MODELLING TECHNIQUES FOR LITHIUM ION BATTERIES

MODELLING TECHNIQUES FOR LITHIUM-ION BATTERIES This document provides a structured overview of the major modeling techniques used to analyze and simulate lithium-ion battery behavior across different physical scales. The material is intended as a concise technical reference that introduces the fundamental concepts, mathematical formulations, and practical applications of battery modeling approaches commonly used in research and engineering. The work begins by presenting the hierarchy of lithium-ion battery models, ranging from simple empirical and equivalent circuit models to advanced physics-based electrochemical frameworks. Particular attention is given to the Doyle–Fuller–Newman (DFN) model, widely regarded as the reference electrochemical model for lithium-ion batteries, along with its reduced-order forms such as the Single Particle Model (SPM) and the Single Particle Model with Electrolyte (SPMe). These models are discussed in terms of their underlying assumptions, governing equations, and computational trade-offs. In addition to electrochemical models, the document outlines other important modeling categories, including equivalent circuit models used in battery management systems, data-driven and machine learning approaches for diagnostics and prediction, thermal models for safety and temperature control, and degradation models that describe capacity fade and aging mechanisms. The goal of this reference is not to present a complete textbook, but rather to provide a clear conceptual map of lithium-ion battery modeling methods and how they relate to one another. By combining theoretical explanations with practical insights, the document serves as a useful starting point for students, researchers, and engineers interested in understanding and applying modern battery modeling techniques. Overall, this reference highlights how different modeling approaches balance physical fidelity, computational efficiency, and practical applicability in areas such as battery design, fast-charging strategies, battery management systems, and lifetime prediction.