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In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory.

Idle Stop-and-go (ISG), also known as Auto Stop/Start, is a fuel saving technology common to many modern vehicles that enables the engine to shut down when the vehicle comes to a stop. Although it may help with fuel efficiency, many drivers in the North American market find the feature to be an annoyance due to hesitation in vehicle re-launch and engine shudder during stop or restart. This paper introduces the usage of traffic signal phase and timing (SPaT) information for controlling the activation of ISG with the goal of reducing driver complaints and increasing acceptance of the function. Previous studies proposed the utilization of Advanced Driver Assistance System (ADAS) to introduce adaptability in powertrain controls to traffic situation changes.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

This paper introduces the advancement of Engine Idle Stop-and-Go (ISG, also known as Auto Engine Stop-Start) and Neutral Coasting Control (NCC) with utilizing Advanced Driver Assistance System (ADAS) and GPS. The ISG and the In-Neutral Coasting (also known as Sailing or Gliding) have been widely implemented in recent vehicles for improving their fuel economy. However, many drivers find them somewhat disturbing because they basically change behaviors of their cars from what they used to. This annoyance discourages usages of those functions and eventually undermines their benefit of fuel saving. In order to mitigate the problem, new ISG and NCC algorithms are proposed. As opposed to the conventional logics that rely only on driver’s pedal action, the new algorithms determine whether or not to enable those functions for the given driving condition, based on the traffic information obtained using ADAS sensors and the location data from GPS and navigation map.

It has been discussed in numerous prior studies that in-neutral coasting, or sailing, can accomplish considerable amount of fuel saving when properly used. The driving maneuver basically makes the vehicle sail in neutral gear when propulsion is unnecessary. By disengaging a clutch or shifting the gear to neutral, the vehicle may better utilize its kinetic energy by avoiding dragging from the engine side. This strategy has been carried over to series production recently in some of the vehicles on the market and has become one of the eco-mode features available in current vehicles. However, the duration of coasting must be long enough to attain more fuel economy benefit than Deceleration Fuel Cut-Off (DFCO) - which exists in all current vehicle powertrain controllers - can bring. Also, the transients during shifting back to drive gear can result in a drivability concern.

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states.

Hyundai-Kia Motor Company recently developed a multi-way, electrical coolant valve for engine thermal management module (TMM). The main purposes of the TMM are to boost fuel economy by accelerating engine warm-up and also to enhance engine thermal efficiency by actively controlling the operating temperature. In addition to those, the system can improve vehicle heating and cooling performance as well. The electrical coolant valve is a key component in the TMM as it modulates the amount of coolant flow to individual components in cooling system such as engine oil heat exchanger, heater core, and radiator. The coolant flow modulation is done by controlling the electric valve’s position with using an electric motor attached to the valve. The objective of the valve control is to manage coolant temperature at a desired level that varies depending on vehicle’s operating condition. This paper discusses the control algorithm developed for controlling electrical coolant valve.

Many leading companies in the automotive industry have been putting tremendous amount of efforts into developing new designs and technologies to make their products more energy efficient. It is straightforward to evaluate the fuel economy benefit of an individual technology in specific systems and components. However, when multiple technologies are combined and integrated into a whole vehicle, estimating the impact without building and testing an actual vehicle becomes very complex, because the efficiency gains from individual components do not simply add up. In an early concept phase, a projection of fuel efficiency benefits from new technologies will be extremely useful; but in many cases, the outlook has to rely on engineer’s insight since it is impractical to run tests for all possible technology combinations.

The fuel economy of a vehicle can be improved by recuperating the kinetic energy when the vehicle is decelerated. However, if there is no electrical traction component, the recuperated energy can be used only by the other electrical systems of the vehicle. Thus, the fuel economy improvement can be maximized by balancing the recuperated energy and the consumed energy. Also, suitable alternator and battery management is required to maximize the fuel economy. This paper describes a design optimization process of the alternator and battery system equipped with recuperation control algorithms for a mid-sized sedan based on the fuel economy and system cost. A vehicle model using AVL Cruise is developed for cycle simulations and validated with experimental data. The validated model is used for the parametric study and design optimization of the alternator and battery systems with single and dual energy storage.

A Battery Test Facility (BTF) has been constructed at United States Environmental Protection Agency (EPA) to test various automotive battery packs for HEV, PHEV, and EV vehicles. Battery pack tests were performed in the BTF using a battery cycler, testing controllers, battery pack cooler, and a temperature controlled chamber. For e-machine testing and HEV power pack component testing, a variety of different battery packs are needed to power these devices to simulate in-vehicle conditions. For in-house e-machine testing and development, it is cost prohibitive to purchase a variety of battery packs, and also very time-consuming to interpret the battery management systems, CAN signals, and other interfaces for different vehicle manufacturers.

