Deep Learning Based SoH Estimation of Lithium-ion Batteries

Károlyi, György and Göllei, Attila and Magyar, Attila (2020) Deep Learning Based SoH Estimation of Lithium-ion Batteries. In: 4th SDEWES Conference, 28th June – 2nd July 2020, Sarajevo, Bosnia and Herzegovina (online).

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Abstract

An artificial intelligence-based approach of estimating remaining useful life for Li-ion batteries has been used in this work, where two different recursive neural networks were set up, trained and investigated for two different scenarios. The investigated battery type is the widely used 18650 battery class. The training and prediction of both networks are performed on a publicly available high-quality dataset that serves as a base for several related research works. The batteries are charged/discharged until they reach their end of life by means of capacity degradation. The charge and discharge were performed under different charging current/load profiles. Out of the available data-driven methods, LSTM (long-short term memory) and GRU (Gated Recurrent Unit) neural networks are the most promising candidate since they are capable of the handling of long-term processes, such as battery aging. The two networks are parameterized, trained and tested for two different scenarios.

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