CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Comparative system dynamic modeling of a conventional and hybrid electric powertrain
Authors
Crowther A.
Crowther A. R.
+6 more
De La Cruz M.
Fredriksson J.
Hartani K.
Heijden A. V. D.
Kuo K.-L.
Proceedings
Publication date
1 January 2016
Publisher
'Informa UK Limited'
Doi
Cite
Abstract
© 2017 Taylor & Francis Group, London. Hybrid Electric Vehicles (HEVs) provide many known benefits over conventional vehicles, including reduced emissions, increased fuel economy, and performance. The high cost of HEVs has somewhat limited their widespread adoption, especially in developing countries. Conversely, it is these countries that would benefit most from the environmental benefits of HEV technology. As part of our ongoing project to develop a cost-effective and viable mild HEV for these markets, dynamic simulations are required to ensure that the proposed designs are to achieve their desired targets. In this paper, mathematical models of the powertrain are used to analyze and compare the dynamics of both a conventional power train and one with the addition of components required for the Mild Hybrid system. Using Matlab and Simulink, simulations of both powertrains under particular driving conditions are performed to observe the advantages of the MHEV over conventional drivetrains. These benefits include torque-hole filling between gear changes, increased fuel efficiency and performance
Similar works
Full text
Available Versions
OPUS - University of Technology Sydney
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:opus.lib.uts.edu.au:10453/...
Last time updated on 18/10/2019
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1201%2F97813153868...
Last time updated on 06/08/2021