Qian Xun

Bio: Qian Xun is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Voltage & Computer science. The author has an hindex of 6, co-authored 20 publications receiving 119 citations.

Application of SiC power electronic devices in secondary power source for aircraft

Abstract: In order to improve the efficiency, reliability and maintainability of the aircraft, the aerospace world has found in progressive electrification that reduces or removes the hydraulic, mechanical and pneumatic power systems. Power devices are widely applied in power system, while conventional Si power devices have reached the theoretical limitation. Admittedly, many advantages, such as high breakdown electric field strength, high saturated electron drift velocity and high thermal conductivity can be seen from SiC semiconductor material, and SiC power electronic devices made of which can have the ability to adapt to high voltage, high power, high frequency, high temperature and other harsh environment. This paper presents an overview of SiC power electronic devices used for the secondary power source in aerospace, through the comparison of power electronic devices between SiC and Si, the advantages of SiC devices and the development are analyzed, on the basis of which, emphatically discussed the application status of SiC devices in secondary power source, including aeronautical static inverter, transformer rectifier unit, DC-DC converter, and motor drive. In the end, the existing problems in the application of SiC power electronic devices are discussed, as well as the impacts on aviation technology.

Silicon Carbide Converters and MEMS Devices for High-temperature Power Electronics: A Critical Review

Abstract: The significant advance of power electronics in today's market is calling for high-performance power conversion systems and MEMS devices that can operate reliably in harsh environments, such as high working temperature. Silicon-carbide (SiC) power electronic devices are featured by the high junction temperature, low power losses, and excellent thermal stability, and thus are attractive to converters and MEMS devices applied in a high-temperature environment. This paper conducts an overview of high-temperature power electronics, with a focus on high-temperature converters and MEMS devices. The critical components, namely SiC power devices and modules, gate drives, and passive components, are introduced and comparatively analyzed regarding composition material, physical structure, and packaging technology. Then, the research and development directions of SiC-based high-temperature converters in the fields of motor drives, rectifier units, DC-DC converters are discussed, as well as MEMS devices. Finally, the existing technical challenges facing high-temperature power electronics are identified, including gate drives, current measurement, parameters matching between each component, and packaging technology.

A Comparative Study of Fuel Cell Electric Vehicles Hybridization with Battery or Supercapacitor

Abstract: This paper deals with the conception and the achievement of a hybrid power source using a fuel cell combined with a battery or a supercapacitor. In which, the fuel cell supplies the main power to the drive system; while the battery or the supercapacitor is used as an auxiliary power source. This gives the benefit that the regenerative energy is stored in battery or supercapacitor during the deceleration and it is transferred back to the drive system during the acceleration when compared to electric vehicles solely powered by a fuel cell. Different energy storage devices, such as fuel cell, battery, and supercapacitor are compared., and then several structures of fuel cell-based electric vehicles are analyzed in the paper. Following that a conventional topology based on fuel cell and battery using a DC/DC converter with the connection between the fuel cell and the inverter, and a floating voltage topology powered by fuel cell and supercapacitor without any DC/DC converters are chosen for the simulation analysis. Simulation results show that power variations of the fuel cell in floating voltage topology can be smoother, and its rated power is downsized, which can extend the fuel cell lifetime and take full advantages of the fuel cell and the supercapacitor.

LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

Abstract: Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors. A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer. The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features. Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer. Bayesian optimization is used to select the optimal combined features. The effectiveness of the proposed model is verified on two actual PV power plants in one area of China. The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model.

Design and experimental verification of a fuel cell/supercapacitor passive configuration for a light vehicle

Abstract: The fuel cell/supercapacitor passive configuration without using any DC/DC converters is promising in automotive applications as it can downsize the fuel cell stack, maintain the peak power capability, improve the system efficiency, and remove the need of additional control. This paper presents the design and characterization of a fuel cell/supercapacitor passive hybrid system for a 60 V light vehicle. A detailed design procedure for the passive hybrid test platform is presented with each component modelled and experimentally verified. The voltage error of the fuel cell and the supercapacitor model in the steady state is within 2% and 3%, respectively. Experimental results also validate the function of the passive configuration under conditions of a step load and a drive cycle. The simulation model of the passive hybrid system matches the measurements when a step load current is applied. The supercapacitor provides the transient current due to its smaller resistance while the fuel cell handles the steady state current, which makes it possible to downsize the fuel cell stack. For the drive cycle examined in this paper, the fuel cell stack can be downsized to one third of the load peak power.

