The study of SOC estimation using incremental learning

As an energy storage medium, the dynamic changes of current and voltage of power battery in actual vehicle use are fluctuating compared to constant current and voltage tests under laboratory conditions, so the distribution of its training data set is also fluctuating and changing. To address the above problems, this paper proposes a support vector regression algorithm (SVR) using incremental learning to improve the estimation accuracy and adapt to a variety of application scenarios. The details of the study are as follows. State of charge (SOC) - one of the key characteristic quantities of power batteries. To improve the generalizability of the SOC estimation model to complex dynamic operating conditions, an incremental learning approach is incorporated into the support vector machine regression algorithm (SVR) to adapt to changes in the distribution of the data set under dynamic operating conditions by incremental learning of additional data sets. A combination of error-driven and sample-labeling methods is used for accurate samples. Finally, the prediction results of both ISVR and SVR algorithms are compared using data from both DST and FUDS dynamic conditions. The experimental results from offline data show that the average absolute error of the incremental SVR prediction results for complex conditions is less than 0.008, which is about 0.044% lower than that of the general SVR.