A control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid unmanned aerial vehicles

Fuel cell/battery hybrid energy storage system (HESS) powered unmanned aerial vehicle (UAV) has the outstanding advantage of long endurance time. Trajectory tracking motion is a commonly used task execution mode of UAVs, especially in autonomous UAVs. This study aims at developing a control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid UAVs. Its position tracking control adopts model predictive control (MPC) and an extended state observer to eliminate the modeling errors and effect of interference. The attitude tracking control adopts an auto-disturbance rejection controller having a quick response. The obtained control parameters are given as an input to the energy management block. Energy management strategies (EMSs) based on online dynamic programming and hierarchical MPC have been proposed. The results obtained from a simulation show that the proposed trajectory tracking control architecture can track the target trajectory stably with a small tracking error. The tracking performance is stable under interference. Experimental results show that dynamic programming is solved online with good control performance. Compared to ordinary EMSs, dynamic programming and hierarchical MPC can increase endurance time by 2.69% and 1.27%, respectively. The proposed control architecture verifies the coordination of energy management and trajectory tracking control, and prospected the advantages of the combination of fuel cell and autonomous driving for long endurance UAVs in the future.     

Experimental investigation on the online fuzzy energy management of hybrid fuel cell/battery power system for UAVs

The hybrid powerplant combining a fuel cell and a battery has become one of the most promising alternative power systems for electric unmanned aerial vehicles (UAVs). To enhance the fuel efficiency and battery service life, highly effective and robust online energy management strategies are needed in real applications.In this work, an energy management system is designed to control the hybrid fuel cell and battery power system for electric UAVs. To reduce the weight, only one programmable direct-current to direct-current (dcdc) converter is used as the critical power split component to implement the power management strategy. The output voltage and current of the dcdc is controlled by an independent energy management controller. An executable process of online fuzzy energy management strategy is proposed and established. According to the demand power and battery state of charge, the online fuzzy energy management strategy produces the current command for the dcdc to directly control the output current of the fuel cell and to indirectly control the charge/discharge current of the battery based on the power balance principle.Another two online strategies, the passive control strategy and the state machine strategy, are also employed to compare with the proposed online fuzzy strategy in terms of the battery management and fuel efficiency. To evaluate and compare the feasibility of the online energy management strategies in application, experiments with three types of missions are carried out using the hybrid power system test-bench, which consists of a commercial fuel cell EOS600, a Lipo battery, a programmable dcdc converter, an energy management controller, and an electric load. The experimental investigation shows that the proposed online fuzzy strategy prefers to use the most power from the battery and consumes the least amount of hydrogen fuel compared with the other two online energy management strategies.     

Fuel cell system with sodium borohydride as hydrogen source for unmanned aerial vehicles

In this study, we design and fabricate a fuel cell system for application as a power source in unmanned aerial vehicles (UAVs). The fuel cell system consists of a fuel cell stack, hydrogen generator, and hybrid power management system. PEMFC stack with an output power of 100 W is prepared and tested to decide the efficient operating conditions; the stack must be operated in the dead-end mode with purge in order to ensure prolonged stack performance. A hydrogen generator is fabricated to supply gaseous hydrogen to the stack. Sodium borohydride (NaBH₄) is used as the hydrogen source in the present study. Co/Al₂O₃ catalyst is prepared for the hydrolysis of the alkaline NaBH₄ solution at room temperature. The fabricated Co catalyst is comparable to the Ru catalyst. The UAV consumes more power in the takeoff mode than in the cruising mode. A hybrid power management system using an auxiliary battery is developed and evaluated for efficient energy management. Hybrid power from both the fuel cell and battery powers takeoff and turning flight operations, while the fuel cell supplies steady power during the cruising flight. The capabilities of the fuel-cell UAVs for long endurance flights are validated by successful flight tests.     

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.     

Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems

In this paper, an observer-based type-2 fuzzy method is proposed for control and energy management strategy (EMS) of the hybrid energy storage system (HESS) which can be composed of the fuel cell (FC), battery (BA), and supercapacitor (SC). The objective and main contribution of the suggested strategy is to provide: 1) Appropriate tracking performance of power sources by an observer-based control method in the presence of noise and signal ripples. 2) An observer-based composite adaptive type-2 fuzzy (OCAT2F) to approximate the voltage of power sources. 3) A dynamical model of DC-bus to guarantee the stability of closed-loop system. 4) An intelligent EMS. To have a high-power supply, the proposed EMS includes two parts; a type-2 fuzzy logic control rule table (T2FLCRT), and an observer-based robust adaptive fuzzy type-2 fuzzy (ORAT2F). Furthermore, stability analyses of the closed-loop system are provided by the input-output linearization (I-OL) approach and based on the Lyapunov theorem. The simulation results of the proposed control scheme under MATLAB/Simulink indicate that the suggested strategy can provide a suitable control performance, and stability of the whole system is achieved.     

