Innovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets

About 60%of emissions into the earth’s atmosphere are produced by the transport sector,caused by exhaust gases from conventional internal combustion engines.An effective solution to this problem is electric mobility,which significantly reduces the rate of urban pollution.The use of electric vehicles(EVs)has to be encouraged and facilitated by new information and communication technology(ICT)tools.To help achieve this goal,this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience.The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle’s current position,battery type,state of charge,nearby charge point availability,and compatibility.In particular,the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points(CPs).To this purpose,two virtual sensors(VSs)are designed,modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking.In particular,the first VS is devoted to locate and find available CPs in a preferred area,whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase.A UML activity diagram describes VSs operations and cooperation,while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors(CP operator,EV manufacturer,etc.).Furthermore,two timed Petri Nets(TPNs)are designed to model the proposed VSs,functioning and interactions as discrete event systems.The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs.

Rational design of natural leather-based water evaporator for electricity generation and functional applications

In recent years,water evaporation-induced electricity has attracted a great deal of attention as an emerging green and renewable energy harvesting technology.Although abundant materials have been developed to fabricate hydrovoltaic devices,the limitations of high costs,inconvenient storage and transport,low environmental benefits,and unadaptable shape have restricted their wide applications.Here,an electricity generator driven by water evaporation has been engineered based on natural biomass leather with inherent properties of good moisture permeability,excellent wettability,physicochemical stability,flexibility,and biocompatibility.Including numerous nano/microchannels together with rich oxygen-bearing functional groups,the natural leather-based water evaporator,Leather_(Emblic-NPs-SA/CB),could continuously produce electricity even staying outside,achieving a maximum output voltage of∼3 V with six-series connection.Furthermore,the leather-based water evaporator has enormous potential for use as a flexible self-powered electronic floor and seawater demineralizer due to its sensitive pressure sensing ability as well as its excellent photothermal conversion efficiency(96.3%)and thus fast water evaporation rate(2.65 kg m^(−2)h^(−1)).This work offers a new and functional material for the construction of hydrovoltaic devices to harvest the sustained green energy from water evaporation in arbitrary ambient environments,which shows great promise in their widespread applications.

Driving and Braking Control of PM Synchronous Motor Based on Low-resolution Hall Sensor for Battery Electric Vehicle

Resolvers are normally employed for rotor positioning in motors for electric vehicles, but resolvers are expensive and vulnerable to vibrations. Hall sensors have the advantages of low cost and high reliability, but the positioning accuracy is low. Motors with Hall sensors are typically controlled by six-step commutation algorithm, which brings high torque ripple. This paper studies the high-performance driving and braking control of the in-wheel permanent magnetic synchronous motor （PMSM） based on low-resolution Hall sensors. Field oriented control （FOC） based on Hall-effect sensors is developed to reduce the torque ripple. The positioning accuracy of the Hall sensors is improved by interpolation between two consecutive Hall signals using the estimated motor speed. The position error from the misalignment of the Hall sensors is compensated by the precise calibration of Hall transition timing. The braking control algorithms based on six-step commutation and FOC are studied. Two variants of the six-step commutation braking control, namely, half-bridge commutation and full-bridge commutation, are discussed and compared, which shows that the full-bridge commutation could better explore the potential of the back electro-motive forces （EMF）, thus can deliver higher efficiency and smaller current ripple. The FOC braking is analyzed with the phasor diagrams. At a given motor speed, the motor turns from the regenerative braking mode into the plug braking mode if the braking torque exceeds a certain limit, which is proportional to the motor speed. Tests in the dynamometer show that a smooth control could be realized by FOC driving control and the highest efficiency and the smallest current ripple could be achieved by FOC braking control, compared to six-step commutation braking control. Therefore, FOC braking is selected as the braking control algorithm for electric vehicles. The proposed research ensures a good motor control performance while maintaining low cost and high reliability.

