A battery model construction method and system

Abstract

The application provides a battery model construction method and system, and belongs to the field of new energy batteries. The battery model construction method comprises the following steps: establishing an electrochemical model and an equivalent circuit model of a target battery; determining a solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery; the solid-phase diffusion compensation voltage is used for compensating the influence of a lithium ion solid-phase diffusion concentration difference on the battery voltage; and an optimized battery model of the target battery is determined according to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery. The application combines the advantages of the electrochemical model and the equivalent circuit model, improves the accuracy of the battery model, and does not increase the complexity of the battery model.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy batteries, in particular to a battery model construction method and system. BACKGROUND

[0002] The battery model is the basis of supporting the battery digital twin technology, determines the accuracy of the digital twin, and is currently applied in various fields such as battery cloud remote diagnosis, high-computing-power platform battery management system, battery SOH calculation, Kalman filter SOC calculation, etc. The current battery model mainly includes equivalent circuit model (ECM), fractional-order model (FOM), and electrochemical model. The ECM is simple to calculate and has low precision; the FOM model requires a large amount of data and has good precision; and the electrochemical model has a large amount of calculation and high precision. However, when high-rate current and some special working conditions (such as mountainous areas) occur, the above models will all have reduced precision.

[0003] In summary, most of the existing battery models have the problem of increased error of the battery model under high-rate current conditions, and if the error is to be reduced, the complexity of the model needs to be significantly increased, resulting in increased computing power requirements and making it impossible to achieve engineering application. SUMMARY

[0004] The purpose of the present application is to provide a battery model construction method and system that can improve the precision of the battery model.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] A battery model construction method, comprising:

[0007] establishing an electrochemical model and an equivalent circuit model of a target battery;

[0008] determining a solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery; the solid-phase diffusion compensation voltage is used to compensate for the influence of lithium ion solid-phase diffusion concentration difference on the battery voltage;

[0009] determining an optimized battery model of the target battery according to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery;

[0010] performing voltage simulation on the target battery based on the optimized battery model.

[0011] Optionally, determining a solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery specifically comprises:

[0012] obtaining the rated capacity of the target battery;

[0013] setting a plurality of initial states of charge of the target battery;

[0014] for any initial state of charge, obtaining a battery capacity value corresponding to each time of the target battery at the initial state of charge;

[0015] based on the electrochemical model, determining the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the positive and negative electrodes of the target battery at the initial state of charge;

[0016] for the k time at the initial state of charge, based on the equivalent circuit model, determining the open circuit voltage original value of the target battery at the k time at the initial state of charge;

[0017] According to the rated capacity of the target battery, the initial state of charge, the battery capacity value corresponding to each time of the target battery at the initial state of charge, the open circuit voltage original value of the target battery at the k time at the initial state of charge, the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the positive and negative electrodes of the target battery at the initial state of charge, the compensation voltage of the target battery at the k time at the initial state of charge is determined.

[0018] According to the compensation voltage of the target battery at each time at each initial state of charge, the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the target battery at each initial state of charge, the optimal initial state of charge is determined.

[0019] According to the compensation voltage of the target battery at each time at the optimal initial state of charge, the solid phase diffusion compensation voltage is determined.

[0020] Optionally, obtaining the battery capacity value corresponding to each time of the target battery at the initial state of charge, specifically includes:

[0021] obtaining the current and voltage of the target battery at each time at the initial state of charge;

[0022] According to the current and voltage of the target battery at each time at the initial state of charge, the ampere-hour integral transfer function is used to determine the battery capacity value corresponding to each time of the target battery at the initial state of charge.

[0023] Optionally, the compensation voltage of the target battery at the kth moment under the initial state of charge is determined according to the rated capacity of the target battery, the initial state of charge, the battery capacity value corresponding to the target battery at the kth moment under the initial state of charge, the open-circuit voltage original value of the target battery at the kth moment under the initial state of charge, the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the positive and negative electrodes of the target battery under the initial state of charge, and specifically includes:

[0024] The state of charge value of the target battery at the kth moment under the initial state of charge is determined according to the rated capacity of the target battery, the initial state of charge, the battery capacity value corresponding to the target battery at the kth moment under the initial state of charge, and the battery capacity value of the target battery at the initial moment under the initial state of charge.