Comparative Review of Energy Storage Systems, Their Roles, and Impacts on Future Power Systems

Abstract: It is an exciting time for power systems as there are many ground-breaking changes happening simultaneously. There is a global concensus in increasing the share of renewable energy-based generation in the overall mix, transitioning to a more environmental-friendly transportation with electric vehicles as well as liberalizing the electricity markets, much to the distaste of traditional utility companies. All of these changes are against the status quo and introduce new paradigms in the way the power systems operate. The generation penetrates distribution networks, renewables introduce intermittency, and liberalized markets need more competitive operation with the existing assets. All of these challenges require using some sort of storage device to develop viable power system operation solutions. There are different types of storage systems with different costs, operation characteristics, and potential applications. Understanding these is vital for the future design of power systems whether it be for short-term transient operation or long-term generation planning. In this paper, the state-of-the-art storage systems and their characteristics are thoroughly reviewed along with the cutting edge research prototypes. Based on their architectures, capacities, and operation characteristics, the potential application fields are identified. Finally, the research fields that are related to energy storage systems are studied with their impacts on the future of power systems.

Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies

Abstract: With the development of technologies in recent decades and the imposition of international standards to reduce greenhouse gas emissions, car manufacturers have turned their attention to new technologies related to electric/hybrid vehicles and electric fuel cell vehicles. This paper focuses on electric fuel cell vehicles, which optimally combine the fuel cell system with hybrid energy storage systems, represented by batteries and ultracapacitors, to meet the dynamic power demand required by the electric motor and auxiliary systems. This paper compares the latest proposed topologies for fuel cell electric vehicles and reveals the new technologies and DC/DC converters involved to generate up-to-date information for researchers and developers interested in this specialized field. From a software point of view, the latest energy management strategies are analyzed and compared with the reference strategies, taking into account performance indicators such as energy efficiency, hydrogen consumption and degradation of the subsystems involved, which is the main challenge for car developers. The advantages and disadvantages of three types of strategies (rule-based strategies, optimization-based strategies and learning-based strategies) are discussed. Thus, future software developers can focus on new control algorithms in the area of artificial intelligence developed to meet the challenges posed by new technologies for autonomous vehicles.

Purcell Enhancement of a Single Silicon Carbide Color Center with Coherent Spin Control.

Abstract: Silicon carbide has recently been developed as a platform for optically addressable spin defects. In particular, the neutral divacancy in the 4H polytype displays an optically addressable spin-1 ground state and near-infrared optical emission. Here, we present the Purcell enhancement of a single neutral divacancy coupled to a photonic crystal cavity. We utilize a combination of nanolithographic techniques and a dopant-selective photoelectrochemical etch to produce suspended cavities with quality factors exceeding 5000. Subsequent coupling to a single divacancy leads to a Purcell factor of ∼50, which manifests as increased photoluminescence into the zero-phonon line and a shortened excited-state lifetime. Additionally, we measure coherent control of the divacancy ground-state spin inside the cavity nanostructure and demonstrate extended coherence through dynamical decoupling. This spin-cavity system represents an advance toward scalable long-distance entanglement protocols using silicon carbide that require the interference of indistinguishable photons from spatially separated single qubits.

Photovoltaic Power Forecasting With a Hybrid Deep Learning Approach

Abstract: Solar energy is the key to clean energy, which can generate large amounts of electricity for the future smart grid. Unfortunately, the randomness and intermittency of solar energy resources bring difficulties to the stable operation and management of the power systems. To reduce the negative impact of photovoltaic (PV) plants accessing on the power systems, it is great significant to predict PV power accurately. In light of this, we propose a hybrid deep learning approach based on convolutional neural network (CNN) and long-short term memory recurrent neural network (LSTM) for the PV output power forecasting. The CNN model is leveraged to discover the nonlinear features and invariant structures exhibited in the previous output power data, thereby facilitating the prediction of PV power. The LSTM is used to model the temporal changes in the latest PV data, and predict the PV power of next time step. Then, the prediction results in the two models are comprehensively considered to obtain the expected output power. The proposed approach is extensively evaluated on real PV data in Limberg, Belgium, and numerical results demonstrate that the proposed approach can provide good prediction performance in PV systems.