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Hybrid Electric Vehicle Powertrain Control Based on Reinforcement Learning
Journal Article
14-11-02-0013
ISSN: 2691-3747, e-ISSN: 2691-3755
Sector:
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, https://doi.org/10.4271/14-11-02-0013.
Language:
English
Abstract:
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.