Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a c...Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a comprehensive comparative analysis of four Kalman filter variants Extended Kalman Filter(EKF),Extended Kalman-Bucy Filter(EKBF),Unscented Kalman Filter(UKF),and Unscented Kalman-Bucy Filter(UKBF)under varying battery parameter conditions.These include temperature fluctuation,self-discharge,current direction,cell capacity,process noise,and measurement noise.Our findings reveal significant variations in the performance of SOC and SOH predictions across filters,emphasizing that UKF demonstrates superior robustness to noise,while EKF performs better under accurate system dynamics.The study underscores the need for adaptive filtering strategies that can dynamically adjust to evolving battery parameters,thereby enhancing BMS reliability and extending battery lifespan.展开更多
为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境...为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。展开更多
[目的/意义]针对温室温湿度预测中多传感器数据融合可靠性低、传统模型忽略温湿度动态耦合,以及参数调优依赖人工经验等问题。[方法]首先,对传统卡尔曼(Kalman)滤波算法实施改进,通过动态调整过程噪声协方差和观测噪声协方差,结合新息...[目的/意义]针对温室温湿度预测中多传感器数据融合可靠性低、传统模型忽略温湿度动态耦合,以及参数调优依赖人工经验等问题。[方法]首先,对传统卡尔曼(Kalman)滤波算法实施改进,通过动态调整过程噪声协方差和观测噪声协方差,结合新息方差动态分配多传感器权重。其次,针对温湿度的强耦合性及其协同控制的需求,构建多输出长短期记忆-注意力机制(Long Short-Term Memory-Attention,LSTM-Attention)模型,以温湿度协同预测为目标,引入注意力机制自适应加权关键环境因子,并采用灰狼优化算法(Grey Wolf Optimizer,GWO)自动对超参数进行寻优。[结果和讨论]提出的自适应卡尔曼滤波算法在多点温湿度融合中的平均绝对偏差分别为1.59℃和8.64%,比传统卡尔曼滤波算法分别降低1.24%、8.57%。以该算法融合结果作为模型训练集,模型在温湿度预测中决定系数R2分别达到98.2%和99.3%,比传统Kalman提升4.7%和4.3%。GWO-LSTM-Atten⁃tion模型的温湿度预测均方根误差分别为0.7768℃和2.0564%,比LSTM、LSTM-Attention时间序列预测模型分别降低15.6%、6.6%,湿度分别降低29.2%、5.7%。[结论]提出的自适应卡尔曼融合算法能够有效抑制异常值影响,可在非平稳环境变化下实现多传感器数据可靠融合。在温室多环境因子预测中,GWO-LSTM-Attention模型温湿度预测值在未来可作为控制温室环境的重要参考,进而实现对温室环境的实时调控。展开更多
To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Fil...To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Filter(ESKF).Several adaptive mechanisms are introduced within the standard ESKF framework:first,the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions;second,the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time,mitigating disturbances caused by external accelerations and magnetic fields.Additionally,a dual-mode correction strategy is proposed for yaw angle estimation:a computationally efficient quaternion-based direct correction method is used for small-angle errors,while the system switches to a higher-precision adaptive ESKF algorithm for large-angle deviations.This strategy ensures estimation accuracy while effectively reducing computational complexity.Experimental results in mixed static-dynamic scenarios show that the proposed algorithmachieves the lowest rootmean square error(RMSE)in roll(5.638°)and yaw(6.315°),and ranks first in pitch(2.616°),validating the effectiveness of the improvements.In magnetic interference tests,it delivers the best overall performance,achieving the highest accuracy in roll and yaw and near-optimal performance in pitch,highlighting its excellent anti-interference capability and dynamic tracking performance.Complexity analysis further confirms a significant reduction in computational time compared to the standard ESKF.The results consistently demonstrate that the proposed method offers higher estimation accuracy and robustness under complex conditions,making it suitable for practical applications involving magnetic disturbances and rapid motions.展开更多
