Battery health state calculation method and device, computer equipment and storage medium

Abstract

The invention relates to a battery health state calculation method and device, computer equipment, a storage medium and a computer program product. According to the method, charging condition data of a target battery and to-be-identified parameters of a target equivalent circuit model are acquired; determining an initial value of the to-be-identified parameter according to the charging condition data; determining at least two parameter groups; determining a parameter value of each to-be-identified parameter in the at least two parameter groups according to the charging condition data; and determining the health state of the target battery based on the parameter value of each to-be-identified parameter in the at least two parameter groups and the initial value of each to-be-identified parameter. According to the whole scheme, the to-be-identified parameters are subjected to two times of parameter identification, compared with instability of single equivalent circuit model identification, the more accurate parameter values of the to-be-identified parameters can be obtained, and then the more accurate battery health state can be obtained based on the accurate parameter values of the to-be-identified parameters.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for calculating the health status of a battery. Background Art

[0002] Batteries represented by lithium-ion batteries have been widely used as energy storage carriers, such as mobile phones, laptops, medical devices, electric vehicles, energy storage power stations, signal base stations, etc. In order to ensure the safe and efficient operation of batteries, they are generally equipped with a battery management system (BMS) to perform battery state estimation, fault diagnosis, and power balancing. Lithium battery state of health (SOH) estimation is a key factor in battery system management because it reflects the remaining available capacity and life of lithium batteries. With an accurate lithium battery SOH estimate, the overall life of the battery can be estimated and the charge and discharge strategy can be improved to avoid battery abuse.

[0003] The current battery health status estimation method is mainly based on the equivalent circuit model for estimation, but the battery health status calculated by the current method has low accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide a high-precision battery health status calculation method, device, computer equipment, computer-readable storage medium and computer program product to address the above technical problems.

[0005] In a first aspect, the present application provides a method for calculating a battery health status. The method comprises:

[0006] Obtaining charging condition data of a target battery and parameters to be identified of a target equivalent circuit model;

[0007] Determining an initial value of the parameter to be identified according to the charging condition data;

[0008] Determine at least two parameter groups, each of which includes at least part of the parameters to be identified;

[0009] Determining parameter values ​​of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data;

[0010] The health state of the target battery is determined based on the parameter values ​​of each parameter to be identified in at least two of the parameter groups and the initial value of each parameter to be identified.

[0011] In the above embodiment, first, the parameters to be identified of the target equivalent circuit model are initially identified according to the charging condition data to obtain the initial values ​​of the parameters to be identified. Secondly, at least two parameter groups are determined based on the parameters to be identified, and then the parameters to be identified in the parameter groups are again identified according to the charging condition data. Finally, the battery health state is determined based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial values ​​of each parameter to be identified. Compared with the instability of the single identification of the parameters to be identified of the equivalent circuit model, more accurate parameter values ​​of the parameters to be identified can be obtained through two identifications of the parameters to be identified, and then based on the accurate parameter values ​​of the parameters to be identified, a more accurate battery health state can be obtained.

[0012] In one of the embodiments, the determining of at least two parameter groups includes: sampling the parameters to be identified based on a preset sampling rule and a preset number of parameter groups to obtain at least two parameter groups, and the number of the preset parameter groups is greater than or equal to two.

[0013] In the above embodiment, parameter groups are determined by multiple sampling rules to obtain more random parameter groups, and thus the parameter values ​​of at least two groups of parameter groups and the initial values ​​of the parameters to be identified, the parameter values ​​of the parameters to be identified of the target equivalent circuit model obtained have higher robustness.

[0014] In one embodiment, determining the health state of the target battery based on the parameter values ​​of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified includes:

[0015] Determining the parameter value of the parameter to be identified of the target equivalent circuit model based on the parameter value of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified;

[0016] The health state of the target battery is determined based on the parameter value of the to-be-identified parameter of the target equivalent circuit model.

[0017] In the above embodiment, after the parameter values ​​of the parameters to be identified of the target equivalent circuit model are determined, it is equivalent to that the target equivalent circuit model has been determined. Subsequently, the battery health status can be determined in real time based on the parameter values ​​of the parameters to be identified of the determined target equivalent circuit model, thereby improving the real-time update of the battery health status.

[0018] In one embodiment, the charging condition data includes input current data and output voltage data;

[0019] The step of determining the initial value of the parameter to be identified according to the charging condition data includes:

[0020] Processing the input current data through the target equivalent circuit model to obtain first simulation voltage data;

[0021] Based on the error value between the first simulation voltage data and the output voltage data, the parameter value of the parameter to be identified in the target equivalent circuit model is corrected, and the step of processing the input current data through the target equivalent circuit model is returned until the correction end condition is reached to obtain the initial value of the parameter to be identified.

[0022] In the above embodiment, by determining the initial value of the parameter to be identified, the situation where the parameter value of the target equivalent circuit model is inaccurate due to the different initial value selection can be reduced. In addition, the initial parameter identification is performed based on the input current data and the output voltage data, so that the initial value can be determined quickly and accurately.

[0023] In one embodiment, determining the parameter value of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data includes:

[0024] Setting the parameter value of the to-be-identified parameter of the target equivalent circuit model to the initial value;

[0025] Processing the input current data through the target equivalent circuit model to obtain second simulation voltage data;

[0026] Based on the error value between the second simulation voltage data and the output voltage data, the parameter value of the parameter to be identified in the target equivalent circuit model is corrected, and the step of processing the input current data through the target equivalent circuit model is returned until the correction end condition is reached, and the parameter value of each parameter to be identified in at least two groups of the parameter groups is obtained.

[0027] In the above embodiment, in the process of determining the parameter values ​​of the parameters to be identified in the parameter group, by using the initial values ​​of the parameters to be identified as the initial parameter values ​​of the target equivalent circuit model, the sensitivity problem existing in the selection of the initial values ​​of the parameters to be identified can be reduced. The target equivalent circuit model is trained according to the actual input current data and output current data of the target battery, and the parameter values ​​of each parameter in the parameters to be identified in the obtained parameter group have low sensitivity.

[0028] In one embodiment, the determining the parameter value of the parameter to be identified of the target equivalent circuit model based on the parameter value of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified includes:

[0029] Based on the parameter values ​​of the parameters to be identified in at least two of the parameter groups, obtaining at least one group of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model;

[0030] A fusion calculation is performed on at least one group of intermediate parameter values ​​of the to-be-identified parameters of the target equivalent circuit model to obtain parameter values ​​of the to-be-identified parameters of the target equivalent circuit model.

[0031] In the above embodiment, the parameter values ​​of the parameters to be identified of the target equivalent circuit model are determined by using multiple groups of intermediate parameter values, which can reduce the problem of inaccurate parameter values ​​calculated by a single model. By fusing the calculations of multiple groups of intermediate parameter values ​​obtained through two stages of parameter identification, the accuracy of the parameter values ​​can be further improved, and the generalization ability of the parameter values ​​of the parameters to be identified of the target equivalent circuit model can be improved.

