Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles
State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-12-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/7/12/8446 |
| _version_ | 1811308242278547456 |
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| author | Yong Tian Bizhong Xia Mingwang Wang Wei Sun Zhihui Xu |
| author_facet | Yong Tian Bizhong Xia Mingwang Wang Wei Sun Zhihui Xu |
| author_sort | Yong Tian |
| collection | DOAJ |
| description | State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF) and adaptive slide mode observer (ASMO) are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model. |
| first_indexed | 2024-04-13T09:19:35Z |
| format | Article |
| id | doaj.art-1e869244257043a3b085e1a2ff94f885 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T09:19:35Z |
| publishDate | 2014-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1e869244257043a3b085e1a2ff94f8852022-12-22T02:52:38ZengMDPI AGEnergies1996-10732014-12-017128446846410.3390/en7128446en7128446Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric VehiclesYong Tian0Bizhong Xia1Mingwang Wang2Wei Sun3Zhihui Xu4Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, the University Town, Shenzhen 518055, ChinaDivision of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, the University Town, Shenzhen 518055, ChinaSunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, ChinaSunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, ChinaSunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, ChinaState of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF) and adaptive slide mode observer (ASMO) are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model.http://www.mdpi.com/1996-1073/7/12/8446lithium-ion batterystate of chargeadaptive unscented Kalman filteradaptive slide mode observer |
| spellingShingle | Yong Tian Bizhong Xia Mingwang Wang Wei Sun Zhihui Xu Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles Energies lithium-ion battery state of charge adaptive unscented Kalman filter adaptive slide mode observer |
| title | Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles |
| title_full | Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles |
| title_fullStr | Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles |
| title_full_unstemmed | Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles |
| title_short | Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles |
| title_sort | comparison study on two model based adaptive algorithms for soc estimation of lithium ion batteries in electric vehicles |
| topic | lithium-ion battery state of charge adaptive unscented Kalman filter adaptive slide mode observer |
| url | http://www.mdpi.com/1996-1073/7/12/8446 |
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