A life-prediction method for lithium-ion batteries based on a fusion model and an attention mechanis

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The current life-prediction models for lithium-ion batteries have several problems,such as the construction of complex feature structures,a high number of feature dimensions,and inaccurate prediction results.To overcome these problems,this paper proposes a deep-learning model combining an autoencoder network and a long short-term memory network.First,this model applies the characteristics of the autoencoder to reduce the dimensionality of the high-dimensional features extracted from the battery data set and realize the fusion of complex time-domain features,which overcomes the problems of redundant model information and low computational efficiency.This model then uses a long short-term memory network that is sensitive to time-series data to solve the long-path dependence problem in the prediction of battery life.Lastly,the attention mechanism is used to give greater weight to features that have a greater impact on the target value,which enhances the learning effect of the model on the long input sequence.To verify the efficacy of the proposed model,this paper uses NASA's lithium-ion battery cycle life data set.
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