The hybrid power system has been widely adopted in the unmanned aerial vehicle (UAV) to meet various flight profiles. In this paper, an optimal sizing strategy for fuel cell (FC)-Li-ion battery-supercapacitor (SC) based hybrid power system in the UAV is proposed. The required energy from the FC is optimized based on the optimal efficiency range. In addition, using the frequency-based power split algorithm, a multi-objective function, minimizing the weight and economic cost, for the battery-SC subsystem is constructed. The PSO intelligent optimization algorithm is used to find the optimal sizing for the system. Finally, some simulations have been carried out to verify the correctness of the proposed algorithm.

With a large number of constant power loads (CPLs) connected to the DC microgrid (MG), the injected negative impedance drastically deteriorates the stability of the DCMG. To address this issue, a stabilization strategy for the DCMG with CPLs fed by the four-switch Buck-Boost (FSBB) converters is proposed in this paper. Due to the adopted DC machine technique, a virtual capacitor is paralleled to the output port of the FSBB converter to support the bus voltage in the presences of CPL power changes. The stability of the system is analyzed based on the Middlebrook impedance stability criterion, and the effectiveness of the proposed control strategy is verified by MATLAB/Simulink simulation.

Compared to single fuel cell powertrain, the multi-source hybrid system addresses the slow dynamic response and inability to recover and recycle braking energy capability inherent to fuel cell system. In aviation, hybrid systems enhance energy management efficiency and enable rapid power response to dynamic demands through the synergistic integration of multiple power sources. Concurrently, the energy management strategy coordinates the operational states of all sources in real time and optimally allocates power loads, thereby enhancing system operational stability and extending service life. Integrating Equivalent Consumption Minimization (ECM) principle, an Optimized Parasitism-Predation Algorithm (OPPA) is proposed. Compared with the traditional PPA, the optimized algorithm features two key enhancements. First, it integrates gradient information analysis and adopts a probabilistic acceptance mechanism for suboptimal solutions, thereby significantly expediting the local search process. Second, certain fixed parameters within the algorithm are adaptively adjusted over time, enabling the algorithm to effectively achieve the exploration-exploitation trade off. Simulation results demonstrate that the proposed strategy effectively balances multiple performance metrics within the model. When benchmarked against the State Machine Strategy and the Equivalent Consumption Minimization Strategy (ECMS), it outperforms in enhancing system efficiency and stability while concurrently reducing system costs.

The LLC resonant converter (LLC)possesses the features of high efficiency, high power density and galvanic isolation, which makes it a promising candidate for the energy storage converter in DC microgrid applications. In DC microgrid, power electronic loads are widely used, such as motor drivers. The negative impedance characteristics of these constant power loads (CPL) will affect the stability of the source converter. In this paper, a control method is proposed for the LLC resonant converter in the energy storage system. By adopting the nonlinear disturbance observer (NDO), the negative impedances caused by the power electronics loads are compensated. In addition, a reduced-order model for the LLC resonant converter is used to simplify the controller design. A Lyapunov based stability analysis is carried out, and the simulation results of using Matlab/Simulink are given to show the advantages of the proposed control method.

For the operation process of proton exchange membrane fuel cells (PEMFCs) system, fault diagnosis plays a crucial role in ensuring the safety and reliability of system. To improve the accuracy and rapidity of fault diagnosis, a fault diagnosis method of PEMFC based on data space feature decision tree group (DTS) and extreme learning machine (ELM) is proposed. Firstly, principal component analysis (PCA) is used to reduce the dimension of sensor data such as current, temperature and gas flow rate of system, which reflects the operating states of fuel cell. Besides, spatial features of system can be extracted and processed by random rotation matrix. Furthermore, multiple sets of spatial rotation feature data are set to diagnose the health state of fuel cell system by combining decision tree group and extreme learning machine (DTS-ELM). Finally, the proposed method was verified and analyzed, and the experimental results indicate that this method can quickly identify four operating states such as normal state, flooding, membrane dying and hydrogen leakage. The diagnostic accuracy and operating time of this method are 99.54% 0.261s, respectively. Therefore, on-line rapid fault diagnosis of proton exchange membrane fuel cell system can be realized by the proposed method.

