Exploitation of a Particle Swarm Optimization Algorithm for Designing a Lightweight Parallel Hybrid Electric Vehicle

The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers' cars. Nevertheless, the large amount of possible powertrain designs does not help the development of an exhaustive sizing process. In this research, a multi-objective particle swarm optimization algorithm is proposed to find the optimal layout of a parallel P2 hybrid electric vehicle powertrain with the aim of maximizing fuel economy capability and minimizing production cost. A dynamic programming-based algorithm is used to ensure the optimal vehicle-level energy management. The results show that diverse powertrain layouts may be suggested when different weights are assigned to the sizing targets related to fuel economy and production cost, respectively. Particularly, upsizing the power sources and increasing the number of gears might be advised to enhance HEV fuel economy capability through the efficient exploitation of the internal combustion engine (ICE) operation. On the other hand, reduction of the HEV production cost could be achieved by downsizing the power sources and limiting the number of gears with respect to conventional ICE-powered vehicles thanks to the interaction between ICE and electric motor.

Automated summary

This research uses a multi-objective particle swarm optimization algorithm to find the optimal layout of a hybrid electric vehicle powertrain, aiming to maximize fuel economy capability and minimize production cost. The results show that different powertrain layouts may be suggested by assigning different weights to sizing targets related to fuel economy and production cost, respectively, to achieve higher fuel economy or lower production cost.