State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error
The state of charge (SOC) is one of the crucial states for battery management. The Kalman filter (KF) family algorithms are promising for SOC estimation. Based on the KF theory, a sufficiently accurate system model is the precondition for a better performance of the algorithm. Thus, we manage to imp...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8630916/ |
| _version_ | 1830357523780075520 |
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| author | Weihua Wang Jiayi Mu |
| author_facet | Weihua Wang Jiayi Mu |
| author_sort | Weihua Wang |
| collection | DOAJ |
| description | The state of charge (SOC) is one of the crucial states for battery management. The Kalman filter (KF) family algorithms are promising for SOC estimation. Based on the KF theory, a sufficiently accurate system model is the precondition for a better performance of the algorithm. Thus, we manage to improve the algorithm by estimating the battery model error. In this paper, the sources that may cause model errors are analyzed. Then, in order to estimate the unknown error term, the bias term characterizing the model error is adjoined to the original state vector to form a new state vector, and the KF is utilized to estimate the new state vector. It is a joint estimation algorithm for both SOC and the model error. Subsequently, by decoupling this joint estimation algorithm, a battery model error observer has been built. Finally, to verify the robustness of the proposed method against the battery model error, different types of errors such as open circuit voltage drift and voltage sensor drift are injected. The results indicate that the improved SOC estimation algorithm has better robustness and accuracy against the model mismatch compared with the standard KF algorithm. |
| first_indexed | 2024-12-20T02:24:12Z |
| format | Article |
| id | doaj.art-05363b2d5c0f4553876c8da30299211f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:24:12Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-05363b2d5c0f4553876c8da30299211f2022-12-21T19:56:44ZengIEEEIEEE Access2169-35362019-01-017292232923510.1109/ACCESS.2019.28953778630916State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model ErrorWeihua Wang0https://orcid.org/0000-0003-2973-5185Jiayi Mu1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaThe state of charge (SOC) is one of the crucial states for battery management. The Kalman filter (KF) family algorithms are promising for SOC estimation. Based on the KF theory, a sufficiently accurate system model is the precondition for a better performance of the algorithm. Thus, we manage to improve the algorithm by estimating the battery model error. In this paper, the sources that may cause model errors are analyzed. Then, in order to estimate the unknown error term, the bias term characterizing the model error is adjoined to the original state vector to form a new state vector, and the KF is utilized to estimate the new state vector. It is a joint estimation algorithm for both SOC and the model error. Subsequently, by decoupling this joint estimation algorithm, a battery model error observer has been built. Finally, to verify the robustness of the proposed method against the battery model error, different types of errors such as open circuit voltage drift and voltage sensor drift are injected. The results indicate that the improved SOC estimation algorithm has better robustness and accuracy against the model mismatch compared with the standard KF algorithm.https://ieeexplore.ieee.org/document/8630916/Electric vehicleKalman filterlithium-ion batterymodel error observerstate of charge |
| spellingShingle | Weihua Wang Jiayi Mu State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error IEEE Access Electric vehicle Kalman filter lithium-ion battery model error observer state of charge |
| title | State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error |
| title_full | State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error |
| title_fullStr | State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error |
| title_full_unstemmed | State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error |
| title_short | State of Charge Estimation for Lithium-Ion Battery in Electric Vehicle Based on Kalman Filter Considering Model Error |
| title_sort | state of charge estimation for lithium ion battery in electric vehicle based on kalman filter considering model error |
| topic | Electric vehicle Kalman filter lithium-ion battery model error observer state of charge |
| url | https://ieeexplore.ieee.org/document/8630916/ |
| work_keys_str_mv | AT weihuawang stateofchargeestimationforlithiumionbatteryinelectricvehiclebasedonkalmanfilterconsideringmodelerror AT jiayimu stateofchargeestimationforlithiumionbatteryinelectricvehiclebasedonkalmanfilterconsideringmodelerror |