Techniques for SOC and SOH Estimation in Li-Ion Batteries: A Detailed Review

Abstract

In the rapidly evolving realm of energy storage, lithium-ion (Li-ion) batteries have emerged as a transformative force, powering everything from portable gadgets to electric vehicles. However, their widespread adoption has brought to the fore the critical challenge of accurately estimating their State of Charge (SOC) and State of Health (SOH). This research delves into the intricacies of these estimations, shedding light on the multifaceted methodologies proposed over the years. Through a meticulous examination, we unravel the strengths and limitations of each technique, from Coulomb Counting's susceptibility to drifts to the adaptability of Kalman Filtering techniques and the complexity of impedance-based methods. Our study is rooted in a comprehensive design that juxtaposes foundational literature with real-world case studies, offering a holistic perspective on the current state of SOC and SOH estimation techniques. Given the variability in battery chemistries and external operating conditions, the findings underscore the need for a unified approach. Conclusively, this research synthesizes existing knowledge and charts a path forward, emphasizing the refinement of estimation techniques to ensure Li-ion batteries' longevity, safety, and efficiency.