Compared to single-stack fuel cell systems, multi-stack fuel cell systems (MFCS) can increase fault tolerance through redundancy. In this paper, an improved energy management control strategy (EMS) based on hierarchical is proposed for power allocation of fuel cells and the battery based on a multi-stack fuel cell hybrid power system. This strategy aims to maintain the state of charge (SOC) of a battery within a healthy range and reduce hydrogen consumption. At the overall level, power distribution is carried out between the battery and fuel cell; At the local level, power distribution is then applied to individual fuel cells. Fuel cell weighting factors were used and range reduction control was applied to SOC. The simulation results show that when the SOC is high, low, or within the expected range, comparing the improved state machine strategy with the traditional state machine strategy, the SOC of the improved state machine strategy changes faster to the expected range, and the hydrogen consumption is also reduced by 11.550g, 9.200g, and 9.710g, respectively. Thus, the economy and durability are improved.

Electrochemical impedance spectroscopy (EIS) has broad application prospects in the field of structural analysis and performance optimization of proton exchange membrane fuel cells (PEMFC), and the establishment of an equivalent impedance model based on EIS can intuitively and accurately characterize the internal characteristics of fuel cells. In this paper, an EIS diagram is drawn based on experimental data, and an equivalent impedance model that can well characterize PEMFC is proposed, and compared with commonly used equivalent models. The correspondence between each topology in the model design and the actual PEMFC electrochemical process is explained, and the electrochemical reaction principle of the catalytic layer is explained based on the model. The fitting analysis shows that the accuracy (chi-square error) of the designed model on the experimental data is improved by about 58.16%. This model can guide the process of EIS image analysis and establishing equivalent circuit models, and provide guiding ideas for related mechanism research.

Thermal management control of cathode open PEMFC system is important for its durability and reliability. To adjust the temperature of PEMFC stack to near the optimal temperature, this paper adopted the control method of second-order ADRC to realize the optimal output of power. On this basis, an improved ADRC thermal management control method for fuel cell system based on sliding mode observer was proposed, and relevant control parameters were also adjusted. A PEMFC thermal management control platform had been built to compare and analyze the traditional strategy with the proposed improvement strategy. The experimental results show that compared with traditional control methods, the proposed strategy significantly reduces the overshoot and response time of the fuel cell under load and unload conditions. At the same time, it can quickly and stably track the optimal control temperature of the system, reduce the output steady-state error range, ensure that the system operates more smoothly, and effectively improve the efficiency and output performance of the fuel cell.

With the advantages of light weight, small size, simple structure, high efficiency, and high power density, permanent magnet synchronous motors have become the main choice of major new energy vehicle enterprises. However, under complex vehicle driving conditions, the electromagnetic parameters of permanent magnet synchronous motors change due to temperature changes, cross-coupling effects, and magnetic saturation of the core, which affect the performance of the motor control system. Therefore, fast and accurate identification of motor electromagnetic parameters is required to ensure the performance and reliability of the motor control system. By summarizing the literature, this paper analyzes the reasons for electromagnetic parameter transformation, compares the advantages and disadvantages of various electromagnetic parameter identification algorithms, points out the potential future development direction of identification algorithms, and provides ideas for future research on parameter identification algorithms.

To last longer in the air, fuel cell (FC) hybrid unmanned aerial vehicles (UAV) need to control the energy flow from the different power sources. In this work, multiple power sources such as photovoltaic (PV) panels, battery systems and supercapacitors (SCs) are used to supplement the FC over a flight simulation. According to the requirements of the flight scheme, a 1.1 kW proton exchange membrane fuel cell (PEMFC) system, 1kWh li-ion battery, 185W PV panel and a 1F/72V SC are considered here. To ensure minimum hydrogen consumption and degradation and a stable dc bus voltage, three energy management strategies (EMSs) are implemented and compared. A suitable topology is used, with unidirectional, bidirectional converters and their controllers.

A reasonably designed energy management strategy can guarantee the stable and efficient operation of fuel cell UAV. The Pontryagin’s Minimal Principle (PMP) -based strategies are usually used to solve the problem of reducing system hydrogen consumption system. The conventional PMP strategies may fail to maintain optimal performance when system operating conditions change. To solve this problem, this paper proposes a PMP energy management strategy based on state switching (S-PMP). The comorphic variable of the PMP can be adjusted actively according to the SOC of the battery. When the SOC of battery lower than the threshold, the algorithm adjusts the comorphic variable through the deviation between the real-time collected SOC and the expected value of the SOC. When the SOC of battery higher than the threshold, the algorithm adjusts the comorphic variable by the deviation of the load power and the battery power. The simulation results show that the proposed S-PMP algorithm can effectively reduce the consumption of hydrogen, maintain the SOC of the battery, and improve the operating efficiency of the fuel cell system under different operating conditions of the system.

