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Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory’s FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls.

This work presents a methodology to determine the off-cycle fuel economy benefit of a 2-Layer HVAC system which reduces ventilation and heat rejection losses of the heater core versus a vehicle using a standard system. Experimental dynamometer tests using EPA drive cycles over a broad range of ambient temperatures were conducted on a highly instrumented 2016 Lexus RX350 (3.5L, 8 speed automatic). These tests were conducted to measure differences in engine efficiency caused by changes in engine warmup due to the 2-Layer HVAC technology in use versus the technology being disabled (disabled equals fresh air-considered as the standard technology baseline). These experimental datasets were used to develop simplified response surface and lumped capacitance vehicle thermal models predictive of vehicle efficiency as a function of thermal state.

It is widely understood that cold ambient temperatures negatively impact vehicle system efficiency. This is due to a combination of factors: increased friction (engine oil, transmission, and driveline viscous effects), cold start enrichment, heat transfer, and air density variations. Although the science of quantifying steady-state vehicle component efficiency is mature, transient component efficiencies over dynamic ambient real-world conditions is less understood and quantified. This work characterizes wheel assembly efficiencies of a conventional and electric vehicle over a wide range of ambient conditions. For this work, the wheel assembly is defined as the tire side axle spline, spline housing, bearings, brakes, and tires. Dynamometer testing over hot and cold ambient temperatures was conducted with a conventional and electric vehicle instrumented to determine the output energy losses of the wheel assembly in proportion to the input energy of the half-shafts.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

Response Surface Methodology (RSM) techniques are applied to develop brake specific fuel consumption (BSFC) maps of a test vehicle over standard drive cycles under various ambient conditions. This technique allows for modeling and predicting fuel consumption of an engine as a function of engine operating conditions. Results will be shown from Federal Test Procedure engine starts of 20°C, and colder conditions of -7°C. Fueling rates under a broad range of engine temperatures are presented. Analysis comparing oil and engine coolant as an input factor of the model is conducted. Analysis comparing the model to experimental datasets, as well as some details into the modeling development, will be presented. Although the methodology was applied to data collected from a vehicle, the same technique could be applied to engines run on dynamometers.

For this work, a methodology of modeling and predicting fuel consumption in a hybrid vehicle as a function of the engine operating temperature has been developed for cold ambient operation (-7°C, 266°K). This methodology requires two steps: 1) development of a temperature dependent engine brake specific fuel consumption (BSFC) map, and, 2) a data-fitting technique for predicting engine temperature to be used as an input to the temperature dependent BSFC maps. For the first step, response surface methodology (RSM) techniques were applied to generate brake specific fuel consumption (BSFC) maps as a function of the engine thermal state. For the second step, data fitting techniques were also used to fit a simplified lumped capacitance heat transfer model using several experimental datasets. Utilizing these techniques, an analysis of fuel consumption as a function of thermal state across a broad range of engine operating conditions is presented.

Testing plug-In hybrid vehicles over standardized and real world drive cycles has shown relatively large efficiency differences between ambient cold starts and hot starts(1,2) (CS/HS). This variation is dependent upon the drive cycle and powertrain architecture, and is significant in magnitude. Quantifying this inefficiency is non-trivial as charge-depleting modes, coupled with vehicle calibration sensitivity, consume small portions of fuel resulting in test variations in which thermal effects cannot be decoupled from slight calibration changes that mask thermal influences. In this paper, a methodology for modeling and analyzing the fuel efficiency of a plug-in hybrid vehicle powertrain as a function of the engine operating temperature will be presented. Response surface methodology (RSM) techniques have been applied to generate brake specific fuel consumption (BSFC) maps as a function of the engine thermal state indicated by crankcase oil temperature.

It is a very challenging problem to reliably ignite extremely lean mixtures, especially for the low speed, high load conditions of large-bore natural gas engines. If these engines are to be use for the distributed power generation market, it will require operation with higher boost pressures and even leaner mixtures. Both place greater demands on the ignition system. The railplug is a very promising ignition system for lean burn natural gas engines with its high-energy deposition and high velocity plasma arc. It requires care to properly design railplugs for this new application, however. For these engines, in-cylinder pressure and mixture temperature are very high at the time of ignition due to the high boost pressure. Hot spots may exist on the electrodes of the ignitor, causing pre-ignition problems. A heat transfer model is proposed in this paper to aid the railplug design. The electrode temperature was measured in an operating natural gas engine.

A liquid-phase port injection system for liquefied petroleum gas (LPG) generally consists of a fuel storage tank with extended capability of operating up to 600 psi, a fuel pump, and suitable fuel lines to and from the LPG fuel injectors mounted in the fuel rail manifold. Port injection of LPG in the liquid phase is attractive due to engine emissions and performance benefits. However, maintaining the LPG in the liquid phase at under-hood conditions and re-starting after hot soak can be difficult. Multiphase behavior within a liquid-phase LPG injection system was investigated computationally and experimentally. A commercial chemical equilibrium code (ASPEN PLUS™) was used to model various LPG compositions under operating conditions.

A quasidimensional engine simulation which uses the concepts of fractal geometry to model the effects of turbulence on flame propagation in a homogeneous charge SI engine has been developed. Heat transfer and blowby/crevice flow submodels are included in this code and the submodels chosen are found to be reasonable. The model predictions of cylinder pressure histories are then compared with experimental data over a range of loads, equivalence ratios, and engine speeds. The model is not adjusted in any manner to yield better agreement with the data, other than by tuning the simple turbulence model used so as to yield agreement with data for the nonreacting flow. However, current information about the flame wrinkling scales in an engine is inadequate. Therefore, predictions are made for three different assumptions about the flame wrinkling scales which span the range of physically possible scales.