Systematic Process for Physics Based Modeling and Model Predictive Control of Engine and Aftertreatment Systems

To make engine and aftertreatment hardware choices for new emissions programs, it is often useful to prototype several candidate engine, aftertreatment and control systems, and run certification cycles. Building such a prototype requires a hardware selection control system that gracefully adapts to new hardware and rapidly achieves a level of performance similar to that of a production calibration. This paper assesses an implementation of Model Predictive Controls (MPC) against requirements for a hardware selection control system. We use metrics captured from 15 projects at 12 automotive OEMs over a period of several years. Metrics include hardware variety, test bed usage, and certification cycle performance relative to production calibrations for projects where production calibrations were available. The data show that a controls design process using MPC and encapsulated in Honeywell OnRAMP software achieves near-production calibrations in a mean time of 6 days of test bed time. We conclude with remarks on the applicability of such a modeling and control framework in an industrial context and an outlook to the future.