Energy Management for Series Hybrid Aircraft Based on a Dual Adaptive Equivalent Fuel Minimization Strategy

Series hybrid tiltrotor aircraft operate under highly dynamic and multi-phase flight missions. This paper addresses the resulting energy management challenge. Moving beyond the direct application of existing optimization algorithms, this study proposes a novel codesign framework that synergizes offline global optimization with online fuzzy adaptive control. The key theoretical innovation lies in transforming the online, real-time optimization problem of determining the optimal equivalent factor into an offline global optimization problem for the parameters of a fuzzy-PI controller. This is achieved through a hybrid particle swarm optimization and genetic algorithm (PSOGA), which optimizes the controller's scaling factors and gains. The optimized controller embeds mission-level economic information into the online generation of the equivalent factor, so that the power-split decision is determined not only by the instantaneous operating conditions, but also by the overall economic objective of the mission. Simulation results under typical and stochastic flight conditions validate the framework's effectiveness. Compared to benchmark strategies, the proposed PSOGA-Fuzzy-A-ECMS achieves a 6.7% improvement in fuel economy while robustly maintaining the battery state of charge within a safe and stable range. This study provides a systematic and theoretically-grounded design paradigm for real-time energy management in complex, time-varying systems.