基于WOA-UKF算法的锂电池容量与SOC联合估计

锂电池的荷电状态(SOC)和有效容量是表征电池当前剩余电量和电池寿命的重要参数,提出一种锂离子电池有效容量和SOC的联合估计方法。在电池全寿命周期内,给出一种开路电压与SOC和电池有效容量非线性模型的两变量多项式描述;当电池循环使用次数超过预设值,采用鲸鱼优化算法估计当前电池容量与电池模型参数,根据模型参数与容量值采用无迹卡尔曼滤波器估计电池SOC;在SOC估计过程中,采用鲸鱼优化算法更新无迹卡尔曼滤波器的观测噪声方差和过程噪声方差,实现噪声方差的自适应调节,进而提高估计精度。实验结果验证了该方法的有效性和联合估计方案的可行性。     

基于改进无迹卡尔曼滤波器的锂电池荷电状态估计

针对无迹卡尔曼滤波算法(UKF)估算锂电池荷电状态(SOC)存在的精度低、稳定性差的问题,在二阶模型的基础上,提出一种基于奇异值分解(SVD)的改进无迹卡尔曼滤波算法。建立锂电池的数学模型,通过带遗忘因子的最小二乘法(FFRLS)得到电池模型参数,将辨识出的模型参数实时导入改进UKF算法中,估计锂电池的荷电状态,并与UKF进行比较。在DST工况下,通过仿真实验可知,与UKF相比,SVD-UKF算法的AAE降低3.29%,RMSE降低3.78%。实验结果表明,改进算法的SOC估算精度和自适应性能更高。     

基于RFF及GWO-PF的锂电池SOC估计

为了提高锂电池剩余电量估计的准确性,提出一种在线参数辨识与改进粒子滤波算法相结合的锂电池SOC估计方法。针对粒子滤波中的粒子退化问题,引入灰狼算法,利用灰狼算法较强的全局寻优能力优化粒子分布,保证粒子多样性,有效抑制粒子退化现象,提高滤波精度。采用带遗忘因子的递推最小二乘法实时更新模型参数,并与改进粒子滤波算法交替运行,进一步提高SOC的估计精度。实验结果表明,改进算法的平均估计误差始终保持在±0.15%以内,相比扩展卡尔曼滤波与无迹卡尔曼滤波算法,在电池SOC估计上有更高的估计精度与稳定性。     

多频未知时变扰动下的结构微振动鲁棒自适应控制

本文针对多频窄带未知和时变扰动,基于内模原理和Y-K参数化方法,提出一种反馈鲁棒自适应振动的主动控制算法。该算法通过设计PID中央鲁棒控制器,有效解决了次级通道模型未知情况下的鲁棒控制器参数设计问题。同时提出一种变步长最小均方（Variable Step Size Least Mean Square,VSSLMS）方法,可以在保证稳态误差的基础上大幅提升收敛速度,并通过系统辨识实验验证了所提VSSLMS方法相较于其他VSSLMS算法在收敛性能上的优越性。通过结构微振动主动控制实时实验,对比验证了单独采用滤波x最小均方（Least Mean Square,LMS）自适应控制算法、基于LMS算法的鲁棒自适应控制算法和基于VSSLMS算法的鲁棒自适应控制算法的抑振效果。实验结果表明,本文基于VSSLMS算法的鲁棒自适应控制算法在面向双频正弦窄带扰动以及其频谱、幅值突变情况时,都具有较好的收敛性和鲁棒性。     

交互式多模型的锂电池荷电状态估计

针对锂电池工作在温度不恒定的环境中时单个模型无法捕捉电池参数动态变化,导致电池模型误差增大,荷电状态估计精度下降的问题,提出使用交互式多模型平方根无迹卡尔曼滤波算法来估计电池荷电状态.以戴维宁电池模型为基础建立了一组电池温度子模型,并实现参数辨识;在平方根无迹卡尔曼滤波的基础上,利用交互式多模型方法估计电池荷电状态,通过实时调整模型概率来实现各个模型间的软切换.实验结果表明,交互式多模型方法融合了不同温度下的电池信息,增强了荷电状态估计的精度和鲁棒性.     

