Broadband Load Torque Estimation in Mechatronic Powertrains using Nonlinear Kalman Filtering

摘要

An important bottleneck in the design, operation and exploitation of mechatronic powertrains is the lack of accurate knowledge of broadband external loading. This is caused by the intrusive nature of regular torque measurements. This paper proposes a novel non-intrusive approach to obtain torsional load information on mechatronic powertrains. Online coupled state/input estimation is performed through an augmented nonlinear Kalman filter. This estimation approach exploits general lumped-parameter physics-based models in order to create a widely applicable framework. This work considers both extended (EKF) and unscented Kalman filtering (UKF) approaches. Contrary to previous works, no considerable difference in accuracy is obtained from experiments, with a considerably lower computational load for the EKF. This work reveals the benefits of including rotational acceleration measurements from a theoretical perspective, which is demonstrated through experimental validation. This drastically increases the broadband accuracy. The result of this work is an accurate and non-invasive virtual torque sensor with a sufficiently broad bandwidth for use in condition monitoring, control and future design optimization.