A model of PEMFC-battery system to evaluate inner operating status and energy consumption under different energy management strategies

A hybrid system with jointed battery and PEMFC is popular and of great potential in New Energy Vehicle (NEV) application. However, reliability and efficiency remain to be improved for commercial products. To reflect the complicated physics inside the proton exchange membrane fuel cell (PEMFC), the PEMFC model consisting of inner muti-physics process and other accessories was built, then a complete hybrid system was established when a matched battery, DC/DC, regenerative braking were taken into consideration. Based on the above model, the stack state and system performance under standard cycle for heavy duty vehicle-CWTVC were obtained. According to the simulation results, fuel cell states such as pressure, water content and voltage suffers severe oscillation with external load, especially in the highway cycle. Membrane electrode assembly (MEA) suffers from pressure impact with average value of more than 24 kPa in highway cycle. In the aspect of relative humidity, the PEMFC stack is most threatened in road cycle. As for the hybrid system, its efficiency and state of charge (SOC) fluctuation perform worst in urban cycle and road cycle respectively, while its highest efficiency occurs in road test. Operating mode of fuel cell has influence on hybrid system. When 3-level mode of fuel cell output was applied, the efficiency increased to its peak value at medium level of 28 kW and then declined gradually. H₂ consumption had an opposite trend compared to efficiency. In the aspect of battery SOC, it declines in operating process and its fluctuations decreases when medium level got bigger. The 3-level mode and 4-level mode were compared using this model. It can be concluded that although 3-level mode performs slightly better in hybrid system efficiency, H₂ consumption, pressure impact, it does not have absolute advantage over 4-level mode in other indicators.     

Single-switch boost-buck DC-DC converter for industrial fuel cell and photovoltaics applications

The realization of dc-dc converters performs a vital function in exploiting renewable energy sources such as solar photovoltaic (PV) and fuel cell applications. This paper demonstrates a single-switch unidirectional buck-boost dc-dc converter for continuous power flow control, excluding the hybrid switched-capacitor. The proposed converter utilizes a limited number of passive components, only four diodes and three inductors required, in addition to six capacitors. The converter can operate at a wide input voltage range with continues input current. The converter has experimented under real-time conditions with 660 W PV system. The obtained efficiency ranges from 93% to 98%. Furthermore, the converter has interfaced with 550 W fuel cell operated under different fuel pressure. The realized efficiency ranges from 91% to 97%. The maximum measured inductance current ripple is limited to under 0.70 A in both scenarios, whereas 0.16 V is the maximum output voltage ripple.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

浅谈电子电工技术在电力系统中的应用

本文针对电子电工技术在电力系统中的作用进行了分析,同时为该技术的推广提出了相应的建议。     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

基于阶段注意力机制的电力线提取算法

电力系统维护是电力系统稳定运行的重要保障，应用智能算法的无人机电力巡检则为电力系统维护提供便捷。电力线提取是自主电力巡检以及保障飞行器低空飞行安全的关键技术，结合深度学习理论进行电力线提取是电力巡检的重要突破点。本文将深度学习方法用于电力线提取任务，结合电力线图像特点嵌入改进的图像输入策略和注意力模块，提出一种基于阶段注意力机制的电力线提取模型（SA-Unet）。本文提出的SA-Unet模型编码阶段采用阶段输入融合策略（Stage input fusion strategy， SIFS），充分利用图像的多尺度信息减少空间位置信息丢失。解码阶段通过嵌入阶段注意力模块（Stage attention module，SAM）聚焦电力线特征，从大量信息中快速筛选出高价值信息。实验结果表明，该方法在复杂背景的多场景中具有良好的性能。     

Steering feel improvement by mathematical modeling of the Electric Power Steering system

Currently, the Electric Power Steering (EPS) system is an essential component of the vehicle because it provides assistive steering torque to the driver. To ensure a faster steering response, the position of the EPS in some vehicles is moved closer to the tire rather than the steering wheel. The steering torque, which is provided by the EPS in the steering system, mainly affects the driver’s feel while steering. Therefore, the driver often feels uncomfortable owing to such positioning of the EPS in the steering system. In particular, the nonlinearity of the Universal Joint (UJ), which is one of parts of the steering system, can be felt at the steering wheel side.In this paper, we proposed an algorithm based on the mathematical model of the steering torque in the steering system to improve the steering feel. The mathematical model is structured using parameters that can be obtained from the information of the steering system. Moreover, the formulation of the steering torque consists of the two parts, namely the deformation part, which describes the propagation inside the steering system, and the friction part that describes the inherent friction in the UJ.Simulation and experiments were conducted to verify the proposed mathematical model with similar conditions to the real tire load during the steering motion. Furthermore, to improve the driver’s feel during steering, a torque compensation algorithm is proposed and verified through experiments.     

基于特征建模的TDD系统大气波导干扰控制体系研究

大气波导干扰是特定气象条件下发生的时分双工（time-division duplex，TDD）系统内干扰，是TDD移动通信系统大规模组网面临的顽疾。在总结分析大气波导干扰成因和分类等的基础上，对大气波导干扰进行建模和表征，验证了海量干扰源在时域和频域的功率集总特征，并结合大量4G/5G现网实测数据给出了典型条件下内陆波导和海面波导的量化干扰信号传播模型，对于干扰的预测和预防具有重要意义。基于干扰特征，给出了TDD系统预防大气波导干扰的帧结构与组网的4项设计原则，5G现网数据表明干扰控制方案有效，上行干扰下降10 dB以上，相关原则对于6G系统的设计也具有指导意义。     

Courses Based on iGEM/BIOMOD Competitions Are the Ideal Format for Research-Based Learning of Xenobiology

Synthetic biology and especially xenobiology, as emerging new fields of science, have reached an intellectual and experimental maturity that makes them suitable for integration into the university curricula of chemical and biological disciplines. Novel scientific fields that include laboratory work are perfect playgrounds for developing highly motivating research-based teaching modules. We believe that research-based learning enriched by digital tools is the best approach for teaching new emerging essentials of academic education. This is especially true when the scientific field as such is still not canonized with text books and best-practice examples. Our experience shows that iGEM/BIOMOD competitions represent an excellent basis for designing research-based courses in xenobiology. Therefore, we present a report on “iGEM–Synthetic Biology” offered at the Technische Universität Berlin as an example.