Energy Management for Hev Combining Naturalistic Driving Data and Deep Reinforcement Learning with High Generalization
Generalization to unseen environments is still a challenge for deep reinforcement learning
(DRL)-based energy management strategies (EMSs). This paper proposes a scenario …
(DRL)-based energy management strategies (EMSs). This paper proposes a scenario …
Deep Reinforcement Learning based Energy Management for Heavy Duty HEV considering Discrete-Continuous Hybrid Action Space
To reduce the fuel consumption of heavy duty logistic vehicles (HDLVs), P2 parallel
hybridization is a promising solution, and deep reinforcement learning (DRL) is a promising …
hybridization is a promising solution, and deep reinforcement learning (DRL) is a promising …
Soft actor-critic with discrete-continuous hybrid action space for energy management of heavy duty hev combining multi-dimensional naturalistic driving data
Real-time application (generalization capability) in an unfamiliar environment is still a
challenge for deep reinforcement learning (DRL) based energy management strategies …
challenge for deep reinforcement learning (DRL) based energy management strategies …
Actor-critic TD3-based deep reinforcement learning for energy management strategy of HEV
In the last decade, deep reinforcement learning (DRL) algorithms have been employed in
the design of energy management strategy (EMS) for hybrid electric vehicles (HEVs) …
the design of energy management strategy (EMS) for hybrid electric vehicles (HEVs) …
An apprenticeship-reinforcement learning scheme based on expert demonstrations for energy management strategy of hybrid electric vehicles
D Hu, H Xie, K Song, Y Zhang, L Yan - Applied Energy, 2023 - Elsevier
Deep reinforcement learning (DRL) is a potential solution to develop efficient energy
management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the …
management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the …
Deep reinforcement learning based on driver experience embedding for energy management strategies in hybrid electric vehicles
D Hu, Y Zhang - Energy Technology, 2022 - Wiley Online Library
Reinforcement learning (RL) is a solution with great potential for hybrid electric vehicle
(HEV) energy management strategies (EMS). However, traditional deep reinforcement …
(HEV) energy management strategies (EMS). However, traditional deep reinforcement …
Time Series Prediction Integrated RNN-DRL for Energy-Emission Management in HEVs
J Luan, L Guo, B Ma, H Chen - 2023 7th CAA International …, 2023 - ieeexplore.ieee.org
Learning-based strategies have gained widespread adoption in the field of energy
management strategy (EMS) for hybrid electric vehicles (HEVs). However, existing …
management strategy (EMS) for hybrid electric vehicles (HEVs). However, existing …
High robustness energy management strategy of hybrid electric vehicle based on improved soft actor-critic deep reinforcement learning
W Sun, Y Zou, X Zhang, N Guo, B Zhang, G Du - Energy, 2022 - Elsevier
As a hybrid electric vehicle (HEV) key control technology, intelligent energy management
strategies (EMSs) directly affect fuel consumption. Investigating the robustness of EMSs to …
strategies (EMSs) directly affect fuel consumption. Investigating the robustness of EMSs to …
A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle
Energy management strategy (EMS) has a huge impact on the energy efficiency of hybrid
electric vehicles (HEVs). Recently, fast-growing number of studies have applied different …
electric vehicles (HEVs). Recently, fast-growing number of studies have applied different …
[HTML][HTML] A transferable energy management strategy for hybrid electric vehicles via dueling deep deterministic policy gradient
Due to the high mileage and heavy load capabilities of hybrid electric vehicles (HEVs),
energy management becomes crucial in improving energy efficiency. To avoid the over …
energy management becomes crucial in improving energy efficiency. To avoid the over …