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LSTM -Attention Mechanism based Remaining Useful Life Prediction of Lithium Batteries

Lithium batteries are widely used in vehicles, portable devices and aerospace applications. With the increase of charge and discharge times, the performance of Lithium batteries also begins to gradually decline, and their safety and reliability is a very important issue. Therefore, accurate prediction of Remaining useful life (RUL) of Lithium batteries has very important application value. For the problems of complex feature construction process, high feature dimensionality and inaccurate prediction results in existing lithium battery life prediction models, we propose an LSTM-Attention Mechanism based method in this paper. First, we use Auto Encoder network to reduce the dimensionality of the extracted multidimensional key features, and then use LSTM network model to achieve the prediction. The attention mechanism is also introduced to enhance the learning effect of the model for long input sequences. Experiments show that the proposed ALSTM model algorithm significantly improves the precision of RUL forecasts. The attention layer is added to determine the extent to which each of the components in the hidden layer affects the results, and the larger the weight coefficient, the greater the influence on the result. Thus, the network is focused on the change of one or several dimensions.