Digital Twin-Based Adaptive Optimization of Cruise Control Systems for Eco-Driving Using Real-World OBD Data

This study presents a data-driven Digital Twin framework designed to optimize closed-loop Adaptive Cruise Control (ACC) operational parameters for eco-driving without modifying electronic control unit (ECU) firmware. High-frequency telematics data (OBD-II and GPS) were recorded from a production vehicle over an 11.57 km test corridor to engineer and calibrate a virtual Digital Twin in PTV Vissim using Wiedemann 99 car-following logic. The validated model demonstrated high empirical fidelity, achieving a mean velocity error of 2.84 km/h, a GEH statistic of 2.67, and an absolute fuel consumption variance of just 0.01 L/100 km. An iterative parametric optimization (~100 simulation runs) was executed to tune acceleration limits, convergence time constants, and target velocity bounds while maintaining strict safety constraints. The optimized Eco configuration achieved a 12% reduction in fuel consumption (4.91 to 4.33 L/100 km) alongside a 14% increase in trip duration (893 to 1019 s). Bivariate state-space analysis confirmed the complete elimination of high-intensity accelerations and a 0.48 percentage point reduction in hard braking. The framework serves as an algorithmic decision support tool for energy-aware fleet management and adaptive vehicle control.