A novel bi-subgroup adaptive evolutionary algorithm for optimizing degree of hybridization of HEV bus

• Published in: Cluster computing Vol. 20; no. 1; pp. 497 - 505
• Main Authors: Yan, Wei, Sun, Jing, Liu, Zhenggang, Hu, Yuping
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2017; Springer Nature B.V
• Subjects: Adaptive algorithms; Computer Communication Networks; Computer Science; Cost analysis; Electric vehicles; Energy consumption; Evolutionary algorithms; Genetic algorithms; Hybrid electric vehicles; Internal combustion engines; Multiple objective analysis; Mutation; Operating Systems; Optimization; Power sources; Processor Architectures; Search algorithms; Self adaptive control systems; Simulation models; Subgroups; Torque; HEV; DOH; Bi-subgroup; Adaptive evolutionary algorithm; Match and optimization
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-017-0753-3

A six-gear bus with traditional internal combustion engine is modified to a hybrid electric vehicle (HEV) which uses both engine and motor as power sources. Vehicle simulation model is set up in AVL-Cruise software simulation platform for researching and optimizing relevant technical parameters. Logic threshold control strategy based on ruling required torque is written to control vehicle driving mode and torque portion. After that, effects of different degree of hybrid (DOH) on vehicle performance are studied with the selected engine by matching different motors. Based on the results of simulation models by the standard condition of road spectrum, vehicle performance and cost under different DOH is analyzed to build the multi-objective function and constraint condition. This paper also develops a new method which has a better performance of global search, and the local search algorithm can improve the quality of the solutions with bi-subgroup self-adaptive evolutionary programming. In this novel algorithm, evolution of Cauchy operator and Gauss operator are parallel performed with different mutation strategies, and the Gauss operator owns the ability of self-adaptation according to the variation of adaptability function. Then this algorithm is used to seek the Pareto optimal solution of the multi-objective function of the HEV, and the best DOH for this model is obtained. The validity of this method is verified in later experiment.