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In this paper, the available correlations proposed in the literature for the gas-side heat transfer in the intake and exhaust system of a spark-ignition internal combustion engine were surveyed. It was noticed that these only by empirically fitted constants. This similarity provided the impetus for the authors to explore if a universal correlation could be developed. Based on a scaling approach using microscales of turbulence, the authors have fixed the exponential factor on the Reynolds number and thus reduced the number of adjustable coefficients to just one; the latter can be determined from a least squares curve-fit of available experimental data. Using intake and exhaust side data, it was shown that the universal correlation The correlation coefficient of this proposed heat transfer model with all available experimental data is 0.845 for the intake side and 0.800 for the exhaust side.

A regenerative braking model for a parallel Hybrid Electric Vehicle (HEV) is developed in this work. This model computes the line and pad pressures for the front and rear brakes, the amount of generator use depending on the state of deceleration (i.e. the brake pedal position), and includes a wheel lock-up avoidance algorithm. The regenerative braking model has been developed in the symbolic programming environment of MATLAB/SIMULINK/STATEFLOW for downloadability to an actual HEV's control system. The regenerative braking model has been incorporated in NREL's HEV system simulation called ADVISOR. Code modules that have been changed to implement the new regenerative model are described. Resulting outputs are compared to the baseline regenerative braking model in the parent code. The behavior of the HEV system (battery state of charge, overall fuel economy, and emissions characteristics) with the baseline and the proposed regenerative braking strategy are first compared.

A hybrid electric vehicle simulation tool (HE-VESIM) has been developed at the Automotive Research Center of the University of Michigan to study the fuel economy potential of hybrid military/civilian trucks. In this paper, the fundamental architecture of the feed-forward parallel hybrid-electric vehicle system is described, together with dynamic equations and basic features of sub-system modules. Two vehicle-level power management control algorithms are assessed, a rule-based algorithm, which mainly explores engine efficiency in an intuitive manner, and a dynamic-programming optimization algorithm. Simulation results over the urban driving cycle demonstrate the potential of the selected hybrid system to significantly improve vehicle fuel economy, the improvement being greater when the dynamic-programming power management algorithm is applied.