Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data
The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processe...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Advances in Applied Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266679242400026X |
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| author | Kosaku Nakano Sophia Vögler Kenji Tanaka |
| author_facet | Kosaku Nakano Sophia Vögler Kenji Tanaka |
| author_sort | Kosaku Nakano |
| collection | DOAJ |
| description | The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development. |
| format | Article |
| id | doaj-art-353ccf4394314ef491143dcf48081ef2 |
| institution | OA Journals |
| issn | 2666-7924 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Applied Energy |
| spelling | doaj-art-353ccf4394314ef491143dcf48081ef22025-08-20T01:59:09ZengElsevierAdvances in Applied Energy2666-79242024-12-011610018810.1016/j.adapen.2024.100188Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world dataKosaku Nakano0Sophia Vögler1Kenji Tanaka2Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 1138656, Tokyo, Japan; Corresponding authors.School of Engineering and Design, Technical University of Munich, Arcisstrasse 21, Munich, 80333, Bavaria, GermanyGraduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 1138656, Tokyo, Japan; Corresponding authors.The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.http://www.sciencedirect.com/science/article/pii/S266679242400026XLithium-ion batteryElectric vehicleState of health estimationDeep learningTransformer |
| spellingShingle | Kosaku Nakano Sophia Vögler Kenji Tanaka Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data Advances in Applied Energy Lithium-ion battery Electric vehicle State of health estimation Deep learning Transformer |
| title | Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data |
| title_full | Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data |
| title_fullStr | Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data |
| title_full_unstemmed | Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data |
| title_short | Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data |
| title_sort | advancing state of health estimation for electric vehicles transformer based approach leveraging real world data |
| topic | Lithium-ion battery Electric vehicle State of health estimation Deep learning Transformer |
| url | http://www.sciencedirect.com/science/article/pii/S266679242400026X |
| work_keys_str_mv | AT kosakunakano advancingstateofhealthestimationforelectricvehiclestransformerbasedapproachleveragingrealworlddata AT sophiavogler advancingstateofhealthestimationforelectricvehiclestransformerbasedapproachleveragingrealworlddata AT kenjitanaka advancingstateofhealthestimationforelectricvehiclestransformerbasedapproachleveragingrealworlddata |