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Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries

Author

Listed:
  • Zhongwei Deng

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Lin Yang

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yishan Cai

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Hao Deng

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance ( R ). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R . Combining the positive strategy ( i.e. , online identification) and negative strategy ( i.e. , fuzzy model), a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePO 4 batteries.

Suggested Citation

  • Zhongwei Deng & Lin Yang & Yishan Cai & Hao Deng, 2016. "Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries," Energies, MDPI, vol. 9(6), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:6:p:472-:d:72373
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    References listed on IDEAS

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    Cited by:

    1. Biao Yang & Yinshuang Wang & Yuedong Zhan, 2022. "Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 15(13), pages 1-18, June.
    2. Bo Zhang & Jianping Yuan & Jianfei Pan & Xiaoyu Wu & Jianjun Luo & Li Qiu, 2017. "Controllability and Leader-Based Feedback for Tracking the Synchronization of a Linear-Switched Reluctance Machine Network," Energies, MDPI, vol. 10(11), pages 1-18, October.

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