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Researchers and engineers are utilizing big data analytics to draw further insights into transportation systems. Large amounts of data at the individual vehicle trip level are being collected and stored. The true potential of such data is still to be determined. In this paper, we are presenting a data-driven, novel, and intuitive approach to model driver behaviors using microscopic traffic simulation. Our approach utilizes metaheuristic methods to create an analytical tool to assess vehicle performance. Secondly, we show how microscopic simulation run outputs can be post-processed to obtain vehicle and trip level performance metrics. The methodology will form the basis for a data-driven approach to unearthing trip experiences as realized by drivers in the real world. The methodology will contribute to, A.) Using vehicle trajectory traces to identify underlying vehicle maneuver distributions as obtained from real-world driver data, B.)

Historical driver behavior and drive style are crucial inputs in addition to V2X connectivity data to predict future events as well as fuel consumption of the vehicle on a trip. A trip is a combination of different maneuvers a driver executes to navigate a route and interact with his/her environment including traffic, geography, topography, and weather. This study leverages big data analytics on real-world customer driving data to develop analytical modeling methodologies and algorithms to extract maneuver-based driving characteristics and generate a corresponding maneuver distribution. The distributions are further segmented by additional categories such as customer group and type of vehicle. These maneuver distributions are used to build an aggressivity distribution database which will serve as the parameter basis for further analysis with traffic simulation models.

The FMEA and DV&PV process of developing automotive products requires identifying and repeatedly testing critical vehicle attributes and their response to noise factors that may impair vehicle function. Ford has developed a new automated scripting tool to streamline in-vehicle robustness testing and produce more accurate and repeatable results. Similar noise factors identified during the FMEA process are grouped together, condensed, and scripts are developed to simulate these noise factors using calibration parameters and vehicle controls. The automated testing tool uses the API of a calibration software tool and a graphical scripting interface to consistently simulate driver inputs with greater precision than a human calibrator and enable more sophisticated controls, which would have previously required experimental software builds. The noise factor scripts are executed with minimal intervention from a human operator, and the collected data is analyzed to determine robustness.

In Hybrid Electric Vehicles, Regenerative Braking is an essential function to convert vehicle kinetic energy into electrical energy, which charges the battery during a braking event to make a portion of captured kinetic energy available for use later. In comparison, conventional vehicles use friction brakes only and kinetic energy is dissipated as heat and not made available for later use. This paper introduces methods of evaluating Regenerative Braking Efficiency, including multiple efficiency definitions that lead to different attributes. The paper proposes regenerative brake event definitions during the FTP cycle and how they are used for control strategy and calibration updates. Also, we apply the efficiency metrics to four different vehicles from four automotive manufacturers for comparison. The paper presents a sample comparison result.

Ford recently introduced an industry first Modular Hybrid Transmission (MHT) in the Model U concept vehicle at the 2003 North American International Auto Show. The MHT is a full function hybrid system (i.e. capable of electric drive) that utilizes a modular approach to leverage high volume conventional driveline components to create a lower-cost hybrid system [1]. In the MHT, the torque converter of a conventional automatic transmission is removed and in its place is packaged a single high voltage electric machine and an engine disconnect clutch. Advanced controls are used to enable hybrid functions. A critical element in the development of the MHT is the ability to replicate the functions of the torque converter without compromise to the vehicle drivability. In this paper, the control of four transmission functions in the MHT will be discussed: 1) transmission engagement, 2) vehicle launch, 3) power-on up-shift and 4) coast downshift.

Ford's H2RV is a Hydrogen engine propelled Hybrid Electric concept Vehicle that was unveiled and driven at Ford's Centennial Show in June 2003. This vehicle is an industry first by an OEM that demonstrates the concept and the marriage of a HEV powertrain with a supercharged Hydrogen ICE that propels the vehicle. Just as Model T was the car of the 20th century, Model U is the vehicle for the 21st century. The powertrain utilizes compressed gaseous hydrogen as fuel, a supercharged 2.3L internal combustion engine, a 25 kW traction motor drive, the electric converterless transmission, regenerative braking, an advanced lithium ion battery, electric power assist steering, electronic throttle and Vehicle System Controller (VSC). The vehicle could deliver a projected fuel economy of 45 mpg and near zero emissions without compromise to performance.