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23 May 2023 Study on the remaining useful life prediction of lead-acid battery based on bilayered neural network
Yuqiang Fan, Longtao Qiu, Jiafei Wang, Long Shan, Junyuan Chen
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Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126453D (2023) https://doi.org/10.1117/12.2680855
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China
Abstract
The Bilayered Neural Network (BNN) prediction model uses to predict the State of Health (SOH) of lead-acid batteries used in aerial work platforms. After the cycle life test of lead-acid batteries, a multi-layer structure prediction model builds to improve the accuracy of the prediction results. Compared with the linear model, tree model, Support Vector Regression (SVR) model and Gaussian Process Regression (GPR) model, the training and test prediction results of BNN are better, and the test RMSE is 0.0562.
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Yuqiang Fan, Longtao Qiu, Jiafei Wang, Long Shan, and Junyuan Chen "Study on the remaining useful life prediction of lead-acid battery based on bilayered neural network", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126453D (23 May 2023); https://doi.org/10.1117/12.2680855
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KEYWORDS
Batteries
Data modeling
Artificial neural networks
Neural networks
Performance modeling
Signal processing
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