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Battery-Supercapacitor State-of-Health Estimation for Hybrid Energy Storage System Using a Fuzzy Brain Emotional Learning Neural Network

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Abstract

This study proposes an efficient estimator and uses it to estimate the health of a lithium-ion battery and a supercapacitor in the hybrid energy storage system (HESS). A new type of online health estimator that uses a fuzzy brain emotional learning neural network (FBELNN) is proposed. This neural network is different to a conventional brain emotional learning neural network where the fuzzy inference system and a new reward signal are used. The effect capacity fading on the output of energy storage components is also determined. The proposed method uses a discrete wavelet transform (DWT) and principal component analysis (PCA) to extract features from the response signal for the impulse load. The DWT-PCA can reduce the workload for feature extraction. The parameter adaptation laws and convergence analysis for the FBELNN are derived and the internal parameters for the FBELNN are optimized using a genetic algorithm (GA). A neural network estimates the capacity of a supercapacitor and lithium-ion battery in real-time to better ensure the safety of HESS. The sample set is collected from the voltage response signal in the HESS simulation platform and practical experimental platform. Simulation and experimental results show that the proposed method has a faster learning speed and is more accurate than other methods.

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Acknowledgements

The authors appreciate the financial support in part from Technology Innovation Fund Support Project by the company of Kehua Hengsheng under Grant KHHS20170416 and from the Ministry of Science and Technology of the Republic of China under Grant MOST 106-2221-E-155-MY3.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China

    Qiongbin Lin & Zhifan Xu

  2. Yuan Ze University, Taoyuan, Taiwan

    Zhifan Xu & Chih-Min Lin

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  1. Qiongbin Lin
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  2. Zhifan Xu
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  3. Chih-Min Lin
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Correspondence to Chih-Min Lin.

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Lin, Q., Xu, Z. & Lin, CM. Battery-Supercapacitor State-of-Health Estimation for Hybrid Energy Storage System Using a Fuzzy Brain Emotional Learning Neural Network. Int. J. Fuzzy Syst. 24, 12–26 (2022). https://doi.org/10.1007/s40815-021-01120-y

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  • DOI: https://doi.org/10.1007/s40815-021-01120-y

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