Abstract:

Lithium ion battery energy storage plays an extremely important role in the fields of clean energy use, electric vehicles, mobile devices, and renewable energy storage. Once a malfunction occurs, it is easy to cause a series of problems, so conducting fault analysis to understand its actual health status is of great significance. This article provides an overview of lithium battery fault analysis techniques under deep learning mechanisms. Based on a deep understanding of deep learning diagnostic theories such as multi-layer perception and recurrent neural networks, this paper elaborates on the evaluation framework and process of the latest lithium battery fault diagnosis model (LSTM). Through practical applications, it can be determined that the deep learning based lithium battery fault analysis model has advantages such as high recyclability and good accuracy, and is worthy of further research and exploration.

Key words: deep learning, lithium batteries, evaluation model