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15 October 2021 Application of improved adaptive genetic algorithm in energy consumption optimization of electric refrigerated vehicles
Haiying Song, Minghua Cai, Jian Cen III, Chenhua Xu IV, Qingmeng Zeng
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Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119332G (2021) https://doi.org/10.1117/12.2615284
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China
Abstract
In view of the large energy loss problem of common mechanical refrigerated vehicles on the market, an improved adaptive genetic algorithm(AGA) is proposed to apply to the energy consumption optimization of the refrigeration system of electric refrigerated vehicles, and the crossover operator and mutation operator are adaptively adjusted through nonlinear adjustment. Probability enables the algorithm to generate better individuals during the evolution process to obtain the global optimal solution, which minimizes the energy consumption of the refrigeration system. This paper takes a certain type of electric refrigerated vehicle as the research object, uses TRNSYS to model the electric refrigerated vehicle compartment and its refrigeration system, simulates the annual refrigeration conditions, and uses an AGA to optimize the blower flow rate and refrigerant delivery pump flow rate. AGA can reduce the total energy consumption of refrigeration system by 9.8% and improve the COP by 4.7% compared with GA. The improved AGA enhances the local search ability of genetic algorithm (GA), improves the efficiency of the algorithm, and effectively reduces the energy consumption of refrigerated vehicle.
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Haiying Song, Minghua Cai, Jian Cen III, Chenhua Xu IV, and Qingmeng Zeng "Application of improved adaptive genetic algorithm in energy consumption optimization of electric refrigerated vehicles", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119332G (15 October 2021); https://doi.org/10.1117/12.2615284
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KEYWORDS
Optimization (mathematics)
Control systems
Genetic algorithms
Mathematical modeling
Solar radiation models
MATLAB
Genetics
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