Battery ion concentration determination method and device, computer equipment and storage medium

Abstract

The invention relates to a battery ion concentration determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring battery working condition parameters of a to-be-detected battery at each moment in a current time period; wherein the battery working condition parameters comprise current information, battery capacity and position information of lithium ions in the to-be-detected battery; based on the target diffusion single particle model of the to-be-detected battery, determining the lithium ion concentration of the to-be-detected battery at each moment in the current time period according to the battery working condition parameters of the to-be-detected battery at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing physical parameters in the initial diffusion single particle model based on a physical information neural network. By adopting the method, the lithium ion concentration in the to-be-detected battery can be accurately and efficiently determined.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a method, device, computer equipment and storage medium for determining the ion concentration of a battery. Background Art

[0002] The development of electric vehicles (EVs) is widely considered to be an effective means to address the challenges of excessive carbon emissions and the depletion of fossil oil. Lithium-ion batteries (LIBs) play a leading role in EV energy storage due to their high energy density and long service life. In order to better manage battery systems, it is essential to establish battery models and study battery algorithms.

[0003] One of the models widely used in academia and industry is the pseudo-two-dimensional model. The LIB mechanism model represented by the pseudo-two-dimensional model is very detailed in describing the dynamics of lithium ion insertion and extraction at the microscale. However, the mechanism model of lithium-ion batteries often requires dozens of electrochemical parameters to describe and is composed of nonlinear partial differential equations, which hinders its application in vehicle-mounted battery management systems. Therefore, how to find an accurate simplified model and quickly solve the simplified model is a difficult problem currently faced. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment and storage medium for determining ion concentration in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining the ion concentration of a battery, comprising:

[0006] Obtaining battery operating parameters of the battery to be tested at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery to be tested;

[0007] Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0008] In one embodiment, the target diffusion single particle model is obtained by:

[0009] Acquire sample data; wherein the sample data includes battery operating parameters and lithium ion concentration of the battery to be tested at each historical moment in a historical period;

[0010] Initializing a physical information neural network based on physical parameters in an initial diffusion single particle model of the battery to be tested to obtain a physical information network model of the battery to be tested;

[0011] The sample data is used to train the physical information network model to obtain a target diffusion single particle model.

[0012] In one embodiment, the initial diffusion single particle model includes a solid phase lithium ion diffusion model and a liquid phase lithium ion diffusion model, and the initial diffusion single particle model is obtained by the following method, including:

[0013] Constructing the solid-phase lithium ion diffusion model according to the solid-phase lithium ion diffusion coefficient and the solid-phase lithium ion concentration function; wherein the solid-phase lithium ion concentration function is related to the position information of the lithium ions in the battery to be tested at different times;

[0014] The liquid-phase lithium-ion diffusion model is constructed according to the liquid-phase lithium-ion diffusion coefficient, the electrolyte volume fraction, the lithium-ion migration number and the liquid-phase lithium-ion concentration function; wherein the liquid-phase lithium-ion concentration function is related to the position information of the lithium ions in the battery to be tested at different times.

[0015] In one embodiment, the adopting the sample data to train the physical information network model to obtain a target diffusion single particle model includes:

[0016] The physical information network model is trained using a target loss function and the sample data to obtain a target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model, and the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model.

[0017] In one embodiment, the target loss function is obtained by:

[0018] Constructing an equation loss function according to the mean square error function of the solid-phase lithium ion diffusion model and the mean square error function of the liquid-phase lithium ion diffusion model;

[0019] Constructing a boundary loss function according to the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model;

[0020] Determine the initial value loss function according to the value of the solid phase lithium ion concentration function at the time when the battery to be tested is powered on and the value of the liquid phase lithium ion concentration function at the time when the battery to be tested is powered on;

[0021] The sum of the equation loss function, the boundary loss function and the initial value loss function is used as the target loss function.

[0022] In one embodiment, the determining of the ion concentration of the battery under test at each moment in the current time period based on the target diffusion single particle model of the battery under test and the battery operating parameters includes:

[0023] Normalizing the battery operating condition parameters to obtain normalized battery operating condition parameters;

[0024] The normalized battery operating condition parameters are input into the target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each time in the current time period.

[0025] In one embodiment, the method further comprises:

[0026] According to the battery capacity and lithium ion concentration of the battery to be tested at each moment in the current time period, a corresponding curve of the battery capacity and lithium ion concentration of the battery to be tested is determined.

