Energy Efficient Dynamic Driving Technique Modeling with Flower Pollination and Grey Wolf Optimization Algorithms in Urban Rail Transportation.

This article presents the development and validation of a novel Energy-Efficient Dynamic Driving Technique (EEDDT) model for urban rail transport, specifically applied to a 4.6 km segment of the Samsun tramway system in Türkiye. The EEDDT model optimizes speed profiles and driving regimes by simultaneously minimizing traction energy consumption (MTEC), maximizing regenerative braking energy production (MRBEP), and minimizing total travel time (MTT) through a multi-objective framework. Two nature-inspired metaheuristic algorithms—the Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO)—are employed to solve the high-dimensional, nonlinear optimization problem, incorporating real-world factors such as track gradients, horizontal curvature, speed-dependent resistance, and passenger load variability. Simulation results demonstrate that the model achieves up to a 48.95% reduction in traction energy consumption, a 137.60% increase in regenerative braking energy recovery, and maintains travel time adherence above 99.6% compared to conventional driving strategies. The study highlights the complementary strengths of FPA and GWO, with FPA providing higher solution accuracy and GWO offering faster convergence, and underscores the model's practical applicability for enhancing energy efficiency in urban rail systems.