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
电池健康状态(state of health,SOH)的准确估计是电池管理系统的关键技术之一,对保障电动汽车安全、可靠运行至关重要。针对当前高斯过程回归(gaussian process regression,GPR)中单一核函数泛化性能不足,超参数选取易陷入局部最优导致...电池健康状态(state of health,SOH)的准确估计是电池管理系统的关键技术之一,对保障电动汽车安全、可靠运行至关重要。针对当前高斯过程回归(gaussian process regression,GPR)中单一核函数泛化性能不足,超参数选取易陷入局部最优导致SOH估计精度较低的问题,提出一种灰狼优化算法(grey wolf optimization,GWO)和组合核函数改进GPR的SOH估计方法。首先,基于容量增量分析法提取用于表征电池老化的特征,对电池恒流充电的容量-电压曲线插值并以差分法计算容量增量(increment capacity,IC)曲线,应用Savitzky-Golay滤波平滑处理,提取峰值高度、峰值电压及峰面积作为健康特征;其次,引入多维尺度变换(multidimensional scaling,MDS)消除特征冗余性同时降低模型计算复杂度,利用Pearson系数验证所提健康特征与SOH的相关性;然后,结合SOH退化轨迹的非线性和电池容量再生的准周期性特点,将神经网络核函数与周期核函数组合作为GPR的协方差核函数,以GWO对组合核函数超参数的初值进行优化;最后,基于NASA电池数据集将所提方法与SVR、ELM、GPR模型作对比,检验GWO-GPR模型的准确性,估计结果的最大均方根误差(root mean square error,RMSE)和平均绝对误差(mean absolute error,MAE)分别为1.03%和0.5%,以第60、80、100个循环为估计起始点,验证模型的鲁棒性,结果显示最大RMSE控制在1.03%以内。展开更多
锂离子电池的荷电状态(state of charge,SOC)估计是电池管理系统的重要组成部分。更加精确的SOC估计结果,有利于储能电站的并网和控制。该文提出一种基于Vi-RNN的储能电池SOC估计算法,该算法将储能电池端口电压和电压增量作为输入,荷电...锂离子电池的荷电状态(state of charge,SOC)估计是电池管理系统的重要组成部分。更加精确的SOC估计结果,有利于储能电站的并网和控制。该文提出一种基于Vi-RNN的储能电池SOC估计算法,该算法将储能电池端口电压和电压增量作为输入,荷电状态作为输出,RNN算法作为框架,实现在线更高精度的SOC估计。采用储能锂离子电池在0.2C和0.3C充放过程中的测量数据进行仿真分析。结果显示:相较于MEA-BP算法,该方法估计结果的均方误差和相对误差更低,均方误差降低约20%。展开更多
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.
文摘电池健康状态(state of health,SOH)的准确估计是电池管理系统的关键技术之一,对保障电动汽车安全、可靠运行至关重要。针对当前高斯过程回归(gaussian process regression,GPR)中单一核函数泛化性能不足,超参数选取易陷入局部最优导致SOH估计精度较低的问题,提出一种灰狼优化算法(grey wolf optimization,GWO)和组合核函数改进GPR的SOH估计方法。首先,基于容量增量分析法提取用于表征电池老化的特征,对电池恒流充电的容量-电压曲线插值并以差分法计算容量增量(increment capacity,IC)曲线,应用Savitzky-Golay滤波平滑处理,提取峰值高度、峰值电压及峰面积作为健康特征;其次,引入多维尺度变换(multidimensional scaling,MDS)消除特征冗余性同时降低模型计算复杂度,利用Pearson系数验证所提健康特征与SOH的相关性;然后,结合SOH退化轨迹的非线性和电池容量再生的准周期性特点,将神经网络核函数与周期核函数组合作为GPR的协方差核函数,以GWO对组合核函数超参数的初值进行优化;最后,基于NASA电池数据集将所提方法与SVR、ELM、GPR模型作对比,检验GWO-GPR模型的准确性,估计结果的最大均方根误差(root mean square error,RMSE)和平均绝对误差(mean absolute error,MAE)分别为1.03%和0.5%,以第60、80、100个循环为估计起始点,验证模型的鲁棒性,结果显示最大RMSE控制在1.03%以内。
文摘锂离子电池的荷电状态(state of charge,SOC)估计是电池管理系统的重要组成部分。更加精确的SOC估计结果,有利于储能电站的并网和控制。该文提出一种基于Vi-RNN的储能电池SOC估计算法,该算法将储能电池端口电压和电压增量作为输入,荷电状态作为输出,RNN算法作为框架,实现在线更高精度的SOC估计。采用储能锂离子电池在0.2C和0.3C充放过程中的测量数据进行仿真分析。结果显示:相较于MEA-BP算法,该方法估计结果的均方误差和相对误差更低,均方误差降低约20%。