HIGH PERFORMANCE ELECTRIC FIELD MICRO SENSOR WITH COMBINED DIFFERENTIAL STRUCTURE

This paper presents a high performance electric field micro sensor with combined differential structure.The sensor consists of two backward laid micro-machined chips,each packaged by polymer and metal.The novel combined differential structure effectively reduces various environmental affections,such as thermal drift,humidity drift and electrostatic charge accumulation.The sensor is tested in near-ground place as well as balloon-borne sounding.In different weather conditions,the measurement results showed good agreement with those of the commercial electric field mill.

An adaptive electrical resistance tomography sensor with flow pattern recognition capability

The all traditional electrical resistance tomography (ERT) sensors have a static structure, which cannot satisfy the intelligent requirements for adaptive optimization to ERT sensors that is subject to flow pattern changes during the real-time detection of two-phase flow. In view of this problem, an adaptive ERT sensor with a dynamic structure is proposed. The electrodes of the ERT sensor are arranged in an array structure, the flow pattern recognition technique is introduced into the ERT sensor design and accordingly an ERT flow pattern recognition method based on signal sparsity is proposed. This method uses the sparse representation of the signal to express the sampling voltage of the ERT system as a sparse combination and find its sparse solution to achieve the classification of different flow patterns. With the introduction of flow identification information, the sensor has an intelligent function of adaptively and dynamically adapting the sensor structure according to the real-time flow pattern change. The experimental results show that the sensor can automatically identify four typical flow patterns: core flow, bubble flow, laminar flow and circulation flow with recognition rates of 91%, 93%, 90% and 88% respectively. For different flow patterns, the dynamically optimized sensor can significantly improve the quality of ERT image reconstruction.

Biomimetic Sensors:Active Electrolocation of Weakly Electric Fish as a Model for Active Sensing in Technical Systems

Instead of vision, many nocturnal animals use alternative senses for navigation and object detection in their dark environment. For this purpose, weakly electric mormyrid fish employ active electrolocation, during which they discharge a specialized electric organ in their tail which discharges electrical pulses. Each discharge builds up an electrical field around the fish, which is sensed by cutaneous electroreceptor organs that are distributed over most of the body surface of the fish. Nearby objects distort this electrical field and cause a local alteration in current flow in those electroreceptors that are closest to the object. By constandy monitoring responses of its electroreceptor organs, a fish can detect, localize, and identify environmental objects. Inspired by the remarkable capabilities of weakly electric fish in detecting and recognizing objects, we designed technical sensor systems that can solve similar problems of remote object sensing. We applied the principles of active electrolocation to technical systems by building devices that produce electrical current pulses in a conducting medium （water or ionized gases） and simultaneously sense local current density. Depending on the specific task a sensor was designed for devices could （i） detect an object, （ii） localize it in space, （iii） determine its distance, and （iv） measure properties such as material properties, thickness, or material faults. Our systems proved to be relatively insensitive to environmental disturbances such as heat, pressure, or turbidity. They have a wide range of applications including material identification, quality control, non-contact distance measurements, medical applications and many more. Despite their astonishing capacities, our sensors still lag far behind what electric fish are able to achieve during active electrolocation. The understanding of the neural principles governing electric fish sensory physiology and the corresponding optimization of our sensors to solve certain technical tasks therefore remain ongoing goals of our research.

Effect of Fluid Electrical Property Changes on Surface Transverse Wave Sensors

A fluid sensor based on the surface transverse wave (STW) delay line on ST-cut quartz has been developed and tested in a large number of fluids with different viscosity and permittivity levels.Influence of fluid mechanical and electrical properties on the sensor's response has been determined and the sensor's performance has been compared with a bulk acoustic wave (BAW) viscosity sensor.The result shows that the viscosity sensitivity of the developed STW sensor represented by the signal to noise ratio is lower than that of a 5 MHz BAW sensor.Applications of the sensor in detecting the quality of industrial fluids are discussed.

