Analysis of the Adaptation of a New Method for Four-Wheel-Hub Electric Vehicle Online-Mass Estimation

  • Jin Zhang
  • Zhuoping Yu
  • Lu Xiong
  • Yuan Feng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 194)


An accurate estimation of vehicle mass is important in automation of vehicle, vehicle following manoeuvres and traditional power train control schemes. It is easy for four-wheel-hub electric motor to get accurate speed signals and torque signals. Based on this feature we introduce a new algorithm for electric vehicle online-mass estimation by decoupling vehicle mass and road grade. In the Matlab/Simulink simulation environment we establish the new estimation algorithm model and an 18 degree of freedom vehicle model. We analyze the accuracy of this online-mass estimation method by changing the value of different parameters respectively, for example, different masses, different rolling resistances… This new mass estimation method is fast and reaches a high accuracy without extra sensors.


Mass estimation Longitudinal dynamic Decoupling Algorithm Simulation 



This work was supported by National Basic Research Program of China. (No.2011CB711200)


  1. 1.
    ONISR, Les grandes donn’ees de laccidentologie (2005) In rapport ONISR.
  2. 2.
    Breen MT (1996) System and method for determining relative vehicle mass. No. 5,482,359Google Scholar
  3. 3.
    Genise T (1994) Control method system including determination of an updated value indicative of gross combination weight of vehicles. No. 5,490,063Google Scholar
  4. 4.
    Bae HS, Ryu J, Gerdes JC (2001) Road grade and vehicle parameter estimation for longitudinal control using GPS. In: Proceedings of the IEEE intelligent transportation systems conferenceGoogle Scholar
  5. 5.
    Vahidi A, Stefanopoulou A, Peng H (2005) Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Veh Syst Dyn 43:1, 31–55Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Tongji UniversityShanghaiChina

Personalised recommendations