创新阻抗跟踪技术可精确计算电池剩余电量

Cypress半导体公司目前推出其符合SMPTE(标准电影与电视工程师学会)标准的全功能HOTLink-On-Demand系列视频物理层(PHY)器件,基于业界首款四独立通道并包含12款器件的HOTLink-On-Demand产品库为设计者提供了专业视频设备设计所需的可扩缩性和灵活性,包括制作转换开关、信号分配放大器、D/A和A/D转换器以及摄像机控制装置.     

创新阻抗跟踪技术可精确计算电池剩余电量

德州仪器(TI)最近在电池管理方面又取得突破性进展，推出功能独特的阻抗跟踪(Impedance Track)“电量监测计”技术，该技术可在电池整个寿命周期内以高达99％的精确度计算锂电池组的剩余电量。     

电量监测计准确测算手持设备电池剩余电量

随着移动设备中高清视频和数据传输等新功能的增加,消费者希望移动设备能像笔记本电脑一样工作,也能显示剩余电池电量.     

电池剩余电量指示的单芯片解决方案

本文首先介绍了手持设备中基于电池电压和库仑计的电池剩余电量预测方式，然后对Maxim新推出的单芯片锂电池电量计DS2780的硬件结构和工作原理进行了分析。     

TI阻抗跟踪技术推进电量监测到新水平

德州仪器 (TI)研发出一种功能独特的阻抗跟踪技术,能够持续不断地正确读取锂电池组剩余电量。TI便携式DC/DC产品系统工程经理王新涛表示:目前这种阻抗跟踪技术主要针对多体,也就是2—4节锂离子电池组,涉及到的终端有笔记本电脑、工业设备以及诸如除颤计、血糖检测器等医疗设备。但也能够支持其他类型的电池,如镍氢(NiMH) 或镍镉 (NiCD)。TI 还计划对该技术作更深层的研发,让它适用于智能电话、数码相机以及 PDA等单体锂离子供电的设备。通常电量监测IC采用的是一种数学模型的方法,需要在电池的研发阶段对其性能进行分析,得出的各种…     

bq2028x：带有阻抗跟踪技术的电量监测计芯片组

基于闪存的bq2028x电量监测计芯片组具有TI的创新型阻抗跟踪技术．可精确监测阻抗改变或由电池老化、温度以及循环模式造成的电阻．能够持续不断地正确读取电池组剩余电量．从而准确预计双节电池组、三节电池组和四节电池组的运行时间．精确度达到99％。阻抗跟踪依靠动态建模算法得知电池随着老化、温度或使用产生了多少衰减。目前许多电量监测技术均依赖静态而不可靠的建模技术．要求创建大型数据库才能测量数百种可用电池参数的具体属性。     

螺旋带色散特性和耦合阻抗的精确计算

应用一种改进的计算螺旋带色散和耦合阻抗的方法,对面电流密度进行Chebyshev多项式展开后,去除了螺旋带上的面电流密度假设。在介质的高阶径向分层时为了避免高阶矩阵的求逆运算,采用转移矩阵和连接矩阵处理边界的场匹配。编写可径向任意分层的普适性程序,计算了螺旋线行波管的一些典型结构。研究表明：该理论的计算结果与实验结果有很好的一致;谐波次数以及面电流Chebyshev展开的项数完全可以依据结果的收敛性进行选取;分层的数目主要与结构的径向规则程度有关,径向越不规则,所要求的分层数目越多。     

基于Kalman的滤波目标精确跟踪技术研究

阐述了基本kalman滤波方法,讨论了针对非线性系统应用的kalman滤波方法的改进形式EKF、UKF。为了增进UKF运算效能,提出了引入衰减系数的滤波形式DCUKF。针对二维平面非匀速运动的目标的精确跟踪定位系统,仿真对比分析了EKF、UKF及DCUKF的滤波计算效果。结果表明DCUKF有更好的滤波效果。     

新电池技术:能储存3倍电量,不易短路

<正>锂离子电池目前被广泛运用在笔记本电脑、手机和平板电脑当中,而锂离子电池的发明者之一、已有94岁的高龄的约翰·古迪纳夫(John Goodenough)公布了新一代电池技术:能量密度是当前锂电池3倍,且安全系数更高的全新固态电池。古迪纳夫是费米奖得主,对材料科学与技术,特别是锂离子电池领域做出了重要贡献。他既是钴酸锂、锰酸锂和磷酸铁锂阳极材料的发明人,还发现钴酸锂材料可用作锂离子电池阴极,加快了锂离子电池     

An analytical model for predicting the remaining battery capacity of lithium-ion batteries

Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing and applying an effective dynamic power management policy for the device. This paper starts up by showing that a 30% error in predicting the battery capacity of a lithium-ion battery can result in up to 20% performance degradation for a dynamic voltage and frequency scaling algorithm. Next, this paper presents a closed form analytical expression for predicting the remaining capacity of a lithium-ion battery. The proposed high-level model, which relies on online current and voltage measurements, correctly accounts for the temperature and cycle aging effects. The accuracy of the high-level model is validated by comparing it with DUALFOIL simulation results, demonstrating a maximum of 5% error between simulated and predicted data.     

High system capacity pre-scheduler for multi-cell wireless networks

Wireless Networks - With the constant increase of throughput demands, maximizing the wireless network capacity has always been a crucial issue. Network densification becomes one of the adopted...     

Sum capacity of the reverse link for multi-cell multi-user cellular systems

This letter studies the information-theoretic sum capacity of the reverse link for multi-cell, multi-user cellular systems subjected to a peak power constraint. It is proven that, for the optimal scheduling, there will be at most one user transmitting at part of the peak power within each cell. Therefore, we approximate the optimization scheduling problem to a combination optimization problem which can be solved by standard simulated annealing algorithm. Further, we propose a low complexity cell greedy scheduling algorithm which can achieve almost the same performance as simulated annealing algorithm.     