Co-optimization strategies for connected and automated fuel cell hybrid vehicles in dynamic curving scenarios

The performance of speed planning and energy management for connected and automated fuel cell hybrid vehicles (CAFCHVs) in the curve directly affects the curve passage, operating safety and energy economy. However, the uncertainty of complex traffic conditions (such as the dynamic state of the preceding and ego vehicle, road adhesion coefficient, and curve radius) and the lateral stability of CAFCHV lead to the difficulty of online speed planning and energy management. To address this problem, a co-optimization strategy is implemented in this study. First, according to the stability condition of CAFCHV in the curve, the critical safe speed is obtained by phase plane analysis. In addition, combing the timeliness, future information of driving conditions, and the current state of preceding and ego-vehicles, the gradient-based model prediction control (GRAMPC) leveraging the fast projection gradient method is adopted to calculate the safe and optimal speed sequence. Meanwhile, the energy management strategy (EMS) based on the power ratio adaptive equivalent consumption minimization strategy (PR-AECMS) is utilized for energy distribution. A multi-objective performance function is introduced to evaluate the total cost of hydrogen consumption and battery life extension. The simulation results reveal that the proposed strategy can obtain a safe and optimal speed sequence when CAFCHV operates on the curve road. And compared with the mode of tracking the speed of the preceding vehicle, the hydrogen consumption, SOC, battery degradation, and total cost are reduced by 1.4%, 1.9%, 9.9%, and 1.8% regulated by the planning mode, respectively.     

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

The power management strategy (PMS) plays an important role in the optimum design and efficient utilization of hybrid energy systems. The power available from hybrid systems and the overall lifetime of system components are highly affected by PMS. This paper presents a novel method for the determination of the optimum PMS of hybrid energy systems including various generators and storage units. The PMS optimization is integrated with the sizing procedure of the hybrid system. The method is tested on a system with several widely used generators in off-grid systems, including wind turbines, PV panels, fuel cells, electrolyzers, hydrogen tanks, batteries, and diesel generators. The aim of the optimization problem is to simultaneously minimize the overall cost of the system, unmet load, and fuel emission considering the uncertainties associated with renewable energy sources (RES). These uncertainties are modeled by using various possible scenarios for wind speed and solar irradiation based on Weibull and Beta probability distribution functions (PDF), respectively. The differential evolution algorithm (DEA) accompanied with fuzzy technique is used to handle the mixed-integer nonlinear multi-objective optimization problem. The optimum solution, including design parameters of system components and the monthly PMS parameters adapting climatic changes during a year, are obtained. Considering operating limitations of system devices, the parameters characterize the priority and share of each storage component for serving the deficit energy or storing surplus energy both resulted from the mismatch of power between load and generation. In order to have efficient power exploitation from RES, the optimum monthly tilt angles of PV panels and the optimum tower height for wind turbines are calculated. Numerical results are compared with the results of optimal sizing assuming pre-defined PMS without using the proposed power management optimization method. The comparative results present the efficacy and capability of the proposed method for hybrid energy systems.     

Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system

This paper proposes a method of maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on adaptive fuzzy logic to control a switch of a boost converter. Adaptive fuzzy logic controllers provide attractive features such as fast response, good performance. In addition, adaptive fuzzy logic controllers can also change the fuzzy parameter for improving the control system. The single phase inverter uses predictive current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. Both conventional fuzzy logic controller and adaptive fuzzy logic controller are simulated and implemented to evaluate performance. Simulation and experimental results are provided for both controllers under the same atmospheric condition. From the simulation and experimental results, the adaptive fuzzy logic controller can deliver more power than the conventional fuzzy logic controller.     

A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.     

Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method

Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy.     

Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system

The aim of this study is to introduce a comprehensive comparison of various energy management strategies of fuel cell/supercapacitor/battery storage systems. These strategies are utilized to manage the energy demand response of hybrid systems, in an optimal way, under highly fluctuating load condition. Two novel strategies based on salp swarm algorithm (SSA) and mine-blast optimization are proposed. The outcomes of these strategies are compared with commonly used strategies like fuzzy logic control, classical proportional integral control, the state machine, equivalent fuel consumption minimization, maximization, external energy maximization, and equivalent consumption minimization. Hydrogen fuel economy and overall efficiency are used for the comparison of these different strategies. Results demonstrate that the proposed SSA management strategy performed best compared with all other used strategies in terms of hydrogen fuel economy and overall efficiency. The minimum consumed hydrogen and maximum efficiency are found 19.4 gm and 85.61%, respectively.     