A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

In this paper,we propose a BPR-CNN(Biometric Pattern Recognition-Convolution Neural Network)classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF(Electric Field)sensors.Currently,an EF sensor or EPS(Electric Potential Sensor)system is attracting attention as a next-generationmotion sensing technology due to low computation and price,high sensitivity and recognition speed compared to other sensor systems.However,it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion,due to the variance of the electric-charge state by heterogeneous surroundings and operational conditions.This hinders the further utilization of the EF sensing;thus,it is critical to design the robust and credible methodology for detecting and extracting signals derived from the motion movement in order to make use and apply the EF sensor technology to electric consumer products such as mobile devices.In this study,we propose a motion detection algorithm using a dynamic offset-threshold method to overcome uncertainty in the initial electrostatic charge state of the sensor affected by a user and the surrounding environment of the subject.This method is designed to detect hand motions and extract its genuine motion signal frame successfully with high accuracy.After setting motion frames,we normalize the signals and then apply them to our proposed BPR-CNN motion classifier to recognize their motion types.Conducted experiment and analysis show that our proposed dynamic threshold method combined with a BPR-CNN classifier can detect the hand motions and extract the actual frames effectively with 97.1%accuracy,99.25%detection rate,98.4%motion frame matching rate and 97.7%detection&extraction success rate.

ELECTRIC FIELD SENSORS BASED ON MEMS TECHNOLOGY

The design and optimization of two types of novel miniature vibrating Electric Field Sensors (EFSs) based on Micro Electro Mechanical Systems (MEMS) technology are presented.They have different structures and vibrating modes. The volume is much smaller than other types of charge-induced EFSs such as field-mills. As miniaturizing, the induced signal is reduced enormously and a high sensitive circuit is needed to detect it. Elaborately designed electrodes can increase the amplitude of the output current, making the detecting circuit simplified and improving the signal-to-noise ratio. Computer simulations for different structural parameters of the EFSs and vibrating methods have been carried out by Finite Element Method (FEM). It is proved that the new structures are realizable and the output signals are detectable.

Sensor monitoring and online measurement correction in electric capacitance tomography system

The recalibration of electrical capacitance tomography (ECT) system is one of the key problems in keeping the system running steadily.However, for engineering application in solids/gas transport,online calibration can not be implemented and the data from this sensor may be unreliable due to the sensor pipe interior wall abrasion during pneumatic transport,so the solids concentration calculated from the reconstructed image based on these data will be highly inaccurate.The simulations show that, the inter-electrode relative capacitance variation of electrode pair spacing 1 is the most sensitive to the abrasion of sensor pipe interior wall, so this relative capacitance variation when the sensor is filled with air can be used as an indicator demanding offline system recalibration when the wall abrasion goes significant.Furthermore, while the pipe interior wall abrasion is not very serious, online correcting measured inter-electrode capacitance with wall capacitance variation can improve the accuracy of concentration calculation.

石墨烯柔性压阻传感器微织构压缩应变机制

为满足智能机器人、电子皮肤等领域对柔性传感器高灵敏度日益增加的需求,通过设计高表面积形貌以制备石墨烯凸台微织构柔性传感器,并对比分析有无微织构传感器的灵敏度;基于结构力学方程与静电方程,建立微织构柔性传感器模型,开展了电场作用不同基底厚度以及微织构间距下柔性传感器微织构应变的数值模拟,研究柔性压阻传感器微织构压缩应变机制,探寻机械载荷和电载荷的交互作用关系。结果表明,柔性基底的微织构化处理能有效提高柔性压阻传感器的灵敏度。模型总位移的最大值随基底厚度的增加呈非线性增加,微织构应变随着厚度的增加而减小。电场作用下微织构应变受机电耦合压力叠加的影响均大于无电场作用。框状微织构类似于悬臂梁,作用在微织构上的力矩随间距增加而增大,同时刚度减小,微织构的压缩形变增加,应变增大。组合尺寸微织构的应变随着微织构间距的增加而增大,制备多尺寸微结构传感器可使用(150+350)μm的组合尺寸,能够有效增加接触面积提高传感器灵敏度。机械载荷和电载荷的耦合作用,组合尺寸对力矩与刚度的分配有效增加了基底的应变,提高了柔性传感器的灵敏度。

