Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction
It is challenging for a battery management system to estimate the State-Of-Charge (SOC) of batteries. A novel model-based method, using a Dual Extended Kalman Filtering algorithm (DEKF) and Back Propagation Neural Network (BPNN), is proposed to estimate and correct lithium-ion batteries. The results...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2118675 |
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| author | Likun Xing Liuyi Ling Xianyuan Wu |
| author_facet | Likun Xing Liuyi Ling Xianyuan Wu |
| author_sort | Likun Xing |
| collection | DOAJ |
| description | It is challenging for a battery management system to estimate the State-Of-Charge (SOC) of batteries. A novel model-based method, using a Dual Extended Kalman Filtering algorithm (DEKF) and Back Propagation Neural Network (BPNN), is proposed to estimate and correct lithium-ion batteries. The results of acceptable SOC estimation are achieved using the DEKF to estimate the battery SOC and simultaneously update model parameters online, while the SOC estimation error is in real-time predicted by the trained BPNN. To further reduce the SOC estimation error, the SOC estimated by the DEKF is corrected by adding the predicted estimation error. The SOC estimation results between the original DEKF and BPNN-based updated DEKF methods under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST), the Beijing Dynamic Stress Test (BJDST) and the US06 Highway Driving Schedule are compared. Experimental results show that the SOC error reduces considerably after correcting the estimated SOC. The corrected SOC Root-Mean Square Errors (RMSEs) decrease by an average of seven times compared with the case of no correction. The constant current discharge test verifies the generality and robustness of the proposed method. The modification to the SOC estimation results using ordinary EKF under the above four sophisticated dynamic tests verifies the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-a7a57582418442beb51fbb6e014e4da5 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-a7a57582418442beb51fbb6e014e4da52025-08-20T03:18:46ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013412332236310.1080/09540091.2022.21186752118675Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correctionLikun Xing0Liuyi Ling1Xianyuan Wu2Anhui University of Science and TechnologyAnhui University of Science and TechnologyAnhui University of Science and TechnologyIt is challenging for a battery management system to estimate the State-Of-Charge (SOC) of batteries. A novel model-based method, using a Dual Extended Kalman Filtering algorithm (DEKF) and Back Propagation Neural Network (BPNN), is proposed to estimate and correct lithium-ion batteries. The results of acceptable SOC estimation are achieved using the DEKF to estimate the battery SOC and simultaneously update model parameters online, while the SOC estimation error is in real-time predicted by the trained BPNN. To further reduce the SOC estimation error, the SOC estimated by the DEKF is corrected by adding the predicted estimation error. The SOC estimation results between the original DEKF and BPNN-based updated DEKF methods under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST), the Beijing Dynamic Stress Test (BJDST) and the US06 Highway Driving Schedule are compared. Experimental results show that the SOC error reduces considerably after correcting the estimated SOC. The corrected SOC Root-Mean Square Errors (RMSEs) decrease by an average of seven times compared with the case of no correction. The constant current discharge test verifies the generality and robustness of the proposed method. The modification to the SOC estimation results using ordinary EKF under the above four sophisticated dynamic tests verifies the effectiveness of the proposed method.http://dx.doi.org/10.1080/09540091.2022.2118675state-of-chargelithium-ion batteryneural networkstate-of-charge correctiondual extended kalman filter |
| spellingShingle | Likun Xing Liuyi Ling Xianyuan Wu Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction Connection Science state-of-charge lithium-ion battery neural network state-of-charge correction dual extended kalman filter |
| title | Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction |
| title_full | Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction |
| title_fullStr | Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction |
| title_full_unstemmed | Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction |
| title_short | Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction |
| title_sort | lithium ion battery state of charge estimation based on a dual extended kalman filter and bpnn correction |
| topic | state-of-charge lithium-ion battery neural network state-of-charge correction dual extended kalman filter |
| url | http://dx.doi.org/10.1080/09540091.2022.2118675 |
| work_keys_str_mv | AT likunxing lithiumionbatterystateofchargeestimationbasedonadualextendedkalmanfilterandbpnncorrection AT liuyiling lithiumionbatterystateofchargeestimationbasedonadualextendedkalmanfilterandbpnncorrection AT xianyuanwu lithiumionbatterystateofchargeestimationbasedonadualextendedkalmanfilterandbpnncorrection |