[0027] In a second aspect, the present application also provides a device for determining the ion concentration of a battery, comprising:

[0028] A parameter acquisition module, used to acquire the battery operating parameters of the battery to be tested at each moment in the current period; wherein the battery operating parameters include current information, battery capacity and position information of lithium ions in the battery to be tested;

[0029] A concentration determination module is used to determine the lithium ion concentration of the battery to be tested at each moment in the current time period based on the target diffusion single particle model of the battery to be tested and according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0030] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Obtaining battery operating parameters of the battery to be tested at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery to be tested;

[0032] Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0033] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0034] Obtaining battery operating parameters of the battery to be tested at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery to be tested;

[0035] Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0036] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0037] Obtaining battery operating parameters of the battery to be tested at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery to be tested;

[0038] Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0039] The above-mentioned method, device, computer equipment and storage medium for determining the ion concentration of the battery obtain the battery operating condition parameters of the battery to be tested at each moment in the current period, including current information, battery capacity and position information of lithium ions in the battery to be tested; based on the target diffusion single particle model of the battery to be tested, according to the battery operating condition parameters of the battery to be tested at each moment in the current period, the lithium ion concentration of the battery to be tested at each moment in the current period is determined; wherein, the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on the physical information neural network. The above scheme optimizes the physical parameters in the initial diffusion single particle model through the physical information neural network, so that the values ​​of each physical parameter in the target diffusion single particle model are the optimal values, further ensuring the accuracy of the target diffusion single particle model; further, by introducing current information and battery capacity, and combining the target diffusion single particle model, the accuracy of the lithium ion concentration in the battery to be tested is guaranteed, and the lithium ion concentration of the battery to be tested under various application current conditions and various battery capacity states can be obtained, which has stronger adaptability and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical 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 paying creative work.

[0041] Figure 1 A diagram showing an application environment of a method for determining an ion concentration of a battery in one embodiment;

[0042] Figure 2 A schematic flow chart of a method for determining the ion concentration of a battery in one embodiment;

[0043] Figure 3 A schematic diagram of a process for constructing a target diffusion single particle model in one embodiment;

[0044] Figure 4 A schematic diagram of a process for constructing an initial diffusion single particle model in one embodiment;

[0045] Figure 5 A schematic diagram of a process for constructing a target loss function in one embodiment;

[0046] Figure 6 A schematic diagram of a process for determining ion concentration in one embodiment;

[0047] Figure 7 is a schematic flow chart of a method for determining the ion concentration of a battery in another embodiment;

[0048] Figure 8 is a structural block diagram of a device for determining ion concentration of a battery in one embodiment;

[0049] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] 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.

[0051] The method for determining the ion concentration of a battery provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the battery management system 101 is a system in the target vehicle for monitoring and managing the battery to be tested; the monitoring terminal 102 is used to monitor and calculate the lithium ion concentration of the battery to be tested in the target vehicle; the battery to be tested 103 is a power battery in the target vehicle. Optionally, the monitoring terminal 102 obtains the battery operating parameters of the battery to be tested 103 at each moment in the current time period; wherein the battery operating parameters include current information, battery capacity and location information of lithium ions in the battery to be tested; based on the target diffusion single particle model of the battery to be tested, according to the battery operating parameters of the battery to be tested at each moment in the current time period, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on the physical information neural network.

[0052] In one embodiment, Figure 2 As shown, a method for determining the ion concentration of a battery is provided, and the method is applied to Figure 1 Taking the monitoring terminal 102 in the example as an example, the following steps are specifically included:

[0053] S201, obtaining battery operating condition parameters of the battery to be tested at various times in the current time period.

[0054] The battery to be tested is a lithium-ion battery whose lithium ion concentration needs to be measured; the current period is a period within a preset time before the current moment. The battery operating parameters characterize the operating conditions of the battery to be tested during operation; in the embodiment of the present application, the battery operating parameters include but are not limited to current information, battery capacity, and position information of lithium ions in the battery to be tested; the current information characterizes the charge and discharge current of the battery to be tested.

[0055] Optionally, the battery operating condition parameters of the battery to be tested at various times in the current time period may be obtained through a monitoring device connected to the battery or from a battery management system.

[0056] Exemplarily, current information corresponding to each moment in the current time period of the battery under test, battery capacity, and location information of lithium ions in the battery under test, etc., can be extracted from the battery management system as battery operating condition parameters of the battery under test.

[0057] S202 , based on a target diffusion single particle model of the battery to be tested and according to battery operating parameters of the battery to be tested at each moment in the current time period, determine the lithium ion concentration of the battery to be tested at each moment in the current time period.

[0058] The diffusion single particle model describes the continuous movement and diffusion process of lithium ions in the battery; the target diffusion single particle model describes the continuous movement and diffusion process of lithium ions in the battery to be tested. In the embodiment of the present application, the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on the physical information neural network.

[0059] Optionally, the battery operating parameters of the battery to be tested at each moment in the current time period can be input into the target diffusion single particle model of the battery to be tested, so that the target diffusion single particle model calculates the battery operating parameters according to the optimized physical parameters and outputs the lithium ion concentration of the battery to be tested at each moment in the current time period.

