Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization - Zhou, Quan; Zhang, Wei; Cash, Scott; Olatunbosun, Oluremi; Xu, Hongming; Lu, Guoxiang (Applied energy, 2017)
•A novel algorithm for hybrid electric powertrain intelligent sizing is introduced and applied.•The proposed CAPSO algorithm is capable of finding the real optimal result with much higher reputation.•Logistic mapping is the most effective strategy to build CAPSO.•The CAPSO gave more reliable results and increased the efficiency by 1.71%. This paper firstly proposed a novel HEV sizing method using the Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm and secondly provided a demonstration on sizing a series hybrid electric powertrain with investigations of chaotic mapping strategies to achieve the global optimization. In this paper, the intelligent sizing of a series hybrid electric powertrain is formulated as an integer multi-objective optimization issue by modelling the powertrain system. The intelligent sizing mechanism based on APSO is then introduced, and 4 types of the most effective chaotic mapping strategy are investigated to upgrade the standard APSO into CAPSO algorithms for intelligent sizing. The evaluation of the intelligent sizing systems based on standard APSO and CAPSOs are then performed. The Monte Carlo analysis and reputation evaluation indicate that the CAPSO outperforms the standard APSO for finding the real optimal sizing result with much higher reputation, and CAPSO with logistic mapping strategy is the most effective algorithm for HEV powertrain components intelligent sizing. In addition, this paper also performs the sensitivity analysis and Pareto analysis to help engineers customize the intelligent sizing system.