基于ELM和MCSCKF的锂离子电池SOC估计

为了减少噪声对锂离子电池荷电状态估计的影响,本文提出一种新颖的基于极限学习机和最大相关熵平方根容积卡尔曼滤波的SOC估计方法。首先,利用泛化性好、运行速度快的极限学习机作为卡尔曼滤波的测量方程;其次,基于灰狼优化算法,极限学习机的超参数被优化以提高电池荷电状态的估计精度;最后,基于最大相关熵平方根容积卡尔曼滤波,极限学习机的测量噪声被进一步减弱。所提方法可以简化极限学习机繁琐的调参过程,且为闭环的SOC估计方法。所提方法在多工况和宽温度范围内被测试以验证其泛化性能。测试结果显示,所提方法明显地提高了锂离子电池的荷电状态估计精度。同时,对比其他算法,所提方法的平均运行时间仅仅为长短时序列和循环门控单元网络的三分之一。当行驶工况复杂、温度变化区间较大时,所提方法的均方根误差小于1%,最大误差小于3%。当存在初始误差与环境噪声时,所提方法显示出了优越的鲁棒性。      

基于融合模型的锂离子电池荷电状态在线估计

针对锂离子电池荷电状态（Stage of charge,SOC）在线估计精度不高,等效电路模型法估计精度与模型复杂度相矛盾的问题,本文对扩展卡尔曼滤波算法进行了改进,并以电池工作电压、电流为输入,对应等效电路模型法的SOC估计误差为输出,采用极限学习机算法,建立基于输入输出数据的SOC估计误差预测模型,采用物理–数据融合方法,基于误差预测模型,建立了等效电路模型法结合极限学习机的锂离子电池SOC在线估计模型。仿真结果表明,改进扩展卡尔曼滤波算法提高了算法的估计精度,而物理–数据融合的锂离子电池SOC在线估计模型减小了由电压、电流测量所引入的估计误差,克服了等效电路模型法估计精度与模型复杂度之间相矛盾的问题,进一步提高了SOC的估计精度,满足估计误差不超过5%的应用需求。      

基于鲁棒H∞滤波的锂离子电池SOC估计

荷电状态（State of charge, SOC）估计是电池管理系统的核心功能之一,它在电动汽车的生命周期中起着重要作用。针对锂离子电池温度影响模型参数,进而导致SOC估计不准确的问题,本文提出了基于鲁棒H_∞滤波的SOC估计方法。首先,以二阶Thevenin等效电路模型做为锂离子电池基础模型,并将温度对电池模型参数的影响建模为标称电阻值和电池总容量的加性变量,视温度变化为系统的外部扰动。其次,采用滑动线性法对电池模型进行线性化,并在此基础上运用线性矩阵不等式技术设计了对SOC进行估计的鲁棒H_∞滤波器。最后,分别采用四种不同类型的动态电流激励进行仿真实验验证,并将SOC的估计结果与kalman滤波对SOC的估计结果进行对比。结果表明所设计的鲁棒H_∞滤波器能够实现对SOC更为准确的跟踪,同时对外部扰动具有较好的鲁棒性。      

平方根卡尔曼滤波分光光度法同时测定钼、钨和锡

本文提出了平方根卡尔曼滤波分光光度法同时测定钢样中钼、钨、锡含量的新方法。平方根卡尔曼滤波是对普通卡尔曼滤波的改进，以克服由计算中截尾误差引起的滤波数值发散。它能有效地滤除信号中的高斯白噪声和分辨重叠峰。实验时采用苯基荧光酮－ＣＴＭＡＢ三元络合物显色体系，对合成样品及实际钢样的分析结果满意。由于不需分离，方法简便快速。     

氢燃料电池汽车不同制氢方案的全生命周期评价及情景模拟研究

为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态（State of charge, SOC）、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略. 通过基于误差反向传播的神经网络来实现未来短期车速的预测,分析未来车辆需求功率变化,同时借助全球定位系统规划一条通往目的地的路径,智能交通系统便可获取整个行程的交通流量信息,利用行驶里程和SOC实时动态修正等效消耗最小策略中的等效因子,实现能量管理策略的自适应性. 基于MATLAB/Simulink软件,搭建整车仿真模型与传统的能量管理策略进行仿真对比验证. 仿真结果表明,采用基于神经网络的工况预测算法能够较好地预测未来短期工况,其预测精度相较于马尔可夫方法提高12.5%,所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降55.6%. 硬件在环试验表明,在市郊循环工况 (EUDC)下的氢气消耗比CD/CS策略下降26.8%,仿真验证结果表明了所提出的策略相比于CD/CS策略在氢气消耗方面的优越性能,并通过硬件在环实验验证了所提策略的有效性.

