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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.

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.

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.

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.

Suspension systems are an integral part of land vehicles and contribute significantly to the vehicle performance in terms of its ride comfort and road holding characteristics. In the case of Space Exploration Vehicles (SEVs), the requirement of these unmanned vehicles is to rove, collect pictures and transmit data back to the earth. This is generally performed with the help of exteroceptive, and proprioceptive sensors mounted on the main chassis of the SEV. The design of various components of such vehicles is dictated by the assumption of extreme terrain and environmental conditions that it might face. The Mars Exploration Rovers (MERs) have incorporated the use of the “Rocker-Bogie” mechanism for the suspension system which provides relative stability to the MER for various maneuvers. In this work, the “Rocker-Bogie” mechanism is modeled and simulated as a planar kinematic model using parameters of the Perseverance rover.

Rapid adoption of battery electric vehicles means improving the energy consumption and energy efficiency of these new vehicles is a top priority. One method of accomplishing this is regenerative braking, which converts kinetic energy to electrical energy stored in the battery pack while the vehicle is decelerating. Coasting is an alternative strategy that minimizes energy consumption by decelerating the vehicle using only road load. A battery electric vehicle model is refined to assess regenerative braking, coasting, and other deceleration strategies. A road load model based on public test data calculates tractive effort requirements based on speed and acceleration. Bidirectional Willans lines are the basis of a powertrain model simulating battery energy consumption. Vehicle tractive and powertrain power are modeled backward from prescribed linear velocity curves, and the coasting trajectory is forward modeled given zero tractive power.

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.

This paper provides a simulation analysis of a novel interconnected roll stability control (RSC) system for improving the roll stability of semitrucks with double trailers. Different from conventional RSC systems where each trailer’s RSC module operates independently, the studied interconnected RSC system allows the two trailers’ RSC systems to communicate with each other. As such, if one trailer’s RSC activates, the other one is also activated to assist in further scrubbing speed or intervening sooner. Simulations are performed using a multi-body vehicle dynamics model that is developed in TruckSim® and coupled with the RSC model established in Simulink®. The dynamic model is validated using track test data. The simulation results for a ramp steer maneuver (RSM) and sine-with-dwell (SWD) maneuver indicate that the proposed RSC system reduces lateral acceleration and rollover index for both trailers, decreasing the likelihood of wheel tip-up and vehicle rollover.

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

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.

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 influence of driver modeling and drive cycle target speed trace modification on vehicle dynamics within energy consumption simulations is studied. EPA dynamometer speed error criteria and the SAE J2951 Drive Quality Evaluation for Chassis Dynamometer Testing standard are applied to simulation outputs as proposed components of simulation validation, providing guidelines for acceptable vehicle speed outputs and allowing comparison of simulation results to reported EPA dynamometer test statistics. The combined effect of driver model tuning and drive cycle interpolation methods is investigated for the UDDS, HwFET and US06 drive cycles, with EPA-specified linearly interpolated speed trace and a PI controller driver as a baseline result.

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.

A new and unique electric vehicle powertrain model based on bidirectional power flow for propel and regenerative brake power capture is developed and applied to production battery electric vehicles. The model is based on a Willans line model to relate power input from the battery and power output to tractive effort, with one set of parameters (marginal efficiency and an offset loss) for the bidirectional power flow through the powertrain. An electric accessory load is included for the propel, brake and idle phases of vehicle operation. In addition, regenerative brake energy capture is limited with a regen fraction (where the balance goes to friction braking), a power limit, and a low-speed cutoff limit. The purpose of the model is to predict energy consumption and range using only tractive effort based on EPA published road load and test mass (test car list data) and vehicle powertrain parameters derived from EPA reported unadjusted UDDS and HWFET energy consumption.

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.

Modern thermal propulsion systems (TPS) as part of hybrid powertrains are becoming increasingly complex. They have an increased number of components in comparison to traditionally powered vehicles leading to increased demand in packaging requirements. Many of the components in these systems relate to achieving efficiency gains, weight saving and pollutant reduction. This includes turbochargers and diesel or gasoline particulate filters for example and these are known to be very sensitive to inlet boundary conditions. When overcoming packaging requirements, sub-optimal flow distributions throughout the TPS can easily occur. Moreover, the individual components are often designed in isolation assuming relatively flat and artificially quiescent inlet flow conditions in comparison to those they are actually presented with. Thus, some of the efficiency benefits are lost through reduced component aerodynamic efficiency.

Multi-mode combustion strategies may provide a promising pathway to improve thermal efficiency in light-duty spark ignition (SI) engines by enabling switchable combustion modes, wherein an engine may operate under advanced compression ignition (ACI) at low load and spark-assisted ignition at high load. The extension from the SI mode to the ACI mode requires accurate control of intake charge conditions, e.g., pressure, temperature and equivalence ratio, in order to achieve stable combustion phasing and rapid mode-switches. This study presents results from computational fluid dynamics (CFD) analysis to gain insights into mixture charge formation and combustion dynamics pertaining to auto-ignition processes. The computational study begins with a discussion of thermal wall boundary condition that significantly impacts the combustion phasing.

The study of spray-wall interaction is of great importance to understand the dynamics that occur during fuel impingement onto the chamber wall or piston surfaces in internal combustion engines. It is found that the maximum spreading length of an impinged droplet can provide a quantitative estimation of heat transfer and energy transformation for spray-wall interaction. Furthermore, it influences the air-fuel mixing and hydrocarbon and particle emissions at combusting conditions. In this paper, an analytical model of a single diesel droplet impinging on the wall with different inclined angles (α) is developed in terms of βm (dimensionless maximum spreading length, the ratio of maximum spreading length to initial droplet diameter) to understand the detailed impinging dynamic process.

Mechanical and thermal properties of the rubber compounds of a tire play an important role in the overall performance of the tire when it is in contact with the terrain. Although there are many studies conducted on the properties of the rubber compounds of the tire to improve some of the tire characteristics such as the wear of the tread, there is a limited number of studies that focused on the performance of the tire when it is in contact with ice. This study is a part of a more comprehensive project looking into tire-ice performance and modeling. A significant part of this study is the experimental investigation of the effect of rubber compounds on tire performance in contact with ice. For this, four tires have been selected for testing. Three of them are completely identical in all tire parameters (such as tire dimensions), except for the rubber compounds. Several tests were conducted for the chosen tires in three modes: free rolling, braking, and traction.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.