Hierarchical Co-Design for Multi-Race Strategy Optimization in Formula 1

This work presents a hierarchical optimization framework for season-level decision-making in Formula 1, combining monotone co-design with sequential optimization. By integrating component design, energy deployment, and long-term degradation within a unified structure, the approach captures both local (lap and race) trade-offs and global (seasonal) constraints. The co-design layer generates track-dependent Pareto-optimal mappings between performance and wear, which are then composed into a finite-horizon optimization problem governing component usage and replacement decisions. Applied to a hybrid-electric Formula 1 power unit, the framework determines optimal battery sizing, deployment, and replacement timing across an entire season. Results show that exploiting regulatory constraints strategically, accepting local penalties to enable global gains, can increase cumulative championship points. Furthermore, while race order does not alter the attainable total reward, it modifies the optimal control policy, illustrating the temporal coupling inherent to multi-stage decision problems. Beyond motorsport, the proposed formulation provides a general template for long-horizon resource management and co-design of complex engineering systems.