Research on energy saving optimization method of electric refrigerated truck based on genetic algorithm

• Published in: International journal of refrigeration Vol. 137; pp. 62 - 69
• Main Authors: Song, Haiying, Cai, Minghua, Cen, Jian, Xu, Chenhua, Zeng, Qingmeng
• Format: Journal Article
• Language: English
• Published: Paris Elsevier B.V 01.05.2022; Elsevier Science Ltd
• Subjects: Algorithme génétique; Camion frigorifique électrique; Electric refrigerated truck; Electricity; Electricity consumption; Energy consumption; Energy management; Energy saving; Flow velocity; Genetic algorithm; Genetic algorithms; Optimization; Parameters; Refrigerants; Refrigeration; TRNSYS; Économie d’énergie; TRNSYS; Genetic algorithm; Algorithme génétique; Économie d’énergie; Electric refrigerated truck; Energy saving; Camion frigorifique électrique; Optimization
• ISSN: 0140-7007; 1879-2081; 1879-2081
• DOI: 10.1016/j.ijrefrig.2022.02.003

•The energy optimization method for refrigeration systems is proposed and tested.•The parameters of GA for optimizing electrical refrigerated truck are researched.•Compare to different regions to verify the applicability of optimization method. To extend the working time of battery of the electric refrigerated truck, the optimization method of the refrigeration system of a certain electric refrigerated truck is researched in this paper. The compartment of the electric refrigerated truck and the working conditions of refrigeration is simulated by TRNSYS in a whole day. The genetic algorithm (GA) is used to optimize the operating parameters of the refrigeration system. Firstly, the appropriate optimization variables were selected. Secondly, the objective function was established in order to minimize energy consumption, then the constraints were set according to the actual working conditions. Finally, the parameters such as refrigerant flow rate, refrigerant transfer pump flow rate, and air supply flow rate are computed by genetic algorithm. The results show that with the reasonable parameters, the genetic algorithm can be effectively applied to the optimization of refrigerating truck refrigeration system, which can reduce the energy consumption of refrigerating truck. After optimization, the total energy consumption of refrigerating system can be reduced by average 0.5 kW, and the coefficient of performance (COP) can be increased by 0.15 equally. In addition, the endurance of the electric refrigerated truck will be greatly improved, and the cost of electricity consumption will be reduced.