[0060] In the above-mentioned method for determining the ion concentration of the battery, the battery operating condition parameters including current information, battery capacity and position information of lithium ions in the battery to be tested are obtained at each moment in the current time period; based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating condition parameters of the battery to be tested at each moment in the current time period; wherein, the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on the physical information neural network. In the above-mentioned scheme, the physical parameters in the initial diffusion single particle model are optimized by the physical information neural network, so that the values ​​of each physical parameter in the target diffusion single particle model are the optimal values, further ensuring the accuracy of the target diffusion single particle model; further, by introducing the current information and battery capacity, and combining the target diffusion single particle model, the accuracy of the lithium ion concentration in the battery to be tested is ensured, and the lithium ion concentration of the battery to be tested under various application current conditions and various battery capacity states can be obtained, which has stronger adaptability and scalability.

[0061] Optionally, in one embodiment, Figure 3 As shown, a method for constructing a target diffusion single particle model is provided, which specifically includes the following steps:

[0062] S301, obtaining sample data.

[0063] The sample data includes the battery operating parameters and lithium ion concentration of the battery to be tested at each historical moment in the historical period.

[0064] Optionally, the battery operating parameters and lithium ion concentration of the battery to be tested at each historical moment in a historical period may be obtained from the battery management system of the battery to be tested as sample data.

[0065] S302, initializing the physical information neural network based on the physical parameters in the initial diffusion single particle model of the battery to be tested, to obtain the physical information network model of the battery to be tested.

[0066] The initial diffusion single particle model is a diffusion single particle model without optimizing the physical parameters; the physical information neural network is a neural network that combines the physical laws. The physical information network model is a physical information neural network that combines the physical parameters in the initial diffusion single particle model.

[0067] Optionally, the physical information network model of the battery to be tested can be obtained by initializing the framework of the physical information neural network using the physical parameters in the initial diffusion single particle model of the battery to be tested and based on the framework of the physical information neural network.

[0068] S303, using sample data to train the physical information network model to obtain a target diffusion single particle model.

[0069] Optionally, the sample data may be used to train the physical information network model to obtain the optimal solution for each physical parameter in the physical information network model and obtain a target diffusion single particle model.

[0070] In this embodiment, the physical information neural network is initialized by the physical parameters in the initial diffusion single particle model of the battery to be tested, and a physical information network model combining the physical information neural network and the initial diffusion single particle model is constructed; further, the physical information network model is trained with sample data to ensure the accuracy of the obtained target diffusion single particle model.

[0071] Optionally, the initial diffusion single particle model includes a solid phase lithium ion diffusion model and a liquid phase lithium ion diffusion model. Based on the above embodiment, in one embodiment, Figure 4 As shown, a method for constructing an initial diffusion single particle model is provided, which specifically includes the following steps:

[0072] S401, constructing a solid phase lithium ion diffusion model according to the solid phase lithium ion diffusion coefficient and the solid phase lithium ion concentration function.

[0073] Among them, the solid-phase lithium ion diffusion coefficient is a measure of the speed and difficulty of lithium ion diffusion in the battery to be tested; the solid-phase lithium ion concentration function is a function that characterizes the concentration of lithium ions contained in the battery to be tested; in the embodiment of the present application, the solid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0074] Optionally, a solid-phase lithium ion diffusion model can be constructed based on the solid-phase lithium ion diffusion theory according to the solid-phase lithium ion diffusion coefficient and the solid-phase lithium ion concentration function. Specifically, the constructed solid-phase lithium ion diffusion model can be expressed by the following formula (1):

[0075] (1)

[0076] in, is the area of ​​the battery cell to be tested, is the negative electrode region, is the diaphragm area, is the positive electrode area, Expressed as solid phase lithium ion concentration, is the radial coordinate, determined by the position information, is the solid phase lithium ion diffusion coefficient.

[0077] S402, constructing a liquid phase lithium ion diffusion model according to the liquid phase lithium ion diffusion coefficient, the electrolyte volume fraction, the lithium ion migration number and the liquid phase lithium ion concentration function.

[0078] Among them, the liquid-phase lithium ion diffusion coefficient is the rate of ion diffusion of lithium ions in the electrode material; the liquid-phase lithium ion concentration function is a function that characterizes the liquid-phase lithium ion concentration between the positive and negative electrodes of the battery to be tested during the charging and discharging process; in the embodiment of the present application, the liquid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0079] Optionally, a liquid-phase lithium-ion diffusion model can be constructed based on the liquid-phase lithium-ion diffusion theory according to the liquid-phase lithium-ion diffusion coefficient, electrolyte volume fraction, lithium-ion migration number and liquid-phase lithium-ion concentration function. Specifically, the constructed liquid-phase lithium-ion diffusion model can be expressed by the following formula (2):

[0080] (2)

[0081] in, Expressed as solid phase lithium ion concentration, is the effective liquid diffusion coefficient, which can be expressed as express, is the liquid phase diffusion coefficient, is the electrolyte volume fraction, is the Brugmann constant, represents the lithium ion migration number, is the active surface area per unit electrode volume, is the interface current density; Indicates location information.

