Singapore Institute of Technology
Browse

File(s) stored somewhere else

Please note: Linked content is NOT stored on Singapore Institute of Technology and we can't guarantee its availability, quality, security or accept any liability.

A Hybrid Data-Driven and Model-based Method for Modeling and Parameter Identification of Lithium-Ion Batteries

journal contribution
posted on 2023-09-27, 02:36 authored by Bin Gou, Yan Xu, Xue FengXue Feng

An accurate and practical model of lithium-ion batteries (LIBs) is necessary for state and health monitoring and battery energy management. This paper proposes a hybrid method for dynamic modeling and parameter identification for LIBs. Firstly, a fractional-order model (FOM) with free derivative orders is proposed to accurately describe electrochemical dynamic behaviors of the LIBs. Two constant phase elements (CPE) and a Warburg component are used to describe the impedance characteristics of the LIBs. Then, an ensemble learning structure based on random forest (RF) is designed to accurately extract the mapping relationship between the open circuit voltage (OCV) and state of charge (SOC) at different temperatures. Based on the dynamic stress test (DST) dataset, particle swarm optimization (PSO) algorithm is used to optimally identify the parameters of the FOM by comprehensively considering the identification accuracy and efficiency. Finally, the accuracy and robustness of the proposed FOM are verified and compared at different temperatures using the highly dynamic US06 highway driving schedule and the federal urban driving schedule (FUDS) test data. Compared with the second-order model with curve fitting methods, the proposed method has an overall higher accuracy and robustness at all temperatures and works well for low and high SOC ranges.

History

Journal/Conference/Book title

IEEE Transactions on Industry Applications

Publication date

2023-07-31

Usage metrics

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC