CN111426952A - Lithium ion battery life prediction method - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 30
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 15
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 15
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Abstract
The invention relates to a method for predicting the service life of a lithium ion battery, which belongs to the technical field of battery service life evaluation. And finally, substituting the actual working condition information of the battery into the second capacity attenuation model so as to obtain the predicted service life of the battery. Compared with the method for simply predicting the storage life or the cycle life in the prior art, the method is more suitable for the actual service condition of the battery, and can quickly predict the actual service life of the battery.
Description
Technical Field
The invention belongs to the technical field of battery life assessment, and particularly relates to a life prediction method of a lithium ion battery.
Background
At present, the technical schemes of battery life prediction are more, but most of the schemes only consider individual or a plurality of related factors to predict the storage life or the cycle life, for example, the chinese patent application with publication number CN107144790A discloses a method for predicting the cycle life of a lithium ion battery, and the method only predicts the life of the battery under a characteristic cycle test condition; chinese patent application publication No. CN106443497A discloses a method for predicting storage life of a lithium ion battery, which only predicts storage life. However, due to the fact that the single storage life prediction method or the single cycle life prediction method is considered to be related in a one-sided manner, the service life of the battery predicted by the storage life prediction method or the cycle life prediction method is inconsistent with the actual application working condition of the power battery, the actual service life of the battery cannot be effectively predicted, and the practical use guiding significance of the power battery is not great.
Disclosure of Invention
The invention aims to provide a method for predicting the service life of a lithium ion battery, which is used for solving the problem that the prior art cannot effectively predict the actual service life of the battery.
In order to solve the technical problem, the invention provides a method for predicting the service life of a lithium ion battery, which comprises the following steps:
1) establishing a first capacity attenuation model representing the storage life of the battery according to the storage temperature of the battery, the set reference temperature, the charge state of the battery and the set reference charge state, acquiring storage experiment data for performing a storage experiment on the battery, wherein the storage experiment data comprises the capacity loss rate of the battery, the corresponding storage temperature, the charge state and the storage time, and solving unknown parameters in the first capacity attenuation model according to the storage experiment data;
2) determining a second capacity attenuation model comprehensively representing the storage life and the cycle life of the battery according to the obtained first capacity attenuation model and the charging and discharging cycle times of the battery within the set time, acquiring cycle experiment data for performing a charging and discharging cycle experiment on the battery, wherein the cycle experiment data comprises the capacity loss rate of the battery, the corresponding charge state and the charging and discharging cycle experiment time, and solving unknown parameters in the second capacity attenuation model according to the cycle experiment data;
3) and substituting the working condition information of the actual use of the battery into the second capacity attenuation model to predict the service life of the battery, wherein the working condition information of the actual use of the battery comprises the storage temperature and the charge state of the actual work of the battery and the charge-discharge cycle number of the battery within the set time.
The method for predicting the service life of the battery comprises the steps of firstly establishing a first capacity attenuation model representing the storage service life of the battery, solving unknown parameters of the first capacity attenuation model according to storage experiment data of a storage experiment, then establishing a second capacity attenuation model according to the determined first capacity attenuation model and the charge-discharge cycle times of the battery within set time, and solving the unknown parameters of the second capacity attenuation model according to the cycle experiment data of the charge-discharge cycle experiment, so that the second capacity attenuation model representing the storage service life and the cycle service life of the battery is finally determined. And finally, substituting the actual working condition information of the battery into the second capacity attenuation model so as to obtain the predicted service life of the battery, and improving the prediction accuracy of the service life of the battery compared with the prior art.
Further, the first capacity fade model is as follows:
Q1(t)＝a*tz
wherein Q1(t) is capacity loss rate, a is intermediate variable, t is storage time, Z is coefficient to be solved related to material chemical system, Ca、CT、CsocThe parameters to be solved are all parameters to be solved, the coefficient to be solved and the parameters to be solved are unknown parameters in the first capacity attenuation model in the step 1), T is the storage temperature of the battery0For a set reference temperature, SOC is the initial state of charge, SOC, of the battery0For the set reference state of charge, △ T, △ SOC are set values.
For solving a parameter C to be solved in a first capacity fading modela、CTAnd a coefficient Z to be solved, wherein the storage experiment in the step 1) comprises the following steps:
setting the state of charge stored by the battery as the reference state of charge, and performing storage experiments at different times at the storage temperature;
and setting the storage temperature of the battery as the reference temperature, and performing a storage experiment under the state of charge stored by the battery.