[0082] In the embodiment of the present application, by separately constructing a solid-phase lithium ion diffusion model and a liquid-phase lithium ion diffusion model, it is ensured that the constructed initial diffusion single particle model can fully represent the movement and diffusion state of lithium ions in the battery to be tested.

[0083] Optionally, in one embodiment, the target loss function and sample data may be used to train the physical information network model to obtain the target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model and the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model. Optionally, the sample data may be used to train the physical information network model, and when the target loss function reaches a preset condition, the training process is completed to obtain the target diffusion single particle model.

[0084] Optionally, in order to ensure the accuracy of the constructed target loss function, in one embodiment, Figure 5 As shown, a method for constructing a target loss function is provided, which specifically includes the following steps:

[0085] S501, constructing an equation loss function according to the mean square error function of the solid phase lithium ion diffusion model and the mean square error function of the liquid phase lithium ion diffusion model.

[0086] Optionally, according to the mean square error function of the solid phase lithium ion diffusion model and the mean square error function of the liquid phase lithium ion diffusion model, the process of constructing the equation loss function can be expressed by the following formula (3):

[0087] (3)

[0088] in, represents the loss function of the equation; represents the number of samples of the solid phase diffusion equation; Represents the number of samples of the liquid phase diffusion equation.

[0089] S502, constructing a boundary loss function according to the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model.

[0090] Optionally, according to the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model, the process of constructing the boundary loss function can be expressed by the following formula (4):

[0091] (4)

[0092] in, represents the boundary loss function; represents the number of samples of the internal boundary conditions of the ion; represents the number of samples of ion surface boundary conditions; Indicates the number of lithium ion concentration samples on the left side of the electrolyte diaphragm; Indicates the number of lithium ion concentration samples on the right side of the electrolyte diaphragm; Indicates the number of lithium ion concentration gradient samples on the left side of the electrolyte diaphragm; Indicates the number of lithium ion concentration gradient samples on the right side of the electrolyte diaphragm; Indicates the coordinate value of the right side of the diaphragm; Indicates the coordinate value on the left side of the positive pole; Indicates the coordinate value on the right side of the negative pole; Indicates the coordinate value of the left side of the diaphragm.

[0093] S503, determining an initial value loss function according to the value of the solid phase lithium ion concentration function when the battery to be tested is powered on and the value of the liquid phase lithium ion concentration function when the battery to be tested is powered on.

[0094] The power-on time is the time when the battery to be tested changes from a non-power-on state to a power-on state during the charging process or the discharging process.

[0095] Optionally, according to the value of the solid phase lithium ion concentration function and the value of the liquid phase lithium ion concentration function of the battery to be tested at the time of power-on, the process of determining the initial value loss function can be expressed by the following formula (5):

[0096] (5)

[0097] in, represents the boundary loss function; represents the initial lithium ion concentration in the solid phase; represents the initial lithium ion concentration in the liquid phase; represents the number of samples of initial lithium ion concentration in the solid phase; Represents the number of samples of initial lithium ion concentration in liquid phase.

[0098] S504, taking the sum of the equation loss function, the boundary loss function and the initial value loss function as the target loss function.

[0099] Optionally, the equation loss function, the boundary loss function, and the initial value loss function may be added together, and the sum obtained is used as the target loss function. Specifically, the target loss function may be expressed by the following formula (6):

[0100] (6)

[0101] in, represents the target loss function.

[0102] The embodiment of the present application fully considers the additional physical constraints brought by the diffusion single particle model by introducing the equation loss function, the boundary loss function and the initial value loss function, ensures the accuracy of the determined target loss function, and further ensures the accuracy of the target diffusion single particle model obtained by training based on the target loss function.

[0103] Optionally, in one embodiment, Figure 6 As shown, a method for determining ion concentration is provided, which specifically includes the following steps:

[0104] S601, normalizing the battery operating condition parameters to obtain the normalized battery operating condition parameters.

[0105] Optionally, in order to ensure the calculation accuracy of the target diffusion single particle model and the adaptability of the battery operating parameters to the target diffusion single particle model, the battery operating parameters need to be normalized to convert the battery operating parameters into values ​​between 0 and 1. Specifically, the battery operating parameters can be normalized by a normalization tool to obtain the normalized battery operating parameters.

[0106] S602, inputting the normalized battery operating condition parameters into the target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each time in the current time period.

