Deep Reinforcement Learning based Multi-Objective Energy Management Strategy for a Plug-in Hybrid Electric Bus Considering Driving Style Recognition
Author:
Affiliation:
1. School of Control Science and Engineering, Shandong University,Jinan,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9964479/9964492/09965049.pdf?arnumber=9965049
Reference12 articles.
1. Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles
2. Development a new power management strategy for power split hybrid electric vehicles
3. A simulation study of the impact of driving patterns and driver behavior on fuel economy of hybrid transit buses
4. Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus
5. An identification algorithm of driver steering characteristics based on backpropagation neural network
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1. Reinforcement Learning-Based Energy Management for Hybrid Power Systems: State-of-the-Art Survey, Review, and Perspectives;Chinese Journal of Mechanical Engineering;2024-05-17
2. Evaluation of the Driving Behaviour Models with Newly Collected Data;Learning and Analytics in Intelligent Systems;2024
3. Optimal energy management strategies for hybrid electric vehicles: A recent survey of machine learning approaches;Journal of Engineering Research;2024-01
4. Review of Data-Driven Artificial Intelligence Applications in Electric Machines and Drive Systems;2023 IEEE International Future Energy Electronics Conference (IFEEC);2023-11-20
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