[0032] In one embodiment, the step of fusing and calculating at least one set of intermediate parameter values ​​of the to-be-identified parameters of the target equivalent circuit model to obtain parameter values ​​of the to-be-identified parameters of the target equivalent circuit model includes:

[0033] When the number of intermediate parameter value groups is greater than or equal to two groups, at least one group of intermediate parameter values ​​of the to-be-identified parameter of the target equivalent circuit model is averaged to obtain parameter values ​​of the to-be-identified parameter of the target equivalent circuit model.

[0034] In the above embodiment, since the average calculation processing efficiency is high, the average calculation based on multiple groups of intermediate parameter values ​​can efficiently and accurately determine the parameter values ​​of the final target equivalent circuit model, while improving the SOH estimation accuracy and further improving the SOH estimation efficiency.

[0035] In one embodiment, the step of obtaining the parameters to be identified of the target equivalent circuit model includes:

[0036] Obtaining a target equivalent circuit model;

[0037] Determining a circuit equation of the target equivalent circuit model, and discretizing the circuit equation to obtain a discretized circuit equation;

[0038] According to the discretized circuit equation, the parameters to be identified of the target equivalent circuit model are obtained.

[0039] In the above embodiment, by discretizing the target equivalent circuit model to determine the parameters to be identified, the parameters to be identified that have a greater impact on the battery charging and discharging process can be obtained, thereby improving the accuracy of determining the parameters to be identified.

[0040] In one of the embodiments, the target equivalent circuit model includes a second-order equivalent circuit model that takes hysteresis effects into account.

[0041] In the above embodiment, by determining the parameters to be identified using a second-order equivalent circuit model that takes into account the hysteresis effect, more accurate parameters to be identified can be obtained, which can more accurately describe the charging and discharging process of the target battery, thereby obtaining a more accurate battery health status.

[0042] In a second aspect, the present application also provides a battery health status calculation device. The device comprises:

[0043] An acquisition module, used to acquire charging condition data of a target battery and parameters to be identified of a target equivalent circuit model;

[0044] An initial value determination module, used to determine the initial value of the parameter to be identified according to the charging condition data;

[0045] A parameter group determination module, used to determine at least two parameter groups, each of which includes at least part of the parameters to be identified;

[0046] A parameter value determination module, used to determine the parameter value of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data;

[0047] The health state determination module is used to determine the health state of the target battery based on the parameter values ​​of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified.

[0048] 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 the processor implements the following steps when executing the computer program:

[0049] Obtaining charging condition data of a target battery and parameters to be identified of a target equivalent circuit model;

[0050] Determining an initial value of the parameter to be identified according to the charging condition data;

[0051] Determine at least two parameter groups, each of which includes at least part of the parameters to be identified;

[0052] Determining parameter values ​​of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data;

[0053] The health state of the target battery is determined based on the parameter values ​​of each parameter to be identified in at least two of the parameter groups and the initial value of each parameter to be identified.

[0054] 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:

[0055] Obtaining charging condition data of a target battery and parameters to be identified of a target equivalent circuit model;

[0056] Determining an initial value of the parameter to be identified according to the charging condition data;

[0057] Determine at least two parameter groups, each of which includes at least part of the parameters to be identified;

[0058] Determining parameter values ​​of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data;

[0059] The health state of the target battery is determined based on the parameter values ​​of each parameter to be identified in at least two of the parameter groups and the initial value of each parameter to be identified.

[0060] In a fifth aspect, the present application further 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:

[0061] Obtaining charging condition data of a target battery and parameters to be identified of a target equivalent circuit model;

[0062] Determining an initial value of the parameter to be identified according to the charging condition data;

[0063] Determine at least two parameter groups, each of which includes at least part of the parameters to be identified;

[0064] Determining parameter values ​​of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data;

[0065] The health state of the target battery is determined based on the parameter values ​​of each parameter to be identified in at least two of the parameter groups and the initial value of each parameter to be identified.

[0066] The above-mentioned battery health status calculation method, device, computer equipment, storage medium and computer program product obtain the charging condition data of the target battery and the parameters to be identified of the target equivalent circuit model; determine the initial values ​​of the parameters to be identified based on the charging condition data; determine at least two groups of parameter groups, each parameter group includes at least part of the parameters to be identified; determine the parameter value of each parameter to be identified in at least two groups of parameter groups based on the charging condition data; determine the health status of the target battery based on the parameter value of each parameter to be identified in at least two groups of parameter groups and the initial value of each parameter to be identified. The entire scheme first performs initial parameter identification on the parameters to be identified of the target equivalent circuit model according to the charging condition data to obtain the initial values ​​of the parameters to be identified. Secondly, at least two parameter groups are determined based on the parameters to be identified. Then, the parameters to be identified in the parameter groups are again identified according to the charging condition data. Finally, the battery health status is determined based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified. Compared with the instability of the single identification of the parameters to be identified of the equivalent circuit model, more accurate parameter values ​​of the parameters to be identified can be obtained through two identifications of the parameters to be identified, and then based on the accurate parameter values ​​of the parameters to be identified, a more accurate battery health status can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0068] Figure 1 A diagram of an application environment of a battery health status calculation method in one embodiment;

[0069] Figure 2 is a flowchart of a method for calculating a battery health status in one embodiment;

[0070] Figure 3 A schematic flow chart of a battery health status calculation step in another embodiment;

[0071] Figure 4 A schematic diagram of a flow chart of battery health status calculation in another embodiment;

[0072] Figure 5 is a circuit diagram of a target equivalent circuit model in one embodiment;

[0073] Figure 6 A schematic diagram of a flow chart of battery health status calculation in another embodiment;

[0074] Figure 7 is a structural block diagram of a battery health status calculation device in one embodiment;

[0075] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with 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.

[0077] Lithium battery health state estimation is a key factor in battery system management because it reflects the remaining available capacity and life of the lithium battery. With an accurate lithium battery SOH estimate, the overall battery life can be estimated and the charge and discharge strategy can be improved to avoid battery abuse.

[0078] The parameter identification method based on the equivalent circuit model (ECM) is currently a commonly used SOH estimation method, which can generally ensure good model accuracy. However, this method usually has the following problems: it has high requirements on data quality. If a certain segment of data used contains noise or part of the data contains noise, the accuracy and stability of the method will be greatly reduced; it is sensitive to the initial value of the parameter to be identified. Once the initial value is poorly selected, the parameter identification result will also be affected; the high-order equivalent circuit model has higher model accuracy in theory, but due to the high coupling between parameters and the complexity of the model, this method has a high probability of falling into a local solution, that is, the identified parameter value is not the optimal parameter.

