基于灰狼算法的柴油机高海拔油气参数优化

田宇; 张付军; 崔涛; 巴建栋

doi:1001-2222.2023.06.008

车用发动机 >

2023 , Vol. 0 >Issue 6: 54 - 60

DOI: https://doi.org/1001-2222.2023.06.008

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基于灰狼算法的柴油机高海拔油气参数优化

田宇 ,
张付军 ,
崔涛 ,
巴建栋

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（1.北京理工大学，北京 100081；2.陕西北方动力有限责任公司技术研发中心，陕西宝鸡 721300）

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Optimization of High-Altitude Fuel Injection and Turbocharging Parameters for Diesel Engine Based on Grey Wolf Algorithm

TIAN Yu ,
ZHANG Fujun ,
CUI Tao ,
BA Jiandong

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(1.Beijing Institute of Technology,Beijing 100081,China;2.Technology R&D Center，Shaanxi North Dynamic Co.,Baoji 721300,China)

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摘要

针对高原环境柴油机工作恶化的问题，利用灰狼算法对柴油机可调增压参数和喷油参数进行协同优化，得到了满足柴油机状态约束条件的最优参数组合。建立某V8可调两级增压柴油机GT-Power仿真模型，利用其生成不同输入参数下的样本数据训练神经网络，生成面向优化的MLP神经网络模型。通过灰狼算法获取不同转速下的最优高低压级涡轮旁通阀开度和循环喷油量组合，在海拔4 000 m（环境压力57.6 kPa）条件下，对于最大扭矩点2 000 r/min最高转速点3 000 r/min，可以在油耗上升5.73%和5.90%时使功率分别达到平原环境的94.88%和77.42%。

关键词： 柴油机; 高原; 可调两级增压; 灰狼算法; 神经网络; 参数优化

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田宇 , 张付军 , 崔涛 , 巴建栋 . 基于灰狼算法的柴油机高海拔油气参数优化[J]. 车用发动机, 2023 , 0(6) : 54 -60 . DOI: 1001-2222.2023.06.008

Abstract

For the performance deterioration of diesel engine operation in plateau environment, the grey wolf algorithm(GWO) was used to cooptimize the regulatedturbocharging and injection parameters, and the optimal parameter combinations that satisfied the state constraints of diesel engine were obtained. A simulation model of V-type 8-cylinder regulated two-stage turbocharging diesel engine was established, and the sample data under different input parameters were used to train the neural network and the optimization-oriented neural network MLP model was acquired. The optimal combinations of high-pressure and low-pressure stage turbine bypass valve openings and cyclic injection mass at different speeds were obtained by the grey wolf algorithm. Under the condition of 4 000 m altitude(ambient pressure 57.6 kPa), the powers of 2 000 r/min maximum torque point and 3 000 r/min maximum speed point could reach 94.88% and 77.42% of the plain environment through increasing the fuel consumption by 5.73% and 5.90%, respectively.

Key words： diesel engine; plateau; regulated two-stage turbocharging; grey wolf algorithm; neural network; parameter optimization

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