Accuracy Assessment of Battery State of Charge Estimation Methods


Battery technologies have come a long way, and their usage is becoming more common in everyday life. From personal devices like smartphones to electric vehicles, the demand for efficient and reliable battery state-of-charge (SoC) estimation methods is growing. In this article, we will explore and assess various SoC estimation methods and their accuracy.


To assess the accuracy of different SoC estimation methods, we will analyze some of the latest research studies in this field. Our focus will be on the most commonly used methods like open-circuit voltage method, coulomb counting, Kalman filter, and adaptive model-based methods.

Open-Circuit Voltage (OCV) Method:

One of the widespread methods for SoC estimation is the OCV method. This method uses the relationship between the battery's SoC and its terminal voltage to estimate the battery's SoC. However, this method has some limitations, such as the dependency on temperature, the effect of self-discharge, and the influence of the battery's discharge rate.

Coulomb Counting:

Another frequently used method is coulomb counting, which relies on integrating the battery's current over time. Coulomb counting has demonstrated good accuracy under ideal conditions, but it is sensitive to measurement errors and parasitic losses, leading to inaccurate SoC estimation in real-world applications.

Kalman Filter:

The Kalman filter method is a recursive mathematical algorithm that utilizes a set of equations to estimate the battery's SoC. This method has the advantage of being adaptable to different models and having a low computational cost, but it can be sensitive to errors and system uncertainties.

Adaptive Model-Based Methods:

Adaptive model-based methods have shown great promise in the field of SoC estimation. These methods rely on building models of the battery's behavior and updating them based on the system's measurements, resulting in accurate SoC estimation. However, the accuracy of these methods depends on the quality of the model used.


In conclusion, accurate SoC estimation is critical for the reliable operation of batteries in various applications. We have explored some of the most common methods used for SoC estimation and their advantages and limitations. While some methods show good accuracy, they are not suitable for all applications. Therefore, evaluating the requirements of the specific application and selecting the most appropriate SoC estimation method is crucial for achieving accurate and reliable battery performance.

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