State of Charge Centralized Estimation of Road Condition Information Based on Fuzzy Sunday Algorithm

Abstract

Accurate estimation of the state of charge (SOC) is critical for battery management systems. A backpropagation neural network (BPNN) based on a modified fuzzy Sunday algorithm is proposed to improve the accuracy of SOC predictions of lithium-ion batteries (LIBs). The road condition information relating to the data is obtained using the fuzzy Sunday algorithm, and the acquired feature information is used to estimate SOC using BPNN based on the Levenberg–Marquardt (L–M) training process. The change from exact character matching to fuzzy number matching is an improvement to the Sunday algorithm. The quantification of the road condition is innovatively integrated into the neural network. At present, this kind of feature is new to the estimation process, and our experiment proved that the effect is good. To quickly estimate the SOC under different driving conditions, the same network was used to predict the data of different road conditions. In addition, a strategy is proposed for SOC estimation under unknown road conditions, which improves the estimation accuracy. Studies have shown that the model used in the experiment is more accurate than other machine learning models. This model assures prediction accuracy, reliability, and timeliness.