CN116359771A - Lithium ion battery life prediction method, electronic equipment and readable storage medium - Google Patents
Lithium ion battery life prediction method, electronic equipment and readable storage medium Download PDFInfo
- Publication number
- CN116359771A CN116359771A CN202111632630.7A CN202111632630A CN116359771A CN 116359771 A CN116359771 A CN 116359771A CN 202111632630 A CN202111632630 A CN 202111632630A CN 116359771 A CN116359771 A CN 116359771A
- Authority
- CN
- China
- Prior art keywords
- battery
- target
- moment
- model
- curve
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The embodiment of the application provides a lithium ion battery life prediction method, electronic equipment and a readable storage medium, wherein the method comprises the following steps: obtaining a target pool relaxation curve corresponding to a battery to be predicted; acquiring a target equivalent circuit model and a target lumped parameter thermal model corresponding to the battery, wherein the target lumped parameter thermal model is used for determining 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 parameter of the target equivalent circuit model at the current moment; and predicting the service life of the battery based on a target service life prediction model according to the current Chi Yu curve, the target equivalent circuit model and the target lumped parameter thermal model, wherein the target service life prediction model at least takes the instantaneous temperature of the battery as a parameter to perform service life prediction.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of lithium ion batteries, and more particularly relates to a lithium ion battery life prediction method, electronic equipment and a computer readable storage medium.
Background
With the strong demand of renewable energy solutions, lithium ion batteries are becoming more and more widely used, which also makes life prediction of lithium ion batteries increasingly important. The current life prediction method for lithium ion batteries generally predicts the life of the lithium ion batteries only for the storage capacity loss of the batteries, or considers the cycle capacity loss of the batteries, but the two methods lack effective coupling modes, so that the predicted life data of the batteries may not be accurate enough.
Disclosure of Invention
It is an object of the present disclosure to provide a new solution for predicting the life of a lithium ion battery to improve the accuracy of the predicted battery life data.
According to a first aspect of the present disclosure, there is provided an embodiment of a lithium ion battery life prediction method, including:
obtaining a target pool relaxation curve corresponding to a battery to be predicted, wherein the target pool relaxation curve comprises a curve reflecting the change of a current value or an electric power value of the battery along with time in the running process;
acquiring a target equivalent circuit model and a target lumped parameter thermal model corresponding to the battery, wherein the target lumped parameter thermal model is used for determining 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 parameter of the target equivalent circuit model at the current moment;
And predicting the service life of the battery according to the target pool relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model and based on a target service life prediction model, wherein the target service life prediction model at least takes the instantaneous temperature of the battery as a parameter to perform service life prediction.
Optionally, the predicting the life of the battery according to the target pool relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model and based on a target life prediction model includes:
obtaining a first current value of the battery at a current first moment according to the target pool relaxation curve;
determining a first real-time heat generation power, a first state of charge, a first depth of discharge and a first actual use duration of the battery at the first moment based on the target equivalent circuit model and according to the first current value and a first RC parameter, wherein the first RC parameter is determined according to a 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;
calculating a 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 a first environmental temperature of the environment at the current moment;
Acquiring a capacity attenuation curve corresponding to the battery, and acquiring a second capacity attenuation amount of the battery at the second moment, which is obtained based on the target life prediction model, wherein the capacity attenuation pool relaxation curve comprises a plurality of 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;
and predicting a first capacity attenuation amount of the battery at the first moment based on the target life prediction model according to the first charge state, the first current value, the first depth of discharge, the first actual use duration, the first instantaneous temperature, the second capacity attenuation amount and the capacity attenuation pool relaxation curve so as to predict first life data of the battery at the first moment.
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;
Wherein, the formula 1 is:
q represents the capacity attenuation of the battery at the current moment，Q loss1 Representing the current battery storage capacity loss, Q loss2 The battery circulation capacity loss at the current moment is represented, and I is the battery current value at the current moment;
the formula 2 is: q (Q) loss1 ＝(a+b*SOC c )·exp(-E a /RT)·t z ，E a The method comprises the steps of expressing apparent activation energy, T expressing instantaneous temperature of a battery at the current moment, R expressing molar gas constant, T expressing battery calibration using time, a, b and c being experience parameters corresponding to the charge state of the battery, and z being attenuation coefficients;
the formula 3 is:
a represents a pre-factor, DOD represents the discharge depth of the battery at the current moment, and t represents the battery calibration use duration.
Optionally, the predicting, based on the target lifetime prediction model, the first capacity reduction of the battery at the first moment according to the first state of charge, the first current value, the first depth of discharge, the first actual usage period, the first instantaneous temperature, the second capacity reduction, and the capacity reduction pool relaxation curve includes:
determining a first sub-curve corresponding to the first instantaneous temperature from the capacity-fading-pool relaxation curve;
determining a first estimated extension time corresponding to the battery at the first moment according to the second capacity attenuation amount and the first sub-curve, wherein the first estimated extension time is the time corresponding to the second capacity attenuation amount in the first sub-curve;
Obtaining a first calibration use duration according to the first actual use duration and the first estimated extension time;
under the condition that the first current value is zero, according to the first charge state, the first instantaneous temperature and the first calibration use duration, obtaining a first storage capacity loss as the first capacity attenuation amount based on the formula 2;
and under the condition that the first current value is not zero, according to the first charge state, the first depth of discharge, the first instantaneous temperature and the first calibration use duration, obtaining a first circulation capacity loss as the first capacity attenuation based on the formula 3.
