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The desire for improved vehicle fuel economy, driven by high gas prices and concerns over energy independence, have sparked interest and demand for hybrid electric vehicles. Hybrid electric vehicle propulsion systems exhibit complex interactions which need to be understood in order to maximize fuel economy over the range of operating modes. Model-based development processes which use vehicle system models capable of representing the functional behaviors with embedded controls are needed for fast, efficient design of vehicle control systems which manage overall energy usage. Model-based vehicle system development processes have been employed for a Dual Drive HEV system. The process for creating these vehicle system models is described along with an approach for using these models to develop HEV systems. Details of key subsystem models and the process for integration of full vehicle implementation level controls are discussed.

Impact harshness characterizes interior sound and vibration resulting from tire interactions with discrete road disturbances. Typical interactions are expansion joints, railroad crossings, and other road discontinuities at low-to-medium vehicle speeds. One goal of the current study was to validate for light trucks and SUVs the metric that was developed for cars: a weighted combination of peak loudness values from the front and rear impacts after lowpass filtering at 1 kHz. Another goal was to see if other sound characteristics of impact harshness needed to be captured with a metric. A listening study was conducted with participants evaluating several different trucks and SUVs for impact harshness. Results show that the existing metric correlates well with subjective preferences for most of the vehicles.

A hybrid electric vehicle (HEV) system model, which directly simulates vehicle drive cycles with interactions among driver, environment, vehicle hardware and vehicle controls, is a critical CAE tool used through out the product development process to project HEV fuel economy (FE) capabilities. The accuracy of the model is essential and directly influences the HEV hardware designs and technology decisions. This ultimately impacts HEV product content and cost. Therefore, improving HEV system model accuracy and establishing high-level model-test correlation are imperative. This paper presents a Parameter Diagram (P-Diagram) based model-test correlation framework which covers all areas contributing to potential model simulation vs. vehicle test differences. The paper describes each area in detail and the methods of characterizing the influences as well as the correlation metrics.