Estimation of State of Health for Lithium-ion Batteries Using Least Square Methods

Modern energy storage systems, especially those in electric vehicles, rely heavily on lithium-ion batteries. These power sources are favoured for their high energy density, extended lifespan, and adaptability to various applications. Accurate estimation of their State of Health (SoH), a key metric for assessing capacity degradation over time, is critical for ensuring safety, performance, and efficient battery management. The Battery Management System (BMS) mitigates risks such as temperature variations, overcharging, and over-discharging by continuously monitoring metrics like State of Charge (SoC) and SoH, ensuring optimal battery utilization and reliability. This study evaluates least square methods—WLSM, PLSM, FMPLSM, and geometric method for online SoH estimation, demonstrating geometric method’s superior accuracy (error < 2%) on real-time EV battery data. Special emphasis is placed on online SoH estimation, enabling real-time battery monitoring and management during vehicle operation. These advancements maximize the utility of energy storage systems and support the transition toward sustainable, eco-friendly mobility solutions. By enhancing SoH estimation accuracy, the Geometric Least square Method contributes to improved battery performance, extended operational lifetimes, and better energy management systems.