Due to the substantial increase in the number of electrically driven systems onboard more electric aircraft (MEA), the onboard electric power systems (EPSs) are becoming more and more complex. Therefore, there is a need to develop a control strategy to manage the overall EPS energy flow and ensure the operation of safety-critical systems (which are electrical loads) under different operating scenarios and to consider EPS losses minimization, exploiting the thermal capability of generators, different load priorities, and available batteries with their charging and discharging schedules. This article presents an energy management (EM) strategy that considers the aforementioned objectives. The optimal droop gain approach is employed as a power-sharing method to minimize the total EPS losses in MEA. A finite state machine (FSM) has been used to implement the control strategy to realize the EPS reconfiguration

This paper provides a design, a charging control, and energy management of a movable Photo Voltaic (PV) charging station with an Automatic Battery Replacement (ABR) system to enable drones for ongoing missions. The paper represents the first stage of a three-staged project titled Fall Armyworm (FAW) insect killer. The other two stages involve the flight control of drones and detecting and killing FAW insects. Without chemical methods, the project aims to eliminate harmful FAW insects that are rapidly spreading in Africa and Asia. The power source is a hybrid PV system with energy storage devices (batteries and supercapacitors). The maximum power from PV panels is tracked using three different online methods (PSO, IC, and P&O), and the best method with the highest accuracy is selected. The experimental and simulation results approved that PSO is the recommended method used in this project among the studied methods because of its high target reach (about 97%) and low steady-state oscillation (maximum 2.15%). An intelligent energy management system is investigated and designed to efficiently utilize solar power with a constant-current constantvoltage charger for LiPo batteries. A new Battery Selection System (BSS) is designed and verified to efficiently utilize the harvested energy and increase the mission time. The BSS targets to manage the selection of the appropriate battery to charge and control its charging rate. The system performance is tested using MATLAB software. Then, an experimental setup for the system is built to validate simulation results. The results of simulations and experiments proved the reliability of BSS