Hybrid physics-data-driven online modelling: Framework, methodology and application to electric vehicles

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 representa...

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Bibliographic Details
Published in:Mechanical systems and signal processing Vol. 185; p. 109791
Main Authors: Chen, Hao, Lou, Shanhe, Lv, Chen
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.02.2023
Subjects:
Electric vehicles
Hybrid modelling
Online learning
Physics-data-driven method
H
Electric vehicles
Hybrid modelling
Online learning
Physics-data-driven method
ISSN:0888-3270, 1096-1216
Online Access:Get full text
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Summary: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. •A novel hybrid physics-data-driven framework for system online modelling is proposed.•The data-driven online modelling can adapt to the fast dynamics of vehicle systems.•H∞-based learning shows fast convergency and robustness for parameter identification.
ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2022.109791