[0025] The open-circuit voltage difference value of the target battery at the kth moment under the initial state of charge is determined according to the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the positive and negative electrodes of the target battery under the initial state of charge; the open-circuit voltage difference value represents the difference between the surface open-circuit voltage value and the internal average open-circuit voltage value caused by the difference between the surface concentration and the bulk concentration; the open-circuit voltage difference value includes a positive electrode open-circuit voltage difference value and a negative electrode open-circuit voltage difference value.

[0026] The particle surface open-circuit voltage of the target battery at the kth moment under the initial state of charge is determined according to the state of charge value and the open-circuit voltage difference value of the target battery at the kth moment under the initial state of charge.

[0027] The compensation voltage of the target battery at the kth moment under the initial state of charge is determined according to the particle surface open-circuit voltage and the open-circuit voltage original value of the target battery at the kth moment under the initial state of charge.

[0028] Optionally, the following formula is used to determine the state of charge value of the target battery at the kth moment under the initial state of charge:

[0029] θ(k) = SOC begin - Q(1) / Q n + Q(k) / Q n ;

[0030] Wherein, θ(k) is the state of charge value of the target battery at the kth moment under the initial state of charge, SOC begin is the initial state of charge, Q(1) is the battery capacity value of the target battery at the initial moment under the initial state of charge, Q(k) is the battery capacity value of the target battery at the kth moment under the initial state of charge, and Q n is the rated capacity of the target battery.

[0031] Optionally, the open-circuit voltage difference value of the target battery at time k under the initial state of charge is determined using the following formula:

[0032] ΔOCV p (k) = h p (c surf_p / c max_p )-h p (c 0_p / c max_p );

[0033] ΔOCV n (k) = h n (c surf_n / c max_n )-h n (c 0_n / c max_n );

[0034] wherein ΔOCV p (k) is the positive electrode open-circuit voltage difference value of the target battery at time k under the initial state of charge, ΔOCV n (k) is the negative electrode open-circuit voltage difference value of the target battery at time k under the initial state of charge, c surf_p is the particle surface lithium ion concentration of the positive electrode of the target battery under the initial state of charge, c surf_n is the particle surface lithium ion concentration of the negative electrode of the target battery under the initial state of charge, c 0_p is the average particle internal lithium ion concentration of the positive electrode of the target battery under the initial state of charge, c 0_n is the average particle internal lithium ion concentration of the negative electrode of the target battery under the initial state of charge, c max_p is the maximum lithium intercalation amount of the positive electrode of the target battery, c max_n is the maximum lithium intercalation amount of the negative electrode of the target battery, h p () represents the relationship function between the lithium intercalation degree of the positive electrode of the target battery and the positive electrode open-circuit potential, h n () represents the relationship function between the lithium intercalation degree of the negative electrode of the target battery and the negative electrode open-circuit potential.

[0035] Optionally, the particle surface open-circuit voltage of the target battery at time k under the initial state of charge is determined using the following formula:

[0036] U oc2 (k) = OCV(θ(k)) - ΔOCV n (k) + ΔOCV p (k);

[0037] wherein U oc2(k) is the particle surface open circuit voltage of the target battery at the initial state of charge at time k, θ(k) is the state of charge value of the target battery at the initial state of charge at time k, OCV(θ(k)) is the open circuit voltage corresponding to θ(k), ΔOCV n (k) is the negative electrode open circuit voltage difference of the target battery at the initial state of charge at time k. p (k) is the positive electrode open circuit voltage difference of the target battery at the initial state of charge at time k.

[0038] Optionally, according to the compensation voltage of the target battery at each time under each initial state of charge, the particle surface lithium ion concentration and the average lithium ion concentration in the particle of the target battery under each initial state of charge, the optimal initial state of charge is determined, and specifically includes:

[0039] The particle surface lithium ion concentration and the average lithium ion concentration in the particle of the target battery under each initial state of charge are fitted with the compensation voltage of the target battery at each time under each initial state of charge, and a key coefficient is determined.

[0040] For any initial state of charge, according to the key coefficient, the particle surface lithium ion concentration and the average lithium ion concentration in the particle of the target battery under the initial state of charge, the compensation voltage corresponding to the initial state of charge is determined.

[0041] According to the compensation voltage corresponding to the initial state of charge and the compensation voltage of the target battery at each time under the initial state of charge, the target function value corresponding to the initial state of charge is determined; the initial state of charge with the minimum target function value is the optimal initial state of charge.

