Hybrid power system with adaptive adjustment of weight coefficients multi-objective model predictive control

Abstract

Considering the complex flight conditions of aircraft and the difficulty of the oil-electric hybrid system to rationally allocate the energy power and give full play to the various energy advantages under different flight conditions, an energy management strategy of the hybrid system based on multi-objective model prediction is proposed to optimize the engine working area, minimize the engine dynamic characteristics, and optimally manage the charge state of the energy storage battery. First, the fuel consumption efficiency curve identification based on the maximum likelihood method is proposed to obtain the optimal working power area of the engine. The real-time estimation of load power is realized by designing two load power filters. Then, combined with the working characteristics of the engine and energy storage battery, an optimal energy management strategy based on multi-objective model predictive control is designed. The fuzzy control method is adopted to dynamically adjust the weight coefficients of each constraint term according to the flight conditions to achieve the global optimization. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments. The results show that in the proposed optimization management strategy, the engine works in the optimal fuel consumption area, which is more economical than the pure fuel drive, avoids the overcharging and discharging of the energy storage battery and the severe fluctuation of the engine power, and greatly improves the economic security and smoothness of the system.