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On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model

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  • Allafi, Walid
  • Uddin, Kotub
  • Zhang, Cheng
  • Mazuir Raja Ahsan Sha, Raja
  • Marco, James

Abstract

The accuracy of identifying the parameters of models describing lithium ion batteries (LIBs) in typical battery management system (BMS) applications is critical to the estimation of key states such as the state of charge (SoC) and state of health (SoH). In applications such as electric vehicles (EVs) where LIBs are subjected to highly demanding cycles of operation and varying environmental conditions leading to non-trivial interactions of ageing stress factors, this identification is more challenging. This paper proposes an algorithm that directly estimates the parameters of a nonlinear battery model from measured input and output data in the continuous time-domain. The simplified refined instrumental variable method is extended to estimate the parameters of a Wiener model where there is no requirement for the nonlinear function to be invertible. To account for nonlinear battery dynamics, in this paper, the typical linear equivalent circuit model (ECM) is enhanced by a block-oriented Wiener configuration where the nonlinear memoryless block following the typical ECM is defined to be a sigmoid static nonlinearity. The nonlinear Weiner model is reformulated in the form of a multi-input, single-output linear model. This linear form allows the parameters of the nonlinear model to be estimated using any linear estimator such as the well-established least squares (LS) algorithm. In this paper, the recursive least square (RLS) method is adopted for online parameter estimation. The approach was validated on experimental data measured from an 18650-type Graphite/Lithium-Nickel-Cobalt-Aluminium-Oxide (C6/LiNiCoAlO2) lithium-ion cell. A comparison between the results obtained by the proposed method and by nonparametric frequency-based approaches for obtaining the model parameters is presented. It is shown that although both approaches give similar estimates, the advantages of the proposed method are (i) the simplicity by which the algorithm can be employed on-line for updating nonlinear equivalent circuit model (NL-ECM) parameters and (ii) the improved convergence efficiency of the on-line estimation.

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  • Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:497-508
    DOI: 10.1016/j.apenergy.2017.07.030
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    5. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature," Energies, MDPI, vol. 11(9), pages 1-17, September.
    6. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
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    9. Fan, Chuanxin & O’Regan, Kieran & Li, Liuying & Higgins, Matthew D. & Kendrick, Emma & Widanage, Widanalage D., 2022. "Data-driven identification of lithium-ion batteries: A nonlinear equivalent circuit model with diffusion dynamics," Applied Energy, Elsevier, vol. 321(C).
    10. György Károlyi & Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2022. "An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm," Energies, MDPI, vol. 15(2), pages 1-23, January.
    11. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
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    13. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).

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