In this paper, a robust voltage control is designed for a floating interleaved boost converter with high voltage gain. The proposed controller has an inner loop based on super-twisting sliding mode algorithm, who has continuous control signal and the sliding surface is defined for the inductor current. The reference current value is generated by active disturbance rejection control (ADRC) algorithm in the outer loop, based on the output voltage error. The stability of the sliding mode inner loop and the ADRC outer loop are proven using Lyapunov stability theorem and Routh-Hurwitz criteria, respectively. The robustness of the proposed controller is analyzed in depth and validated by the simulations and experimental results obtained with a 100W prototype converter.

A reasonably designed energy management strategy (EMS) can guarantee the safe and stable operation of fuel cell hybrid electric vehicle (FCHEV). To optimize the energy conversion efficiency of FCHEV, this research proposes a three-level efficiency optimized EMS based on the dual reward functions Q-learning algorithm. Focused on the system overall efficiency, a hardware-in-the-loop experimental platform was built first to compare the effectiveness between the proposed EMS and other existed methods. The results shown that compared with other state-of-art methods, the proposed strategy can effectively improve the energy efficiency of the system, and can slow down the aging of the fuel cell by reducing its operating stress. To further verify the effectiveness of the proposed strategy, varying driving loads profile were tested based on a 1.2-kW hybrid electric vehicle developed in the laboratory. The FCHEV real-time experiment results indicated that the proposed EMS can achieve the average load power matching error of 0.19 W and can optimize the system average overall system efficiency to 52%. The proposed method can help to contribute to the massive commercialization and implementation of the FCHEV.

Unmanned aerial vehicles (UAVs) can be regarded as one of the most emerging technologies. Compared with piloted aerial vehicles, UAVs qualify for higher safety and lower cost, and they are more suitable for dangerous missions. Recently, fuel cell (FC) UAVs have obtained more and more attention due to their relatively higher energy density. However, their propulsion system still needs to be further improved to meet different requirements of performing difficult tasks. This article aims to give a comprehensive review of the key technologies of FC UAVs. First, various architectures of the propulsion system and corresponding advantages and disadvantages are introduced. Then, three kinds of management strategies for FC UAVs are reviewed. Next, some special applications and considered design procedures are summarized. Compared with other review articles, this article tries to cover more aspects of FC UAVs. Since these aspects have a tight relationship with each other, giving a comprehensive review can be more beneficial to us to understand the state-of-the-art development of propulsion systems for FC UAVs. The conclusions show that advanced propulsion architectures, efficient online energy management strategies, and professional design procedures are still in urgent demand in the commercial process of FC UAVs.

Maintaining the cathode pressure stable and avoiding oxygen starvation are crucial for the air supply of proton exchange membrane fuel cell (PEMFC). In most applications, the cathode pressure and oxygen excess ratio (OER) are unmeasurable parameters, which makes it difficult to precisely control the air supply system. This article proposes a fractional order PID (FOPID) controller based on an unknown input nonlinear observer for the fuel cell air supply system. The proposed nonlinear observer is able to estimate internal states including the cathode pressure of the PEMFC system. Then, the OER is obtained based on the observed system states. A fractional-order PID controller with a parameter optimization algorithm is developed to regulate as fast the OER and the cathode pressure to their desired values. Both the steady and transient performances of the proposed control method are simulated and experimentally validated compared with supertwisting sliding mode controller and traditional PID controller.

As the global environmental pollution and energy crisis become more serious, lithium-ion batteries (LIB) received an increasing research interest due to their low self-discharge rates, long cycle life and high power-density for the transportation applications. Battery management system (BMS) play an essential part for the LIB system, can guarantee an efficient and stable operation through LIB state of charge (SOC) estimation. In this paper, a nonlinear observer with terminal voltage feedback injection (VFNO) is designed based on electrochemical single particle (SP) model to monitor the SOC of LIB. The convergence of the SP-VFNO system in consideration of measurement error is proved in terms of Lyapunov stability theory. The current, the terminal voltage and the SOC reference value are measured in battery testing system. Besides, the extended Kalman filter (EKF) based on both the SP model and the second-order resistor-capacitor (SORC) model to estimate SOC are adopted for comparison. The experimental results indicate that the proposed SP-VFNO method has superiority with a faster convergence rate and a higher estimation precision, which can help the accurate SOC estimation for BMS in practical application.

This paper presents the modeling and control of an ultrahigh-speed centrifugal compressor for the air management of proton exchange membrane fuel-cell (PEMFC) systems. Centrifugal compressors have the advantages of compactness, high efficiency, and low noise, compared with other kinds of displacement compressors. Moreover, ultrahigh-speed technology can also reduce the size and weight of the compressor, which makes it more feasible for automotive applications. However, the adoption of a centrifugal compressor results in control being difficult because of coupling between mass flow and pressure. In this paper, a neural network model of the compressor is developed, and a decentralized sliding-mode controller based on twisting and super twisting algorithms is proposed and tested to control the compressor pressure and mass flow. The experimental results show good dynamic characteristics and faster response compared with conventional proportional-integral (PI) control.