内燃叉车发动机与液力变矩器匹配计算参数优化  

Parameter Optimization for Matching Calculation between Engine and Torque Converter of Internal Combustion Forklift Truck

作  者:王玲 王娟 吴睿 张经纬 万凯 Wang Ling;Wang Juan;Wu Rui

机构地区:[1]合肥职业技术学院 [2]安徽合力股份有限公司

出  处:《工程机械》2025年第3期148-151,I0007,共5页Construction Machinery and Equipment

基  金:2023年度合肥职业技术学院自然科学一般项目(2023KJB15);2023年度安徽省高校自然科学重点项目(2023AH052552);2023年度安徽省高校自然科学重点项目(2023AH040340);2022年度安徽省质量工程项目项目(2022cxtd132)。

摘  要:发动机与液力变矩器匹配程度对于内燃叉车的整体性能有着至关重要的影响,匹配的合理性直接决定叉车的工作效率和能源消耗。其中,泵损失扣除数、机械传动效率和黏附系数是影响匹配计算的几个关键参数。基于内燃发动机和液力变矩器的理论计算,结合整车性能试验数据,对这些关键参数进行深入分析与优化研究。首先,确定泵损失扣除数为20 N·m,以减少能量损耗并提高系统效率;其次,将机械传动效率优化为0.85,以确保动力传递的高效和平稳;最后,调整黏附系数为0.85,以提高液力变矩器在各种工况下的适应性和稳定性。结合试验验证,优化效果明显。The degree of matching between the engine and the torque converter has a crucial impact on the overall performance of the internal combustion forklift truck,and the reasonableness of the matching directly determines the working efficiency and energy consumption of the forklift truck.Thereinto,the pump loss deduction,mechanical transmission efficiency and sticking coefficient are the key parameters affecting the matching calculation.Based on the theoretical calculation of internal combustion engine and torque converter and combined with the overall performance test data,in-depth analysis and optimization research are conducted on these key parameters.Firstly,the pump loss deduction is determined to be 20 N·m to reduce energy loss and improve system efficiency;secondly,the mechanical transmission efficiency is optimized to be 0.85 to ensure high efficiency and smoothness of power transmission;finally,the sticking coefficient is adjusted to be 0.85 to improve the adaptability and stability of the torque converter under various working conditions.In conjunction with test verification,the optimization effect is obvious.

关 键 词:发动机 液力变速箱 匹配 参数优化 

分 类 号:TH2[机械工程—机械制造及自动化]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象