Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor

Interest in hybrid electric agricultural tractors (HEAT) is growing as traditional agricultural tractors consume large amounts of non-renewable resources. Energy management strategy (EMS) plays an important role in improving fuel consumption. This paper studies a deep reinforcement learning (DRL)-based EMS for HEAT. The model of HEAT is built and rotary tillage condition is defined for better conformity with the actual situation. Simulation results show that the DRL-based EMS achieves better performance.