An optimization scheduling method of electric vehicle virtual energy storage to track planned output based on multiobjective optimization

Electric vehicle virtual energy storage technology can effectively improve the utilization of renewable energy. Aiming at the impact of the uncertainty of electric vehicle on the power grid, an optimized dispatching method of hybrid energy storage systems based on multiobjective optimization in the scenario of tracking plan output is proposed in this paper. The predicted value of the photovoltaic power obtained by the particle swarm optimization (PSO)-back propagation (BP) neural network is used to formulate the planned output of photovoltaic power generation, and the principle component analysis algorithm is used to extract the main features affecting photovoltaic power generation to further improve the prediction accuracy of photovoltaic output power. From the perspective of the service life of electric vehicles, a two-stage optimal control method of hybrid energy storage systems based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to achieve energy distributions between electric vehicles and supercapacitors. Fully consider the benefits of electric vehicle users and the capacity of tracking plans, a multiobjective optimization model of hybrid energy storage systems to track planned output is established, and the nondominated sorted genetic algorithm-III is adopted to solve the model. The validity of the model is verified by a simulation test of actual operating data of a business park in China. The simulation results show that after the optimized control, the average absolute error of the deviation power reduces from 1.092 to 0.0528 MW, power fluctuating times of electric vehicles decreases from 151 to 80, and the daily income benefit increases from $404.468 to $483.116 in the cloudy day. The method proposed in this paper can effectively improve the controllability of renewable energy, and provide a theoretical basis for the application of electric vehicle virtual energy storage technology.     

Adaptive maximum power point tracking based on Kalman filter for hydrogen fuel cell in hybrid unmanned aerial vehicle applications

In this paper, an adaptive real-time estimation method based on Kalman filter is proposed for tracking the maximum power point (MPP) of a hydrogen fuel cell (FC) in hybrid unmanned aerial vehicle (UAV) applications. To achieve the adaptive MPP tracking (MPPT), a mathematical model for the hydrogen FC is established. Then, the recursive least square method is employed to identify the initial values of model parameters. On this basis, the MPP of the hydrogen FC under steady conditions can be derived. Furthermore, the state and observation equations based on Kalman filter are introduced to adaptively estimate the model parameters in real-time. Moreover, the real-time model parameters would be used to optimize the MPP in accordance with the operating conditions such that the adaptive MPPT can be achieved. Finally, various simulations and experiments are conducted to verify the effectiveness and accuracy of the adaptive MPPT for the hydrogen FC in hybrid UAV applications. Results show that the adaptive MPPT can not only track the MPP accurately in real-time, but also reduce the oscillation of the hydrogen FC. Compared with the MPPT methods based on perturb and observe (P&O) and particle swarm optimization (PSO), the maximum power tracking error of the adaptive MPPT can be improved by 2.83% and 1.10%, respectively.     

Energy storage and control optimization for an electric vehicle

Two big issues involving electric vehicles are energy supply and power management control. To deal with the energy supply problem, this paper proposes the application of a hybrid energy source system, composed of battery pack and ultracapacitor bank. The power management control between the energy supplies was defined by a fuzzy logic with inference rules optimized through genetic algorithm. The genetic algorithm optimizes lower and upper limits of membership functions aiming to reduce the hybrid energy source system total mass while maximizing the electric vehicle drive range and performance. Through the Pareto frontier, we found the best trade‐off solution.     

A novel maximum power fuzzy logic controller for photovoltaic solar energy systems

The maximum power tracking problem and efficient energy utilization of a stand-alone photovoltaic array (PVA) feeding voltage controlled linear and nonlinear loads is studied. A novel and simple on-line fuzzy logic-based dynamic search, detection and tracking controller is developed to ensure maximum power point (MPP) operation under excursions in solar insolation, ambient temperature and electric load variations. A computer simulation model of the PVA renewable utilization scheme including the effects of temperature and solar irradiation changes was developed and fully simulated. The load voltage is controlled by a DC chopper and kept constant at the required rated voltage. A permanent magnet DC motor (PMDC) driving a fan-type load was connected in parallel to an RL passive load. A speed control scheme is also used for the PMDC motor drive so that the drive can be operated at specified speeds. Different controllers have been employed in the unified PVA scheme to control three separate loads at MPP tracking condition namely voltage at load bus and speed of the PMDC motor. The main objective of the paper is to present a novel enhanced, cost-effective MPP detector (MPPD) and dynamic MPP tracking (MPPT) controller for a hybrid mix of electric loads.     

Novel energy management method for suppressing fuel cell degradation in hydrogen and electric hybrid energy storage systems compensating renewable energy fluctuations

This research investigates an energy management method utilized in a hydrogen and electric hybrid energy storage system (HESS), which is utilized as an ancillary system for renewable energy electricity generation. To suppress the performance degradation of the fuel cell (FC), the newly proposed energy management method deals with main FC degradation causes, such as low humidification and frequent and rapid voltage changes. The entire HESS's performance is demonstrated using the proposed energy management method. In addition, a simulation is conducted to evaluate the proposed energy management method's performance in terms of both suppressing the FC's degradation and ensuring system efficiency. The results of the experiment and simulation show that the proposed energy management method can suppress the FC's harmful working states while maintaining high system efficiency.     

