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A Spatiotemporal Cluster Method for Trajectory Data Read chapter Read first chapter

2015 | OriginalPaper | Chapter

17. SOC Prediction Method of a New Lithium Battery Based on GA-BP Neural Network

Authors : Kai Guan, Zhiqiang Wei, Bo Yin

Published in: Proceedings of the 4th International Conference on Computer Engineering and Networks

Publisher: Springer International Publishing

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Abstract

The prediction of a battery’s state of charge (SOC) is one of the key tasks of battery management. Lithium battery internal chemical reactions are complex and have many factors; its SOC prediction has strong nonlinear characteristics. This paper discussed a SOC prediction model which is based on hybrid genetic algorithm and BP neural network. Set BP neural network’s training error as genetic algorithm fitness value, and then iterate to find the optimal individual as the neural network initialization thresholds and weights. Simulation results show that this method can accurately predict the new kind of a lithium battery’s SOC and have higher accuracy compared with BP neural network.

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previous chapter Artificial Neural Networks in Biomedicine Applications
next chapter Compressed Sensing for Channel State Information (CSI) Feedback in MIMO Broadcast Channels
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Metadata
Title
SOC Prediction Method of a New Lithium Battery Based on GA-BP Neural Network
Authors
Kai Guan
Zhiqiang Wei
Bo Yin
Copyright Year
2015
Book
Proceedings of the 4th International Conference on Computer Engineering and Networks

Print ISBN: 978-3-319-11103-2

Electronic ISBN: 978-3-319-11104-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-11104-9_17