基于PSO-GPR的发动机性能与排放预测方法

doi:10.13229/j.cnki.jdxbgxb20210111

摘要/Abstract

摘要：

针对发动机标定试验成本大、开发周期长等问题，提出一种基于粒子群算法优化的高斯过程回归（PSO-GPR）模型，用于处理非线性、复杂的发动机性能和排放预测，以提高试验效率。基于一款汽油发动机的点火角标定试验，结合间隔填充试验设计，通过模型使用少量的试验数据预测扭矩、比油耗、IMEPcov及HC、NO _x 和CO排放等发动机性能和排放参数，并引入R²、RAAE和RMAE对模型泛化能力进行评估。在此基础上研究了不同训练集数量对模型泛化能力的影响，并基于3种不同的机型对模型的普适性进行了验证。结果表明：PSO-GPR模型可以对发动机性能和排放参数进行预测，且精度优于传统的GPR模型和多元多项式回归（MPR）模型，同时该模型具有普适性，为减少发动机标定工作量提供了参考。

关键词: 内燃机工程, 高斯过程回归, 粒子群优化算法, 排放预测

Abstract:

Aiming to overcome the problems of high cost and long development cycle of engine calibration test， a Gaussian Process Regression model based on Particle Swarm Optimization （PSO-GPR） is proposed to deal with nonlinear and complex engine performance and emission prediction for improving test efficiency. Working with calibration tests to the ignition angle of a gasoline engine and combined with the test design of gap filling， engine operating parameters such as torque， fuel consumption， IMEPcov， HC， NO _x and CO emissions have been predicted by using the model with a small amount of test data. R²， RAAE and RMAE were introduced to evaluate the model generalization ability. On this basis， the influence of different number of training sets on the generalization ability of the model was studied， and the universality of the model has been verified with three different engines. The results show that the PSO-GPR model can predict the engine performance and emission parameters with expected accuracy which is better than traditional GPR model and Multivariate Polynomial Regression （MPR） model. The study suggests that the model is universal， which provides a reference for reducing the workload of engine calibration.

Key words: internal-combustion engine engineering, Gaussian process regression, particle swarm optimization, emission prediction

中图分类号:

TK411

秦静,郑德,裴毅强,吕永,苏庆鹏,王膺博. 基于PSO-GPR的发动机性能与排放预测方法[J]. 吉林大学学报(工学版), 2022, 52(7): 1489-1498.

Jing QIN,De ZHENG,Yi-qiang PEI,Yong LYU,Qing-peng SU,Ying-bo WANG. Prediction method of engine performance and emission based on PSO-GPR[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1489-1498.

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参数名称	参数信息
发动机型号	1.5T
结构形式	直列三缸
缸径/mm	43
冲程/mm	92.17
压缩比	11.3
排量/L	1.493
最大扭矩/（N·m）	235
最高转速/（r·min^-1）	6000
额定功率/kW	120

工况	点火角/ （°CA）	类型	扭矩/ （r·min^-1）	比油耗/ ［g·（kW·h）^-1］	IMEPcov/%	HC/10^-6	NO _x /10^-6	CO/10^-6
1000 r/min 25%	22.13	试验值	29.50	325.33	2.28	2007.54	335.65	3068.38
	22.13	预测值	30.26	332.54	2.16	2065.65	328.72	3218.11
	27.78	试验值	30.70	316.86	2.19	2017.54	245.74	3080.53
	27.78	预测值	30.71	317.93	2.26	1958.22	252.53	3201.09
	33.94	试验值	28.80	329.61	3.78	2081.65	122.62	2827.46
	33.94	预测值	28.41	328.52	3.76	2031.98	132.81	2844.04
2800 r/min 50%	15.77	试验值	78.60	245.95	1.68	1421.72	1689.28	3514.42
	15.77	预测值	78.72	247.53	1.72	1357.78	1705.10	3701.63
	21.70	试验值	79.30	241.48	0.99	1522.34	1906.94	3887.85
	21.70	预测值	79.52	240.85	1.09	1424.72	1781.70	4161.76
	27.50	试验值	80.30	239.67	1.07	1549.29	2176.60	4051.66
	27.50	预测值	80.54	238.00	1.09	1486.16	2168.79	4222.16
5200 r/min 105%	7.36	试验值	156.60	280.58	2.84	1139.27	1203.45	28280.46
	7.36	预测值	159.05	287.15	2.66	1079.68	1207.46	26859.22
	11.62	试验值	164.40	249.82	2.05	857.27	2595.32	7198.79
	11.62	预测值	168.83	246.63	2.15	888.90	2563.75	7238.00
	14.11	试验值	168.00	241.03	1.65	798.76	3035.94	4962.77
	14.11	预测值	171.51	239.60	1.57	877.16	2913.31	4762.93

机型	压缩比	缸数	缸径×行程/mm×mm
1.6T汽油机	9.5	4	79.7×81.1
1.8T汽油机	9.6	4	82.5×84.1
2.0T汽油机	11.65	4	82.5×92.8

机型	训练集数据量∶预测集数据量
1.6T汽油机	123∶134
1.8T汽油机	116∶128
2.0T汽油机	123∶135

机型	评价指标	比油耗	HC	NO _x
1.6T	R²	0.9804	0.9706	0.9886
	RAAE	0.0845	0.1240	0.0737
	RMAE	0.8025	0.8766	0.5442
1.8T	R²	0.9867	0.9600	0.9922
	RAAE	0.0695	0.1556	0.0596
	RMAE	0.5757	0.7527	0.3291
2.0T	R²	0.9891	0.9788	0.9781
	RAAE	0.0592	0.1055	0.1032
	RMAE	0.5827	0.6251	0.5152