Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning

Joonki Hong, Dongheon Lee, Eui-Rim Jeong and Yung Yi

Applied Energy, 2020, vol. 278, issue C, No S0306261920311429

Abstract: This paper presents the first full end-to-end deep learning framework for the swift prediction of lithium-ion battery remaining useful life. While lithium-ion batteries offer advantages of high efficiency and low cost, their instability and varying lifetimes remain challenges. To prevent the sudden failure of lithium-ion batteries, researchers have worked to develop ways of predicting the remaining useful life of lithium-ion batteries, especially using data-driven approaches. In this study, we sought a higher resolution of inter-cycle aging for faster and more accurate predictions, by considering temporal patterns and cross-data correlations in the raw data, specifically, terminal voltage, current, and cell temperature. We took an in-depth analysis of the deep learning models using the uncertainty metric, t-SNE of features, and various battery related tasks. The proposed framework significantly boosted the remaining useful life prediction (25X faster) and resulted in a 10.6% mean absolute error rate.

Keywords: Lithium-ion battery; Remaining useful life; End-to-end deep learning; Dilated convolutional neural networks; Prediction uncertainty (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (36)

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DOI: 10.1016/j.apenergy.2020.115646

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