     

带有遗忘因子的滤波器型迭代学习直线伺服系统

针对永磁直线同步电机伺服系统,提出开闭环迭代学习控制器,实现期望直线位置的跟踪控制.分析了永磁直线同步电机的2-D模型及迭代学习直线伺服系统的收敛性.通过减小系统输入误差协方差矩阵迹的方式得到优化的遗忘因子,来修正控制输入的迭代学习律,同时采用零相位FIR数字滤波器对前馈学习控制器中的误差信号进行滤波处理.实验结果表明,带有遗忘因子的滤波器型迭代学习控制器能够保证直线伺服系统在不断的迭代学习中提高性能,有效抑制端部推力波动,系统具有很好的学习收敛速度、动态响应及控制精度.     

一种估计混合动力用Ni-MH电池单体SOC的方法

搭建了混合动力汽车动力电池的性能实验平台,针对车辆实际行驶工况,在不同环境温度下对动力电池进行了相关充放电实验.利用实验系统采集到的动力电池电压与电流,采用自校正模糊神经网络控制算法对常温25℃下的动力电池荷电状态(State of Charge,SOC)进行计算,并与Arbin动力电池测试设备计算出的动力电池荷电状态进行了比较.理论分析和实验结果表明,采用自校正模糊神经网络控制算法计算出的电池SOC满足混合动力汽车电池SOC所需的精度要求.     

电动汽车制动能量回收控制策略研究现状分析

以载重50 t纯电动矿用汽车为研究对象,提出了一种基于深度强化学习优化算法的再生制动回馈策略. 首先建立了纯电动矿用自卸车的数学模型. 随后提出了一种考虑载重和坡度变化的基于自动熵调节Soft actor-critic (SAC)和深度确定性策略梯度算法（DDPG）的能量管理策略. 其中车速、加速度、车辆质量与道路坡度、动力电池荷电状态（SOC)及充放电倍率作为状态变量;变速箱挡位作为动作变量;动力电池SOC及电池寿命作为奖励函数. 仿真结果表明,基于动态规划的控制策略和所提出的基于SAC算法与基于DDPG算法的优化控制策略回馈效率分别提高了18.15%、17.18%和16.63%,电池寿命分别提升了57.31%、56.87%和57.38%. 最后通过比较两种基于深度强化学习算法策略的奖励曲线,可以看出与基于DDPG算法的控制策略相比,所提出的基于SAC的能量管理控制策略的收敛速度提升了166.7%.

     

电力系统状态估计算法综述与研究趋势

状态估计的核心部分是状态估计算法,简要介绍了电力系统状态估计的基本概念和数学模型,阐述了近年来研究较多的几种电力系统状态估计算法,其中包括:最小二乘算法、快速分解算法、正交变换算法、量测变换状态估计算法以及分区协凋算法等,并对各个算法的优缺点加以评述.最后对状态估计算法的发展趋势进行了展望.     

变量矩阵算法及一类曲线最佳拟合问题的解

讨论1种对无约束目标函数采用变量矩阵算法求解最优值的方法,变量矩阵算法的特点是利用矩阵迭代计算,收敛速度快,过程较稳定.对于一类可逼近模拟曲线的最佳拟合,可以迭代出符合要求的参数值.作为较典型的应用实例。设计1种幅度均衡器.通过迭代搜索找出满足衰耗误差的元件值,说明曲线拟合达到了预期的效果.     

鲁棒UKF滤波算法在SINS初始对准中的应用

针对系统存在不确定性扰动时传统UKF滤波算法的滤波精度和鲁棒性均下降的问题,提出了一种基于H∞范数的鲁棒UKF滤波算法.该算法在Krein空间内对简化UKF滤波算法进行改进,增加了一个鲁棒环节.鲁棒环节通过引入给定正常数调整滤波增益从而提高滤波算法的鲁棒性能.在SINS大方位失准角初始对准中对简化UKF滤波算法和鲁棒UKF滤波算法进行了对比研究.仿真结果表明:与简化UKF滤波算法相比,鲁棒UKF滤波算法的方位失准角估计误差由16.9'缩小到4.3'.鲁棒UKF滤波算法降低了系统对扰动的敏感度,具有更好的滤波性能.     

Improved Estimation of ADCP Apparent Bed-Load Velocity Using a Real-Time Kalman Filter

An estimate of apparent bed-load velocity (v) can be derived from the difference between differential global positioning system (DGPSs) and acoustic Doppler current profiler (ADCP) bottom track (BT) measurements when BT is biased by a moving bottom. A Kalman filter has been developed to integrate GPS and bottom track data to improve estimation of boat velocity during ADCP measurements under moving bed conditions (Rennie and Rainville, 2008, J. Hydraulic Eng., in review). The boat velocity estimated using the Kalman filter is superior to boat velocity from raw GPS data. In this paper we assess the improvement in estimation of v using the Kalman filter as opposed to raw GPS data. Specifically, a synthetic moving bed bias was generated for 22 repeat transects of the Gatineau River, Quebec. The synthetic moving bed bias had mean, variance, and distribution across the section as typically observed during bed-load transport conditions, and had the advantage that it was known explicitly. The errors in estimated apparent bed-load velocity derived using either raw DGPS data or the Kalman filter boat velocity were compared. It was found that the improved boat velocity from the Kalman filter yielded significantly (α = 0.05) better estimates of v, (e.g., 61% reduction in error when the Kalman filter boat velocity was used instead of wide area augmentation system GGA), because boat velocity errors were reduced. Tests with real moving bed data confirmed the Kalman filter was able to significantly reduce errors in bed load calculated with stand alone GPS.     

