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For simulation and analysis of vehicles there is a need to have a means of generating drive cycles which have properties similar to real world driving. A method is presented which uses measured vehicle speed from a number of vehicles to generate a Markov chain model. This Markov chain model is capable of generating drive cycles which match the statistics of the original data set. This Markov model is then used in an iterative fashion to generate drive cycles which match constraints imposed by the user. These constraints could include factors such number of stops, total distance, average speed, or maximum speed. In this paper, systematic analysis was done for a PHEV fleet which consists of 9 PHEVs that were instrumented using data loggers for a period of approximately two years. Statistical analysis using principal component analysis and a clustering approach was carried out for the real world velocity profiles.

Plug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption.

The evolution of the diagnostic equipment for automotive application is the direct effect of the implementation of sophisticated and high technology control systems in the new generation of passenger cars. One of the most challenging issues in automotive diagnostics is the ability to assess, to analyze, and to integrate all the information and data supplied by the vehicle's on-board computer. The data available might be in the form of fault codes or sensors and actuators voltages. Moreover, as environmental regulations get more stringent, knowledge of the concentration of different species emitted from the tailpipe during the inspection and maintenance programs can become of great importance for an integrated powertrain diagnostic system. A knowledge-based diagnostic tool is one of the approaches that can be adopted to carry out the challenging task of detecting and diagnosing faults related to the emissions control system in an automobile.

A prototype CO sensor based on anatase TiO2 was fabricated and tested in a Ford V6 engine. Fuel combustion was programmed to be near stoichiometric conditions, and emissions were monitored with an FT-IR analytical instrument. The sensor, positioned near the oxygen sensor in the exhaust manifold, was successfully tested for 50 cycles of revving and idling, and was observed to respond quickly and reproducibly. The sensor response was correlated to the CO concentration at specific engine temperatures and was found to vary systematically with increasing concentrations. This sensor has promising potentials to monitor the efficiency of the catalytic converter.

Crankshaft angular position measurements are fundamental to all modern automotive engines. These measurements are required to control fuel injection timing as well as ignition timing. However, many other functions can be performed from such measurements through the use of advanced signal processing. These additional functions are essentially diagnostic in nature although there is potential for substitution of primary fuel and ignition control functions. This paper illustrates the application of crankshaft angular position measurement to the estimation of individual cylinder indicated and/or brake torque in IC engines from measurement of crankshaft position/velocity.