Statistics

Efficient energy use

Knowledge transfer

Convergence (economics)

Engineering

Artificial intelligence

Economics

Power (physics)

Maximum power transfer theorem

Physics

Control engineering

Energy (signal processing)

Economic growth

Aerospace engineering

Mathematics

Hybrid vehicle

Knowledge management

Reinforcement learning

Electric vehicle

Quantum mechanics

Operating system

Computer science

Process (computing)

Automotive industry

Electrical engineering

Transfer of learning

Automotive engineering

Energy management

Artificial neural network

IEEE Transactions on Vehicular Technology

Developing energy management strategies (EMSs) for different types of hybrid electric vehicles (HEVs) is a time-consuming and laborious task for automotive engineers. Experienced engineers can reduce the developing cycle by exploiting the commonalities between different types of HEV EMSs. Aiming at improving the efficiency of HEV EMSs development automatically, this paper proposes a transfer learning based method to achieve the cross-type knowledge transfer between deep reinforcement learning (DRL) based EMSs. Specifically, knowledge transfer among four significantly different types of HEVs is studied. We first use massive driving cycles to train a DRL-based EMS for Prius. Then the parameters of its deep neural networks, wherein the common knowledge of energy management is captured, are transferred into EMSs of a power-split bus, a series vehicle and a series-parallel bus. Finally, the parameters of 3 different HEV EMSs are fine-tuned in a small dataset. Simulation results indicate that, by incorporating transfer learning (TL) into DRL-based EMS for HEVs, an average 70% gap from the baseline in respect of convergence efficiency has been achieved. Our study also shows that TL can transfer knowledge between two HEVs that have significantly different structures. Overall, TL is conducive to boost the development process for HEV EMS.

Cross-Type Transfer for Deep Reinforcement Learning Based Hybrid Electric Vehicle Energy Management