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.