[0042] Optionally, the optimized battery model is:

[0043] U t = U oc -U d -R i *I-U comp ;

[0044] Wherein, U t is the battery terminal voltage, U oc is the battery open circuit voltage, U d is the capacitor-impedance parallel module voltage in the equivalent circuit model, R i is the battery impedance, I is the battery current, and U comp is the solid-phase diffusion compensation voltage.

[0045] To achieve the above purpose, the present application also provides the following scheme:

[0046] A battery model construction system, comprising:

[0047] a model establishing module, configured to establish an electrochemical model and an equivalent circuit model of a target battery;

[0048] a compensation voltage determining module, connected with the model establishing module, configured to determine a solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery; the solid-phase diffusion compensation voltage is used to compensate the influence of lithium ion solid-phase diffusion concentration difference on the battery voltage;

[0049] a model optimizing module, connected with the compensation voltage determining module, configured to determine an optimized battery model of the target battery according to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery.

[0050] According to the embodiments of the present application, the following technical effects are achieved: the solid-phase diffusion compensation voltage is determined based on the electrochemical model and the equivalent circuit model of the target battery, the influence of lithium ion solid-phase diffusion concentration difference on the battery voltage is compensated, the optimized battery model of the target battery is determined according to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery, the advantages of the electrochemical model and the equivalent circuit model are combined, the precision of the battery model is improved, and the complexity of the battery model is not increased. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0052] Figure 1 a flow chart of the battery model construction method provided by the present application;

[0053] Figure 2 a schematic diagram of a traditional equivalent circuit model;

[0054] Figure 3 a schematic diagram of the optimized battery model established by the present application;

[0055] Figure 4 a schematic diagram of the battery model construction system provided by the present application. DETAILED DESCRIPTION

[0056] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0057] The present application aims to provide a battery model construction method and system. On the basis of ECM, the influence of lithium ion solid-phase diffusion concentration difference on battery voltage is compensated to form an improved ECM model, i.e. an optimized battery model. The optimized battery model is simple to calculate and has high precision, solves the problem of reduced precision under high-rate current, and realizes simple and high-precision simulation of battery terminal voltage and open-circuit voltage.

[0058] In order to make the above-mentioned purposes, characteristics and advantages of the present application more apparent and easy to understand, the present application will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0059] Embodiment one

[0060] As shown in the figure, the present embodiment provides a battery model construction method, comprising: Figure 1

[0061] Step 100: Establishing an electrochemical model and an equivalent circuit model of a target battery. The target battery involved in the present application can be a new energy vehicle battery system or a energy storage system.

[0062] Specifically, when establishing the electrochemical model, a plurality of key parameters of the battery are set, such as the maximum lithium ion concentration of the positive and negative electrodes, the particle diameter of the positive and negative electrodes, the capacity, the negative electrode surplus amount, the diffusion coefficient of the positive and negative electrodes, etc. The electrochemical model can be a single-particle electrochemical model or a pseudo two-dimensional electrochemical model.

[0063] Step 200: Determining a solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery. The solid-phase diffusion compensation voltage is used to compensate the influence of lithium ion solid-phase diffusion concentration difference on battery voltage.

[0064] Further, step 200 comprises:

[0065] (21) Obtaining the rated capacity of the target battery.

[0066] (22) Setting a plurality of initial state-of-charge of the target battery.

[0067] (23) For any initial state-of-charge, obtaining the battery capacity value corresponding to each time under the initial state-of-charge of the target battery.

[0068] ​Specifically, first, the current and voltage of the target battery at each time point under the initial state of charge are obtained. Then, according to the current and voltage of the target battery at each time point under the initial state of charge, the battery capacity value corresponding to each time point of the target battery under the initial state of charge is determined by using an ampere-hour integral transfer function. Q(k) = Q(k-1) + I(k)*[k-(k-1)]; wherein Q(k) is the battery capacity value at time k, Q(k-1) is the battery capacity value at time k-1, and I(k) is the current at time k. The battery capacity value at the initial time point can be the rated capacity, 0, or other set values.

[0069] In this embodiment, the voltage can be the highest / lowest single cell voltage value of all single cells in the battery system at each time point, or the voltage value of a specific single cell. The current is negative when charging and positive when discharging. Alternatively, the current can be positive when charging and negative when discharging.

