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A regenerative braking control strategy for electric vehicle with four in-wheel motors

  • Jilin University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Regenerative braking control technology of electric vehicle plays a vital role in automotive energy-saving and environmental protection. Actually, there are two important aspects included in regenerative braking control. First and foremost is to maintain the vehicle safety during the braking process, and secondly is to maximize the energy recovery and minimize the energy consumption as far as possible. This paper proposes a regenerative braking control strategy to meet the above two aspects. In this research, the electric vehicle is assumed to keep straight line driving with a driver. First, according to the desired braking torques of the driver during braking process, the brake torque on front and rear axle respectively are allocated based on the tire load ratio, which makes sure that maximizing the use of tire adhesion during deceleration. Second, in order to deal with multi-objections and constraints for maximizing the energy recovery and minimizing the energy consumption, an model predictive controller is designed to distribute the brake torque between the hydraulic brake mode and the electric motor brake mode. In the end, the effectiveness of the proposed strategy is verified through the simulations of the electric vehicle model with four individually driven in-wheel motors based on Matlab/Simulink and AMESim software.

Original languageEnglish
Title of host publicationProceedings of the 35th Chinese Control Conference, CCC 2016
EditorsJie Chen, Qianchuan Zhao, Jie Chen
PublisherIEEE Computer Society
Pages8671-8676
Number of pages6
ISBN (Electronic)9789881563910
DOIs
StatePublished - 26 Aug 2016
Externally publishedYes
Event35th Chinese Control Conference, CCC 2016 - Chengdu, China
Duration: 27 Jul 201629 Jul 2016

Publication series

NameChinese Control Conference, CCC
Volume2016-August
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference35th Chinese Control Conference, CCC 2016
Country/TerritoryChina
CityChengdu
Period27/07/1629/07/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • electric vehicle
  • model predictive control
  • optimal control
  • regenerative braking

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