Ecological Cruising Control of Connected Electric Vehicle: A Deep Reinforcement Learning Approach

Dec 1, 2022·
Qun Wang
,
Fei Ju
,
Weichao Zhuang
,
Liangmo Wang
· 0 min read
Abstract
This work presents a deep reinforcement learning (DRL) based ecological cruising strategy for connected electric vehicles. The agent learns to jointly optimize speed tracking and energy economy using vehicle-to-infrastructure (V2I) information about upcoming terrain and signalized intersections. Results demonstrate measurable energy savings over rule-based benchmarks in realistic driving cycles.
Type
Publication
Science China Technological Sciences
publications
Deep Reinforcement Learning Electric Vehicles Eco-Cruising Energy Management
Authors
Qun Wang
Postdoctoral Fellow
Qun Wang is a Postdoctoral Fellow at the AIMS Lab, Hong Kong Polytechnic University, working with Assistant Professor Hailong Huang. His research focuses on energy-saving optimization and safety-guaranteed control of autonomous electrified vehicles, with emphasis on reinforcement learning for eco-driving, human driving behavior modeling, and multi-agent car-following control. He has published over 10 papers in leading journals including IEEE Transactions on ITS, TTE, TCST and Energy.