Title: Control strategy of genetic algorithm for a hybrid electric container loader

Authors: Jian Li; Hong Shu; Zhien Xu; Weizhou Huang

Addresses: School of Automotive Engineering, Chongqing University, Chongqing 400044, China ' School of Automotive Engineering, Chongqing University, Chongqing 400044, China ' School of Automotive Engineering, Chongqing University, Chongqing 400044, China ' Beijing Kangmu Fute Technology Co., Ltd., Beijing, 102200, China

Abstract: A genetic algorithm is applied to optimise the control parameters of a hybrid electric loader. Based on the optimal control parameters, a multidimensional response surface model for control parameters was established by a response surface method. The simulation shows that under multiple loads, battery temperatures and initial SOCs, the fuel saving of the hybrid loader is significant, the battery maintains the charge sustain or reaches within the optimal range, the battery temperature rise is kept within a reasonable range, and the battery charge and discharge rate is controlled within 1C. The fuel consumption of the hybrid electric loader is reduced by more than 20% compared with the traditional loader under the full load conditions. Compared with the original calculation model optimised by genetic algorithms and the dynamic programming, it was verified that the calculation accuracy and fuel saving significance of the response surface model for control parameters.

Keywords: HEVs; hybrid electric vehicles; control strategy; genetic algorithm; response surface; hybrid electric loader; container loader; energy management strategy; fuel consumption; simulation.

DOI: 10.1504/IJVP.2021.116062

International Journal of Vehicle Performance, 2021 Vol.7 No.3/4, pp.324 - 340

Received: 29 Feb 2020
Accepted: 04 Jul 2020

Published online: 06 May 2021 *

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