Optionally, the calculating, according to the first real-time generated heat power and the first environmental temperature of the environment where the current moment is located, the first instantaneous temperature of the battery at the first moment based on the target lumped parameter thermal model includes:
acquiring a first cooling temperature of a cooling system where the battery is located at the current moment;
calculating a first heat exchange amount of the battery and the environment and a second heat exchange amount of the cooling system according to the first environment temperature and the first cooling temperature through a first equivalent heat convection coefficient and a second equivalent heat convection coefficient which are obtained in advance and correspond to the battery, wherein the first equivalent heat convection coefficient is a heat exchange coefficient of the battery and the environment, and the second equivalent heat convection coefficient is a heat exchange coefficient of the battery and the cooling system;
And 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.
Optionally, the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient are obtained by:
acquiring a first reference curve and a second reference curve, wherein the first reference curve is a curve reflecting the change of the highest temperature of the battery with time under the starting working condition of a cooling system, and the second reference curve is a curve reflecting the change of the lowest temperature of the battery with time under the starting working condition of the cooling system;
and taking the first reference curve and the second reference curve as references, and obtaining the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient according to the mass, the specific heat capacity and the temperature change rate of the battery.
Optionally, the predicting the life of the battery according to the target pool relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model and based on a target life prediction model includes:
dividing the battery into N sub-battery cells, and predicting and obtaining N sub-capacity attenuation amounts respectively corresponding to the N sub-battery cells at the current first moment according to the target pool relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model and based on the target life prediction model, wherein N is a positive integer greater than 0;
And predicting first life data of the battery at the first moment according to the N sub-capacity attenuation amounts.
Optionally, the first RC parameter is determined by:
acquiring a second instantaneous temperature calculated by the target lumped parameter thermal model at a second moment;
and obtaining the first RC parameter according to the second instantaneous temperature and preset mapping data, wherein the preset mapping data is data reflecting the corresponding relation between the battery temperature and the RC parameter, and the preset mapping data is obtained by carrying out parameter identification processing on the target equivalent circuit model in advance.
Optionally, the capacity-fading-pool relaxation curve is obtained by:
and respectively carrying out storage test and charge-discharge cycle test on the battery at different temperatures and different charge states, stopping the test until the health state of the battery reaches a preset health state, and fitting test data obtained in the test process to obtain the capacity attenuation relaxation curve.
According to a second aspect of the present disclosure, there is provided an embodiment of an electronic device, comprising:
a memory for storing executable instructions;
a processor for executing the method according to the first aspect of the present specification according to the control of the instruction.
According to a third aspect of the present disclosure, there is provided an embodiment of a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in the first aspect of the present description.
According to the embodiment of the disclosure, the target relaxation curve corresponding to the battery to be predicted is obtained, the target equivalent circuit model and the target lumped parameter thermal model corresponding to the battery are obtained, then the instantaneous temperature of the battery at each moment and the corresponding RC parameters can be determined based on the target relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model, and accordingly the state of charge, the depth of discharge and other data of the battery can be accurately estimated based on the determined instantaneous temperature and the RC parameters in the dynamic environment temperature, and life data of the battery can be efficiently and accurately predicted and obtained by predicting the life of the battery based on the target life prediction model.
Other features of the present specification and its advantages will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description, serve to explain the principles of the specification.
Fig. 1 is a schematic flow chart of a method for predicting life of a lithium ion battery according to an embodiment of the disclosure.
Fig. 2 is a schematic diagram of a capacity fade relaxation curve provided by an embodiment of the present disclosure.
Fig. 3 is a schematic diagram of a target equivalent circuit model provided by an embodiment of the present disclosure.
FIG. 4 is a logic diagram of coupling multiple models to predict battery life data according to an embodiment of the present disclosure.
Fig. 5 is a schematic hardware structure of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
< method example >
In the related art, when predicting battery life data, battery life data is usually predicted only by predicting battery storage capacity loss, for example, the battery life data can be predicted by establishing a lithium ion battery thermal-electrochemical coupling model and calibrating model parameters by using battery rest experiments under different working conditions, determining the correlation between side reaction and temperature, and finally predicting battery life data by substituting ambient temperature to predict battery storage capacity loss.
However, on the one hand, in the use process of the lithium ion battery, besides the storage capacity loss, there is usually a circulation capacity loss, especially on the operation vehicles such as taxis and buses, the circulation capacity loss of the battery is not negligible, so the existing method for predicting the service life data of the battery based on the prediction of the storage capacity loss of the battery is usually inaccurate; on the other hand, during the use of the battery, heat may be generated and exchanged with the ambient temperature and the cooling system, so that the prediction result may deviate from the actual result by a method of predicting the battery life data based on the ambient temperature.
To solve the above-mentioned problems, the embodiments of the present disclosure provide a method for effectively coupling an equivalent circuit model, a thermal model and a life prediction model of a battery to perform life prediction, please refer to fig. 1, which is a schematic flow chart of a lithium ion battery life prediction method provided by the embodiments of the present disclosure, and the method may be implemented in an electronic device, for example, may be implemented in an electronic device running a battery management system, or may also be applied in a simulation platform, for example, a life prediction simulation platform built based on Matlab software or Simulink software, which is not limited herein.
