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-cobaltaluminum 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. Drive cycle statistics impacting battery life are compared to standard test cycles.

While it is well known that "MPG will vary" based on how one drives, little independent research exists on the aggregate fuel savings potential of improving driver efficiency and on the best ways to motivate driver behavior changes. This paper finds that reasonable driving style changes could deliver significant national petroleum savings, but that current feedback approaches may be insufficient to convince many people to adopt efficient driving habits. To quantify the outer bound fuel savings for drive cycle modification, the project examines completely eliminating stop-and-go driving plus unnecessary idling, and adjusting acceleration rates and cruising speeds to ideal levels. Even without changing the vehicle powertrain, such extreme adjustments result in dramatic fuel savings of over 30%, but would in reality only be achievable through automated control of vehicles and traffic flow. Considering the effects of real-world driving conditions, efficient driving behaviors could reduce fuel use by 20% on aggressively driven cycles and by 5-10% on more moderately driven trips.To evaluate potential receptiveness to changing driving habits, the project team conducted a literature survey of driver behavior influences and observed pertinent factors from on-road experiments with different driving styles. This effort highlighted important driver influences such as surrounding vehicle behavior, anxiety over trying to get somewhere quickly, and the power/torque available from the vehicle. Existing feedback approaches often effectively deliver efficiency information and instruction, but do not always do so in an easy way that avoids unintended consequences and helps trump other driving behavior influences. Based on these findings the report details three recommendations for maximizing fuel savings from potential drive cycle improvement: (1) Leverage applications with enhanced incentives, (2) Use an approach that makes it easy and is widely-deployable to motivated drivers, and (3) Utilize connected vehicle and automation technologies to achieve large and widespread efficiency improvements.

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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. Unaddressed, these errors significantly impact the reliability of source data and limit the effectiveness of traditional drive cycle analysis approaches and vehicle simulation software. Without reliable speed and time information, the validity of derived metrics for drive cycles, such as acceleration, power, and distance become questionable. This study explores some of the common sources of error present in collected raw GPS data and presents a detailed filtering process designed to correct for these issues. To illustrate the effectiveness of the proposed filtration process across the range of vehicle vocations, test data from both light-and medium/heavy-duty applications are examined. Graphical comparisons of raw and filtered cycles are presented, and statistical analyses performed to determine the effects of the proposed filtration process on raw data. Finally, the paper concludes with an evaluation of the overall benefits of data filtration on raw GPS data and presents potential areas for continued research.

As vehicle powertrain efficiency increases through electrification, consumer travel and driving behavior have significantly more influence on the potential fuel consumption of these vehicles. Therefore, it is critical to have a good understanding of in-use or "real world" driving behavior if accurate fuel consumption estimates of electric drive vehicles are to be achieved. Regional travel surveys using Global Positioning System (GPS) equipment have been found to provide an excellent source of in-use driving profiles. In this study, a variety of vehicle powertrain options were developed and their performance was simulated over GPS-derived driving profiles for 783 vehicles operating in Texas. The results include statistical comparisons of the driving profiles versus national data sets, driving performance characteristics compared with standard drive cycles, and expected petroleum displacement benefits from the electrified vehicles given various vehicle charging scenarios.