Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric Vehicles
The performance of a battery system is critical to the development of electric vehicles (EVs). Battery capacity decays with the use of EVs and an advanced onboard battery management system is required to estimate battery capacity accurately. However, the acquired capacity suffers from poor accuracy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/8/9/112 |
| _version_ | 1797491235811753984 |
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| author | Bo Jiang Xuezhe Wei Haifeng Dai |
| author_facet | Bo Jiang Xuezhe Wei Haifeng Dai |
| author_sort | Bo Jiang |
| collection | DOAJ |
| description | The performance of a battery system is critical to the development of electric vehicles (EVs). Battery capacity decays with the use of EVs and an advanced onboard battery management system is required to estimate battery capacity accurately. However, the acquired capacity suffers from poor accuracy caused by the inadequate utilization of battery information and the limitation of a single estimation method. This paper investigates an innovative fusion method based on the information fusion technique for battery capacity estimation, considering the actual working conditions of EVs. Firstly, a general framework for battery capacity estimation and fusion is proposed and two conventional capacity estimation methods running in different EV operating conditions are revisited. The error covariance of different estimations is deduced to evaluate the estimation uncertainties. Then, a fusion state–space function is constructed and realized through the Kalman filter to achieve the adaptive fusion of multi-dimensional capacity estimation. Several experiments simulating the actual battery operations in EVs are designed and performed to validate the proposed method. Experimental results show that the proposed method performs better than conventional methods, obtaining more accurate and stable capacity estimation under different aging statuses. Finally, a practical judgment criterion for the current deviation fault is proposed based on fusion capacity. |
| first_indexed | 2024-03-10T00:44:33Z |
| format | Article |
| id | doaj.art-d832ac6fd372412d8377461dfbf39812 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-10T00:44:33Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-d832ac6fd372412d8377461dfbf398122023-11-23T15:03:07ZengMDPI AGBatteries2313-01052022-09-018911210.3390/batteries8090112Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric VehiclesBo Jiang0Xuezhe Wei1Haifeng Dai2Postdoctoral Station of Mechanical Engineering, School of Automotive Studies, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, School of Automotive Studies, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, School of Automotive Studies, Tongji University, Shanghai 201804, ChinaThe performance of a battery system is critical to the development of electric vehicles (EVs). Battery capacity decays with the use of EVs and an advanced onboard battery management system is required to estimate battery capacity accurately. However, the acquired capacity suffers from poor accuracy caused by the inadequate utilization of battery information and the limitation of a single estimation method. This paper investigates an innovative fusion method based on the information fusion technique for battery capacity estimation, considering the actual working conditions of EVs. Firstly, a general framework for battery capacity estimation and fusion is proposed and two conventional capacity estimation methods running in different EV operating conditions are revisited. The error covariance of different estimations is deduced to evaluate the estimation uncertainties. Then, a fusion state–space function is constructed and realized through the Kalman filter to achieve the adaptive fusion of multi-dimensional capacity estimation. Several experiments simulating the actual battery operations in EVs are designed and performed to validate the proposed method. Experimental results show that the proposed method performs better than conventional methods, obtaining more accurate and stable capacity estimation under different aging statuses. Finally, a practical judgment criterion for the current deviation fault is proposed based on fusion capacity.https://www.mdpi.com/2313-0105/8/9/112lithium-ion batterystate estimationbattery capacityadaptive fusionestimation uncertainty |
| spellingShingle | Bo Jiang Xuezhe Wei Haifeng Dai Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric Vehicles Batteries lithium-ion battery state estimation battery capacity adaptive fusion estimation uncertainty |
| title | Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric Vehicles |
| title_full | Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric Vehicles |
| title_fullStr | Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric Vehicles |
| title_full_unstemmed | Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric Vehicles |
| title_short | Development of a Fusion Framework for Lithium-Ion Battery Capacity Estimation in Electric Vehicles |
| title_sort | development of a fusion framework for lithium ion battery capacity estimation in electric vehicles |
| topic | lithium-ion battery state estimation battery capacity adaptive fusion estimation uncertainty |
| url | https://www.mdpi.com/2313-0105/8/9/112 |
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