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Other than in Japan, medium and heavy duty vehicles (MHDVs) are not regulated despite accounting for a significant portion of the fuel consumed (about 26% in the US in 2008). Government agencies worldwide are currently evaluating options to address that issue. Due to the large number of vehicle applications, some of them being “one of a kind”, vehicle modelling and simulation offers an attractive solution to medium and heavy duty regulations. This paper discusses the advantages and challenges of vehicle simulation to support regulations.

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.

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.

Validation of a vehicle simulation model of the Chevrolet Volt 2016 was conducted. The Chevrolet Volt 2016 is equipped with the new “Voltec” extended-range propulsion system introduced into the market in 2016. The second generation Volt powertrain system operates in five modes, including two electric vehicle modes and three extended-range modes. Model development and validation were conducted using the test data performed on the chassis dynamometer set in a thermal chamber of Argonne National Laboratory’s Advanced Powertrain Research Facility. First, the components of the vehicle, such as the engine, motor, battery, wheels, and chassis, were modeled, including thermal aspects based on the test data. For example, engine efficiency changes dependent on the coolant temperature, or chassis heating or air-conditioning operations according to the ambient and cabin temperature, were applied.

Improvements in vehicle technology impact the purchase price of a vehicle and its operating cost. In this study, the monetary benefit of a technology improvement includes the potential reduction in vehicle price from using cheaper or smaller components, as well as the discounted value of the fuel cost savings. As technology progresses over time, the value and benefit of improving technology varies as well. In this study, the value of improving a few selected technologies (battery energy density, electric drive efficiency, tire rolling resistance, aerodynamics, light weighting) is studied and the value of the associated cost saving is quantified. The change in saving as a function of time, powertrain selection and vehicle type is also quantified. For example, a 10% reduction in aerodynamic losses is worth $24,222 today but only $8,810 in 2030 in an electric long haul truck. The decrease in value is primarily due to expected battery cost reduction over time.

Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. Aerodynamic drag can also be reduced by close-gap platooning using information from vehicle-to-vehicle communications. In order to achieve these goals, the designers of control strategies need to simulate a wide range of driving situations in which vehicles interact with other vehicles and the infrastructure in a closed-loop fashion. RoadRunner is a new model-based system engineering platform based on Autonomie software, which can collectively provide the necessary tools to predict energy consumption for various driving decisions and scenarios such as car-following, free-flow, or eco-approach driving, and thereby can help in developing control algorithms.

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.

This paper presents the vehicle model development and validation process for the Ford Focus battery electric vehicles (BEVs) using Autonomie and test results from Advanced Powertrain Research Facility in Argonne National Laboratory. The parameters or characteristic values for the important components such as the electric machine and battery pack system are estimated through analyzing the test data of the multi cycle test (MCT) procedure under the standard ambient condition. A novel process was used to import vehicle test data into Autonomie. Through this process, a complete vehicle model of the Ford Focus BEV is developed and validated under ambient temperature for different drive cycles (UDDS, HWFET, US06 and Steady-State). The simulation results of the developed vehicle model show coincident results with the test data within 0.5% ∼ 4% discrepancies for electrical consumption.

This paper introduces control strategy analysis and performance degradation for the 2010 Toyota Prius under different thermal conditions. The goal was to understand, in as much detail as possible, the impact of thermal conditions on component and vehicle performances by analyzing a number of test data obtained under different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory. A previous study analyzed the control behavior and performance under a normal ambient temperature; thus the first step in this study was to focus on the impact when the ambient temperature is cold or hot. Based on the analyzed results, thermal component models were developed in which the vehicle controller in the simulation was designed to mimic the control behavior when temperatures of the components are cold or hot. Further, the performance degradation of the components was applied to the mathematical models based on analysis of the test data.

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.

The Toyota Prius Prime is a new generation of Toyota Prius plug-in hybrid electric vehicle, the electric drive range of which is 25 miles. This version is improved from the previous version by the addition of a one-way clutch between the engine and the planetary gear-set, which enables the generator to add electric propulsive force. The vehicle was analyzed, developed and validated based on test data from Argonne National Laboratory’s Advanced Powertrain Research Facility, where chassis dynamometer set temperature can be controlled in a thermal chamber. First, we analyzed and developed components such as engine, battery, motors, wheels and chassis, including thermal aspects based on test data. By developing models considering thermal aspects, it is possible to simulate the vehicle driving not only in normal temperatures but also in hot, cold, or warmed-up conditions.

Near-term advances in spark ignition (SI) engine technology (e.g., variable value lift [VVL], gasoline direct injection [GDI], cylinder deactivation, turbo downsizing) for passenger vehicles hold promise of delivering significant fuel savings for vehicles of the immediate future. Similarly, trends in transmissions indicate higher (8-speed, 9-speed) gear numbers, higher spans, and a focus on downspeeding to improve engine efficiency. Dual-clutch transmissions, which exhibit higher efficiency in lower gears, than the traditional automatics, and are being introduced in the light-duty vehicle segment worldwide. Another development requiring low investment and delivering immediate benefits has been the adaptation of start-stop (micro hybrids or idle engine stop technology) technology in vehicles today.

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.

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 Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.

The National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes.