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Adaptive estimation for time-varying state-of-charge of lithium-ion battery with consideration of temperature distribution

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Abstract

Usually, state-of-charge (SOC) is a time-varying process and its parameters are influenced by temperature distribution. However, almost all existing identification methods regard this process as an invariant system and less consider the influence of temperature distribution. Here, an adaptive method with consideration of temperature distribution is proposed for the estimation of time-varying state-of-charge in lithium-ion battery (LIB). First, a spatiotemporal kernel-local-embedding modeling (STKLLE) method is developed to obtain the temperature distribution. Then, a least squares support vector machine (LS-SVM) is used to build the relation of inherent parameters of the equivalent circuit model (ECM) with temperature distribution, current and SOC. Furthermore, a novel adaptive robust observer is designed to realize the online estimation of SOC. In this observer, the time-varying relations between open circuit voltage (OCV) and SOC are constructed by an online Takagi–Sugeno (T–S) fuzzy model, and the deviation of SOC estimation caused by disturbances is suppressed by the robust law of sliding mode observer (SMO). The effectiveness of the strategy is verified by experiments of the LIB at different conditions. The results demonstrate that the estimated errors under different working rates are constrained in small range (≤ 0.03) for both charging and discharging processes. Besides, the errors for the proposed method are bounded in [− 0.01, 0.02], which presents better model performance compared with the other two commonly used methods.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (52075556), and the Key R&D Program of Hunan Province (2021SK2016).

Funding

This work was partially supported by National Natural Science Foundation of China (52075556), and the Key R&D Program of Hunan Province (2021SK2016).

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Authors and Affiliations

  1. State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, China

    Bowen Xu, Xinjiang Lu, Yunxu Bai, Jie Xu & Wei Luo

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  1. Bowen Xu
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  2. Xinjiang Lu
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Correspondence to Xinjiang Lu.

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Xu, B., Lu, X., Bai, Y. et al. Adaptive estimation for time-varying state-of-charge of lithium-ion battery with consideration of temperature distribution. Nonlinear Dyn 111, 17379–17392 (2023). https://doi.org/10.1007/s11071-023-08735-w

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