Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition
This work addresses energy conservation and emission reduction for hybrid electric vehicles, but it is incremental as it builds on existing reinforcement learning methods.
The authors tackled energy management in hybrid electric vehicles by developing a transferred energy management strategy that combines reinforcement learning with driving condition recognition, achieving improved computational efficiency and fuel economy.
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.