Open AccessAccelerated adaptive super twisting sliding mode observer‐based drive shaft torque estimation for electric vehicle with automated manual transmission
Author(s) -
Lin Cheng,
Sun Shengxiong,
Yi Jiang,
Walker Paul,
Zhang g
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5038
Subject(s) - control theory (sociology) , powertrain , observer (physics) , torque , vibration , engineering , torsional vibration , sliding mode control , computer science , nonlinear system , physics , control (management) , artificial intelligence , quantum mechanics , thermodynamics
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.