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A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation

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  • Ibrahim M. Safwat

    (Electrical Engineering Department, Northwestern Polytechnical University, Xi’an 710065, China)

  • Weilin Li

    (Electrical Engineering Department, Northwestern Polytechnical University, Xi’an 710065, China)

  • Xiaohua Wu

    (Electrical Engineering Department, Northwestern Polytechnical University, Xi’an 710065, China)

Abstract

State-of-charge (SOC) estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC) of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.

Suggested Citation

  • Ibrahim M. Safwat & Weilin Li & Xiaohua Wu, 2017. "A Novel Methodology for Estimating State-Of-Charge of Li-Ion Batteries Using Advanced Parameters Estimation," Energies, MDPI, vol. 10(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1751-:d:117234
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    References listed on IDEAS

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    1. Zhihao Yu & Ruituo Huai & Linjing Xiao, 2015. "State-of-Charge Estimation for Lithium-Ion Batteries Using a Kalman Filter Based on Local Linearization," Energies, MDPI, vol. 8(8), pages 1-20, July.
    2. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
    3. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    4. Friedrich Pukelsheim, 2007. "Optimum Experimental Designs, with SAS by Anthony Atkinson, Alexander Donev, Randall Tobias," International Statistical Review, International Statistical Institute, vol. 75(3), pages 413-413, December.
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    Cited by:

    1. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    2. Can Aksakal & Altug Sisman, 2018. "On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 11(1), pages 1-14, January.
    3. Ragab El-Sehiemy & Mohamed A. Hamida & Ehab Elattar & Abdullah Shaheen & Ahmed Ginidi, 2022. "Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm," Energies, MDPI, vol. 15(13), pages 1-20, June.
    4. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.

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