engine calibration
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2022 ◽  
Vol 305 ◽  
pp. 117894
Author(s):  
Xunzhao Yu ◽  
Ling Zhu ◽  
Yan Wang ◽  
Dimitar Filev ◽  
Xin Yao

2021 ◽  
pp. 1-32
Author(s):  
Andrew Mansfield ◽  
Varun Chakrapani ◽  
Qingyu Li ◽  
Margaret Wooldridge

Abstract The use of genetic optimization algorithms (GOA) has been shown to significantly reduce the resource intensity of engine calibration, motivating investigation into the development of these methods. The objective of this work was to quantify the sensitivity of GOA performance to the algorithm search parameter values, in a case study of engine calibration. A GOA was used to calibrate four combustion system control parameters for a direct-injection gasoline engine at a single operating condition, with an optimization goal to minimize brake specific fuel consumption (BSFC) for a specified engine-out NOx concentration limit. The calibration process was repeated for two NOx limit values and a wide range of values for five GOA search parameters, including the number of genes, mutation rate, and convergence criteria. Results indicated GOA performance is very sensitive to algorithm search parameter values, with converged calibrations yielding BSFC values from 1 to 14% higher than the global minimum value, and the number of iterations required to converge ranging from 10 to 3,000. Broadly, GOA performance sensitivity was found to increase as the NOx limit was decreased from 4,500 to 1,000 ppm. GOA performance was the most sensitive to the number of genes and the gene mutation rate, whereas sensitivity to convergence criteria values was minimal. Identification of one set of algorithm search parameter values which universally maximized GOA performance was not possible as ideal values depended strongly on engine behavior, NOx limit, and the maximum level of error acceptable to the user.


Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7606
Author(s):  
Johannes Ritzmann ◽  
Oscar Chinellato ◽  
Richard Hutter ◽  
Christopher Onder

In this work, the potential for improving the trade-off between fuel consumption and tailpipe NOx emissions through variable engine calibration (VEC) is demonstrated for both conventional and hybrid electric vehicles (HEV). First, a preoptimization procedure for the engine operation is proposed to address the challenge posed by the large number of engine control inputs. By excluding infeasible and suboptimal operation offline, an engine model is developed that can be evaluated efficiently during online optimization. Next, dynamic programming is used to find the optimal trade-off between fuel consumption and tailpipe NOx emissions for various vehicle configurations and driving missions. Simulation results show that for a conventional vehicle equipped with VEC and gear optimization run on the worldwide harmonized light vehicles test cycle (WLTC), the fuel consumption can be reduced by 5.4% at equivalent NOx emissions. At equivalent fuel consumption, the NOx emissions can be reduced by 80%. For an HEV, the introduction of VEC, in addition to the optimization of the torque split and the gear selection, drastically extended the achievable trade-off between fuel consumption and tailpipe NOx emissions in simulations. Most notably, the region with very low NOx emissions could only be reached with VEC.


2021 ◽  
Author(s):  
Federico Millo ◽  
Andrea Piano ◽  
Alessandro Zanelli ◽  
Giulio Boccardo ◽  
Marcello Rimondi ◽  
...  

Author(s):  
Jialin Liu ◽  
Qingquan Zhang ◽  
Jiyuan Pei ◽  
Hao Tong ◽  
Xudong Feng ◽  
...  

AbstractEngine calibration aims at simultaneously adjusting a set of parameters to ensure the performance of an engine under various working conditions using an engine simulator. Due to the large number of engine parameters to be calibrated, the performance measurements to be considered, and the working conditions to be tested, the calibration process is very time-consuming and relies on the human knowledge. In this paper, we consider non-convex constrained search space and model a real aero-engine calibration problem as a many-objective optimisation problem. A fast many-objective evolutionary optimisation algorithm with shift-based density estimation, called fSDE, is designed to search for parameters with an acceptable performance accuracy and improve the calibration efficiency. Our approach is compared to several state-of-the-art many- and multi-objective optimisation algorithms on the well-known many-objective optimisation benchmark test suite and a real aero-engine calibration problem, and achieves superior performance. To further validate our approach, the studied aero-engine calibration is also modelled as a single-objective optimisation problem and optimised by some classic and state-of-the-art evolutionary algorithms, compared to which fSDE not only provides more diverse solutions but also finds solutions of high-quality faster.


Author(s):  
Anuj Pal ◽  
Yan Wang ◽  
Ling Zhu ◽  
Guoming George Zhu

Abstract A surrogate assisted optimization approach is an attractive way to reduce the total computational budget for obtaining optimal solutions. This makes it special for its application to practical optimization problems requiring a large number of expensive evaluations. Unfortunately, all practical applications are affected by measurement noises, and not much work has been done to address the issue of handling stochastic problems with multiple objectives and constraints. This work tries to bridge the gap by demonstrating three different frameworks for performing surrogate assisted optimization on multiobjective constrained problems with stochastic measurements. To make the algorithms applicable to real-world problems, heteroscedastic (non-uniform) noise is considered for all frameworks. The proposed algorithms are first validated on several multiobjective numerical problems (unconstrained and constrained) to verify their effectiveness, and then applied to the diesel engine calibration problem, which is expensive to perform and has measurement noises. A GT-SUITE model is used to perform the engine calibration study. Three control parameters, namely variable geometry turbocharger vane position, exhaust-gas-recirculating valve position, and the start of injection, are calibrated to obtain the trade-off between engine fuel efficiency performance (brake specific fuel consumption) and NOx emissions within the constrained design space. The results show that all three proposed extensions can handle the problems well with different measurement noise levels at a reduced evaluation budget. For the engine calibration problem, a good approximation of the optimal region is observed with more than 80\% reduction in evaluation budget for all the proposed methodologies.


2021 ◽  
Author(s):  
Alexandre Tadeu Mencacci Esteves ◽  
Alexandre Massayuki Kawamoto ◽  
André Pelisser ◽  
David Gazitto Carmelutti

2021 ◽  
Author(s):  
José Márcio Fachin ◽  
Gilberto Reynoso-Meza ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho

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