Zhang, H;
Lei, N;
Peng, W;
Li, B;
Lv, S;
Chen, B;
Wang, Z;
(2025)
Bi-Level Transfer Learning for Lifelong-Intelligent Energy Management of Electric Vehicles.
IEEE Transactions on Intelligent Transportation Systems
pp. 1-14.
10.1109/TITS.2025.3571673.
(In press).
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Abstract
Automotive energy management systems (EMSs) are evolving to achieve intelligence across their entire lifecycle, from initial product development to real-world customer usage. This paper proposes a bi-level transfer approach with model-agnostic meta-learning (MAML) to realize cross-platform transferable and online-adaptive EMS. In the development phase, MAML calibrates the heuristic control maps of an instantaneous optimization-based EMS, with a per-unit state-action space design ensuring seamless knowledge transfer between vehicle platforms. During the usage phase, real driving data are adopted to refine onboard control parameters. The framework is validated through real vehicle experiments. Firstly, the entire MAML-assisted V-cycle development is implemented to validate the optimality and knowledge transfer of the EMS, resulting in zero-shot transfer for EMS calibration on new vehicle products. Additionally, real vehicle experimental tests show that a correction of 8.0%~9.5% fuel economy is improved against the convention reinforcement learning-based EMS during usage via online-adaptation, effectively bridging the gap between pre-trained policies and real-world optimal energy management.
| Type: | Article |
|---|---|
| Title: | Bi-Level Transfer Learning for Lifelong-Intelligent Energy Management of Electric Vehicles |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TITS.2025.3571673 |
| Publisher version: | https://doi.org/10.1109/tits.2025.3571673 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Energy management, Engines, Mechanical power transmission, Mathematical models, Research and development, Logistics, Data models, Calibration, Transfer learning, State of charge |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210189 |
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