[0107] Optionally, the normalized battery operating parameters are input into a target diffusion single particle model, so that the target diffusion single particle model processes and calculates the normalized battery operating parameters based on physical parameters, and outputs the lithium ion concentration of the battery to be tested at each moment in the current time period.

[0108] In the embodiment of the present application, the calculation accuracy of the target diffusion single particle model and the compatibility of the battery operating parameters with the target diffusion single particle model are guaranteed by normalizing the battery operating parameters.

[0109] Optionally, after obtaining the lithium ion concentration of the battery to be tested at each moment in the current time period, in order to more intuitively reflect the corresponding relationship between the battery capacity and the lithium ion concentration, the corresponding curve of the battery capacity and the lithium ion concentration of the battery to be tested can also be determined according to the battery capacity and the lithium ion concentration of the battery to be tested at each moment in the current time period. Specifically, the battery capacity and the lithium ion concentration of the battery to be tested at each moment in the current time period can be input into the regression model so that the regression model outputs the corresponding curve of the battery capacity and the lithium ion concentration of the battery to be tested.

[0110] Figure 7FIG. 1 is a flow chart of a method for determining the ion concentration of a battery in another embodiment. Based on the above embodiment, this embodiment provides an optional example of a method for determining the ion concentration of a battery. Figure 7 The specific implementation process is as follows:

[0111] S701, obtaining battery operating condition parameters of the battery to be tested at various times in the current time period.

[0112] The battery operating parameters include current information, battery capacity and location information of lithium ions in the battery to be tested.

[0113] S702, obtaining sample data.

[0114] The sample data includes the battery operating parameters and lithium ion concentration of the battery to be tested at each historical moment in the historical period.

[0115] S703, constructing a solid phase lithium ion diffusion model according to the solid phase lithium ion diffusion coefficient and the solid phase lithium ion concentration function.

[0116] Among them, the solid phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0117] S704, constructing a liquid phase lithium ion diffusion model according to the liquid phase lithium ion diffusion coefficient, the electrolyte volume fraction, the lithium ion migration number and the liquid phase lithium ion concentration function.

[0118] The liquid phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0119] S705 , initializing the physical information neural network based on the physical parameters in the initial diffusion single particle model of the battery to be tested, to obtain the physical information network model of the battery to be tested.

[0120] S706, using sample data to train the physical information network model to obtain a target diffusion single particle model.

[0121] Optionally, the physical information network model is trained using a target loss function and sample data to obtain a target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model, and the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model.

[0122] Optionally, an equation loss function is constructed based on the mean square error function of the solid-phase lithium ion diffusion model and the mean square error function of the liquid-phase lithium ion diffusion model; a boundary loss function is constructed based on the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model; an initial value loss function is determined based on the value of the solid-phase lithium ion concentration function at the time when the battery to be tested is powered on and the value of the liquid-phase lithium ion concentration function at the time when the battery to be tested is powered on; and the sum of the equation loss function, the boundary loss function and the initial value loss function is used as the target loss function.

[0123] S707, normalize the battery operating condition parameters to obtain normalized battery operating condition parameters.

[0124] S708, inputting the normalized battery operating condition parameters into the target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each time in the current time period.

[0125] The specific process of the above S701-S708 can be found in the description of the above method embodiment. The implementation principle and technical effect are similar and will not be repeated here.

[0126] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned 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 does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can 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 to be 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.

[0127] Based on the same inventive concept, the embodiment of the present application also provides a battery ion concentration determination device for implementing the above-mentioned battery ion concentration determination method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the one or more battery ion concentration determination devices provided below can refer to the limitations of the battery ion concentration determination method above, and will not be repeated here.

[0128] In one embodiment, Figure 8 As shown, a device 800 for determining the ion concentration of a battery is provided, comprising: a parameter acquisition module 810 and a concentration determination module 820, wherein:

[0129] The parameter acquisition module 810 is used to obtain the battery operating parameters of the battery under test at each time in the current period; wherein the battery operating parameters include current information, battery capacity and position information of lithium ions in the battery under test;

[0130] The concentration determination module 820 is used to determine the lithium ion concentration of the battery to be tested at each moment in the current time period based on the target diffusion single particle model of the battery to be tested and the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on the physical information neural network.

[0131] The above-mentioned device for determining the ion concentration of the battery obtains the battery operating parameters of the battery to be tested at each moment in the current time period, including the current information, the battery capacity and the position information of the lithium ions in the battery to be tested; based on the target diffusion single particle model of the battery to be tested, according to the battery operating parameters of the battery to be tested at each moment in the current time period, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined; wherein, the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on the physical information neural network. The above scheme optimizes the physical parameters in the initial diffusion single particle model through the physical information neural network, so that the values ​​of each physical parameter in the target diffusion single particle model are the optimal values, further ensuring the accuracy of the target diffusion single particle model; further, by introducing the current information and the battery capacity, and combining the target diffusion single particle model, the accuracy of the lithium ion concentration in the battery to be tested is ensured, and the lithium ion concentration of the battery to be tested under various application current conditions and various battery capacity states can be obtained, which has stronger adaptability and scalability.

