Battery Aging-Robust Driving Range Prediction of Electric Bus

Abstract

The prediction of driving range is very important for electric bus, but there is usually a difficulty: battery aging affects the accuracy of driving range prediction. In order to solve this problem, this paper proposes a driving range prediction method for electric bus, which is robust to the battery aging effect. Firstly, we extract the features that affect the driving range from the real-world dataset, quantify the correlation between them and the driving range by grey correlation analysis. Then through the feature enhancement technology, the time window processing is used to mitigate the influence of battery aging, and the time information hidden in the historical period sequence is deeply excavated. On this basis, we establish the driving range prediction model based on k-nearest neighbors regression, where the key parameters are optimized with the particle swarm optimization algorithm. Numerous experimental results show that compared with the classical methods, the method proposed in this paper has higher prediction accuracy especially when the batteries undergo significant aging effects.