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 constant-voltage 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 in different operating cases with an efficiency higher than 97%.

This paper will review, analyze, model, and check experimentally the most effective techniques of photovoltaic (PV) cooling and control of its temperature in general. This will be analyzed and discussed to choose the suitable methods for Middle East Countries, especially Egypt. Namely, it is well known that an increase in electrical efficiency is a direct result of any decrease in the panel temperature. Several cooling techniques will be studied, analyzed, modelled, and check experimentally the best based on active, passive water and air cooling, as these are the simplest techniques. Improving electrical efficiency depends mainly on the technique used in cooling, geographical position, type and size of the module, and the month or season of the year. Usually, the overall efficiency records improvement with a rise of 10-15 %. Finally, a comparison on deferent cooling techniques for PV panels will be made to discuss which type is suitable for Egypt.

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation.

Bidirectional electric vehicle (EV) chargers must regulate current and voltage accurately in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes while respecting the tight real-time computational budget of embedded hardware. This paper compares three controllers, active disturbance rejection control (ADRC), a linear model predictive controller (MPC), and a reduced-order quadratic-programming predictive variant (rMPC), for a bidirectional charger supplying a 96-cell nickel–manganese–cobalt battery modeled as a second-order equivalent circuit. The gains of all three controllers are tuned systematically with particle swarm optimization (PSO) and a genetic algorithm (GA) against a weighted objective covering current tracking, terminal-voltage error, disturbance recovery, and control effort. Each controller is evaluated over a constant-current/taper charging profile, a V2G discharge, and injected load disturbances, with robustness quantified by a 500-run Monte Carlo sweep of inductance, resistance, and temperature. After tuning, every controller meets the 5% normalized-error target across all phases, with constant-current tracking error below 2%. ADRC’s mean per-step solve time (∼14.5 $\mathrm{\mu }$s) is 19–30× smaller than that of the predictive controllers. A 20-seed statistical comparison at equal budget shows PSO and GA to be statistically equivalent tuners, and the decisive practical distinction among three comparably accurate and robust controllers is computational: ADRC offers bounded, negligible worst-case timing, while rMPC and MPC offer marginally faster disturbance recovery at a far higher and less predictable solve cost that exceeds the real-time budget at the 99th percentile.