Preventive maintenance decision model of electric vehicle charging pile based on mutation operator and life cycle optimization

• Published in: Energy science & engineering Vol. 12; no. 6; pp. 2616 - 2626
• Main Authors: Cai, Jian, Ding, Xiaoyin, Jiang, Zhibo, Chen, Jingyun, Cen, Zhijia
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.06.2024; Wiley
• Subjects: Age factors; Aging; Algorithms; Decision making; Efficiency; electric vehicle; Electric vehicle charging; Electric vehicles; Energy management; life cycle optimization; Life cycles; maintenance of charging pile; Mutation; mutation operator; Optimization algorithms; Particle swarm optimization; Preventive maintenance
• ISSN: 2050-0505; 2050-0505
• DOI: 10.1002/ese3.1766

This paper proposes a preventive maintenance decision model for electric vehicle charging stations based on mutation operators and lifecycle optimization to address the impact of potential faults on maintenance effectiveness. By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was conducted to establish an indicator system for the operation status of charging piles, and a potential fault identification model was constructed. By optimizing the life cycle, the balance problem between optimal maintenance life and optimal opportunity maintenance life has been solved, thus completing preventive maintenance decisions. The experimental results show that the accuracy of this method in preventive maintenance decision‐making for electric vehicle charging piles can reach 98%, with an average preventive maintenance decision‐making time of 1.6 s for load piles. At the same time, the risk probability value and load loss value are effectively controlled. This study has good application prospects in improving the preventive maintenance effect of electric vehicle charging piles. By introducing a particle swarm optimization algorithm with mutation operators, the model can accurately identify potential faults in charging piles and construct a comprehensive operational status indicator system. Meanwhile, using lifecycle optimization methods, the model found the optimal balance between maintenance cost, maintenance effectiveness, and service life, effectively solving the conflict between optimal maintenance life and optimal opportunity maintenance life.