Optimal energy management strategy for fuel cell hybrid vehicles via SVD-based model order reduction and non-linear MPC

Fuel cell electric vehicles (FCEVs) offer a sustainable transportation solution by combining high efficiency with zero local emissions. However, the complex dynamics of air-path management and the demands of real-time energy control pose significant challenges. This work proposes a non-linear model predictive control (NLMPC) strategy leveraging singular value decomposition (SVD) to obtain a reduced-order model from a detailed seven-state physical representation, including battery state-of-charge (SOC) and air-path dynamics. By preserving the system’s dominant dynamics, the reduced model allows for computationally efficient control within a CasADi-based MPC framework. The proposed approach is evaluated across multiple reduction levels, analyzing trade-offs between model complexity, computational cost, and hydrogen consumption. Simulations demonstrate fuel savings of up to 6.4%, with up to 3.3% achieved under the selected real-time feasibility threshold. Experimental data were used to calibrate and validate the high-fidelity plant model, and the control strategy was assessed in closed-loop simulations under realistic driving cycles.