Title: Load torque estimation for an automotive electric rear axle drive by means of virtual sensing using Kalman filtering

Authors: Robert Kalcher; Katrin Ellermann; Gerald Kelz

Addresses: AMSD Advanced Mechatronic System Development KG, Reininghausstrasse 13a, 8020 Graz, Austria ' Institute of Mechanics, Graz University of Technology, Kopernikusgasse 24/IV, 8010 Graz, Austria ' AMSD Advanced Mechatronic System Development KG, Reininghausstrasse 13a, 8020 Graz, Austria

Abstract: Load torque signal information in hybrid or battery electric vehicles would be beneficial for control applications, extended diagnosis or load spectrum acquisition. Due to the high cost of the sensor equipment and because of the inaccuracies of state-of-the-art estimation methods, however, there is currently a lack of accurate load torque signals available in series production vehicles. In response to this, this work presents a novel model-based load torque estimation method using Kalman filtering for an electric rear axle drive. The method implements virtual sensing by using measured twist motions of the electric rear axle drive housing and appropriate simulation models within a reduced-order unscented Kalman filter. The proposed method is numerically validated with help of sophisticated multibody simulation models, where influences of hysteresis, torque dynamics, road excitations and several driving manoeuvres such as acceleration and braking are analysed.

Keywords: load torque estimation; electric rear axle drive; virtual sensing; Kalman filtering; unscented Kalman filter; UKF; unscented Kalman filter; reduced-order unscented Kalman filter; ROUKF; reduced-order unscented Kalman filter; HEV; hybrid electric vehicles; BEV; battery electric vehicles; MBS; multi-body simulations; vehicle systems modelling.

DOI: 10.1504/IJVP.2022.119432

International Journal of Vehicle Performance, 2022 Vol.8 No.1, pp.1 - 30

Received: 01 May 2020
Accepted: 25 Oct 2020
Published online: 05 Dec 2021 *

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