Energy-efficiency-oriented optimal control for electrical environmental control system based on advanced neural network

Abstract

Electric environmental control system (EECS) adopts the unique architecture of electric compressor bleed air and multi-stage expansion refrigeration. The state of EECS is regulated by the electric compressor and multiple regulation valves together, resulting in flow and temperature of each node strongly coupled. Traditional methods use a single valve to control each objective without considering the effect of control scheme on energy efficiency, which will not only lead to poor control effect, but also limit the coefficient of performance (COP) of EECS. This paper proposes an energy-efficiency-oriented optimal control strategy to improve the COP and control performance simultaneously. Part of the regulation valves are regarded as optimization variables and the corresponding control objectives are regarded as constraints to maximize COP. On this basis, an advanced control algorithm is proposed to further improve the control effect. The COP prediction model is developed with an offline trained neural network and the optimal valve openings are solved by the differential evolution algorithm. The controller is developed with another neural network online trained by adaptive learning rate algorithm to adapt to complex conditions. The results show that the COP and control performance of EECS are significantly improved. Compared to other strategies, the optimal control strategy can increase COP by up to 61.14%. This study provides an effective solution to the optimal control problem of systems with similar structure to EECS.