Advancements in Battery State of Charge Estimation Methods for Battery Management Systems: A Comprehensive Literature Review

This paper offers an extensive examination of methods for estimating the State of Charge (SoC) in batteries within the context of Battery Management Systems (BMS). SoC is a crucial parameter that indicates the remaining charge in a battery during its current cycle. Accurately estimating the SoC is vital to prevent the battery from operating in unfavorable conditions, such as low charge, and to ensure its safe and efficient operation, ultimately extending its service life. This motivates the exploration and comparison of various SoC estimation methods. The Ampere-Hour Integration (AHI) method is the simplest but lacks the ability to correct estimation errors due to its open-loop nature. The Kalman Filter (KF) method combines aspects of the first two methods, leveraging system observation errors to make timely corrections to the state estimates. It is suitable for in use estimation when accompanied by an appropriate battery model, resulting in a high estimation accuracy. This paper aims to summarize each SoC estimation method and explore potential avenues for improvement. By analyzing the limitations and challenges of the SoC estimation algorithms in practical engineering applications, it provides insights into the future development of online SoC estimation.