同济大学学报（自然科学版）2024，Vol.52Issue(z1)：39-45,7.DOI:10.11908/j.issn.0253-374x.24794
基于MI特征选择的车辆能耗高精度预测方法
High-Precision Vehicle Energy Consumption Prediction using Mutual Information Feature Selection
摘要
Abstract
In recent years,machine learning methods have been widely adopted for real-time vehicle energy consumption predictions.However,the accuracy and generalizability of these predictions are often hindered by challenges such as data imprecision,missing fields,multicollinearity,and substantial difference in driving conditions and driver behaviors among identical vehicle models.To address these issues,this study systematically considers factors such as feature redundancy,data balance,freight trip frequency,transport capacity,traffic congestion and driving duration.Subsequently,an energy consumption prediction model with high precision is developed using a combination of machine learning methods such as XGBoost,Random Forest(RF),and Multilayer Perceptron(MLP).The model utilizes key features selected through the Mutual Information(MI)method,along with a constructed driver profile that captures characteristic behaviors as an independent feature.The proposed method is validated using T-BOX data collected from 120 light trucks.Experimental results indicate that the prediction method significantly enhances the prediction accuracy of energy consumption under various driving behaviors and conditions.This research contributes to the development of models with high precision in estimating the fuel consumption of light trucks.关键词
车辆能耗预测/轻型卡车/交互信息方法/司机特征画像/机器学习Key words
vehicle energy consumption prediction/light trucks/mutual information method/driver profile/machine learning分类
交通工程引用本文复制引用
王宁,李秀峰,聂辽栋,刘登程,于勤,樊华春,徐炜..基于MI特征选择的车辆能耗高精度预测方法[J].同济大学学报（自然科学版）,2024,52(z1):39-45,7.基金项目
南昌智能新能源汽车研究院科研项目(TPD-TC202303-11) （TPD-TC202303-11）
