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.