Battery State of Charge Estimation: Methods and Implementation

Accurate battery state-of-charge (SOC) estimation is the cornerstone of modern battery management systems, ensuring safety, longevity, and efficiency in electric vehicles and renewable storage. Understanding how to track a battery's remaining energy requires a solid grasp of both physical electrochemical behaviors and mathematical estimation algorithms. This text-based course guides you from the foundational definitions of battery states through to advanced algorithmic estimation techniques. You will learn to evaluate different SOC estimation strategies, understand their trade-offs, and implement them using step-by-step written mathematical frameworks and code-based exercises. What you'll learn: - Understand the core concepts of battery chemistry, state-of-charge definitions, and the key factors influencing battery degradation. - Implement simple SOC estimation techniques, including open-circuit voltage lookup and Coulomb counting, while analyzing their inherent limitations. - Apply sequential probabilistic inference and Kalman filtering algorithms to achieve highly accurate, dynamic state-of-charge tracking. - Evaluate the unique estimation challenges posed by modern cell chemistries like Lithium Iron Phosphate (LFP) compared to traditional lithium-ion. - Explore modern data-driven and machine learning approaches to SOC estimation alongside classic physics-based models. Starting with foundational definitions of voltage, current, and cell behavior, the course builds sequentially into mathematical modeling, filter design, and modern battery management implementation strategies. Through detailed written explanations and code snippets, you will gain a practical toolkit for real-world engineering scenarios. This course is designed for engineers, developers, and students new to battery management systems, requiring only basic programming logic and introductory algebra. Start mastering the algorithms that power modern energy storage solutions today.