Prediction analysis of autonomous mining trucks fuel consumption based on machine learning
LIU Haifeng
Abstract：In order to optimize energy management and enhance transportation efficiency of autonomous mining trucks,this article establishes a fuel consumption prediction model based on key variables including payload,total resistance,and actual speed,analyzez the performance of three machine learning models,multiple linear regression,artificial neural network and support vector machine for fuel consumption prediction.This article holds significant practical value for rationally planning driving strategies of autonomous systems,improving energy utilization efficiency,and serving as a reference for evaluating the performance of autonomous mining trucks.Based on real-world application scenarios,the article investigates the modeling and evaluation of diesel consumption for mining trucks.We utilize a dataset of 300,000 operational records from an open-pit coal mine in Inner Mongolia,the models are evaluated using metrics such as the coefficient of determination(R2),mean squared error(MSE).The ANN model achieves the highest prediction accuracy(R2=0.9051,MSE=486.32),which provide a reliable basis for energy-efficient route planning and task scheduling of autonomous mining trucks.
Keywords：autonomous mining truckfuel consumption predictionmultiple linear regressionartificial neural networksupport vector machineperformance evaluation of autonomous mining truck
Publication Date：2025-10-15
Online Publishing Date：2025-12-12(First online date of this platform, not the publication date of the document)
Pages：6( 56-61 )