As shown in fig. 1, the method of the present embodiment may include the following steps S1100-S1300, which are described in detail below.
Step S1100, a target cell relaxation curve corresponding to a battery to be predicted is obtained, wherein the target cell relaxation curve comprises a curve reflecting the change of a current value or an electric power value of the battery along with time in the running process.
The target relaxation curve can be a curve reflecting the current value or the change of the electric power value of the battery with time in the actual working condition.
Step S1200, 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 an instantaneous temperature of the battery at a next moment according to a real-time heat generating power of the battery at a current moment, and the real-time heat generating power of the battery at the current moment is determined according to a current value of the battery at the current moment and an RC parameter of the target equivalent circuit model at the current moment.
Hereinafter, a brief description will be given of the target equivalent circuit model and how to recognize RC parameters of the target equivalent circuit model.
Please refer to fig. 2, which is a schematic diagram of a target equivalent circuit model provided by an embodiment of the present disclosure. In particular, in embodiments of the present disclosure, the target equivalent circuit model may be a second order equivalent circuit model, in which, as shown in figure 2,u represents terminal voltage, and the unit is V; i represents current, and the unit is A; u (U) ocv The open circuit voltage is represented by V; r is R 0 、R 1 、R 2 Ohmic internal resistance and two polarized internal resistances related to the state of charge, namely SOC, temperature and current, are respectively expressed in omega; c (C) 1 And C 2 Representing the polarization capacitance in F.
Wherein, in the target equivalent circuit model, for example, the second-order equivalent circuit model shown in FIG. 2, R flows through 1 Is the current i of (2) R1 Voltage U 1 And flow through R 2 Is the current i of (2) R2 Voltage U 2 According to kirchhoff's law, the relationship between them can be expressed based on the following formula:
U＝U ocv -U 1 -U 2 -i*R 0
in particular implementations, RC parameters in the target equivalent circuit model, e.g., R as shown in FIG. 2 1 、R 2 、C 1 And C 2 The voltage and current data can be obtained through pulse charge and discharge testing of mixed power pulse characteristics (HPPC, hybrid PulsePower Characteristic) of different SOCs (0% SOC-100% SOCs, 10% SOCs at intervals) and different multiplying powers (0.2C charge and discharge, 0.5C charge and discharge, 1C charge and discharge and 2C charge and discharge) of the battery at-30 ℃, 15 ℃, 0 ℃, 15 ℃, 30 ℃, 45 ℃ and 60 ℃, and then the RC parameters of the battery corresponding to the temperature, the SOCs and the multiplying power can be obtained through identification of preset parameter identification algorithms such as genetic algorithm, particle swarm algorithm and the like.
The detailed description is given above on the objective equivalent circuit model and how to identify the RC parameters thereof, and the thermal model for predicting the instantaneous temperature of the battery, that is, the objective lumped parameter thermal model, provided by the embodiments of the present disclosure is described below.
Specifically, the target lumped parameter thermal model may be a one-dimensional simplified thermal model, and in the embodiment of the disclosure, the thermal model of the battery, that is, the real-time thermal power of the battery, may be expressed by the following formula, without considering the radiation and heat dissipation of the battery, and setting the thermophysical parameter to be a constant value at the same time:
wherein Q represents the real-time heat generation power of the battery, and the unit is W; r is R 0 、R 1 、R 2 For identifying the corresponding internal resistance of the battery in the obtained target equivalent circuit model, the unit is omega; i, i R1 And i R2 The unit is A for determining the currents flowing through different resistors based on RC parameters obtained through identification based on kirchhoff law;
the unit is V/K, T is the temperature of the battery cell, and the unit is K.
The target lumped parameter thermal model may be expressed as:
wherein m represents the mass of the battery in kg; t represents the temperature of the battery, and the unit is K; c (C) p The specific heat capacity of the battery is expressed in J/K; h is a 1 And h 2 The first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient of the battery, the environment and the cooling system are respectively represented, and the unit is W/(m 2. Times.K); a is that 1 And A 2 Representing the contact area of the battery with the environment and the cooling system, respectively, with the unit being m 2 ；T cooling The temperature of the coolant is expressed in K.
Indicating the rate of change of the battery temperature with time, h 1 A 1 (T amb T) represents the heat exchange quantity of the battery and the environment, i.e. with the air, h 2 A 2 (T cooling -T) means battery and coolingThe heat exchange capacity of the system.
That is, in the embodiment of the present disclosure, the instantaneous temperature of the battery at each moment may be determined by calculating the real-time heat generating power of the battery at the moment, then the real-time ambient temperature of the environment where the battery is located at the moment and the real-time cooling temperature of the cooling system are obtained, and then the total heat exchange amount of the battery at the moment may be obtained by calculating the heat exchange amounts of the battery, the environment where the battery is located and the cooling system respectively through the first equivalent convective heat exchange coefficient and the second equivalent convective heat exchange system which are obtained in advance and correspond to the battery; and subtracting the total heat exchange amount from the total heat generation power at the current moment to obtain the accurate instantaneous temperature of the battery.
