Efficient Powertrain Design -- A Mixed-Integer Geometric Programming Approach

Abstract

The powertrain of battery electric vehicles can be optimized to maximize the travel distance for a given amount of stored energy in the traction battery. To achieve this, a combined control and design problem has to be solved which results in a non-convex Mixed-Integer Nonlinear Program. To solve this design task more efficiently, we present a new systematic optimization approach that leads to a convex Mixed-Integer Nonlinear Program. The solution process is based on a combination of Geometric Programming and a Benders decomposition. The benefits of this approach are a fast solution time, a global convergence, and the ability to derive local sensitivities in the optimal design point with no extra cost, as they are computed in the optimization procedure by solving a dual problem. The presented approach is suitable for the evaluation of a complete driving cycle, as this is commonly done in powertrain system design, or for usage in a stochastic approach, where multiple scenarios are sampled from a given probability density function. The latter is useful, to account for the uncertainty in the driving behavior to generate solutions that are optimal in an average sense for a high variety of vehicles and drive conditions. For the powertrain model we use a transmission model with up to two selectable transmission ratios and an electric motor model that is based on a scaled efficiency map representation. Furthermore, the shown model can also be used to model a continuously variable transmission to show beneficial energy savings based on this additional degree of freedom. The presented design approach is also applicable to a wide variety of design tasks for technical systems besides the shown use case.