智能传感器在农业机械电气控制领域的应用与发展

智慧农业、智能农机已成为我国现阶段农业发展的主流趋势,农业智能传感监测技术是实现这一目标的核心技术。传感器作为一种监测感知特定物体状态信息的设备,其主要工作原理是将监测到的物理信息转换成为更易读取的数字信号或其他种类的电子信号,因其具有较高的反应速度、测量精度等特点,传感器已被广泛应用到众多领域。农业机械对于传感器技术的相关应用主要包括农用姿态监测传感器、视觉识别传感器和物料感知传感器等。该文通过分析研究智能传感器在农业机械电气控制的主要应用现状,阐述了国内外关于利用传感器技术进行农机作业状态监测、作物生长信息采集以及农机自动控制等研究的丰富成果,分析了智能传感器在农业机械应用过程中仍存在的部分问题,最后对智能传感器在农业机械电气控制领域的未来发展进行了详细的展望。

里德堡原子电场传感器的自校准性解析及测量不确定度评定

传统电场传感器需要标准场或更高准确度等级的传感器进行校准,而基于原子固有能级结构和光谱特征的里德堡原子电场测量可溯源至基本物理常数,量子传感自带校准功能。该文阐述了里德堡原子与外场相互作用的量子相干效应,说明了里德堡原子传感器的电场测量原理,并解析了其自校准性。基于所构建的针对低频电场的里德堡原子传感测量系统,分析了误差来源,定义和计算了各种不确定度分量,得到合成相对标准不确定度为3.885%;保守考虑,在大于或等于95.45%的包含概率下得到扩展不确定度为7.77%。在此过程中,明确了不确定度的主要影响因素并提出了减小其影响的建议。最后给出传感系统实测电磁诱导透明(EIT)光谱频移量与电场强度之间的拟合函数,拟合确定系数达到0.9969,验证了所构建的低频电场量子测量系统的准确性。

低轴间耦合的三维电场传感器

使用正交排列的电容式传感单元的三维电场传感器(Three-dimensional electric field sensor,3D EFS)测量空间电场时,轴间耦合效应会严重影响EFS的测量精度。为此,提出了一种电场屏蔽电极,以降低3D EFS的轴间耦合效应,提高测量精度。首先,利用多物理场仿真软件构建电场分布模型。其次,根据仿真结果建立带屏蔽电极的3D EFS电容式传感单元的屏蔽电极模型。最后,建立任意角度的测试平台,将带有屏蔽电极的3D EFS和无屏蔽电极的3D EFS进行实验对比。结果显示,有屏蔽电极的3D EFS的测量偏差在3.2%以内,比无屏蔽电极的3D EFS的测量偏差减少12%。因此,所设计的基于电场屏蔽结构的3D EFS可以使解耦矩阵更加可靠,有效降低空间电场测量偏差。

半导体激光器驱动器的SPICE仿真及其在气体传感系统的应用

为了提高激光器的工作可靠性和稳定性,借助TINA-SPICE软件对其驱动电路进行辅助设计,并通过电调制和光电检测系统实验检验设计效果。对驱动器的恒流输出电路的幅频特性和瞬态响应进行仿真,预测潜在的电流振荡现象,并提出一种用于补偿环路增益的易用方案。通过对比补偿前后的仿真曲线,验证了该方案的可行性。在此基础上,以STM32H743作为中央处理核心,通过触摸屏图形化设置和监测驱动电流参数,从而调度激光器驱动电路的运行。在296 K环境温度下,利用该电路进行半导体激光器电调制特性实验,通过拟合波长与电流得到调制系数为0.045 cm-1/mA。进一步地,将该控制器引入基于红外光谱吸收技术的气体传感系统,完成了对甲烷气体的定量分析。分析结果表明,所设计的电路能稳定地驱动激光器并实现对波长的精准调制,获得的气体传感系统的检测下限低于8 ppm。

