基于IGA-BP神经网络的锂电池健康状态估算

针对锂离子电池健康状态(SOH)估算精度低、传统遗传算法(GA)易陷入局部最优、收敛速度慢的问题,为提高锂电池健康状态的估算精度,提出了交叉概率和变异概率自适应的调整策略对传统GA进行改进,在改进遗传算法(IGA)的作用下,使优良个体仍保持较好的进化能力,算法初期搜索范围、后期局部搜索能力以及收敛速度也得到加强。提取间接健康因子,再用改进的遗传算法对BP神经网络的初始参数寻优得到IGA-BP神经网络模型,基于NASA锂电池数据集分别用GA-BP与IGA-BP神经网络算法对SOH进行估算。结果表明:IGA-BP神经网络算法估算精度更高,且具备快速收敛的优势,平均绝对百分比误差和均方根误差分别下降了0.422%和0.412,拟合程度提高了8.1%。     

基于分段电容阵列的改进型逐次逼近型ADC

为缩短高速模数转换器(ADC)中高位(MSB)电容建立时间以及减小功耗,提出了一种基于分段式电容阵列的改进型逐次逼近型(SAR)ADC结构,通过翻转小电容阵列代替翻转大电容阵列以产生高位数字码,并利用180 nm CMOS工艺实现和验证了此ADC结构。该结构一方面可以缩短产生高位数码字过程中的转换时间,提高量化速度;另一方面其可以延长大电容的稳定时间,减小参考电压的负载。通过缩小比较器输入对管的面积以减小寄生电容带来的误差,提升高位数字码的准确度。同时,利用一次性校准技术减小比较器的失配电压。最终,采用180 nm CMOS工艺实现该10 bit SAR ADC,以验证该改进型结构。结果表明,在1.8 V电源电压、780μW功耗、有电路噪声和电容失配情况下,该改进型SAR ADC得到了58.0 dB的信噪失真比(SNDR)。     

基于超级电容的列车制动能量回收控制策略

针对列车电制动回馈能量回收与利用问题,文中通过分析列车运行特性,重点探究了列车运行过程与牵引网电压波动之间的关系。结合牵引变电所供电功率与列车用电功率动态不匹配特性,采用车载式制动能量回收利用系统的方案,提出了基于列车用电功率动态变化的超级电容充放电控制策略。该方案主要由超级电容串并联储能单元和双向DC-DC变换器组成。利用MATLAB进行建模仿真分析,结果表明,该控制策略在有效回收利用列车制动能量的同时抑制了牵引网电压波动,为列车制动能量回收与利用提供了有效的解决方案。     

丝网状镍钴硫金属化合物电化学性能研究

通过简单两步水热的方法在泡沫镍基底上成功制备出丝网状三维网络结构的Ni Co2S4,研究了材料的电化学性能,结果表明,Ni Co2S4@泡沫镍电极材料具有高的面积比电容,在电流密度为10 m A/cm~2时,面积比电容可达到2.87 F/cm~2;电流密度从10 m A/cm~2增大到50 m A/cm~2,电容保持率为60.9%;在负载量为6.21 mg/cm~2时,在30 m A/cm~2的高电流密度下充放电1 000次,电容保持率仍为67.3%。     

基于改进式电容充放电的纳秒级时间测量技术

提出一种基于改进式电容充放电原理实现纳秒级时间精度测量的方法.该方法仅对电容充放电一次,从而有效地延长了电容的使用寿命,消除了误差累计,同时提高了系统的稳定性和测量的准确性.     

高速嵌入式系统中的电源完整性设计方法

描述了高速嵌入式系统中电源分布网络（Power distribution network,PDN）的组成和特性。解释了目标阻抗和电源分布网络阻抗的概念,以及它们与电源完整性之间的关系。以OMAP5910为例,说明了在实际设计中如何选择、放置去耦电容。最后,给出了板级实现方案。     

Modeling the high pH sensitivity of Suspended Gate Field Effect Transistor (SGFET)

High sensitivity to chemical species of sub-micron gap Suspended-Gate FETs (more than 200 mV/pH for example) is explained from the charge distribution induced by the high field in the sub-micronic gap under the gate-bridge. Modeling of Metal-Electrolyte-Insulator-Silicon (MEIS) capacitor, which is the basic vertical structure of the transistor, is performed to highlight this effect through the response to the pH change of the solution filling the gap. The analytical model is based on the 2D-numerical resolution of Poisson's equation. The response of quasi-static C(V) plots versus pH is simulated using both electrolyte charge distribution and site-binding theory considering the influence of sites densities on silicon nitride. Device modeling and simulated/experimental electrical characteristics are presented. Effect of the gap thickness on the pH sensitivity is also discussed in this study.     

Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.     

A frame‐based EM‐simulation for design of LC oscillator with MoM capacitor banks

This article presents a simulation method for the design of a digitally controlled oscillator (DCO). Electromagnetic (EM) simulations are essential and inevitable for modern LC oscillator design. Although EM‐simulators provide high accuracy, the EM‐simulation time is very long when metal‐oxide‐metal (MoM) capacitors are present. The proposed frame‐based EM‐simulation can significantly reduce the EM‐simulation time even in the presence of MoM capacitors without influencing the accuracy. To verify the proposed method, a DCO was fabricated using a 55‐nm CMOS process. Measurements of the DCO are in good agreement with the frame‐based post‐layout simulation results. In addition, the DCO has good performances with a low power consumption of approximately 0.68 mW.     

基于ARMA模型的工业电容退化状态研究方法

随着中国制造2025计划的推广,工业电源正处于高速发展期,大型铝电解电容的需求越来越大.电容的故障会令工业流水线中断,产生极大的损失,因此工业电容的寿命预测（RUL）具有重要意义.本文通过对铝电解电容充电状态EIS频谱分析,建立电容的状态的量化模型.根据电容状态量化数据,建立ARMA电容退化预测模型.最后通过美国航天航空局的等效串联电阻EIS频谱数据集进行验证.结果表明ARMA电容退化模型对铝电解电容的状态预测有很大的准确性.