[0132] In one embodiment, the battery ion concentration determination device 800 further includes:

[0133] The data acquisition module is used to acquire sample data; wherein the sample data includes the battery operating parameters and lithium ion concentration of the battery to be tested at each historical moment in a historical period.

[0134] The model optimization module is used to initialize the physical information neural network based on the physical parameters in the initial diffusion single particle model of the battery to be tested, so as to obtain the physical information network model of the battery to be tested.

[0135] The model training module is used to train the physical information network model using sample data to obtain a target diffusion single particle model.

[0136] In one embodiment, the model optimization module is further used to:

[0137] A solid-phase lithium ion diffusion model is constructed based on the solid-phase lithium ion diffusion coefficient and the solid-phase lithium ion concentration function; wherein the solid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times; a liquid-phase lithium ion diffusion model is constructed based on the liquid-phase lithium ion diffusion coefficient, the electrolyte volume fraction, the lithium ion migration number and the liquid-phase lithium ion concentration function; wherein the liquid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0138] In one embodiment, the model training module is specifically used to:

[0139] The physical information network model is trained using the target loss function and sample data to obtain a target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model, as well as the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model.

[0140] In one embodiment, the model training module is further used to:

[0141] An equation loss function is constructed based on the mean square error function of the solid-phase lithium ion diffusion model and the mean square error function of the liquid-phase lithium ion diffusion model; a boundary loss function is constructed based on the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model; an initial value loss function is determined based on the value of the solid-phase lithium ion concentration function at the time when the battery to be tested is powered on and the value of the liquid-phase lithium ion concentration function at the time when the battery to be tested is powered on; the sum of the equation loss function, the boundary loss function and the initial value loss function is used as the target loss function.

[0142] In one embodiment, the concentration determination module 820 is used to:

[0143] The battery operating condition parameters are normalized to obtain normalized battery operating condition parameters; the normalized battery operating condition parameters are input into a target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each moment in the current time period.

[0144] In one embodiment, the battery ion concentration determination device 800 is further used for:

[0145] According to the battery capacity and lithium ion concentration of the battery to be tested at each moment in the current time period, a corresponding curve of the battery capacity and lithium ion concentration of the battery to be tested is determined.

[0146] Each module in the above-mentioned battery ion concentration determination 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 a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0147] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the ion concentration of a battery is implemented.

[0148] Those skilled in the art will understand that Fig. 9 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.

[0149] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0150] Obtaining battery operating parameters of the battery under test at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery under test;

[0151] Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0152] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0153] Acquire sample data; wherein the sample data includes battery operating parameters and lithium ion concentrations of the battery to be tested at each historical moment in a historical period; initialize the physical information neural network based on the physical parameters in the initial diffusion single particle model of the battery to be tested to obtain the physical information network model of the battery to be tested; use the sample data to train the physical information network model to obtain a target diffusion single particle model.

[0154] In one embodiment, the initial diffusion single particle model includes a solid phase lithium ion diffusion model and a liquid phase lithium ion diffusion model, and the processor further implements the following steps when executing the computer program:

[0155] A solid-phase lithium ion diffusion model is constructed based on the solid-phase lithium ion diffusion coefficient and the solid-phase lithium ion concentration function; wherein the solid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times; a liquid-phase lithium ion diffusion model is constructed based on the liquid-phase lithium ion diffusion coefficient, the electrolyte volume fraction, the lithium ion migration number and the liquid-phase lithium ion concentration function; wherein the liquid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0156] In one embodiment, when the processor executes the computer program to train the physical information network model using sample data to obtain the target diffusion single particle model, the following steps are also implemented:

[0157] The physical information network model is trained using the target loss function and sample data to obtain a target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model, as well as the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model.

[0158] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0159] An equation loss function is constructed based on the mean square error function of the solid-phase lithium ion diffusion model and the mean square error function of the liquid-phase lithium ion diffusion model; a boundary loss function is constructed based on the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model; an initial value loss function is determined based on the value of the solid-phase lithium ion concentration function at the time when the battery to be tested is powered on and the value of the liquid-phase lithium ion concentration function at the time when the battery to be tested is powered on; the sum of the equation loss function, the boundary loss function and the initial value loss function is used as the target loss function.

[0160] In one embodiment, when the processor executes the computer program to determine the ion concentration of the battery under test at each time in the current period based on the target diffusion single particle model of the battery under test and the battery operating parameters, the processor also implements the following steps:

[0161] The battery operating condition parameters are normalized to obtain normalized battery operating condition parameters; the normalized battery operating condition parameters are input into a target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each moment in the current time period.