In specific implementation, the first equivalent convective heat transfer coefficient h corresponding to the battery to be predicted 1 And a second equivalent convective heat transfer coefficient h 2 The method can be obtained by the following steps: acquiring a first reference curve and a second reference curve, wherein the first reference curve is a curve reflecting the change of the highest temperature of the battery with time under the starting working condition of the cooling system, and the second reference curve is a curve reflecting the change of the lowest temperature of the battery with time under the starting working condition of the cooling system; and taking the first reference curve and the second reference curve as references, and obtaining the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient according to the mass, the specific heat capacity and the temperature change rate of the battery.
Specifically, curves reflecting the change of the highest temperature and the lowest temperature of the battery along with time under the starting working condition of a cooling system can be respectively obtained through experimental tests or three-dimensional thermal simulation to serve as a first reference curve and a second reference curve, and then the first equivalent convective heat transfer coefficient h is adjusted according to the target lumped parameter thermal model 1 And a second equivalent convective heat transfer coefficient h 2 The one-dimensional simulation result is matched with the reference curve, so that h is calibrated 1 And h 2 。
And step S1300, carrying out life prediction on the battery based on a target life prediction model according to the current Chi Yu curve, the target equivalent circuit model and the target lumped parameter thermal model, wherein the target life prediction model at least takes the instantaneous temperature of the battery as a parameter to carry out life prediction.
And (3) after the target equivalent circuit model, the target lumped parameter thermal model and the target relaxation curve of the battery under the actual working condition are obtained based on the step (S1100) and the step (S1300), the service life of the battery can be predicted based on the target service life prediction model.
Referring to fig. 3, a logic diagram of coupling multiple models to predict battery life data according to an embodiment of the present disclosure is shown. As shown in fig. 3, the method provided by the embodiment of the present disclosure establishes the coupling relationship between the thermal model, i.e., the target lumped parameter thermal model and the target equivalent circuit model and the target life prediction model based on the instantaneous temperature of the battery calculated by the thermal model, specifically, at the initial time, the RC parameter of the target equivalent circuit model may be determined according to the initial temperature, i.e., the ambient temperature, to calculate the initial battery real-time generated heat power, and then the real-time generated heat power is provided to the target lumped parameter thermal model, and then the first equivalent convective heat transfer coefficient h corresponding to the battery determined in the above step S1200 is determined according to the real-time generated heat power 1 And a second equivalent convective heat transfer coefficient h 2 The instantaneous temperature of the battery can be calculated and determined by acquiring the first ambient temperature of the battery at the current moment and the first cooling temperature of the cooling system, the instantaneous temperature is provided to the target equivalent circuit model, the RC parameter of the battery at the next moment can be accurately confirmed based on the instantaneous temperature, and further the instantaneous temperature of the battery at the next moment is accurately calculated according to the real-time heat generation power of the next moment obtained by the RC parameter, so that iteration of the target equivalent circuit model and the target lumped parameter thermal model can be realized; when the target life prediction model predicts the life data of the battery at each moment, the storage capacity loss or the cycle capacity loss of the battery is calculated based on the battery SOC, the depth of discharge (DOD, depth Of Discharge) and the instantaneous temperature determined based on the target lumped parameter thermal model which are estimated based on the RC parameter of the target equivalent circuit model at the current moment, so that the accuracy of the parameter for life prediction at each moment can be improved through the repeated iteration, and the final improvement is further improvedAccuracy of the results.
Specifically, as shown in fig. 3, in one embodiment, the predicting the life of the battery based on the target life prediction model according to the current Chi Yu 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 a target pool relaxation curve; determining a first real-time heat generation power, a first state of charge, a first depth of discharge and a first actual use duration of the battery at a first moment based on a target equivalent circuit model according to a first current value and a first RC parameter, wherein the first RC parameter is determined according to a 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; according to the first real-time heat generation power and the first environmental temperature of the environment where the current moment is located, calculating the first instantaneous temperature of the battery at the first moment based on a target lumped parameter thermal model; acquiring a capacity attenuation curve corresponding to the battery, and acquiring a second capacity attenuation amount of the battery at a second moment, which is obtained based on a target life prediction model, wherein the capacity attenuation pool relaxation curve comprises a plurality of 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; and predicting the first capacity attenuation of the battery at the first moment based on the target life prediction model according to the first charge state, the first current value, the first depth of discharge, the first actual use duration, the first instantaneous temperature, the second capacity attenuation and the capacity attenuation pool relaxation curve so as to predict the first life data of the battery at the first moment.
As shown in fig. 4, the capacity-fading relaxation curve may be a curve composed of a plurality of sub-curves reflecting the change with time of the capacity fading amounts of the battery at different temperatures. In specific implementation, the capacity attenuation relaxation curve can be obtained by performing storage test and charge-discharge cycle test on the battery to be predicted at different temperatures and different SOCs respectively, and fitting test data obtained according to the test.
That is, in one embodiment, the capacity-fading pool relaxation curve may be obtained by: and respectively carrying out storage test and charge-discharge cycle test on the battery at different temperatures and different charge states, stopping the test until the health state of the battery reaches a preset health state, and fitting test data obtained in the test process to obtain the capacity attenuation relaxation curve.
For example, the test can be stopped by performing storage tests of 100% soc, 80%, 60%, 40%, 20% soc at 25 ℃, 35 ℃, 45 ℃ and 60 ℃ until the battery state of health, i.e. SOH reaches 80%; and, it is also possible to stop the test by performing charge-discharge cycle tests of different depths of discharge of 0.5C-0.5C at 25 ℃, 35 ℃, 45 ℃ and 60% until SOH of the battery reaches 80%; the capacity-decay relaxation curve is constructed by fitting test data obtained during the test.
