Energy consumption prediction of PEVs incorporating traffic flow information

The problem of range anxiety caused by the discrepancy between the mileage on the dashboard and the driving mileage of pure electric vehicles (PEVs) is one of the most important reasons hindering the development of PEVs. Prediction of energy consumption can effectively reduce the driver's range anxiety and provide support for energy management strategies optimization and energy-efficient route plan. To this end, this paper analyzes the effects of velocity, environment and driving style on energy consumption from the real driving data of PEVs. Based on the analysis, a driving condition prediction method combining with real-time traffic information is proposed, and integrated into the energy consumption prediction model. The real-world trip test results show that the prediction errors of RMSE and MAPE of the proposed method are respectively reduced by 66.37% and 68.10% compared to the conventional method.