An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle

Abstract

With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype（RCP）experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.

A Comprehensive Review on Fuel Cell UAV Key Technologies: Propulsion System, Management Strategy, and Design Procedure

“…The reliability of the power system is improved as no power sources will be depleted in advance. [52] State machine To do FC system power management Fuel cell and battery simulation EMS is coupled with the fuel cell compressor optimal power control, which identified 2.6% energy saving compared with the constant compressor power…”

“…A rapid control prototype is built to testify simulation result, and the overall efficiency is more than 90% under different initial SOC states with the proposed method. To ensure the energy utility rate difference of the fuel cell and the battery are controlled in an acceptable range when the fuel consumption is the least, a rule-based dynamic balance EMS is proposed [52]. Three states are set based on the difference between SOC and the remaining hydrogen rate.…”

An Improved Optimization Function to Integrate the User’s Comfort Perception into a Smart Home Controller Based on Particle Swarm Optimization and Fuzzy Logic

“…General aspects regarding the development of the PSO algorithm, along with discussion of the constraint factors, inertia weights, dynamic tracking systems, adaptive parameter adjustments, and more, are available in [ 41 , 42 ]. In addition, [ 16 , 43 , 44 , 45 ] provide examples of the use of PSO and fuzzy strategies in other applications.…”

A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots

“…A mobile robot path planning algorithm based on FL and neural networks was designed in [49]. Finally, Chelsea and Kelly [7] presented an FL controller for UAVs in a 2-dimensional environment, while Lei et al [50] did so in 3-dimensional space with full cell or battery hybrid power. Ten, the navigation in 3-dimensional space also was presented in [51] using FL for aerial robots and [52] for underwater robots.…”