Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm. (1st February 2020)

• Record Type: Journal Article
• Authors: Lü, Xueqin; Wu, Yinbo; Lian, Jie; Zhang, Yangyang; Chen, Chao; Wang, Peisong; Meng, Lingzheng
• Is Part Of: Energy conversion and management. Volume 205(2020)
• Publication Date: 2020-02-01
• Subjects: Fuel cell hybrid electric vehicle -- Energy management strategy -- Hybrid power system -- Genetic algorithm -- Optimization parameters and objectives; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.112474
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Summarized the topological structure types of fuel cell hybrid vehicles. Summarize the optimal parameters in the energy management strategy. Expounded several objectives of energy management optimization. Analyzed and summarized the GA optimization effect on EMS. Prospecting the challenge and direction of energy management optimization. Abstract: Under the background of current environmental pollution and serious shortage of fossil energy, the development of electric vehicles driven by clean new energy is the key to solve this problem, especially the hybrid electric vehicle driven by fuel cell is the most effective solution. Many scholars have found that the output performance of hybrid system is an important reason to determine the life of fuel cell. Unreasonable output will affect the control characteristics of the drive system, resulting in a series of serious consequences such as the reduction of the life of fuel cell hybrid power system. Therefore, the energy management strategy and performance optimization of hybrid system is the key to ensure the normal operation of the system. At present, many excellent researchers have carried out relevant research in this field. Genetic algorithm is a heuristic algorithm, which has better optimization performance. It can easily choose satisfactory solutions according to the optimization objectives, and make up for these shortcomings by using its own characteristics. These characteristics make genetic algorithm have outstanding advantages in the iterative optimization of energy management strategy. This paper analyzes and summarizes the optimization effect of genetic algorithm in various energy management strategies, aiming to analyze and select the optimization rules and parameters, optimization objects and optimization objectives. This paper hopes to provide guidance for the optimal control strategy and structural design of the fuel cell hybrid power system, contribute to the research on improving the energy utilization efficiency of the hybrid power system and extending the life of the fuel cell, and provide more ideas for the optimization of energy management in the future.