[0162] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0163] According to the battery capacity and lithium ion concentration of the battery to be tested at each moment in the current time period, a corresponding curve of the battery capacity and lithium ion concentration of the battery to be tested is determined.

[0164] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0165] Obtaining battery operating parameters of the battery under test at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery under test;

[0166] Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] Acquire sample data; wherein the sample data includes battery operating parameters and lithium ion concentrations of the battery to be tested at each historical moment in a historical period; initialize the physical information neural network based on the physical parameters in the initial diffusion single particle model of the battery to be tested to obtain the physical information network model of the battery to be tested; use the sample data to train the physical information network model to obtain a target diffusion single particle model.

[0169] In one embodiment, the initial diffusion single particle model includes a solid phase lithium ion diffusion model and a liquid phase lithium ion diffusion model, and the processor further implements the following steps when executing the computer program:

[0170] A solid-phase lithium ion diffusion model is constructed based on the solid-phase lithium ion diffusion coefficient and the solid-phase lithium ion concentration function; wherein the solid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times; a liquid-phase lithium ion diffusion model is constructed based on the liquid-phase lithium ion diffusion coefficient, the electrolyte volume fraction, the lithium ion migration number and the liquid-phase lithium ion concentration function; wherein the liquid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0171] In one embodiment, when the processor executes the computer program to train the physical information network model using sample data to obtain the target diffusion single particle model, the following steps are also implemented:

[0172] The physical information network model is trained using the target loss function and sample data to obtain a target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model, as well as the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model.

[0173] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0174] An equation loss function is constructed based on the mean square error function of the solid-phase lithium ion diffusion model and the mean square error function of the liquid-phase lithium ion diffusion model; a boundary loss function is constructed based on the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model; an initial value loss function is determined based on the value of the solid-phase lithium ion concentration function at the time when the battery to be tested is powered on and the value of the liquid-phase lithium ion concentration function at the time when the battery to be tested is powered on; the sum of the equation loss function, the boundary loss function and the initial value loss function is used as the target loss function.

[0175] In one embodiment, when the processor executes the computer program to determine the ion concentration of the battery under test at each time in the current period based on the target diffusion single particle model of the battery under test and the battery operating parameters, the processor also implements the following steps:

[0176] The battery operating condition parameters are normalized to obtain normalized battery operating condition parameters; the normalized battery operating condition parameters are input into a target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each moment in the current time period.

[0177] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0178] According to the battery capacity and lithium ion concentration of the battery to be tested at each moment in the current time period, a corresponding curve of the battery capacity and lithium ion concentration of the battery to be tested is determined.

[0179] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0180] Obtaining battery operating parameters of the battery under test at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery under test;

[0181] Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] Acquire sample data; wherein the sample data includes battery operating parameters and lithium ion concentrations of the battery to be tested at each historical moment in a historical period; initialize the physical information neural network based on the physical parameters in the initial diffusion single particle model of the battery to be tested to obtain the physical information network model of the battery to be tested; use the sample data to train the physical information network model to obtain a target diffusion single particle model.

[0184] In one embodiment, the initial diffusion single particle model includes a solid phase lithium ion diffusion model and a liquid phase lithium ion diffusion model, and the processor further implements the following steps when executing the computer program:

[0185] A solid-phase lithium ion diffusion model is constructed based on the solid-phase lithium ion diffusion coefficient and the solid-phase lithium ion concentration function; wherein the solid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times; a liquid-phase lithium ion diffusion model is constructed based on the liquid-phase lithium ion diffusion coefficient, the electrolyte volume fraction, the lithium ion migration number and the liquid-phase lithium ion concentration function; wherein the liquid-phase lithium ion concentration function is related to the position information of lithium ions in the battery to be tested at different times.

[0186] In one embodiment, when the processor executes the computer program to train the physical information network model using sample data to obtain the target diffusion single particle model, the following steps are also implemented:

[0187] The physical information network model is trained using the target loss function and sample data to obtain a target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model, as well as the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model.

[0188] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0189] An equation loss function is constructed based on the mean square error function of the solid-phase lithium ion diffusion model and the mean square error function of the liquid-phase lithium ion diffusion model; a boundary loss function is constructed based on the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model; an initial value loss function is determined based on the value of the solid-phase lithium ion concentration function at the time when the battery to be tested is powered on and the value of the liquid-phase lithium ion concentration function at the time when the battery to be tested is powered on; the sum of the equation loss function, the boundary loss function and the initial value loss function is used as the target loss function.