Fuel cell has received more and more attention in the field of green aviation in recent years due to their zero-emission and pollution-free advantages. In this paper, a flight mission model of a distributed electric propulsion aircraft is established, and an energy management strategy based on an improved multi-dimensional coupled state machine is designed by considering factors such as multi-mission profile power requirements and failure emergency measures for the aviation electric propulsion hybrid power system. The strategy is based on a state machine and divides the operation of the distributed electric propulsion aircraft into four states: climb, cruise, descent, and malfunction. The overall operation test of the aircraft model is carried out based on the four states, and the research results indicate that compared with the load-following mode control strategy, the proposed strategy increases the average fuel cell (FC) efficiency by 3.83%. In addition, the proposed strategy saves 7.95% of hydrogen and can effectively ensure flight safety in the event of a malfunction.

Fuel cell diagnosis is very important to ensure the reliability of its operation and application. The data-driven method is concerned for its simplicity and accuracy. This paper proposes a fuel cell fault diagnosis method based on multi-Grained Cascade Forest (gcForest) and principal component analysis (PCA). This method uses PCA to reduce the dimensionality of the fault data and extract appropriate features. Based on relatively simplified features, the classification algorithm of gcForest is used to diagnose the fault status of the fuel cell. Through experimental analysis, this proposed method can quickly identify the three health states of membrane drying, hydrogen leakage, and normal state. The diagnostic accuracy of this method is 99.39%, and the diagnosis period is 0.372s. Therefore, the method proposed in this paper is suitable for online fault identification of proton exchange membrane fuel cell systems with large data samples and multi-dimensional data.

As a renewable and efficient power source, proton exchange membrane fuel cells (PEMFCs) are receiving more and more attention from the world, but it still has shortcomings with poor durability and insufficient reliability, so the purpose of this paper is to effectively improve the reliability and durability of PEMFCs by a data-driven fault diagnosis method. In this method, principal component analysis (PCA) is first adopted to reduce the dimensionality of fault data. Then, a classification method named eXtreme Gradient Boosting (XGBoost) which based on boosting algorithm is used to classify these data. In the end, the experiment is proposed to verify the diagnostic performance of this method. The result shows that the method can effectively identify four healthy states of membrane drying failure, hydrogen leakage failure, normal state as well as unknown state, the diagnostic accuracy can reach 99.72%, and the diagnosis period is 0.17751 s, which is suitable for online implementation.

This paper presents an observer based power switch open-circuit fault diagnosis method for a two-phase interleaved boost converter (IBC). Motivated by the simplicity and robustness of immersion and invariant (I&I) theory, I&I observers are adopted for residual generation. The inductor current and output voltage used in controllers are selected for input voltage estimation, which avoids the use of extra sensors. Furtherly, to eliminate the unexpected effects, which are caused by parameter uncertainty, an adaptive threshold is designed. The simulation results obtained under different operation situations have demonstrated that the preselected threshold has a strong robustness against internal and external disturbances with no false alarms. What' more, the simulation results also show that the switch open-circuit diagnosis is equal to one switch period under various conditions. Thus, the effectiveness of the proposed diagnosis method is validated rigorously.

The power converters can play an important role in fuel cell systems, and robust control design of the converters can improve both the system efficiency and stability. In this paper, an advanced control strategy of a two-phase interleaved boost converter for fuel cell applications is proposed and implemented, which consists of the outer voltage regulation loop and the inner current tracking loop. The current inner loop is designed based on the Super-Twisting sliding mode control to track its reference current tightly. The active disturbance rejection control (ADRC) is applied to the voltage outer loop to regulate the output voltage and generate the reference current for the inner loop. The stability of the control loops is proved through the Lyapunov stability theory and Routh-Hurwitz criteria. Finally, the effectiveness of the proposed control strategy is demonstrated by numerical simulation results, which indicates that strong robustness can be achieved when compared with traditional dual-loop PI controller.

A novel control scheme for two phases floating interleaved boost DC-DC converter (FIBC) is presented in this paper. The proposed controller is based on second order sliding mode method combined with an extend state observer (ESO), which comprises two loops: the outer loop is a voltage regulation loop whereas the inner loop is an instantaneous current regulation loop. The outer loop is accomplished by SOSM and ESO, which aims to regulate the output voltage of the converter and reject the disturbance of the load variations. The SOSM strategy is also applied to inner loop to make the current track its reference accurately. Through the proposed approach, the system can obtain a stronger robustness, and the chattering problem is also suppressed. Both theoretical analysis and simulation results are demonstrated. Moreover, in order to verify the effectiveness of the proposed control scheme, comparisons with dual loops super-twisting control scheme (ST + ST) and outer active disturbance reject control plus inner super-twisting control scheme (ADRC + ST) in case of parameter uncertainties and loads disturbances have been made in this paper.