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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.

With the increasing demand for Battery Electric Vehicles (BEVs) capable of extended mileage, optimizing their efficiency has become paramount for manufacturers. However, the challenge lies in balancing the need for climate control within the cabin and precise thermal regulation of the battery, which can significantly reduce a vehicle's driving range, often leading to energy consumption exceeding 50% under severe weather conditions. To address these critical concerns, this study embarks on a comprehensive exploration of the impact of weather conditions on energy consumption and range for the 2019 Nissan Leaf Plus. The primary objective of this research is to enhance the understanding of thermal management for BEVs by introducing a sophisticated thermal management system model, along with detailed thermal models for both the battery and the cabin.

Hybrid electric vehicles (HEVs) have seen tremendous improvements in performance, fuel economy and cost over the last two decades. As battery and motor prices decrease, HEVs are likely to be even more attractive to consumers. This study considers how HEVs can improve and whether advancements in engines and other components will play a large role in the HEV segment. Past studies have relied on a rule-based component sizing approach for hybrids to meet certain performance criteria. By going beyond this approach, we can explore the design space by varying engine power and electric drivetrain power. This can provide more insights into the fuel-saving potential of HEVs, and the trade-offs required on performance or cost characteristics to achieve those savings. In this study, we examine the fuel-saving potential of three main hybrid powertrain architectures (parallel, series, and power-split) with varying degrees of hybridization (DOH) and using various engine technologies.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.

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.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

This paper examines, for several major markets, the fuel savings achievable with advanced engine technologies as “drop-in” substitutions for existing engines, as well as from increased electric hybridization of the powertrain. Key segments of light duty vehicles in major automotive markets including the US, China, EU, Japan, India, and Saudi Arabia were examined. Representative vehicles for each market were simulated using advanced vehicle modeling tools and evaluated on the relevant local regulatory cycle or cycles. In all cases, to ensure meaningful results, the performance of a given vehicle was maintained as engine and powertrain technology was varied through appropriate resizing of powertrain components. In total, 4 engine technologies and 5 powertrain architectures were simulated for 5 different markets.

Gasoline compression ignition (GCI) has been shown to offer benefits in the NOx-soot tradeoff over conventional diesel combustion while still achieving high fuel efficiency. However, due to gasoline’s low reactivity, it is challenging for GCI to attain robust ignition and stable combustion under cold operating conditions. Building on previous work to evaluate glow plug-assisted GCI combustion at cold idle, this work evaluates the use of a spark plug to assist combustion. The closed-cycle 3-D CFD model was validated against GCI test results at a compression ratio of 17.3 during extended cold idle operation under laboratory-controlled conditions. A market representative, ethanol-free, gasoline (RON92, E0) was used in both the experiment and the numerical analysis. Spark-assisted simulations were performed by incorporating an ignition model with the spark energy required for stable combustion at cold start.

Connected and automated technologies poised to change the way vehicles operate are starting to enter the mainstream market. Methods to accurately evaluate these technologies, in particular for their impact on safety and energy, are complex due to the influence of static and environmental factors, such as road environment and traffic scenarios. Therefore, it is important to develop modeling and testing frameworks that can support the development of complex vehicle functionalities in a realistic environment. Microscopic traffic simulations have been increasingly used to assess the performance of connected and automated vehicle technologies in traffic networks. In this paper, we propose and apply an evaluation method based on a combination of microscopic traffic simulation (AIMSUN) and a chassis dynamometer-based vehicle-in-the-loop environment, developed at Argonne National Laboratory.

The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles. A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation.

Advanced compression ignition (ACI) operation in gasoline direct injection (GDI) engines is a promising concept to reduce fuel consumption and emissions at part load conditions. However, combustion phasing control and the limited operating range in ACI mode are a perennial challenge. In this study the combined impact of fuel properties and engine control strategies in ACI operation are investigated. A design of experiments method was implemented using a three level orthogonal array to determine the sensitivity of engine control parameters on the engine load, combustion noise and stability under low load ACI operation for three RON 98 gasoline fuels, each exhibiting disparate chemical composition. Furthermore, the thermodynamic state of the compression histories was studied with the aid of the pressure-temperature framework.

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.

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.

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.

Wireless Power Transfer (WPT) promises automated and highly efficient charging of electric and plug-in-hybrid vehicles. As commercial development proceeds forward, the technical challenges of efficiency, interoperability, interference and safety are a primary focus for this industry. The SAE Vehicle Wireless Power and Alignment Taskforce published the Recommended Practice J2954 to help harmonize the first phase of high-power WPT technology development. SAE J2954 uses a performance-based approach to standardizing WPT by specifying ground and vehicle assembly coils to be used in a test stand (per Z-class) to validate performance, interoperability and safety. The main goal of this SAE J2954 bench testing campaign was to prove interoperability between WPT systems utilizing different coil magnetic topologies. This type of testing had not been done before on such a scale with real automaker and supplier systems.

While spark-ignition (SI) engine technology is aggressively moving towards challenging (dilute and boosted) combustion regimes, advanced ignition technologies generating non-equilibrium types of plasma are being considered by the automotive industry as a potential replacement for the conventional spark-plug technology. However, there are currently no models that can describe the low-temperature plasma (LTP) ignition process in computational fluid dynamics (CFD) codes that are typically used in the multi-dimensional engine modeling community. A key question for the engine modelers that are trying to describe the non-equilibrium ignition physics concerns the plasma characteristics. A key challenge is also represented by the plasma formation timescale (nanoseconds) that can hardly be resolved within a full engine cycle simulation.

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.