Lithium-ion Battery Life Prediction Method, Electronic Device, and Readable Storage Medium

Abstract

An embodiment of the present application provides a method for predicting the life of a lithium-ion battery, an electronic device, and a readable storage medium. The method includes: obtaining a target relaxation curve corresponding to the battery to be predicted; obtaining a target equivalent circuit model and a target lumped parameter thermal model corresponding to the battery, where the target lumped parameter thermal model is used to determine the instantaneous temperature of the battery at the next moment according to the real-time heat generation power of the battery at the current moment, and the real-time heat generation power of the battery at the current moment is determined according to the current value of the battery at the current moment and the RC parameters of the target equivalent circuit model at the current moment; based on the current relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, performing life prediction on the battery based on a target life prediction model, where the target life prediction model performs the life prediction at least with the instantaneous temperature of the battery as a parameter.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the technical field of lithium-ion batteries, and more particularly, to a method for predicting the life of a lithium-ion battery, an electronic device, and a computer-readable storage medium. Background Art

[0002] With the strong demand for renewable energy solutions, lithium-ion batteries have been increasingly widely used, which makes the prediction of the life of lithium-ion batteries become more and more important. Currently, the methods for predicting the life of lithium-ion batteries usually either only predict the life of the battery based on the loss of storage capacity of the battery, or although they also consider the loss of cycle capacity of the battery, but there is no effective coupling mode between the two, so that the predicted battery life data may not be accurate enough. Summary of the Invention

[0003] An object of the present disclosure is to provide a new technical solution for predicting the life of a lithium-ion battery, so as to improve the accuracy of the predicted battery life data.

[0004] According to a first aspect of the present disclosure, an embodiment of a method for predicting the life of a lithium-ion battery is provided, including:

[0005] Obtain a target relaxation curve corresponding to the battery to be predicted, where the target relaxation curve includes a curve reflecting the change of the current value or electric power value of the battery over time during operation;

[0006] Obtain a target equivalent circuit model and a target lumped parameter thermal model corresponding to the battery, where the target lumped parameter thermal model is used to determine the instantaneous temperature of the battery at the next moment according to the real-time heat generation power of the battery at the current moment, and the real-time heat generation power of the battery at the current moment is determined according to the current value of the battery at the current moment and the RC parameters of the target equivalent circuit model at the current moment;

[0007] Predict the life of the battery according to the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, and based on a target life prediction model, where the target life prediction model performs the life prediction at least with the instantaneous temperature of the battery as a parameter.

[0008] Optionally, the predicting the life of the battery according to the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, and based on a target life prediction model includes:

[0009] Obtain a first current value of the battery at a current first moment according to the target relaxation curve;

[0010] Based on the first current value and the first RC parameter, and based on the target equivalent circuit model, determine the first real-time heat generation power, the first state of charge, the first depth of discharge, and the first actual usage duration of the battery at the first moment, where the first RC parameter is determined according to the second instantaneous temperature calculated by the target lumped parameter thermal model at a second moment earlier than the first moment;

[0011] Based on the first real-time heat generation power and the first ambient temperature of the environment at the current moment, calculate the first instantaneous temperature of the battery at the first moment based on the target lumped parameter thermal model;

[0012] Obtain the capacity attenuation curve corresponding to the battery, and obtain the second capacity attenuation amount of the battery at the second moment based on the target life prediction model, where the capacity attenuation relaxation curve includes multiple sub-curves, and each sub-curve is a curve reflecting the change of the capacity attenuation amount of the battery with time at the corresponding temperature;

[0013] Based on the first state of charge, the first current value, the first depth of discharge, the first actual usage duration, the first instantaneous temperature, the second capacity attenuation amount, and the capacity attenuation relaxation curve, and based on the target life prediction model, predict the first capacity attenuation amount of the battery at the first moment to predict the first life data of the battery at the first moment.

[0014] Optionally, the target life prediction model includes a battery storage capacity loss prediction sub-model and a battery cycle capacity loss prediction sub-model. The function mapping relationship of the target life prediction model is expressed as the following formula 1, the function mapping relationship of the battery storage capacity loss prediction sub-model is expressed as the following formula 2, and the function mapping relationship of the battery cycle capacity loss prediction sub-model is expressed as the following formula 3;

[0015] Among them, the formula 1 is: Q represents the battery capacity attenuation amount at the current moment, Q loss1 represents the battery storage capacity loss at the current moment, Q loss2 represents the battery cycle capacity loss at the current moment, and I is the battery current value at the current moment;

[0016] The formula 2 is: Q loss1 =(a + b * SOC c )·exp(-E a / RT)·t z E a represents the apparent activation energy, T represents the instantaneous temperature of the battery at the current moment, R represents the molar gas constant, t represents the calibrated usage duration of the battery, a, b, c are empirical parameters corresponding to the state of charge of the battery, and z is the attenuation coefficient;

[0017] The formula 3 is as follows: A represents the pre-factor, DOD represents the depth of discharge of the battery at the current moment, and t represents the usage duration for battery calibration.

