A Framework for Optimization of the Traction Motor Design Based on the Series-HEV System Level Goals

Ivančo, Andrej; Zhou, Kan; Hofmann, Heath; Filipi, Zoran

doi:10.4271/2014-01-1801

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…For every design iteration step the comp map is altered and the powertrain desig maximize the fuel economy. In a previously [5], we have used FEA analysis to capture di the in-wheel electric motor with the aim to m efficiency zone of the e-machine with most points determined by the power demand from This paper is focusing on the powerpack desi adding a degree of freedom as the powerpack independently from the drive cycle power dem alleviate the computational burden and to optimization, instead of generating the compo an FEA approach [5,6], the engine and elec scaled based on the well-known Willans line m approach the engine or electric machine effi linearly fitted and their losses approximated a maximum power. In this way, the output pow together with losses of the energy conversion relative efficiency stays unchanged.…”

Section: Component Scalingmentioning

confidence: 98%

Framework for Powerpack Optimization in the SHEV: Two Stage Optimization for Best Efficiency in the Hybrid Electric Vehicle Powertrain Design

Ivančo¹,

Filipi²

2015

2015 IEEE Vehicle Power and Propulsion Conference (VPPC)

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In order to meet the future fuel economy and Greenhouse gas emission standards, such as the EPA first-ever 2014-2017 Commercial Vehicle regulation, advanced vehicle powertrain concepts have to be introduced to the market. Configurations like hybrid or plug-in hybrid electric vehicle are becoming more accessible and popular due to their perception of efficiency and lower direct environmental impact. However the penetration rates of these advanced powertrain configurations remains still low, due to their higher cost compared to the wellestablished internal combustion engine. Therefore the relation between cost and benefit needs to be addressed early on in the powertrain design stage to find the right balance for the customer. This paper proposes a powertrain design optimization framework, demonstrated on the series hybrid electric vehicle configuration, where the customer benefit is represented by the decrease of the fuel consumption for a given duty cycle.The scope of this paper is to demonstrate this design methodology on the power pack, which acts as the principal energy source in the series hybrid electric vehicle (SHEV) configuration. A two stage optimization framework is developed to maximize the fuel economy within the power demand constraints coming from the drive cycle. The Genetic Algorithm is used in the first stage to generate a particular engine-generator configuration, using Willans line description for component sizing. The candidate configuration is then evaluated in the second stage by using Dynamic Programming to optimize the supervisory control and generate a fuel-economy benchmark over a given drive cycle. By using this two stage design optimization framework, the maximum fuel economy is found for the given drive cycle within the power demand constraints. Generator power [kW] 35 36 37 38 39 40 L/100km

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Section: Component Scalingmentioning

confidence: 98%