Title: Research on multi objective intelligent shifting schedule of electric vehicle AMT considering ride comfort and economy

Authors: Donghui Lv; Lin Yuan; Bo Zhu; Zhidong Liu; Xue Bai

Addresses: The college of Automobile and Rail Transit, Tianjin Sino-German University of Applied Sciences, Tianjin, 300350, China ' Proving Ground Management Department, CATARC Yancheng Automotive Test Ground Co. Ltd., Yancheng, 224100, China ' Intelligent Manufacturing Institute, Hefei University of Technology, Hefei, 230009, China ' The college of Automobile and Rail Transit, Tianjin Sino-German University of Applied Sciences, Tianjin, 300350, China ' The college of Automobile and Rail Transit, Tianjin Sino-German University of Applied Sciences, Tianjin, 300350, China

Abstract: Considering the current shifting strategy of multi-speed automatic manual transmission (AMT) separates the steady-state shifting from the transient shifting process in the pure electric vehicle, it is difficult to find a comprehensive improvement of shifting quality, dynamic performance, and driving economy. In this paper, taking advantage of the artificial intelligence technology, a fuzzy neural network (FNN) based T-S model is established via obtaining the training data from skilled drivers' experience and expert knowledge. A two-speed AMT pure electric vehicle model is used to investigate the fuzzy shifting strategy performance. According to the co-simulation results of AMESim and SIMULINK, the average jerk of 10.006 is recorded, compared to the value of 16.472 based on an ordinary shifting schedule. The results show that FNN-based schedule fully reflects drivers' shifting intentions in pursuing shifting smoothness, at the same time, improving vehicle dynamic performance with negligible economic performance loss.

Keywords: two-speed; AMT; automatic manual transmission; gear shift smoothness; FNN; fuzzy neural network; shifting schedule; electric vehicle; ride comfort; economy performance; multi-objective.

DOI: 10.1504/IJVD.2023.134737

International Journal of Vehicle Design, 2023 Vol.92 No.2/3/4, pp.278 - 299

Received: 10 Jan 2022
Received in revised form: 19 Sep 2022
Accepted: 26 Oct 2022
Published online: 09 Nov 2023 *

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