WANG Xian-bao,WU Fei-teng,YAO Ming-hai.A life-prediction method for lithium-ion batteries based on a fusion model and an attention mechanism[J].Optoelectronics Letters,2020,16(6):410-417 A life-prediction method for lithium-ion batteries based on a fusion model and an attention mechanism Author Name Affiliation WANG Xian-bao  College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China  WU Fei-teng  College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China  YAO Ming-hai  College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China  Abstract:       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. Hits: 931 Download times: 0 View Full Text    Download reader