Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information

Since fuel consumption of parallel Plug-in hybrid electric vehicles (PHEVs) is significantly different in various driving cycles, an adaptive optimal control strategy based on route information is proposed. First, the energy management strategy structure of adaptive Pontryagin's Minimum Principle (APMP) is established. Second, genetic algorithm is employed to obtain a Map, which is co-state function of driving distances and state of charge (SOC) and makes this strategy used online. Furthermore, co-state value is updated in real time according to route information and reference SOC, which improves the adaptability of APMP control strategy. Finally, MATLAB/Simulink and AVL/Cruise software are employed as simulation tools to verify the proposed APMP control strategy. The performance of APMP control strategy is compared to rule-based (RB) control strategy. Results show that the proposed APMP control strategy could greatly improve the fuel economy of the parallel PHEV for different driving conditions.