Optoelectronics Letters, Volume. 16, Issue 6, 410(2020)
A life-prediction method for lithium-ion batteries based on a fusion model and an attention mechanism
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.
Get Citation
Copy Citation Text
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
Received: Dec. 17, 2019
Accepted: Feb. 5, 2020
Published Online: Dec. 25, 2020
The Author Email: Xian-bao WANG (wxb@zjut.edu.cn)