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Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 11, 2005 by SAE International in United States
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With the adoption of complex technologies such as multiple injections, EGR and variable geometry turbocharging, it has become increasingly onerous to develop optimal engine control calibrations for either light- or heavy-duty diesel engines. The addition of NOx and PM aftertreatment systems increases further the calibration burden, as both diesel particulate filters and NOx absorbers require regeneration initiated by the engine management system. There is significant interest in the industry in reducing development costs by moving as much of the engine calibration process as is feasible from the engine test cell to the virtual desktop environment.
This paper describes the development of a model-based calibration optimization system that offers significant advantages in reducing the time and effort required to obtain certification-quality engine calibrations. Unlike design of experiments (DoE)-based systems, which are typically used to reduce the size of the experimental matrix required to optimally map an engine under steady-state operating conditions, this system utilizes high-fidelity dynamic or transient engine modeling. The data required to develop the engine model is obtained by operating the engine through a set of transient dynamometer tests while the engine calibration is perturbed in real-time by a reconfigurable rapid prototyping control system. The fully predictive engine model produced in this fashion utilizes a combination of equation-based and neural network data-driven methods.
The dynamic engine model is then used in an off-line calibration environment to produce optimal engine calibrations that meet emissions standards on transient test and regulatory cycles, while minimizing fuel consumption and meeting engine operating constraints. Finally, the optimized engine calibration map set is verified in the test cell.
The application of this methodology to a 2004-specification multi-parameter diesel engine is presented, along with emissions, performance and fuel consumption results.
CitationAtkinson, C. and Mott, G., "Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines," SAE Technical Paper 2005-01-0026, 2005, https://doi.org/10.4271/2005-01-0026.
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- Managing the Challenges of Automotive Embedded Software Development Using Model-Based Methods for Design and Specification Yeaton M. SAE 2004-01-0720
- Simulation-Based Engine Calibration: Tools, Techniques and Applications Rask E. Sellnau M. SAE 2004-01-1264
- Neural Network Modeling of the Emissions and Performance of a Heavy-Duty Diesel Engine Thompson G. Atkinson C.M. Clark N.N. Long T.W. Hanzevack E.L. Proc. Inst. Mech. Engrs., Part D, Journal of Automobile Engineering 214 111 126 2000
- Approximation and Control of the Engine Torque using Neural Networks Mueller R. Schneider B. SAE 2000-01-0929
- Application of Neural Networks for Prediction and Optimization of Exhaust Emissions in a H.D. Diesel Engine Desantes J.M. Lopez J.J. Garcia J.M. Hernandez L. SAE 2002-01-1144
- Steps Toward an Optimization of the Dynamic Emission Behavior of IC Engines: Measurement Strategies, Modeling and Model-Based Optimization Hafner M. SAE 2001-01-1793
- Neural Network-Based Diesel Engine Emissions Prediction using In-Cylinder Combustion Pressure Traver M.L. Atkinson R.J. Atkinson C.M. SAE 1999-01-1532