基于百度地图API的纯电动汽车未来行驶能耗预测  

Prediction of future driving conditions for electrical vehicles based on Baidu maps API

作　　者：黄新朝 张毅 HUANG Xinchao;ZHANG Yi(School of Vehicle Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学车辆工程学院,重庆400054

出　　处：《汽车安全与节能学报》2023年第6期715-722,共8页Journal of Automotive Safety and Energy

基　　金：重庆市教委科学技术研究计划青年项目(KJQN202001105)。

摘　　要：为了预测纯电动车的未来行驶能耗,利用从百度地图应用程序接口(API)所获取道路车流数据,并在云计算系统的在环平台(in-loop platform)上进行实车上路实验验证。用百度提供的道路车流数据,可以计算剩余里程、路径规划、能量管理策略以及充电桩布置等。这些数据与实车行驶数据结合,一并作为训练数据集。用k-means聚类分析算法与支持向量机(SVM)分类算法,来预测未来行驶能耗。对比池剩余电量(SOC)的预测值和实车上路实验所得的实际值。结果表明:对于一个40 min、约20 km的行驶工况,能耗预测的误差可以限制在一个标准差σ之内。从而验证了本文基于百度地图API车流数据的未来行驶能耗预测算法的准确性。The road traffic flow data obtained from the Baidu Map Application Programming Interface(API)was used to predict the future driving energy-consumption of pure electric vehicles,and a vehicle on-road experimental verification was conducted on the in-loop platform of the cloud computing system.Used the road traffic data obtained by Baidu to calculate the remaining mileage,the path planning,the energy-management strategies,and the charging pile layout,etc.These data were combined with vehicle-driving data and used as a training data set.The future energy-consumption was predicted with the k-means cluster analysis algorithm and the support vector machine(SVM)classifcation algorithm.The predicted value of the remaining battery state of charge(SOC)was compared with the actual value obtained from the vehicle on-road experiments.The results show that the error of future driving energy-consumption prediction are limited to inset of one-standard-deviationσfor a 40 min driving condition(about 20 km),based on Baidu Map API traffic flow data.Therefore,the accuracy of the proposed prediction algorithm in this paper is verified.

关 键 词：自动驾驶汽车 纯电动车 车联网 行驶工况 百度地图应用程序接口(API) 聚类分析 支持向量机 云计算 

分 类 号：U467.14[机械工程—车辆工程]