Specifically, after the first capacity reduction amount of the battery at the first time is obtained, the first life data of the battery at the first time can be obtained by subtracting the first capacity reduction amount from the nominal capacity. For example, if the first capacity fade at the first time is 10% and the nominal capacity of the battery is typically 100%, the first life data for the battery is 90% by 100% -10%.
From the above description, the first RC parameter of the battery at the current first moment may be determined by: acquiring a second instantaneous temperature calculated by the target lumped parameter thermal model at a second moment; and obtaining a first RC parameter according to the second instantaneous temperature and preset mapping data, wherein the preset mapping data is data reflecting the corresponding relation between the battery temperature and the RC parameter, and is obtained by carrying out parameter identification processing on the target equivalent circuit model in advance.
In addition, after the first RC parameter of the target equivalent battery model at the current moment is obtained, the state of charge of the battery at the current moment, that is, the SOC, may be obtained by an ampere-hour integration method or other methods, details of the detailed processing thereof will not be described herein, in addition, the first depth of discharge of the battery at the first moment may be obtained by calculating a difference between the maximum SOC and the minimum SOC of the battery at the corresponding interval, and the detailed obtaining method thereof will not be described herein because of detailed description in the related art.
Unlike the method of predicting the life of the battery by calculating only the battery storage capacity loss in the related art, in the embodiment 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 represented by the following formula 1, the function mapping relationship of the battery storage capacity loss prediction sub-model is represented by the following formula 2, and the function mapping relationship of the battery cycle capacity loss prediction sub-model is represented by the following formula 3:
Q loss1 ＝(a+b*SOC c )·exp(-E a /RT)·t z equation 2
Wherein Q represents the capacity attenuation of the battery at the current moment, Q loss1 Representing the current battery storage capacity loss, Q loss2 The current time battery circulation capacity loss is represented, and I represents the current value of the current time battery; e (E) a The apparent activation energy is expressed in J/mol, T is expressed as the instantaneous temperature of the battery at the current moment, K is expressed in K, R is expressed as a molar gas constant, J/(mol x K), T is expressed as the battery calibration use time, a, b and c are empirical parameters corresponding to the battery state of charge, and z is an attenuation coefficient; a represents a pre-factor, and DOD represents the depth of discharge of the battery at the current moment. In the above formula 2, t is specifically identified as a storage duration, and may be in units of days, and in the above formula 3, t is specifically used to denote a cycle duration, and is in units of hours.
In particular implementations, the above formulas may be determined based on a fit of the test data during the process of obtaining the test data and fitting the capacity-fading pool relaxation curve, respectively.
In one embodiment, predicting the first capacity fade of the battery at the first time 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 use duration, the first instantaneous temperature, the second capacity fade, and the capacity fade cell relaxation curve comprises: determining a first sub-curve corresponding to a first instantaneous temperature from the capacity-fading-pool relaxation curve; determining a first estimated extension time corresponding to the battery at a first moment according to the second capacity attenuation and the first sub-curve, wherein the first estimated extension time is the time corresponding to the second capacity attenuation in the first sub-curve; obtaining a first calibration use duration according to the first actual use duration and the first estimated extension time; under the condition that the first current value is zero, according to the first charge state, the first instantaneous temperature and the first calibration use duration, obtaining a first storage capacity loss as a first capacity attenuation amount based on the formula 2; and, when the first current value is not zero, obtaining a first cyclic capacity loss as a first capacity attenuation amount based on the above-described formula 3 according to the first state of charge, the first depth of discharge, the first instantaneous temperature, and the first calibration use period.
Specifically, when predicting life data of the battery at each moment based on the target life prediction model, determining whether the current battery is in a storage state or a circulation state according to whether the current value at the current moment is zero, and if the current value at the current moment, namely the first current value is zero, calculating the circulation capacity loss of the battery as the capacity attenuation amount at the current moment according to the formula 2 through the first instantaneous temperature, the first depth of discharge and the battery calibration use time length at the current temperature obtained through the capacity attenuation curve at the current moment; if the current value at the current time, that is, the first current value is not zero, the storage capacity loss of the battery may be calculated as the capacity attenuation amount at the current time based on the above equation 3 by calibrating the battery at the first instantaneous temperature, the first state of charge, and the current temperature obtained by the capacity attenuation curve.