自适应滤波在平台自标定数据处理中的应用

目的：研究自适应卡尔曼滤波技术在平台自标定数据处理中的应用。方法：对平台标定中数据处理的一种方法是对角度传感器输出分析，解算出漂移角速率，并在此基础上辨识出漂移参数，由于平台自标定往往在动基座条件下进行，角度传感器输出中的噪声统计难以确定，并且会出现时变的情况，传统的卡尔曼滤波方法不能适用，自适应卡尔曼滤波在估计状态的同时，利用观测数据带来的信息，可在线估计噪声的统计特性，从而不断地改进滤波器的设计，结果与结论：对输出数据建立常速度模型，采用Sage-Husa自适应滤波算法，进行参数辨识，得到较好的效果。     

基于自适应H_(2)/H_(∞)滤波的锂电池SOC和SOH联合估计北大核心CSCD

准确、实时地估计电池的荷电状态(state of charge,SOC)和健康状态(state of health,SOH)是现代电池管理系统的关键任务。通过自适应H_(2)/H_(∞)滤波器可对锂电池的SOC和SOH进行联合估计。该方法基于锂电池的二阶RC等效电路模型,采用AFFRLS法在线辨识锂电池的模型参数,并利用H_(2)/H_(∞)滤波器估计锂电池的SOC,AFFRLS辨识与H_(2)/H_(∞)滤波交替进行,得到一种自适应H_(2)/H_(∞)滤波器。SOH依据AFFRLS辨识的电池内阻进行估计,实现了锂电池SOC与SOH的联合估计。实验结果表明:自适应H_(2)/H_(∞)滤波算法的估计精度高且鲁棒性强,电池的SOC和SOH的平均估计误差始终保持在±0.19%以内,相比于EKF和H_(∞)滤波算法有更高的估计精度与稳定性。     

基于自适应鲁棒滤波的SINS/DVL动基座初始对准方法

针对传统的基于矢量重构的鲁棒粗对准方法对DVL量测噪声敏感的问题,提出基于自适应鲁棒滤波的动基座鲁棒粗对准方法,实现动态跟踪DVL量测噪声,提高粗对准精度。首先建立姿态确定模型和观测矢量误差模型;然后分析矢量重构模型,提出基于自适应鲁棒滤波的参数估计技术,动态跟踪速度噪声,优化参数估计过程,提高矢量重构精度;接着通过最优基算法获得最优姿态四元数;最后进行仿真实验验证。实验表明,该方法较传统方法鲁棒性更强,收敛速度更快,600 s对准结束时刻准确度提高79%。     

风力发电机自适应鲁棒保性能控制器设计

本文针对变速变桨距风力发电机系统对于风能利用率、动态稳定性要求高的特点,建立了风能转换系统的线性参数变化（LPV）模型,给出了设计一个具有自适应机制的鲁棒保性能控制器的充分条件,进而设计出了一种新的自适应鲁棒保性能控制器。首先,运用自适应方法估计出系统的不确定参数,然后利用参数估计和鲁棒保性能控制方法设计系统的状态反馈控制器,其反馈增益阵是通过求解一组线性矩阵不等式（LMI）的约束而得到,并且满足某二次型性能指标。仿真表明,无论在低风速下还是在高风速下,控制器不仅使系统具有良好的动态特性和鲁棒性,而且使系统具有抑制外界干扰能力。     

State-of-charge estimation of lithium-ion batteries in electric vehicles based on an adaptive extended Kalman filter

进行了用自适应扩展卡尔曼滤波(AEKF)算法估计电动车用锂离子动力电池的荷电状态(SOC)的研究.基于混合脉冲功率特性(HPPC)试验,利用遗传优化算法改进Thevenin电路模型参数辨识方法,且从充放电两个方向来获得模型参数,然后在动态应力测试(DST)工况下对改进的模型进行仿真验证分析,基于改进的模型和联邦城市行驶工况(FUDS),应用AEKF算法开展SOC估计研究.仿真和台架试验结果对比表明,改进的Thevenin电路模型和AEKF算法均具有较高的精度,最大估算误差分别为1.70％和2.53％;同时AEKF算法具有较好的鲁棒性,可以有效地解决初始估算不准和累计误差的问题.     

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.     

Modeling and state of charge estimation of lithium-ion battery

Modeling and state of charge(SOC) estimation of lithium-ion(Li-ion) battery are the key techniques of battery pack management system(BMS) and critical to its reliability and safety operation.An auto-regressive with exogenous input(ARX) model is derived from RC equivalent circuit model(ECM) due to the discrete-time characteristics of BMS.For the time-varying environmental factors and the actual battery operating conditions,a variable forgetting factor recursive least square(VFFRLS)algorithm is adopted as an adaptive parameter identification method.Based on the designed model,an SOC estimator using cubature Kalman filter(CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure.In the battery tests,experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter(EKF) algorithm,which is widely used for Li-ion battery SOC estimation,and the maximum estimation error is about 2.3%.     

