An efficient energy management strategy for hybrid electric unmanned aerial vehicles considering exhaust gas temperature of the turboshaft engine

Amid the rise of the low-altitude economy, hybrid electric unmanned aerial vehicles (HEUAVs) have emerged as a promising solution to mitigate energy scarcity and advance carbon neutrality. Owing to their high power-to-mass ratio, turboshaft engines are widely adopted as the primary power source for HEUAVs. Modern energy management strategies (EMSs) optimize power distribution between the turboshaft engine-generator pack (TGP) and the battery to enhance fuel economy. Besides energy efficiency, turbine durability necessitates incorporating the turboshaft engine’s exhaust gas temperature (EGT) into EMS design, as prolonged over-temperature exposure risks turbine damage. The inherent nonlinearity of the TGP alone presents significant challenges, and the integration of nonlinear EGT dynamics further compounds the complexity, making the EMS a highly nonconvex optimization problem with severe computational demands. To address these challenges, this work proposes an efficient EMS for HEUAVs that jointly optimizes fuel economy and EGT regulation. First, the multi-objective EMS is formulated as a quadratic constrained quadratic programming problem under a model predictive control framework. Second, this problem is recast as a semidefinite programming (SDP) problem, where new-induced nonconvexities are consolidated into a rank-one constraint. Third, leveraging the sparsity of constraint matrices, the rank-one constraint is decomposed into smaller submatrix constraints to reduce computational complexity. An iterative rank minimization method then converts the nonconvex SDP into a sequence of convex subproblems, enabling efficient solutions. Simulations using real-world flight data and hardware-in-loop experiments validate the strategy’s effectiveness, demonstrating an 8.8–15.6 % reduction in fuel consumption, and a 5.5–15.0 % improvement in EGT regulation, compared to the baseline method. Furthermore, hardware-in-loop results demonstrate the strategy’s real-time applicability, underscoring its potential for scalable deployment in HEUAV operations.