Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Abstract

Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.