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

Estiko Rijanto; Latif Rozaqi; Asep Nugroho; Stratis Kanarachos

doi:10.1109/ICA.2017.8068416

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

Estiko Rijanto, Latif Rozaqi, Asep Nugroho, Stratis Kanarachos

Indonesian Institute of Sciences (LIPI)

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

21 Citations (Scopus)

370 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 language	English
Title of host publication	Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation, ICA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	73-77
Number of pages	5
ISBN (Electronic)	978-1-5386-0349-9, 978-1-5386-0348-2
ISBN (Print)	978-1-5386-0350-5
DOIs	https://doi.org/10.1109/ICA.2017.8068416
Publication status	Published - 16 Oct 2017
Event	5th International Conference on Instrumentation, Control, and Automation, ICA 2017 - Yogyakarta, Indonesia Duration: 9 Aug 2017 → 11 Aug 2017

Conference

Conference	5th International Conference on Instrumentation, Control, and Automation, ICA 2017
Country/Territory	Indonesia
City	Yogyakarta
Period	9/08/17 → 11/08/17

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICA.2017.8068416

Post-PrintAccepted author manuscript, 2.63 MB

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). Article 8068416 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICA.2017.8068416

RLS with Optimum Multiple Adaptive Forgetting Factors for SoC and SoH Estimation of Li-Ion Battery. / Rijanto, Estiko; Rozaqi, Latif; Nugroho, Asep et al.
Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation, ICA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 73-77 8068416.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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., 8068416, Institute of Electrical and Electronics Engineers Inc., pp. 73-77, 5th International Conference on Instrumentation, Control, and Automation, ICA 2017, Yogyakarta, Indonesia, 9/08/17. https://doi.org/10.1109/ICA.2017.8068416

Rijanto E, Rozaqi L, Nugroho A, Kanarachos S. 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. Institute of Electrical and Electronics Engineers Inc. 2017. p. 73-77. 8068416 Epub 2017 Jul 17. doi: 10.1109/ICA.2017.8068416

@inproceedings{48bbb0a307234defbbed2b6d1ca65940,

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

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.",

keywords = "RLS, forgetting factor, Li-Ion, Battery, SOC, SOH",

author = "Estiko Rijanto and Latif Rozaqi and Asep Nugroho and Stratis Kanarachos",

note = "{\textcopyright} 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.; 5th International Conference on Instrumentation, Control, and Automation, ICA 2017 ; Conference date: 09-08-2017 Through 11-08-2017",

year = "2017",

month = oct,

day = "16",

doi = "10.1109/ICA.2017.8068416",

language = "English",

isbn = "978-1-5386-0350-5",

pages = "73--77",

booktitle = "Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation, ICA 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

TY - GEN

T1 - RLS with Optimum Multiple Adaptive Forgetting Factors for SoC and SoH Estimation of Li-Ion Battery

AU - Rijanto, Estiko

AU - Rozaqi, Latif

AU - Nugroho, Asep

AU - Kanarachos, Stratis

N1 - © 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.

PY - 2017/10/16

Y1 - 2017/10/16

N2 - 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.

AB - 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.

KW - RLS

KW - forgetting factor

KW - Li-Ion

KW - Battery

KW - SOC

KW - SOH

UR - http://www.scopus.com/inward/record.url?scp=85037083682&partnerID=8YFLogxK

U2 - 10.1109/ICA.2017.8068416

DO - 10.1109/ICA.2017.8068416

M3 - Conference proceeding

AN - SCOPUS:85037083682

SN - 978-1-5386-0350-5

SP - 73

EP - 77

BT - Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation, ICA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Instrumentation, Control, and Automation, ICA 2017

Y2 - 9 August 2017 through 11 August 2017

ER -

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

Abstract

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this