It should be noted that, in the embodiment of the present disclosure, when determining the storage capacity loss or the cycle capacity loss of the battery based on the above formula 2 or formula 3, on the basis of the actual use time length of the battery, the estimated extended time length is obtained based on the capacity fade curve, and the calibration use time length is further determined for performing the capacity fade prediction, specifically, because, in general, in the actual working condition, the temperature of the battery is instantaneously changed, the capacity fade rates corresponding to different temperatures tend to be different, and the fade rates of the battery during the cycle and the storage process are also different. Thus, in this embodiment, when predicting the life of the battery, the battery is predicted to be at the current time, e.g., t n Capacity attenuation amount at time
At the time, since the battery is at t n Instant battery temperature T n Can be determined by the above-mentioned target lumped parameter thermal model, and thus t can be determined n Instant temperature T of time n The corresponding sub-curve n in the capacity relaxation curve as shown in fig. 2, in order to ensure that at the current t n The time of day may be based on at t n-1 Capacity attenuation obtained at time ∈>
Based on the current instantaneous temperature T n The capacity attenuation amount of the current moment is predicted by the corresponding battery capacity attenuation rate>
Therefore, the battery can be used for the battery at t n-1 At instant of time T n-1 In the case of the corresponding sub-curve n-1 in the capacity relaxation curve as shown in figure 4 +.>
By interpolation into the sub-curve n, fitting to obtain t n-1 Time->
The corresponding independent variable time in the sub-curve n-1, namely the estimated extension time corresponding to the abscissa, is used for correcting the actual use time of the current battery to obtain the calibration use time, and the battery t can be accurately estimated by substituting the calibration use time into the formula 2 or the formula 3 n Capacity attenuation of time>
As can be seen from the above description, in the embodiments of the present disclosure, when predicting battery life data based on a target life prediction model, when predicting battery capacity loss, a capacity relaxation curve obtained based on test data fitting is used, and when predicting life data of a battery at each moment, on one hand, accuracy of prediction can be improved based on accurate RC parameters and instantaneous temperature; on the other hand, the method also ensures that the capacity loss of the battery is predicted at the capacity attenuation rate of the current instantaneous temperature by ensuring that the current estimated extension time length is obtained through the iteration of the capacity relaxation curve on the basis of consistent capacity attenuation so as to calibrate the actual use time length of the battery, thereby further improving the accuracy of life data obtained through prediction.
In the above description, the battery is processed to predict the life data of the battery by predicting the instantaneous temperature of the battery at each moment, however, in the embodiment of the disclosure, considering that the battery is in use and different parts of the battery are different from the ambient temperature and the cooling system, for example, the liquid cooling system may be different when performing heat exchange on heat, this may still cause a certain deviation between the predicted result and the actual result when the battery is considered as the whole for life prediction, so in order to further improve the accuracy of the predicted result, in one embodiment, the life prediction of the battery according to the current Chi Yu curve, the target equivalent circuit model and the target lumped parameter thermal model based on the target life prediction model may also be: dividing the battery into N sub-battery cells, and predicting and obtaining N sub-capacity attenuation amounts respectively corresponding to the N sub-battery cells at the current first moment based on the target pool relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model, wherein N is a positive integer greater than 0; and predicting first life data of the battery at a first moment according to the N sub-capacity attenuation amounts.
That is, in order to solve the problem that the prediction result is not accurate enough due to the fact that the heat exchange amounts of different parts of the battery, the environment and the cooling system may be different, in the specific implementation, the battery may be divided into N sub-battery cells, and the accuracy of the predicted battery life data is improved by calculating the sub-capacity attenuation value of each sub-battery cell.
Specifically, when predicting life data of the battery, the battery can be divided into N blocks from top to bottom, and the real-time heat generation power of the battery is divided into N equal parts to obtain the sub-time heat generation power corresponding to each sub-battery cell respectively; and then, calculating to obtain sub-instantaneous temperatures of each sub-cell at the current moment by pre-fitting the obtained first sub-equivalent convective heat transfer coefficient and the second sub-equivalent convective heat transfer coefficient which are respectively corresponding to each sub-cell.
In addition, parameters such as sub-charge states, sub-discharge depths and the like, which respectively correspond to each sub-battery cell at the current moment, can be obtained based on the same processing thought.
Then, the sub-capacity attenuation value of each sub-cell can be respectively predicted based on the target life prediction model; and then, adding the N predicted sub capacity attenuation values to obtain the total capacity attenuation value of the battery at the current moment, and accurately predicting the service life data of the battery at the current moment based on the nominal capacity of the battery and the total capacity attenuation value.
In summary, according to the method provided by the embodiment of the disclosure, by acquiring the target relaxation curve corresponding to the battery to be predicted, and acquiring 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 parameter can be determined, so that in the dynamic environment temperature, based on the determined instantaneous temperature and the RC parameter, the data such as the state of charge and the depth of discharge of the battery can be accurately estimated, and based on the 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 efficiently and accurately predicted.
< device example >
In this embodiment, referring to fig. 5, a schematic structural diagram of an electronic device is provided.
As shown in fig. 5, the electronic device 5000 may include a processor 500 and a memory 5100, the memory 5100 for storing executable instructions; the processor 5200 is configured to operate the electronic device according to control of the instructions to perform the method according to any embodiment of the present disclosure.
Media examples ]
In correspondence with the method embodiments described above, the present embodiment also provides a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the method described in any of the method embodiments of the present disclosure.
One or more embodiments of the present description may be a system, method, and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement aspects of the present description.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of embodiments of the present description may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present description are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer-readable program instructions, which may execute the computer-readable program instructions.
Various aspects of the present description are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
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 description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The embodiments of the present specification have been described above, and the above description is illustrative, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.
Claims (11)
1. A lithium ion battery life prediction method, comprising:
obtaining a target pool relaxation curve corresponding to a battery to be predicted, wherein the target pool relaxation curve comprises a curve reflecting the change of a current value or an electric power value of the battery along with time in the running process;
acquiring a target equivalent circuit model and a target lumped parameter thermal model corresponding to the battery, wherein the target lumped parameter thermal model is used for determining 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 parameter of the target equivalent circuit model at the current moment;
And predicting the service life of the battery according to the target pool relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model and based on a target service life prediction model, wherein the target service life prediction model at least takes the instantaneous temperature of the battery as a parameter to perform service life prediction.
