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Hybrid Electric Vehicle Powertrain Control Based on Reinforcement Learning

Journal Article
ISSN: 2691-3747, e-ISSN: 2691-3755
Published October 27, 2021 by SAE International in United States
Hybrid Electric Vehicle Powertrain Control Based on Reinforcement
Citation: Yao, Z. and Yoon, H., "Hybrid Electric Vehicle Powertrain Control Based on Reinforcement Learning," SAE Int. J. Elec. Veh. 11(2):165-176, 2022,
Language: English


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.