Aiming at the problem that the accuracy of the existing pure electric vehicle mileage prediction algorithm is difficult to improve

an online prediction algorithm for the remaining cruising range of pure electric vehicles based on real-time traffic flow information is proposed. The algorithm uses the EM machine learning algorithm to cluster historical data and establish a support vector machine (SVM) model. It uses the real-time traffic flow information of the online digital map (Baidu map) to predict the future driving conditions on each given road section

including energy and time consumption. Then

the battery SOC variation and remaining cruising range are estimated. Finally

a 20 km real-vehicle on-road experiment was conducted on the Internet-distributed vehicle-in-the-loop platform. Experimental results prove that this online mileage prediction method has high accuracy.