[0018] Optionally, predicting the first capacity attenuation of the battery at the first moment based on the target life prediction model according to the first state of charge, the first current value, the first depth of discharge, the first actual usage duration, the first instantaneous temperature, the second capacity attenuation, and the capacity attenuation relaxation curve includes:

[0019] Determine a first sub-curve corresponding to the first instantaneous temperature from the capacity attenuation relaxation curve;

[0020] Determine a first estimated extended time corresponding to the battery at the first moment according to the second capacity attenuation and the first sub-curve, where the first estimated extended time is the time corresponding to the second capacity attenuation in the first sub-curve;

[0021] Obtain a first calibration usage duration according to the first actual usage duration and the first estimated extended time;

[0022] When the first current value is zero, obtain a first storage capacity loss as the first capacity attenuation based on the formula 2 according to the first state of charge, the first instantaneous temperature, and the first calibration usage duration;

[0023] When the first current value is not zero, obtain a first cycle capacity loss as the first capacity attenuation based on the formula 3 according to the first state of charge, the first depth of discharge, the first instantaneous temperature, and the first calibration usage duration.

[0024] Optionally, calculating the first instantaneous temperature of the battery at the first moment based on the target lumped parameter thermal model according to the first real-time heat generation power and the first ambient temperature of the environment where the current moment is located includes:

[0025] Obtain a first cooling temperature of the cooling system where the battery is located at the current moment;

[0026] According to the first ambient temperature and the first cooling temperature, calculate a first heat exchange amount between the battery and the environment where it is located, and a second heat exchange amount with the cooling system through the pre-obtained first equivalent convective heat transfer coefficient and second equivalent convective heat transfer coefficient corresponding to the battery, where the first equivalent convective heat transfer coefficient is the heat transfer coefficient between the battery and the environment where it is located, and the second equivalent convective heat transfer coefficient is the heat transfer coefficient between the battery and the cooling system;

[0027] Based on the first real-time heat generation power, the first heat exchange amount, and the second heat exchange amount, the first instantaneous temperature is obtained.

[0028] Optionally, the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient are obtained through the following steps:

[0029] Obtain a first reference curve and a second reference curve, where the first reference curve is a curve reflecting the change of the highest temperature of the battery over time under the condition that the cooling system is turned on, and the second reference curve is a curve reflecting the change of the lowest temperature of the battery over time under the condition that the cooling system is turned on;

[0030] Taking the first reference curve and the second reference curve as references, based on the mass, specific heat capacity, and temperature change rate of the battery, the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient are obtained.

[0031] Optionally, the life prediction of the battery based on the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, and based on the target life prediction model includes:

[0032] Divide the battery into N sub-cell cores, and based on the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, and based on the target life prediction model, predict the N sub-capacity attenuation amounts corresponding to the N sub-cell cores at the current first moment respectively, where N is a positive integer greater than 0;

[0033] Based on the N sub-capacity attenuation amounts, predict the first life data of the battery at the first moment.

[0034] Optionally, the first RC parameter is determined through the following steps:

[0035] Obtain the second instantaneous temperature calculated by the target lumped parameter thermal model at the second moment;

[0036] Based on the second instantaneous temperature and the preset mapping data, the first RC parameter is obtained, where the preset mapping data is data reflecting the corresponding relationship between the battery temperature and the RC parameter, and the preset mapping data is obtained by performing parameter identification processing on the target equivalent circuit model in advance.

[0037] Optionally, the capacity attenuation relaxation curve is obtained through the following steps:

[0038] Perform storage tests and charge-discharge cycle tests on the battery at different temperatures and different state of charge until the health state of the battery reaches the preset health state, and stop the test. Fit the test data obtained during the test to obtain the capacity attenuation relaxation curve.

[0039] According to a second aspect of the present disclosure, an embodiment of an electronic device is provided, including:

[0040] A memory for storing executable instructions;

[0041] A processor for operating the electronic device according to the control of the instructions to execute the method described in the first aspect of this specification.

[0042] According to a third aspect of the present disclosure, an embodiment of a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method described in the first aspect of this specification is implemented.

[0043] One beneficial effect of the embodiments of the present disclosure is that, according to the embodiments of the present disclosure, by obtaining the target relaxation curve corresponding to the battery to be predicted, and obtaining the target equivalent circuit model and the target lumped parameter thermal model corresponding to the battery, then, based on the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, the instantaneous temperature of the battery at each moment and the corresponding RC parameters can be determined. Thus, in a dynamic ambient temperature, based on the determined instantaneous temperature and RC parameters, data such as the state of charge and depth of discharge of the battery can be accurately evaluated. Through this type of data, the life of the battery can be predicted based on the target life prediction model, and the life data of the battery can be predicted efficiently and accurately.

[0044] Through the following detailed description of the exemplary embodiments of this specification with reference to the accompanying drawings, other features and advantages of this specification will become clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings incorporated in the specification and constituting a part of the specification illustrate the embodiments of the specification and, together with the description, are used to explain the principles of the specification.

[0046] Figure 1 is a schematic flow chart of a method for predicting the life of a lithium-ion battery provided by an embodiment of the present disclosure.

[0047] Figure 2 is a schematic diagram of a capacity attenuation relaxation curve provided by an embodiment of the present disclosure.

[0048] Figure 3 is a schematic diagram of the target equivalent circuit model provided by an embodiment of the present disclosure.

[0049] Figure 4 is a logical diagram of coupling multiple models to predict battery life data provided by an embodiment of the present disclosure.

[0050] Figure 5It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0051] Now, various exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. It should be noted that: unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present invention.

[0052] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way limits the present invention or its application or use.

[0053] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the specification.

