针对现有锂离子电池组健康状态(state of health,SOH)精确估计难题,设计了一种融合电池组整体与单体不一致性多尺度特征的高精度SOH估计方法。在该方法中,提出了一种结合卷积神经网络(convolutional neural network,CNN)、柯尔莫可洛夫...针对现有锂离子电池组健康状态(state of health,SOH)精确估计难题,设计了一种融合电池组整体与单体不一致性多尺度特征的高精度SOH估计方法。在该方法中,提出了一种结合卷积神经网络(convolutional neural network,CNN)、柯尔莫可洛夫-阿诺德网络(Kolmogorov-Arnold network,KAN)与Bahdanau注意力(Bahdanau attention,BA)机制的深度学习模型CNN-KANBA。在提出的SOH估计过程中,首先通过对6节串联18650电池组开展系统老化实验,获取全生命周期数据。进而,采用增量能量分析(incremental energy analysis,IEA)方法提取表征电池组整体衰退的增量能量曲线长度特征,同时计算组内单体电压中位数绝对偏差量与温度峰度作为反映不一致性演化的关键个体特征,从而构建了全面描述电池组“整体-单体”协同衰退的多尺度特征集。利用训练数据的特征训练了CNN-KAN-BA估计模型,并对测试数据进行了验证,结果表明该方法可实现高精度SOH估计,其平均绝对误差为0.5874%,均方根误差为0.6990%,平均决定系数高于98%,均优于其他常见的SOH估计方法。所提出的方法可有效解决锂离子电池组SOH精确估计问题。展开更多
Damage to elevated water tanks in past earthquakes can be attributed to the poor performance of their supporting frame staging. In order to ascertain the performance of these elevated water tanks, it is crucial to cat...Damage to elevated water tanks in past earthquakes can be attributed to the poor performance of their supporting frame staging. In order to ascertain the performance of these elevated water tanks, it is crucial to categorize the damage in quantifiable damage states. Among various parameters to quantify the damage states, the top drift of frame staging can be conveniently correlated to the different damage levels. In literature, drift limits corresponding to different damage states of the frame staging of the elevated water tank are not available. In the present study, drift limits for RC frame staging in elevated water tanks corresponding to different seismic damage states have been proposed. Various damage states of the elevated water tank have been determined using the Park and Ang damage index. The Park and Ang damage index utilizes results of both pushover analysis and incremental dynamic analysis. Twelve models of elevated water tanks have been developed considering variation in staging height and tank capacity. Incremental dynamic analysis has been performed using the suite of twelve actual earthquake ground motions. Based on the regression analysis between damage indexes and drift, limiting drift values for each damage state are proposed.展开更多
时变干扰问题在风洞流场控制中较为常见,其中典型的例子是跨声速连续变迎角试验中迎角变化对马赫数控制造成的干扰。为提高存在时变干扰情况下的流场控制精度,本文创新性地提出一种新型的前馈+反馈复合控制方案。该方案中,前馈控制采用...时变干扰问题在风洞流场控制中较为常见,其中典型的例子是跨声速连续变迎角试验中迎角变化对马赫数控制造成的干扰。为提高存在时变干扰情况下的流场控制精度,本文创新性地提出一种新型的前馈+反馈复合控制方案。该方案中,前馈控制采用基于超前校正的增量式扩张状态观测器(Lead Correction based Incremental Extend State Observer,LIESO),反馈控制则采用增量式比例积分(Proportional–Integral,PI)控制。针对1.2 m跨超声速风洞连续变迎角试验,对该复合控制方法进行了试验验证。结果表明:LIESO+PI复合控制能够有效抑制时变干扰,鲁棒性良好,在不同模型堵塞度和试验马赫数下均表现出较好的适应性,具备较高的工程应用价值。展开更多
