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Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition
- Publication Year :
- 2020
- Publisher :
- arXiv, 2020.
-
Abstract
- Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission reduction. This work presents a transferred EMS for a parallel HEV via combining the reinforcement learning method and driving conditions recognition. First, the Markov decision process (MDP) and the transition probability matrix are utilized to differentiate the driving conditions. Then, reinforcement learning algorithms are formulated to achieve power split controls, in which Q-tables are tuned by current driving situations. Finally, the proposed transferred framework is estimated and validated in a parallel hybrid topology. Its advantages in computational efficiency and fuel economy are summarized and proved.<br />Comment: 6 pages, 5 figures
- Subjects :
- Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Energy management
020209 energy
020208 electrical & electronic engineering
Markov process
Topology (electrical circuits)
02 engineering and technology
Automotive engineering
Machine Learning (cs.LG)
Reduction (complexity)
Energy conservation
symbols.namesake
0202 electrical engineering, electronic engineering, information engineering
symbols
FOS: Electrical engineering, electronic engineering, information engineering
Reinforcement learning
Process control
Markov decision process
Electrical Engineering and Systems Science - Signal Processing
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....0e02935a7d940cc0c553bdc49ed60a55
- Full Text :
- https://doi.org/10.48550/arxiv.2007.08337