Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review
The rising adoption of electric vehicles (EVs) utilizing lithium-ion batteries necessitates a robust understanding of state-of-health (SOH) estimation. The existing literature highlights various SOH estimation models, but a comprehensive comparative analysis is lacking. This paper addresses this gap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/337 |
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| author | Jianqiang Gong Bin Xu Fanghua Chen Gang Zhou |
| author_facet | Jianqiang Gong Bin Xu Fanghua Chen Gang Zhou |
| author_sort | Jianqiang Gong |
| collection | DOAJ |
| description | The rising adoption of electric vehicles (EVs) utilizing lithium-ion batteries necessitates a robust understanding of state-of-health (SOH) estimation. The existing literature highlights various SOH estimation models, but a comprehensive comparative analysis is lacking. This paper addresses this gap by conducting an exhaustive review of diverse SOH estimation approaches for EV battery applications, including the direct measurement method, physical-based and data-driven approaches. Results highlight that data-driven methods, particularly those utilizing machine learning techniques, offer superior accuracy and adaptability but often require extensive datasets. In contrast, physical-based approaches provide interpretable insights but are computationally intensive, and direct measurement methods, though simple, lack generalizability. In addition, this paper also systematically reviews the indicators of battery SOH, influential factors affecting battery SOH, and various datasets used for SOH modeling. Future research should focus on integrating multiple modeling methodologies to leverage their combined strengths, enhancing the collection of comprehensive battery lifecycle datasets to support robust model development, and extending the scope of SOH estimation beyond individual cells to encompass entire battery packs. |
| format | Article |
| id | doaj-art-8bb641c8a5bd4c51949a978ac82bdaff |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-8bb641c8a5bd4c51949a978ac82bdaff2025-01-24T13:31:06ZengMDPI AGEnergies1996-10732025-01-0118233710.3390/en18020337Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature ReviewJianqiang Gong0Bin Xu1Fanghua Chen2Gang Zhou3Key Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway Ministry of Transport, Beijing 100088, ChinaCollege of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaKey Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway Ministry of Transport, Beijing 100088, ChinaKey Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway Ministry of Transport, Beijing 100088, ChinaThe rising adoption of electric vehicles (EVs) utilizing lithium-ion batteries necessitates a robust understanding of state-of-health (SOH) estimation. The existing literature highlights various SOH estimation models, but a comprehensive comparative analysis is lacking. This paper addresses this gap by conducting an exhaustive review of diverse SOH estimation approaches for EV battery applications, including the direct measurement method, physical-based and data-driven approaches. Results highlight that data-driven methods, particularly those utilizing machine learning techniques, offer superior accuracy and adaptability but often require extensive datasets. In contrast, physical-based approaches provide interpretable insights but are computationally intensive, and direct measurement methods, though simple, lack generalizability. In addition, this paper also systematically reviews the indicators of battery SOH, influential factors affecting battery SOH, and various datasets used for SOH modeling. Future research should focus on integrating multiple modeling methodologies to leverage their combined strengths, enhancing the collection of comprehensive battery lifecycle datasets to support robust model development, and extending the scope of SOH estimation beyond individual cells to encompass entire battery packs.https://www.mdpi.com/1996-1073/18/2/337electric vehiclesstate of healthestimationcomparative analysis |
| spellingShingle | Jianqiang Gong Bin Xu Fanghua Chen Gang Zhou Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review Energies electric vehicles state of health estimation comparative analysis |
| title | Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review |
| title_full | Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review |
| title_fullStr | Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review |
| title_full_unstemmed | Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review |
| title_short | Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review |
| title_sort | predictive modeling for electric vehicle battery state of health a comprehensive literature review |
| topic | electric vehicles state of health estimation comparative analysis |
| url | https://www.mdpi.com/1996-1073/18/2/337 |
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