Motor-Temperature-Aware Predictive Energy Management Strategy for Plug-In Hybrid Electric Vehicles Using Rolling Game Optimization

Abstract

Energy management strategy (EMS) design aims at obtaining excellent fuel economy for plug-in hybrid electric vehicles (PHEVs) through the coordination of multiple energy sources. To reach this goal, a motor needs to start frequently or work for long time. Under these circumstances, the motor temperature inevitably increases and even will trigger motor thermal protection (MTP). Fuel economy and dynamic performance of PHEV would be influenced by such MTP, as it typically reduces the output torque of motor to lower its temperature and prevent it from overheating. Therefore, designing an efficient EMS for PHEVs with the consideration of motor temperature has been a challenging research issue. To solve this problem, this article proposes a predictive EMS using rolling game optimization (RGO) for PHEVs. First, a 2-D Markov chain is designed to predict the future possible velocities, which can provide input information for EMS of PHEVs. Second, the EMS problem in the studied parallel hybrid powertrain is formulated in the model predictive control (MPC) framework, and the motor temperature is added as a key optimization term in the cost function. Third, based on the MPC framework, the min-max game model is established to deal with the worst driving conditions in the future, and the RGO algorithm is proposed to solve this optimization problem. Finally, the comparison work is conducted under three driving cycles and the results show that the proposed strategy improves the fuel economy of the studied PHEV by 20.9% and 11.7% over that using the rule-based (RB) strategy, under the China typical urban driving cycle and real-world driving cycle, respectively. Meanwhile, the proposed strategy effectively limits the motor temperature in a reasonable range.