In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress h...In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress high-frequency noise,an infinite impulse response filter(IIRF)is introduced at the front end of ASTUKF to preprocess the original data.Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering.After that,the data from the experiment were tested on the hardware experiment platform.The experimental results show that compared to the traditional extended Kalman filter(EKF)and unscented Kalman filter(UKF)algorithms,the root mean square error(RMSE)of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5%and 36.1%;the RMSE of the pitch axis results decreases by nearly 58.4%and 51.5%,respectively;and the RMSE of the yaw axis results decreases almost 62.8%and 50.9%,correspondingly.The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.展开更多
机动飞行条件下高速转子系统会同时受到环境载荷以及转子自身的共同激励而产生强烈的强迫响应。为研究其复杂的振动特性,本文采用Vold-Kalman滤波(Vold-Kalman Filter,VKF)对不同基础运动激励下转子系统的实测振动信号进行阶次跟踪滤波...机动飞行条件下高速转子系统会同时受到环境载荷以及转子自身的共同激励而产生强烈的强迫响应。为研究其复杂的振动特性,本文采用Vold-Kalman滤波(Vold-Kalman Filter,VKF)对不同基础运动激励下转子系统的实测振动信号进行阶次跟踪滤波。为验证VKF的有效性及参数设置的可靠性,通过转子动力特性计算生成系统响应的仿真信号,并通过加噪处理模拟测量信号,然后通过VKF提取目标阶次的时域波形。通过陀螺运动转子动力学试验,测得不同基础转动激起的系统振动响应,组合使用VKF和计算阶次跟踪(Computed Order Tracking,COT)提取并分离了转子转频信号和基础低频信号的时域和阶次信息。结果表明,单轴滚转或俯仰运动均会激起与其频率一致的低频振动响应,且滚转、俯仰角速度的大小会影响该低频信号的幅值大小;随着基础运动角速度的变化,转子前四阶振动分量没有发生明显的变化,而基础运动频率与转频之间的频带区域有显著变化。此方法有效地提升了机动飞行下转子支承系统振动信号处理与分析的准确度和效率,降低了信号噪声。展开更多
基金supported by the Royal Academy of Engineering,UK,in the scheme of Distinguished International Associate(DIA-2424-5-134).
文摘Accurate estimation of the State of Charge(SOC),State of Health(SOH),and Terminal Resistance(TR)is crucial for the effective operation of Battery Management Systems(BMS)in lithium-ion batteries.This study conducts a comprehensive comparative analysis of four Kalman filter variants Extended Kalman Filter(EKF),Extended Kalman-Bucy Filter(EKBF),Unscented Kalman Filter(UKF),and Unscented Kalman-Bucy Filter(UKBF)under varying battery parameter conditions.These include temperature fluctuation,self-discharge,current direction,cell capacity,process noise,and measurement noise.Our findings reveal significant variations in the performance of SOC and SOH predictions across filters,emphasizing that UKF demonstrates superior robustness to noise,while EKF performs better under accurate system dynamics.The study underscores the need for adaptive filtering strategies that can dynamically adjust to evolving battery parameters,thereby enhancing BMS reliability and extending battery lifespan.