[0079] Based on this, the present application uses the equivalent circuit model as the base learner. In the first stage, based on an equivalent circuit model, the parameter value of the parameter to be identified is obtained. In the second stage, each ECM selects different (may have intersections) parameter subsets from the parameter sample set to be identified for parameter identification. Finally, the results of multiple equivalent circuit base models are fused to improve the stability and accuracy of the model. On the one hand, since each equivalent circuit base model uses a different sample subset, it can reduce the impact of noise in some data on the identification results; on the other hand, the integration of multiple equivalent circuit base models can effectively alleviate the problem that a single equivalent circuit model is sensitive to the selection of initial parameter values.

[0080] On the basis of this inventive concept, the present application obtains the charging condition data of the target battery and the parameters to be identified of the target equivalent circuit model; determines the initial values ​​of the parameters to be identified based on the charging condition data; determines at least two parameter groups, each parameter group including at least part of the parameters to be identified; determines the parameter value of each parameter to be identified in at least two parameter groups based on the charging condition data; determines the health status of the target battery based on the parameter value of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified. The entire scheme first performs initial parameter identification on the parameters to be identified of the target equivalent circuit model according to the charging condition data to obtain the initial values ​​of the parameters to be identified. Secondly, at least two parameter groups are determined based on the parameters to be identified. Then, the parameters to be identified in the parameter groups are again identified according to the charging condition data. Finally, the battery health status is determined based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified. Compared with the instability of the single identification of the parameters to be identified of the equivalent circuit model, more accurate parameter values ​​of the parameters to be identified can be obtained through two identifications of the parameters to be identified, and then based on the accurate parameter values ​​of the parameters to be identified, a more accurate battery health status can be obtained.

[0081] The battery health status calculation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0082] In a possible implementation, the terminal 102 obtains the charging condition data of the target battery and the parameters to be identified of the target equivalent circuit model; and determines the initial values ​​of the parameters to be identified according to the charging condition data. The charging condition data is stored in the server 104. The terminal 102 determines at least two parameter groups, each parameter group includes at least part of the parameters to be identified; determines the parameter values ​​of each parameter to be identified in at least two parameter groups according to the charging condition data; and determines the health status of the target battery based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial values ​​of each parameter to be identified.

[0083] In another possible implementation, the server 104 obtains the charging condition data of the target battery and the parameters to be identified of the target equivalent circuit model; determines the initial values ​​of the parameters to be identified based on the charging condition data; determines at least two parameter groups, each parameter group includes at least part of the parameters to be identified; determines the parameter values ​​of each parameter to be identified in at least two parameter groups based on the charging condition data; determines the health status of the target battery based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial values ​​of each parameter to be identified. Among them, the terminal 104 can be but is not limited to various personal computers, laptops, smart phones, tablet computers, Internet of Things devices, and the Internet of Things devices can be smart TVs, smart car devices, etc. The server 106 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0084] In one embodiment, Figure 2 As shown, a battery health status calculation method is provided, and the method is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate, and the following steps are included:

[0085] Step 200, obtaining charging condition data of a target battery and parameters to be identified of a target equivalent circuit model.

[0086] The target battery refers to the battery whose health status needs to be determined. It can be different types of batteries, that is, batteries with different cell types. The target equivalent circuit model is the equivalent circuit model selected for calculating the health status of the target battery. The equivalent circuit model abandons the complex electrochemical internal mechanism analysis and uses common electrical components such as voltage sources, resistors, capacitors, inductors, or some nonlinear components to simulate the charging and discharging effects of the battery.

[0087] The charging condition data is the condition data collected during the actual charging and discharging process of the target battery, and can also be the driving condition data collected during the actual driving process of the vehicle equipped with the target battery. Different condition data can be selected according to the characteristics of the target equivalent circuit model, for example, the specific condition data to be used can be determined according to the collection accuracy of the charging condition data or driving condition data in different equivalent circuit models. The charging condition data may include temperature, battery state of charge, voltage, current and other parameters related to battery performance, which can be used to identify the parameters of the target equivalent circuit model. The parameters to be identified are model parameters in the target equivalent circuit model. The parameters to be identified represent the relevant parameters that affect the battery performance, and may include resistance, battery capacity, battery state of charge, voltage and other parameters. The parameters to be identified of different equivalent circuit models may be exactly the same or partially the same.

[0088] In some embodiments, a user triggers a battery health status calculation request on a terminal, and the battery health status calculation request carries the charging condition data of the target battery and the target equivalent circuit model. The terminal listens to and responds to the battery health status calculation request, obtains the charging condition data of the target battery and the target equivalent circuit model, extracts parameters from the target equivalent circuit model, and obtains parameters to be identified. Among them, the battery health status calculation request may also carry a target battery identifier. When the battery health status calculation request carries the target battery identifier, the charging condition data corresponding to the target battery identifier and the parameters to be identified of the target equivalent circuit model are obtained from a local database of the terminal or a remote server according to the target battery identifier.

[0089] Step 400: Determine the initial value of the parameter to be identified according to the charging condition data.

[0090] In some embodiments, the terminal uses the charging condition data as training data, and trains the target equivalent circuit model according to the charging condition data to determine the initial values ​​of the parameters to be identified of the target equivalent circuit model. In one embodiment, based on an optimization algorithm, the target equivalent circuit model is trained according to the charging condition data to determine the initial values ​​of the parameters to be identified of the target equivalent circuit model. Furthermore, the initial values ​​of the parameters to be identified of the target equivalent circuit model can be determined based on a particle swarm optimization algorithm, and the particle swarm optimization algorithm is used to solve the constrained nonlinear least squares problem to solve the optimal solution of the parameters to be identified. The convergence speed is fast, and the large-scale computing efficiency of the cloud can be improved. In this embodiment, other optimization algorithms can also be selected to determine the initial values ​​of the parameters to be identified of the target equivalent circuit model, such as the least squares method to determine the initial values ​​of the parameters to be identified of the target equivalent circuit model, which is not limited to the particle swarm optimization algorithm and the least squares method.

[0091] Step 600, determining at least two groups of parameters.

[0092] The number of parameter groups is greater than or equal to two, each parameter group includes at least part of the parameters to be identified, and may also include all of the parameters to be identified. The number of parameters in each parameter group may be the same or different.

[0093] In some embodiments, the terminal obtains a preset sampling rule from the local, and then samples the parameters to be identified based on the preset sampling rule to obtain at least two groups of parameter groups. The preset sampling rule is a rule for sampling the parameters to be identified to obtain parameter groups, and the preset sampling rule can be set by the staff and can include sampling rules in different ways.

[0094] Step 800: Determine parameter values ​​of each parameter to be identified in at least two parameter groups according to charging condition data.

[0095] In some embodiments, the terminal may use the charging condition data as training data, set the default value of the parameter to be identified in each parameter group according to the empirical value of the parameter to be identified, and then identify the parameters to be identified in at least two parameter groups again according to the charging condition data to obtain the parameter value of each parameter to be identified in at least two parameter groups.