[0070] (24) Based on the electrochemical model, the particle surface lithium ion concentration and the average lithium ion concentration inside the particle of the positive and negative electrodes of the target battery under the initial state of charge are determined.

[0071] According to the principles of solid-phase diffusion and potential conservation, the particle surface lithium ion concentration and the average lithium ion concentration inside the particle of the positive and negative electrodes of the battery are calculated by using a three-parameter model and other methods with current as the input quantity.

[0072] (25) For the k time point under the initial state of charge, based on the equivalent circuit model, the open circuit voltage original value of the target battery at the k time point under the initial state of charge is determined.

[0073] In this embodiment, the battery current and voltage data are processed by using an identification algorithm to obtain the open circuit voltage original value corresponding to each time point of voltage by using ECM. When the battery is in a large current working condition or a special mountain working condition, the open circuit voltage original value will have a large error. The identification algorithm can be all algorithms capable of identifying the open circuit voltage (OCV), such as the least square identification algorithm, the Kalman filter algorithm, the H-infinity algorithm, the intelligent machine learning optimization algorithm, and the like.

[0074] (26) According to the rated capacity of the target battery, the initial state of charge, the battery capacity value corresponding to each time point of the target battery under the initial state of charge, the open circuit voltage original value of the target battery at the k time point under the initial state of charge, and the particle surface lithium ion concentration and the average lithium ion concentration inside the particle of the positive and negative electrodes of the target battery under the initial state of charge, the compensation voltage of the target battery at the k time point under the initial state of charge is determined.

[0075] Specifically, step (26) comprises:

[0076] 1) determining the state of charge value of the target battery at the kth moment under the initial state of charge according to the rated capacity of the target battery, the initial state of charge, the battery capacity value of the target battery at the kth moment under the initial state of charge and the battery capacity value of the target battery at the initial moment under the initial state of charge.

[0077] In this embodiment, the state of charge value of the target battery at the kth moment under the initial state of charge is determined by using the following formula:

[0078] θ(k) = SOC begin - Q(1) / Q n + Q(k) / Q n ;

[0079] wherein θ(k) is the state of charge value of the target battery at the kth moment under the initial state of charge, SOC begin is the initial state of charge, Q(1) is the battery capacity value of the target battery at the initial moment under the initial state of charge, Q(k) is the battery capacity value of the target battery at the kth moment under the initial state of charge, and Q n is the rated capacity of the target battery.

[0080] 2) determining the open circuit voltage difference value of the target battery at the kth moment under the initial state of charge according to the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the positive and negative electrodes of the target battery under the initial state of charge. The open circuit voltage difference value represents the difference between the surface open circuit voltage value and the internal average open circuit voltage value caused by the difference between the surface concentration and the bulk concentration. The open circuit voltage difference value includes the positive electrode open circuit voltage difference value and the negative electrode open circuit voltage difference value.

[0081] In this embodiment, the open circuit voltage difference value of the target battery at the kth moment under the initial state of charge is determined by using the following formula:

[0082] ΔOCV p (k) = h p (c surf_p / c max_p )- h p (c 0_p / c max_p );

[0083] ΔOCV n (k) = h n (c surf_n / c max_n )- h n (c 0_n / c max_n );

[0084] wherein, ΔOCV p (k) is the positive electrode open circuit voltage difference value of the target battery at time k under the initial state of charge, ΔOCV n (k) is the negative electrode open circuit voltage difference value of the target battery at time k under the initial state of charge, ΔOCV p (k) and ΔOCV n (k) can be ignored as 0, c surf_p is the particle surface lithium ion concentration of the positive electrode of the target battery under the initial state of charge, c surf_n is the particle surface lithium ion concentration of the negative electrode of the target battery under the initial state of charge, c 0_p is the average particle internal lithium ion concentration of the positive electrode of the target battery under the initial state of charge, c 0_n is the average particle internal lithium ion concentration of the negative electrode of the target battery under the initial state of charge, c max_p is the maximum lithium intercalation amount of the positive electrode of the target battery, c max_n is the maximum lithium intercalation amount of the negative electrode of the target battery, h p () represents the relationship function between the lithium intercalation degree of the positive electrode of the target battery and the positive electrode open circuit potential (OCP), h n () represents the relationship function between the lithium intercalation degree of the negative electrode of the target battery and the negative electrode open circuit potential.

