跳到主要导航 跳到搜索 跳到主要内容

Accelerated adaptive super twisting sliding mode observer-based drive shaft torque estimation for electric vehicle with automated manual transmission

  • Cheng Lin
  • , Shengxiong Sun*
  • , Jiang Yi
  • , Paul Walker
  • , Nong Zhang
  • *此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

The suddenly released torque that accumulated in the elastic drive shaft will bring torsional vibration and jerking feel at the shifting moment. A novel sliding mode observer is proposed to estimate the torque in drive shaft for a motor-transmission integrated powertrain system. Non-linear external characteristics of a driving motor and non-linear drag torque are considered in the electric powertrain system. In order to attenuate the chatting problem, the second-order super twisting sliding mode algorithm with an adaptive gain is adopted. Furthermore, a term 'system damping' is introduced to accelerate the estimation error convergence. The proposed estimation algorithm is tested on test rig for typical operating conditions. The results show that the torque in drive shaft can be estimated satisfactorily and the tracking error converges to 0 in a short time.

源语言英语
页(从-至)160-167
页数8
期刊IET Intelligent Transport Systems
13
1
DOI
出版状态已出版 - 1 1月 2019

指纹

探究 'Accelerated adaptive super twisting sliding mode observer-based drive shaft torque estimation for electric vehicle with automated manual transmission' 的科研主题。它们共同构成独一无二的指纹。

引用此