The design, calibration and integration of powertrain control algorithms has become significantly more complex in recent years, as the automotive industry faces increasing challenges in meeting consumer requirements and government regulations. Traditionally, the powertrain control engineering design process develops the engine and transmission controllers independently and then integrates them after an initial calibration. This process can lead to suboptimal performance and requires additional calibration and verification steps to improve the coordination of the various subsystems.
This paper proposes a novel approach to achieve a systematic, high-level coordination, and optimization of the control strategy in an automotive powertrain system that will reduce overall calibration effort. Optimized set-points for engine and transmission controls are generated based on joint optimization of fuel consumption and drivability using Model Predictive Control to manage both continuous and discrete control variables. Simulation results confirm the control decisions made by the proposed coordinator match a well-calibrated production ECU with little tuning effort.