[0085] 3) determining the particle surface open circuit voltage of the target battery at time k under the initial state of charge according to the state of charge value and the open circuit voltage difference value of the target battery at time k under the initial state of charge.

[0086] In this embodiment, the following formula is used to determine the particle surface open circuit voltage of the target battery at time k under the initial state of charge:

[0087] U oc2 (k) = OCV(θ(k)) - ΔOCV n (k) + ΔOCV p (k) ;

[0088] wherein, U oc2 (k) is the particle surface open circuit voltage of the target battery at time k under the initial state of charge, θ(k) is the state of charge value of the target battery at time k under the initial state of charge, OCV(θ(k)) is the open circuit voltage corresponding to θ(k), which is obtained by the open circuit voltage fitting formula or the interpolation function, ΔOCV n (k) is the negative electrode open circuit voltage difference value of the target battery at time k under the initial state of charge, ΔOCV p(k) is the open-circuit voltage difference value of the positive electrode of the target battery at the initial state of charge at time k.

[0089] Specifically, the function used to fit the OCV(0(k)) can be a Gaussian function, or a polynomial, hyperbolic tangent, or no function fitting, only smoothing the original array.

[0090] 4) Determine the compensation voltage of the target battery at the initial state of charge at time k according to the particle surface open-circuit voltage and the open-circuit voltage original value of the target battery at the initial state of charge at time k.

[0091] In this embodiment, the following formula is used to determine the compensation voltage of the target battery at the initial state of charge at time k:

[0092] U comp (k) = U oc1 (k) - U oc2 (k);

[0093] wherein, U comp (k) is the compensation voltage of the target battery at the initial state of charge at time k, U oc1 (k) is the open-circuit voltage original value of the target battery at the initial state of charge at time k.

[0094] In order to better understand the technical solutions of the present application, the implementation process of steps (22) to (26) is specifically introduced as follows: first, set a SOC begin parameter representing the starting time real state of charge (battery state of charge, SOC) of the working condition, then according to the SOC begin and its corresponding Q(k) array, the particle surface open-circuit voltage corresponding to the SOC begin is calculated, and the compensation voltage is calculated according to the particle surface open-circuit voltage corresponding to the SOC begin . Change the value of SOC begin , repeat the calculation of the compensation voltage under different SOC begin , and obtain a series of compensation voltage arrays corresponding to different SOC begin . The compensation voltage array includes the compensation voltage corresponding to each time under the corresponding SOC begin . A plurality of compensation voltage arrays corresponding to SOC begin form a compensation voltage matrix ER of N rows and M columns, N represents the length of the compensation voltage array, and M represents the number of SOC begin .

[0095] wherein, change the SOC beginThe step length can be a fixed value or a variable value, the minimum step length is greater than 0.01, the maximum step length is not more than 0.10, 10≤M≤101, and SOC begin The maximum value is 1 and the minimum value is 0.

[0096] (27) According to the compensation voltage of the target battery at each initial state of charge at each time, the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the target battery at each initial state of charge, the optimal initial state of charge is determined.

[0097] Specifically, step (27) comprises:

[0098] 1) The particle surface lithium ion concentration and the particle internal average lithium ion concentration of the target battery at each initial state of charge are fitted with the compensation voltage of the target battery at each initial state of charge at each time, and the key coefficients are determined.

[0099] In this embodiment, machine learning or Matlab fitting tool is used to determine c surf (i), c0(i) and the optimal fitting formula f of ER(:, i), and the key coefficients in the optimal fitting formula are extracted. Wherein, c surf (i) is the particle surface lithium ion concentration of the target battery at the i-th initial state of charge, c0(i) is the particle internal average lithium ion concentration of the target battery at the i-th initial state of charge, and ER(:, i) is the i-th column array of the ER matrix, i.e. the compensation voltage array of the target battery at the i-th initial state of charge.

[0100] The number of key coefficients is determined according to the fitting function, such as 2 key coefficients when linear fitting, 3 key coefficients when quadratic polynomial, and 4 key coefficients when 2-term exponential function.

[0101] 2) For any initial state of charge, the key coefficients, the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the target battery at the initial state of charge are determined to determine the compensation voltage corresponding to the initial state of charge.

