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

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 ne...

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Bibliographic Details
Published in:International journal of fuzzy systems 2022-02, Vol.24 (1), p.12-26
Main Authors: Lin, Qiongbin, Xu, Zhifan, Lin, Chih-Min
Format: Article
Language:English
Subjects:
Accuracy
Aging
Alternative energy sources
Artificial Intelligence
Artificial neural networks
Brain
Climate change
Computational Intelligence
Computer simulation
Discrete Wavelet Transform
Energy storage
Engineering
Feature extraction
Fuzzy logic
Genetic algorithms
Hybrid systems
Impulse loading
Learning
Lithium
Lithium-ion batteries
Management Science
Neural networks
Operations Research
Parameters
Principal components analysis
Rechargeable batteries
Renewable resources
Supercapacitors
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Summary: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.
ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-021-01120-y