文摘为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。
文摘[目的/意义]针对温室温湿度预测中多传感器数据融合可靠性低、传统模型忽略温湿度动态耦合,以及参数调优依赖人工经验等问题。[方法]首先,对传统卡尔曼(Kalman)滤波算法实施改进,通过动态调整过程噪声协方差和观测噪声协方差,结合新息方差动态分配多传感器权重。其次,针对温湿度的强耦合性及其协同控制的需求,构建多输出长短期记忆-注意力机制(Long Short-Term Memory-Attention,LSTM-Attention)模型,以温湿度协同预测为目标,引入注意力机制自适应加权关键环境因子,并采用灰狼优化算法(Grey Wolf Optimizer,GWO)自动对超参数进行寻优。[结果和讨论]提出的自适应卡尔曼滤波算法在多点温湿度融合中的平均绝对偏差分别为1.59℃和8.64%,比传统卡尔曼滤波算法分别降低1.24%、8.57%。以该算法融合结果作为模型训练集,模型在温湿度预测中决定系数R2分别达到98.2%和99.3%,比传统Kalman提升4.7%和4.3%。GWO-LSTM-Atten⁃tion模型的温湿度预测均方根误差分别为0.7768℃和2.0564%,比LSTM、LSTM-Attention时间序列预测模型分别降低15.6%、6.6%,湿度分别降低29.2%、5.7%。[结论]提出的自适应卡尔曼融合算法能够有效抑制异常值影响,可在非平稳环境变化下实现多传感器数据可靠融合。在温室多环境因子预测中,GWO-LSTM-Attention模型温湿度预测值在未来可作为控制温室环境的重要参考,进而实现对温室环境的实时调控。
文摘To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Filter(ESKF).Several adaptive mechanisms are introduced within the standard ESKF framework:first,the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions;second,the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time,mitigating disturbances caused by external accelerations and magnetic fields.Additionally,a dual-mode correction strategy is proposed for yaw angle estimation:a computationally efficient quaternion-based direct correction method is used for small-angle errors,while the system switches to a higher-precision adaptive ESKF algorithm for large-angle deviations.This strategy ensures estimation accuracy while effectively reducing computational complexity.Experimental results in mixed static-dynamic scenarios show that the proposed algorithmachieves the lowest rootmean square error(RMSE)in roll(5.638°)and yaw(6.315°),and ranks first in pitch(2.616°),validating the effectiveness of the improvements.In magnetic interference tests,it delivers the best overall performance,achieving the highest accuracy in roll and yaw and near-optimal performance in pitch,highlighting its excellent anti-interference capability and dynamic tracking performance.Complexity analysis further confirms a significant reduction in computational time compared to the standard ESKF.The results consistently demonstrate that the proposed method offers higher estimation accuracy and robustness under complex conditions,making it suitable for practical applications involving magnetic disturbances and rapid motions.
基金supported by the Key Research and Development Program of Shaanxi Province(No.2024NC-YBXM-246)the Shaanxi Provincial Science and Technology Department(No.2024JC-YBQN-0725)+1 种基金the Education Department of Shaanxi Province(No.23JK0371)the Shaanxi University of Technology(No.SLGRCQD2318).
文摘In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress high-frequency noise,an infinite impulse response filter(IIRF)is introduced at the front end of ASTUKF to preprocess the original data.Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering.After that,the data from the experiment were tested on the hardware experiment platform.The experimental results show that compared to the traditional extended Kalman filter(EKF)and unscented Kalman filter(UKF)algorithms,the root mean square error(RMSE)of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5%and 36.1%;the RMSE of the pitch axis results decreases by nearly 58.4%and 51.5%,respectively;and the RMSE of the yaw axis results decreases almost 62.8%and 50.9%,correspondingly.The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.
文摘机动飞行条件下高速转子系统会同时受到环境载荷以及转子自身的共同激励而产生强烈的强迫响应。为研究其复杂的振动特性,本文采用Vold-Kalman滤波(Vold-Kalman Filter,VKF)对不同基础运动激励下转子系统的实测振动信号进行阶次跟踪滤波。为验证VKF的有效性及参数设置的可靠性,通过转子动力特性计算生成系统响应的仿真信号,并通过加噪处理模拟测量信号,然后通过VKF提取目标阶次的时域波形。通过陀螺运动转子动力学试验,测得不同基础转动激起的系统振动响应,组合使用VKF和计算阶次跟踪(Computed Order Tracking,COT)提取并分离了转子转频信号和基础低频信号的时域和阶次信息。结果表明,单轴滚转或俯仰运动均会激起与其频率一致的低频振动响应,且滚转、俯仰角速度的大小会影响该低频信号的幅值大小;随着基础运动角速度的变化,转子前四阶振动分量没有发生明显的变化,而基础运动频率与转频之间的频带区域有显著变化。此方法有效地提升了机动飞行下转子支承系统振动信号处理与分析的准确度和效率,降低了信号噪声。
