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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 automotive industry is currently experiencing a massive transformation, one like it has not quite seen in the past. With the advent of highly software-driven, always on, connected vehicles, the automotive industry is experiencing itself at a crossroads. While the traditional component-driven design approach to vehicle development worked in the favor of the industry for decades due to vehicles being mostly mechanical in nature, the industry now finds itself struggling to develop well-integrated vehicle solutions with the large dependency on software systems. The fast-paced nature of the software world makes it imperative to approach the development of automobiles from a Systems Engineering perspective. A function-based approach to the development of vehicle architectures can ensure cohesive systems development and a well-integrated vehicle.

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

Improving passenger safety inside vehicle cabins requires continuously monitoring vehicle seat occupancy statuses. Monitoring a vehicle seat’s occupancy status includes detecting if the seat is occupied and classifying the seat’s occupancy type. This paper introduces an innovative non-intrusive technique that employs capacitive sensing and an occupancy classifier to monitor a vehicle seat’s occupancy status. Capacitive sensing is facilitated by a meticulously constructed capacitance-sensing mat that easily integrates with any vehicle seat. When a passenger or an inanimate object occupies a vehicle seat equipped with the mat, they will induce variations in the mat’s internal capacitances. The variations are, in turn, represented pictorially as grayscale capacitance-sensing images (CSI), which yield the feature vectors the classifier requires to classify the seat’s occupancy type.

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.

The internal combustion engine (ICE) has long dominated the heavy-duty sector by using liquid fossil fuels such as diesel but global commitments by countries and OEMs to reduce lifecycle carbon dioxide (CO2) emissions has garnered interest in alternative fuels like hydrogen. Hydrogen is a unique gaseous fuel that contains zero carbon atoms and has desired thermodynamic properties of high energy density per unit mass and high flame speeds. However, there are challenges related to its adoption to the heavy-duty sector as a drop-in fuel replacement for compression ignition (CI) diesel combustion given its high autoignition resistance. To overcome this fundamental barrier, engine manufacturers are exploring dual fuel combustion engines by substituting a fraction of the diesel fuel with hydrogen which enables fuel flexibility when there is no infrastructure and retrofittability to existing platforms.

The performance of autonomous vehicle (AV) sensors, such as lidars or cameras, is often hindered during rain. Rain droplets on the AV sensors can cause beam attenuation and backscattering, which in turn causes inaccurate sensor readings and misjudgment by AV algorithms. Most AV systems are equipped with cleaning systems to remove contaminants, such as rain, from AV sensors. One such mechanism is to blow high-speed air over the AV sensors. However, the cleaning air can be hindered by incoming headwind, especially at higher vehicle speeds. An innovative idea proposed here is to use a visor to improve the cleaning performance of AV cleaning systems at higher vehicle speeds. The effectiveness of a baseline visor design was studied using computational fluid dynamics (CFD) air flow analysis and Lagrangian rain droplet tracking. The baseline visor improved the AV sensor cleaning performance in two ways. First, the visor protects the cleaning air flow from being disturbed by headwind.

In a recent time, which new vehicle lines comes with a huge number of sensors, control units, embedded technologies, and the complexity of these systems (electronics, electrical and electromechanical parts) increases in an exponential way. Considering these events, the expressive generated data amount grows in the same pace, so, consume, transform, and analyze all these data to better understand the modern customer, their needs and how they use the car features becomes necessary. Through that scenario, connected vehicles developed by Ford Motor Company has been generating opportunities to feature’s improvement and cost reduction based on data analysis. This growing quantity of data might be used to optimize feature systems and help engineering teams to understand how the features have been used and enhance the systems engineering design for new or existing features.

The rise of greenhouse gas emissions has reached historic levels, with 37 billion tons of CO2 released into the atmosphere in 2018 alone. In the European Union, 32% of these emissions come from transportation, with 73.3% of that percentage coming from vehicles. To address this problem, solutions such as cleaner fuels and more efficient engines are necessary. Artificial Intelligence can also play a crucial role in climate analysis and verification to move towards a more sustainable future. By utilizing connected vehicle data, automakers can analyze real-time vehicle performance data to identify opportunities for improvement and reduce carbon emissions. This approach benefits the environment, improves vehicle quality, and reduces engineering work time, making it a win-win solution. Connected vehicle data offers a wealth of information on vehicle performance, such as fuel consumption and carbon emissions.

In recent years, the automotive industry has seen an exponential increase in the replacement of mechanical components with electronic-controlled components or systems. engine, transmission, brake, exhaust gas recirculation (EGR), lighting, driver-assist technologies, etc. are all monitored and/or controlled electronically. Connected vehicles are increasingly being used by Original Equipment Manufacturers (OEMs) to collect and transmit vehicle data in real-time via the use of various sensors, actuators, and communication technologies. Vehicle telematics devices can collect and transmit data about the vehicle location, speed, fuel efficiency, State Of Charge (SOC), auxiliary battery voltage, emissions, performance, and more. This data is sent over to the cloud via cellular networks, where it can be processed and analyzed to improve their products and services by automotive companies and/or fleet management.

