This paper presents an optimization work on hybrid electric vehicle (HEV) powertrain using Genetic Algorithm (GA) method. It focused on optimization of the parameters of powertrain components including supercapacitors to obtain maximum fuel economy. Vehicle modelling is based on Quasi-Static-Simulation (QSS) backward-facing approach. A combined city (FTP-75)-highway (HWFET) drive cycle is utilized for the design process. Seeking global optimum solution, GA was executed with different initial settings to obtain sets of optimal parameters. Starting from a benchmark HEV, optimization results in a smaller engine (2 l instead of 3 l) and a larger battery (15.66 kWh instead of 2.01 kWh). This leads to a reduction of 38.3% in fuel consumption and 30.5% in equivalent fuel consumption. Optimized parameters are also compared with actual values for HEV in the market.
|Journal||IOP Conference Series: Materials Science and Engineering|
|Publication status||Published - Apr 3 2017|
|Event||3rd International Conference on Mechanical, Automotive and Aerospace Engineering, ICMAAE 2016 - Kuala Lumpur, Malaysia|
Duration: Jul 25 2016 → Jul 27 2016
ASJC Scopus subject areas
- Materials Science(all)