Effect of automotive electrical system changes on fuel consumption using incremental efficiency methodology

There has been a continuous increase in automotive electric power usage. Future projections show no sign of it decreasing. Therefore, the automotive industry has a need to either improve the current 12 Volt automotive electrical system or move to a higher voltage vehicle electric system. Both of these choices are likely to increase cost of the system. Performance improvements will be needed to justify the increased cost to the Original Equipment Manufacturer. This thesis is investigating the potential for fuel economy improvements and their associated economic advantages for different vehicle electric systems. The objective is to determine the effects on fuel consumption of electrical system choices under a variety of drive and load cycle circumstances. Incremental, or marginal, efficiencies will be used to determine the relationship between loads and fuel consumptions. ADVISOR, a model developed by the National Renewable Energy Lab, has been adapted for use in this application. This included the implementation of industry standard engine performance map and alternator efficiency map data in the ADVISOR model.

Department dc:contributor.department

Massachusetts Institute of Technology. Technology and Policy Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Hardin, Christopher William

Advisor dc:contributor.advisor

• Richard Roth, Thomas A. Keim, Gary DesGroseilliers and Frank R. Field, III.

• Technology and Policy Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/34514

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34514