锂离子电池被广泛应用于新能源汽车和电化学储能系统,是实现碳中和目标的重要支撑。准确获取健康状态(State of health,SOH)是锂离子电池安全和高效应用的基础,然而,健康状态是电池内部隐含状态,难以直接测量。针对电池外部表征参数难...锂离子电池被广泛应用于新能源汽车和电化学储能系统,是实现碳中和目标的重要支撑。准确获取健康状态(State of health,SOH)是锂离子电池安全和高效应用的基础,然而,健康状态是电池内部隐含状态,难以直接测量。针对电池外部表征参数难以准确映射内部老化状态问题,提出一种基于容量增量分析的磷酸铁锂锂离子电池容量在线估计方法。首先,分析容量增量曲线特征在不同老化状态和工作温度下的变化规律;其次,提取与电池容量强相关的曲线特征作为健康因子;随后,构建健康因子与电池老化状态的映射关系;最后,针对充电温度对估计结果的影响引入补偿机制,最终实现不同充电工况下电池最大可用容量的准确估计。验证结果表明,电池容量估计最大误差为0.36 A·h,对应的估计结果为47.747 A·h,最大相对误差为0.75%。展开更多
文摘针对现有锂离子电池组健康状态(state of health,SOH)精确估计难题,设计了一种融合电池组整体与单体不一致性多尺度特征的高精度SOH估计方法。在该方法中,提出了一种结合卷积神经网络(convolutional neural network,CNN)、柯尔莫可洛夫-阿诺德网络(Kolmogorov-Arnold network,KAN)与Bahdanau注意力(Bahdanau attention,BA)机制的深度学习模型CNN-KANBA。在提出的SOH估计过程中,首先通过对6节串联18650电池组开展系统老化实验,获取全生命周期数据。进而,采用增量能量分析(incremental energy analysis,IEA)方法提取表征电池组整体衰退的增量能量曲线长度特征,同时计算组内单体电压中位数绝对偏差量与温度峰度作为反映不一致性演化的关键个体特征,从而构建了全面描述电池组“整体-单体”协同衰退的多尺度特征集。利用训练数据的特征训练了CNN-KAN-BA估计模型,并对测试数据进行了验证,结果表明该方法可实现高精度SOH估计,其平均绝对误差为0.5874%,均方根误差为0.6990%,平均决定系数高于98%,均优于其他常见的SOH估计方法。所提出的方法可有效解决锂离子电池组SOH精确估计问题。
文摘Damage to elevated water tanks in past earthquakes can be attributed to the poor performance of their supporting frame staging. In order to ascertain the performance of these elevated water tanks, it is crucial to categorize the damage in quantifiable damage states. Among various parameters to quantify the damage states, the top drift of frame staging can be conveniently correlated to the different damage levels. In literature, drift limits corresponding to different damage states of the frame staging of the elevated water tank are not available. In the present study, drift limits for RC frame staging in elevated water tanks corresponding to different seismic damage states have been proposed. Various damage states of the elevated water tank have been determined using the Park and Ang damage index. The Park and Ang damage index utilizes results of both pushover analysis and incremental dynamic analysis. Twelve models of elevated water tanks have been developed considering variation in staging height and tank capacity. Incremental dynamic analysis has been performed using the suite of twelve actual earthquake ground motions. Based on the regression analysis between damage indexes and drift, limiting drift values for each damage state are proposed.
文摘时变干扰问题在风洞流场控制中较为常见,其中典型的例子是跨声速连续变迎角试验中迎角变化对马赫数控制造成的干扰。为提高存在时变干扰情况下的流场控制精度,本文创新性地提出一种新型的前馈+反馈复合控制方案。该方案中,前馈控制采用基于超前校正的增量式扩张状态观测器(Lead Correction based Incremental Extend State Observer,LIESO),反馈控制则采用增量式比例积分(Proportional–Integral,PI)控制。针对1.2 m跨超声速风洞连续变迎角试验,对该复合控制方法进行了试验验证。结果表明:LIESO+PI复合控制能够有效抑制时变干扰,鲁棒性良好,在不同模型堵塞度和试验马赫数下均表现出较好的适应性,具备较高的工程应用价值。
文摘锂离子电池被广泛应用于新能源汽车和电化学储能系统,是实现碳中和目标的重要支撑。准确获取健康状态(State of health,SOH)是锂离子电池安全和高效应用的基础,然而,健康状态是电池内部隐含状态,难以直接测量。针对电池外部表征参数难以准确映射内部老化状态问题,提出一种基于容量增量分析的磷酸铁锂锂离子电池容量在线估计方法。首先,分析容量增量曲线特征在不同老化状态和工作温度下的变化规律;其次,提取与电池容量强相关的曲线特征作为健康因子;随后,构建健康因子与电池老化状态的映射关系;最后,针对充电温度对估计结果的影响引入补偿机制,最终实现不同充电工况下电池最大可用容量的准确估计。验证结果表明,电池容量估计最大误差为0.36 A·h,对应的估计结果为47.747 A·h,最大相对误差为0.75%。
