Accurate prediction of remaining useful life for lithium-ion battery using deep neural networks with memory features

Abstract

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Li-ion batteries degrade with time and usage, caused by factors like the growth of solid electrolyte interface (SEI), lithium plating, and several other irreversible electrochemical reactions. These failure mechanisms exacerbate degradation and reduce the remaining useful life (RUL). This paper highlights the importance of feature engineering and how a careful presentation of the data can capture the hidden trends in the data. It develops a novel framework of deep neural networks with memory features (DNNwMF) to accurately predict the RUL of Li-ion batteries using features of current and previous n cycles. The results demonstrate that introducing memory in this form significantly improves the accuracy of RUL prediction as root mean square error (RMSE) decreases more than twice with memory compared to without memory. The optimal value of n, referred to as nopt, is also determined, which minimizes the prediction error. Moreover, the number of optimization parameters reduces by more than an order of magnitude if an autoencoder is used in conjunction with the proposed framework (DNNwMF). The framework in this paper results in a trade-off between accuracy and computational complexity as the accuracy improves with the encoding dimensions. To validate the generalizability of the developed framework, two different datasets, i) from the National Aeronautics and Space Administration’s Prognostic Center of excellence and ii) from the Center for Advanced Life Cycle Engineering, are used to validate the results.

Keywords

• lithium-ion battery
• remaining useful life (RUL)
• prognostics and health management (PHM)
• autoencoder
• deep learning
• feed-forward neural network