Using multiobjective optimization for automotive component sizing

Abstract

This paper shows how a multiobjective problem is formulated and solved in order to size the components of a vehicle with a split hybrid transmission, such as a Toyota Prius. The goal is to explore feasible design options and the trade-offs between fuel economy and vehicle cost. Eight input variables are provided for this optimization, including plant variables such as maximum power ratings for engine, motors, and battery; final drive ratio; and control variables that determine how the battery energy is utilized. Three constraints are used: achievement of the battery charge balance, ability to trace the drive cycle, and ability to achieve a zero to 60 mph acceleration performance within 10 seconds. We describe a multiobjective optimization algorithm that we have implemented in Autonomie, a simulation tool developed at Argonne, and we demonstrate its ability to utilize parallel computing capabilities of Matlab. A parallel/distributed-computing infrastructure is used to simultaneously evaluate multiple combinations of input parameters, over multiple drive cycles, thereby reducing the overall time taken to perform the optimization and hence reduce the total solution time. The optimization produces several design choices, which form a Pareto front. The search algorithm ensures that as the number of iterations increases, more and more points are added on or near the Pareto front. All the points that form the front are relevant design choices, and the front characterizes the balance between conflicting goals such as fuel economy and performance.