This study provides an optimization methodology in powertrain configurations and energy management strategy for a multi-mode plug-in hybrid electric vehicle. The challenge in this study is that the energy management strategy is highly intertwined with the powertrain configurations; the problem-specific complexity of this powertrain makes it even more difficult. Based on the analysis of the powertrain architecture, a simulation model is developed. Two self-adaptive solutions, a fuzzy PID driver and an adaptive shifting schedule are established to accommodate the powertrain parameter variations during the particle swarm optimization process. In order to obtain optimal and applicable results of both the powertrain and the energy management strategy configurations, a two-layer optimization controller is proposed. In the first layer, sub-optimal results are obtained by ACOR, additionally, the second layer is designed to final confirm both mode changes and gear shifting. At last, two possible applications—modifications to the shifting schedule and the energy management strategy, as well as developing a neuro network controller utilizing the optimal results—are analyzed. The results suggest that the hybrid optimization presented in this paper provides a solution to squeeze the potential of a plug-in hybrid electric vehicle while maintaining its practicability.
Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming
