To improve the real-time capability, adaptivity, and efficiency of the energy management strategy in the actual driving cycle, a real-time energy management strategy is investigated for commute hybrid electric vehicles, which integrates mode switching with variable threshold and adaptive equivalent consumption minimization strategy. The proposed strategy includes offline and online parts. In the offline part based on the historical traffic data on the route of the commute vehicle, particle swarm optimization is applied to optimize all the thresholds of mode switching, equivalence factor of the equivalent consumption minimization strategy, and the engine torque and speed at the engine-alone propelling mode so as to establish their mappings on the battery state of charge and power demand. In the online part, the established mappings are involved in the energy management supervisor to generate timely appropriate mode switching signals, and an adaptive equivalence factor for instantaneous optimization equivalent consumption minimization strategy and the optimal engine torque and speed at engine-alone propelling mode. To fully demonstrate the effectiveness of the proposed strategy, the simulation results and comparison with some other strategies and the benchmark dynamic programming strategy are presented by implementing the strategies on the GT-SUITE test platform. The comparison result indicates that the control effect of the proposed energy management strategy is much nearer to that of the benchmark dynamic programming than those of other strategies (the rule-based control, the conventional equivalent consumption minimization strategy, the adaptive equivalent consumption minimization strategy, the rule-based-equivalent consumption minimization strategy, and the stochastic dynamic programming strategy) with the respective improvement in fuel efficiency by 25.9%, 13.25%, 4.6%, 1.32%, and 1.13%.
Design of Optimized Energy Management Strategy for All-Wheel-Drive Electric Vehicles
