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Optimization of Energy Management Strategy for Range-Extended Electric Vehicle Using Reinforcement Learning and Neural Network

Nipun Mittal, Aditya Pundlikrao Bhagat, Shubham Bhide, Bharadwaj Acharya, Bin Xu, Chris Paredis
  • Technical Paper
  • 2020-01-1190
To be published on 2020-04-14 by SAE International in United States
A Range-Extended Electric Vehicle (REEV) uses battery as the primary energy source and engine as the secondary source to extend the total range of the vehicle. Deep Orange 11 program at Clemson University is proposing a REEV for solving the mobility needs in the year 2040. Designing the Energy Management System (EMS) of such a vehicle is a critical aspect of the problem statement of this program to improve the vehicle economy and bring down the cost of operation of the vehicle. This paper proposes a reinforcement learning based algorithm for designing the EMS of such a vehicle. Q-learning is a model-free algorithm which seeks to improve the cumulative reward by finding the best policy over the course of operation. A rule-based strategy is first used to establish a baseline model of engine operation during the operation of vehicle over an EPA drive-cycle (FHDS). The Q-learning strategy is then deployed which learns over the baseline strategy as the vehicle travels over the drive cycle and improves the fuel economy of the vehicle. A high-fidelity vehicle…