Engine universal characteristic modeling based on improved ant colony optimization

Chen Fuen, Jiang Shihui, Xie Xin, Chen Longhan, Lan Yubin

Abstract


There have been some mathematics methods to model farm vehicle engine universal characteristic mapping (EUCM). Nevertheless, any of different mathematics methods used would possess its own strengths and weaknesses. As a result, these modeling methods about EUCM are not the same among the most vehicle manufacturers. In order to obtain a better robustness EUCM, an improved ant colony optimization was introduced into a traditional cubic surface regression method for modeling EUCM. Based on this method, the test data were regressed into a three-dimensional cubic surface, after that it was cut by some equal specific fuel consumption (ESFC) planes, more than twenty two-dimensional ESFC equations were obtained. Furthermore, the engine speed in every ESFC equation was discretized to obtain a set of ESFC points, and this set of ESFC points was linked into a closed curve by a given sequence via the improved ant colony algorithm. In order to improve the modeling speed, dimensionality reduction and discretization methods were adopted. In addition, a corresponding simulation platform was also developed to obtain an optimal system configuration. There were 48 000 simulation search tests carried out on the platform, and the major parameters of the algorithm were determined. In this way the EUCM was established successfully. In contrast with other methods, as a result of the application of the novel bionic intelligent algorithm, it has better robustness, less distortion and higher calculating speed, and it is available for both gasoline engines and diesel engines.
Keywords: engines, universal characteristics, improved ant colony algorithm, genetic algorithm, cubic surface regression
DOI: 10.3965/j.ijabe.20150805.1802

Citation: Chen F E, Jiang S H, Xie X, Chen L H, Lan Y B. Engine universal characteristic modeling based on improved ant colony algorithm. Int J Agric & Biol Eng, 2015; 8(5): 26－35.

Keywords


engines, universal characteristics, improved ant colony algorithm, genetic algorithm, cubic surface regression

Full Text:

PDF

References


Jiang F C, Wang M L, Li L. Software design of engine characteristic simulation. Journal of Software, 2012; 7(2): 316–321.

Xin Q. Overview of diesel engine applications for engine system design - Part 2: General performance characteristics. SAE Technical Paper. 2011. http://papers.sae.org/2011-01- 2179/. Accessed on [2011-09-13].

Heywood J B, Welling O Z. Trends in performance characteristics of modern automobile SI and diesel engines. SAE Technical Paper. 2009. http://papers.sae.org/2009-01- 1892/. Accessed on [2009-06-15].

Ausserer J K, Litke P J, Rowton A, Polanka M, Grinstead K. Development of test bench and characterization of performance in small internal combustion engines. SAE Technical Paper. 2013. http://papers.sae.org/2013-32-9036/. Accessed on [2013-10-15].

Nasser S H, Weibermel V, Wiek J. Computer simulation of vehicle’s performance and fuel consumption under steady and dynamic driving conditions. SAE Technical Paper. 1998. http://papers.sae.org/981089/. Accessed on [1998-02-01].

Gjirja S, Olsson E. Performance and emission analysis of a non-conventional gasoline engine. SAE Technical Paper. 2000. http://papers.sae.org/2000-01-1840/. Accessed on [2000-06-19].

Chaim M B, Shmerling E. A Model of Vehicle Fuel Consumption at Conditions of the EUDC. International Journal of Mechanics, 2013; 1(7): 10–17.

Zeng M, Wang B B, Zhang S M, Wang W M. A research on fitting methods of universal performance curve for air motor based on MATLAB. Oil Field Equipment, 2013; 12(12): 21–25. (in Chinese with English abstract)

Li X H, Luo F Q, Tang D. Drawing engine universal performance characteristics map using polynomial interpolation. Transactions of the CSAE. 2004; 20(5): 138–140. (in Chinese with English abstract)

Durković R, Damjanović M. Regression models of specific fuel consumption curves and characteristics of economic operation of internal combustion engines. Mechanical Engineering, 2006; 4(1): 17–26.

Bloomenthal J. Polygonization of implicit surfaces. Computer-Aided Geometric Design, 1988; 5(4): 341–355.

Van Nieuwstadt M J, Kolmanovsky I V, Brehob D, Haghgooie M. Heat release regressions for GDI engines. SAE Technical Paper. 2000. http://papers.sae.org/ 2000-01-0956/. Accessed on [2000-03-06].

Yan F W, Wang H F, Tian S P, Yuan Z J. Application of BP neural network to engine universal characteristics. Journal of WUT, 2010; 32(3): 399–402. (in Chinese with English abstract)

Zhang C J, Mu G Y, Chen H, Sun T Z, Pang L Y. Distributed service discovery algorithm based on ant colony algorithm. Journal of Software, 2014; 9(1): 70–75.

Abousleiman R, Rawashdeh O. An application of ant colony optimization to energy efficient routing for electric vehicles. SAE Technical Paper. 2013. http://papers.sae.org/ 2013-01-0337/. Accessed on [2013-04-08].

Liu M Y, Liu S F, Wang X Y, Qu M, Hu C H. Knowledge-domain semantic searching and recommendation based on improved ant colony algorithm. Journal of Bionic Engineering, 2013; 10(4): 532–540.

Middendorf M, Reischle F, Schmeck H. Multi colony ant algorithms. Journal of Heuristics, 2002; 8(3): 305–320.

Dong H, Zhao X H, Qu L D, Chi X F, Cui X Y. Multi-hop routing optimization method based on improved ant algorithm for vehicle to roadside network. Journal of Bionic Engineering, 2014; 11(3): 490–496.

Chaves R D O. Genetic Algorithms Applied on Route Optimization. SAE Technical Paper. 1999. http://papers.sae.org/1999-01-2993/. Accessed on [1999-12-01].

Harik G R, Harik G R, Lobo F G, Goldberg D E. The compact genetic algorithm. IEEE Transactions on Evolutionary Computation, 1999; 3(4): 287–297.

Lei X S, Du Y H. A linear domain system identification for small unmanned aerial rotorcraft based on adaptive genetic algorithm. Journal of Bionic Engineering, 2010; 7(2): 142–149.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office