Data-driven energy management for electric vehicles using offline reinforcement learning
Abstract Energy management technologies have significant potential to optimize electric vehicle performance and support global energy sustainability. However, despite extensive research, their real-world application remains limited due to reliance on simulations, which often fail to bridge the gap b...
Saved in:
| Main Authors: | Yong Wang, Jingda Wu, Hongwen He, Zhongbao Wei, Fengchun Sun |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58192-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on the Application of Reinforcement Learning in Hybrid Electric Vehicle Energy Management
by: ZHENG Chunhua, et al.
Published: (2020-08-01) -
Data augmented offline deep reinforcement learning for stochastic dynamic power dispatch
by: Wencong Xiao, et al.
Published: (2025-08-01) -
Temporal-Sequence Offline Reinforcement Learning for Transition Control of a Novel Tilt-Wing Unmanned Aerial Vehicle
by: Shiji Jin, et al.
Published: (2025-05-01) -
A Scalable and Coordinated Energy Management for Electric Vehicles Based on Multiagent Reinforcement Learning Method
by: Ruien Bian, et al.
Published: (2024-01-01) -
Adaptive Energy Management Strategy for Hybrid Electric Vehicles in Dynamic Environments Based on Reinforcement Learning
by: Shixin Song, et al.
Published: (2024-10-01)