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Extended range electric vehicles (EREVs) are a potential solution for fossil fuel usage mitigation and on-road emissions reduction. The use of EREVs can be shown to yield significant fuel economy improvements when proper energy management strategies (EMSs) are employed. However, many in-use EREVs achieve only moderate fuel reduction compared to conventional vehicles due to the fact that their EMS is far from optimal. This paper focuses on in-use rule-based EMSs to improve the fuel efficiency of EREV last-mile delivery vehicles equipped with two-way Vehicle-to-Could (V2C) connectivity. The method uses previous vehicle data collected on actual delivery routes and machine learning methods to improve the fuel economy of future routes. The paper first introduces the main challenges of the project, such as inherent uncertainty in human driver behavior and in the roadway environment. Then, the framework of our practical physics-model guided data-driven approach is introduced.

A new methodological tool was developed consisting of a patchwork thermal cool/warm grid with great flexibility to manipulate the temperature on different areas of the body. Through conflicting temperatures on the body surface, it is possible to direct heat current to different distal or proximal areas. The effectiveness of the use of a cooled hood, gloves, socks on the overheated body was evaluated as countermeasures for balancing heat exchange. Temperature in the magistral vessels was the main source of information for understanding the mechanism of the relationship between core and shell, and shell and distal parts of the limb.

Psychological, group interaction, and task performance characteristics were evaluated in four polar expedition teams varying in national and gender composition. Leaders played a crucial role in promoting strong group cohesiveness and morale. North American members were more highly focused on achievement strivings, Russians on avoidance of failure. Gender differences in behavior were also evident. An all women's team demonstrated a high level of cooperativeness and social support of other team members. Across teams, anxiety, tension, and health concerns increased in the early stages of the expedition and decreased significantly at later stages. The overall findings indicate the need to focus on the interaction of personality, cultural, gender, and task performance demands in personnel selection and during long duration missions. Implications for the optimal design of space vehicles and habitats are discussed.

Particulate emissions from heavy-duty buses were measured in real time under conditions encountered during the standard Central Business District (CBD) driving cycle. The buses tested were equipped with 1994 Detroit Diesel Engine Corporation 6V92-TA engines, and some included after treatment devices on the exhaust. Instantaneous, time-resolved measurements of CO2 and amorphous carbon concentrations were obtained using an optical extinction technique and compared to simultaneous results obtained using conventional dilution tunnel sampling methods. Good agreement was obtained between the real-time extinction measurements and the diluted CO2 and cycle-integrated filter measurements. The instantaneous measurements revealed that acceleration transients accounted for roughly 80% of the particulate mass emitted during the cycle but only about 45% of the fuel consumption.

This paper summarizes the state-of-the-art of various alternative fuel technologies for heavy-duty transit applications and compares them to conventional and “ clean” diesel engines. Alternative powerplants considered include compressed natural gas (CNG), liquefied natural gas (LNG), methanol, ethanol, liquefied petroleum gas (LPG), hydrogen, and several electric technologies. The various technologies are ranked according to emissions, operating and capital costs, safety, development status, driveability, and long term fuel supply. A simple spreadsheet-based rating system is presented; it not only provides a versatile, semi-quantitative way to rank technologies using both quantitative and qualitative information, but also helps identify critical areas which limit implementation for a given application. An example is given for urban transit buses.