LAUSR

Abstract For the plug-in hybrid electric vehicle (PHEV) with repeated routes, the route information is very useful for energy optimization. SOC (state of charge) of power battery is the most important parameter for PHEV, so the information is usually utilized to calculate reference SOC. However, it is not the optimal trajectory, which might restrict the improvement of economic performance. In this paper, the route information is processed by dynamic programming and a group of optimal state of charge trajectories are obtained. Then, a novel state-of-charge-constraint-based cost function is firstly proposed, which can be obtained by presented method based on history cycle data. Next, a nonlinear model predictive controller is designed as energy management strategy to evaluate different cost functions. Experiment results show that proposed cost function can save fuel consumption by 5.9% and 10.8% compared with other two common cost functions.