基于PSO-GPR的发动机性能与排放预测方法

doi:10.13229/j.cnki.jdxbgxb20210111

Abstract

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

CLC Number:

TK411

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.

Figures/Tables 14

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Table 2

Fig.5

Fig.6

Table 3

Fig.7

Fig.8

Table 4

Table 5

Table 6

References 23

1	张文. 基于运行工况的发动机基本标定研究[D]. 上海:上海交通大学机械与动力工程学院, 2019.
	Zhang Wen. Research on basic engine calibration based on operating conditions[D]. Shanghai:School of Mechanical Engineering, Shanghai Jiao Tong University, 2019.
2	李志辉. 基于模型的电控发动机标定技术[J]. 内燃机与配件, 2020(21): 71-72.
	Li Zhi-hui. Model-based electronic-controlled engine calibration technology [J]. Internal Combustion Engine & Parts, 2020(21): 71-72.
3	杜乐. 基于模型的直喷汽油机标定技术研究[D]. 长春:吉林大学机械科学与工程学院, 2013.
	Du Le. Research on model-based calibration of GDI engine[D]. Changchun: College of Mechanical Sciences and Engineering,Jilin University, 2013.
4	Martínez M, Palacios H, Velázquez C. Modeling engine fuel consumption and NO _x with RBF neural network and MOPSO algorithm[J]. International Journal of Automotive Technology, 2015, 16(6):1041-1049.
5	Francesco D N, Giovanni G, Alfredo G, et al. Volumetric efficiency estimation based on neural networks to reduce the experimental effort in engine base calibration[J]. Fuel, 2019, 244: 31-39.
6	Rask E, Sellnau M. Simulation-based engine calibration: tools, techniques, and applications[J]. SAE Transactions, 2004, 113:821-832.
7	Gao J, Shen T. Online calibration of spark advance for combustion phase control of gasoline SI engines[C]//Seventh International Conference on Intelligent Control and Information Processing (ICICIP), Dali, China, 2016.
8	Carvalho R, Machado G B, Colaço M, et al. Estimating gasoline performance in internal combustion engines with simulation metamodels[J]. Fuel, 2017, 193: 230-240.
9	Zutta E, Acosta D, Andrés D, et al. Development of simulation metamodels to predict the performance and exhaust emission parameters of a spark ignition engine[J]. International Journal on Interactive Design and Manufacturing (IJIDeM), 2020, 14(1):1-12.
10	侯献军,高寒,席光维, 等. 基于BP神经网络的发动机最佳点火提前角MAP优化分析[J]. 武汉理工大学学报:交通科学与工程版, 2018, 42(3): 353-356.
	Hou Xian-jun, Gao Han, Xi Guang-wei, et al. Optimization analysis of engine ignition advance angle MAP based on BP neural network [J]. Journal of Wuhan University of Technology (Traffic Science and Engineering Edition), 2018, 42(3): 353-356.
11	何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述[J]. 控制与决策, 2013(8): 1121-1129.
	He Zhi-kun, Liu Guang-bin, Zhao Xi-jing, et al. A review of gaussian process regression methods[J]. Control and Decision, 2013, 28(8): 1121-1129.
12	王阳, 唐朝晖, 王紫勋, 等. 选用改进高斯过程回归模型的碳排放短期预测[J]. 计算机工程与应用, 2018, 54(23): 246-251, 258.
	Wang Yang, Tang Chao-hui, Wang Zi-xun, et al. Short-term forecasting of carbon emission by using improved gaussian process regression[J]. Computer Engineering and Applications, 2018, 54(23): 246-251, 258.
13	Keprate A, Chandima Ratnayake R M, Sankararaman S. Experimental validation of the adaptive gaussian process regression model used for prediction of stress intensity factor as an alternative to FEM[J]. Journal of Offshore Mechanics and Arctic Engineering, 2018, 141(2):1-11.
14	钟美, 赵兵涛, 黄朔. 基于高斯过程回归的燃煤烟气汞排放预测[J]. 动力工程学报, 2016, 36(12): 987-992.
	Zhong Mei, Zhao Bing-tao, Huang Shuo. Prediction of Mercury emission from coal-fired flue gas based on Gaussian process regression[J]. Chinese Journal of Power Engineering, 2016, 36(12): 987-992.
15	黄南天, 齐斌, 刘座铭, 等. 采用面积灰关联决策的高斯过程回归概率短期负荷预测[J]. 电力系统自动化, 2018, 42(23): 64-71.
	Huang Nan-tian, Qi Bin, Liu Zuo-ming, et al. Gaussian process regression probability short-term load forecasting based on area grey relational decision[J]. Automation of Electric Power Systems, 2018, 42(23): 64-71.
16	任利强, 张立民, 王海鹏, 等. 基于IPSO-GPR的短期负荷区间预测[J]. 计算机工程与设计, 2019, 40(10): 3002-3008.
	Ren Li-qiang, Zhang Li-min, Wang Hai-peng, et al. Short-term power load interval forecasting based on improved particle swarm optimization and Gaussian process regression[J]. Computer Engineering and Design, 2019, 40(10): 3002-3008.
17	杜常清, 曹锡良, 何彪, 等. 基于混合粒子群算法的双离合变速器参数优化设计[J]. 吉林大学学报: 工学版, 2020, 50(5): 1556-1564.
	Du Chang-qing, Cao Xi-liang, He Biao, et al. Parameters optimization of dual clutch transmission based on hybrid particle swarm optimization[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(5): 1556-1564.
18	张静, 刘向东. 混沌粒子群算法优化最小二乘支持向量机的混凝土强度预测[J]. 吉林大学学报: 工学版, 2016, 46(4): 1097-1102.
	Zhang Jing, Liu Xiang-dong. Prediction of concrete strength based on least square support vector machine optimized by chaotic particle swarm optimization[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(4): 1097-1102.
19	杨景明, 马明明, 车海军, 等. 多目标自适应混沌粒子群优化算法[J]. 控制与决策, 2015, 30(12): 2168-2174.
	Yang Jing-ming, Ma Ming-ming, Che Hai-jun, et al. Multi-objective adaptive chaotic particle swarm optimization algorithm[J]. Control and Decision, 2015, 30(12): 2168-2174.
20	徐从东, 陈春. 一种自适应动态控制参数的粒子群优化算法[J]. 计算机工程, 2013, 39(10): 203-207.
	Xu Cong-dong, Chen Chun. A particle swarm optimization algorithm of adaptive dynamic control parameter[J]. Computer Engineering, 2013, 39(10): 203-207.
21	李建平. 仿真元建模中的拟合方法及其应用研究[D]. 长沙:国防科学技术大学计算机学院, 2007.
	Li Jian-ping. Study on fitting methods of simulation metamodelling and its application[D]. Changsha: College of Computer Science and Technology, National University of Defense Technology, 2007.
22	Lee S, Chang J H. On Using Multivariate Polynomial Regression Model with Spectral Difference for Statistical Model-based Speech Enhancement[M]. London: Elsevier North-Holland, Inc., 2016.
23	郭栋, 高松, 王晓原, 等. 轻型车微观排放模型的建立与比较分析[J]. 中南大学学报: 自然科学版, 2012, 43(10): 4123-4128.
	Guo Dong, Gao Song, Wang Xiao-yuan, et al. Comparative analysis of light-duty vehicle emission model[J]. Journal of Central South University (Science and Technology), 2012, 43(10): 4123-4128.

