Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles
The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-06-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/8/7/6509 |
| _version_ | 1811302221909852160 |
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| author | Zhentong Liu Hongwen He |
| author_facet | Zhentong Liu Hongwen He |
| author_sort | Zhentong Liu |
| collection | DOAJ |
| description | The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected. |
| first_indexed | 2024-04-13T07:24:12Z |
| format | Article |
| id | doaj.art-fe0eb75c34a94df5a603cef2d35d52e8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T07:24:12Z |
| publishDate | 2015-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fe0eb75c34a94df5a603cef2d35d52e82022-12-22T02:56:32ZengMDPI AGEnergies1996-10732015-06-01876509652710.3390/en8076509en8076509Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric VehiclesZhentong Liu0Hongwen He1Collaborative Innovation Center of Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaCollaborative Innovation Center of Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, ChinaThe battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.http://www.mdpi.com/1996-1073/8/7/6509lithium-ion batteryfault diagnosisfaults effects analysisextended Kalman filter |
| spellingShingle | Zhentong Liu Hongwen He Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles Energies lithium-ion battery fault diagnosis faults effects analysis extended Kalman filter |
| title | Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles |
| title_full | Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles |
| title_fullStr | Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles |
| title_full_unstemmed | Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles |
| title_short | Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles |
| title_sort | model based sensor fault diagnosis of a lithium ion battery in electric vehicles |
| topic | lithium-ion battery fault diagnosis faults effects analysis extended Kalman filter |
| url | http://www.mdpi.com/1996-1073/8/7/6509 |
| work_keys_str_mv | AT zhentongliu modelbasedsensorfaultdiagnosisofalithiumionbatteryinelectricvehicles AT hongwenhe modelbasedsensorfaultdiagnosisofalithiumionbatteryinelectricvehicles |