A Review on the Prediction of Health State and Serving Life of Lithium‐Ion Batteries

Abstract

The monitoring and prediction of the health status and the end of life of batteries during the actual operation plays a key role in the battery safety management. However, although many related studies have achieved exciting results, there are few systematic and comprehensive reviews on these prediction methods. In this paper, the current prediction models of remaining useful life of lithium‐ion batteries are divided into mechanism‐based models, semi‐empirical models and data‐driven models. Their advantages, technical obstacles, improvement methods and prediction performance are summarized, and the latest research results are shown by comparison. We highlight that the fusion models of convolution neural network, long short term memory network and so on, which have great practical application prospects because of their outstanding computing efficiency and strong modeling ability. Finally, we look forward to the future work in simplifying the model and improving its interpretability.

Prediction of available capacity of power battery based on autoregressive integrated moving average model

“…In addition, for the mapping relationship between the battery capacity decline and the aging characteristics, selecting the appropriate efficient algorithm is also the key to improve the estimation accuracy. Currently, data-driven methods include including time series models, artificial neural networks, support vector machines and related vector machines [8] . Literature [9] uses convolutional neural networks for rapid battery capacity estimation, but as the number of layers increases, the backpropagation gradient in the network becomes unstable and becomes particularly large or particularly small with the continuous multiplication.…”

A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning

“…Researchers have developed various models to achieve reliable RUL prediction, which can be classified into three main types: mechanism-based, semi-empirical, and data-driven. Each category offers its unique approach to capturing the intricate dynamics of battery aging and providing insights for RUL estimation [2]. Mechanism-based models offer a quantitative approach to assessing capacity loss by simulating the battery system's electrochemical responses and aging mechanisms [3], [4].…”