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A transmission system model is developed at various complexities in order to capture the transient behaviors in drivability and fuel economy simulations. A large number of model parameters bring more degree of freedom to correlate with vehicular test data. However, in practice, it requires extensive time and effort to tune the parameters to satisfy the model performance requirements. Among the transmission model, a hydraulic clutch actuator plays a critical role in transient shift simulations. It is particularly difficult to tune the actuator model when it is over-parameterized. Therefore, it is of great importance to develop a hydraulic actuator model that is easy to adjust while retaining sufficient complexity for replicating realistic transient behaviors. This paper describes a systematic approach for reducing the hydraulic actuator model into a piecewise 1st order representation based on piston movement.

Recent developments of hybrid vehicle technology have promoted another wave of vehicle electrification and introduction of pure electric vehicles (PEVs), such as Nissan Leaf and Ford Transit Connect Electric. The energy efficiency of these PEVs with an electric drive can be potentially further improved by introducing a two-speed or multi-speed gearbox to ensure the electric machine to operate at peak performance. In this work, a powertrain model of the Transit Connect Electric is built to examine the powertrain efficiency improvement potentials using a two-speed gearbox. The HEV and EV powertrain modeling tool, AUTONOMIE from US Argonne National Lab, is used for the powertrain modeling, and partially verified using vehicle testing data from US Environment Protection Agency (EPA). An optimization method, whose kernel is Dynamic Programming (DP), is combined with the model to find the possible minimum energy consumption and corresponding gear ratios.

Transmission system models require a high level of fidelity and details in order to capture the transient behaviors in drivability and fuel economy simulations. Due to model fidelity, manufacturing tolerances, frictional losses and other noise sources, parametrization and tuning of a large number of parameters in the plant model is very challenging and time consuming. In this paper, we used particle swarm optimization as the key algorithm to fast correlate the open-loop performance of an automatic transmission system plant model to vehicle launch and coast down test data using vehicle control inputs. During normal operations, the model correlated well with test data. For error states, due to the lack of model fidelity, the model cannot reproduce the same error state quantitatively, but provided a valuable methodology for qualitatively identifying error states at the early stages.