After determining the first capacity fade model, the second capacity fade model is as follows:
Q2(t)＝eβ*N*Q1(t)
wherein Q2(t) is the capacity loss rate of the second capacity fading model, Q1(t) is the capacity loss rate of the first capacity fading model, N is the number of charge and discharge cycles within the set time, β is the constant coefficient to be obtained, which is the unknown parameter in the second capacity fading model in step 2).
The cycle experimental data are obtained by performing a charge/discharge cycle experiment on the battery at a set charge/discharge current in order to obtain the unknown parameter β in the second capacity fade model.
Before predicting the life of the battery, the method further comprises the following steps: and optimizing parameters in a second capacity attenuation model by combining the second capacity attenuation model according to the actual attenuation data of the battery. After the parameters of the second capacity fading model are optimized, the finally predicted battery life is more accurate.
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Fig. 1 is a flowchart of a method for predicting the lifetime of a lithium ion battery according to the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
In the method for predicting the service life of the lithium ion battery provided in the embodiment, the first capacity fading model is determined through a storage experiment, the determined first capacity fading model is substituted into the second capacity fading model, the unknown parameter of the second capacity fading model is determined through a charge-discharge cycle experiment, the service life of the battery is predicted through the finally obtained second capacity fading model, and the predicted service life of the battery is closer to the actual service life of the battery under the influence of the storage service life and the cycle service life in a comprehensive consideration.
Before the storage experiment and the charge-discharge cycle experiment were performed, the conditions of the storage experiment and the charge-discharge cycle experiment were determined as follows:
the storage temperature of the battery, the battery temperature in the charge-discharge cycle experiment process, the charge current and the discharge current are all within the allowable range of the normal use of the battery, and the relevant experiment data of capacity fading caused by abuse of the battery are not in line with the conditions and cannot be used for solving the first capacity fading model and the second capacity fading model.
The following explains the concrete steps of the lithium ion battery life prediction method:
according to the storage temperature of the battery, the set reference temperature, the state of charge of the battery and the set reference state of charge, establishing a first capacity fading model for representing the storage life of the battery, wherein the first capacity fading model comprises the following steps:
Q1(t)＝a*tz
wherein Q1(t) is capacity loss rate, a is intermediate variable, t is storage time, Z is coefficient to be solved related to material chemical system, Ca、CT、CsocThe parameters to be solved are all parameters to be solved, the coefficients to be solved and the parameters to be solved are unknown parameters in the first capacity attenuation model, T is the storage temperature of the battery, and T is the storage temperature of the battery0For a set reference temperature, SOC is the state of charge of the battery, SOC0For the set reference state of charge, △ T, △ SOC are set values.
Setting T0＝25℃，SOC0100%, △ T10 ℃, △ SOC 10%, the following storage experiments were performed on the batteries:
in the first experiment, the state of charge stored in a battery is set to be 100%, and the experiment is stored at different storage temperatures;
and in the second experiment, the storage temperature of the battery is set to be 25 ℃ of the reference temperature, and the storage experiment is carried out under the charge states stored by different batteries.
According to experiment one, the first capacity fade model is logarithmically deformed as follows:
Ln(Q1(t))＝Lna+Z*Ln(t)
Lna＝Ln(Ca)+(T-T0)/△T*Ln(CT)+(100％-SOC0)/△SOC*Ln(CSOC)
＝Ln(Ca)+(T-25)/10*Ln(CT)+(100％-100％)/10％*Ln(CSOC)
＝Ln(Ca)+(T-25)/10*Ln(CT)
substituting the stored experimental data including the capacity loss rate, the storage time and the corresponding storage temperature of the battery in the first experiment into the formula to solve the parameter C to be solved in the first capacity attenuation modela、CTAnd the coefficient Z to be solved.
According to experiment two, the first capacity fade model is logarithmically deformed as follows:
Lna＝Ln(Ca)+(T-T0)/△T*Ln(CT)+(100％-SOC0)/△SOC*Ln(CSOC)
＝Ln(Ca)+(25-25)/10*Ln(CT)+(SOC-100％)/10％*Ln(CSOC)
＝Ln(Ca)+(SOC-100％)/10％*Ln(CSOC)
substituting the stored experimental data including the capacity loss rate and the storage time of the battery and the corresponding state of charge in the second experiment into the formula to solve the parameter C to be solved in the first capacity attenuation modelSOC。
According to the obtained first capacity fading model and the charge-discharge cycle number of the battery in the set time, determining a second capacity fading model comprehensively representing the storage life and the cycle life of the battery, wherein the second capacity fading model is as follows:
Q2(t)＝eβ*N*Q1(t)
wherein Q2(t) is a capacity loss rate of the second capacity fading model, t in Q2(t) represents aging time including working time (charge-discharge time) and storage time, Q1(t) is a capacity loss rate of the first capacity fading model, N is a number of charge-discharge cycles within the set time, β is a constant coefficient to be obtained, and the constant coefficient is an unknown parameter in the second capacity fading model.
