An empirical model based transient calibration process for diesel engines has been developed to optimize diesel emissions, torque response and fuel consumption over transient cycles. Modeling and optimization methods suitable for transient emissions were found to be significantly different from corresponding steady state methods. These differences and related challenges related to data acquisition, data processing, choice of empirical techniques, the size of the optimization problem and the challenge of preventing quasi-static optimums have been described along with proposed solutions and corresponding results. A transient data collection methodology spanning the calibration spectrum is proposed for robust model building. Models for accounting for transport delay and sensor lag have been proposed. Simple less powerful empirical methods like global regression have been recommended for modeling transient emissions rather than more powerful methods such as neural networks, when working with model inputs that are strongly correlated but lacking causative information. Second order dynamic constraint models that constrain model inputs to achievable transient states at all instants of time have been developed to prevent the optimization algorithm from returning solutions that are realistic only if every transient point is achievable at steady state. Surface parameters rather than table entries have been optimized to reduce the size of the optimization problem. Each of the individual processes from data acquisition to search methodology has been justified and compared with the corresponding steady-state calibration process.
The challenges of an empirical model based transient calibration process
2011
13 Seiten, 11 Bilder, 1 Tabelle, 11 Quellen
Aufsatz (Konferenz)
Englisch
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