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
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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.
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(5):758-779.
Wu, Jie; Li, Kun; Jiang, Yifei; Lv, Qin; Shang, Li; Sun, Yihe. 2011. "Large-Scale Battery System Development and User-Specific
Driving Behavior Analysis for Emerging Electric-Drive Vehicles." Energies 4, no. 5: 758-779.