摘要
针对共轨系统中高压泵的动力和能量控制策略复杂及多元件参数整定困难的问题,提出基于PWM控制计算高压泵压力功率分配策略及燃油计量阀和泵参数整定方法。该策略以泵的供油速率、功率和进口压力响应时间指标为优化对象,进行共轨系统多参数匹配计算,获得对高压泵响应特性影响权重较大的参考值。利用SPSS正交试验整次秩和比综合评价法以及遗传算法对泵凸轮转速、平板阀弹簧刚度、平板阀阀芯直径、燃油计量阀电磁铁励磁电压和线圈匝数5个影响因素进行综合优化,使目标储存时间缩短了44.7%、关闭延迟时间减少了55.1%、关闭时间降低了68.7%、功率延迟时间下降了42.1%、功率下降时间缩短了38.1%。
Aiming at the complexity of power and energy control strategy of the high-pressure pump in common rail system and the difficulty in multi-component parameter setting, a pressure and power distribution strategy of the high-pressure pump based on PWM control and a method of the fuel metering valve and the pump parameter setting were proposed. Through taking oil supply rate, power and inlet pressure response time of the pump as the optimization object, this method performed multi-parameter matching calculation of the common rail system to obtain the reference value that has a great influence on the response characteristics of the high-pressure pump. Making use of the comprehensive evaluation method of whole rank order and rank sum ratio of SPSS orthogonal test and genetic algorithm to optimize five influencing factors like the pump CAM speed, flat valve spring stiffness, flat valve spool diameter and the electromagnet excitation voltage of fuel measuring valve and coil turns show that, the target storage time can be shortened by 44.7%, the shutdown delay time reduced by 55.1%, the shutdown time reduced by 68.7%, the power delay time reduced by 42.1% and the power drop time shortened by 38.1%.
作者
刘沛汉
尹翠
贾娜
LIU Pei-han;YIN Cui;JIA Na(School of Energy Engineering,MOE Key Laboratory of Green Mining of Coal Resources in Xinjiang,XinjiangInstitute of Technology;Ultrahigh Voltage Branch,State Grid Xinjiang Electric Power Co.,Ltd.)
出处
《化工机械》
CAS
2023年第6期808-818,共11页
Chemical Engineering & Machinery
基金
新疆维吾尔自治区自然科学基金(批准号:2022D01A240)资助的课题
国家自然科学基金(批准号:52266018)资助的课题
新疆维吾尔自治区重点研发项目(批准号:2022B1016-1)资助的课题
新疆维吾尔自治区科技厅重大专项项目(批准号:2022A01001-2)资助的课题。
关键词
共轨系统
高压泵
秩和比法
遗传群算法
混合优化
scommon rail system
high pressure pump
rank sum ratio method
genetic group algorithm
hybrid optimization