Liangcai Xu

The power converter plays a crucial role in energy conversion systems, while advanced control algorithms can help stabilize the bus voltage and match the power demand. In this work, a novel fast terminal sliding mode control is proposed for boost converters to strengthen the disturbance rejection ability. First, a full-order sliding mode manifold is proposed to guarantee the finite-time convergence of system states, and an integral-type switching control law is proposed to deal with large disturbances. Second, the disturbance estimation method is used to identify the system uncertainties and theoretical analysis indicates that it can further enhance the robustness and reduce the chattering. Third, a parameter update mechanism is developed to calculate the model parameters at the current operating point, which contributes to improving the control performance and flexibility to different working conditions. Finally, simulation and experimental tests are conducted to verify the proposed method.

The dc-dc converter is a crucial component in energy conversion system. However, designing an advanced controller for the dc-dc converter to maintain satisfactory control performance is challenging due to its inherent nonlinearity, internal uncertainty, and external disturbance. Although active disturbance rejection control (ADRC) method is a promising solution, its control ability still needs enhancement due to the limitation of the linear extended state observer (LESO). Therefore, this paper tries to propose an adaptive ADRC by introducing an additional regulation law of $b_0$ , which is a coefficient of the conventional ADRC. When compared to the conventional ADRC, the proposed adaptive ADRC can effectively eliminate the system steady-state error caused by time-varying disturbances, improving the static performance of the closed-loop system. Besides, the estimation burden of LESO can be greatly reduced, leading to improved dynamic performance of the closed-loop system. The proposed adaptive ADRC method is validated on a dc-dc floating interleaved boost converter (FIBC), and both simulation and experimental results demonstrate that it outperforms the conventional ADRC method and other adaptive ADRC method in terms of steady-state and dynamic performance.

The powerful control ability of the active disturbance rejection control (ADRC) method has attracted much attention from both the academic and industrial communities. This method successfully inherits the advantages of the conventional proportional integration (PI) control method, such as error-driven way, simple structure, and low design complexity. In addition, it also absorbs the observer technique from modern control theory. All these factors make the ADRC method show a big potential to be widely adopted in practical industrial applications. To facilitate this development trend, in this work, some advancements of ADRC are discussed in detail, and three typical applications in the power electronics field are briefly introduced, such that the engineers can have a more intuitive understanding of this method and the academics could recognize some topics that still need to be further researched.

Fuel cells are considered as the preferred future power source due to their environmental friendliness and high efficiency, whereas short lifespan and high cost hinder their large-scale commercialization. Fuel cell prognostic can contribute to prolonging the fuel cell life and reducing the overall cost, and it has attracted research attention recently. However, most of the prognostic methods treat the measured voltage as the health indicator and thus can only be applied to the fuel cell that works under constant current condition. To handle the prognostic under frequent load change condition, this article proposes an attention mechanism-based explainable data-driven framework. In the framework, system parameters are used to construct a virtual voltage as the health indicator for degradation prediction. Attention mechanism is integrated to find the essential parameters that affect fuel cell degradation, which can reduce the sensors and provide an explainable prediction result. The proposed framework is testified using aging data obtained under dynamic condition. Results show that the current, outlet air temperature and time are the most important parameters that affect the prediction. Comparisons with conventional methods indicate the proposed method can get a promising prediction.

This article proposes an easily implementable and computationally effective model predictive control (MPC) scheme for fuel cell dc–dc boost converter. The proposed control scheme contains an explicit MPC method just with a single-step predictive horizon. Thus, the computational burden can be greatly reduced. To further enhance its antidisturbance ability, the observer technique is adopted to, respectively, estimate the load impedance and input voltage for the predictive models. Since the estimated variables can be directly sent to the cost function, the satisfactory disturbance rejection performance can be guaranteed with optimal control. Besides, an additional reference current generation module based on the flatness theory is built to deal with the inevitable parameter uncertainties and the estimation errors. Thus, the nominal system model can be directly adopted to further reduce the design complexity. Some simulation and experimental tests based on a multiphase interleaved fuel cell dc–dc boost converter are conducted to verify the superiority of the proposed control scheme. Results show that, with the proposed control scheme, both the dynamic performance and robustness of the closed-loop system are satisfactory, which can help to contribute to the fuel cell applications in transportation electrification.

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.

In this paper, a cascaded extended state observer- based sliding mode control (SMC) is proposed for the typical microgrid (MG) interfaced dc-dc boost converter. For a given MG system, the power fluctuation is unpredictable, which may cause an unstable dc-bus voltage. In that situation, all the dc-bus connected electronic loads cannot work normally. In order to maintain a stable dc-bus voltage, additional power sources with a suitable power converter and the advanced controller are necessary. Especially, the controller for the power converter plays a crucial role in reducing the power fluctuation and regulating the dc-bus voltage. In this paper, to improve the ability of the converters to reject external power disturbance, a cascaded extended state observer is designed to estimate the lumped disturbance more accurately. Besides, the continuous SMC method is adopted to provide a feedback control loop. The corresponding simulation results could highly validate the effectiveness of the proposed control method.

Fault diagnosis is essential for the stable and efficient operation of the proton exchange membrane fuel cell (PEMFC) system. However, the manifold balance of plant (BOP) components and the coupling phenomenon involving multiple physical fields will significantly increase the probability of system fault, which makes it difficult to realize a timely and effective diagnosis. In this study, a novel online diagnosis method for an open-cathode PEMFC system is proposed, which is only based on output voltage measurements, and both the normal state and fault states caused by abnormal BOP components operation are taken into consideration. Specifically, the fault identification is realized based on the fusion of t-Distributed Stochastic Neighbor Embedding (t-SNE) and eXtreme Gradient Boosting (XGBoost), where the t-SNE is adopted to extract the diagnostic features from the individual cell output voltages and the XGBoost is adopted to identify the fault type based on the extracted diagnostic features. A facile method based on the gray relational analysis (GRA) is also proposed to quantify the fault degree, which can contribute to the condition-based maintenance of the system. The results validated by the fuel cell system diagnostic experiments reveal that the novel method can effectively identify the five health states and the fault degree. The overall accuracy is 99.31%, and the diagnosis period is shortened from 0.3375 to 0.1416 s after processing by t-SNE, which indicates that the proposed method can have a better performance compared with the traditional methods.