The SOC Prediction of Lead-Acid Battery Based on MIV-OSELM Algorithm

In this work, an MIV-OSELM prediction model is constructed to predict the state of charge (SOC) of lead-acid battery, which combines the mean impact value (MIV) algorithm and the online sequence extreme learning machine (OSELM) algorithm. This model uses the MIV method to quantitatively calculate the impact value of the input variables on the output variables, and completes selection of the input variables of model; the OSELM method is used to carry out incremental learning of new samples generated during the use of battery, and track the potential impact of battery's state of health (SOH) on the SOC prediction of battery in a timely manner. Compared with the prediction results of other models, the MIV-OSELM method can improve the prediction accuracy of SOC during the charging and discharging processes of lead-acid batteries, which also has the adaptive ability to make dynamic adjustment of the model parameters according to the information of new samples.