A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic Regression
One of the critical challenges posed by the spread of Lithium-ion Batteries (LIBs) within Electric Vehicles (EVs) is the real-time estimation of their State-of-Health (SOH), commonly regarded as the leading indicator of EV aging. However, SOH estimation is still challenging due to the electrochemica...
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2025-01-01
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| author | Raimondo Gallo Tommaso Monopoli Marco Zampolli Remi Jacques Philibert Jaboeuf Paolo Tosco Edoardo Patti Alessandro Aliberti |
| author_facet | Raimondo Gallo Tommaso Monopoli Marco Zampolli Remi Jacques Philibert Jaboeuf Paolo Tosco Edoardo Patti Alessandro Aliberti |
| author_sort | Raimondo Gallo |
| collection | DOAJ |
| description | One of the critical challenges posed by the spread of Lithium-ion Batteries (LIBs) within Electric Vehicles (EVs) is the real-time estimation of their State-of-Health (SOH), commonly regarded as the leading indicator of EV aging. However, SOH estimation is still challenging due to the electrochemical complexity of LIBs. This work proposes a novel, computationally-inexpensive, and chemically agnostic Machine Learning (ML) procedure for onboard real-time SOH estimation. The proposed methodology requires a narrow time window of voltage, current, and State-of-Charge battery data, collected while driving the vehicle. We defined a Simulink-based EV simulator, modeling a specific real-world EV, and we utilized it to generate a synthetic dataset by simulating multiple driving sessions of the EV to compensate for the lack of large-scale publicly available EV monitoring data. Then, we examined three feature extraction methods and three ML regression models, estimating the battery pack’s SOH. We conducted a thorough comparison of the proposed feature extraction methods and ML models, training the ML model with processed synthetic data and inferring over real driving session monitoring data from the corresponding real-world EV model. The best synthetic-trained ML model achieves an MAE of 0.27% and 5.08%, and an RMSE of 0.37% and 5.92% over synthetic and real test data, respectively. Finally, we implemented transfer learning over the ML models, employing a portion of the available real data, reaching the lowest MAE of 1.97%, and an RMSE of 2.56% over the remaining real test set. |
| format | Article |
| id | doaj-art-12a08969a1714851ab83fd0c46ee0d13 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-12a08969a1714851ab83fd0c46ee0d132025-01-10T00:03:15ZengIEEEIEEE Access2169-35362025-01-011332634010.1109/ACCESS.2024.351621510795164A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic RegressionRaimondo Gallo0https://orcid.org/0009-0002-4921-1266Tommaso Monopoli1Marco Zampolli2Remi Jacques Philibert Jaboeuf3Paolo Tosco4Edoardo Patti5https://orcid.org/0000-0002-6043-6477Alessandro Aliberti6https://orcid.org/0000-0001-8828-608XDepartment of Control and Computer Engineering, Politecnico di Torino, Turin, ItalyEdison S.p.A., Milan, ItalyEdison S.p.A., Milan, ItalyEdison S.p.A., Milan, ItalyEdison S.p.A., Milan, ItalyDepartment of Control and Computer Engineering, Politecnico di Torino, Turin, ItalyInter-University Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, ItalyOne of the critical challenges posed by the spread of Lithium-ion Batteries (LIBs) within Electric Vehicles (EVs) is the real-time estimation of their State-of-Health (SOH), commonly regarded as the leading indicator of EV aging. However, SOH estimation is still challenging due to the electrochemical complexity of LIBs. This work proposes a novel, computationally-inexpensive, and chemically agnostic Machine Learning (ML) procedure for onboard real-time SOH estimation. The proposed methodology requires a narrow time window of voltage, current, and State-of-Charge battery data, collected while driving the vehicle. We defined a Simulink-based EV simulator, modeling a specific real-world EV, and we utilized it to generate a synthetic dataset by simulating multiple driving sessions of the EV to compensate for the lack of large-scale publicly available EV monitoring data. Then, we examined three feature extraction methods and three ML regression models, estimating the battery pack’s SOH. We conducted a thorough comparison of the proposed feature extraction methods and ML models, training the ML model with processed synthetic data and inferring over real driving session monitoring data from the corresponding real-world EV model. The best synthetic-trained ML model achieves an MAE of 0.27% and 5.08%, and an RMSE of 0.37% and 5.92% over synthetic and real test data, respectively. Finally, we implemented transfer learning over the ML models, employing a portion of the available real data, reaching the lowest MAE of 1.97%, and an RMSE of 2.56% over the remaining real test set.https://ieeexplore.ieee.org/document/10795164/Electric vehiclebattery packregressionmachine learningstate-of-health |
| spellingShingle | Raimondo Gallo Tommaso Monopoli Marco Zampolli Remi Jacques Philibert Jaboeuf Paolo Tosco Edoardo Patti Alessandro Aliberti A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic Regression IEEE Access Electric vehicle battery pack regression machine learning state-of-health |
| title | A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic Regression |
| title_full | A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic Regression |
| title_fullStr | A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic Regression |
| title_full_unstemmed | A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic Regression |
| title_short | A Novel Procedure for Real-Time SOH Estimation of EV Battery Packs Based on Time Series Extrinsic Regression |
| title_sort | novel procedure for real time soh estimation of ev battery packs based on time series extrinsic regression |
| topic | Electric vehicle battery pack regression machine learning state-of-health |
| url | https://ieeexplore.ieee.org/document/10795164/ |
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