Related Articles 15

[1]	Hai-yan XING,Chao LIU,Cheng XU,Yu-huan CHEN,Song-hong-ze WANG. Quantitative metal magnetic memory classification model of weld grades based on particle swarm optimization fuzzy C⁃means [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 525-532.
[2]	Wei LUO,Bo LU,Fei CHEN,Teng MA. Fault diagnosis method of NC turret based on PSO⁃SVM and time sequence [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 392-399.
[3]	Chang-qing DU,Xi-liang CAO,Biao HE,Wei-qun REN. Parameters optimization of dual clutch transmission based on hybrid particle swarm optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1556-1564.
[4]	Fang-wu MA,Li HAN,Liang WU,Jin-hang LI,Long-fan YANG. Damping optimization of heavy⁃loaded anti⁃vibration platform based on genetic algorithm and particle swarm algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1608-1616.
[5]	Xiang-jun YU,Yuan-hui HUAI,Xue-fei LI,De-wu WANG,An YU. Shoveling trajectory planning method for wheel loader based on kriging and particle swarm optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 437-444.
[6]	Shun-fu JIN,Xiu-chen QIE,Hai-xing WU,Zhan-qiang HUO. Clustered virtual machine allocation strategy in cloud computing based on new type of sleep-mode and performance optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 237-246.
[7]	Hong⁃zhi WANG,Fang⁃da JIANG,Ming⁃yue ZHOU. Power allocation of cognitive radio system based on genetic particle swarm optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1363-1368.
[8]	Yuan-ning LIU,Shuai LIU,Xiao-dong ZHU,Guang HUO,Tong DING,Kuo ZHANG,Xue JIANG,Shu-jun GUO,Qi-xian ZHANG. Iris secondary recognition based on decision particle swarm optimization and stable texture [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1329-1338.
[9]	Kai XU,Zhi⁃gang CHEN,Jing⁃hua ZHAO,Lu DAI,Feng LI. Layout design method of star sensor based on particle swarm optimization algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 972-978.
[10]	ZHAO Dong,SUN Ming-yu,ZHU Jin-long,YU Fan-hua,LIU Guang-jie,CHEN Hui-ling. Improved moth-flame optimization method based on combination of particle swarm optimization and simplex method [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1867-1872.
[11]	YANG Shuai, FENG Zhi-wei, ZHAO Zhi-guo, ZHOU Yi. 1-Dimensional simulation analysis about the influence of different Miller cycle strategies on diesel engine operating process [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1444-1454.
[12]	LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.
[13]	ZANG Guo-shuai, SUN Li-jun. Method based on inertial point for setting depth to rigid layer [J]. 吉林大学学报(工学版), 2018, 48(4): 1037-1044.
[14]	HUANG Hui, FENG Xi-an, WEI Yan, XU Chi, CHEN Hui-ling. An intelligent system based on enhanced kernel extreme learning machine for choosing the second major [J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[15]	LIU Ying, ZHANG Kai, YU Xiang-jun. Multi-objective optimization of hydrostatic bearing of hollow shaft based on surrogate model [J]. 吉林大学学报(工学版), 2017, 47(4): 1130-1137.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

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

Prediction method of engine performance and emission based on PSO-GPR

RICH HTML

PDF (PC)