Multitarget Evaluation of Hybrid Electric Vehicle Powertrain Architectures Considering Fuel Economy and Battery Lifetime

2020-37-0015

06/30/2020

Features
Event
CO2 Reduction for Transportation Systems Conference
Authors Abstract
Content
Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options. Subsequently, three different HEV powertrain architectures are considered as test cases for the developed HEV assessment methodology including parallel P2, series-parallel P1P2 and power-split layouts. A comparison of numerical results for the three HEV powertrain test cases is then performed in terms of optimal fuel economy capabilities while ensuring a specific battery lifetime over several defined driving missions. Engineers could thus adopt the developed methodology to enhance the evaluation of HEV design options by considering fuel economy and battery lifetime at the same time.
Meta TagsDetails
DOI
https://doi.org/10.4271/2020-37-0015
Pages
17
Citation
Anselma, P., Kollmeyer, P., Belingardi, G., and Emadi, A., "Multitarget Evaluation of Hybrid Electric Vehicle Powertrain Architectures Considering Fuel Economy and Battery Lifetime," SAE Technical Paper 2020-37-0015, 2020, https://doi.org/10.4271/2020-37-0015.
Additional Details
Publisher
Published
Jun 30, 2020
Product Code
2020-37-0015
Content Type
Technical Paper
Language
English