Engine Modelling with Smart Online DoE

2024-26-0338

01/16/2024

Features
Event
Symposium on International Automotive Technology
Authors Abstract
Content
The implementation of TREM/CEV 5 emission norms on farm equipment will bring in cost pressure due to the need for exhaust after treatment systems. This cost increase needs to be reduced by bringing in more efficient and effective processes to shorten the development phase and to provide better fuel efficiencies.
In this work ETAS ASCMO Online DoE with Constraint Modelling (ODCM) was applied to execute smart online DoE on a new common rail diesel engine with EGR, whose exact bounds of operation was not available. A Global test plan with ASCMO Static was created without much focus on detailed constraints of engine operation, other than the full load curve. The parameters which were selected were Speed, Torque, Rail Pressure, Main Timing, EGR Valve Position, Pilot Separation and Quantity and Post Quantity and Separation. For these parameters, the safe operating bounds were not available.
This ASCMO Static test plan is automated and executed on engine test cell with ETAS INCAFlow. ODCM at each step delivers a next operating point to be measured on testbench and if this operating point is within the defined limits of engine operations, the point is measured or if it outside the limits, the point is skipped. At every step, ODCM uses the information generated to modify the order of remaining DoE points in which only feasible measurement are suggested and those which are not feasible are skipped.
The results of this work showed a 35% reduction in the total efforts. Since the model was Global, other variants were also covered with the same ASCMO Static Models without any requirements for additional test runs/measurements. Stringent cycle SFC mandates, which was not possible with earlier methods, was also realized with ODCM approach as ODCM extends the coverage range of measurements. The results also reduced the need for new engine development to meet the stringent requirements.
Meta TagsDetails
DOI
https://doi.org/10.4271/2024-26-0338
Pages
6
Citation
Paulraj, L., Varsha, A., Karadi, S., and Kumar, D., "Engine Modelling with Smart Online DoE," SAE Technical Paper 2024-26-0338, 2024, https://doi.org/10.4271/2024-26-0338.
Additional Details
Publisher
Published
Jan 16
Product Code
2024-26-0338
Content Type
Technical Paper
Language
English