[0096] In some embodiments, the terminal may also determine the parameter values ​​of each parameter to be identified in at least two parameter groups according to the charging condition data and the initial values ​​of the parameters to be identified. Furthermore, the terminal uses the charging condition data as training data, sets the initial values ​​of the parameters to be identified as the default values ​​of the parameters to be identified in each parameter group, and then identifies the parameters to be identified in at least two parameter groups again according to the charging condition data to obtain the parameter values ​​of each parameter to be identified in at least two parameter groups. Among them, the default value represents the initial value of the parameter to be identified in the parameter group. In this embodiment, by using the initial value of the parameter to be identified as the default value of the target equivalent circuit model, the target equivalent circuit model is trained according to the charging condition data to obtain the parameter values ​​of each parameter to be identified in each parameter value group, which can reduce the instability of the initial value selection of the parameter to be identified, improve the accuracy of the identification of the parameter to be identified, and thus improve the stability of the identification result of the parameter value of the parameter to be identified of the target equivalent circuit model.

[0097] The charging condition data selected in the process of identifying the parameter value of the parameter to be identified and the charging condition data selected in the process of identifying the initial value of the parameter to be identified can use charging condition data of different time series to improve the stability and accuracy of the parameter identification result. The charging condition data selected in the process of identifying the parameter value of the parameter to be identified and the charging condition data selected in the process of identifying the initial value of the parameter to be identified can also partially overlap to improve the stability and accuracy of the parameter identification result. Based on the charging condition data, the first condition data is obtained as the training data in the process of determining the initial value of the parameter to be identified, and the second charging condition data is obtained as the training data in the process of determining the parameter value of the parameter to be identified. The first charging condition data and the second charging condition data can be completely different or partially overlapped.

[0098] Step 1000 , determining a health state of a target battery based on parameter values ​​of each parameter to be identified in at least two parameter groups and an initial value of each parameter to be identified.

[0099] In some embodiments, the terminal performs fusion calculation based on the parameter values ​​of each parameter in each parameter group and the initial value of each parameter to be identified, determines the fusion value of each parameter to be identified, and then extracts the fusion value related to the battery capacity from the fusion value of the parameter to be identified, and determines the fusion value related to the battery capacity as the actual capacity of the target battery. The fusion calculation process can be implemented in a variety of fusion methods, including but not limited to weighted fusion, Kalman filtering and other fusion methods.

[0100] Afterwards, the terminal obtains the nominal capacity of the target battery, that is, the maximum capacity when leaving the factory, from a local or remote server, inputs the actual capacity and nominal capacity of the target battery into the calculation expression of the battery health status, and obtains the health status of the target battery based on the ratio of the actual capacity to the nominal capacity.

[0101] The calculation expression of battery health status is as follows:

[0102]

[0103] Among them, Q * Indicates the actual capacity of the target battery.

[0104] In the above-mentioned battery health status calculation method, the charging condition data of the target battery and the parameters to be identified of the target equivalent circuit model are obtained; the initial values ​​of the parameters to be identified are determined based on the charging condition data; at least two parameter groups are determined, each parameter group includes at least part of the parameters to be identified; the parameter value of each parameter to be identified in at least two parameter groups is determined based on the charging condition data; based on the parameter value of each parameter to be identified in at least two parameter groups, and the initial value of each parameter to be identified, the health status of the target battery is determined. The entire scheme first performs initial parameter identification on the parameters to be identified of the target equivalent circuit model according to the charging condition data to obtain the initial values ​​of the parameters to be identified. Secondly, at least two parameter groups are determined based on the parameters to be identified. Then, the parameters to be identified in the parameter groups are again identified according to the charging condition data. Finally, the battery health status is determined based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified. Compared with the instability of the single identification of the parameters to be identified of the equivalent circuit model, more accurate parameter values ​​of the parameters to be identified can be obtained through two identifications of the parameters to be identified, and then based on the accurate parameter values ​​of the parameters to be identified, a more accurate battery health status can be obtained.

[0105] In some optional embodiments, such as Figure 3 As shown, determining at least two parameter groups includes: step 620, sampling the parameters to be identified based on a preset sampling rule and the number of preset parameter groups to obtain at least two parameter groups.

[0106] Among them, the preset sampling rules include random sampling rules and fixed sampling rules. The random sampling rule is to randomly extract at least part of the parameters from the parameters to be identified. The number of extracted parameters can be the preset number of parameters, which can be set by the staff. The number of preset parameters for each parameter group can be the same or different, and the preset number of parameters is less than or equal to the total number of parameters to be identified. The fixed sampling rule can select the parameters to be identified at a fixed position for each parameter group. For example, the first parameter group selects the parameters to be identified at an odd position, the second parameter group selects the parameters to be identified at an even position, and the third parameter group selects the parameters at other positions. The fixed sampling rule can be set by the staff, including but not limited to parameter sampling based on fixed positions. The number of parameter groups can be the number of preset parameter groups. Similarly, the number of preset parameter groups can be set by the staff, and the number of preset parameter groups is greater than or equal to two.

[0107] In some embodiments, the terminal samples the parameters to be identified for a preset number of times based on a random sampling rule to obtain a set of parameter groups. After that, the terminal repeats the above process for the preset number of parameter groups to obtain the preset number of parameter groups. Further, for each parameter group, the terminal randomly samples from the parameters to be identified to obtain a set of parameter groups. For example, if there are n parameters to be identified, and the n parameters to be identified constitute a set of parameters to be identified, the number of parameters contained in each parameter group may be m, and the terminal uses a random sampling (bootstrap) method to select parameters from the set of parameters to be identified θ=[θ 1 ,θ 2 ,…,θ n ], randomly select m (m≤n) parameters to be identified from the parameter set, and use the parameter subset generated by random sampling to obtain a set of parameter groups. Then, repeat T times to obtain T groups of parameter groups, and the T groups of parameter groups can contain repeated parameters to be identified.

[0108] In other embodiments, the terminal obtains the number of preset parameter groups and the sampling rules corresponding to each parameter group, samples the corresponding parameter groups based on the sampling rules of each parameter group in the fixed sampling rules, and obtains parameter groups of the preset number of parameter groups.

[0109] In the above embodiment, parameter groups are determined by multiple sampling rules to obtain more random parameter groups, and thus the parameter values ​​of at least two groups of parameter groups and the initial values ​​of the parameters to be identified, the parameter values ​​of the parameters to be identified of the target equivalent circuit model obtained have higher robustness.

[0110] In some optional embodiments, the health state of the target battery is determined based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified, including: determining the parameter values ​​of the parameters to be identified of the target equivalent circuit model based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified; determining the health state of the target battery based on the parameter values ​​of the parameters to be identified of the target equivalent circuit model.