And carrying out a charge-discharge cycle experiment on the battery under the set charge-discharge current, and acquiring cycle experiment data of the charge-discharge cycle experiment on the battery, wherein the cycle experiment data comprises the capacity loss rate of the battery, the corresponding charge state and the time of the charge-discharge cycle experiment.
The second capacity fade model is logarithmically deformed as follows:
Ln(Q2(t))＝Ln(eβ*N)+Ln(a)+Z*Ln(t)
＝β*N+Ln(a)+Z*Ln(t)
in the above equation, a and Z are both obtained by a storage experiment, and the obtained cycle experiment data is substituted into the above equation to obtain the unknown parameter β in the second capacity attenuation model.
Determining a second capacity attenuation model for representing the storage life and the cycle life of the battery, and enabling the condition information of the actual use of the battery with the life to be predicted to comprise N, T, T0、△T、SOC、SOC0And △ SOC into the second capacity fade model to obtain the predicted life of the battery.
The invention can predict the storage life (Q1 (t)) of the battery, also can predict the life (Q2 (t)) of the battery under the actual use condition, and can attenuate various correlation factors (Z, C) of the battery life when establishing a second capacity attenuation model of Q2(t)a、CT、Csoc) The method has the advantages that the method has better prediction accuracy, better accords with the actual service condition of the battery compared with the method for simply predicting the storage life or the cycle life in the prior art, can quickly predict the actual service life of the battery, and avoids the concentrated outbreak of market problems caused by the early arrival of the attenuation degree of the battery because the service life of the battery does not meet the quality guarantee requirement.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art.
For example, in order to improve the accuracy of predicting the battery life, it is further required to optimize the unknown parameters already found in the first capacity fade model and/or the second capacity fade model, and the optimizing step includes:
(1) taking an unknown parameter in the second capacity attenuation model as a parameter to be optimized, since the second capacity attenuation model is obtained according to the first capacity attenuation model, the unknown parameter as the parameter to be optimized also includes the unknown parameter in the first capacity attenuation model, i.e. at β, Z, Ca、CT、CsocOne of the parameters is selected, the actual attenuation data (including capacity loss rate, storage time, storage temperature, reference temperature, initial charge state and reference charge state) of the battery is substituted into the second capacity attenuation model to obtain the parameter value of the parameter to be optimized, and the optimization of an unknown parameter is completed;
(2) and (3) then, selecting the next unknown parameter as a parameter to be optimized, and performing parameter optimization according to the content in the step (1) until all the unknown parameters are optimized.
For another example, the first capacity fading model in this embodiment may also be replaced by a capacity fading model for predicting storage life in the prior art, for example, the following capacity fading model disclosed in chinese patent application with publication number CN 106443497A:
Q1(t)＝α*eβT*(1/T-1/To)*eβSOC*(SOC-SOCo)*tz
wherein Q1(T), T, Z, T0SOC and SOC0The meaning of (c) is the same as that of the corresponding parameter in the first capacity fade model mentioned in the present embodiment, α, βT、βsocAre all parameters to be solved.
For another example, the formula of the second capacity attenuation model in this embodiment may be modified, such as Q2(t) β'. eNQ1(t), β' instead of β is the constant factor to be claimed, therefore, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (6)
1. A method for predicting the service life of a lithium ion battery is characterized by comprising the following steps:
1) establishing a first capacity attenuation model representing the storage life of the battery according to the storage temperature of the battery, the set reference temperature, the charge state of the battery and the set reference charge state, acquiring storage experiment data for performing a storage experiment on the battery, wherein the storage experiment data comprises the capacity loss rate of the battery, the corresponding storage temperature, the charge state and the storage time, and solving unknown parameters in the first capacity attenuation model according to the storage experiment data;
2) determining a second capacity attenuation model comprehensively representing the storage life and the cycle life of the battery according to the obtained first capacity attenuation model and the charging and discharging cycle times of the battery within the set time, acquiring cycle experiment data for performing a charging and discharging cycle experiment on the battery, wherein the cycle experiment data comprises the capacity loss rate of the battery, the corresponding charge state and the charging and discharging cycle experiment time, and solving unknown parameters in the second capacity attenuation model according to the cycle experiment data;
3) and substituting the working condition information of the actual use of the battery into the second capacity attenuation model to predict the service life of the battery, wherein the working condition information of the actual use of the battery comprises the storage temperature and the charge state of the actual work of the battery and the charge-discharge cycle number of the battery within the set time.
