AI-DRIVEN LITHIUM-ION BATTERY HEALTH PREDICTION: A REVIEW OF SOC, SOH AND RUL ESTIMATION TECHNIQUES

Abstract

Necessity for precise Lithium-ion Battery (LiB) monitoring and prognostics has grown significantly with increasing use of Electric Vehicles (EVs) and Renewable Energy System (RES) technologies. State-of-Charge (SOC), State-of-Health (SOH) and Remaining Useful Life (RUL) are three critical performance metrics that are crucial in providing battery safety, functional dependability and effective energy utilization. Nonlinear degradation patterns, operating uncertainties and dynamic environmental fluctuations are frequently difficult for traditional electrochemical and empirical assessment methodologies to capture. As a result, recent research has focused toward advanced data-driven techniques to comprehensive battery state estimation, notably those based on Artificial Intelligence (AI) and Deep Learning (DL). This review provides an organized review of current methodologies for SOC, SOH and RUL prediction, with a focus on modern DL architectures like transformer networks, autoencoder-based latent modelling, neural basis expansion techniques, attention mechanisms and hybrid optimization-driven frameworks. The paper examines new advances in intelligent battery prognostics critically, emphasizing their potential in estimation precision, resilience and flexibility at practical operating scenarios. In addition, it identifies future research directions such as enhanced optimization-assisted frameworks, hybrid physics-informed learning and Explainable Artificial Intelligence (XAI). This paper intends to offer a comprehensive overview of modern battery prognostics approaches to aid in the development of dependable, efficient and long-term Battery Management Systems (BMS) solutions for electric mobility and energy storage systems.