Battery Remaining Useful Life Prediction Supported by Long Short-Term Memory Neural Network

被引：2

作者：

Marri, Iacopo ^{[1
]}

Petkovski, Emil ^{[1
]}

Cristaldi, Loredana ^{[1
]}

Faifer, Marco ^{[1
]}

机构：

[1] Politecn Milan, DEIB, Milan, Italy

来源：

2023 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC | 2023年

关键词：

lithium-ion batteries; machine learning; remaining useful life (RUL); neural network; aging; ION BATTERIES; MODEL;

D O I：

10.1109/I2MTC53148.2023.10176105

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The rise of renewable energy and electric vehicles has led to enormous growth and development in the field of lithium-ion batteries. Ensuring long-life and safe operation of these batteries requires accurate estimation of key parameters such as state of charge, state of health (SoH), and remaining useful life (RUL). In this paper, a long short-term memory neural network (LSTM NN) is presented for RUL prediction. Furthermore, the predictors used are discussed in detail, and a comparison between the two models is presented. The network has been trained and tested on a substantial dataset of 124 batteries, aged under various fast charging conditions, and published by the Toyota Research Institute in collaboration with MIT and Stanford. Despite their vastly different cycle lives, the proposed LSTM NN structure has performed very accurate RUL prediction for all tested cells.

引用

页数：6

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