[0111] In some embodiments, the terminal performs fusion calculation based on the parameter values ​​of each parameter in each parameter group and the initial value of each parameter to be identified, determines the fusion value of each parameter to be identified, and determines the fusion value of each parameter to be identified as the parameter value of the parameter to be identified of the target equivalent circuit model. The fusion calculation process can be implemented in a variety of fusion methods, including but not limited to weighted fusion, Kalman filtering and other fusion methods.

[0112] Next, the terminal determines the actual capacity of the target battery from the parameter values ​​of the parameters to be identified in the target equivalent circuit model, and then obtains the nominal capacity of the target battery, that is, the maximum capacity at the factory, from a local or remote server, and inputs the actual capacity and the nominal capacity of the target battery into the calculation expression of the battery health status, and obtains the health status of the target battery based on the ratio of the actual capacity to the nominal capacity.

[0113] In some application embodiments, after determining the parameter value of the parameter to be identified of the target equivalent circuit model, in order to update the battery health status in real time to obtain a more accurate battery health status, real-time charging condition data within the current preset time period can be obtained, and the parameter value of the parameter to be identified of the target equivalent circuit model is updated based on the real-time charging condition data to obtain the updated value of the parameter to be identified of the target equivalent circuit model, and the updated value related to the battery capacity is extracted from the updated value of the parameter to be identified, and the updated value related to the battery capacity is determined as the current actual capacity of the target battery.

[0114] In the above embodiment, after the parameter values ​​of the parameters to be identified of the target equivalent circuit model are determined, it is equivalent to that the target equivalent circuit model has been determined. Subsequently, the battery health status can be determined in real time based on the parameter values ​​of the parameters to be identified of the determined target equivalent circuit model, thereby improving the real-time update of the battery health status.

[0115] In some optional embodiments, such as Figure 4 As shown, according to the charging condition data, the initial values ​​of the parameters to be identified are determined, including:

[0116] Step 420, processing the input current data through the target equivalent circuit model to obtain first simulation voltage data;

[0117] Step 440, based on the error value between the first simulation voltage data and the output voltage data, correct the parameter value of the parameter to be identified in the target equivalent circuit model, and return to the step of processing the input current data through the target equivalent circuit model until the correction end condition is reached to obtain the initial value of the parameter to be identified.

[0118] Among them, the charging condition data includes input current data and output voltage data. The input current data is the input data of the target equivalent circuit model, and the output voltage data is the output data of the target equivalent circuit model. Compared with other training data, only selecting the input current data and the output voltage data can quickly and accurately determine the initial value of the parameter to be identified.

[0119] In some embodiments, the terminal obtains a default value of the parameter to be identified, which can be determined based on an empirical value. Then, the initial parameter value of the parameter to be identified of the target equivalent circuit model is determined based on the default value of the parameter to be identified, and the upper and lower limit constraints of each parameter to be identified are introduced. The input current data in the first charging condition data is processed by the target equivalent circuit model, and the model outputs the corresponding simulation terminal voltage sequence, i.e., the first simulation voltage data; then, the error value between the first simulation voltage data and the output voltage data in the first charging condition data is calculated based on the RMSE (Root Mean Square Error) loss function; when the error value between the first simulation voltage data and the output voltage data is greater than a preset error threshold, the parameter value of the parameter to be identified in the target equivalent circuit model is corrected based on the error value between the first simulation voltage data and the output voltage data. Afterwards, the parameter value of the parameter to be identified after correction is used as the model parameter of the target equivalent circuit model, and the input current data is processed based on the target equivalent circuit model to obtain the first simulation voltage data; the error value between the first simulation voltage data and the output voltage data is calculated again until the correction end condition is reached, and the parameter value of the parameter to be identified, that is, the initial value of the parameter to be identified is obtained. The preset correction condition can be that the error value between the first simulation voltage data and the output voltage data is less than or equal to a preset error threshold.

[0120] The RMSE loss function is as follows:

[0121]

[0122]

[0123] Where n represents the number of parameters to be identified, U k Indicates the output voltage value, represents the first simulation voltage value, represents the parameter value of the parameter to be identified, argmin represents the variable value when the RMSE loss function f(x) takes the minimum value, LB represents the lower limit value of the parameter to be identified, and UB represents the upper limit value of the parameter to be identified.

[0124] In the above embodiment, by determining the initial value of the parameter to be identified, the situation where the parameter value of the target equivalent circuit model is inaccurate due to the different initial value selection can be reduced. In addition, the initial parameter identification is performed based on the input current data and the output voltage data, so that the initial value can be determined quickly and accurately.

[0125] In some optional embodiments, the parameter value of each parameter to be identified in at least two parameter groups is determined according to the charging condition data, including: setting the parameter value of the parameter to be identified in the target equivalent circuit model to an initial value; processing the input current data through the target equivalent circuit model to obtain second simulation voltage data; based on the error value between the second simulation voltage data and the output voltage data, correcting the parameter value of the parameter to be identified in the target equivalent circuit model, and returning to the step of processing the input current data through the target equivalent circuit model until the correction end condition is reached to obtain the parameter value of each parameter to be identified in the parameter group.

[0126] In some embodiments, the terminal determines the initial parameter value of the parameter to be identified of the target equivalent circuit model according to the initial value of the parameter to be identified, and introduces the upper and lower limit constraints of each parameter to be identified. The input current data in the second charging condition data is processed by the target equivalent circuit model, and the model outputs the corresponding analog terminal voltage sequence, that is, the second simulation voltage data; then the error value between the second simulation voltage data and the output voltage data in the second charging condition data is calculated based on the RMSE loss function; when the error value between the second simulation voltage data and the output voltage data is greater than the preset error threshold, the particle swarm optimization algorithm is used to correct the parameter value of the parameter to be identified in the target equivalent circuit model based on the error value between the second simulation voltage data and the output voltage data. Afterwards, the parameter value of the corrected parameter to be identified is used as the model parameter of the target equivalent circuit model, and the input current data is processed based on the target equivalent circuit model to obtain the second simulation voltage data; the error value between the second simulation voltage data and the output voltage data is calculated again until the correction end condition is reached, and the parameter value of the parameter to be identified in the parameter group is obtained. The preset correction condition can be that the error value between the second simulation voltage data and the output voltage data is less than or equal to the preset error threshold.

[0127] In the above embodiment, in the process of determining the parameter values ​​of the parameters to be identified in the parameter group, by using the initial values ​​of the parameters to be identified as the initial parameter values ​​of the target equivalent circuit model, the sensitivity problem existing in the selection of the initial values ​​of the parameters to be identified can be reduced. The target equivalent circuit model is trained according to the actual input current data and output current data of the target battery, and the parameter values ​​of each parameter in the parameters to be identified in the obtained parameter group have low sensitivity.

