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

Jie Wu, Kun Li, Yifei Jiang, Qin Lv, Li Shang and Yihe Sun
Additional contact information
Jie Wu: Institute of Microelectronics, Tsinghua University, Beijing 10084, China
Kun Li: Department of Electrical, Computer and Energy Engineering, University of Colorado at Boulder,
Yifei Jiang: Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA
Qin Lv: Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA
Li Shang: Department of Electrical, Computer and Energy Engineering, University of Colorado at Boulder,
Yihe Sun: Institute of Microelectronics, Tsinghua University, Beijing 10084, China

Energies, 2011, vol. 4, issue 5, 1-22

Abstract: 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.

Keywords: battery system; user-specific driving pattern; hybrid vehicle; aging effect (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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