2. The method of claim 1, wherein said predicting the lifetime of the battery based on the target pool relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model and based on a target lifetime prediction model comprises:
obtaining a first current value of the battery at a current first moment according to the target pool relaxation curve;
determining a first real-time heat generation power, a first state of charge, a first depth of discharge and a first actual use duration of the battery at the first moment based on the target equivalent circuit model and according to the first current value and a first RC parameter, wherein the first RC parameter is determined according to a 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;
calculating a 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 a first environmental temperature of the environment at the current moment;
Acquiring a capacity attenuation curve corresponding to the battery, and acquiring a second capacity attenuation amount of the battery at the second moment, which is obtained based on the target life prediction model, wherein the capacity attenuation pool relaxation curve comprises a plurality of 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;
and predicting a first capacity attenuation amount of the battery at the first moment based on the target life prediction model according to the first charge state, the first current value, the first depth of discharge, the first actual use duration, the first instantaneous temperature, the second capacity attenuation amount and the capacity attenuation pool relaxation curve so as to predict first life data of the battery at the first moment.
3. The method according to claim 2, wherein the target life prediction model includes a battery storage capacity loss prediction sub-model and a battery cycle capacity loss prediction sub-model, a function mapping relationship of the target life prediction model is represented by the following formula 1, a function mapping relationship of the battery storage capacity loss prediction sub-model is represented by the following formula 2, and a function mapping relationship of the battery cycle capacity loss prediction sub-model is represented by the following formula 3;
Wherein, the formula 1 is:
q represents the capacity attenuation of the battery at the current moment, Q loss1 Representing the current battery storage capacity loss, Q loss2 The current time battery circulation capacity loss is represented, and I represents the current value of the current time battery;
the formula 2 is: q (Q) loss1 ＝(a+b*SOC c )·exp(-E a /RT)·t z ，E a The method comprises the steps of expressing apparent activation energy, T expressing instantaneous temperature of a battery at the current moment, R expressing molar gas constant, T expressing battery calibration using time, a, b and c being experience parameters corresponding to the charge state of the battery, and z being attenuation coefficients;
the formula 3 is:
a represents a pre-factor, DOD represents the discharge depth of the battery at the current moment, and t represents the battery calibration use duration.
4. The method of claim 3, wherein predicting a first capacity fade of the battery at the first time based on the target life prediction model based on the first state of charge, a first current value, a first depth of discharge, a first length of actual use, a first instantaneous temperature, the second capacity fade, and the capacity fade cell relaxation curve comprises:
determining a first sub-curve corresponding to the first instantaneous temperature from the capacity-fading-pool relaxation curve;
determining a first estimated extension time corresponding to the battery at the first moment according to the second capacity attenuation amount and the first sub-curve, wherein the first estimated extension time is the time corresponding to the second capacity attenuation amount in the first sub-curve;
Obtaining a first calibration use duration according to the first actual use duration and the first estimated extension time;
under the condition that the first current value is zero, according to the first charge state, the first instantaneous temperature and the first calibration use duration, obtaining a first storage capacity loss as the first capacity attenuation amount based on the formula 2;
and under the condition that the first current value is not zero, according to the first charge state, the first depth of discharge, the first instantaneous temperature and the first calibration use duration, obtaining a first circulation capacity loss as the first capacity attenuation based on the formula 3.
5. The method of claim 2, wherein calculating a first instantaneous temperature of the battery at the first time based on the target lumped parameter thermal model from the first real-time generated heat power and a first ambient temperature of an environment in which the current time is located comprises:
acquiring a first cooling temperature of a cooling system where the battery is located at the current moment;
calculating a first heat exchange amount of the battery and the environment and a second heat exchange amount of the cooling system according to the first environment temperature and the first cooling temperature through a first equivalent heat convection coefficient and a second equivalent heat convection coefficient which are obtained in advance and correspond to the battery, wherein the first equivalent heat convection coefficient is a heat exchange coefficient of the battery and the environment, and the second equivalent heat convection coefficient is a heat exchange coefficient of the battery and the cooling system;
And 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 of claim 5, wherein the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient are obtained by:
acquiring a first reference curve and a second reference curve, wherein the first reference curve is a curve reflecting the change of the highest temperature of the battery with time under the starting working condition of a cooling system, and the second reference curve is a curve reflecting the change of the lowest temperature of the battery with time under the starting working condition of the cooling system;
and taking the first reference curve and the second reference curve as references, and obtaining the first equivalent convective heat transfer coefficient and the second equivalent convective heat transfer coefficient according to the mass, the specific heat capacity and the temperature change rate of the battery.
7. The method of claim 1, wherein said predicting the lifetime of the battery based on the target pool relaxation curve, the target equivalent circuit model, and the target lumped parameter thermal model and based on a target lifetime prediction model comprises:
Dividing the battery into N sub-battery cells, and predicting and obtaining N sub-capacity attenuation amounts respectively corresponding to the N sub-battery cells at the current first moment according to the target pool relaxation curve, the target equivalent circuit model and the target lumped parameter thermal model and based on the target life prediction model, wherein N is a positive integer greater than 0;
and predicting first life data of the battery at the first moment according to the N sub-capacity attenuation amounts.
