Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning

Zhengyu Yao, Hwan-Sik Yoon () and Yang-Ki Hong
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Zhengyu Yao: Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Hwan-Sik Yoon: Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Yang-Ki Hong: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA

Energies, 2023, vol. 16, issue 2, 1-18

Abstract: Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms.

Keywords: hybrid electric vehicle; reinforcement learning; powertrain control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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