[0054] In all the examples shown and discussed herein, any specific values should be construed as merely exemplary and not as a limitation. Thus, other examples of the exemplary embodiments may have different values.

[0055] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0056] <Method embodiment>

[0057] In the related art, when predicting battery life data, it is usually only by predicting the loss of battery storage capacity to predict the battery life data. For example, a lithium-ion battery thermal-electrochemical coupling model can be established, and the model parameters can be calibrated by battery storage experiments under different working conditions to determine the correlation between side reactions and temperature. Finally, the battery life data can be predicted by substituting the ambient temperature to predict the loss of battery storage capacity.

[0058] However, on the one hand, during the use of lithium-ion batteries, in addition to the loss of storage capacity, there is usually also a loss of cycle capacity. Especially in operating vehicles such as taxis and buses, the loss of cycle capacity of the battery cannot be ignored. Therefore, the existing method of predicting battery life data only based on predicting the loss of battery storage capacity is usually inaccurate; on the other hand, during the use of the battery, while generating heat, it may also exchange heat with the ambient temperature and the cooling system. Therefore, the existing method of predicting battery life data based on the ambient temperature may cause a large deviation between the prediction result and the actual result.

[0059] To solve the above problems, an embodiment of the present disclosure provides a method for effectively coupling an equivalent circuit model, a thermal model, and a life prediction model of a battery for life prediction. Please refer toFigure 1 , which is a schematic flowchart of a method for predicting the lifespan of a lithium-ion battery provided by an embodiment of the present disclosure. This method can be implemented in an electronic device. For example, it can be implemented in an electronic device running a battery management system, or it can also be applied in a simulation platform, such as a lifespan prediction simulation platform built based on Matlab software or Simulink software. No special limitation is made here.

[0060] As Figure 1 shown, the method of this embodiment may include the following steps S1100 - S1300, which will be described in detail below.

[0061] Step S1100: Obtain a target relaxation curve corresponding to the battery to be predicted, where the target relaxation curve includes a curve reflecting the change of the current value or electric power value of the battery over time during operation.

[0062] The target relaxation curve can be a curve reflecting the change of the current value or electric power value of the battery over time under actual working conditions.

[0063] Step S1200: Obtain the target equivalent circuit model and the target lumped parameter thermal model corresponding to the battery, where the target lumped parameter thermal model is used to determine the instantaneous temperature of the battery at the next moment according to the real-time heat generation power of the battery at the current moment, and the real-time heat generation power of the battery at the current moment is determined according to the current value of the battery at the current moment and the RC parameters of the target equivalent circuit model at the current moment.

[0064] Hereinafter, a brief description will be first given to the target equivalent circuit model and how to identify the RC parameters of the target equivalent circuit model.

[0065] Please refer to Figure 2 , which is a schematic diagram of the target equivalent circuit model provided by an embodiment of the present disclosure. Specifically, in the embodiment of the present disclosure, the target equivalent circuit model can be a second-order equivalent circuit model. In the second-order equivalent circuit model, as Figure 2 shown, U represents the terminal voltage with the unit of V; I represents the current with the unit of A; U ocv represents the open-circuit voltage with the unit of V; R0, R1, and R2 respectively represent the ohmic internal resistance and two polarization internal resistances related to the state of charge, i.e., SOC, temperature, and current, with the unit of Ω; C1 and C2 represent the polarization capacitors with the unit of F.

[0066] Among them, in the target equivalent circuit model, such as Figure 2 shown in the second-order equivalent circuit model, the current i R1 flowing through R1, the voltage U1, and the current i R2, voltage U2. According to Kirchhoff's law, the relationship between them can be expressed based on the following formula:

[0067]

[0068] U = U ocv -U1 - U2 - i*R0

[0069] In specific implementation, the RC parameters in the target equivalent circuit model, for example, Figure 2 the shown R1, R2, C1, and C2 can be obtained by performing hybrid power pulse characteristic (HPPC) pulse charge and discharge tests on the battery at -30°C, -15°C, 0°C, 15°C, 30°C, 45°C, and 60°C with different SOCs (0% SOC - 100% SOC, tested every 10% SOC interval) and different rates (0.2C charge and discharge, 0.5C charge and discharge, 1C charge and discharge, and 2C charge and discharge) to acquire voltage and current data, and then identifying the battery RC parameters corresponding to temperature, SOC, and rate through a preset parameter identification algorithm, such as a genetic algorithm, particle swarm algorithm, etc.

[0070] The above has elaborated in detail on the target equivalent circuit model and how to identify its RC parameters. Next, the thermal model provided in the embodiments of the present disclosure for predicting the instantaneous temperature of the battery, that is, the target lumped parameter thermal model, will be described.

[0071] Specifically, the target lumped parameter thermal model can be a one-dimensional simplified thermal model. In the embodiments of the present disclosure, radiation heat dissipation of the battery is not considered, and at the same time, the thermal physical properties parameters are also set as constant values. Then, the heat generation model of the battery, that is, the real-time heat generation power of the battery, can be expressed by the following formula:

[0072]

[0073] Among them, Q represents the real-time heat generation power of the battery, with the unit of W; R0, R1, and R2 are the corresponding internal resistances of the battery in the identified target equivalent circuit model, with the unit of Ω; i, i R1 and i R2 are the currents flowing through different resistances determined based on the identified RC parameters according to Kirchhoff's law, with the unit of A; is the entropy heat coefficient of the battery cell, with the unit of V / K, and T is the temperature of the battery cell, with the unit of K.

