Hybrid Electric Vehicle Powertrain Control Based on Reinforcement Learning
Hybrid Electric Vehicles (HEVs) achieve better fuel economy than conventional vehicles by employing two different power sources: a mechanical engine and an electrical motor. These power sources have conventionally been controlled by a rule-based algorithm or optimization-based control. Besides these conventional approaches, reinforcement learning-based control algorithms have actively been studied recently. To investigate the benefits of the reinforcement learning-based approach, a model-free control algorithm for an HEV is proposed in this article using a Twin Delayed Deep Deterministic policy gradient (TD3), which is an online, off-policy Deep Reinforcement Learning (DRL) algorithm. The effectiveness of the proposed algorithm is studied by applying the TD3 algorithm to a 48V mild HEV (MHEV) model and the optimal operating strategy is obtained for maximum fuel economy. The simulation results show that the proposed TD3-based algorithm improves the average fuel economy by 1.89% on standard driving cycles and 2.20% on real-world driving cycles when compared to the Deep Deterministic Policy Gradient (DDPG) algorithm.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/26913747
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Supplemental Notes:
- Abstract reprinted with permission of SAE International.
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Authors:
- Yao, Zhengyu
- Yoon, Hwan-Sik
- Publication Date: 2021-10-27
Language
- English
Media Info
- Media Type: Digital/other
- Features: References;
- Pagination: pp 165-176
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Serial:
- SAE International Journal of Electrified Vehicles
- Volume: 11
- Issue Number: 2
- ISSN: 2691-3747
- EISSN: 2691-3755
- Serial URL: https://www.sae.org/publications/collections/content/E-JOURNAL-14
Subject/Index Terms
- TRT Terms: Advanced vehicle control systems; Electric vehicles; Fuel consumption; Hybrid vehicles; Machine learning; Mathematical models
- Subject Areas: Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01830415
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 14-11-02-0013
- Files: TRIS, SAE
- Created Date: Dec 16 2021 4:08PM