The Advanced Light-Duty Powertrain and Hybrid Analysis tool was created by EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles. It is a physics-based, forward-looking, full vehicle computer simulator capable of analyzing various vehicle types combined with different powertrain technologies. The software tool is a freely-distributed, MATLAB/Simulink-based desktop application. Version 1.0 of the ALPHA tool was applicable only to conventional, non-hybrid vehicles and was used to evaluate off-cycle technologies such as air-conditioning, electrical load reduction technology and road load reduction technologies for the 2017-2025 LD GHG rule. The next version of the ALPHA tool will extend its modeling capabilities to include power-split and P2 parallel hybrid electric vehicles and their battery pack energy storage systems. Future versions of ALPHA will incorporate plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) architectures.

The Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created by EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles. It is a physics-based, forward-looking, full vehicle computer simulator capable of analyzing various vehicle types combined with different powertrain technologies. The software tool is a freely-distributed, MATLAB/Simulink-based desktop application. Version 1.0 of the ALPHA tool was applicable only to conventional, non-hybrid vehicles and was used to evaluate off-cycle technology such as air-conditioning, electrical load reduction technology and road load reduction technologies for the 2017-2025 LD GHG and Fuel Economy rule. The next version of the ALPHA tool extends its modeling capabilities to include power-split and P2 parallel hybrid electric vehicles and their battery pack energy storage systems. Future versions of ALPHA will incorporate plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) architectures.

The Advanced Light-Duty Powertrain and Hybrid Analysis tool was created by Environmental Protection Agency to evaluate the greenhouse gas emissions and fuel efficiency of light-duty vehicles. It is a physics-based, forward-looking, full vehicle computer simulator that is capable of analyzing various vehicle types equipped with different powertrain technologies. The software is built on MATLAB/Simulink. This first version release of the simulation tool models conventional vehicles and is capable of evaluating effects of off-cycle technologies on greenhouse gas emissions, such as air conditioning, electrical load reduction, road load reduction by active aerodynamics, and engine start-stop. This paper introduces the simulation tool by describing its basic model architecture and presenting its underlying physics as well as model formulations. It describes the simulation capability along with its graphical user interface of the tool, designed for off-cycle technology analysis purposes.

Of all existing modes of transportation, onroad motor vehicles are the largest contributor to greenhouse gas emissions and fuel usage. The Environmental Protection Agency and the National Highway Traffic Safety Administration finalized regulations in April 2010 to reduce greenhouse gas emissions and improve fuel economy for 2012-2016 model year light-duty vehicles. In November 2010, both agencies jointly proposed the first ever greenhouse gas standards for medium- and heavy-duty trucks which are expected to take effect for model years starting in 2014. Vehicles of light-duty families are subject to mandatory testing for certification and compliance. Unlike the light-duty sector where a vast majority of vehicles are mass produced for generally similar purposes, medium- and heavy-duty vehicles are commonly custom-made.

A fuel-cycle model-called the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model-has been developed at Argonne National Laboratory to evaluate well-to-wheels (WTW) energy and emission impacts of motor vehicle technologies fueled with various transportation fuels. The new GREET version has up-to-date information regarding energy use and emissions for fuel production activities and vehicle operations. In this study, a complete WTW evaluation targeting energy use, greenhouse gases (CO2, CH4, and N2O), and typical criteria air pollutants (VOC, NOX, and PM10) includes the following fuel options-gasoline, diesel, and hydrogen; and the following vehicle technologies-spark-ignition engines with or without hybrid configurations, compression-ignition engines with hybrid configurations, and hydrogen fuel cells with hybrid configurations.

In this paper, the detection and isolation of actuator faults (both measured and commanded) occurring in the engine breathing and the fueling systems of a spark-ignition direct-injection (SIDI) engine are described. The breathing system in an SIDI engine usually consists of a fresh air induction path via an electronically controlled throttle (ECT) and an exhaust gas recirculation (EGR) path via an EGR valve. They are dynamically coupled through the intake manifold to form a gas mixture, which eventually enters the engine cylinders for a subsequent combustion process. Meanwhile, the fueling system is equipped with a high-pressure common-rail injection for a precise control of the fuel quantity directly injected into the engine cylinders. Since the coupled system is highly nonlinear in nature, the fault diagnosis will be performed by generating residuals based on multiple nonlinear observers.

This paper presents the results of Argonne National Laboratory's assessment of the fuel-cycle energy and emissions impacts of 13 combinations of fuels and propulsion systems that are potential candidates for light-duty vehicles with tripled fuel economy (3X vehicles). These vehicles are being developed by the Partnership for a New Generation of Vehicles (PNGV). Eleven fuels were considered: reformulated gasoline (RFG), reformulated diesel (RFD), methanol, ethanol, dimethyl ether, liquefied petroleum gas (LPG), compressed natural gas (CNG), liquefied natural gas (LNG), biodiesel, Fischer-Tropsch diesel and hydrogen. RFG, methanol, ethanol, LPG, CNG and LNG were assumed to be burned in spark-ignition, direct-injection (SIDI) engines. RFD, Fischer-Tropsch diesel, biodiesel and dimethyl ether were assumed to be burned in compression-ignition, direct-injection (CIDI) engines. Hydrogen, RFG and methanol were assumed to be used in fuel-cell vehicles.