[0074] Then, the target lumped parameter thermal model can be expressed by the following formula:

[0075]

[0076] where, m represents the mass of the battery, with the unit of kg; T represents the temperature of the battery, with the unit of K; C p represents the specific heat capacity of the battery, with the unit of J / K; h1 and h2 respectively represent the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient between the battery and the surrounding environment and the cooling system, with the unit of W / (m2*K); A1 and A2 respectively represent the contact areas between the battery and the surrounding environment and the cooling system, with the unit of m 2 ; T cooling represents the temperature of the coolant, with the unit of K. represents the rate of change of the battery temperature with time, h1A1(T amb -T) represents the heat exchange amount between the battery and the surrounding environment, that is, and the air, h2A2(T cooling -T) represents the heat exchange amount between the battery and the cooling system.

[0077] That is, in the embodiments of the present disclosure, the instantaneous temperature of the battery at each moment can be first determined by calculating the real-time heat generation power of the battery at that moment, then obtaining the real-time ambient temperature of the environment where the battery is located and the real-time cooling temperature of the cooling system at that moment. After that, by using the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer system corresponding to the battery obtained in advance, calculate the heat exchange amounts between the battery and the surrounding environment and the cooling system respectively, and then the total heat exchange amount of the battery at that moment can be obtained; by subtracting the total heat generation power at the current moment from the total heat exchange amount, the relatively accurate instantaneous temperature of the battery can be obtained.

[0078] In specific implementation, the first equivalent convective heat transfer coefficient h1 and the second equivalent convective heat transfer coefficient h2 corresponding to the battery to be predicted can be obtained through the following steps: obtain the first reference curve and the second reference curve, where the first reference curve is a curve reflecting the change of the highest temperature of the battery with time under the condition that the cooling system is turned on, and the second reference curve is a curve reflecting the change of the lowest temperature of the battery with time under the condition that the cooling system is turned on; with the first reference curve and the second reference curve as references, obtain the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient according to the mass, specific heat capacity, and rate of change of temperature of the battery.

[0079] Specifically, through experimental testing or three-dimensional thermal simulation, obtain the curves reflecting the change of the highest temperature and the lowest temperature of the battery with time under the condition that the cooling system is turned on as the first reference curve and the second reference curve respectively, and then adjust the first equivalent convective heat transfer coefficient h1 and the second equivalent convective heat transfer coefficient h2 according to the above target lumped parameter thermal model to make the one-dimensional simulation results coincide with the reference curve, so as to calibrate h1 and h2.

[0080] Step S1300: Based on the current relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, perform life prediction on the battery based on a target life prediction model, where the target life prediction model performs the life prediction with at least the instantaneous temperature of the battery as a parameter.

[0081] After obtaining the target equivalent circuit model, the target lumped parameter thermal model of the battery, and the target relaxation curve under the actual working conditions of the battery based on the above steps S1100 and S1300, the life of the battery can be predicted based on the target life prediction model.

[0082] Please refer to Figure 3 , which is a logical schematic diagram of coupling multiple models to predict battery life data in an embodiment of the present disclosure. As Figure 3 shown, the method provided in the embodiment of the present disclosure establishes a coupling relationship between the target equivalent circuit model and the target life prediction model based on the instantaneous temperature of the battery calculated by the thermal model, that is, the target lumped parameter thermal model. Specifically, at the initial moment, the RC parameters of the target equivalent circuit model can be determined according to the initial temperature, for example, the ambient temperature, to calculate the initial real-time heat generation power of the battery, and then the real-time heat generation power is provided to the target lumped parameter thermal model. After that, according to the real-time heat generation power and the first equivalent convective heat transfer coefficient h1 and the second equivalent convective heat transfer coefficient h2 corresponding to the battery determined in the above step S1200, by obtaining the first ambient temperature of the battery and the first cooling temperature of the cooling system at the current moment, the instantaneous temperature of the battery can be calculated and determined, and the instantaneous temperature is provided to the target equivalent circuit model. Based on the instantaneous temperature, the RC parameters at the next moment can be accurately confirmed, and then the real-time heat generation power at the next moment obtained according to the RC parameters can be used to accurately calculate the instantaneous temperature of the battery at the next moment, so as to realize the iteration of the target equivalent circuit model and the target lumped parameter thermal model; when the target life prediction model predicts the life data of the battery at each moment, it calculates the storage capacity loss or cycle capacity loss of the battery based on the SOC and depth of discharge (DOD) of the battery estimated according to the RC parameters of the target equivalent circuit model at the current moment and the instantaneous temperature determined based on the target lumped parameter thermal model. Through the above repeated iteration, the accuracy of the parameters used for life prediction at each moment can be improved, and thus the accuracy of the final result can be improved.

