In order to enhance the energy efficiency of unmanned aerial vehicles (UAVs) during flight operations in mountainous terrain, this research paper proposes an improved particle swarm optimization (PSO) algorithm-based optimal energy path planning method, which effectively reduces the non-essential energy consumption of UAV during the flight operations through a reasonable path planning method. First, this research designs a 3D path planning method based on the PSO optimization algorithm with the goal of achieving optimal energy consumption during UAV flight operations. Then, to overcome the limitations of the classical PSO algorithm, such as poor global search capability and susceptibility to local optimality, a parameter adaptive method based on deep deterministic policy gradient (DDPG) is introduced. This parameter adaptive method dynamically adjusts the main parameters of the PSO algorithm by monitoring the state of the particle swarm solution set. Finally, the improved PSO algorithm based on parameter adaptive improvement is applied to path planning in mountainous terrain environments, and an optimal energy-consuming path-planning algorithm for UAVs based on the improved PSO algorithm is proposed. Simulation results show that the path-planning algorithm proposed in this research effectively reduces non-essential energy consumption during UAV flight operations, especially in more complex terrain scenarios.

Fuel cell/battery hybrid construction vehicles (FCHCVs) have shown great promise; however, the complex working conditions of construction vehicles pose considerable challenges to the performance and energy management of a fuel cell/battery hybrid system. In this paper, multiobjective optimal model predictive control (MOMPC)-based energy management for FCHCVs is explored. A system model is established that includes an economic model and a lifetime model. In the MOMPC framework, multiobjective optimization is conducted to enhance fuel cell durability and battery lifetime while minimizing costs. Since the energy management problem is a nonlinear problem with hard state constraints, it can be difficult to resolve online. The multiobjective approach employs an adaptive weight-adjustment method based on a fuzzy logic algorithm. An economic evaluation of the FCHCV is conducted over its life cycle with respect to the power source size. Simulation results indicate economic savings and prolonged battery lifetime with the MOMPC-based strategy, compared with conventional benchmarks.

The UAV (unmanned aerial vehicle) accelerometer is a sensor that measures the acceleration of the UAV by detecting the components of gravity along the three axes. The use of self-powered technology in UAV accelerometers will become inevitable. This article proposes an asymmetric electrode arc triboelectric nanogenerator (ASA-TENG) based on the single-electrode mode. It is a potential self-powered acceleration sensor that is installed on the UAV to detect the acceleration in the cruise state. It is characterized by a simple structure, low quality, high sensitivity, large detection range, low cost, and environmental friendliness. Two ASA-TENG structures are stacked into a cross along the x-axis and y-axis to detect the components of actual acceleration along the two axes to detect the acceleration of random directions. Finally, the actual acceleration is obtained through the principle of acceleration synthesis, which can accurately detect the direction of acceleration. The ASA-TENG acceleration sensor can detect accelerations in the range of 0.1–40 m/s ² . It exhibits a high sensitivity, and its response time has a good fitting relationship with acceleration (R ² =0.997). In addition, the voltage signal amplitude generated by the ASA-TENG sensor shows negligible fluctuations after 6 hours of continuous operation. Comparison experiments with commercial acceleration sensor and the maximum relative error of only 2.9%. Therefore, the ASA-TENG acceleration sensors can be potentially used under cruise conditions of UAV as it presents numerous advantages.

In this study, titanium alloy (Ti-4Al-1.5Mn), magnesium alloy (Mg-Li9-A3-Zn3), or aluminum alloy (Al7075-T6) were used to construct the shell model of helicopter rotor blade to study the solid particle erosion of helicopter rotor blades. The erosion resistance of the three materials at different angles of attack (6°, 3°, or 0°) and particle collision speeds (70, 150, or 220 m/s) was examined using the finite volume method, the discrete phase method, and erosion models. In addition, the leading edge of the helicopter blades was coated with two types of bionic anti-erosion coating layers (V- and VC-type), in an attempt to improve erosion resistance at the angles of attack and particle collision speeds given above. The results showed that Ti-4Al-1.5Mn had the best erosion resistance at high speed, followed by Al7075-T6 and Mg-Li9-A3-Zn3. The angle of attack appeared to affect only the surface area of the blade erosion, while the erosion rate was not affected. Finally, the results of this article showed that the V-type bionic coating had better erosion resistance than the VC-type coating at the same impact speeds. The angle of attack did not have a significant effect on the erosion rate of the bionic coating.