E-COSM 2021 Paper Abstract

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Paper MoBT3.6

He, Weiliang (Beijing Institute of Technology), Huang, Ying (Beijing Institute of Technology)

Real-Time Energy Optimization of Hybrid Electric Vehicle in Connected Environment Based on Deep Reinforcement Learning

Scheduled for presentation during the Invited session "Special session on benchmark challenging (1)" (MoBT3), Monday, August 23, 2021, 18:10−18:30, Room T3

6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, August 23-25, 2021, Tokyo, Japan

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Hybrid and Electric Vehicles, Energy Management, Powertrain Control

Abstract

In this paper, a real-time control method of hybrid electric vehicle is proposed based on rule-based speed planning and deep deterministic policy gradient (DDPG) energy management algorithm. This method can optimize fuel economy in real-time based on all traffic information in a connected environment, and satisfy the constraints of driving safety and driving time. The results show that the proposed deep reinforcement learning algorithm DDPG can achieve lower fuel consumption. In addition, the proposed speed planning algorithm will not violate traffic rules and has good results.

 

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