Development of solid oxide fuel cell and battery hybrid power generation system

Solid oxide fuel cell hybrid generation system is the best scheme for the load tracking of off-grid monitoring stations. But there are still potential problems that need to be addressed: preventing fuel starvation and ensuring thermal safety while meeting load tracking in hybrid power generation system. In order to solve these problems, a feasible hybrid power generation system structure scheme is proposed which combined SOFC subsystem and Li-ion battery subsystem. Then a model of the hybrid power generation system is built based on the proposed system structure. On this basis, an adaptive controller, include the adaptive energy management algorithm and current feedforward gas supply strategy, is applied to manage the power-sharing in this hybrid system as well as keep the system operating within the safety constraints. The constraints, including maintaining the bus voltage at the desired level, keeping SOFC operating temperature in safety, and mitigating fuel starvation are explicitly considered. The stability of the proposed energy management algorithm is analyzed. Finally, the developed control algorithm is applied to the hybrid power generation system model, the operation result proves the feasibility of the designed controller strategy for hybrid generation system and effectively prevent fuel starvation and ensure thermal safety.     

A new Fuzzy&Wavelet-based adaptive thresholding method for detecting PQDs in a hydrogen and solar-energy powered EV charging station

This study presents a hybrid fuzzy decision-maker (FDM) and un-decimated wavelet transform (UWT)-based method for detecting power quality disturbances (PQDs) in a developed hydrogen and solar energy-powered electric vehicle (EV) charge station. The proposed adaptive FDM&UWT-based hybrid method eliminated the lack of performance of threshold-based signal analysis methods in noise-containing signals and it is implemented for a reliable PQD detection and integration in a developed microgrid. Also, the proposed method has eliminated the need for a processing-intensive filtering process to reduce noise from the signal. With this adaptive approach, detection errors in boundary conditions in threshold value methods are avoided and at the same time, cost and computational burden are minimized by using only the peak values in the detail coefficients of the voltage signal. The mean test accuracy is 96.13% for the FDM method using pyramidal UWT in noise-free conditions. Besides, the pyramidal UWT-FDM has a mean classification accuracy of 94.96% under 20–40 dB high-level noise conditions. The effectiveness of the UWT-FDM method is also tested using an experimental setup. The mean test accuracy for experimental data is 96.66%.     

Stored energy control for long-term continuous operation of an electric and hydrogen hybrid energy storage system for emergency power supply and solar power fluctuation compensation

In order to realize a large-capacity stand-alone emergency power supply that enables highly reliable and high-quality power supply at the time of a large-scale natural disaster and enables effective use of solar power generation, we proposed an electric and hydrogen hybrid energy storage system (HESS). It is composed of an electric double-layer capacitor bank, fuel cell, electrolyzer, and hydrogen storage (buffer gas tank and metal hydride). In an emergency, this HESS is expected to supply power for loads together with photovoltaics panels for a long time. In usual time, it should not only cooperate with external electricity grids to convert unstable photovoltaic output power into reliable power supply, but also maintain sufficient stored energy in case of emergency. To realize the continuous operation of the HESS in both emergency and usual time, we proposed an electric double-layer capacitor's state-of-charge feedback control method and a hydrogen energy feedback control method, coordinating an energy management method based on Kalman filter algorithm. An experiment and a simulation demonstrated the operations of a 10-kW scale model HESS in emergency and usual time mode, respectively. The demonstrations verified the correct performance of the proposed HESS with the proposed control methods and enabled the continuous operation of the HESS.     

Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles

The energy management strategy (EMS) is a key to reduce the equivalent hydrogen consumption and slow down fuel cell performance degradation of the plug-in fuel cell hybrid electric vehicles. Global optimal EMS based on the whole trip information can achieve the minimum hydrogen consumption, but it is difficult to apply in real driving. This paper tries to solve this problem with a novel hierarchical EMS proposed to realize the real-time application and approximate global optimization. The long-term average speed in each future trip segment is predicted by KNN, and the short-term speed series is predicted by a new model averaging method. The approximate global optimization is realized by introducing hierarchical reinforcement learning (HRL), and the strategy within the speed forecast window is optimized by introducing upper confidence tree search (UCTS). The vehicle speed prediction and the proposed EMS have been verified using the collected real driving cycles. The results show that the proposed strategy can adapt to driving style changes through self-learning. Compared with the widely used rule-based strategy, it can evidently reduce hydrogen consumption by 6.14% and fuel cell start-stop times by 21.7% on average to suppress the aging of fuel cell. Moreover, its computation time is less than 0.447 s at each step, and combined with rolling optimization, it can be used for real-time application.