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The objective draw by this project is to develop tools for Plug-in Hybrid Electric Vehicle (PHEV) design, energy analysis and energy management, with the aim of analyzing the effect of design, driving cycles, charging frequency and energy management on performance, fuel economy, range and battery life. A Chevrolet Equinox fueled by bio diesel B20 has been hybridized at the Center for Automotive Research (CAR), at The Ohio State University. The vehicle model has been developed in Matlab/Simulink environment, and validated based on laboratory and test. The PHEV battery pack has been modeled starting from Li-Ion batteries experimental data and then implemented into the simulator. In order to simulate “real world” scenarios, custom driving cycles/typical days were identified starting from average driving statistics and well-known cycles.

Highway and roadway surface measurement is a practice that has been ongoing for decades now. This sort of measurement is intended to ensure a safe level of road perturbances. The measurement may be conducted by a slow moving apparatus directly measuring the elevation of the road, at varying distance intervals, to obtain a road profile, with varying degrees of resolution. An alternate means is to measure the surface roughness at highway speeds using accelerometers coupled with high speed distance measurements, such as laser sensors. Vehicles out rigged with such a system are termed inertial profilers. This type of inertial measurement provides a sort of filtered roadway profile. Much research has been conducted on the analysis of highway roughness, and the associated metrics involved. In many instances, it is desirable to maintain an off-road course such that the course will provide sufficient challenges to a vehicle during durability testing.

This paper presents the results of a design space exploration based on the simulations of the MTV (Medium Tactical Vehicle) 5.0 Ton Cargo Truck using MSC-ADAMS (Automatic Dynamic Analysis of Mechanical System). Design space study is conducted using ADAMS/Car and ADAMS/Insight to consider parametric design changes in suspension and the tires of the cargo truck. The methodology uses an industry acknowledged multibody dynamics simulation software (ADAMS) for the modeling of the cargo truck and a flexible optimization architecture to explore the design space. This research is a part of the work done for the U.S. Army TACOM (Tank Automotive and Armaments Command) at the Center for Automotive Research, The Ohio State University.

Research has been conducted to develop a methodology for the generation of driving and duty cycles for refuse vehicles in conjunction with a larger effort in the design of a hybrid-electric refuse vehicle. This methodology includes the definition of real-world data that was collected, as well as a data analysis procedure based on sequencing of the collected data into micro-trips and hydraulic cycles. The methodology then applies multi-variate statistical analysis techniques to the sequences for classification. Finally, driving and duty cycles are generated based on matching the statistical metrics and distributions of the generated cycles to the collected database. Simulated vehicle fuel economy for these cycles is also compared to measured values.