[0102] Specifically, the formula ΔU(i)=f(c surf (i), c0(i), a, b, c) is used to determine the compensation voltage ΔU(i) corresponding to the i-th initial state of charge.

[0103] 3) According to the compensation voltage corresponding to the initial state of charge and the compensation voltage of the target battery at each time at the initial state of charge, the target function value corresponding to the initial state of charge is determined; the initial state of charge with the minimum target function value is the optimal initial state of charge.

[0104] Specifically, the intelligent algorithm or recognition algorithm is used to optimize and solve the target function value J, and the target is the SOC corresponding to the minimum value of the target function value J begin and the key coefficient. J = std(ER(:, i)-ΔU(i)). Where std() represents the standard deviation function. The intelligent algorithm can be all solvable methods such as traversal algorithm, genetic algorithm, particle swarm algorithm, intelligent machine learning algorithm, etc.

[0105] (28) According to the particle surface lithium ion concentration and the average lithium ion concentration inside the particle of the target battery at the optimal initial state of charge, the solid-phase diffusion compensation voltage is determined.

[0106] The SOC begin corresponding to the solution result and the key coefficient are brought into the formula of the above compensation voltage to obtain the corresponding ΔU, that is, the solid-phase diffusion compensation voltage. According to the solid-phase diffusion compensation voltage, the battery terminal voltage U t of the corresponding model and the compensated battery open-circuit voltage U oc2 and other model key parameters can be obtained. The compensated battery open-circuit voltage is closer to the true value than the original value of the open-circuit voltage U oc1 , reduces the model error, and improves the accuracy of battery simulation.

[0107] Step 300: According to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery, an optimized battery model of the target battery is determined. The optimized battery model is used for voltage simulation of the target battery or for solving the open-circuit voltage, etc. As shown in Figure 2 is a traditional equivalent circuit model, as shown in Figure 3 is an optimized battery model, Figure 2 and Figure 3 , C d is the capacitance of the capacitance-impedance parallel module in the equivalent circuit model, and R d is the impedance of the capacitance-impedance parallel module in the equivalent circuit model.

[0108] Specifically, the optimized battery model is:

[0109] U t = U oc -U d -R i *I-U comp ;

[0110] Wherein, U t is the battery terminal voltage, U oc is the battery open-circuit voltage, U d is the voltage of the capacitance-impedance parallel module in the equivalent circuit model, and R ifor the battery impedance, I is the battery current, U is the battery voltage comp for the solid-phase diffusion compensation voltage.

[0111] The present application combines the electrochemical model and the equivalent circuit model, combines the characteristics of the electrochemical model that can calculate the particle surface lithium ion concentration and the bulk lithium ion concentration, and combines the advantages of the equivalent circuit model calculation, forms an optimized battery model, calculates the particle surface lithium ion concentration and the bulk lithium ion concentration of the positive and negative electrode sheets by using the electrochemical model, considers the factors that the large current causes the solid-phase concentration difference to increase and affects the voltage, calculates the solid-phase diffusion compensation voltage, and realizes error compensation under large current and special working conditions, thereby improving the simulation accuracy of the battery voltage under large current and special working conditions such as mountainous areas, and the parameter optimization calculation amount is small, the calculation process time is short, the calculation complexity does not obviously increase, the algorithm requirement of ECM does not need to be changed, the hardware cost will not be increased, the application difficulty of battery model failure under large current and special working conditions is overcome, and the present application is suitable for online simulation.

[0112] Embodiment two

[0113] In order to perform the method corresponding to the above-mentioned embodiment one, to realize the corresponding functions and technical effects, the following provides a battery model construction system.

[0114] As shown in Figure 4 The battery model construction system provided by the present embodiment comprises: a model establishing module 21, a compensation voltage determining module 22, and a model optimization module 23.

[0115] The model establishing module 21 is used for establishing the electrochemical model and the equivalent circuit model of the target battery.

[0116] The compensation voltage determining module 22 is connected with the model establishing module 21, and the compensation voltage determining module 22 is used for determining the solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery. The solid-phase diffusion compensation voltage is used for compensating the influence of the lithium ion solid-phase diffusion concentration difference on the battery voltage.

[0117] The model optimization module 23 is connected with the compensation voltage determining module 22, and the model optimization module 23 is used for determining the optimized battery model of the target battery according to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery.

