Verification of the optimum hybridization factor as design parameter of hybrid electric vehicles

Abstract

Automotive companies are developing hybrid electric vehicles in different ways. There are several system topologies and component configurations used for prototype and mass production. To save time and money during the design process empirical values are necessary. In this paper, the hybridization factor is advanced to a design parameter using well-known characteristics of existing vehicles. In addition, a full vehicle simulation system is used to verify the practical results. A scalable model that is able to vary the hybridization factor is defined and applied. The requirements of machine design and control strategy are also presented. Closing, the similarities and differences of practice and simulation results are discussed.

Multi-objective optimization of hybrid electric vehicles considering fuel consumption and dynamic performance

“…This contains wheel-slip calculations and includes an anti wheel-slip device for getting realistic results. The electrical motor model is scalable by changing the nominal torque and is described in [11]. So the power of the motor can be varied during the optimization.…”

“…A characteristic of the CS is that no efficiency tables of the components have to be used. This leads to a generalized structure that ensures an operation which is independant of the hybridization factor [11]. However, it contains parameters that were introduced as decision variables for the optimization process and allow the adaption of the strategy to a specific type of engine for example.…”

“…Therefore, switching points can be characterized by the proportion of the engine torque needed for propulsion T ICE and the engine speed n ICE . As a result, a state-of-charge-based characteristic curve of decision can be used that is explained in [11]. The CS had been manually tuned for good fuel economy for one combination of drive train components.…”

“…Therefore it is desirable that besides component sizing also the control strategy can be optimized. For that, three decision variables were added to the strategy of [11] for allowing a tuning of the CS by the optimization procecss. So, the decision for recharge is: CS.a changes the SOC-dependent adjustion of the curve for prohibiting a depletion or an overload of the batteries and so the SOC stays between defined limits SOC low and SOC high .…”

An Efficient Methodology Proposed For Deciding About the Number of Battery Modules In Hybrid Electric Vehicles

“…In parallel hybrid electric vehicles, Degree of Hybridization (DOH) is usually defined as equation (1) [2,5,[7][8][9][14][15][16][17][18][19]. …”