Search Results

The paper presents a robust adaptive control technique for precise regulation of a port fuel injection + direct injection (PFI+DI) system, a dual fuel injection configuration adopted in modern gasoline engines to boost performance, fuel efficiency, and emission reduction. Addressing parametric uncertainties on the actuators, inherent in complex fuel injection systems, the proposed approach utilizes an indirect model reference adaptive control scheme. To accommodate the increased control complexity in PFI+DI and the presence of additional uncertainties, a nonlinear plant model is employed, incorporating dynamics of the exhaust burned gas fraction. The primary objective is to optimize engine performance while minimizing fuel consumption and emissions in the presence of uncertainties. Stability and tracking performance of the adaptive controller are evaluated to ensure safe and reliable system operation under various conditions.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

A linear parameter-varying model predictive control (LPVMPC) is proposed to enhance the longitudinal vehicle speed control of a gas-engine vehicle, with potential application in autonomous vehicles. To achieve this objective, an advanced vehicle dynamic model and a sophisticated fuel consumption model are derived, forming a control-oriented model for the proposed control system. The vehicle dynamic model accurately captures the motions of the tires and the vehicle body. The fuel consumption model incorporates new powertrain modes such as automatic engine stop/start, active fuel management, and deceleration fuel cut-off, etc. The performance of the proposed LPV-MPC is evaluated by comparing it to a PID controller. Both simulation tests and vehicle-in-the-loop tests demonstrate the superior performance of the proposed controller. The results indicate that the LPV-MPC provides improved longitudinal vehicle speed control and reduced fuel consumption.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

A gasoline’s distillation profile is directly related to its hydrocarbon composition and the volatility (boiling points) of those hydrocarbons. Generally, the volatility profiles of U.S. market fuels are characterized using a very simple, low theoretical plate distillation separation, detailed in the ASTM D86 test method. Because of the physical chemistry properties of some compounds in gasoline, this simple still or retort distillation has some limitations: separating azeotropes, isomers, and heavier hydrocarbons. Chemists generally rely on chromatographic separations when more detailed and precise results are needed. High-boiling aromatic compounds are the primary source of particulate emissions from spark ignited (SI), internal combustion engines (ICE), hence a detailed understanding and high-resolution separation of these heavy compounds is needed.

Methanol is currently being evaluated as a promising alternative fuel for internal combustion engines, due to being attainable by carbon neutral or negative pathways (renewable energy and carbon capture technology). The low ignitability of methanol has made it attractive mostly as a fuel for spark ignition engines, however the low sooting properties of the fuel could potentially reduce the NOx-soot tradeoff present in compression ignition engines. In this work, using a 4-cylinder engine with compression ratio modified from 16:1 to 19:1, methanol combustion is evaluated under five operating conditions in terms of fuel consumption, criteria pollutants, CO2 emissions and engine efficiency in addition to the qualitative assessment of the combustion stability. It was found that combustion is stable at medium to high loads, with medium load NOx emissions levels at least 30% lower than the original diesel engine and comparable emissions at maximum load conditions.

Heating devices are effective technologies to strengthen emission robustness of AfterTreatment Systems (ATS) and to guarantee emission compliance in the new boundaries given by upcoming legislations. Moreover, they allow to manage the ATS warm-up independently from engine operating conditions, thereby reducing the need for specific combustion strategies. Within heating devices, an attractive solution to provide the required thermal power without mandating a 48V platform is the fuel burner. In this work, a model-based control coordinator to manage the interaction between engine, ATS and fuel burner device has been developed, virtually validated, and optimized. The control function features a burner model and a control logic to deliver the needed amount of thermal energy, while ensuring ATS hardware protection.

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEISimDis equation are detailed herein. The PEISimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time.

As the automotive industry is quickly changing towards electric vehicles, we can highlight the importance of aerodynamics and its critical role in reaching extended battery ranges for electric cars. With all new smooth underbodies, a lot of attention has turned into the effects of rim designs and tires brands and the management of these tire wakes with the vehicle. Tires are one of the most challenging areas for aerodynamic drag prediction due to its unsteady behavior and rubber deformation. With the simulation technologies evolving fast regarding modeling spinning tires for aerodynamics, this paper takes the prior work and data completed by the authors and investigates the impact on the flow fields and aerodynamic forces using the most recent developments of an Immerse Boundary Method (IBM). IBM allows us to mimic realistically a rotating and deformed tire using Lattice Boltzmann methods.

The power loss of bearings is a significant factor in the overall efficiency in a drive unit system. Such bearings are subject to combined radial and axial loading needed to support the gear mesh forces. An experimental methodology has been developed to perform sets of power loss measurements on TRB, 4PCBB and DGBB. These measurements were performed under a variety of speed, load, temperature, and lubrication conditions. The loss behaviors of these types of the bearings are discussed, along with the tradeoff of different bearing arrangements for the fuel economy cycles. Several power loss models are employed to assess the accuracy of the estimations as compared to the experimental measurements. At low speed some models showed good correlations for TRB and DGBB, while at higher speed, they start deviating from the testing results. A higher fidelity model for estimating the losses at high speed, especially speed around 20krpm and beyond, needs to be developed.

