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.
Hybrid Electric Vehicle Powertrain Control Based on Reinforcement Learning
Sae Int. J. Elec. Veh
Sae International Journal of Electrified Vehicles ; 11 , 2 ; 165-176
2021-10-27
12 pages
Conference paper
English
Sensitivity Analysis of Reinforcement Learning-Based Hybrid Electric Vehicle Powertrain Control
SAE Technical Papers | 2021
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