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

• 来源期刊: 光电子快报（英文版）
• 年，卷（期）: 2020,（6）
• 所属期刊栏目: Devices
• 页码范围: 410-417
• 页数: 8页
• 语种: 英文
• DOI: 10.1007/s11801-020-9214-y

摘要

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.