Python Machine Learning Projects - Useful Life Assessment for Lithium-ion Batteries - ClickMyProject
The prediction of a Lithium-ion battery’s lifetime is very important for ensuring safety and reliability. In addition, it is utilized as an early warning system to prevent the battery’s failure. Recent advance in Machine Learning (ML) is an enabler for new data-driven estimation approaches. In this paper, we suggest a hybrid method, named the CNN-LSTM, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) for the estimation of the battery’s remaining useful life (RUL) and improving prediction accuracy with acceptable execution time.
A comparison against various ML estimation algorithms is carried out to show the superiority of the proposed hybrid estimation approach. For that, two statistical indicators, i.e. the MSE, MAE, R², and RMSE, are selected to assess numerically the prediction results.
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