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A novel battery state estimation model based on unscented Kalman filter

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Abstract

Accurate estimation of the state of charge (SOC) of batteries is very important for real-time monitoring and safety control of electric vehicles. Four aspects of efforts are applied to promote the accuracy of SOC estimation. Firstly, the state-space equation of the battery model based on the Thevenin model is established and the parameters of the model are identified by the forgetting factor recursive least square method. Secondly, aiming at the nonlinear relationship between the open-circuit voltage (OCV) and SOC, the least square support vector machine is proposed to establish the mapping relationship between OCV and SOC. Thirdly, the influence of fitting accuracy of the OCV-SOC curve on SOC estimation is analyzed. Based on this, an error model is proposed, and a joint estimator using an adaptive unscented Kalman filter algorithm combining the error model is proposed. Finally, compared with the estimated SOC results of the traditional SOC estimation method, the experimental results show that the proposed model has better estimation ability and robustness.

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Funding

This work was supported by the National Natural Science Foundation of China (No. 51805041), the Fundamental Research Funds for the Central Universities (No. 300102259204), Science and technology innovation team of Shaanxi Province (2020TD0012) and the Key Technological Special Project of Xinxiang City (No. ZD19007).

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Authors and Affiliations

  1. National Engineering Laboratory for Highway Maintenance Equipment, Chang’an University, Xi’an, 710064, China

    Jiabo Li, Min Ye, Kangping Gao, Shengjie Jiao & Xinxin Xu

  2. Henan Gaoyuan Maintenance Technology of Highway Co., Ltd., Xinxiang, 453003, China

    Xinxin Xu

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  1. Jiabo Li
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  2. Min Ye
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  3. Kangping Gao
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  4. Shengjie Jiao
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  5. Xinxin Xu
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Correspondence to Min Ye.

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Li, J., Ye, M., Gao, K. et al. A novel battery state estimation model based on unscented Kalman filter. Ionics 27, 2673–2683 (2021). https://doi.org/10.1007/s11581-021-04021-0

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  • DOI: https://doi.org/10.1007/s11581-021-04021-0

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