6 citations · 26 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles
Teng Liu, Bo Wang, Dongpu Cao +2
To improve the driving mobility and energy efficiency of connected autonomous electrified vehicles, this paper presents an integrated longitudinal speed decision-making and energy…
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…
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
Jiangdong Liao, Teng Liu, Xiaolin Tang +3
Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making p…
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…
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition
Teng Liu, Xiaolin Tang, Jiaxin Chen +3
Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission red…