A real-time optimization energy management of range extended electric vehicles for battery lifetime and energy consumption

Due to the complexity of two power source control for range extended electric vehicles, this paper proposes a real-time multi-objective prediction energy management strategy that can find a tradeoff between minimizing auxiliary power unit fuel cost, minimizing electric cost, and minimizing battery degradation cost. First, the multiobjective energy management problem is formulated as minimizing total operating cost. The model predictive control based real-time multi-objective prediction energy management strategy is designed to achieve real-time energy management control. Furthermore, a novel energy management optimizer based on the direct multiple shooting method and sequential quadratic programming algorithm is proposed to improve the real-time performance of the online optimization process. Finally, the total operating cost of the proposed strategy is reduced by 8.05% and 13.12% compared with the equivalent consumption minimization strategy and the chargedepleting charge-sustaining strategy. The calculation duration of the proposed strategy for each rolling optimization step is only about 0.15 s. The simulation results manifest the excellent performance of the proposed real-time multi-objective prediction energy management strategy.

Automated summary

This paper proposes a real-time multi-objective prediction energy management strategy for range extended electric vehicles, aiming to find a tradeoff between different costs. By utilizing an optimization algorithm, the strategy achieves significant energy savings and fast computational speed.