[0083] Specifically, as Figure 3As shown, in one embodiment, the life prediction of the battery based on the target life prediction model according to the current relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model includes: obtaining a first current value of the battery at a current first moment according to the target relaxation curve; determining a first real-time heat generation power, a first state of charge, a first depth of discharge, and a first actual usage duration of the battery at the first moment based on the first current value and a first RC parameter according to the target equivalent circuit model, where the first RC parameter is determined according to a second instantaneous temperature calculated by the target lumped parameter thermal model at a second moment earlier than the first moment; calculating a first instantaneous temperature of the battery at the first moment based on the first real-time heat generation power and a first ambient temperature of the environment at the current moment according to the target lumped parameter thermal model; obtaining a capacity attenuation curve corresponding to the battery, and obtaining a second capacity attenuation amount of the battery at the second moment based on the target life prediction model, where the capacity attenuation relaxation curve includes multiple sub-curves, and each sub-curve is a curve reflecting the change of the capacity attenuation amount of the battery with time at a corresponding temperature; predicting a first capacity attenuation amount of the battery at the first moment based on the first state of charge, the first current value, the first depth of discharge, the first actual usage duration, the first instantaneous temperature, the second capacity attenuation amount, and the capacity attenuation relaxation curve according to the target life prediction model to predict a first life data of the battery at the first moment.

[0084] As Figure 4 shown, the capacity attenuation relaxation curve may be a curve composed of multiple sub-curves reflecting the change of the capacity attenuation amount of the battery with time at different temperatures. In a specific implementation, the capacity attenuation relaxation curve may be obtained by respectively performing storage tests and charge-discharge cycle tests on the battery to be predicted at different temperatures and different SOCs, and fitting the test data obtained from the tests to obtain the capacity attenuation relaxation curve.

[0085] That is, in one embodiment, the capacity attenuation relaxation curve may be obtained through the following steps: respectively performing storage tests and charge-discharge cycle tests on the battery at different temperatures and different states of charge, stopping the test when the health state of the battery reaches a preset health state, and fitting the test data obtained during the test to obtain the capacity attenuation relaxation curve.

[0086] For example, storage tests at 100% SOC, 80%, 60%, 40%, and 20% SOC may be performed at 25°C, 35°C, 45°C, and 60°C until the health state of the battery, i.e., SOH reaches 80% and the test is stopped; and charge-discharge cycle tests with different discharge depths of 0.5C - 0.5C may also be performed at 25°C, 35°C, 45°C, and 60°C until the battery SOH reaches 80% and the test is stopped; fitting the test data obtained during the test to construct the capacity attenuation relaxation curve.

[0087] Specifically, after obtaining the first capacity attenuation of the battery at the first moment, the first life data of the battery at the first moment can be obtained by subtracting the first capacity attenuation from the nominal capacity. For example, if the first capacity attenuation at the first moment is 10% and the nominal capacity of the battery is usually 100%, then the first life data of the battery can be obtained as 90% by 100% - 10%.

[0088] According to the above description, the first RC parameter of the battery at the current first moment can be determined through the following steps: obtaining the second instantaneous temperature calculated by the target lumped parameter thermal model at the second moment; obtaining the first RC parameter according to the second instantaneous temperature and the preset mapping data, where the preset mapping data is data reflecting the corresponding relationship between the battery temperature and the RC parameter, and the preset mapping data is obtained by performing parameter identification processing on the target equivalent circuit model in advance.

[0089] In addition, after obtaining the first RC parameter of the target equivalent battery model at the current moment, the state of charge of the battery at the current moment, i.e., SOC, can be calculated by the ampere-hour integration method or other methods, and the detailed processing will not be elaborated here. In addition, the first depth of discharge of the battery at the first moment can be obtained by calculating the difference between the maximum SOC and the minimum SOC in the corresponding interval, and the detailed obtaining method will not be elaborated here because it is described in detail in the related art.

[0090] Different from the method in the prior art that only predicts the battery life by calculating the loss of battery storage capacity, in the embodiments of the present disclosure, in order to improve the accuracy of the predicted life data, the target life prediction model includes a battery storage capacity loss prediction sub-model and a battery cycle capacity loss prediction sub-model. The function mapping relationship of the target life prediction model is expressed as the following formula 1, the function mapping relationship of the battery storage capacity loss prediction sub-model is expressed as the following formula 2, and the function mapping relationship of the battery cycle capacity loss prediction sub-model is expressed as the following formula 3:

[0091]

[0092] Q loss1 =(a + b*SOC c )·exp(-E a / RT)·t z Formula 2

[0093]

[0094] Among them, Q represents the battery capacity attenuation at the current moment, Q loss1 represents the battery storage capacity loss at the current moment, Q loss2represents the battery cycle capacity loss at the current moment, I represents the battery current value at the current moment; E a represents the apparent activation energy, with the unit of J / mol, T represents the instantaneous temperature of the battery at the current moment, with the unit of K, R represents the molar gas constant, with the unit of J / (mol*K), t represents the duration of battery calibration, a, b, and c are empirical parameters corresponding to the state of charge of the battery, z is the attenuation coefficient; A represents the pre-exponential factor, and DOD represents the depth of discharge of the battery at the current moment. It should be noted that in the above formula 2, t is specifically identified as the storage duration, and its unit can be days. In the above formula 3, t is specifically used to represent the cycle duration, and its unit is hours.

[0095] In specific implementation, the above formulas can be respectively determined based on the test data fitting in the process of obtaining the test data and fitting the above-mentioned capacity attenuation relaxation curve.