In the present work, five surrogate components (n-Hexadecane, n-Tetradecane, Heptamethylnonane, Decalin, 1-Methylnaphthalene) are proposed to represent liquid phase of diesel fuel, and another different five surrogate components (n-Decane, n-Heptane, iso-Octane, MCH (methylcyclohexane), Toluene) are proposed to represent vapor phase of diesel fuel. For the vapor phase, a 5-component surrogate chemical kinetic mechanism has been developed and validated. In the mechanism, a recently updated H2/O2/CO/C1 detailed sub-mechanism is adopted for accurately predicting the laminar flame speeds over a wide range of operating conditions, also a recently updated C2-C3 detailed sub-mechanism is used due to its potential benefit on accurate flame propagation simulation. For each of the five diesel vapor surrogate components, a skeletal sub-mechanism, which determines the simulation of ignition delay times, is constructed for species C4-Cn.

Over the past couple of years, Argonne National Laboratory has tested, analyzed, and validated automobile models for the light duty vehicle class, including several types of powertrains including conventional, hybrid electric, plug-in hybrid electric and battery electric vehicles. Argonne’s previous works focused on the light duty vehicle models, but no work has been done on medium and heavy-duty vehicles. This study focuses on the validation of shifting control in advanced automatic transmission technologies for medium duty vehicles by using Argonne’s model-based high-fidelity, forward-looking, vehicle simulation tool, Autonomie. Different medium duty vehicles, from Argonne’s own fleet, including the Ram 2500, Ford F-250 and Ford F-350, were tested with the equipment for OBD (on-board diagnostics) signal data record. For the medium duty vehicles, a workflow process was used to import test data.

This is an extension of simple fuel consumption modeling toward HEV. Previous work showed that in urban driving the overhead of running an ICEV engine can use as much fuel as the traction work. The bidirectional character and high efficiency of electric motors enables HEVs to run as a BEV at negative and low traction powers, with no net input from the small battery. The ICE provides the net work at higher traction powers where it is most efficient. Whereas the network reduction is the total negative work times the system round-trip efficiency, the reduction in engine running time requires knowledge of the distribution of traction power levels. The traction power histogram, and the work histogram derived from it, provide the required drive cycle description. The traction power is normalized by vehicle mass, so that the drive trace component becomes invariant, and the road load component nearly invariant to vehicle mass.

A DMS (Driver Monitoring System) is one of the most important safety features that assist in the monitoring functions and alert drivers when distraction or drowsiness is detected. The system is based in a DSMC (Driver Status Monitoring Camera) mounted in the vehicle's dash, which has a predefined set of operational requirements that must be fulfilled to guarantee the correct operation of the system. These conditions represent a trade space analysis challenge for each vehicle since both the DSMC and the underlying vehicle’s requirements must be satisfied. Relying upon the camera’s manufacturer evaluation for every iteration of the vehicle’s design has proven to be time-consuming, resources-intensive, and ineffective from the decision-making standpoint.

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.

With the proliferation of ADAS and autonomous systems, the quality and quantity of the data to be used by vehicles has become crucial. In-vehicle sensors are evolving, but their usability is limited to their field of view and detection distance. V2X communication systems solve these issues by creating a cooperative perception domain amongst road users and the infrastructure by communicating accurate, real-time information. In this paper, we propose a novel Consolidated Object Data Service (CODS) for multi-Radio Access Technology (RAT) V2X communication. This service collects information using BSM packets from the vehicular network and perception information from infrastructure-based sensors. The service then fuses the collected data, offering the communication participants with a consolidated, deduplicated, and accurate object database. Since fusing the objects is resource intensive, this service can save in-vehicle computation costs.

The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have Vehicle to Everything (V2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the Vehicle to Vehicle (V2V) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules.

Fretting fatigue was observed in standard cylindrical fatigue samples at the regions in contact with the grips of the test frames during fatigue testing for AlSi10Mg aluminum alloy produced by laser powder bed fusion process (L-PBF). The failure of the fatigue sample grips occurs much earlier than the failure of the gauge section. This results in a damaged sample and the sample cannot be reused to continue the test. This type of failure is rarely seen in materials produced by traditional manufacturing processes. In this study, X-ray residual stress analysis was performed to understand the cause of failure for L-PBF AlSi10Mg with the as-built surface condition. The result indicates that the fretting fatigue failure was caused by the strong tensile residual stress in the as-built state combining with the fretting wear between the sample and the grip. A few potential solutions to avoid the fretting fatigue failure were investigated.

To enable smooth and low-risk autonomous driving in the presence of other road users, such as cyclists and pedestrians, appropriate predictive safe speed control strategies relying on accurate and robust prediction models should be employed. However, difficulties related to driving scene understanding and a wide variety of features influencing decisions of other road users significantly complexifies prediction tasks and related controls. This paper proposes a hierarchical neural network (NN)-based prediction model of pedestrian crossing behavior, which is aimed to be applied within an autonomous vehicle (AV) safe speed control strategy. Additionally, different single-level prediction models are presented and analyzed as well, to serve as baseline approaches.

There are a multitude of dynamics faced by any industry. There is also a consistent search and development of technological platforms and services to address these changes. This necessitates a shared work philosophy which involves multiple stakeholders. Verification and validation are integral part of any development irrespective of product, process, or services. Also, every industry has a regulatory compliance to adhere too. But the extent of complexity and the level of dependencies or interactions between modules as well as stakeholders involved, creates slippage at some or other level. Nowadays the industries are also driven by reuse for cost effectiveness. Though it marks the significant improvement in the capability to compete, compatibility is a key measure to a successful product or service launch and sustainability.