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In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

As a result of increasingly stringent regulations and higher customer expectations, auto manufacturers have been considering numerous technology options to improve vehicle fuel economy. Transmissions have been shown to be one of the most cost-effective technologies for improving fuel economy. Over the past couple of years, transmissions have significantly evolved and impacted both performance and fuel efficiency. This study validates the shifting control of advanced automatic transmission technologies in vehicle systems by using Argonne National Laboratory's model-based vehicle simulation tool, Autonomie. Different midsize vehicles, including several with automatic transmission (6-speeds, 7-speeds, and 8-speeds), were tested at Argonne's Advanced Powertrain Research Facility (APRF). For the vehicles, a novel process was used to import test data.

Building a vehicle model with sufficient accuracy for fuel economy analysis is a time-consuming process, even with the modern-day simulation tools. Obtaining the right kind of data for modeling a vehicle can itself be challenging, given that while OEMs advertise the power and torque capability of their engines, the efficiency data for the components or the control algorithms are not usually made available for independent verification. The U.S. Department of Energy (DOE) funds the testing of vehicles at Argonne National Laboratory, and the test data are publicly available. Argonne is also the premier DOE laboratory for the modeling and simulation of vehicles. By combining the resources and expertise with available data, a process has been created to automatically develop a model for any conventional vehicle that is tested at Argonne. This paper explains the process of analyzing the publicly available test data and computing the parameters of various components from the analysis.

The Prius - a power-split hybrid electric vehicle from Toyota - has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration - an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory's Advanced Powertrain Research Facility (ARRF).

Alternative electric motor geometry with potentially increased efficiency is being considered for hybrid electric vehicle applications. An axial flux motor with a dynamically adjustable air gap (i.e., mechanical field weakening) has been tested, analyzed, and modeled for use in a vehicle simulation tool at Argonne National Laboratory. The advantage of adjusting the flux is that the motor torque-speed characteristics can better match the vehicle load. The challenge in implementing an electric machine with these qualities is to develop a control strategy that takes advantage of the available efficiency improvements without using excessive energy to mechanically adjust the air gap and thus reduce the potential energy savings. Motor efficiency was mapped in terms of speed, torque, supply voltage, and rotor-to-stator air gap.

The energy density and power density comparison of conventional fuels and batteries is often mentioned as an advantage of conventional vehicles over electric vehicles. Such an analysis often shows that the batteries are at least an order of magnitude behind fuels like gasoline. However this incomplete analysis ignores the impact of powertrain efficiency and mass of the powertrain itself. When we compare the potential of battery electric vehicles (BEVs) as an alternative for conventional vehicles, it is important to include the energy in the fuel and their storage as well as the eventual conversion to mechanical energy. For instance, useful work expected out of a conventional vehicle as well as a BEV is the same (to drive 300 miles with a payload of about 300 lb). However, the test weight of a Conventional vehicle and BEV will differ on the basis of what is needed to convert their respective stored energy to mechanical energy.

With the success of hybrid electric vehicles (HEVs) and the still uncertain long-term solution for vehicle transportation, Plug-in Hybrid Electric Vehicles (PHEV) appear to be a viable short-term solution and are of increasing interest to car manufacturers. Like HEVs, PHEVs offer two power sources that are able to independently propel the vehicle. They also offer additional electrical energy onboard. In addition to choices about the size of components for PHEVs, choices about powertrain configuration must be made. In this paper, we consider three potential architectures for PHEVs for 10- and 40-mi All Electric Range (AER) and define the components and their respective sizes to meet the same set of performance requirements. The vehicle and component efficiencies in electric-only and charge-sustaining modes will be assessed.

Dual-fuel combustion using port-injection of low reactivity fuel combined with direct injection of a higher reactivity fuel, otherwise known as Reactivity Controlled Compression Ignition (RCCI), has been shown as a method to achieve high efficiency combustion with moderate peak pressure rise rates, low engine-out soot and NOx emissions. A key requirement for extending to high-load operation is reduce the reactivity of the premixed charge prior to the diesel injection. One way to accomplish this is to use a very low reactivity fuel such as natural gas. In this work, experimental testing was conducted on a 13L multi-cylinder heavy-duty diesel engine modified to operate using RCCI combustion with port injection of natural gas and direct injection of diesel fuel. Natural gas/diesel RCCI engine operation is compared over the EPA Heavy-Duty 13 mode supplemental emissions test with and without EGR.

The Center for Transportation Research (CTR) Vehicle Systems team modified a Nissan CK-2 Continuously Variable Transmission (CVT) for a diesel hybrid powertrain application. Mechanical and electrical modifications were made to the CVT, both internal and external to the transmission. The goal of this experiment was to investigate and demonstrate the potential of CVT for diesel engines hybrid electric vehicles (HEVs) in fuel economy and emissions. The test set-up consisted of a diesel engine coupled to an electric motor driving a Continuously Variable Transmission (CVT). This hybrid drive is connected to a dynamometer and a DC electrical power source creating a vehicle context by combining advanced computer models and emulation techniques. The experiment focuses on the impact particular transmission control strategies have on measured fuel economy and emissions specifically, nitrogen oxides (NOx) and particulate matter (PM).

