Machine Learning-Driven Prediction of Lithium-Ion Battery Power Capability for eVTOL Aircraft
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908575374245888 |
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| author | Tu, Hao Wang, Yebin Mou, Shaoshuai Fang, Huazhen |
| author_facet | Tu, Hao Wang, Yebin Mou, Shaoshuai Fang, Huazhen |
| contents | Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery management, a prominent one of which is the prediction of the power capability of their lithium-ion battery systems. The challenge originates from the high C-rate discharging conditions required during eVTOL flights as well as the complexity of lithium-ion batteries' electro-thermal dynamics. This paper, for the first time, formulates a power limit prediction problem for eVTOL which explicitly considers long prediction horizons and the possible occurrence of emergency landings. We then harness machine learning to solve this problem in two intertwined ways. First, we adopt a dynamic model that integrates physics with machine learning to predict a lithium-ion battery's voltage and temperature behaviors with high accuracy. Second, while performing search for the maximum power, we leverage machine learning to predict the remaining discharge time and use the prediction to accelerate the search with fast computation. Our validation results show the effectiveness of the proposed study for eVTOL operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Driven Prediction of Lithium-Ion Battery Power Capability for eVTOL Aircraft Tu, Hao Wang, Yebin Mou, Shaoshuai Fang, Huazhen Systems and Control Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery management, a prominent one of which is the prediction of the power capability of their lithium-ion battery systems. The challenge originates from the high C-rate discharging conditions required during eVTOL flights as well as the complexity of lithium-ion batteries' electro-thermal dynamics. This paper, for the first time, formulates a power limit prediction problem for eVTOL which explicitly considers long prediction horizons and the possible occurrence of emergency landings. We then harness machine learning to solve this problem in two intertwined ways. First, we adopt a dynamic model that integrates physics with machine learning to predict a lithium-ion battery's voltage and temperature behaviors with high accuracy. Second, while performing search for the maximum power, we leverage machine learning to predict the remaining discharge time and use the prediction to accelerate the search with fast computation. Our validation results show the effectiveness of the proposed study for eVTOL operations. |
| title | Machine Learning-Driven Prediction of Lithium-Ion Battery Power Capability for eVTOL Aircraft |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.03497 |