The Soft Sensing of Battery Capacity Based on RReliefF Algorithm and Neural Network

Ying Ying Su; Xing Hua Liu; Jing Zhe Li; Tai Fu Li; Ke Sheng Yan

doi:10.4028/www.scientific.net/AMR.765-767.809

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767The Soft Sensing of Battery Capacity Based on...

The Soft Sensing of Battery Capacity Based on RReliefF Algorithm and Neural Network

Abstract:

To solve the problem of too many variable numbers which makes the model complex, a kind of auxiliary variables selection method is established. After that, soft sensing of lead-acid battery capacity is put forward. First, the RReliefF method is adopted to define quantitatively the influence of auxiliary variables. Then, the soft sensing model is built up with all the combination of auxiliary variables with BP neural network. Simulation results show that the soft sensing of battery capacity is established ideal. It provides theoretical feasibility to omit the battery discharge capacity in the process of production inspection process.

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Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

809-812

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.809

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Online since:

September 2013

Authors:

Ying Ying Su, Xing Hua Liu, Jing Zhe Li, Tai Fu Li, Ke Sheng Yan

Keywords:

Auxiliary Variable, Lead-Acid Batteries, Neural Network (NN), RReliefF, Soft Sensing

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References

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