Stochastic Preignition (SPI) is an abnormal combustion phenomenon for internal combustion engines (ICE), which has been a significant impact to automotive companies developing high efficiency, turbocharged, direct fuel injection, spark ignited engines. It is becoming clearer what fuel properties are related to the cause of SPI, whether directly with fuel preparation in the cylinder, or mechanisms related to the deposit build-up which contributes to initial and follow-on SPI events. The purpose of this paper is to provide a summary of global market gasoline fuel properties with special attention given to properties and specific compounds from the fuel and fuel additives that can contribute to SPI and the deposit build-up in engines. Based on a review of the global fuel quality, it appears that the fuel quality has not caught up to meet the technology requirements for fuel economy from modern technology engines.

De-fossilization is an increasingly important trend in the energy sector. In the transport sector the de-fossilization efforts have been centered in promoting the electrification of vehicles, nonetheless other pathways, like the use of carbon neutral or carbon-offsetting fuels under current vehicle fleets, are also worth considering. Low-carbon fuels (LCF) can be synthetized from sources that can take advantage of the carbon already present in the atmosphere (either by technologies like direct carbon capture or biological processes like photosynthesis in biofuels) and use energy from renewable sources for the necessary industrial processes. Although, LCFs can be compared to fossil fuels as energy sources for internal combustion engines, their composition is not the same and their properties can modify the engine combustion and emissions.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample.

Gasoline Particulate Filters (GPF) are widely employed in exhaust aftertreatment systems of gasoline engines to meet the stringent particulate emissions requirements of Euro6 and China6 standard. While providing an effective filtration of particles, the GPF increases the engine backpressure as a penalty due to accumulation of soot. To clean the accumulated soot, periodical burning of soot is achieved by the onboard control models and lot of effort is spent on calibrating the same. In order to understand pressure drop behavior across GPF, detailed pressure drop measurements were conducted at clean, soot and ash loaded conditions at engine dynamometer and at vehicle conditions. Effect of degreening of GPF was studied to take into account any change in pressure drop characteristics of onboard control models during GPF aging in the vehicle.

Stringent requirements for high fuel economy and energy efficiency mandate using increasingly complex vehicle thermal systems in most types of electrified vehicles (xEVs). Enabling the maximum benefits of such complex thermal systems under the full envelope of their operating modes demands designing complex thermal control systems. This is becoming one of the most challenging problems for electrified vehicles. Typically, the thermal systems of such vehicles have several modes of operation, constituting nonlinear multiple-input/multiple-output (MIMO) dynamic systems that cannot be efficiently controlled using classical or rule based strategies. This paper covers the different steps towards the design of a model-based control (MBC) strategy that can improve the overall performance of xEV thermal control systems. To achieve the above objective, the latter MBC strategy is applied to control cooling of the cabin and high voltage battery.

For flows around a tire rotating over a ground plane, the Reynolds number is probably the most important parameter influencing the transition mechanism leading to flow separation from the tire surface, as it determines the viscous response of the boundary layer in the vortex-wall interaction. The present work investigates the effects of Reynolds number on an isolated tire model using a commercial Computational Fluid Dynamics (CFD) code. It validates the baseline simulation for this purpose against the Particle Image Velocimetry (PIV) data from Stanford University got using a Toyota Formula 1 race car tire model. Time-resolved velocity fields and vortex structures from the PIV data are used to correlate local and global flow phenomena to identify unsteady boundary-layer separation and the subsequent flow structures. The study will highlight the pre to post critical flow regimes where the aero coefficients and vortex structure will be studied.

Stochastic Preignition (SPI) is an abnormal combustion event that occurs in a turbocharged engine and can lead to the loss in fuel economy and engine hardware damage, and in turn result in customer dissatisfaction. It is a significant limiting factor on the use and continued downsizing of turbocharged spark ignited direct injection (SIDI) gasoline engines. Understanding and mitigating all the factors that cause and influence the rate and severity of SPI occurrence are of critical importance to the engine’s continued use and fuel economy improvements for future designs. Previous studies have shown that the heavy molecular weight components of the fuel formulations are one factor that influences the rate of SPI from a turbocharged SIDI gasoline engine. All the previous studies have involved analyzing the fuel’s petroleum hydrocarbon chemistry, but not specifically the additives that are put in the fuel to protect and clean the internal components over the life of the engine.

This paper will focus on automotive development highlights of FCA US LLC magnesium intensive closures components. Deploying lightweight materials is one of many key strategies that has been implemented to reduce vehicle mass and improve overall fuel economy while maintaining rigorous functional objective performance. This paper will outline some basic design and manufacturing considerations for magnesium closures. The development of the 2017 Chrysler Pacifica liftgate and 2018 Jeep® Wrangler swing gate along with the two generations of magnesium spare tire brackets will be the focus.

One of the advantages of hybrid vehicles is the ability to operate the engine more optimally at a low brake specific fuel consumption (BSFC) as compared to conventional vehicles. This ability of hybrid vehicles is a major factor contributing to the fuel economy improvement over conventional vehicles. Unlike conventional gasoline powertrains, hybrid powertrains allow engine to be switched off and use battery power to propel vehicles. In order to maintain battery state of charge neutral operation between the start and end of a drive cycle, the net electrical energy consumption from the battery requires to be zero. An optimization algorithm can be developed and calibrated in different ways to achieve net zero battery energy over the cycle. For instance, the engine can be operated at powers higher than the power of the drive cycle to charge the battery. This accumulated energy can be used for all-electric propulsion by turning off the engine.