[0128] In some optional embodiments, determining the parameter value of the parameter to be identified of the target equivalent circuit model based on the parameter value of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified includes:

[0129] Based on the parameter values ​​of the parameters to be identified in the at least two parameter groups, obtaining at least one set of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model;

[0130] A fusion calculation is performed on at least one set of intermediate parameter values ​​of the to-be-identified parameters of the target equivalent circuit model to obtain parameter values ​​of the to-be-identified parameters of the target equivalent circuit model.

[0131] Among them, the number of groups of intermediate parameter values ​​can be multiple groups, and multiple groups are two groups or more than two groups. Each group of intermediate parameter values ​​of the parameters to be identified can include parameter values ​​of each parameter to be identified in a parameter group, and initial values ​​of parameters other than the parameters to be identified in the parameter group. Optionally, the intermediate parameter values ​​of the parameters to be identified can be a group. This group of intermediate parameter values ​​can be based on the error value in the process of determining the parameters to be identified in the multiple parameter groups, to select the parameter group with the smallest error in turn to obtain the intermediate parameter value, until the selected intermediate parameter value contains the parameter values ​​of all the parameters to be identified, and the intermediate parameter values ​​here are all parameter values ​​of the parameters to be identified in the multiple parameter groups. Optionally, the intermediate parameter values ​​of the parameters to be identified can be a group. This group of intermediate parameter values ​​can be based on the error value in the process of determining the parameters to be identified in the multiple parameter groups, to select the parameter value of a group of parameters to be identified with the smallest error, and initial values ​​of parameters other than the parameters to be identified in the parameter group.

[0132] Preferably, taking the case where there are two or more groups of intermediate parameter values, the terminal obtains the parameter values ​​of the parameters to be identified of at least two groups of parameter groups, and determines the other out-of-group parameters in each parameter group except the parameter to be identified in the parameter group, and determines the parameter values ​​of the other out-of-group parameters based on the initial values ​​of the parameters to be identified, and obtains multiple groups of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model. Afterwards, the multiple groups of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model are fused and calculated to obtain the parameter values ​​of the parameters to be identified of the target equivalent circuit model.

[0133] In the above embodiment, the parameter values ​​of the parameters to be identified of the target equivalent circuit model are determined by using multiple groups of intermediate parameter values, which can reduce the problem of inaccurate parameter values ​​calculated by a single model. By fusing the calculations using multiple groups of intermediate parameter values ​​obtained through two stages of parameter identification, the accuracy of the parameter values ​​can be further improved, and the generalization ability of the parameter values ​​of the parameters to be identified of the target equivalent circuit model can be improved.

[0134] In some optional embodiments, performing fusion calculation on at least one set of intermediate parameter values ​​of the to-be-identified parameters of the target equivalent circuit model to obtain parameter values ​​of the to-be-identified parameters of the target equivalent circuit model includes:

[0135] When the number of intermediate parameter value groups is greater than or equal to two groups, at least one group of intermediate parameter values ​​of the to-be-identified parameters of the target equivalent circuit model is averaged to obtain parameter values ​​of the to-be-identified parameters of the target equivalent circuit model.

[0136] In some embodiments, during the process of fusing multiple groups of intermediate parameter values, multiple groups of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model are averaged to obtain parameter values ​​of the parameters to be identified of the target equivalent circuit model.

[0137] Furthermore, the average calculation process may include simple average calculation and weighted average calculation. Optionally, for each identical parameter in multiple sets of intermediate parameter values, the parameter value of the parameter is accumulated, and the average value of the accumulated results is calculated to obtain the parameter value of the parameter. The determination process of other parameters is similar, and the parameter value of the parameter to be identified of the target equivalent circuit model is obtained. The specific simple average calculation expression is as follows:

[0138]

[0139] Where T represents the number of groups of intermediate parameter values, Indicates different intermediate parameter values.

[0140] Optionally, the weight of each group of intermediate parameter values ​​can also be obtained, and the weighted average calculation processing can be performed on multiple groups of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model to obtain the parameter values ​​of the parameters to be identified of the target equivalent circuit model. Furthermore, the weight of each group of intermediate parameter values ​​can be determined according to the error value in the determination process of each group of intermediate parameter values; then, a weighted average calculation is performed based on the weight of each group of intermediate parameter values ​​and the corresponding intermediate parameter values ​​to obtain the parameter values ​​of the parameters to be identified of the target equivalent circuit model. The specific weighted average calculation expression is as follows:

[0141]

[0142] Among them, α jRepresents the weight of each group of intermediate parameter values. The smaller the error value, the higher the weight of the group to which the intermediate parameter value belongs.

[0143] In the process of fusing and calculating multiple sets of intermediate parameter values ​​of the target equivalent circuit model's parameters to be identified, other fusion algorithms may also be used to determine the parameter values ​​of the target equivalent circuit model's parameters to be identified, not limited to average calculation and weighted average calculation.

[0144] In the above embodiment, since the average calculation processing efficiency is high, the average calculation based on multiple groups of intermediate parameter values ​​can efficiently and accurately determine the parameter values ​​of the final target equivalent circuit model, while improving the SOH estimation accuracy and further improving the SOH estimation efficiency.

[0145] In some optional embodiments, obtaining the to-be-identified parameters of the target equivalent circuit model includes:

[0146] Obtaining a target equivalent circuit model;

[0147] Determine a circuit equation of a target equivalent circuit model, and discretize the circuit equation to obtain a discretized circuit equation;

[0148] According to the discretized circuit equation, the parameters to be identified of the target equivalent circuit model are obtained.

[0149] In some embodiments, a target equivalent circuit model is obtained. In the actual charging and discharging process, the polarization of the battery causes a certain hysteresis effect when the polarization voltage suddenly changes in the current size or direction. Therefore, this embodiment uses a second-order equivalent circuit model that takes the hysteresis effect into account as an example of the target equivalent circuit model to model the nonlinear characteristics of the lithium battery. The second-order equivalent circuit model that takes the hysteresis effect into account describes the nonlinear characteristics of the battery through two RC (Resistance-CapacitanceCircuits, phase-shift circuit) networks, and characterizes the complex electrochemical process of the battery through the relationship between the input current and the output voltage. The schematic diagram of the target equivalent circuit model is shown in FIG. Figure 5 As shown, R 1 and R 2 Represent electrochemical polarization resistance and concentration polarization resistance, C 1 and C 2 Represent the electrochemical polarization capacitance and concentration polarization capacitance respectively. 1 and U 2 Represents the polarization voltage at both ends of the two RC networks, U L Indicates terminal voltage, U OCV It represents the open circuit voltage OCV of the battery, which is a function of SOC. I represents the discharge current (positive) and the charge current (negative).

