18 citations · 32 across the 5 of their papers we have counts for
5 papers
Driving Conditions-Driven Energy Management for Hybrid Electric Vehicles: A Review
Teng Liu, Wenhao Tan, Xiaolin Tang +3
Motivated by the concerns on transported fuel consumption and global air pollution, industrial engineers, and academic researchers have made many efforts to construct more efficien…
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment
Teng Liu, Xiaolin Tang, Jinwei Zhang +3
As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-ma…
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle
Xiaowei Guo, Teng Liu, Bangbei Tang +4
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to…
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences
Hao Chen, Xiaolin Tang, Guo Hu +1
Development of hybrid electric vehicles depends on an advanced and efficient energy management strategy (EMS). With online and real-time requirements in mind, this article presents…
Inverse Reinforce Learning with Nonparametric Behavior Clustering
Siddharthan Rajasekaran, Jinwei Zhang, Jie Fu
Inverse Reinforcement Learning (IRL) is the task of learning a single reward function given a Markov Decision Process (MDP) without defining the reward function, and a set of demon…