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This paper determines the impact of ambient temperature on energy consumption of a variety of vehicles in the laboratory. Several conventional vehicles, several hybrid electric vehicles, a plug-in hybrid electric vehicle and a battery electric vehicle were tested for fuel and energy consumption under test cell conditions of 20°F, 72°F and 95°F with 850 W/m₂ of emulated radiant solar energy on the UDDS, HWFET and US06 drive cycles. At 20°F, the energy consumption increase compared to 72°F ranges from 2% to 100%. The largest increases in energy consumption occur during a cold start, when the powertrain losses are highest, but once the powertrains reach their operating temperatures, the energy consumption increases are decreased. At 95°F, the energy consumption increase ranges from 2% to 70%, and these increases are due to the extra energy required to run the air-conditioning system to maintain 72°F cabin temperatures.

The recovery of braking energy through regenerative braking is a key enabler for the improved efficiency of Hybrid Electric Vehicles, Plug-in Hybrid Electric, and Battery Electric Vehicles (HEV, PHEV, BEV). However, this energy is often treated in a simplified fashion, frequently using an overall regeneration efficiency term, ξrg [1], which is then applied to the total available braking energy of a given drive-cycle. In addition to the ability to recapture braking energy typically lost during vehicle deceleration, hybrid and plug-in hybrid vehicles also allow for reduced or zero engine fueling during vehicle decelerations. While regenerative braking is often discussed as an enabler for improved fuel economy, reduced fueling is also an important component of a hybrid vehicle's ability to improve overall fuel economy.

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).

A key strategy to improving the real-world fuel consumption and emissions of medium and heavy duty vehicles is the hybridization of these applications. Unlike the passenger vehicle market, medium and heavy duty applications are typically comprised of a range of components from a variety of manufacturers. The vocational market diversity and size places considerable demand on fuel efficiency and emission compliance. Medium and heavy duty applications have the ability to be successfully hybridized in ways that are not currently, or would not be practical within a passenger vehicle. This would also drive greater truck and bus vertical integration of the hybrid components. However, medium and heavy duty manufacturers have been prevented from certifying a full vehicle level platform due to the current engine only certification requirements.

As batteries become a major component of numerous advanced vehicles, significant efforts have been allocated towards characterizing and estimating battery energy capability over the lifetime of a vehicle. Currently, battery State of Charge (SOC) is one of the primary values used for this characterization; however SOC usage has several issues when implemented in Electric Vehicle (EV), Hybrid Electric Vehicle (HEV), and Plug-In Hybrid Electric Vehicle (PHEV) systems. One of the main issues with reporting battery SOC as a characterization of battery energy capability is that it only gives a percentage of the energy available to the operator. SOC does not accurately represent the true capability or capacity of the battery, and thus fails to account for the impact to capability with respect to battery size, age, and recent operational history.

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.

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.

Connected automated vehicles (CAVs) are quickly becoming a reality, and their potential ability to communicate with each other and the infrastructure around them has big potential impacts on future mobility systems. Perhaps one of the most important impacts could be on network wide energy consumption. A lot of research has already been performed on the topic of eco-driving and the potential fuel and energy consumption benefits for CAVs. However, most of the efforts to date have been based on simulation studies only, and have only considered conventional vehicle powertrains. In this study, experimental data is presented for the potential eco-driving benefits of two specific intersection approach scenarios, for four different powertrain types.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

Rising fuel costs, increased regulations, and heightened customer sensitivity to energy efficiency has prompted the evaluation of numerous powertrain technology improvements to introduce into production. The actual impact of such technologies can differ broadly, depending on the technology or application. To evaluate the fuel consumption impact, various baseline vehicles have been created and simulated by using Argonne National Laboratory's vehicle modeling and simulation tool, the Powertrain Systems Analysis Toolkit (PSAT). This paper provides a quantitative evaluation of several technologies or combinations of technologies. First, we assess the impact of single technologies, including vehicle/chassis characteristics, such as weight, aerodynamics, or rolling resistance. Next, we consider advanced powertrain technologies, ranging from dieselization to transmissions with a higher gear number, and hybridization.

Connectivity and automation are increasingly being developed for cars and trucks, aiming to provide better safety and better driving experience. As these technologies mature and reach higher adoption rates, they will also have an impact on the energy consumption: Connected and Automated Vehicles (CAVs) may drive more smoothly, stop less often, and move at faster speeds, thanks to overall improvements to traffic flows. These potential impacts are not well studied, and any existing studies tend to focus solely on conventional engine-powered cars, leaving aside electrified vehicles such as Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs). This work intends to address this issue by analyzing the energy impact of various CAV scenarios on different types of electric vehicles using high-fidelity models. The vehicles-all midsize, one HEV, one BEV, and a conventional-are modeled in Autonomie, a high-fidelity, forward-looking vehicle simulation tool.

