Route-Based Optimization Methods for Energy Consumption Modeling of Electric Trucks

This study presents an advanced method for modeling energy consumption in electric trucks by incorporating regenerative braking probability into conventional modeling equations. Traditional models typically assume uniform regenerative energy recovery, ignoring the variability introduced by differing driving behaviors and braking scenarios. To address this gap, the proposed method explicitly integrates regenerative probability, capturing the dynamic interactions between driving conditions and regenerative braking events. The research involves systematic data preprocessing techniques, including outlier detection and correction, to ensure high data integrity. Moreover, a genetic algorithm is employed to optimize critical features such as aerodynamic drag coefficient, rolling resistance, and regenerative braking efficiency and probability, aiming to minimize discrepancies between predicted and actual energy consumption. The validation results demonstrate that the enhanced model provides a significantly improved accuracy in predicting energy recovery and state-of-charge estimations, supporting more effective and sustainable energy management practices for electric truck operations.