[0096] In one embodiment, predicting the first capacity attenuation of the battery at the first moment based on the target life prediction model according to the first state of charge, the first current value, the first depth of discharge, the first actual usage duration, the first instantaneous temperature, the second capacity attenuation, and the capacity attenuation relaxation curve includes: determining a first sub-curve corresponding to the first instantaneous temperature from the capacity attenuation relaxation curve; determining a first estimated extension time corresponding to the battery at the first moment according to the second capacity attenuation and the first sub-curve, where the first estimated extension time is the time corresponding to the second capacity attenuation in the first sub-curve; obtaining a first calibrated usage duration according to the first actual usage duration and the first estimated extension time; in the case where the first current value is zero, obtaining a first storage capacity loss as the first capacity attenuation based on the above formula 2 according to the first state of charge, the first instantaneous temperature, and the first calibrated usage duration; and, in the case where the first current value is not zero, obtaining a first cycle capacity loss as the first capacity attenuation based on the above formula 3 according to the first state of charge, the first depth of discharge, the first instantaneous temperature, and the first calibrated usage duration.

[0097] Specifically, when predicting the life data of the battery at each moment based on the target life prediction model, it is possible to determine whether the current battery is in the storage or cycling state by whether the current value of the current is zero. If the current value at the current moment, that is, the first current value, is zero, then based on the above formula 2, the cyclic capacity loss of the battery is calculated as the capacity attenuation at the current moment through the first instantaneous temperature, the first depth of discharge, and the calibrated usage duration of the battery at the current temperature obtained from the capacity decay curve; if the current value at the current moment, that is, the first current value, is not zero, then based on the above formula 3, the storage capacity loss of the battery is calculated as the capacity attenuation at the current moment through the first instantaneous temperature, the first state of charge, and the calibrated usage duration of the battery at the current temperature obtained from the capacity decay curve.

[0098] It should be noted that in the embodiments of the present disclosure, when determining the storage capacity loss or cyclic capacity loss of the battery based on the above formula 2 or formula 3, the reason for obtaining the estimated extended duration based on the capacity decay curve on the basis of the actual usage duration of the battery and then determining the calibrated usage duration for capacity decay prediction is specifically that, usually in actual working conditions, the temperature of the battery changes instantaneously, and the capacity decay rate corresponding to different temperatures is often different, and the decay rates of the battery during the cycling process and the storage process are also different. Therefore, in this embodiment, when predicting the life of the battery, when predicting the capacity attenuation at the current moment, such as at time t n the capacity attenuation at time t n since the instantaneous temperature T n of the battery at this time can be determined by the above target lumped parameter thermal model, it is possible to determine the instantaneous temperature T n at time t n in the sub-curve n corresponding to the capacity relaxation curve as shown in Figure 2 . In order to ensure that the capacity attenuation at the current moment can be predicted based on the capacity attenuation obtained at time t n at the current instantaneous temperature T n-1 on the basis of the capacity attenuation obtained at time t corresponding to the battery capacity decay rate at the current moment, n therefore, it is possible to fit and obtain the capacity at time t by interpolating the value corresponding to the sub-curve n-1 in the capacity relaxation curve as shown in n-1 at the instantaneous temperature T n-1 at time t Figure 4 into the sub-curve n. in the sub-curve n, n-1 at time t Corresponding to the independent variable time in the sub-curve n - 1, that is, the estimated extended duration corresponding to the abscissa, to correct the actual usage duration of the current battery to obtain the calibrated usage duration, and then by substituting this calibrated usage duration into the above formula 2 or formula 3, the capacity attenuation of the battery at time t can be accurately predicted. n Capacity attenuation

[0099] As can be seen from the above description, in the embodiments of the present disclosure, when predicting battery life data based on the target life prediction model, when predicting battery capacity loss, based on the capacity relaxation curve obtained by fitting the test data, when predicting the life data of the battery at each moment, on the one hand, the prediction accuracy can be improved based on accurate RC parameters and instantaneous temperature; on the other hand, by ensuring that on the basis of the same capacity attenuation, the current estimated extended duration is obtained through the iteration of the capacity relaxation curve to calibrate the actual usage duration of the battery, it is ensured that the battery capacity loss is predicted at the capacity attenuation rate at the current instantaneous temperature, thereby further improving the accuracy of the predicted life data.

[0100] It should be noted that in the above description, the battery as a whole is processed, and the instantaneous temperature of the battery as a whole at each moment is predicted to predict the life data of the battery. However, in the embodiments of the present disclosure, considering that during the use of the battery, the heat exchange situations of different parts of the battery with the ambient temperature and with the cooling system, such as a liquid cooling system, may be different. This makes it possible that when the battery is regarded as a whole for life prediction, due to the existence of this situation, there may still be a certain deviation between the prediction result and the actual result. Therefore, in order to further improve the accuracy of the prediction result, in one embodiment, the life prediction of the battery based on the current relaxation curve, the target equivalent circuit model, the target lumped parameter thermal model, and the target life prediction model can also be: the battery is divided into N sub - battery cells, and based on the target relaxation curve, the target equivalent circuit model, the target lumped parameter thermal model, and the target life prediction model, the N sub - capacity attenuations corresponding to the N sub - battery cells at the current first moment are predicted respectively, where N is a positive integer greater than 0; according to the N sub - capacity attenuations, the first life data of the battery at the first moment is predicted.

[0101] That is, in order to eliminate the problem of inaccurate prediction results caused by the possible difference in the heat exchange amounts of different parts of the battery with the environment and the cooling system, in specific implementation, the battery can be divided into N sub - battery cells, and by calculating the sub - capacity attenuation values of each sub - battery cell respectively, the accuracy of the predicted battery life data can be improved.

