Chemometric Models and Electrochemical Techniques for Studying the Health of Li-Ion Batteries: A Review

This study presents a thorough analysis of machine learning techniques utilized for monitoring the health and predicting the remaining useful life (RUL) of lithium-ion batteries, which is essential for optimizing the performance of electric vehicles (EVs). Given the challenges faced by lithium-ion batteries, such as capacity fading and environmental factors, key indicators like State of Health (SOH) and RUL become vital. The paper reviews several adaptive machine learning methodologies, including Support Vector Regression (SVR), Gaussian Process Regression (GPR), and hybrid neural networks that integrate convolutional neural networks (CNNs) with long short-term memory (LSTM) algorithms. Special emphasis is placed on these models’ effectiveness in addressing the complex nonlinear behaviors associated with battery aging. Moreover, innovative approaches like fuzzy logic systems are examined, showcasing their potential to enhance SOH estimation via adaptable rule-based techniques. The manuscript stresses the necessity of integrating multiple methodologies to enhance predictive accuracy and reliability. By compiling empirical studies, the work aims to elucidate the capabilities of these algorithms, thereby enriching the knowledge base within battery management systems. Ultimately, this research aims to drive advancements in efficient energy storage solutions, which are crucial for the sustainable development of electric mobility.