Comparative Analysis of Mass Estimation Methods for Heavy-Duty Vehicles under Varying Payload Conditions

Keyphrases

• Comparative Analysis 100%
• Estimation Method 100%
• Heavy-duty Vehicles 100%
• Recursive Least Square Method 100%
• Mass Estimation 100%
• Feedforward Neural Network 100%
• K-nearest 100%
• Varying Payload 100%
• Regressor 75%
• Sparse Data 75%
• Mass Estimate 50%
• Accurate Mass 50%
• Electric 25%
• In-motion 25%
• Management Performance 25%
• System Optimization 25%
• Cost Efficiency 25%
• Energy Consumption 25%
• Operational Efficiency 25%
• Network Systems 25%
• Energy Management System 25%
• Cost Reduction 25%
• Performance Optimization 25%
• Accurate Estimation 25%
• Time Application 25%
• Impact Energy 25%
• High-resolution Data 25%
• Mean Absolute Percentage Error 25%
• Time Prediction 25%
• Estimation Approaches 25%
• Vehicle Model 25%
• Battery Sizing 25%
• Robust Performance 25%
• Data-driven Methods 25%
• Computationally Intensive 25%
• Recursive Least Squares 25%
• Energy Estimates 25%
• Model-based Method 25%
• Vehicle Driving 25%
• Route Optimization 25%
• Vehicle Mass 25%
• Energy Consumption Model 25%
• Direct Mass Measurement 25%
• Charging Scheduling 25%
• Range Prediction 25%
• Explainability 25%
• External Weights 25%
• Driving Dataset 25%
• Telematics Data 25%
• On-board Weighing System 25%
• Controller Area Network 25%
• Critical Calculation 25%
• Data Use 25%
• Continuous Data Streams 25%
• Class 8 Trucks 25%
• Effective Energy Management 25%

Engineering

• Comparative Analysis 100%
• Recursive 100%
• Duty Vehicle 100%
• Least Squares Method 100%
• Feedforward 80%
• Nearest Neighbor 80%
• Regressors 60%
• Operational Efficiency 20%
• Impact Energy 20%
• Effective Energy 20%
• Energy Management System 20%
• Optimization Strategy 20%
• High Resolution 20%
• Network System 20%
• Continuous Data 20%
• Controller Area Network 20%
• Data Stream 20%
• Trucks 20%
• Interpretability 20%
• Explainability 20%

Computer Science

• recursive least square 100%
• Comparative Analysis 100%
• Estimation Method 100%
• Least Squares Methods 80%
• Feed Forward Neural Networks 80%
• Data Stream 20%
• Continuous Data 20%
• Real-Time Application 20%
• Interpretability 20%
• Controller Area Network 20%
• Energy Consumption Model 20%
• Performance Optimization Strategy 20%
• Energy Consumption 20%
• Operational Efficiency 20%