Multiobjective Energy Management Strategy for Series Hybrid Electric Vehicles With Prediction Uncertainty Awareness: A Min–Max Game

Hybrid electric vehicles (HEVs) have emerged as a promising solution for achieving carbon neutrality and overcoming energy-related challenges. The efficacy of HEVs, in part, is contingent upon the implementation of effective energy management strategies (EMSs). Recently, on the one hand, most EMSs have primarily emphasized fuel economy while overlooking the potential consequences of frequent and high-power usage of electronic components, which may lead to motor overheating and the degradation of the motor’s maximum capacity. On the other hand, benefiting from future driving conditions in a finite horizon, prediction-based EMSs can proactively make some adjustments to energy allocation in advance, garnering significant attention from numerous researchers. However, inherent prediction uncertainties will mislead predictive EMSs to make overconfident decisions and ultimately introduce risks to thermal dynamics. Targeting the issues above, this article presents a min–max game-based generator temperature-sensitive EMS for series HEVs (SHEVs). First, a multiobjective energy management problem with a comprehensive consideration of fuel economy and generator temperature is formulated. Then, a min–max game framework for robust decision-making is structured to deal with the potential adverse effects of prediction uncertainty. Finally, to improve computational efficiency, a closed-form solution is proposed to solve the Stackelberg equilibrium pair for the min–max game, which avoids performing optimization in each iteration. Simulation and hardware-in-the-loop test results demonstrate that the proposed strategy enhances fuel economy while effectively lowering generator temperature.