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An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

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Abstract

In response to the inaccurate estimation of State of Charge (SOC) in current power battery management systems, and considering that SOC may be subject to offset constraints from State of Health (SOH) when estimated separately. A method combining adaptive robust Kalman filtering with multiple innovation theories and online identification of dual Kalman filtering parameters is proposed. This control method is based on adaptive robust Kalman control. It corrects the estimated values using multiple innovation values and Kalman gains at different times. Dual Kalman filtering is used for online parameter identification and joint estimation of battery health status, which increases the amount of error information and provides an optimized method for accurate estimation of SOC and SOH. To verify the rationality of the algorithm, a second-order RC equivalent circuit model is used to characterize the dynamic characteristics of the battery, and experimental verification is carried out under different operating conditions. The experimental results show that the average error of SOC under three operating conditions: UDDS, FUDS, and US06 is 0.56%, 0.31%, and 1.23%, respectively. The estimated error of SOH after stabilization is less than 1.73%. The estimation error is the lowest among the five estimation algorithms. The proposed algorithm has been verified to have good accuracy and convergence. The multi-innovation adaptive robust dual Kalman filtering algorithm provides a theoretical basis for accurate state estimation and widespread application of lithium batteries.

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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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Funding

This work is supported by the National Natural Science Foundation of China (61563032, 61963025) and the Gansu Provincial Science and Technology Program (22YF7GA164, 22CX8GA131).

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Author notes

  1. Fa Zhi Yang, Tao hua Yu and Aimin An contributed equally to this work.

Authors and Affiliations

  1. College of Electrial and Information Engineering, Lanzhou University of Technology, Qilihe, Lanzhou, 730050, Gansu, China

    Zhe Guan, Fa Zhi Yang, Tao hua Yu & Aimin An

  2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Qilihe, Lanzhou, 730050, Gansu, China

    Aimin An

  3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Qilihe, Lanzhou, 730050, Gansu, China

    Aimin An

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  1. Zhe Guan
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Fazhi YANG, Taohua Yu,Zhe Guan and Aimin An. The first draft of the manuscript was written by Zhe Guan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Aimin An.

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Guan, Z., Yang, F. ., Yu, T.h. et al. An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state. Ionics 30, 991–1006 (2024). https://doi.org/10.1007/s11581-023-05314-2

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