Analysis of Multistage Hybrid Powertrains using Multistage Mixed-Integer Trajectory Optimization 2020-01-0274
Stringent emissions regulations and fuel economy standards are driving the development of increasingly complex hybrid electric vehicle (HEV) powertrains. Early in the design process, it is desirable to rapidly evaluate powertrain architectures using simulation models before committing to costly physical prototyping. However, HEV powertrains have multiple controlled degrees of freedom, such as power split, engine on/off and gear selection, that need to be pre-determined as functions of powertrain operating conditions before a meaningful performance evaluation can be carried out. This has traditionally been accomplished using calibrated feedforward tables. However, obtaining “optimal” feedforward tables is becoming increasingly challenging and resource intensive for complex powertrains with multiple highly coupled degrees of freedom.
In this paper, we describe a multistage mixed-integer trajectory optimization methodology that allows design engineers to rapidly perform performance analyses of complex powertrains. The method can generate optimal input signals for both continuous (engine torque or motor power) and discrete (engine on/off or gear selection) degrees of freedom for a given scenario. Moreover, the method is computationally efficient; it exploits characteristics of powertrain control problems, builds on existing nonlinear programming solvers and is highly parallelizable. We illustrate the utility of this technique by applying it to a Toyota multistage hybrid powertrain of the type used in 2018 LC500h vehicles. We consider several driving scenarios and perform a jerk/acceleration time tradeoff study. Our results indicate that the proposed methodology is effective for performing these kinds of analyses.
Huayi Li, Dominic Liao-McPherson, Ilya Kolmanovsky, Shinhoon Kim, Ken Butts
University of Michigan - Ann Arbor, Toyota Motor North America R&D