Name
Abstract | ||
|---|---|---|
With the demand and supply for electric vehicles (EVs) in rapid growth at a global scale, due to factors such as environmental awareness, tax and monetary incentives, government regulations, and technological advances, a range of technical issues in efficient EV design has been gaining great attention. With consumer concerns around EV range and battery degradation, several initiatives have been investigating technologies which could further address the above issues. Recognizing the need for increasing the driving range and decreasing the impact of scenarios that negatively affect it as prime design issues, one of the resulting areas of interest is to explore options on better managing energy for heating, ventilation and the air conditioning (HVAC) system, which can decrease the range of EVs by as much as 30% when in use. With key driver for identifying the most promising ideas and technologies worth extensive investigations, the objective is to develop a methodology for managing the selection of technologies during conceptual design, through assessing HVAC power demand effects and employing advanced energy management techniques. A methodology for identifying emerging technology concepts has been outlined and demonstrated in the context of a virtual electric vehicle system. As a result, MOTEVETOR, a portable, interactive multi-criteria decision making tool has been created which allows for performing trades between vehicle configurations carrying different technology packages, as part of supporting EV conceptual design. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | Electric Vehicles (EVs), EV batteries, thermal management, HVAC energy optimization, EV range, vehicle performance modeling, EV systems integration, decision support for EV powertrain sizing |
Field | DocType | ISSN |
Incentive,Computer science,Risk analysis (engineering),Driving range,Artificial intelligence,Energy management,Conceptual design,Simulation,Electric vehicle,HVAC,Emerging technologies,Supply and demand,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Bucsan | 1 | 0 | 0.68 |
| Michael Balchanos | 2 | 6 | 3.17 |
| Dimitri N. Mavris | 3 | 50 | 14.47 |
| Jae-Seung Lee | 4 | 58 | 16.02 |
| Masanori Ishigaki | 5 | 0 | 1.35 |
| Atsushi Iwai | 6 | 0 | 0.34 |