基于磁弹效应的电动舵机压力传感器设计

为了实现对电动舵机的电动缸输出轴压力的动态测量,该文设计并实现了一种基于磁弹效应的非接触式压力传感器,它包括传感器探头和信号处理电路两部分。该文首先从磁致伸缩的理论模型中分析并建立了传感器的测量原理,设计了传感器探头的结构。为优化激励源,通过实验研究分析了不同的激励源对传感器信号的影响,结果表明采用105 kHz、幅值为5 V的方波作为激励源时传感器输出信号最优,并以此设计了传感器信号电路。通过对传感器整体的压力标定实验得到传感器灵敏度为0.294 mV/N,重复性误差为3.53%,在运用三阶多项式拟合对传感器输入输出曲线进行校正后得到传感器的非线性误差为1.76%,适用于承压较大的电动缸输出轴及其他大重量设备的动态压力测量。

混合动力汽车转向稳定性多传感器融合控制技术

为了满足汽车行驶稳定性要求,当前依托于单一传感信息进行转向控制,容易忽略很多影响因素,使得控制后汽车转向质心侧偏角依旧较大。因此,混合动力汽车提出转向稳定性多传感器融合控制技术。考虑混合动力汽车转向过程中的动态特性,构建转向动力学模型,并计算出车辆理想横摆角速度。深入分析横摆角速度、滑移率对车辆转向稳定性的影响,得出期望的横摆力矩和滑移率调整力矩。将汽车转向稳定性控制问题看作二次规划问题,结合BP神经网络和常规PID控制器,设计转向稳定性控制算法,利用车辆转向参数的理想值、实际值和偏差值推算出稳定性控制参数。结合融合多传感器实时采集信息,推算出汽车转向姿态误差代入控制结构中,给出优化后的控制参数。实验结果表明:新提出的控制技术应用后,车辆转向的质心侧偏角不超过±0.012 rad/s,满足了混合动力汽车转向行驶要求。

机械设备电气故障自动检测系统优化设计

当前系统不能有效消除干扰信号,数据采集精度低,影响机械设备电气故障诊断,为此设计一种新的机械设备电气故障自动检测系统。采集电气故障检测数据,提取电气故障特征,根据特征判断机械设备的电气故障原因,计算纠正参数完善电气故障检测结果,线性处理电压检测曲线,完成机械设备电气故障检测。测试结果验证,所提系统能够有效消除干扰信号,保证整体功能运行稳定,相对于对比系统,响应时间更短,数据采集精度得到明显改善。

电动汽车PMSM驱动系统车载诊断技术研究

驱动电机是电动汽车驱动系统的核心,车载诊断和失效保护技术是提高其可靠性的有效技术手段。介绍了汽车随车诊断系统对电驱动系统的各种故障判定方法,并运用案例分析了汽车PMSM驱动控制系统中最常见的故障之一驱动电机抖动故障,呈现出了车载诊断系统判定电机位置传感器不良的典型情况;对电机位置传感器故障进行了simulink仿真并讨论了无位置传感器容错控制方案及发展趋势。

一种用于高压电气设备局放超声检测的新型EFPI传感器

文章介绍了一种新型结构的光纤EFPI超声传感器,可内置于变压器、GIS等高压电气设备,这种新型结构的光纤EFPI超声传感器可在不同温度、不同压强条件下,进行局部放电超声信号的检测。新型传感器采用全石英焊接工艺,在F-P腔上开孔,使绝缘介质进入F-P腔,平衡了石英膜片两侧由于温度和绝缘介质密度不同产生的压强差,并在石英膜片内表面上局部镀铝膜,提高膜片内表面的光反射率,提升了传感器的灵敏度。通过用新型传感器在不同温度、不同距离下进行测试,验证了传感器的温度、频率、灵敏度等性能。新型光纤EFPI超声传感器为检测高压电气设备内部局部放电提供了一种新的检测方法。实际应用中也取得了非常好的效果。