安时-卡尔曼交叉运行的电池荷电状态估算策略及其微控制器在环验证

为了提高电池管理系统(BMS)的性能,研究了电池荷电状态(SOC)的估算方法,并根据SOC估算算法精度和系统实时性要求,提出了安时(AH)积分算法-卡尔曼(Kalman)算法(AH-Kalman)交叉运行的SOC估算策略。该策略用开路电压(OCV)法确定SOC初值,以实时性较强的AH积分法为主,采用间歇运行的Kalman滤波法修正安时计量法积分误差。建立了系统仿真模型,验证了卡尔曼滤波算法对安时积分法积累误差的修正作用。将控制算法生成C代码下载到目标控制器,搭建微控制器在环测试验证(PILS)平台,进行了与传统卡尔曼滤波算法的复杂度对比分析。结果表明,所提出AHKalman交叉运行的SOC估算策略在保证了SOC估算精度的同时也具有较好的实时性,便于实际应用。     

Angular velocity estimation from measurement vectors of star tracker

In most spacecraft, there is a need to know the craft's angular rate. Approaches with least squares and an adaptive Kalman filter are proposed for estimating the angular rate directly from the star tracker measurements. In these approaches, only knowledge of the vector measurements and sampling interval is required. The designed adaptive Kalman filter can filter out noise without information of the dynamic model and inertia dyadic. To verify the proposed estimation approaches, simulations based on the orbit data of the challenging minisatellite payload (CHAMP) satellite and experimental tests with night-sky observation are performed. Both the simulations and experimental testing results have demonstrated that the proposed approach performs well in terms of accuracy, robustness, and performance.     

基于模糊自适应Kalman滤波的机械手动态图像雅可比矩阵辨识

提出了一种改进的基于模糊自适应Kalman滤波的动态图像雅可比矩阵辨识方法.该方法在机器人参数和滤波参数未知而且视觉成像模型动态变化的情况下,通过模糊逻辑自适应控制器在线监测滤波残差均值和残差协方差误差,对过程噪声参数Q和量测噪声参数R进行自适应调节,实现了未知环境下动态图像雅可比矩阵的稳定辨识.通过微装配机械手运动实验验证了该方法的有效性.     

Huber-based unscented filtering and its application to vision-based relative navigation

A new algorithm called Huber-based unscented filtering (UF) is derived and applied to estimate the precise relative position, velocity and attitude of two unmanned aerial vehicles in the formation flight. The relative states are estimated using line-of-sight measurements between the vehicles along with acceleration and angular rate measurements of the follower. By making use of the Huber technique to modify the measurement update equations of standard UF, the new filtering could exhibit robustness with respect to deviations from the commonly assumed Gaussian error probability, for which the standard unscented filtering would exhibit severe degradation in estimation accuracy. Furthermore, contrast to standard extended Kalman filtering, more accurate estimation and faster convergence could be achieved from inaccurate initial conditions. During filter design, the global attitude parameterisation is given by a quaternion, whereas a generalised three-dimensional attitude representation is used to define the local attitude error. A multiplicative quaternion-error approach is used to guarantee that quaternion normalisation is maintained in the filter. Simulation results are shown to compare the performance of the new filter with standard UF and standard extended Kalman filtering for non-Gaussian case.     

基于Kriging模型和无迹卡尔曼滤波的转向架构架模型修正

为提高转向架构架模型的修正效率和实时性,提出了一种基于Kriging模型和无迹卡尔曼滤波的模型修正方法.首先,对构架进行模态分析,引入信息熵确定模态阶数来优选频响函数频率区间.其次,构造Kriging模型,将频响函数经过小波变换并提取第4层低频系数作为Kriging模型输出,并通过改进的灰狼算法(grey wolf o...     

结构不对称条件下测速发电机的卡尔曼测速算法

针对基于测速发电机、光电编码器等测速传感器的传统测速系统存在低速测量性能,实时性,对结构不对称、电磁扰动等的抗干扰性等都较差的问题,提出了一种高精度宽范围实时滤波测速方法。以爪极永磁式交流测速电机为例,详细分析了其机械结构特性和工作原理,并在结构不对称条件下建立了爪极永磁式交流测速电机测速模型,提出了一种面向工程应用、计算量小的交互双模自适应降阶无迹卡尔曼滤波算法来实时估计永磁转子转速。该算法同时运行降阶和全阶交互双模自适应算法,当采用降阶估计值保障系统实时性时,在计算耗时较长的全阶算法运行完成一次后修正一次降阶估计值,提高测速精度。仿真结果表明,提出的测速算法对于结构不对称扰动具有良好的鲁棒性,能够适用于宽范围条件下的转速高精度实时跟踪,具有一定的工程指导意义。