2. The method of claim 1, wherein the first capacity fade model is as follows:
Q1(t)＝a*tz
wherein Q1(t) is capacity loss rate, a is intermediate variable, t is storage time, Z is coefficient to be solved related to material chemical system, Ca、CT、CsocThe parameters to be solved are all parameters to be solved, the coefficient to be solved and the parameters to be solved are unknown parameters in the first capacity attenuation model in the step 1), T is the storage temperature of the battery0For a set reference temperature, SOC is electricityState of charge, SOC, of the cell0For the set reference state of charge, △ T, △ SOC are set values.
3. The method for predicting the lifetime of a lithium ion battery according to claim 2, wherein the storage experiment in step 1) comprises:
setting the state of charge stored by the battery as the reference state of charge, and performing storage experiments at different times at the storage temperature;
and setting the storage temperature of the battery as the reference temperature, and performing a storage experiment under the state of charge stored by the battery.
4. The method of predicting the lifetime of a lithium ion battery according to claim 1 or 2, wherein the second capacity fade model is as follows:
Q2(t)＝eβ*N*Q1(t)
wherein Q2(t) is the capacity loss rate of the second capacity fading model, Q1(t) is the capacity loss rate of the first capacity fading model, N is the number of charge and discharge cycles within the set time, β is the constant coefficient to be obtained, which is the unknown parameter in the second capacity fading model in step 2).
5. The method of claim 4, wherein the cycle test data is obtained by performing a charge-discharge cycle test on the battery at a set charge-discharge current.
6. The method of predicting the lifetime of a lithium ion battery according to claim 1, further comprising, before predicting the lifetime of the battery: and optimizing parameters in a second capacity attenuation model by combining the second capacity attenuation model according to the actual attenuation data of the battery.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112014736A (en) * | 2020-08-21 | 2020-12-01 | 中国第一汽车股份有限公司 | Battery life prediction method, device, equipment and storage medium |
| CN112014756A (en) * | 2020-08-11 | 2020-12-01 | 蜂巢能源科技有限公司 | Method and device for determining cycle life of battery, storage medium and electronic equipment |
| CN112364486A (en) * | 2020-10-23 | 2021-02-12 | 昆山宝创新能源科技有限公司 | Method for predicting cycle life of lithium battery and application thereof |
| CN112379297A (en) * | 2020-10-22 | 2021-02-19 | 欣旺达电动汽车电池有限公司 | Battery system service life prediction method, device, equipment and storage medium |
| CN112731164A (en) * | 2020-12-21 | 2021-04-30 | 惠州亿纬锂能股份有限公司 | Battery life evaluation method |
| CN113406522A (en) * | 2021-06-18 | 2021-09-17 | 合肥国轩高科动力能源有限公司 | Method and system for predicting and evaluating service life of lithium battery system of electric vehicle |
| CN114217237A (en) * | 2021-11-05 | 2022-03-22 | 东软睿驰汽车技术（沈阳）有限公司 | Battery health state determination method and device based on storage endurance and electronic equipment |
| CN114444370A (en) * | 2021-10-11 | 2022-05-06 | 崔跃芹 | Method and device for predicting accumulated loss life of rechargeable battery by considering operation conditions, electronic equipment and readable storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106443497A (en) * | 2016-12-09 | 2017-02-22 | 合肥国轩高科动力能源有限公司 | Storage life prediction method of lithium ion battery |
| CN106597305A (en) * | 2016-12-09 | 2017-04-26 | 合肥国轩高科动力能源有限公司 | Method for predicting cycle life of lithium ion battery |
| US20170115358A1 (en) * | 2015-10-27 | 2017-04-27 | Nec Laboratories America, Inc. | Innovative framework combining cycling and calendar aging models |
| CN106772100A (en) * | 2017-02-06 | 2017-05-31 | 联想(北京)有限公司 | A kind of method and device for predicting battery life |
