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In a laboratory environment, it is cost prohibitive to run automotive battery aging experiments across a wide range of possible ambient environment, drive cycle, and charging scenarios. Because worst-case scenarios drive the conservative sizing of electric-drive vehicle batteries, it is useful to understand how and why those scenarios arise and what design or control actions might be taken to mitigate them. In an effort to explore this problem, this paper applies a semi-empirical life model of the graphite/nickel-cobalt-aluminum lithium-ion chemistry to investigate calendar degradation for various geographic environments and simplified cycling scenarios. The life model is then applied to analyze complex cycling conditions using battery charge/discharge profiles generated from simulations of plug-in electric hybrid vehicles (PHEV10 and PHEV40) vehicles across 782 single-day driving cycles taken from a Texas travel survey.

Global Positioning System (GPS) data acquisition devices have proven useful tools for gathering real-world driving data and statistics. The data collected by these devices provide valuable information in studying driving habits and conditions. When used jointly with vehicle simulation software, the data are invaluable in analyzing vehicle fuel use and performance, aiding in the design of more advanced and efficient vehicle technologies. However, when employing GPS data acquisition systems to capture vehicle drive-cycle information, a number of errors often appear in the captured raw data samples. Common sources of error in GPS data include sudden signal loss, extraneous or outlying data points, speed drifting, and signal white noise, all of which combine to limit the quality of field data for use in downstream applications.