Multi-Objective Optimization of Transient Air-Fuel Ratio Limitation of a Diesel Engine Using DoE Based Pareto-Optimal Approach

Paper #:
  • 2017-01-0587

Published:
  • 2017-03-28
Citation:
Gurel, C., Ozmen, E., Yilmaz, M., Aydin, D. et al., "Multi-Objective Optimization of Transient Air-Fuel Ratio Limitation of a Diesel Engine Using DoE Based Pareto-Optimal Approach," SAE Int. J. Commer. Veh. 10(1):299-307, 2017, https://doi.org/10.4271/2017-01-0587.
Pages:
9
Abstract:
Emissions and fuel economy optimization of internal combustion engines is becoming more challenging as the stringency of worldwide emission regulations are constantly increasing. Aggressive transient characteristics of new emission test cycles result in transient operation where the majority of soot is produced for turbocharged diesel engines. Therefore soot optimization has become a central component of the engine calibration development process. Steady state approach for air-fuel ratio limitation calibration development is insufficient to capture the dynamic behavior of soot formation and torque build-up during transient engine operation. This paper presents a novel methodology which uses transient maneuvers to optimize the air-fuel ratio limitation calibration, focusing on the trade-off between vehicle performance and engine-out soot emissions. The proposed methodology features a procedure for determining candidate limitation curves with smoothness criteria considerations. Following the design of test plans, DoE testing is performed on the engine test bed using transient maneuvers which are representative of typical customer behavior. Transient data obtained from DoE tests are then projected into cumulative performance metrics. After modeling, multi-objective optimization is applied for the reconstruction of air-fuel ratio limitation maps considering Pareto optimality between soot emissions and engine performance. The methodology is applied to a diesel engine with Euro VI emission norms and experimental results are presented including the evaluation of performance and drivability metrics on a test vehicle. Potential areas of future work to improve data collection, modeling and optimization processes are also discussed.
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