基于高速数据采集、PWM控制、差压测密封技术,研制了一套防滑阀性能检测系统,实现了对防滑阀的动作响应时间、阶段充排气性能和密封性检测。在模拟实车制动工况条件下,检测系统通过输出快速响应的PWM信号控制进气电磁阀、排气电磁阀高...基于高速数据采集、PWM控制、差压测密封技术,研制了一套防滑阀性能检测系统,实现了对防滑阀的动作响应时间、阶段充排气性能和密封性检测。在模拟实车制动工况条件下,检测系统通过输出快速响应的PWM信号控制进气电磁阀、排气电磁阀高速通断,以此实现对防滑阀性能的高精确检测。测试系统经重复性实验结果表明,阶段充排气性能的最大测量不确定度为0.834 k Pa,密封性检测的最大不确定度为0.011 k Pa。展开更多
EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the ...EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.展开更多
文摘基于高速数据采集、PWM控制、差压测密封技术,研制了一套防滑阀性能检测系统,实现了对防滑阀的动作响应时间、阶段充排气性能和密封性检测。在模拟实车制动工况条件下,检测系统通过输出快速响应的PWM信号控制进气电磁阀、排气电磁阀高速通断,以此实现对防滑阀性能的高精确检测。测试系统经重复性实验结果表明,阶段充排气性能的最大测量不确定度为0.834 k Pa,密封性检测的最大不确定度为0.011 k Pa。
文摘EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.
