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This paper proposes a method for multi-objective parameter optimization of piecewise linear time profiles for control of Automatic Transmission (AT) shifts and presents results obtained on an example of a powertrain with a 10-speed automatic transmission. The paper first outlines the powertrain dynamics model. Then, the AT control trajectory optimization approach is outlined and employed with the aim of getting insights into the optimal shift control profiles and related performance. The parameter optimization problem is to find parameters of piecewise linear shift control profiles, which provide a trade-off between the shift comfort and performance. The optimization problem is solved by using the multi-objective genetic algorithm MOGA-II incorporated within modeFRONTIER environment.

New generation of torque converter automatic transmissions (AT) include a large number of gears for improved fuel economy. Control requirements for such transmissions become more demanding, which calls for the development of new shift optimization and analysis tools. This paper presents two contributions to the field of transmission dynamics analysis: (i) bond graph method-based shift transient analysis, and (ii) deriving a unique set of conditions for beneficial use of a third (normally-open) clutch for any upshift or downshift, with emphasis on inertia phase. The derived conditions are examined on an example of 10-speed AT based on the clutch torque input trajectory optimization results. The examination results point out that the extra clutch has a potential of significant performance improvement for any single-transition upshift in the inertia phase, in terms of reduced vehicle jerk RMS value due to the suppressed inertia bump effect.

A dynamic programming-based algorithm is developed and used for off-line optimization of range extended electric vehicle power train control variables over standardized certification driving cycles. The aim is to minimize the fuel consumption subject to battery state-of-charge constraints and physical limits of different power train variables. The control variables to be optimized include engine torque and electric machine speed, as well as a variable that selects the power train operating mode. The optimization results are presented for four characteristic certification driving cycles and characteristic vehicle operating regimes including electric driving during charge depleting mode, hybrid driving during charge sustaining mode, and combined/blended regime.