A Novel Pigeon-Inspired Optimized RBF Model for Parallel Battery Branch Forecasting

Battery energy storage is the pivotal project of renewable energy systems reform and an effective regulator of energy flow. Parallel battery packs can effectively increase the capacity of battery modules. However, the power loss caused by the uncertainty of parallel battery branch current poses seve...

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Bibliographic Details
Published inComplexity (New York, N.Y.) Vol. 2021; no. 1
Main Authors Zhang, Yanhui, Lin, Shili, Ma, Haiping, Guo, Yuanjun, Feng, Wei
Format Journal Article
LanguageEnglish
Published Hoboken Hindawi 2021
John Wiley & Sons, Inc
Wiley
Subjects
Algorithms
Automation
Birds
Compasses
Electric vehicles
Energy flow
Energy storage
Forecasting
Mathematical models
Modules
Neural networks
Optimization
Product safety
Radial basis function
Rechargeable batteries
System effectiveness
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Summary:Battery energy storage is the pivotal project of renewable energy systems reform and an effective regulator of energy flow. Parallel battery packs can effectively increase the capacity of battery modules. However, the power loss caused by the uncertainty of parallel battery branch current poses severe challenge to the economy and safety of electric vehicles. Accuracy of battery branch current prediction is needed to improve the parallel connection. This paper proposes a radial basis function neural network model based on the pigeon-inspired optimization method and successfully applies the algorithm to predict the parallel branch current of the battery pack. Numerical results demonstrate the high accuracy of the proposed pigeon-inspired optimized RBF model for parallel battery branch forecasting and provide a useful tool for the prediction of parallel branch currents of battery packs.
Bibliography:ObjectType-Article-1
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ISSN:1076-2787
1099-0526
DOI:10.1155/2021/8895496