[0102] Specifically, when predicting the life data of a battery, the battery can be divided into N blocks from top to bottom. By equally dividing the real-time heat generation power of the battery into N parts, the sub-real-time heat generation power corresponding to each sub-cell can be obtained. Then, through the first sub-equivalent convective heat transfer coefficient and the second sub-equivalent convective heat transfer coefficient obtained by pre-fitting and corresponding to each sub-cell respectively, the sub-instantaneous temperature corresponding to each sub-cell at the current moment can be calculated.

[0103] In addition, based on the same processing idea, parameters such as the sub-state of charge and sub-depth of discharge corresponding to each sub-cell at the current moment can also be obtained.

[0104] After that, the sub-capacity attenuation values of each sub-cell can be predicted respectively based on the above target life prediction model. Then, by adding the N predicted sub-capacity attenuation values, the total capacity attenuation value of the battery at the current moment can be obtained. Based on the nominal capacity of the battery and this total capacity attenuation value, the life data of the battery at the current moment can be accurately predicted.

[0105] In summary, the method provided by the embodiments of the present disclosure obtains the target relaxation curve corresponding to the battery to be predicted, and obtains the target equivalent circuit model and the target lumped parameter thermal model corresponding to the battery. Then, based on the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, the instantaneous temperature of the battery at each moment and the corresponding RC parameters can be determined. Thus, in a dynamic ambient temperature, based on the determined instantaneous temperature and RC parameters, the state of charge, depth of discharge, and other data of the battery can be accurately evaluated. Through these data and based on the target life prediction model, the life of the battery can be predicted, and the life data of the battery can be predicted efficiently and accurately.

[0106] <Device Embodiment>

[0107] Corresponding to the above method embodiment, in this embodiment, an electronic device is further provided. Please refer to Figure 5 which is a schematic structural diagram of an electronic device provided by the embodiments of the present disclosure.

[0108] As Figure 5 shown, the electronic device 5000 may include a processor 500 and a memory 5100. The memory 5100 is used to store executable instructions. The processor 5200 is used to control the operation of the electronic device according to the instructions to execute the method according to any embodiment of the present disclosure.

[0109] <Medium Embodiment>

[0110] Corresponding to the above method embodiments, this embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any method embodiment of the present disclosure is implemented.

[0111] One embodiment or multiple embodiments of this specification may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium, on which computer-readable program instructions for causing a processor to implement various aspects of this specification are loaded.

[0112] The computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structures in a groove storing instructions thereon, and any suitable combination of the above. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (for example, an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0113] The computer-readable program instructions described herein may be downloaded from the computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. The network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0114] The computer program instructions for performing the operations of the embodiments of this specification may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages. The programming languages include object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of this specification.

[0115] Aspects of this specification are described herein with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer - readable program instructions.

[0116] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine such that the instructions, when executed by the processor of the computer or other programmable data - processing apparatus, create a means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner. Thus, the computer - readable medium storing the instructions includes a manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0117] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0118] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present specification. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions. As is well known to those skilled in the art, implementation by hardware, implementation by software, and implementation by a combination of software and hardware are equivalent.

[0119] The embodiments of the present specification have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the market, or to enable other ordinary skilled persons in the technical field to understand the embodiments disclosed herein. The scope of the present application is defined by the appended claims.

Claims

1. A method for predicting the lifespan of a lithium-ion battery, characterized in that, Including: Obtain a target relaxation curve corresponding to the battery to be predicted, where the target relaxation curve includes a curve reflecting the change of the current value or electric power value of the battery over time during operation; Obtain a target equivalent circuit model and a target lumped parameter thermal model corresponding to the battery, where the target lumped parameter thermal model is used to determine the instantaneous temperature of the battery at the next moment according to the real-time heat generation power of the battery at the current moment, and the real-time heat generation power of the battery at the current moment is determined according to the current value of the battery at the current moment and the RC parameters of the target equivalent circuit model at the current moment; Based on the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, perform life prediction on the battery based on a target life prediction model, where the target life prediction model performs the life prediction at least with the instantaneous temperature of the battery as a parameter.

2. The method according to claim 1, wherein The performing life prediction on the battery based on the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model, and based on the target life prediction model includes: According to the target relaxation curve, obtain a first current value of the battery at a current first moment; According to the first current value and first RC parameters, and based on the target equivalent circuit model, determine the first real-time heat generation power, first state of charge, first depth of discharge, and first actual usage duration of the battery at the first moment, where the first RC parameters are determined according to the second instantaneous temperature calculated by the target lumped parameter thermal model at a second moment, and the second moment is earlier than the first moment; Based on the first real-time heat generation power and a first ambient temperature of the environment at the current moment, calculate the first instantaneous temperature of the battery at the first moment based on the target lumped parameter thermal model; Obtain a capacity attenuation relaxation curve corresponding to the battery, and obtain a second capacity attenuation amount of the battery at the second moment obtained based on the target life prediction model, where the capacity attenuation relaxation curve includes a plurality of sub-curves, and each sub-curve is a curve reflecting the change of the capacity attenuation amount of the battery over time at a corresponding temperature; Based on the first state of charge, first current value, first depth of discharge, first actual usage duration, first instantaneous temperature, the second capacity attenuation amount, and the capacity attenuation relaxation curve, and based on the target life prediction model, predict the first capacity attenuation amount of the battery at the first moment to predict the first life data of the battery at the first moment.

