With the advancement of energy structure reform, lithium‐ion batteries have been widely used in new energy vehicles due to their advantages of high energy density, low self‐discharge rates, and long cycle life. As a crucial component of these vehicles, real‐time monitoring of the health and operational status of lithium‐ion batteries is essential. Battery management systems play a key role in intelligently managing battery status, with state of charge (SOC) serving as a critical parameter that reflects the remaining energy of lithium‐ion batteries. Accurate SOC estimation enables efficient trip planning and prolongs battery life. This paper discusses the SOC estimation technology, introduces several important methods, and compares their advantages and disadvantages, operating temperature range, and errors. The main focus is on the application of data‐driven methods in SOC estimation, and the characteristics, parameters, dataset size, and accuracy of different support vector machine (SVM) models are compared. At the same time, four new methods of combining traditional models with data‐driven models are introduced. Finally, the challenges and opportunities for future directions in this field are pointed out.
Peng et al. (Sun,) studied this question.
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