电动汽车电池容量的选择

分析了影响电动汽车续驶里程的因素,计算了电动汽车典型城市工况下的电能消耗,提出了电动汽车电池容量的选择方案。     

智能电池监视器

目前,最为流行的智能电话和PDA普遍使用锂离子(Li+)电池供电,以解决由于产品功能的增多而带来的功耗增大等问题。最终用户常常希望能够及时了解剩余电量和剩余工作时间,以便动态控制智能电话或PDA。传统的通过测量电池电压粗略显示电池电量的方法远远不能满足用户的要求,因此,果在智能电话和PDA中实现智能电池监视功能,将为用户带来更多的方便和收效。Li+电池的特性Li+电池放电时电池电压逐渐下降,但在不同的放电电流与温度下对应的电压差别较大。所以,要精确显示电池电量,必须同时测量电流和电压,根据其特征参数进行修正。业界普遍认…     

基于部分频率复用的多小区协作方案及容量分析

针对无线蜂窝网中的小区间干扰问题,给出一种基于部分频率复用的多小区协作传输方案.该方案将小区用户划分为中心用户和边缘用户,中心用户采用频率复用因子为1的本地基站通信,边缘用户采用频率复用因子为3的多基站协作通信,并通过对多个小区的基站进行功率控制,达到抑制小区间干扰、提高系统容量的目的.数值分析表明,相对于传统多小区蜂窝系统(Traditional Multi-Cell System,TMCS)和广义分布式天线系统(Generalized Distributed Antenna System,GDAS),所提多小区协作蜂窝系统(Multi-Cell Cooperative System,MCCS)可以有效地克服小区间干扰,提高系统容量.     

Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

Electrical power system (EPS) is one of the most critical sub-systems of the spacecraft. Lithium-ion battery is the vital component is the EPS. Remaining useful life (RUL) prediction is an effective mean to evaluate the battery reliability. Autoregressive model (AR) and particle filter (PF) are two traditional approaches in battery prognosis. However, the parameters in a trained AR model cannot be updated which will cause the under-fitting in the long term prediction and further decrease the RUL prediction accuracy. On the other hand, the measurement function in the PF algorithm cannot be obtained in the long term prediction process. To address these two challenges, a hybrid method of IND-AR model and PF algorithm are proposed in this work. Compared with basic AR model, a nonlinear degradation factor and an iterative parameter updating method are utilized to improve the long term prediction performance. The capacity prediction results are applied as the measurement function for the PF algorithm. The nonlinear degradation factor can make the linear AR model suitable for nonlinear degradation estimation. And once the capacity is predicted, the state-space model in the PF is activated to obtain an optimized result. Optimized capacity prediction result of each cycle is utilized to re-train the regression model and update the parameters. The predictor keeps working iteratively until the capacity hit the failure threshold to calculate the RUL value. The uncertainty involved in the RUL prediction result is presented by PF algorithm as well. Experiments are conducted based on commercial lithium-ion batteries and real-applied satellite lithium-ion batteries. The results have high accuracy in capacity fade prediction and RUL prediction of the proposed method. The real applied lithium-ion battery can meet the requirement of spacecraft. All the experiments results show great potential of the proposed framework.     

Heuristic Kalman optimized particle filter for remaining useful life prediction of lithium-ion battery

Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of any engineering system. In recent times, the particle filter algorithm and several variants of it have been used as an effective method for this purpose. However, particle filter suffers from sample degeneracy and impoverishment. In this study, we introduce the Heuristic Kalman algorithm, a metaheuristic optimization approach, in combination with particle filtering to tackle sample degeneracy and impoverishment. Our proposed method is compared with the particle swarm optimized particle filtering technique, another popular metaheuristic approach for improvement of particle filtering. The prediction accuracy and precision of our proposed method is validated using several Lithium ion battery data sets from NASA® Ames research center.     

电路板上地保护走线的阻抗数学模型和串扰控制

文章提出了一种基于地保护走线的互连线的特性阻抗闭环计算公式,采用变分法结合横向传输线结构,分析地保护走线引起的两耦合信号线的耦合系数的变化。提出如何让地保护走线能很好的弥补串扰,还可以使传输线达到需求的稳定的特性阻抗。并且用高频三维电磁场仿真软件模拟仿真,并用矢量网络分析仪测量验证。     

An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction

Lithium-ion rechargeable batteries are widely used as power sources for mobile phones, laptops and electric cars, and gradually extended to military communication, navigation, aviation, aerospace and other fields. Accurate remaining useful life (RUL) prediction of lithium-ion battery plays an important role in avoiding serious security and economic consequences caused by failure to supply required power levels. Thus, the RUL prediction for lithium-ion battery has become a critical task in engineering practices. With its superiority in handling nonlinear and non-Gaussian system behaviors, the particle filtering (PF) technique is widely used in the remaining life prediction. However, the choice of importance function and the degradation of diversity in sampling particles limit the estimation accuracy. This paper presents an improved PF algorithm, that is, the unscented particle filter (UPF) based on linear optimizing combination resampling (U-LOCR-PF) to improve the prediction accuracy. In one aspect, the unscented Kalman filter (UKF) is used to generate a proposal distribution as an importance function for particle filtering. In the other aspect, the linear optimizing combination resampling (LOCR) algorithm is used to overcome the particle diversity deficiency. It should be noted that the step coefficient K can affect the performance of LOCR algorithm, and the fuzzy inference system is applied to determine the value of step coefficient K. According to the analysis results, it can be seen that the proposed prognostic method shows higher accuracy in the RUL prediction of lithium-ion battery, compared with the existing PF-based and UPF-based prognostic methods.