Takagi–Sugeno fuzzy model based shaft torque estimation for integrated motor–transmission system. (October 2019) - Zhu, Xiaoyuan; Li, Wei
Abstract: Shaft torque information is of great importance to develop advanced control system for electrified powertrains. However, it is rarely practical to find durable and also affordable physical sensors to achieve accurate torque measurement in commercial vehicles. This paper investigates a model based shaft torque estimation approach for integrated motor–transmission (IMT) system. First, Takagi–Sugeno (T–S) fuzzy modeling approach is adopted to deal with the nonlinearities in driving resistant load, which is directly related to vehicle speed. Based on this T–Sfuzzy model, a reduced order observer is developed to estimate the shaft torque as well as the wheel rotation speed by using the measurement of motor speed only. Considering external road resistance variation that caused by road slope change, H ∞ filtering approach is further adopted to attenuate its negative effect on shaft torque estimation performance. In addition, pole placement technique is also adopted to ensure the transient performance of the proposed reduced order observer. The observer gains are determined by both off-line calculation of a set of linear matrix inequalities and on-line computation of estimated wheel rotation speed related algebraic equations. Finally, Comparison analysis with Luenberger observers is carried out show the effectiveness as well as performance of proposed shaft torque estimation approach. Highlights: A T–S fuzzy reduced-order shaft torque observer is proposed for integrated motor–transmission (IMT) system. T–S fuzzy modeling approach is adopted to deal with the nonlinearity in the air drag torque. Robust H ∞ observer design approach is adopted to handle the road condition variations. Pole placement technique is adopted to improve proposed observer's transient performance. Only the motor speed measurement information is required, both the wheel rotation speed and shaft torque are estimated.