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author:

Lin, Xinyou (Lin, Xinyou.) [1] | Wei, Shenshen (Wei, Shenshen.) [2] | Wu, Jiayun (Wu, Jiayun.) [3]

Indexed by:

EI

Abstract:

The dual-motor coupling-propulsion electric vehicle (DMCP-EV) has the merit of favorable energy efficiency due to its multiple operation modes. In the driving process, the performance of the DMCP-EV depends greatly on the power distribution to meet various driving requests. A power distribution coefficient is optimized by using the genetic-Pareto algorithm (GA-Pareto) algorithm. In addition to this effort, a power distribution strategy based on tradeoff optimization with comprehensive consideration of energy efficiency and ride comfort is proposed. Then, the sensitive analysis of optimized performance is conducted to obtain the three appropriate GA-Pareto algorithm parameters. The benchmark strategies are conducted to validate the proposed tradeoff power distribution strategy and the comparisons of three preference strategies with the hardware-in-loop (HIL) validation are performed. Results of the simulation and HIL experiments demonstrate that the proposed control strategies can address the tradeoff between energy-efficient and ride comfort for DMCP-EV. © 2022 by ASME.

Keyword:

Electric power distribution Electric vehicles Energy efficiency Genetic algorithms Particle swarm optimization (PSO) Powertrains Traction motors

Community:

  • [ 1 ] [Lin, Xinyou]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Wei, Shenshen]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Wu, Jiayun]College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China

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Source :

Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

ISSN: 0022-0434

Year: 2022

Issue: 6

Volume: 144

1 . 7

JCR@2022

1 . 7 0 0

JCR@2023

ESI HC Threshold:66

JCR Journal Grade:3

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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