An efficient optimal sizing strategy for a hybrid electric air-ground vehicle using adaptive spiral optimization algorithm

This paper proposes an efficient optimal sizing strategy for hybrid electric air-ground vehicles. First, to optimize the cost and energy consumption while maintaining driving performance, major design parameters, such as the number of battery packs, power of the engine-generator set, and air/ground electrical motors, are chosen as optimization variables. Second, an adaptive spiral optimization algorithm is proposed in the presented optimal sizing strategy. Third, unique update and punish mechanisms are designed to prevent the sizing process from being stuck in local minima, leading to improved searching efficiency. The simulation is carried out in MATLAB/Simulink using the Bogacki-Shampine solver. And the relevant coding work is implemented in MATLAB. Optimization results show that, compared to the initial sizing, the proposed strategy improves cost, energy consumption, ground-driving, and flying performance by 15.16%, 4.68%, 11.78%, and 1.43%, respectively. Finally, the proposed algorithm outperforms the original spiral optimization algorithm, enhanced genetic algorithm, and adaptive particle swarm optimization algorithm in terms of improving the objective function by 1.9%, 1.18%, and 2.04%, respectively. Theoretical insights for a hybrid electric air-ground vehicle powertrain sizing problem might be provided by the proposed strategy.