Prediction of the remaining mileage of electric vehicles based on the quantitative factors of driving behavior
A model for predicting the remaining mileage of electric vehicles was established by addressing the challenge of overlooking the impact of each sample point in a clustering category on energy consumption when using clustering algorithms to extract driving behaviors.The model is based on the quantitative factors of driving behavior.First,seven driving behavior evaluation indicators,such as vehicle speed and acceleration affecting energy consumption,were selected.The random forest algorithm was employed to assign weights to these indicators,and the normalized weighted sum was used as the quantitative factor of driving behavior.Second,a remaining mileage prediction model for electric vehicles was constructed using the state of charge of the battery,quantitative factors of driving behavior,environmental temperature,electrical load rate,and operational information as inputs.Results showed that the prediction model that considers the quantitative factors of driving behavior has a smaller root mean square error and mean absolute error than the prediction model that does not consider the quantitative factors of driving behavior.Compared with the support vector regression,backpropagation,and recurrent neural network algorithms,the long short-term memory network exhibits the best predictive performance.Finally,using real vehicle operation data,this study validated that the model improves the accuracy of predicting the remaining mileage.This research achievement is significant in enhancing the driving experience of drivers.
electric vehicleremaining mileagedriving behaviorquantitative factorsLSTM
NETL
NSTL
万方数据
