Robust optimization of energy management strategy in hybrid vehicles based on digital twin and PSO algorithm

doi:10.3969/j.issn.1674-8484.2022.03.013

Abstract

Abstract:

A robust particle swarm optimization (PSO) scheme for the development of energy management strategy for hybrid vehicles was proposed based on digital twin. By incorporating global cross-validation with local particle swarm optimization, the proposed scheme aimed to achieve more reliable optimization for energy management. First, a digital twin model for the hybrid vehicle was built based on the chassis dynamometer test data and an adaptive neural fuzzy inference system (ANFIS) was then developed for real-time energy management. By introducing the concept of control utility, which evaluated the vehicle energy efficiency with a penalty factor of battery usage, the robust particle swarm optimisation scheme was deployed to optimize the hyper parameters of the ANFIS controller. The optimization performances were evaluated through experiment based on the hardware-in-the-loop testing platform under worldwide driving cycles including JC08, WLTC, and UDDS. Compared to conventional particle swarm optimisation, the proposed robust particle swarm optimization can achieve more than 11% higher control utility value in both learning cycles and testing cycles and improve the fuel economy by up to 27.92%.

Key words: hybrid electric vehicle, energy management strategy, particle swarm optimization (PSO), robust optimization, cross-validation

CLC Number:

U469.7

ZHOU Quan, ZHANG Cetengfei, LI Yanfei, SHUAI Bin, XU Hongming. Robust optimization of energy management strategy in hybrid vehicles based on digital twin and PSO algorithm[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 517-525.

Figures/Tables 13

车辆质量	1 315 kg
车轮旋转半径	0.35 m
主减速比	9.655
电池额定电压	352 V
电池安全充放 / 电流	413 A / 16 A

	第1集	第2集	第3集	综合效用
第1折	0.199 2	0.273 6	0.225 7	0.199 2
第2折	0.172 1	0.278 6	0.206 7	0.172 1
第3折	0.179 3	0.274 4	0.226 7	0.179 3

	$U$ _high	提升 / %	$U$ _main	提升 / %
鲁棒粒子群能优化	0.195 8	—	0.094 9	—
粒子群优化对照组1	0.190 6	2.66	0.094 6	0.32
粒子群优化对照组2	0.168 5	13.94	0.094 3	0.63

	$U$ _high	提升 / %	$U$ _main	提升 / %
鲁棒粒子群能优化	0.195 8	—	0.094 9	—
粒子群优化对照组1	0.190 6	2.66	0.094 6	0.32
粒子群优化对照组2	0.168 5	13.94	0.094 3	0.63

	V_high / L	提升 / %	V_main / L	提升 / %
鲁棒粒子群能优化	2.883	—	4.599	—
粒子群优化对照组1	3.006	4.27	4.636	0.80
粒子群优化对照组2	3.688	27.92	4.618	0.41

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