Mileage Prediction of Electric Vehicle Based on Multi Model Fusion

The driving mileage prediction of pure electric vehicles is one of the most concerns for drivers. The existing regression models always have the drawbacks of low prediction accuracy and large relative error. This paper developed a machine learning method that combines segment regression prediction and single-point classification prediction to predict the mileage. The prediction method took real vehicle state parameters, environmental information as input, extracted the optimal feature set by clustering and filtering encapsulated feature selection, then selected a prediction method based on the sample size of driving segments, and layered coupling prediction of environmental temperature and battery health state (SOH) to improve the prediction accuracy of fragment regression. The final prediction result was further optimized by the model fusion of single point classification prediction and fragment regression prediction. The RMSRE relative error of the predicted result of the mileage test set is 0.035, and the average relative error is 1.71%, which can accurately and stably achieve the mileage prediction. Copyright © 2020 by Science Press.