Two-Stage Extended Kalman Filters with Derivative-Free Local Linearizations

This paper proposes a derivative-free two-stage extended Kalman filter (2-EKF) especially suited for state and parameter identification of mechanical oscillators under Gaussian white noise. Two sources of modeling uncertainties are considered: (1)?errors in linearization, and (2) an inadequate system model. The state vector is presently composed of the original dynamical/parameter states plus the so-called bias states accounting for the unmodeled dynamics. An extended Kalman estimation concept is applied within a framework predicated on explicit and derivative-free local linearizations (DLL) of nonlinear drift terms in the governing stochastic differential equations (SDEs). The original and bias states are estimated by two separate filters; the bias filter improves the estimates of the original states. Measurements are artificially generated by corrupting the numerical solutions of the SDEs with noise through an implicit form of a higher-order linearization. Numerical illustrations are provided for a few single- and multidegree-of-freedom nonlinear oscillators, demonstrating the remarkable promise that 2-EKF holds over its more conventional EKF-based counterparts.     

Subsurface Radioactive Contaminant Transport Modeling Using Particle and Kalman Filter Schemes

Contamination of groundwater by radioactive contaminants can be harmful to the environment. Various prediction models have been adopted to simulate the state of contaminants in the subsurface. Conventional numerical models are simplified by approximation and the model parameters are assumed to be constant, thereby introducing error to the prediction results. Particle and Kalman filters are used in this research to simulate the radioactive contaminant cobalt-57 transport in a subsurface environment by using a two-dimensional advection-dispersion model. A radioactive contaminant concentration was predicted spatially and temporally within boundary conditions. The errors in the prediction results were assessed by using the root-mean-square-error (RMSE) equation. The results show that the Kalman filter performs better than the particle filter when the prediction model is linear. Furthermore, the results from filters are closer to the true value in comparison with the numerical solution, and the filters are capable of reducing the RMSE of the numerical solution by approximately 80%.     

Bayesian State Estimation Method for Nonlinear Systems and Its Application to Recorded Seismic Response

The focus of this paper is to demonstrate the application of a recently developed Bayesian state estimation method to the recorded seismic response of a building and to discuss the issue of model selection. The method, known as the particle filter, is based on stochastic simulation. Unlike the well-known extended Kalman filter, it is applicable to highly nonlinear systems with non-Gaussian uncertainties. The particle filter is applied to strong motion data recorded in the 1994 Northridge earthquake in a seven-story hotel whose structural system consists of nonductile reinforced-concrete moment frames, two of which were severely damaged during the earthquake. We address the issue of model selection. Two identification models are proposed: a time-varying linear model and a simplified time-varying nonlinear degradation model. The latter is derived from a nonlinear finite-element model of the building previously developed at Caltech. For the former model, the resulting performance is poor since the parameters need to vary significantly with time in order to capture the structural degradation of the building during the earthquake. The latter model performs better because it is able to characterize this degradation to a certain extent even with its parameters fixed. For this case study, the particle filter provides consistent state and parameter estimates, in contrast to the extended Kalman filter, which provides inconsistent estimates. It is concluded that for a state estimation procedure to be successful, at least two factors are essential: an appropriate estimation algorithm and a suitable identification model.     

A double laser filtering approach to vibration noise rejection for hot-rolled strip flatness measurement

This paper proposes a new filtering method based on the Kalman filtering algorithm for hot-rolled strip flatness measurement system. The system involves processing slowly changing signal, which can be considered as a bounded random process, and its model parameters are completely unknown. The noise rejection strategy in double lasers can generate a compensation signal. Since the initial and accumulative error would lead to negative filter effect or even cause divergence, Kalman filter is integrated to effectively deal with the initial error and enhance convergence. In this setting, the noise rejection strategy is used as a prediction model to constitute a similar Kalman filter. The correlated error caused by measurement error is coped with by a compensation model based on the feature of correlated error to enhance the filter effect. Both theoretical analysis and simulations show that the new algorithm has a better filter effect than the traditional Kalman filtering algorithm for the system.     

A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

With the combination of three land surface models (LSMs) and the ensemble Kalman filter (EnKF), a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms: the Simple Model Average (SMA) and the Weighted Average Method (WAM). The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.