Pacing strategy optimization for time trial cyclists with physiological constraints

Abstract

This thesis investigates the optimization of pacing strategies for time-trial cyclists by integrating physiological constraints with course-specific environmental factors. Two complementary methods are developed: one directly optimizes velocity profiles using total energy expenditure as a proxy for current energy, while the other optimizes power output at discrete course points. Both outperform constant power pacing, producing personalized strategies that adapt to terrain and wind while respecting individual cyclists’ power profiles, physiology, and energy reserves. The first optimization scheme produces slightly better completion times, but impractical power curves that could create dangerous forces, so the second scheme is ideal. Distinct pacing patterns emerge across cyclist phenotypes, underscoring the need for athlete-specific strategies. Potential applications include a prototype training app offering real-time coaching cues. This modular framework supports future integration with advanced physiological models, real-time feedback control, and empirical validation, bridging mathematical optimization and sports science to enhance cycling performance.