RLS with Optimum Multiple Adaptive Forgetting Factors for SoC and SoH Estimation of Li-Ion Battery

Estiko Rijanto, Latif Rozaqi, Asep Nugroho, Stratis Kanarachos

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding

    3 Citations (Scopus)
    85 Downloads (Pure)

    Abstract

    Recursive least square (RLS) with a single forgetting factor has been commonly used for parameter and state estimation of dynamical systems. In many applications such as robotics, electric vehicles, renewable energy systems, and smart-grid, accurate battery state of charge (SOC) and state of health (SOH) estimation is essential for the safe and efficient operation. To this end, the challenge lies in identifying and parameterizing the temporal behavior of Lithium-Ion batteries, because their response is nonlinear and time-varying. This paper proposes a new RLS algorithm with optimum multiple adaptive forgetting factors (MAFFs) for SOC and SOH estimation of Li-ion batteries. Particle swarm intelligence is employed for identifying the system parameters. The performance of the optimum MAFF-RLS algorithm is compared to RLS with multiple fixed forgetting factors (MFFFs). Performance evaluation is carried out using the Urban Dynamometer Driving Schedule (UDDS). The simulation results indicate the better performance of MAFF-RLS algorithm compared to MFFF-RLS algorithm in terms of mean square error of SOC and internal resistance.
    Original languageEnglish
    Title of host publicationProceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation, ICA 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages73-77
    Number of pages5
    ISBN (Electronic)978-1-5386-0349-9, 978-1-5386-0348-2
    ISBN (Print)978-1-5386-0350-5
    DOIs
    Publication statusPublished - 16 Oct 2017
    Event5th International Conference on Instrumentation, Control, and Automation, ICA 2017 - Yogyakarta, Indonesia
    Duration: 9 Aug 201711 Aug 2017

    Conference

    Conference5th International Conference on Instrumentation, Control, and Automation, ICA 2017
    CountryIndonesia
    CityYogyakarta
    Period9/08/1711/08/17

    Bibliographical note

    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Keywords

    • RLS
    • forgetting factor
    • Li-Ion
    • Battery
    • SOC
    • SOH

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Control and Systems Engineering
    • Control and Optimization
    • Instrumentation

    Fingerprint Dive into the research topics of 'RLS with Optimum Multiple Adaptive Forgetting Factors for SoC and SoH Estimation of Li-Ion Battery'. Together they form a unique fingerprint.

  • Cite this

    Rijanto, E., Rozaqi, L., Nugroho, A., & Kanarachos, S. (2017). RLS with Optimum Multiple Adaptive Forgetting Factors for SoC and SoH Estimation of Li-Ion Battery. In Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation, ICA 2017 (pp. 73-77). [8068416] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICA.2017.8068416