SoC estimation for optimal ESS’ energy management

Abstract

Battery energy storage systems have become indispensable to modern civilization, enabling the functionality of numerous advanced technologies, including high-performance smartphones, long-range electric vehicles, and various portable electronic, tools, and backup systems. The continuous advancement of battery technology is a key driver for future innovations. A crucial component of battery systems is the Battery Management System (BMS), which monitors and optimizes various operational parameters, including the State of Charge (SoC). SoC represents the remaining useful battery capacity relative to its total capacity, however it cannot be directly measured and must be estimated through computational techniques instead. While existing estimation methods have significantly improved in terms of accuracy and reliability, they remain challenged by complexity, sensitivity to operating conditions, and dependence on dynamic load behavior. Overcoming these challenges is essential for enhancing the performance and longevity of battery systems in next-generation applications. Our contribution is a novel estimation technique that periodically stimulates the battery with a predefined current profile during charging or discharging to determine its State of Charge (SoC). Since this method is not continuous, it is combined with Coulomb counting for calibration. The results demonstrated the method's efficiency and reliability, effectively overcoming dependency on environmental conditions and dynamic load behavior. Its key advantages include independence from operating conditions and dynamic load behavior, as well as, minimal computational complexity without sacrificing accuracy achieving an error of less than 1%. This ensures high reliability and efficiency with reduced complexity