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Energies 2011, 4(5), 758-779; doi:10.3390/en4050758

Large-Scale Battery System Development and User-Specific Driving Behavior Analysis for Emerging Electric-Drive Vehicles

Institute of Microelectronics, Tsinghua University, Beijing 10084, China
Department of Electrical, Computer and Energy Engineering, University of Colorado at Boulder,
Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA
Author to whom correspondence should be addressed.
Received: 15 February 2011 / Revised: 26 March 2011 / Accepted: 13 April 2011 / Published: 29 April 2011
(This article belongs to the Special Issue Hybrid Vehicles)
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Emerging green-energy transportation, such as hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs), has a great potential for reduction of fuel consumption and greenhouse emissions. The lithium-ion battery system used in these vehicles, however, is bulky, expensive and unreliable, and has been the primary roadblock for transportation electrification. Meanwhile, few studies have considered user-specific driving behavior and its significant impact on (P)HEV fuel efficiency, battery system lifetime, and the environment. This paper presents a detailed investigation of battery system modeling and real-world user-specific driving behavior analysis for emerging electric-drive vehicles. The proposed model is fast to compute and accurate for analyzing battery system run-time and long-term cycle life with a focus on temperature dependent battery system capacity fading and variation. The proposed solution is validated against physical measurement using real-world user driving studies, and has been adopted to facilitate battery system design and optimization. Using the collected real-world hybrid vehicle and run-time driving data, we have also conducted detailed analytical studies of users’ specific driving patterns and their impacts on hybrid vehicle electric energy and fuel efficiency. This work provides a solid foundation for future energy control with emerging electric-drive applications. View Full-Text
Keywords: battery system; user-specific driving pattern; hybrid vehicle; aging effect battery system; user-specific driving pattern; hybrid vehicle; aging effect

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Wu, J.; Li, K.; Jiang, Y.; Lv, Q.; Shang, L.; Sun, Y. Large-Scale Battery System Development and User-Specific Driving Behavior Analysis for Emerging Electric-Drive Vehicles. Energies 2011, 4, 758-779.

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