Static Model-Based Optimization and Multi-Input Optimal Control of Automatic Transmission Upshift during Inertia Phase

Abstract

Step-ratio automatic transmission upshift performance can be improved by modulating the off-going (OFG) clutch during the inertia phase. In this paper, a static powertrain performance model is derived and applied for the purpose of numerically efficient, multi-objective shift control parameter optimization for the inertia phase. The optimization is aimed at finding the optimal node parameters for simplified, piecewise linear, open-loop profiles of oncoming (ONC) clutch, OFG clutch, and engine torque reduction control variables. The performance indices, i.e., the optimization objectives, include shift comfort, clutch thermal loss, and shift time. The optimization results in 3D Pareto optimal frontiers, which are then analyzed and compared with those obtained by using the previously developed, nonlinear model-based, genetic algorithm optimization tool. The derived method is employed in order to develop a static model-based predictive control (S-MPC) strategy, which commands ONC clutch torque control input while retaining open-loop controls for engine and OFG clutch control inputs. The S-MPC strategy aims at providing the prespecified shift time, while the shift time accuracy is relaxed to some extent by using a control input dead zone element to avoid chattering effect. The S-MPC system performance is verified through simulation and compared with the genetic algorithm benchmark. The simulation results demonstrate that the S-MPC strategy approaches the benchmark performance.