Article: Development and validation of a Kalman filter based load torque estimation method for electric axle drives Journal: International Journal of Vehicle Performance (IJVP) 2023 Vol.9 No.1 pp.41 - 72 Abstract: Based on the automotive development strategies towards electrification and autonomous driving, the need of enhancements regarding state estimations, extended diagnosis as well as redundant architectures are evident. Accurate, robust and cost-efficient load torque estimation methods can bring significant benefits in this respect. Thus, this work presents a Kalman filter based load torque estimation method for electric axle drives by mean of virtual sensing. The elastically mounted electric rear axle drive of a Renault Twizy 80 was installed at a powertrain test rig. Using the measurement results, the parameters of a multi-body system (MBS) model were adjusted, in order to obtain a reference model for method validation. Subsequently, the basic functioning of the proposed load torque estimation method was shown by several test manoeuvres based on the measurement-adjusted reference model. The investigations revealed favourable results even in the case of reasonable effective torque dynamics (up to 3 Hz) and realistic sensor noise. Inderscience Publishers - linking academia, business and industry through research

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Title: Development and validation of a Kalman filter based load torque estimation method for electric axle drives

Authors: Robert Kalcher; Katrin Ellermann; Gerald Kelz; Karl Heinz Reisinger

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 ' Institute of Automotive Engineering, FH Joanneum Graz University of Applied Sciences, Alte Poststrasse 149, 8020 Graz, Austria

Abstract: Based on the automotive development strategies towards electrification and autonomous driving, the need of enhancements regarding state estimations, extended diagnosis as well as redundant architectures are evident. Accurate, robust and cost-efficient load torque estimation methods can bring significant benefits in this respect. Thus, this work presents a Kalman filter based load torque estimation method for electric axle drives by mean of virtual sensing. The elastically mounted electric rear axle drive of a Renault Twizy 80 was installed at a powertrain test rig. Using the measurement results, the parameters of a multi-body system (MBS) model were adjusted, in order to obtain a reference model for method validation. Subsequently, the basic functioning of the proposed load torque estimation method was shown by several test manoeuvres based on the measurement-adjusted reference model. The investigations revealed favourable results even in the case of reasonable effective torque dynamics (up to 3 Hz) and realistic sensor noise.

Keywords: load torque estimation; electric rear axle drives; ERAD; electric rear axle drive; virtual sensing; Kalman filter; multi-body systems; multi-body simulations; MBS; multi-body system; powertrain test rig; simulation-measurement adjustment.

DOI: 10.1504/IJVP.2023.128064

International Journal of Vehicle Performance, 2023 Vol.9 No.1, pp.41 - 72

Received: 17 May 2021
Accepted: 12 Jan 2022
Published online: 04 Jan 2023 *

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Title: Development and validation of a Kalman filter based load torque estimation method for electric axle drives

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