Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems
Safe and efficient operation of a battery pack requires a battery management system (BMS) that can accurately predict the pack state-of-heath (SOH). Although there is no universal definition for battery SOH, it is often defined based on the increase in the battery’s internal resistance. Te...
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| Format: | Article |
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MDPI AG
2018-06-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/6/1490 |
| _version_ | 1798025940666679296 |
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| author | Manoj Mathew Stefan Janhunen Mahir Rashid Frank Long Michael Fowler |
| author_facet | Manoj Mathew Stefan Janhunen Mahir Rashid Frank Long Michael Fowler |
| author_sort | Manoj Mathew |
| collection | DOAJ |
| description | Safe and efficient operation of a battery pack requires a battery management system (BMS) that can accurately predict the pack state-of-heath (SOH). Although there is no universal definition for battery SOH, it is often defined based on the increase in the battery’s internal resistance. Techniques such as extended Kalman filter (EKF) and recursive least squares (RLS) are two frequently used approaches for online estimation of this resistance. These two methods can, however, be computationally expensive, especially in the case of a battery pack composed of hundreds of cells. In addition, both methods require a battery model as well as chemistry specific parameters. Therefore, this paper investigates the performance of a direct resistance estimation (DRE) technique that requires minimal computational resources and can be implemented without any training data. This approach estimates the ohmic resistance only when the battery experiences sharp pulses in current. Comparison of results from the three algorithms shows that the DRE algorithm can accurately identify a degraded cell under various operating conditions while significantly reducing the required computational complexity. The findings will further advance diagnostic techniques for the identification of a weak cell in a large battery pack. |
| first_indexed | 2024-04-11T18:27:05Z |
| format | Article |
| id | doaj.art-ff92270f5a40492eaa08743b031e6734 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:27:05Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ff92270f5a40492eaa08743b031e67342022-12-22T04:09:36ZengMDPI AGEnergies1996-10732018-06-01116149010.3390/en11061490en11061490Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management SystemsManoj Mathew0Stefan Janhunen1Mahir Rashid2Frank Long3Michael Fowler4Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, CanadaNuvation Energy, 40 Bathurst Dr, Waterloo, ON N2V 1V6, CanadaDepartment of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, CanadaDepartment of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, CanadaDepartment of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, CanadaSafe and efficient operation of a battery pack requires a battery management system (BMS) that can accurately predict the pack state-of-heath (SOH). Although there is no universal definition for battery SOH, it is often defined based on the increase in the battery’s internal resistance. Techniques such as extended Kalman filter (EKF) and recursive least squares (RLS) are two frequently used approaches for online estimation of this resistance. These two methods can, however, be computationally expensive, especially in the case of a battery pack composed of hundreds of cells. In addition, both methods require a battery model as well as chemistry specific parameters. Therefore, this paper investigates the performance of a direct resistance estimation (DRE) technique that requires minimal computational resources and can be implemented without any training data. This approach estimates the ohmic resistance only when the battery experiences sharp pulses in current. Comparison of results from the three algorithms shows that the DRE algorithm can accurately identify a degraded cell under various operating conditions while significantly reducing the required computational complexity. The findings will further advance diagnostic techniques for the identification of a weak cell in a large battery pack.http://www.mdpi.com/1996-1073/11/6/1490lithium-ion batterybattery internal resistanceextended Kalman filterrecursive least squaresstate-of-health |
| spellingShingle | Manoj Mathew Stefan Janhunen Mahir Rashid Frank Long Michael Fowler Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems Energies lithium-ion battery battery internal resistance extended Kalman filter recursive least squares state-of-health |
| title | Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems |
| title_full | Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems |
| title_fullStr | Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems |
| title_full_unstemmed | Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems |
| title_short | Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems |
| title_sort | comparative analysis of lithium ion battery resistance estimation techniques for battery management systems |
| topic | lithium-ion battery battery internal resistance extended Kalman filter recursive least squares state-of-health |
| url | http://www.mdpi.com/1996-1073/11/6/1490 |
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