Evolutionary Search for Smooth Maps in Motor Control Unit Calibration

Poland, Jan; Knödler, Kosmas; Mitterer, Alexander; Fleischhauer, Thomas; Zuber-Goos, Frank; Zell, Andreas

doi:10.1007/3-540-45322-9_7

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…So, the maps were smoothened by eliminating the surface peaks without considering the engine operation. In some other references, such as, Nishio et al, 16 Niedernolte et al, 17 Poland et al, 18 and Nikzadfar and Shamekhi 19 the smoothness condition was satisfied by limiting the allowable operating range of each parameter relative to their neighborhood. In this paper, a method is proposed to determine the direction and value of smoothness so that the control parameters are chosen at the minimum distance of the main optimal value.…”

Section: Objective Function Of Global Optimizationmentioning

confidence: 99%

Automatic smooth map generation of internal combustion engines via local-global model based calibration technique

Soltanalizadeh,

Esfahanian,

Haeri Yazdi

et al. 2024

International Journal of Engine Research

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The addition of new sensors and actuators to the engine, to reduce fuel consumption and emissions besides improving the engine operation, complicates the control commands stored in the engine control unit (ECU). Substitution of mechanical actuators with electronic ones increases the engine’s degrees of freedom and the number of control parameters, which results in the increased engine calibration time and cost. The aim of this paper is to take advantage of optimization techniques to achieve optimal values of control parameters in a fast and automated way. In this regard, it requires replacing the real engine with the virtual model and implementing the model-based calibration by coupling the virtual engine model with optimization algorithms. In this study, deep neural network (DNN) modeling and genetic algorithm (GA, NSGA-II) optimization are used for model-based calibration. The effect of all input control parameters, including ignition angle, continuously variable valve timing, etc., on all output control parameters including, brake-specific fuel consumption, emissions level, knock limit, combustion stability, etc., are investigated simultaneously by a valid global model, which is a remarkable achievement in the model-based calibration. Dynamic lag of some actuators delays the execution of control commands sent from ECU. To avoid abrupt variations in the actuators values, smoothness of the engine maps is considered in the calibration process. To reduce fuel consumption, decrease emission levels and attain smooth maps, the calibration of control parameters is performed by local-multi-objective optimization and global-single-objective optimization. Local-global model-based calibration presented in this study reduces 3.7% of the brake-specific fuel consumption and 7%–10% of emissions level at breakpoints of the engine map compared to manual calibration. In addition, the calibration time and costs while producing better engine performance can be reduced by automating the calibration process. Finally, calibrated maps are stored as a lookup table (LUT) in ECU. Generating an optimal lookup table involves the pre-calculation of several points that cover the calculation domain and allow the interpolation for other points. Selecting the optimal points for exact calculation is of great importance in the size and accuracy of LUT. In this study, an optimization tool is also presented to generate accurate and efficient LUT.

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Section: Objective Function Of Global Optimizationmentioning

confidence: 99%

Automatic smooth map generation of internal combustion engines via local-global model based calibration technique

Soltanalizadeh,

Esfahanian,

Haeri Yazdi

et al. 2024

International Journal of Engine Research

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“…With this approach, an improved measuring arrangement, reduced relaxation times, and overall measuring time were achieved. Poland et al [6] applied GAs to the combinatorial optimization task of choosing the smoothest map from a given family of calibration maps, which was motivated from motor unit calibration. They studied the performance of different codings and found that a direct encoding was more suitable for a GA in the case of combinatorial optimization of engine calibration.…”

Section: Introductionmentioning

confidence: 99%

Investigation of a multi-objective optimization tool for engine calibration

Lin

Duan

2008

Proceedings of the Institution of Mechanical Engineers, Part D:

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Since multi-objective optimizations are becoming more important in engine calibration, the paper investigates multi-objective genetic algorithms in the application of engine optimization. Although several multi-objective genetic algorithms have been developed and some have been applied successfully in the automotive industry, it is difficult to determine which multi-objective genetic algorithm outperforms others in finding the set of optimal solutions or Pareto-optimal front in a practical multi-objective optimization problem. Based on some widely used multi-objective genetic algorithms, the paper proposes a combined scheme to deal with the difficulties in finding the optimal solution set during the engine calibration process. In the proposed approach the real-coded representation is employed in the genetic algorithm and the elitist strategy is applied for each multi-objective genetic algorithm used. To assess the proposed approach, two computational examples are given to minimize the brake specific fuel consumption and to maximize the output power torque simultaneously. The results show that the proposed approach is well suited to multi-objective optimization in engine calibration.

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