Comparison of Cycle Reduction and Model Reduction Strategies for the Design Optimization of Hybrid Powertrains on Driving Cycles

Abstract

Decision-making is a crucial and difficult step in the design process of complex systems such as the hybrid powertrain. Finding an optimal solution requires the system feedback. This can be, depending on the granularity of the models at the component level, highly time-consuming. This is even more true when the system’s performance is determined by its control. In fact, various possibilities can be selected to deliver the required torque to the wheels during a driving cycle. In this work, two different design strategies are proposed to minimize the fuel consumption and the cost of the hybrid powertrain. Both strategies adopt the iterative framework which allows for the separation of the powertrain design problem and its control while leading to system optimality. The first approach is based on model reduction, while the second approach relies on improved cycle reduction techniques. They are then applied to a parallel hybrid vehicle case study, leading to important cost reduction in reasonable delays and are compared using different metrics.

Calculation of Losses in a Motor Fed by a Conventional Inverter and a Battery Distributed Inverter

“…A conventional approach to electrical machine design is to calculate the efficiency of the machine for a given operating point [14]. This results in lower efficiency for variable speed operation and, therefore, the oversizing of the components, which leads to higher costs.…”

“…Another approach, already developed and used in [4,[14][15][16], is to design an electric machine design taking into account the nominal (Tn, Ωn) and maximum (Td, Ωd) operating points of the machine. The operating points of the road cycle are then divided into several operational areas and represented by centers of mass, weighted by the number of points within the area.…”

Kriging Metamodel for Electric Machines: A Drive Cycle Approach

“…The difficulty encountered is finding a way to integrate these points in the optimization process of the machine without the prohibitive computation time. There are several driving cycle reduction approaches to reduce the number of operating points [11], [12]. The k-means data partitioning method is one of the most accurate methods to perform such a task [11].…”

“…There are several driving cycle reduction approaches to reduce the number of operating points [11], [12]. The k-means data partitioning method is one of the most accurate methods to perform such a task [11]. It is used to group desired data according to a defined similarity criterion [13].…”