Mr. Haoqiang Ding Profile

Haoqiang Ding

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Proceedings Article | 15 March 2024 Paper

Remaining useful life prediction for lithium batteries using CNN- BILSTM-Attention hybrid method

Liefa Liao, E Tang, Haoqiang Ding

Proceedings Volume 13075, 130752M (2024) https://doi.org/10.1117/12.3026948

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The precision of predicting the residual service life of lithium-ion batteries is of utmost vital to make sure their effective management and maintenance. Therefore, in order to increase the precision of predicting the residual service life of lithium batteries, this research puts forward a methodology that forecast the remaining useful life of lithium batteries through employing a hybrid model of CNN-BiLSTM and attention mechanism. firstly employing the Bayesian optimization algorithm for the purpose of the parameters optimization of the suggested model, and secondly, using the series connection of CNN and BiLSTM to catch the layered characteristic between same variables which can affect battery degradation, as well as the time dependence embedded into these characteristic, and using the attention mechanism to give more focus to the important features that impact the outcomes, so as to acquire the forecasted outcomes for the remaining lifespan of lithium batteries. The validation analysis performed on the NASA dataset illustrates that the proposed approach attains higher levels of accuracy in comparison to the predictive results obtained from alternative neural network methodologies.

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