[0118] Compared with the prior art, the battery model construction system provided by the present embodiment has the same beneficial effects as the battery model construction method provided by embodiment one, which will not be repeated here.

[0119] Embodiment three

[0120] The embodiment provides an electronic device, comprising a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the battery model construction method in the embodiment one.

[0121] Optionally, the electronic device can be a server.

[0122] In addition, the embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the battery model construction method in the embodiment one.

[0123] In the specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other.

[0124] The principles and implementation manners of the present application are described by using specific examples in the specification, and the above embodiment description is only used to help understand the method and core idea of the present application; meanwhile, for the general technical personnel in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as the limitation of the present application.

Claims

1. A battery model construction method characterized by comprising: The battery model construction method comprises: establishing an electrochemical model and an equivalent circuit model of a target battery; determining a solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery, specifically comprising: obtaining a rated capacity of the target battery; setting a plurality of initial states of charge of the target battery; for any initial state of charge, obtaining a battery capacity value corresponding to each time under the initial state of charge of the target battery; based on the electrochemical model, determining the lithium ion concentration on the surface of the particles and the average lithium ion concentration inside the particles of the positive and negative electrodes of the target battery under the initial state of charge; for the k time under the initial state of charge, based on the equivalent circuit model, determining the open-circuit voltage original value of the target battery at the k time under the initial state of charge; determining the compensation voltage of the target battery at the k time under the initial state of charge according to the rated capacity of the target battery, the initial state of charge, the battery capacity value corresponding to each time under the initial state of charge of the target battery, the open-circuit voltage original value of the target battery at the k time under the initial state of charge, the lithium ion concentration on the surface of the particles and the average lithium ion concentration inside the particles of the target battery under the initial state of charge; determining the optimal initial state of charge according to the compensation voltage of the target battery at each time under each initial state of charge, the lithium ion concentration on the surface of the particles and the average lithium ion concentration inside the particles under each initial state of charge of the target battery; determining the solid-phase diffusion compensation voltage according to the compensation voltage of the target battery at each time under the optimal initial state of charge; the solid-phase diffusion compensation voltage is used to compensate the influence of the lithium ion solid-phase diffusion concentration difference on the battery voltage; constructing an optimized battery model of the target battery according to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery.

2. The battery model building method of claim 1, wherein, obtaining the battery capacity value corresponding to each time under the initial state of charge of the target battery, specifically comprising: obtaining the current and voltage of the target battery at each time under the initial state of charge; determining the battery capacity value corresponding to each time under the initial state of charge of the target battery by using the ampere-hour integral transfer function according to the current and voltage of the target battery at each time under the initial state of charge.

3. The battery model building method of claim 1, wherein, determining the compensation voltage of the target battery at the k time under the initial state of charge according to the rated capacity of the target battery, the initial state of charge, the battery capacity value corresponding to each time under the initial state of charge of the target battery, the open-circuit voltage original value of the target battery at the k time under the initial state of charge, the lithium ion concentration on the surface of the particles and the average lithium ion concentration inside the particles of the target battery under the initial state of charge, specifically comprising: determining the state of charge value of the target battery at the k time under the initial state of charge according to the rated capacity of the target battery, the initial state of charge, the battery capacity value of the target battery at the k time under the initial state of charge and the battery capacity value of the target battery at the initial time under the initial state of charge. According to the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the positive and negative electrodes of the target battery at the initial state of charge, a difference value of the open circuit voltage of the target battery at the initial state of charge at time k is determined; the difference value of the open circuit voltage represents a difference between the surface open circuit voltage value and the internal average open circuit voltage value caused by the difference between the surface concentration and the bulk concentration; the difference value of the open circuit voltage includes a positive electrode open circuit voltage difference value and a negative electrode open circuit voltage difference value; According to the state of charge value and the difference value of the open circuit voltage of the target battery at the initial state of charge at time k, the particle surface open circuit voltage of the target battery at the initial state of charge at time k is determined; According to the particle surface open circuit voltage and the original value of the open circuit voltage of the target battery at the initial state of charge at time k, the compensation voltage of the target battery at the initial state of charge at time k is determined.