The control analysis and model validation of a 2014 BMW i3-Range Extender (REX) was conducted based on the test data in this study. The vehicle testing was performed on a chassis dynamometer set within a thermal chamber at the Advanced Powertrain Research Facility at Argonne National Laboratory. The BMW i3-REX is a series-type plug-in hybrid range extended vehicle which consists of a 0.65L in-line 2-cylinder range-extending engine with a 26.6kW generator, 125kW permanent magnet synchronous AC motor, and 18.8kWh lithium-ion battery. Both component and vehicle model including thermal aspects, were developed based on the test data. For example, the engine fuel consumption rate, battery resistance, or cabin HVAC energy consumption are affected by the temperature. Second, the vehicle-level control strategy was analyzed at normal temperature conditions (22°C ambient temperature). The analysis focuses on the engine on/off strategy, battery SOC balancing, and engine operating conditions.

This paper includes analysis results for the control strategy of the Peugeot 3008 Hybrid4, a diesel-electric hybrid vehicle, under different thermal conditions. The analysis was based on testing results obtained under the different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory (ANL). The objectives were to determine the principal concepts of the control strategy for the vehicle at a supervisory level, and to understand the overall system behavior based on the concepts. Control principles for complex systems are generally designed to maximize the performance, and it is a serious challenge to determine these principles without detailed information about the systems. By analyzing the test results obtained in various driving conditions with the Peugeot 3008 Hybrid4, we tried to figure out the supervisory control strategy.

Advanced internal combustion engines, although generally more efficient than conventional combustion engines, often encounter limitations in multi-cylinder applications due to variations in the combustion process. This study leverages experimental data from an inline 6-cylinder heavy-duty dual fuel engine equipped with a fully-flexible variable intake valve actuation system to study cylinder-to-cylinder variations in power production. The engine is operated with late intake valve closure timings in a dual-fuel combustion mode featuring a port-injection and a direct-injection fueling system in order to improve fuel efficiency and engine performance. Experimental results show increased cylinder-to-cylinder variation in IMEP as IVC timing moves from 570°ATDC to 610°ATDC, indicating an increasingly uneven fuel distribution between cylinders.

The U.S. Department of Energy, Vehicle Technologies Office (U.S. DOE-VTO) has been developing more energy-efficient and environmentally friendly highway transportation technologies that would enable the United States to burn less petroleum on the road. System simulation is an accepted approach for evaluating the fuel economy potential of advanced (future) technology targets. U.S. DOE-VTO defines the targets for advancement in powertrain technologies (e.g., engine efficiency targets, battery energy density, lightweighting, etc.) Vehicle system simulation models based on these targets have been generated in Autonomie, reflecting the different EPA classifications of vehicles for different advanced timeframes as part of the DOE Benefits and Scenario (BaSce) Analysis. It is also important to evaluate the progress of these component technical targets compared to existing technologies available in the market.

Low temperature combustion through in-cylinder blending of fuels with different reactivity offers the potential to improve engine efficiency while yielding low engine-out NOx and soot emissions. A Navistar MaxxForce 13 heavy-duty compression ignition engine was modified to run with two separate fuel systems, aiming to utilize fuel reactivity to demonstrate a technical path towards high engine efficiency. The dual-fuel engine has a geometric compression ratio of 14 and uses sequential, multi-port-injection of a low reactivity fuel in combination with in-cylinder direct injection of diesel. Through control of in-cylinder charge reactivity and reactivity stratification, the engine combustion process can be tailored towards high efficiency and low engine-out emissions. Engine testing was conducted at 1200 rpm over a load sweep.

Their easy availability, lower well-to-wheel emissions, and relative ease of use with existing engine technologies have made ethanol and ethanol-gasoline blends a viable alternative to gasoline for use in spark-ignition (SI) engines. The lower energy density of ethanol and ethanol-gasoline blends, however, results in higher volumetric fuel consumption compared with gasoline. Also, the higher latent heat of vaporization can result in cold-start issues with higher-level ethanol blends. On the other hand, a higher octane number, which indicates resistance to knock and potentially enables more optimal combustion phasing, results in better engine efficiency, especially at higher loads. This paper compares the fuel consumption and emissions of two ethanol blends (E50 and E85) with those for gasoline when used in conventional (non-hybrid) and power-split-type plug-in hybrid electric vehicles (PHEVs).

Stricter CO2 and emissions regulations are pushing spark ignition engines more and more towards downsizing, enabled through direct injection and turbocharging. The advantages which come with direct injection, such as increased charge density and an elevated knock resistance, are even more pronounced when using low carbon number alcohols instead of gasoline. This is mainly due to the higher heat of vaporization and the lower air-to-fuel ratio of light alcohols such as methanol, ethanol and butanol. These alcohols are also attractive alternatives to gasoline because they can be produced from renewable resources. Because they are liquid, they can be easily stored in a vehicle. In this respect, the performance and engine-out emissions (NOx, CO, HC and PM) of methanol, ethanol and butanol were examined on a 4 cylinder 2.4 DI production engine and are compared with those on neat gasoline.

This study examines the dynamics and control of an engine operated with late intake valve closure (LIVC) timings in a dual-fuel combustion mode. The engine features a fuel delivery system in which diesel is direct-injected and natural gas is port-injected. Despite the benefits of LIVC and dual-fuel strategy, combining these two techniques resulted in efficiency losses due to the variability of the combustion process across cylinders. The difference in power production across cylinders ranges from 9% at an IVC of 570°ATDC* to 38% at an IVC of 620 °ATDC and indicates an increasingly uneven fuel distribution as the intake valve remains open longer in the compression stroke. This paper describes an approach for controlling the amount of fuel injected into each cylinders’ port of an inline six- cylinder heavy-duty dual-fuel engine to minimize the variations in fuel distribution across cylinder.

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.