| CN107202960A (en) * | 2017-05-25 | 2017-09-26 | 安徽江淮汽车集团股份有限公司 | Electrokinetic cell life-span prediction method |
| CN107271922A (en) * | 2017-08-02 | 2017-10-20 | 合肥国轩高科动力能源有限公司 | Method for predicting service life of whole new energy automobile battery system |
| US20180059192A1 (en) * | 2015-08-21 | 2018-03-01 | Lg Chem, Ltd. | Apparatus and method for estimating degree of aging of secondary battery |
-
2019
- 2019-01-10 CN CN201910023616.3A patent/CN111426952A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180059192A1 (en) * | 2015-08-21 | 2018-03-01 | Lg Chem, Ltd. | Apparatus and method for estimating degree of aging of secondary battery |
| US20170115358A1 (en) * | 2015-10-27 | 2017-04-27 | Nec Laboratories America, Inc. | Innovative framework combining cycling and calendar aging models |
| CN106443497A (en) * | 2016-12-09 | 2017-02-22 | 合肥国轩高科动力能源有限公司 | Storage life prediction method of lithium ion battery |
| CN106597305A (en) * | 2016-12-09 | 2017-04-26 | 合肥国轩高科动力能源有限公司 | Method for predicting cycle life of lithium ion battery |
| CN106772100A (en) * | 2017-02-06 | 2017-05-31 | 联想(北京)有限公司 | A kind of method and device for predicting battery life |
| CN107202960A (en) * | 2017-05-25 | 2017-09-26 | 安徽江淮汽车集团股份有限公司 | Electrokinetic cell life-span prediction method |
| CN107271922A (en) * | 2017-08-02 | 2017-10-20 | 合肥国轩高科动力能源有限公司 | Method for predicting service life of whole new energy automobile battery system |
Non-Patent Citations (1)
| Title |
|---|
| 夏顺礼;秦李伟;赵久志;朱道吉;: "EV用三元动力电池使用寿命预测方法研究", 合肥工业大学学报(自然科学版), vol. 39, no. 08, pages 1013 - 1016 * |
Cited By (13)
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|---|---|---|---|---|
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| CN112014756B (en) * | 2020-08-11 | 2023-07-25 | 蜂巢能源科技股份有限公司 | Method and device for determining battery cycle life, storage medium and electronic equipment |
| CN112014736A (en) * | 2020-08-21 | 2020-12-01 | 中国第一汽车股份有限公司 | Battery life prediction method, device, equipment and storage medium |
| CN112379297A (en) * | 2020-10-22 | 2021-02-19 | 欣旺达电动汽车电池有限公司 | Battery system service life prediction method, device, equipment and storage medium |
| CN112379297B (en) * | 2020-10-22 | 2024-06-11 | 欣旺达动力科技股份有限公司 | Battery system life prediction method, device, equipment and storage medium |
| CN112364486A (en) * | 2020-10-23 | 2021-02-12 | 昆山宝创新能源科技有限公司 | Method for predicting cycle life of lithium battery and application thereof |
| CN112731164A (en) * | 2020-12-21 | 2021-04-30 | 惠州亿纬锂能股份有限公司 | Battery life evaluation method |
| CN112731164B (en) * | 2020-12-21 | 2024-03-15 | 惠州亿纬锂能股份有限公司 | Battery life assessment method |
| CN113406522A (en) * | 2021-06-18 | 2021-09-17 | 合肥国轩高科动力能源有限公司 | Method and system for predicting and evaluating service life of lithium battery system of electric vehicle |
| CN113406522B (en) * | 2021-06-18 | 2022-05-03 | 合肥国轩高科动力能源有限公司 | Method and system for predicting and evaluating service life of lithium battery system of electric vehicle |
| CN114444370A (en) * | 2021-10-11 | 2022-05-06 | 崔跃芹 | Method and device for predicting accumulated loss life of rechargeable battery by considering operation conditions, electronic equipment and readable storage medium |
| CN114444370B (en) * | 2021-10-11 | 2023-10-10 | 崔跃芹 | Method and device for predicting accumulated loss life of rechargeable battery by considering operation conditions, electronic equipment and readable storage medium |
| CN114217237A (en) * | 2021-11-05 | 2022-03-22 | 东软睿驰汽车技术（沈阳）有限公司 | Battery health state determination method and device based on storage endurance and electronic equipment |
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| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Country or region after: China Address after: No. 6 Yutong Road, Guancheng Hui District, Zhengzhou City, Henan Province Applicant after: Yutong Bus Co.,Ltd. Address before: No.1 Yutong Road, Shibali River, Guancheng Hui District, Zhengzhou City, Henan Province Applicant before: ZHENGZHOU YUTONG BUS Co.,Ltd. Country or region before: China |