4. The battery model building method of claim 3, wherein, The following formula is used to determine the state of charge value of the target battery at the initial state of charge at time k: θ(k) = SOC begin - Q(1) / Q n + Q(k) / Q n ; wherein θ(k) is a state of charge value of the target battery at the initial state of charge at time k, SOC begin is the initial state of charge, Q(1) is a battery capacity value of the target battery at the initial state of charge at an initial time, Q(k) is a battery capacity value of the target battery at the initial state of charge at time k, Q n is a rated capacity of the target battery.

5. The battery model building method of claim 3, wherein, The following formula is used to determine the difference value of the open circuit voltage of the target battery at the initial state of charge at time k: ΔOCV p (k) = h p (c surf_p / c max_p )-h p (c 0_p / c max_p ); ΔOCV n (k) = h n (c surf_n / c max_n )-h n (c 0_n / c max_n ); wherein ΔOCV p (k) is the positive electrode open circuit voltage difference value of the target battery at time k under the initial state of charge, ΔOCV n (k) is the negative electrode open circuit voltage difference value of the target battery at time k under the initial state of charge, c surf_p is the particle surface lithium ion concentration of the positive electrode of the target battery under the initial state of charge, c surf_n is the particle surface lithium ion concentration of the negative electrode of the target battery under the initial state of charge, c 0_p is the particle internal average lithium ion concentration of the positive electrode of the target battery under the initial state of charge, c 0_n is the particle internal average lithium ion concentration of the negative electrode of the target battery under the initial state of charge, c max_p is the maximum lithium intercalation amount of the positive electrode of the target battery, c max_n is the maximum lithium intercalation amount of the negative electrode of the target battery, h p () represents the relationship function between the lithium intercalation degree of the positive electrode of the target battery and the positive electrode open circuit potential, h n () represents the relationship function between the lithium intercalation degree of the negative electrode of the target battery and the negative electrode open circuit potential.

6. The battery model building method of claim 3, wherein, The following formula is used to determine the particle surface open circuit voltage of the target battery at the initial state of charge at time k: U oc2 (k) = OCV(0(k)) - AOCV n (k) + AOCV p (k); wherein U oc2 (k) is the particle surface open circuit voltage of the target battery at time k under the initial state of charge, θ(k) is the state of charge value of the target battery at time k under the initial state of charge, OCV(θ(k)) is the open circuit voltage corresponding to θ(k), ΔOCV n (k) is the negative electrode open circuit voltage difference value of the target battery at time k under the initial state of charge, ΔOCV p (k) is the positive electrode open circuit voltage difference value of the target battery at time k under the initial state of charge.

7. The battery model building method of claim 1, wherein According to the compensation voltage of the target battery at each initial state of charge at each time, the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the target battery at each initial state of charge, the optimal initial state of charge is determined, specifically including: The particle surface lithium ion concentration and the particle internal average lithium ion concentration of the target battery at each initial state of charge are fitted with the compensation voltage of the target battery at each initial state of charge at each time to determine the key coefficient; For any initial state of charge, the key coefficient, the particle surface lithium ion concentration and the particle internal average lithium ion concentration of the target battery at the initial state of charge are used to determine the compensation voltage corresponding to the initial state of charge; According to the compensation voltage corresponding to the initial state of charge and the compensation voltage of the target battery at the initial state of charge at each time, the target function value corresponding to the initial state of charge is determined; the initial state of charge with the minimum target function value is the optimal initial state of charge.

8. The battery model building method of claim 1, wherein, The optimized battery model is: U t = U oc - U d - R i * I - U comp ; where U t is the battery terminal voltage, U oc is the battery open circuit voltage, U d is the voltage of the capacitor-impedance parallel module in the equivalent circuit model, R i is the battery impedance, I is the battery current, U comp is the solid phase diffusion compensation voltage.

9. A battery model construction system applied to the battery model construction method according to any one of claims 1 to 8, characterized by The battery model construction system includes: A model establishing module is configured to establish an electrochemical model and an equivalent circuit model of a target battery; A compensation voltage determining module is connected with the model establishing module and configured to determine a solid-phase diffusion compensation voltage based on the electrochemical model and the equivalent circuit model of the target battery; the solid-phase diffusion compensation voltage is used to compensate the influence of the lithium ion solid-phase diffusion concentration difference on the battery voltage; A model optimizing module is connected with the compensation voltage determining module and configured to construct an optimized battery model of the target battery according to the solid-phase diffusion compensation voltage and the equivalent circuit model of the target battery.

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