8. The method of claim 2, wherein the first RC parameter is determined by:
acquiring a second instantaneous temperature calculated by the target lumped parameter thermal model at a second moment;
and obtaining the first RC parameter according to the second instantaneous temperature and preset mapping data, wherein the preset mapping data is data reflecting the corresponding relation between the battery temperature and the RC parameter, and the preset mapping data is obtained by carrying out parameter identification processing on the target equivalent circuit model in advance.
9. The method of claim 2, wherein the capacity-fading pool relaxation curve is obtained by:
and respectively carrying out storage test and charge-discharge cycle test on the battery at different temperatures and different charge states, stopping the test until the health state of the battery reaches a preset health state, and fitting test data obtained in the test process to obtain the capacity attenuation relaxation curve.
10. An electronic device, comprising:
a memory for storing executable instructions;
a processor for executing the method according to any of claims 1-9, operating the electronic device according to the control of the instructions.
11. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111632630.7A CN116359771A (en) | 2021-12-28 | 2021-12-28 | Lithium ion battery life prediction method, electronic equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111632630.7A CN116359771A (en) | 2021-12-28 | 2021-12-28 | Lithium ion battery life prediction method, electronic equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116359771A true CN116359771A (en) | 2023-06-30 |
Family
ID=86926137
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111632630.7A Pending CN116359771A (en) | 2021-12-28 | 2021-12-28 | Lithium ion battery life prediction method, electronic equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116359771A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117112971A (en) * | 2023-10-25 | 2023-11-24 | 宁德时代新能源科技股份有限公司 | Temperature curve generation method and device, electronic equipment and storage medium |
| CN117192383A (en) * | 2023-11-06 | 2023-12-08 | 宁德时代新能源科技股份有限公司 | Method, device, equipment and medium for determining service life of battery |
-
2021
- 2021-12-28 CN CN202111632630.7A patent/CN116359771A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117112971A (en) * | 2023-10-25 | 2023-11-24 | 宁德时代新能源科技股份有限公司 | Temperature curve generation method and device, electronic equipment and storage medium |
| CN117112971B (en) * | 2023-10-25 | 2024-03-29 | 宁德时代新能源科技股份有限公司 | Temperature curve generation method and device, electronic equipment and storage medium |
| CN117192383A (en) * | 2023-11-06 | 2023-12-08 | 宁德时代新能源科技股份有限公司 | Method, device, equipment and medium for determining service life of battery |
| CN117192383B (en) * | 2023-11-06 | 2024-03-26 | 宁德时代新能源科技股份有限公司 | Method, device, equipment and medium for determining service life of battery |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ding et al. | An improved Thevenin model of lithium-ion battery with high accuracy for electric vehicles | |
| CN109073710B (en) | System and method for generation and use of electrothermal battery models | |
| Pan et al. | A computational multi-node electro-thermal model for large prismatic lithium-ion batteries | |
| Wang et al. | Model‐based unscented Kalman filter observer design for lithium‐ion battery state of charge estimation | |
| Chen et al. | Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications | |
| JP7255020B2 (en) | battery simulation | |
| US10044212B2 (en) | Modeling a change in battery degradation | |
| CN107690585B (en) | Method and apparatus for determining the state of health and state of charge of a lithium sulfur battery | |
| Song et al. | Current profile optimization for combined state of charge and state of health estimation of lithium ion battery based on Cramer–Rao bound analysis | |
| Kwak et al. | Parameter identification and SOC estimation of a battery under the hysteresis effect | |
| JP2020533772A (en) | Methods and systems for controlling rechargeable batteries | |
| Pang et al. | An enhanced temperature‐dependent model and state‐of‐charge estimation for a Li‐Ion battery using extended Kalman filter | |
| CN116359771A (en) | Lithium ion battery life prediction method, electronic equipment and readable storage medium | |
| GB2556076A (en) | Method | |
| Baveja et al. | Predicting temperature distribution of passively balanced battery module under realistic driving conditions through coupled equivalent circuit method and lumped heat dissipation method | |
| WO2006057468A1 (en) | Method and system for battery state and parameter estimation | |
| Mehta et al. | An improved single-particle model with electrolyte dynamics for high current applications of lithium-ion cells | |
| CN113794254B (en) | Thermal management strategy configuration method and device, computer equipment and storage medium | |
| CN103529400A (en) | Battery capacity forecasting method with self-adaptive temperature compensating function | |
| Xu et al. | Surrogate models for lithium-ion battery heat generation based on orthogonal experiments by eliminating external wire connection effect | |
| Zhuo et al. | Diffusion-aware voltage source: An equivalent circuit network to resolve lithium concentration gradients in active particles | |
| Chen et al. | Modeling and state of charge estimation of lithium-ion battery | |
| Dahmardeh et al. | State-of-charge uncertainty of lithium-ion battery packs considering the cell-to-cell variability | |
| CN116256636A (en) | Method and device for identifying parameters of battery equivalent circuit model and readable storage medium | |
| Wett et al. | Method of Lines for flexible coupling of the Single Particle Model for Lithium-Ion Batteries demonstrated by thermal modelling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |