State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter
A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-08-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/9/391 |
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| author | Qian Huang Junting Li Qingshan Xu Chao He Chenxi Yang Li Cai Qipin Xu Lihong Xiang Xiaojiang Zou Xiaochuan Li |
| author_facet | Qian Huang Junting Li Qingshan Xu Chao He Chenxi Yang Li Cai Qipin Xu Lihong Xiang Xiaojiang Zou Xiaochuan Li |
| author_sort | Qian Huang |
| collection | DOAJ |
| description | A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates the ’Data saturation’ problem experienced by least squares methods by dynamically adjusting weights of data. On the other hand, the EKF improves the robustness and adaptability of SOC estimation. Simulation results under Hybrid Pulse Power Characteristic (HPPC) conditions demonstrate that this new approach offers superior performance in SOC estimation in batteries for electric vehicles compared to existing methods, with better tracking of the true SOC curve, reduced estimation error, and improved convergence. |
| format | Article |
| id | doaj-art-d3617839242f424d9d66e2f1c19ff8bd |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-d3617839242f424d9d66e2f1c19ff8bd2025-08-20T01:56:13ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-08-0115939110.3390/wevj15090391State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman FilterQian Huang0Junting Li1Qingshan Xu2Chao He3Chenxi Yang4Li Cai5Qipin Xu6Lihong Xiang7Xiaojiang Zou8Xiaochuan Li9School of Electronics and Information Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaSchool of Electronics and Information Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 211189, ChinaSchool of Electronics and Information Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaSchool of Electronics and Information Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaSchool of Electronics and Information Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaState Grid Electric Power Research Institute, Nanjing 211100, ChinaSchool of Electronics and Information Engineering, Chongqing Three Gorges University, Chongqing 404100, ChinaChongqing Andao Cheng Automobile Technology Limited, Chongqing 404130, ChinaChongqing Hang Ying Automobile Manufacturing Limited, Chongqing 404100, ChinaA new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates the ’Data saturation’ problem experienced by least squares methods by dynamically adjusting weights of data. On the other hand, the EKF improves the robustness and adaptability of SOC estimation. Simulation results under Hybrid Pulse Power Characteristic (HPPC) conditions demonstrate that this new approach offers superior performance in SOC estimation in batteries for electric vehicles compared to existing methods, with better tracking of the true SOC curve, reduced estimation error, and improved convergence.https://www.mdpi.com/2032-6653/15/9/391lithium batteriesState of Charge (SOC)Levenberg–Marquardt Algorithm (LMA)Extended Kalman Filter (EKF)parameter identificationelectric vehicles |
| spellingShingle | Qian Huang Junting Li Qingshan Xu Chao He Chenxi Yang Li Cai Qipin Xu Lihong Xiang Xiaojiang Zou Xiaochuan Li State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter World Electric Vehicle Journal lithium batteries State of Charge (SOC) Levenberg–Marquardt Algorithm (LMA) Extended Kalman Filter (EKF) parameter identification electric vehicles |
| title | State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter |
| title_full | State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter |
| title_fullStr | State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter |
| title_full_unstemmed | State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter |
| title_short | State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter |
| title_sort | state of charge estimation in batteries for electric vehicle based on levenberg marquardt algorithm and kalman filter |
| topic | lithium batteries State of Charge (SOC) Levenberg–Marquardt Algorithm (LMA) Extended Kalman Filter (EKF) parameter identification electric vehicles |
| url | https://www.mdpi.com/2032-6653/15/9/391 |
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