Predictive State-of-Health (SOH) of lithium-ion batteries throughout their lifecycle based on LSTM neural networks

Abstract

With the rapid development of new energy sources, the demand for energy storage systems in the power industry has surged. While lithium-ion battery energy storage systems have gained widespread market acceptance due to their performance advantages, concerns regarding battery safety and durability have also escalated. Therefore, accurately predicting the health status of batteries has become a crucial aspect in assessing their safety and durability. State of Health (SOH), as a vital indicator of battery health status, has become a focal point for many researchers aiming to enhance the accuracy of its prediction. Today, owing to the rapid advancements in machine learning and neural networks, data-driven approaches have gradually emerged as one of the primary methods for SOH prediction. This paper proposes a method based on Long Short-Term Memory (LSTM) neural networks, which combines theoretical analysis with neural network models, utilizing authentic battery aging data from NASA to predict the health status of lithium-ion batteries throughout their lifecycle. Experimental results demonstrate that the proposed method exhibits high accuracy.