reaches the duration threshold; Outputting the optimal real-time charging strategy under the current battery parameter distribution.

5. The method according to claim 3, characterized in that, The generating and outputting the charging performance evaluation results of multiple target dimensions corresponding to the battery parameter distribution according to the optimal real-time charging strategy under different battery parameter distributions through the real-time charging optimization evaluation model includes: Determining the consistency evaluation index of the current battery system according to the structure data and the optimal real-time charging strategy under the current battery parameter distribution; Determining the weight of the consistency evaluation index; Determining the consistency evaluation result of the current battery system under the current battery parameter distribution according to the consistency evaluation index and the corresponding weight.

6. The method according to claim 1, wherein The target dimensions include the average charging speed within the first preset time, the average charging speed within the second preset time, the overall average charging speed until the cut-off charging condition is reached, the consistency data of the current battery system within the first preset time, the consistency data of the current battery system within the second preset time, and the consistency data of the current battery system until the cut-off charging condition is reached.

7. A device for evaluating the charging performance of a battery system, characterized in that, The device includes: A first data acquisition module, configured to acquire the structure data of the current battery system, and establish a system equivalent model of the current battery system according to the structure data, where the current battery system includes at least two single cells; A second data acquisition module, configured to acquire the charging mode of the current battery system; A processing module, configured to establish a real-time charging optimization evaluation model for the current battery system according to the system equivalent model and the charging mode; A performance evaluation module, configured to output the charging performance evaluation results of multiple target dimensions of the current battery system under different battery parameter distributions according to the real-time charging optimization evaluation model.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, 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 the 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 the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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