[0150] Based on Kirchhoff's law, the circuit equation of the target equivalent circuit model is determined. The circuit equation is:

[0151] U 1 =IR 1 [1-exp(-t / τ 1 )]

[0152] U 2 =IR 2 [1-exp(-t / τ 2 )]

[0153]

[0154]

[0155] Among them, η represents the inventory efficiency, Q represents the battery capacity, τ 1 and τ 2 Respectively represent the time constants of the two RC networks, τ 1 =R 1 C 1 ,τ 1 =R 1 C 2 .U H represents the hysteresis voltage, k p Represents the attenuation factor, ε is small enough and positive. SOC 0 is the battery state of charge at the start time of charging, I k is the current at time k.

[0156] In order to apply the target equivalent circuit model in the actual charging condition data, the circuit equation needs to be discretized and the discretized circuit equation is:

[0157]

[0158]

[0159]

[0160]

[0161] According to the discretized circuit equation, the parameters to be identified of the target equivalent circuit model are obtained as follows: θ = [SOC 0 ,Q,R 0 ,k p ,H,τ 1 ,R 1 ,τ 2 ,R 2 ].

[0162] In the above embodiment, the parameters to be identified are determined by discretizing the target equivalent circuit model, so that the parameters to be identified that have a greater impact on the battery charging and discharging process can be obtained, thereby improving the accuracy of determining the parameters to be identified, more accurately describing the charging and discharging process of the target battery, and thus obtaining a more accurate battery health status.

[0163] In some optional embodiments, the target equivalent circuit model is a second-order equivalent circuit model that takes hysteresis effects into account.

[0164] In some embodiments, other equivalent circuit models can be used for parameter identification. The corresponding equivalent circuit model can be selected according to the battery type (such as cell type) of the target battery to perform parameter identification to obtain accurate parameter values ​​of the parameters to be identified, thereby obtaining a more accurate battery health status.

[0165] In the above embodiment, by determining the parameters to be identified using a second-order equivalent circuit model that takes into account the hysteresis effect, more accurate parameters to be identified can be obtained, which can more accurately describe the charging and discharging process of the target battery, thereby obtaining a more accurate battery health status.

[0166] In order to facilitate understanding of the technical solutions provided in the embodiments of the present application, Figure 6 As shown, the battery health status calculation method provided in the embodiment of the present application is briefly described with the complete battery health status calculation process:

[0167] Obtain the charging condition data of the target battery and the parameters to be identified of the target equivalent circuit model; first, use the target equivalent circuit model including the parameters to be identified as the initial model, that is, the first-stage equivalent circuit model. Use the particle swarm optimization algorithm as the parameter identification algorithm to solve the parameters to be identified of the target equivalent circuit model and obtain the initial values ​​of the parameters to be identified. Taking the number of parameters to be identified as 9 as an example, for the set of 9 parameters to be identified of the target equivalent circuit model, θ = [SOC 0 ,Q,R 0 ,k p ,H,τ 1 ,R 1 ,τ 2 ,R 2 ] to solve and generate a set of parameter identification results, and obtain the initial values ​​of the parameters to be identified. The initial values ​​can be used as the default values ​​of the parameters in the secondary model:

[0168]

[0169] Using the idea of ​​Bagging in ensemble learning, multiple base models of equivalent circuits are constructed. There is no dependency between the multiple base models, and they are independent of each other. The base model is the target equivalent circuit model of the second stage that includes a set of parameter values. Multiple independent base models of equivalent circuits can be trained simultaneously based on the time series data of current and voltage under charging conditions. The specific steps are as follows:

[0170] Random parameter sampling: Use random sampling (Bootstrap) method to select parameters from the parameter set θ = [SOC 0 ,Q,R 0 ,k p ,H,τ 1 ,R 1 ,τ 2 ,R 2 ] is sampled with replacement, and m (m≤9) parameters are randomly selected from the parameter set. The unselected parameters in the parameter set are used as non-identified parameters. The initial values ​​of the parameters to be identified by the primary model are used as the parameter values ​​of the non-identified parameters. The secondary model is established using the new parameter subset for parameter identification. In the process of parameter identification of the base model, the parameter values ​​of the non-identified parameters remain unchanged, and only the parameters to be identified in each parameter set are updated. For example, a parameter set containing m parameters to be identified and 9-m fixed parameter values ​​is generated, where the fixed parameter values ​​are the initial values ​​of the parameters obtained by solving the primary model. The parameters to be identified will be used as variables of the secondary model, and the particle swarm optimization algorithm is used to solve the optimal value. For example, θ j =[Q,R 0 ,k p ,H,τ 1 0 ,SOC 0 0 ,R 1 0 ,τ 2 0 ,R 2 0 ],Q,R 0 ,k p , H is the four parameters to be identified for a secondary model, τ 1 0 ,SOC 0 0 ,R 1 0 ,τ 2 0 ,R 2 0 It is a non-identification parameter. The parameter value of the non-identification parameter is the default in the secondary model and is the initial value for the parameter identification of the first stage equivalent circuit model.

[0171] Repeat T times to obtain T parameter sets and overlapping sampling feature subsets. For T groups of charging condition data, T different equivalent circuit models are independently established on each parameter set as secondary basis models, and the particle swarm optimization algorithm is used to solve the optimal value of each parameter set, and finally T groups of optimal parameter sets are obtained.

[0172] According to the characteristics of multiple equivalent circuit base models in the fusion equivalent circuit model, weight coefficients are assigned to different equivalent circuit base models through the root mean square error RMSE of parameter identification, and the parameter identification results of each equivalent circuit base model are weighted averaged in a targeted manner to give full play to the advantages of each equivalent circuit base model, so that the SOH estimation accuracy and stability are further improved compared with the ordinary single equivalent circuit model.

[0173] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0174] Based on the same inventive concept, the embodiment of the present application also provides a battery health status calculation device for implementing the battery health status calculation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more battery health status calculation device embodiments provided below can refer to the limitations of the battery health status calculation method above, and will not be repeated here.

[0175] In one embodiment, Figure 7 As shown, a battery health status calculation device is provided, including: an acquisition module 702, an initial value determination module 704, a parameter group determination module 706, a first parameter value determination module 708, a second parameter value determination module 710 and a health status determination module 712, wherein:

[0176] An acquisition module 702 is used to acquire charging condition data of a target battery and parameters to be identified of a target equivalent circuit model;

[0177] An initial value determination module 704 is used to determine the initial value of the parameter to be identified according to the charging condition data;

[0178] A parameter group determination module 706 is used to determine at least two parameter groups, each parameter group includes at least part of the parameters to be identified;

[0179] A parameter value determination module 708, for determining the parameter value of each parameter to be identified in at least two parameter groups according to the charging condition data;

[0180] The health state determination module 710 is used to determine the health state of the target battery based on the parameter values ​​of each parameter to be identified in at least two parameter groups and the initial value of each parameter to be identified.

[0181] In one embodiment, the parameter group determination module 706 is further used to sample the parameters to be identified based on a preset sampling rule and the number of preset parameter groups to obtain at least two parameter groups, and the number of preset parameter groups is greater than or equal to two.

