Real-Time HEV Energy Management Strategy Considering Road Congestion Based on Deep Reinforcement Learning

Shota Inuzuka, Bo Zhang and Tielong Shen
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Shota Inuzuka: Faculty of Science and Technology, Sophia University, Tokyo 102-8554, Japan
Bo Zhang: Faculty of Science and Technology, Sophia University, Tokyo 102-8554, Japan
Tielong Shen: Faculty of Science and Technology, Sophia University, Tokyo 102-8554, Japan

Energies, 2021, vol. 14, issue 17, 1-20

Abstract: This paper deals with the HEV real-time energy management problem using deep reinforcement learning with connected technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I). In the HEV energy management problem, it is important to run the engine efficiently in order to minimize its total energy cost. This research proposes a policy model that takes into account road congestion and aims to learn the optimal system mode selection and power distribution when considering the far future by policy-based reinforcement learning. In the simulation, a traffic environment is generated in a virtual space by IPG CarMaker and a HEV model is prepared in MATLAB/Simulink to calculate the energy cost while driving on the road environment. The simulation validation shows the versatility of the proposed method for the test data, and in addition, it shows that considering road congestion reduces the total cost and improves the learning speed. Furthermore, we compare the proposed method with model predictive control (MPC) under the same conditions and show that the proposed method obtains more global optimal solutions.

Keywords: HEV energy management; connected technology; deep reinforcement learning (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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