State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree
When using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of an electric vehicle power battery, the change of system noise and model parameters may lead to a reduction in estimation accuracy, due to variable operating conditions, battery aging, and other factors. The NCR18650...
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Editorial Office of Journal of Shanghai Jiao Tong University
2024-12-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-12-1935.shtml |
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| author | WU Jiang, ZHANG Yan, LIU Zelong, CHENG Gang, LEI Dong, JIAO Chaoyong |
| author_facet | WU Jiang, ZHANG Yan, LIU Zelong, CHENG Gang, LEI Dong, JIAO Chaoyong |
| author_sort | WU Jiang, ZHANG Yan, LIU Zelong, CHENG Gang, LEI Dong, JIAO Chaoyong |
| collection | DOAJ |
| description | When using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of an electric vehicle power battery, the change of system noise and model parameters may lead to a reduction in estimation accuracy, due to variable operating conditions, battery aging, and other factors. The NCR18650B ternary lithium-ion battery is selected, and the second-order RC model is established with identified parameters. Then, by using EKF as the main body with a fixed measurement noise covariance and adaptively adjusting process noise covariance based on the maximum likelihood estimation criterion, an adaptive extended Kalman filter is built to estimate the SOC of the battery. Simultaneously, a Kalman filter is used to estimate the ohmic resistance in real time. Thus, an adaptive dual extended Kalman filter (ADEKF) algorithm is formed. Finally, algorithm verifications are performed with testing data and public datasets. The ADEKF proposed is used to estimate the SOC of five groups of aged lithium batteries under three operating conditions, which are constant current, dynamic stress test, and Beijing dynamic stress test, and compared with that of EKF and other algorithms. The results show that compared with EKF, the average absolute error of the estimation results of ADEKF for different aged batteries under three operating conditions decreases by 1.868 percentage points, 2.296 percentage points, and 2.534 percentage points, respectively, which proves that ADEKF algorithm can effectively improve the SOC estimation accuracy under multiple operating conditions, battery aging and the combination of the two factors. |
| format | Article |
| id | doaj-art-a9decc35d3b34485b078235eec3e1a7b |
| institution | DOAJ |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-a9decc35d3b34485b078235eec3e1a7b2025-08-20T02:40:28ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672024-12-0158121935194510.16183/j.cnki.jsjtu.2023.168State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging DegreeWU Jiang, ZHANG Yan, LIU Zelong, CHENG Gang, LEI Dong, JIAO Chaoyong01. School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China;2. Xi’an Key Laboratory of Interconnected Sensing and Intelligent Diagnosis for Electrical Equipment, Xi’an 710048, China;3. NARI Technology Co., Ltd., Nanjing 211106, ChinaWhen using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of an electric vehicle power battery, the change of system noise and model parameters may lead to a reduction in estimation accuracy, due to variable operating conditions, battery aging, and other factors. The NCR18650B ternary lithium-ion battery is selected, and the second-order RC model is established with identified parameters. Then, by using EKF as the main body with a fixed measurement noise covariance and adaptively adjusting process noise covariance based on the maximum likelihood estimation criterion, an adaptive extended Kalman filter is built to estimate the SOC of the battery. Simultaneously, a Kalman filter is used to estimate the ohmic resistance in real time. Thus, an adaptive dual extended Kalman filter (ADEKF) algorithm is formed. Finally, algorithm verifications are performed with testing data and public datasets. The ADEKF proposed is used to estimate the SOC of five groups of aged lithium batteries under three operating conditions, which are constant current, dynamic stress test, and Beijing dynamic stress test, and compared with that of EKF and other algorithms. The results show that compared with EKF, the average absolute error of the estimation results of ADEKF for different aged batteries under three operating conditions decreases by 1.868 percentage points, 2.296 percentage points, and 2.534 percentage points, respectively, which proves that ADEKF algorithm can effectively improve the SOC estimation accuracy under multiple operating conditions, battery aging and the combination of the two factors.https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-12-1935.shtmllithium-ion batterystate of charge(soc)complex operating conditionsbattery agingadaptive dual extended kalman filter (adekf) |
| spellingShingle | WU Jiang, ZHANG Yan, LIU Zelong, CHENG Gang, LEI Dong, JIAO Chaoyong State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree Shanghai Jiaotong Daxue xuebao lithium-ion battery state of charge(soc) complex operating conditions battery aging adaptive dual extended kalman filter (adekf) |
| title | State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree |
| title_full | State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree |
| title_fullStr | State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree |
| title_full_unstemmed | State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree |
| title_short | State of Charge Estimation of Lithium-Ion Battery Considering Operating Conditions and Aging Degree |
| title_sort | state of charge estimation of lithium ion battery considering operating conditions and aging degree |
| topic | lithium-ion battery state of charge(soc) complex operating conditions battery aging adaptive dual extended kalman filter (adekf) |
| url | https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-12-1935.shtml |
| work_keys_str_mv | AT wujiangzhangyanliuzelongchenggangleidongjiaochaoyong stateofchargeestimationoflithiumionbatteryconsideringoperatingconditionsandagingdegree |