Load optimization configuration method for electric vehicle charging stations based on second-order cone programming algorithm

In order to achieve State of Charge (SOC) control of battery packs, optimize grid load, and extend battery life. Propose a load optimization configuration method for electric vehicle charging stations based on second-order cone programming algorithm. Firstly, the equivalent circuit models of first-order RC and second-order RC power batteries were analyzed, and the mathematical relationship between output voltage and input current was established. Next, the ensemble learning method of feature combination stacking fusion is adopted to predict SOC, in order to improve the accuracy of prediction. Finally, based on the predicted SOC values, a charging control strategy was proposed, and the charging behavior was optimized using a second-order cone programming algorithm to reduce grid load fluctuations and peak valley differences. The research results indicate that this method not only improves the accuracy of SOC prediction, but also achieves SOC balancing of the battery pack, which helps to extend the service life of power batteries.