Research Papers

Computational Intelligence Nonmodel-Based Calibration Approach for Internal Combustion Engines

[+] Author and Article Information
He Ma

School of Engineering,
University of Birmingham,
Edgbaston B15 2TT, UK
e-mail: mahe0502@gmail.com

Ziyang Li

School of Engineering,
University of Birmingham,
Edgbaston B15 2TT, UK
e-mail: z.li.6@pgr.bham.ac.uk

Mohammad Tayarani

School of Computer Science,
University of Birmingham,
Edgbaston B15 2TT, UK
e-mail: mo_tayarani@yahoo.com

Guoxiang Lu

School of Engineering,
University of Birmingham,
Edgbaston B15 2TT, UK
e-mail: g.lu.2@bham.ac.uk

Hongming Xu

School of Engineering,
University of Birmingham,
Edgbaston B15 2TT, UK
e-mail: h.m.xu@bham.ac.uk

Xin Yao

Natural Computation Group,
School of Computer Science,
University of Birmingham,
Edgbaston B15 2TT, UK
e-mail: x.yao@cs.bham.ac.uk

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 20, 2016; final manuscript received August 17, 2017; published online November 10, 2017. Assoc. Editor: Jingang Yi.

J. Dyn. Sys., Meas., Control 140(4), 041002 (Nov 10, 2017) (9 pages) Paper No: DS-16-1201; doi: 10.1115/1.4037835 History: Received April 20, 2016; Revised August 17, 2017

Over the past 20 years, with the increase in the complexity of engines, and the combinatorial explosion of engine variables space, the engine calibration process has become more complex, costly, and time consuming. As a result, an efficient and economic approach is desired. For this purpose, many engine calibration methods are under development in original equipment manufacturers and universities. The state-of-the-art model-based steady-state design of experiments (DOE) technique is mature and is used widely. However, it is very difficult to further reduce the measurement time. Additionally, the increasingly high requirements of engine model accuracy and robust testing process with high data quality by high-quality testing facility also constrain the further development of model-based DOE engine calibration. This paper introduces a new computational intelligence approach to calibrate internal combustion engine without the need for an engine model. The strength Pareto evolutionary algorithm 2 (SPEA2) is applied to this automatic engine calibration process. In order to implement the approach on a V6 gasoline direct injection (GDI) engine test bench, a simulink real-time based embedded system was developed and implemented to engine electronic control unit (ECU) through rapid control prototyping (RCP) and external ECU bypass technology. Experimental validations prove that the developed engine calibration approach is capable of automatically finding the optimal engine variable set which can provide the best fuel consumption and particulate matter (PM) emissions, with good accuracy and high efficiency. The introduced engine calibration approach does not rely on either the engine model or the massive test bench experimental data. It has great potential to improve the engine calibration process for industries.

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Fig. 1

Comparison between the current model-based engine calibration and the introduced CINCA

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Fig. 2

Example of dominance: individual A dominates individuals C and D, individual B dominates individuals C and D; individual C dominates individual D; individual D is dominated by individuals A, B, and C; individuals A and B are not dominated by each other; and individuals A and B are at the current Pareto front

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Fig. 3

Working flow of SPEA2 in this research

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Fig. 4

RCP test bench setup for the CINCA

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Fig. 5

Application of intecrio and inca in the system

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Fig. 6

Founded optimal solutions at engine operating condition A

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Fig. 7

Founded optimal solutions at engine operating condition B

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Fig. 8

Founded optimal solutions at engine operating condition C

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Fig. 9

Engine variable values and performance comparison between the CINCA and the original ECU for engine operating condition A

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Fig. 10

Engine variable values and performance comparison between the CINCA and the original ECU for engine operating condition B

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Fig. 11

Engine variable values and performance comparison between the CINCA and the original ECU for engine operating condition C




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