Amirhamzeh Farajollahi

Proper thermal management of lithium-ion cells is vital for safety, longevity, and reliable performance, especially in high-power applications where heat generation is considerable. Among passive cooling strategies, phase change materials (PCMs) have shown great promise due to their ability to absorb large amounts of heat. However, while many studies have experimentally and numerically examined this topic, gaps still exist in the theoretical modeling of phase change-based cooling for cylindrical lithium-ion batteries. This study aims to enhance battery thermal management pack design by developing a comprehensive analytical solution for the coupled heat transfer in a cylindrical lithium-ion cell surrounded by PCM. The model incorporates realistic time-dependent boundary conditions, significantly improving its accuracy and applicability for battery pack design. By integrating the perturbation method for the PCM and the Green’s function method for the cell, the model predicts time-dependent temperature profiles and the position of the moving phase change front. Unlike numerical methods, this analytical framework provides rapid yet accurate insights, validated against experimental and computational results with an error margin of less than 5%. The results confirm that while PCM cooling effectively regulates the surface temperature, the battery core remains relatively hotter. Therefore, improving the thermal conductivity of the battery itself is crucial for maximizing the effectiveness of PCM cooling and achieving a more uniform temperature distribution. Furthermore, increasing the thermal conductivity and latent heat of the PCM can also help control the core temperature up to an optimal threshold.

This paper presents a novel real-time energy management strategy (EMS) for fuel cell/battery hybrid fixed-wing UAVs based on a chaotic augmentation fuzzy logic controller. In this framework, all fuzzy parameters and chaos suppression coefficients are optimized offline by the Fire Hawk Optimizer (FHO). The main innovation of this research is the simultaneous integration of three components that have never been integrated in the UAV literature: (i) Explicit modeling of environmental uncertainties—especially turbulent gusts—through a modified Lorenz system whose behavior is directly mapped to battery SOC and fuel cell hydrogen consumption dynamics; (ii) Real-time suppression of chaotic oscillations resulting from this model using a lightweight hybrid controller OGY + TDFC; and (iii) Single-stage global co-optimization of all tunable parameters (fuzzy membership functions, rule weights, OGY gain, TDFC gain and delay τ) via the recent FHO algorithm. Simulation results on a 5 kg fixed-wing UAV under a realistic 3-hour mission with chaotic wind disturbances show that the proposed strategy achieves 44.35% reduction in fuel-cell energy consumption and extends endurance by more than one hour compared to a baseline FHO-optimized conventional fuzzy EMS (without chaos modeling). Compared to state-of-the-art methods reported in the literature, it provides 14.65–28.75% higher fuel savings than NMPC/AHEMS-based approaches, 24.35% higher savings over PSO-optimized fuzzy EMS, and significantly superior disturbance rejection without requiring accurate future mission preview or heavy online computation, while remaining executable in real-time (< 16 ms/cycle) on typical UAV companion computers. These results highlight the effectiveness of explicit chaos-based uncertainty representation and lightweight hybrid chaos control in achieving superior fuel economy and robustness for long-endurance hybrid UAV operations.

Unmanned aerial vehicles (UAVs) are increasingly being powered by fuel cells, which provide a zero-emission green energy source, improve endurance, and reduce charging/refuelling times. These gadgets are used for anything from aerial photography to military activities. By tackling two problems—power management (PM) of the resources and design optimization (DO) of a hybrid electric source (HES) made up of a fuel cell (FC) and a battery—this study seeks to increase endurance and energy efficiency. It is designed for a fixed-wing electric unmanned aerial vehicle (EUAV) and uses a novel method to lighten the drone. For hybrid electric drones, energy management techniques are crucial. Using fuzzy logic-based programming and Multi-Factor Reinforcement Learning (MFRL), we will apply a reinforcement learning system to regulate the drone’s fuel consumption between the fuel cell and the battery. The Harris Hawk Optimization (HHO) algorithm is used by DO to determine the fuel cell and battery’s maximum power and capacity in order to reduce resource use. In order to choose the best management system, this PMS will use the HHO method to optimize the MFRL parameters and membership functions in the fuzzy logic structure. We have considered the uncertainty that governs the drone’s mobility and the effect of variations in wind speed in order to produce a realistic model. In the fuzzy system, we have also incorporated the wind speed variable for the energy management problem. Through the use of a modelling platform that integrates the UAV and hybrid power system models with a Matlab tool, the proposed method is assessed and yields an 8% weight reduction, saving a total of 70.43 kJ of energy, which can extend the “endurance phase” by more than 30 min. Additionally, the proposed method reduces the amplitude of SoC fluctuations, saving up to 40% of FC energy and enabling the UAV to operate for longer missions.