Next Article in Journal
Development of electromobility in France: Causes, facts and figures
Previous Article in Journal
Benchmarking Charging Infrastructure Utilization
World Electric Vehicle Journal is published by MDPI from Volume 9 issue 1 (2018). Articles in this Issue were published by The World Electric Vehicle Association (WEVA) and its member the European Association for e-Mobility (AVERE), the Electric Drive Transportation Association (EDTA), and the Electric Vehicle Association of Asia Pacific (EVAAP). They are hosted by MDPI on mdpi.com as a courtesy and upon agreement with AVERE.
Open AccessArticle

Development of Fuel Economy Improvement by Using Driving Condition Prediction System for Hybrid Vehicle

Hyunda-Kia Motors, 772-1 Jangduk-Dong Hwaseong-Si Gyeonggi-Do South Korea
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2012, 5(1), 118-124; https://doi.org/10.3390/wevj5010118
Published: 30 March 2012
PDF [657 KB, uploaded 17 May 2018]

Abstract

If the future driving condition such as road information and traffic condition can be predicted, the use of electrical power source will be controlled appropriately in order to improve the fuel economy of Hybrid vehicle. In this paper the algorithm for the driving condition prediction model and the rule-based controller for HEV are developed and verified through simulation and road test. With road information and traffic from 3D navigation, the types of road (uphill, flat or downhill) and the traffic condition (congestion or free driving) can be predicted by the Driving Condition Prediction System (DCPS). The rule-based controller for HEV can determine the control strategy (discharge-oriented, charge-oriented, or normal) depending on the future driving condition. With this technology the system can secure more battery capacity for regenerating when downhill is anticipated and engine can be operated within high efficiency area by discharging battery energy when uphill is anticipated. When congestion is predicted the battery is charged in advance in order to increase electric driving (EV) range and prevent inefficient series-path driving. Compared to the previous study, the methodology to determine future road condition and control strategy of HEV suggested in this paper is simple and fast enough to apply to real-time controller.
Keywords: HEV (hybrid electric vehicle); navigation; power management; city traffic; control system HEV (hybrid electric vehicle); navigation; power management; city traffic; control system
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Kim, I.; Park, T.; Suh, B.; Kim, S. Development of Fuel Economy Improvement by Using Driving Condition Prediction System for Hybrid Vehicle. World Electr. Veh. J. 2012, 5, 118-124.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
World Electr. Veh. J. EISSN 2032-6653 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top