A comparative analysis of route-based power management strategies for real-time application in plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) are promising alternatives for sustainable transportation. Because of their embedded battery pack, they can significantly enhance the fuel economy compared to conventional vehicle. Improving the power management strategy can exploit the full potential of a PHEV powertrain and reduce the fuel consumption considerably. In this study, we compare two outstanding optimal route-based control approaches: model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A-ECMS), for different levels of trip information. The controllers are fine-tuned for the 2013 Toyota Prius plug-in hybrid and implemented as a high-fidelity model in Autonomie software. To evaluate the designed control systems, the rule-based controller of Autonomie is considered as a benchmark power management system. The result of simulations show that MPC and A-ECMS lead to an approximately equal fuel economy, and they can improve the fuel consumption of this PHEV up to 10% in comparison to the benchmark controller. Both strategies can be implemented in real-time although A-ECMS is 15% faster than MPC.