Parameters co-optimization strategy for dual turboshaft engine based hybrid electric propulsion system of flying cars

As a vital part of the Urban Air Mobility, flying cars should have high load capacity and long travelling range. Under the current technology, all-electric propulsion is still immature in practical applications, and a hybrid electric propulsion system (HEPS) using the high power-to-weight ratio gas turbine is a promising solution. Thus, this paper proposes a HEPS configuration consisting of dual modularized turboshaft generation units (TGUs) for a flying car, and further optimizes the HEPS's parameters using an adaptive random enhanced reflection particle swarm optimization (ARER-PSO), which improves both the components size parameters and the energy management strategy parameters. By gradually setting reflective walls to force particles to escape from improper areas, ARER-PSO realizes much better optimization capability. The optimization results show that ARER-PSO has significantly faster convergence speed than the genetic algorithm (GA) and also realizes less cost function. Furthermore, compared with the initial parameters, ARER-PSO effectively reduces the fuel consumption by 14.27 % and enhances the battery state of health (SOH) by 0.29 %, substantially outperforming traditional PSO and GA, which demonstrate the effectiveness of the ARER-PSO based parameter optimization method. At last, a hardware in loop experiment verified the real-time performance of the proposed ARER-PSO optimized energy management strategy.