Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164354
Title: Hybrid physics-data-driven online modelling: framework, methodology and application to electric vehicles
Authors: Chen, Hao
Lou, Shanhe
Lv, Chen
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Chen, H., Lou, S. & Lv, C. (2023). Hybrid physics-data-driven online modelling: framework, methodology and application to electric vehicles. Mechanical Systems and Signal Processing, 185, 109791-. https://dx.doi.org/10.1016/j.ymssp.2022.109791
Journal: Mechanical Systems and Signal Processing
Abstract: This paper proposes a novel hybrid physics-data-driven framework for system modelling by integrating a physical model and an online learning data model to improve model accuracy, interpretability, and generalization. Taking an in-wheel Motor Driven Vehicle (IMDV) as an example, two hybrid representations, i.e. the Dynamic Linearization Data Model (DLDM) and Recurrent High-Order Neural Network (RHONN) are introduced for the planar dynamics modelling of the electric vehicle. However, it is difficult to obtain the statistical information of the operation process and measurement noise when the weight vectors of the data-driven model is updated online. To address this issue, a H∞-based learning algorithm is adopted. The stability and convergence rate are elaborated and compared with an existing Extended Kalman Filter (EKF)-based method. Finally, we compare four methods, including the physics-based, data-based and two hybrid models, to evaluate their performances of modelling the IMDV's dynamics. The feasibility test and comparison studies are conducted in simulations and on a Hardware-in-the-Loop (HiL) test rig. The results demonstrated that the proposed H∞-based hybrid method, which does not make any assumption on measurement noise, has better generalization ability and robustness in practical implementations, compared to other baseline methods.
URI: https://hdl.handle.net/10356/164354
ISSN: 0888-3270
DOI: 10.1016/j.ymssp.2022.109791
Rights: © 2022 Elsevier Ltd. All rights reserved. This paper was published in Mechanical Systems and Signal Processing and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20250222
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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