Summary of Complex Methods for Power Battery SOC Estimation

The State of Charge (SOC) is a critical parameter used to represent the remaining energy in a power battery. It is defined as the ratio of the battery's remaining capacity to its total capacity. Accurate online SOC estimation is essential for maximizing the driving range of electric vehicles and ensuring reliable performance [1]. However, due to factors such as temperature fluctuations, self-discharge, and the degradation of active materials, it is challenging to directly measure the SOC using simple parameters. As a result, advanced methods are continuously being developed to improve the accuracy and reliability of SOC estimation. Several factors influence the accuracy of SOC estimation. The operational conditions of the battery are often unpredictable, with random on/off cycles that complicate the estimation process. Moreover, real-time online estimation is necessary during actual vehicle operation, rather than relying on offline measurements. This requires the estimation method to be robust and convergent, even when errors occur, so that it can still approach the true value after adjustment [1]. Challenges like current surges, ambient temperature changes, and battery aging further complicate the estimation process. To address these issues, various complex algorithms have been proposed, including Kalman filters, neural networks, support vector regression, and hybrid approaches. The Extended Kalman Filter (EKF) is one of the most widely used methods for SOC estimation. It treats the battery as a dynamic system and uses a state-space model to estimate the SOC. EKF requires an accurate battery model, such as the Thevenin or PNGV models. Among these, the Thevenin model is commonly used due to its balance between simplicity and accuracy. The EKF algorithm incorporates system and measurement noise assumptions, and through iterative calculations, it provides a stable estimation under varying conditions. However, it demands high computational resources and precise battery modeling [2]. Neural networks, particularly the Backpropagation (BP) network, offer another approach. These networks can learn complex nonlinear relationships without requiring an explicit mathematical model. They are trained using data from the battery’s voltage, current, and temperature. While BP networks are effective, they require large datasets and extensive training time to achieve high accuracy [6]. Support Vector Regression (SVR) is another powerful technique, especially for small sample sizes. It maps input data into a high-dimensional space to find the best fit for estimating SOC. SVR has good generalization and robustness, but its performance heavily depends on the selection of kernel functions and tuning parameters [10]. To overcome the limitations of individual methods, composite algorithms have been introduced. One such method is the Fuzzy Kalman Filter, which integrates fuzzy logic to adjust noise variance in real-time, improving the accuracy and stability of the EKF. Another is the Adaptive Fuzzy Neural Network (ANFIS), which combines the learning capability of neural networks with the reasoning ability of fuzzy systems. ANFIS can handle uncertainty and improve estimation accuracy, though it also requires significant training data [17]. In summary, while each method has its strengths and weaknesses, the development of more accurate and efficient SOC estimation techniques remains a key challenge. Future research should focus on integrating multiple algorithms, refining battery models, and expanding experimental datasets to enhance the reliability and precision of SOC estimation, ultimately supporting the growth of the electric vehicle industry.

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