TY - JOUR
T1 - Load torque estimation for an automotive electric rear axle drive by means of virtual sensing using Kalman filtering
AU - Kalcher, Robert
AU - Ellermann, Katrin
AU - Kelz, Gerald
N1 - Publisher Copyright:
Copyright © 2022 Inderscience Enterprises Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Battery electric vehicles
KW - Bev
KW - Electric rear axle drive
KW - Hev
KW - Hybrid electric vehicles
KW - Kalman filtering
KW - Load torque estimation
KW - Mbs
KW - Multi-body simulations
KW - Reduced-order unscented kalman filter
KW - Roukf
KW - Ukf
KW - Unscented kalman filter
KW - Vehicle systems modelling
KW - Virtual sensing
UR - http://www.scopus.com/inward/record.url?scp=85121450741&partnerID=8YFLogxK
U2 - 10.1504/IJVP.2022.119432
DO - 10.1504/IJVP.2022.119432
M3 - Article
AN - SCOPUS:85121450741
SN - 1745-3194
VL - 8
SP - 1
EP - 30
JO - International Journal of Vehicle Performance
JF - International Journal of Vehicle Performance
IS - 1
ER -