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As vehicles are getting electrified and more intelligent, the energy consumption of the auxiliary system increases rapidly. The auxiliary battery acts as the backbone of the system to support the proper operation of the vehicle. It is important to ensure the auxiliary battery has enough energy to meet the basic loads regardless the vehicle is in park or running. However, the existing methods only focus on auxiliary energy management when the vehicle is in a dynamic event. To fulfill the gap, we propose an intelligent strategy that detects the low state of charge (SOC) condition, temporarily turns down the auxiliary loads based on their priorities and charges the auxiliary battery at the maximum efficiency of the auxiliary power unit. In addition, the proposed strategy allows the vehicle to get the park duration update and make intelligent decisions on charging the auxiliary battery.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

Reynolds-averaged Navier-Stokes (RANS) turbulence model has been used extensively for diesel engine simulations due to its computational efficiency and is expected to remain the workhorse computational fluid dynamics (CFD) tool for industry in the near future. Alternatively, large eddy simulations (LES) can potentially deal with complex flows and cover a large disparity of turbulence length scales, which makes this technique more and more attractive in the engine community. An n-dodecane spray flame (Spray A from Engine Combustion Network) was simulated using a dynamic structure LES model to understand the transient behavior of this turbulent flame. The liquid spray was treated with a traditional Lagrangian method and the gas-phase reaction was closed using a delta probability density function (PDF) combustion model. A 103-species skeletal mechanism was used for n-dodecane chemical kinetic model.

This paper introduces an intelligent energy distribution scheme for series plug-in hybrid electric vehicles (PHEVs) which incorporates the complexity of human driving behavior. Driving styles can have a significant impact on fuel consumption, but it is often unclear how one should drive to get the optimal fuel efficiency. Hybrid electric vehicles have been shown to improve fuel economy, reduce vehicle emissions and maintain drivability by incorporating electric motors into the drivetrain. Due to the highly complex system design and vehicle architecture, sophisticated energy management strategies (EMS) are required to optimize the vehicle performance. Currently, the power management system is based on static thresholds optimized on a fixed drive cycle for a given vehicle. This paper introduces an adaptive control method for EMS utilizing the complexity of human driving patterns for energy distribution in a series PHEV.

An optically accessible single-cylinder high-speed direct-injection (HSDI) Diesel engine equipped with a Bosch common rail injection system was used to study the spray and combustion processes for European low sulfur diesel, bio-diesel, and their blends at different blending ratio. Influences of injection timing and fuel type on liquid fuel evolution and combustion characteristics were investigated under similar loads. The in-cylinder pressure was measured and the heat release rate was calculated. High-speed Mie-scattering technique was employed to visualize the liquid distribution and evolution. High-speed combustion video was also captured for all the studied cases using the same frame rate. NOx emissions were measured in the exhaust pipe. The experimental results indicated that for all of the conditions the heat release rate was dominated by a premixed combustion pattern and the heat release rate peak became smaller with injection timing retardation for all test fuels.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

Particulate and nitrogen oxide emissions from a 1.9-liter Volkswagen diesel engine were measured for three different fuels: neat diesel fuel, a blend of diesel fuel with 10% ethanol, and a blend of diesel fuel with 15% ethanol. Engine-out emissions were measured on an engine dynamometer for five different speeds and five different torques using the standard engine-control unit. Results show that particulate emissions can be significantly reduced over approximately two-thirds of the engine map by using a diesel-ethanol blend. Nitrogen oxide emissions can also be significantly reduced over a smaller portion of the engine map by using a diesel-ethanol blend. Moreover, there is an overlap between the regions where particulate emissions can be reduced by up to 75% and nitrogen oxide emissions are reduced by up to 84% compared with neat diesel fuel.

This paper presents the results of emission tests of a flexible fuel vehicle (FFV) powered by an SI engine, fueled by M85, and supplied with oxygen-enriched intake air containing nominal 21%, 23%, and 25% oxygen (by volume). Emission data were collected by following the standard federal test procedure (FTP) and U.S. Environmental Protection Agency's (EPA's) “off-cycle” test EPA-REP05. Engine-out total hydrocarbons (THCs) and unburned methanol were considerably reduced in the entire FTP cycle when the oxygen content of the intake air was either 23% or 25%. However, CO emissions did not vary appreciably, and NOx emissions were higher. Formaldehyde emissions were reduced by about 53% in bag 1, 84% in bag 2, and 59% in bag 3 of the FTP cycle when 25% oxygen-enriched intake air was used.

A numerical modeling technique is proposed for computer simulations of high speed valve train dynamics. The dynamic terms in the valve spring reaction forces are calculated using linear vibration theory for given kinematic valve motions. Because the spring dynamics are analyzed before the time stepping integration, spring surge phenomena can be included without using additional computer time. Consequently, valve train dynamics can be simulated very quickly without noticeable errors in accuracy. The experimental results prove the computer model developed here is accurate and also computationally efficient.