Accelerated adaptive super twisting sliding mode observer‐based drive shaft torque estimation for electric vehicle with automated manual transmission. Issue 1 (21st August 2018)

• Record Type: Journal Article
• Authors: Lin, Cheng; Sun, Shengxiong; Yi, Jiang; Walker, Paul; Zhang, Nong
• Is Part Of: IET intelligent transport systems. Volume 13:Issue 1(2019)
• Page Start: 160
• Page End: 167
• Publication Date: 2018-08-21
• Subjects: power transmission (mechanical) -- variable structure systems -- observers -- torque control -- vibrations -- shafts -- electric vehicles -- adaptive control -- estimation theory; size 0.0 A -- chatting problem -- test rig -- system damping -- adaptive gain -- nonlinear drag torqu -- nonlinear external characteristics -- torque estimation -- shifting moment -- estimation algorithm -- estimation error convergence -- second‐order super twisting sliding mode algorithm -- electric powertrain system -- driving motor -- motor‐transmission integrated powertrain system -- jerking feel -- torsional vibration -- elastic drive shaft -- automated manual transmission -- electric vehicle -- accelerated adaptive super twisting sliding mode observer‐based drive shaft torque estimation; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5038
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.