Energy Management Strategy Based on an Improved TD3 Reinforcement Algorithm with Novel Experience Replay
- Resource Type
- Conference
- Authors
- Niu, Zegong; Huang, Ruchen; He, Hongwen; Zhou, Zhiqiang; Su, Qicong
- Source
- 2023 IEEE Vehicle Power and Propulsion Conference (VPPC) Vehicle Power and Propulsion Conference (VPPC), 2023 IEEE. :1-5 Oct, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Deep learning
Systematics
Reinforcement learning
Fuel economy
Propulsion
Energy management
Hybrid electric bus
Energy management strategy
Deep reinforcement learning
TD3
- Language
- ISSN
- 2769-4186
The energy management strategy (EMS) plays an important part in the systematic control of hybrid electric vehicles (HEVs). In recent years, the EMS based on deep reinforcement learning (DRL) receives more attention. This paper proposes an EMS based on TD3 deep reinforcement learning algorithm with novel experience replay. The experience replay is introduced to select samples via an evaluation network aiming to improve the learning ability and the convergence speed. The results show that compared with the traditional TD3-based EMS, the proposed EMS reduces the training time by 8.73% and improves the fuel economy by 2.14%.