Applying Neural Network to Health Estimation and Lifetime Prediction of Lithium-Ion Batteries

Abstract

In recent years, artificial neural networks (ANNs) have significantly advanced in both health estimation and lifetime prediction of lithium-ion batteries. The great success of ANNs stems primarily from their scalability in encoding large-scale data and maneuver billions of model parameters. However, there are still many challenges in balancing predictive accuracy and deployment feasibility. For instance, shallow ANNs are often more efficient but may sometimes sacrifice accuracy, whereas deep hybrid ANNs often achieve strong generalization capabilities, this comes with the trade-off of increased computational demands. To this end, this article presents a comprehensive survey of ANN-based paradigms for estimating state-of-health (SOH) and predicting the remaining useful life (RUL) of lithium-ion batteries. It covers battery aging mechanisms, available datasets, network architecture, training schemes, advanced machine learning (AML) algorithms, and performance comparison. Furthermore, challenges in battery health diagnosis are reviewed in detail, and comments on future research prospects are discussed and forwarded.