A multimode transmission combines several power-split modes and possibly several fixed gear modes, thanks to complex arrangements of planetary gearsets, clutches and electric motors. Coupled to a battery, it can be used in a highly flexible hybrid configuration, which is especially practical for larger cars. The Chevrolet Tahoe Hybrid is the first light-duty vehicle featuring such a system. This paper introduces the use of a high-level vehicle controller based on instantaneous optimization to select the most appropriate mode for minimizing fuel consumption under a broad range of vehicle operating conditions. The control uses partial optimization: the engine ON/OFF and the battery power demand regulating the battery state-of-charge are decided by a rule-based logic; the transmission mode as well as the operating points are chosen by an instantaneous optimization module that aims at minimizing the fuel consumption at each time step.

This paper presents the validation of an entire vehicle model of the Honda Accord Plug-in Hybrid Electric Vehicle (PHEV), which has a new powertrain system that can be driven in both series and parallel hybrid drive using a clutch, including thermal aspects. The Accord PHEV is a series-parallel PHEV with about 21 km of all-electric range and no multi-speed gearbox. Vehicle testing was performed at Argonne’s Advanced Powertrain Research Facility on a chassis dynamometer set in a thermal chamber. First, components (engine, battery, motors and wheels) were modeled using the test data and publicly available assumptions. This includes calibration of the thermal aspects, such as engine efficiency as a function of coolant temperature. In the second phase, the vehicle-level control strategy, especially the energy management, was analyzed in normal conditions in both charge-depleting and charge-sustaining modes.

Hybrid electric vehicles have demonstrated their ability to significantly reduce fuel consumption for several medium- and heavy-duty applications. In this paper we analyze the impact on fuel economy of the hybridization of a tractor-trailer. The study is done in PSAT (Powertrain System Analysis Toolkit), which is a modeling and simulation toolkit for light- and heavy-duty vehicles developed by Argonne National Laboratory. Two hybrid configurations are taken into account, each one of them associated with a level of hybridization. The mild-hybrid truck is based on a parallel configuration with the electric machine in a starter-alternator position; this allows start/stop engine operations, a mild level of torque assist, and a limited amount of regenerative braking. The full-hybrid truck is based on a series-parallel configuration with two electric machines: one in a starter-alternator position and another one between the clutch and the gearbox.

Limited battery power and poor engine efficiency at cold temperature results in low plug in hybrid vehicle (PHEV) fuel economy and high emissions. Quick rise of battery temperature is not only important to mitigate lithium plating and thus preserve battery life, but also to increase the battery power limits so as to fully achieve fuel economy savings expected from a PHEV. Likewise, it is also important to raise the engine temperature so as to improve engine efficiency (therefore vehicle fuel economy) and to reduce emissions. One method of increasing the temperature of either component is to maximize their usage at cold temperatures thus increasing cumulative heat generating losses. Since both components supply energy to meet road load demand, maximizing the usage of one component would necessarily mean low usage and slow temperature rise of the other component. Thus, a natural trade-off exists between battery and engine warm-up.

The U.S. Department of Energy (DOE) has invested considerable research and development (R&D) effort into Plug-in Hybrid Electric Vehicle (PHEV) technology because of the potential fuel displacement offered by the technology. DOE's PHEV R&D Plan [1], which is driven by the desire to reduce dependence on foreign oil by diversifying the fuel sources of automobiles, describes the various activities required to achieve the goals. The U.S. DOE will use Argonne's Powertrain Systems Analysis Toolkit (PSAT) to guide its analysis activities, stating, “Argonne's Powertrain Systems Analysis Toolkit (PSAT) will be used to design and evaluate a series of PHEVs with various ‘primary electric’ ranges, considering all-electric and charge-depleting strategies.” PSAT was used to simulate three possible charge-depleting (CD) PHEV control strategies for a power split hybrid. Trip distance was factored into the CD strategies before the cycle was started.

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.

The U.S. Environmental Protection Agency instrumented and tested a line-haul Class 8 tractor trailer on a 4-wheel-drive heavy-duty chassis dynamometer. A vehicle model was then developed in the Powertrain Systems Analysis Toolkit (PSAT), Argonne National Laboratory's vehicle simulation tool, using the truck technical specifications and the recorded data, which included the Portable Emissions Measurement System (PEMS) and Controller Area Network (CAN) signals. In this paper, we describe the test scenarios and the analysis performed on the data. We then present the vehicle model and assumptions. Finally, we compare the test and simulation data, including fuel consumption and component signals, as well as the main challenges specific to heavy-duty vehicle testing and simulation.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

Plug-in Hybrid Electric Vehicles (PHEVs) use electric energy from the grid rather than fuel energy for most short trips, therefore drastically reducing fuel consumption. Different configurations can be used for PHEVs. In this study, the parallel pre-transmission, series, and power-split configurations were compared by using global optimization. The latter allows a fair comparison among different powertrains. Each vehicle was operated optimally to ensure that the results would not be biased by non-optimally tuned or designed controllers. All vehicles were sized to have a similar all-electric range (AER), performance, and towing capacity. Several driving cycles and distances were used. The advantages of each powertrain are discussed.