[0182] In one embodiment, the charging condition data includes input current data and output voltage data, and the initial value determination module 704 is further used to process the input current data through a target equivalent circuit model to obtain first simulation voltage data; based on the error value between the first simulation voltage data and the output voltage data, correct the parameter value of the parameter to be identified in the target equivalent circuit model, and return to the step of processing the input current data through the target equivalent circuit model until the correction end condition is reached to obtain the initial value of the parameter to be identified.

[0183] In one embodiment, the parameter value determination module 708 is also used to set the parameter value of the parameter to be identified of the target equivalent circuit model to an initial value; process the input current data through the target equivalent circuit model to obtain second simulation voltage data; based on the error value between the second simulation voltage data and the output voltage data, correct the parameter value of the parameter to be identified in the target equivalent circuit model, and return to the step of processing the input current data through the target equivalent circuit model until the correction end condition is reached, and obtain the parameter value of each parameter to be identified in at least two parameter groups.

[0184] In one embodiment, the parameter value determination module 708 is also used to obtain at least one set of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model based on the parameter values ​​of each parameter to be identified in at least two parameter groups; and to perform a fusion calculation on at least one set of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model to obtain the parameter values ​​of the parameters to be identified of the target equivalent circuit model.

[0185] In one embodiment, the parameter value determination module 708 is further used to perform average calculation processing on at least one group of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model when the number of intermediate parameter value groups is greater than or equal to two groups, so as to obtain the parameter value of the parameter to be identified of the target equivalent circuit model.

[0186] In one embodiment, the acquisition module 702 is also used to obtain a target equivalent circuit model; determine a circuit equation of the target equivalent circuit model, and discretize the circuit equation to obtain a discretized circuit equation; and obtain parameters to be identified of the target equivalent circuit model based on the discretized circuit equation.

[0187] In one embodiment, the target equivalent circuit model is a second-order equivalent circuit model that takes hysteresis effects into consideration.

[0188] Each module in the above-mentioned battery health status calculation device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0189] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, 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 an 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 the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a battery health status calculation method is implemented. The display screen of the computer device 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 covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0190] Those skilled in the art will understand that Figure 8 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0191] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0192] 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.

[0193] 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.

[0194] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile 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), 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0195] The technical features of the above embodiments may be arbitrarily combined. 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 specification.

[0196] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for calculating a battery health status, characterized in that: The method comprises: Obtaining charging condition data of a target battery and parameters to be identified of a target equivalent circuit model; Determining an initial value of the parameter to be identified according to the charging condition data; Determine at least two parameter groups, each of which includes at least part of the parameters to be identified; Determining parameter values ​​of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data; The health state of the target battery is determined based on the parameter values ​​of each parameter to be identified in at least two of the parameter groups and the initial value of each parameter to be identified.

2. The method according to claim 1, characterized in that: The determining of at least two groups of parameter groups comprises: Based on a preset sampling rule and a preset number of parameter groups, the parameters to be identified are sampled to obtain at least two parameter groups, and the number of the preset parameter groups is greater than or equal to two.

3. The method according to claim 1 or 2, characterized in that: The determining the health state of the target battery based on the parameter values ​​of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified includes: Determining the parameter value of the parameter to be identified of the target equivalent circuit model based on the parameter value of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified; The health state of the target battery is determined based on the parameter value of the to-be-identified parameter of the target equivalent circuit model.

4. The method according to any one of claims 1 to 3, characterized in that: The charging condition data includes input current data and output voltage data; The step of determining the initial value of the parameter to be identified according to the charging condition data includes: Processing the input current data through the target equivalent circuit model to obtain first simulation voltage data; Based on the error value between the first simulation voltage data and the output voltage data, the parameter value of the parameter to be identified in the target equivalent circuit model is corrected, and the step of processing the input current data through the target equivalent circuit model is returned until the correction end condition is reached to obtain the initial value of the parameter to be identified.

5. The method according to claim 4, characterized in that The step of determining the parameter value of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data includes: Setting the parameter value of the to-be-identified parameter of the target equivalent circuit model to the initial value; Processing the input current data through the target equivalent circuit model to obtain second simulation voltage data; Based on the error value between the second simulation voltage data and the output voltage data, the parameter value of the parameter to be identified in the target equivalent circuit model is corrected, and the step of processing the input current data through the target equivalent circuit model is returned until the correction end condition is reached, and the parameter value of each parameter to be identified in at least two groups of the parameter groups is obtained.

6. The method according to claim 4 or 5, characterized in that: The step of determining the parameter value of the parameter to be identified of the target equivalent circuit model based on the parameter value of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified comprises: Based on the parameter values ​​of the parameters to be identified in at least two of the parameter groups, obtaining at least one group of intermediate parameter values ​​of the parameters to be identified of the target equivalent circuit model; A fusion calculation is performed on at least one group of intermediate parameter values ​​of the to-be-identified parameters of the target equivalent circuit model to obtain parameter values ​​of the to-be-identified parameters of the target equivalent circuit model.

7. The method according to claim 6, characterized in that The step of performing fusion calculation on at least one group of intermediate parameter values ​​of the to-be-identified parameters of the target equivalent circuit model to obtain parameter values ​​of the to-be-identified parameters of the target equivalent circuit model includes: When the number of intermediate parameter value groups is greater than or equal to two groups, at least one group of intermediate parameter values ​​of the to-be-identified parameter of the target equivalent circuit model is averaged to obtain parameter values ​​of the to-be-identified parameter of the target equivalent circuit model.

8. The method according to any one of claims 1 to 7, characterized in that: The step of obtaining the parameters to be identified of the target equivalent circuit model includes: Obtaining a target equivalent circuit model; Determining a circuit equation of the target equivalent circuit model, and discretizing the circuit equation to obtain a discretized circuit equation; According to the discretized circuit equation, the parameters to be identified of the target equivalent circuit model are obtained.

9. The method according to any one of claims 1 to 8, characterized in that: The target equivalent circuit model includes a second-order equivalent circuit model taking hysteresis effects into account.

10. A battery health status calculation device, characterized in that: The device comprises: An acquisition module, used to acquire charging condition data of a target battery and parameters to be identified of a target equivalent circuit model; An initial value determination module, used to determine the initial value of the parameter to be identified according to the charging condition data; A parameter group determination module, used to determine at least two parameter groups, each of which includes at least part of the parameters to be identified; A parameter value determination module, used to determine the parameter value of each parameter to be identified in at least two groups of the parameter groups according to the charging condition data; The health state determination module is used to determine the health state of the target battery based on the parameter values ​​of each parameter to be identified in at least two groups of the parameter groups and the initial value of each parameter to be identified.

11. 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 9 are implemented.

12. 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 9 are implemented.

13. 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 9 are implemented.

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