3. The method according to claim 2, characterized in that, The target life prediction model includes a battery storage capacity loss prediction sub-model and a battery cycle capacity loss prediction sub-model. The function mapping relationship of the target life prediction model is expressed as Formula 1 below, the function mapping relationship of the battery storage capacity loss prediction sub-model is expressed as Formula 2 below, and the function mapping relationship of the battery cycle capacity loss prediction sub-model is expressed as Formula 3 below; Among them, the formula 1 is as follows: Q represents the battery capacity attenuation at the current moment, Q loss1 represents the battery storage capacity loss at the current moment, Q loss2 represents the battery cycle capacity loss at the current moment, and I represents the battery current value at the current moment; The formula 2 is: Q loss1 =(a + b * SOC c )·exp(-E a / RT)·t z , where E a represents the apparent activation energy, T represents the instantaneous temperature of the battery at the current moment, R represents the molar gas constant, t represents the duration of battery calibration, a, b, and c are empirical parameters corresponding to the state of charge of the battery, and z is the attenuation coefficient; The formula 3 is as follows: A represents the pre-factor, DOD represents the depth of discharge of the battery at the current moment, and t represents the duration of battery calibration use.

4. The method according to claim 3, wherein Predicting a first capacity attenuation of the battery at the first moment based on the target life prediction model according to the first state of charge, the first current value, the first depth of discharge, the first actual usage duration, the first instantaneous temperature, the second capacity attenuation amount, and the capacity attenuation relaxation curve includes: Determining a first sub-curve corresponding to the first instantaneous temperature from the capacity attenuation relaxation curve; Determining a first estimated extended time corresponding to the battery at the first moment according to the second capacity attenuation amount and the first sub-curve, where the first estimated extended time is the time corresponding to the second capacity attenuation amount in the first sub-curve; Obtaining a first calibrated usage duration according to the first actual usage duration and the first estimated extended time; When the first current value is zero, obtaining a first storage capacity loss as the first capacity attenuation amount based on the formula 2 according to the first state of charge, the first instantaneous temperature, and the first calibrated usage duration; When the first current value is not zero, obtaining a first cycle capacity loss as the first capacity attenuation amount based on the formula 3 according to the first state of charge, the first depth of discharge, the first instantaneous temperature, and the first calibrated usage duration.

5. The method according to claim 2, characterized in that, Calculating the first instantaneous temperature of the battery at the first moment based on the target lumped parameter thermal model according to the first real-time heat generation power and the first ambient temperature of the environment where the current moment is located includes: Obtaining a first cooling temperature of the cooling system where the battery is located at the current moment; Calculating a first heat exchange amount between the battery and the environment where it is located, and a second heat exchange amount with the cooling system according to the first ambient temperature and the first cooling temperature through the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient obtained in advance and corresponding to the battery, where the first equivalent convective heat transfer coefficient is the heat transfer coefficient between the battery and the environment where it is located, and the second equivalent convective heat transfer coefficient is the heat transfer coefficient between the battery and the cooling system; Obtaining the first instantaneous temperature according to the first real-time heat generation power, the first heat exchange amount, and the second heat exchange amount.

6. The method according to claim 5, wherein The first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient are obtained through the following steps: Obtaining a first reference curve and a second reference curve, where the first reference curve is a curve reflecting the change of the highest temperature of the battery with time under the condition that the cooling system is turned on, and the second reference curve is a curve reflecting the change of the lowest temperature of the battery with time under the condition that the cooling system is turned on; Taking the first reference curve and the second reference curve as references, obtaining the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient according to the mass, specific heat capacity, and temperature change rate of the battery.

7. The method according to claim 1, wherein Predicting the life of the battery based on the target life prediction model according to the target relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model includes: Divide the battery into N sub - battery cells, and based on the target pool relaxation curve, the target equivalent circuit model, the target lumped - parameter thermal model, and the target life prediction model, predict and obtain N sub - capacity attenuation amounts corresponding to the N sub - battery cells at the current first moment respectively, where N is a positive integer greater than 0; Predict the first life data of the battery at the first moment according to the N sub - capacity attenuation amounts.

8. The method according to claim 2, characterized in that, The first RC parameter is determined by the following steps: Obtain the second instantaneous temperature calculated by the target lumped - parameter thermal model at the second moment; Obtain the first RC parameter according to the second instantaneous temperature and the preset mapping data, where the preset mapping data is data reflecting the corresponding relationship between the battery temperature and the RC parameter, and the preset mapping data is obtained by pre - performing parameter identification processing on the target equivalent circuit model.

9. The method according to claim 2, wherein The capacity attenuation relaxation curve is obtained by the following steps: Perform storage tests and charge - discharge cycle tests on the battery at different temperatures and different states of charge respectively, and stop the tests until the health state of the battery reaches the preset health state. Fit the test data obtained during the test process to obtain the capacity attenuation relaxation curve.

10. An electronic device, characterized in that, Comprising: A memory for storing executable instructions; A processor for operating the electronic device to execute the method according to any one of claims 1 - 9 under the control of the instructions.

11. A computer-readable storage medium, characterized in that, A computer program is stored on the computer - readable storage medium, and when the computer program is executed by the processor, the method according to any one of claims 1 - 9 is implemented.

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