[0190] In one embodiment, when the processor executes the computer program to determine the ion concentration of the battery under test at each time in the current period based on the target diffusion single particle model of the battery under test and the battery operating parameters, the processor also implements the following steps:

[0191] The battery operating condition parameters are normalized to obtain normalized battery operating condition parameters; the normalized battery operating condition parameters are input into a target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each moment in the current time period.

[0192] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0193] According to the battery capacity and lithium ion concentration of the battery to be tested at each moment in the current time period, a corresponding curve of the battery capacity and lithium ion concentration of the battery to be tested is determined.

[0194] It should be noted that the data involved in this application (including but not limited to data used for analysis, storage, display, etc.) are all information and data fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0195] Those skilled 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 this 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.

[0196] The technical features of the above embodiments may be combined arbitrarily. 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.

[0197] The above-described 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 determining the ion concentration of a battery, characterized in that: The method comprises: Obtaining battery operating parameters of the battery to be tested at each moment in the current period; wherein the battery operating parameters include current information, battery capacity, and position information of lithium ions in the battery to be tested; Based on the target diffusion single particle model of the battery to be tested, the lithium ion concentration of the battery to be tested at each moment in the current time period is determined according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

2. The method according to claim 1, characterized in that The target diffusion single particle model is obtained by: Acquire sample data; wherein the sample data includes battery operating parameters and lithium ion concentration of the battery to be tested at each historical moment in a historical period; Initializing a physical information neural network based on physical parameters in an initial diffusion single particle model of the battery to be tested to obtain a physical information network model of the battery to be tested; The sample data is used to train the physical information network model to obtain a target diffusion single particle model.

3. The method according to claim 2, characterized in that The initial diffusion single particle model includes a solid phase lithium ion diffusion model and a liquid phase lithium ion diffusion model, and the initial diffusion single particle model is obtained by the following method, including: Constructing the solid-phase lithium ion diffusion model according to the solid-phase lithium ion diffusion coefficient and the solid-phase lithium ion concentration function; wherein the solid-phase lithium ion concentration function is related to the position information of the lithium ions in the battery to be tested at different times; The liquid-phase lithium-ion diffusion model is constructed according to the liquid-phase lithium-ion diffusion coefficient, the electrolyte volume fraction, the lithium-ion migration number and the liquid-phase lithium-ion concentration function; wherein the liquid-phase lithium-ion concentration function is related to the position information of the lithium ions in the battery to be tested at different times.

4. The method according to claim 3, characterized in that The adopting of the sample data to train the physical information network model to obtain a target diffusion single particle model includes: The physical information network model is trained using a target loss function and the sample data to obtain a target diffusion single particle model; wherein the target loss function is constructed based on the mean square error function and boundary conditions of the solid-phase lithium ion diffusion model, and the mean square error function and boundary conditions of the liquid-phase lithium ion diffusion model.

5. The method according to claim 4, characterized in that The objective loss function is obtained in the following way: Constructing an equation loss function according to the mean square error function of the solid-phase lithium ion diffusion model and the mean square error function of the liquid-phase lithium ion diffusion model; Constructing a boundary loss function according to the boundary conditions of the solid-phase lithium ion diffusion model and the boundary conditions of the liquid-phase lithium ion diffusion model; Determine the initial value loss function according to the value of the solid phase lithium ion concentration function at the time when the battery to be tested is powered on and the value of the liquid phase lithium ion concentration function at the time when the battery to be tested is powered on; The sum of the equation loss function, the boundary loss function and the initial value loss function is used as the target loss function.

6. The method according to claim 1, characterized in that The step of determining the ion concentration of the battery to be tested at each moment in the current time period based on the target diffusion single particle model of the battery to be tested and according to the battery operating parameters includes: Normalizing the battery operating condition parameters to obtain normalized battery operating condition parameters; The normalized battery operating condition parameters are input into the target diffusion single particle model to obtain the lithium ion concentration of the battery to be tested at each time in the current time period.

7. The method according to claim 1, characterized in that The method further comprises: According to the battery capacity and lithium ion concentration of the battery to be tested at each moment in the current time period, a corresponding curve of the battery capacity and lithium ion concentration of the battery to be tested is determined.

8. A device for determining the ion concentration of a battery, characterized in that: The device comprises: A parameter acquisition module, used to acquire the battery operating parameters of the battery to be tested at each moment in the current period; wherein the battery operating parameters include current information, battery capacity and position information of lithium ions in the battery to be tested; A concentration determination module is used to determine the lithium ion concentration of the battery to be tested at each moment in the current time period based on the target diffusion single particle model of the battery to be tested and according to the battery operating parameters of the battery to be tested at each moment in the current time period; wherein the target diffusion single particle model is obtained by optimizing the physical parameters in the initial diffusion single particle model based on a physical information neural network.

9. 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 7 are implemented.

10. 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 7 are implemented.

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