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The US EPA and the California Air Resources Board (CARB) require electric vehicle range to be determined according to the Society of Automotive Engineers (SAE) surface vehicle recommended practice J1634 - Battery Electric Vehicle Energy Consumption and Range Test Procedure. In the 2021 revision of the SAE J1634, the Short Multi-Cycle Test (SMCT) was introduced. The proposed testing protocol eases the chassis dynamometer test burden by performing a 2.1-hour drive cycle on the dynamometer, followed by discharging the remaining battery energy into a battery cycler to determine the Useable Battery Energy (UBE). Opting for a cycler-based discharge is financially advantageous due to the extended operating time required to fully deplete a 70-100kWh battery commonly found in Battery Electric Vehicles (BEVs).

Pouch cells are increasingly popular form factors for the construction of energy storage systems in electric vehicles of all classes. Knowledge of the stress generated by these higher capacity pouch cells is critical to properly design battery modules and packs for both normal and abnormal operation. Existing literature predominantly offers data on smaller pouch cells with capacities of less than 10 Ah, leaving a gap in our understanding of the behavior of these larger cells. This experimental study aimed to bridge this knowledge gap by measuring loads and stresses in constrained 65 Ah pouch cells under both cycling and abuse conditions. To capture the desired responses, a load cell was located within a robust fixture to measure cell stress in real time after the application of a preload of approximately 30 kilograms or 294 N, equivalent to a pressure of 0.063 bar, with a fixed displacement.

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.

The widely accepted best practice for spark-ignition combustion is the four-valve pent-roof chamber using a central sparkplug and incorporating tumble flow during the intake event. The bulk tumble flow readily breaks up during the compression stroke to fine-scale turbulent kinetic energy desired for rapid, robust combustion. The natural gas engines used in medium- and heavy-truck applications would benefit from a similar, high-tumble pent-roof combustion chamber. However, these engines are invariably derived from their higher-volume diesel counterparts, and the production volumes are insufficient to justify the amount of modification required to incorporate a pent-roof system. The objective of this multi-dimensional computational study was to develop a combustion chamber addressing the objectives of a pent-roof chamber while maintaining the flat firedeck and vertical valve orientation of the diesel engine.

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.

Vehicle-to-everything (V2X) communication, primarily designed for communication between vehicles and other entities for safety applications, is now being studied for its potential to improve vehicle energy efficiency. In previous work, a 20% reduction in energy consumption was demonstrated on a 2017 Prius Prime using V2X-enabled algorithms. A subsequent phase of the work is targeting an ambitious 30% reduction in energy consumption compared to a baseline. In this paper, we present the Eco-routing algorithm, which is key to achieving these savings. The algorithm identifies the most energy-efficient route between an Origin-Destination (O-D) pair by leveraging information accessible through commercially available Application Programming Interfaces (APIs). This algorithm is evaluated both virtually and experimentally through simulations and dynamometer tests, respectively, and is shown to reduce vehicle energy consumption by 10-15% compared to the baseline over real-world routes.

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. Zero Trust (ZT) originated as an Information Technology (IT) principle of “never trust, always verify”; it is the concept that a network must never assume assets can be trusted regardless of their ownership or network location. This research focused on drastically improving security of the cyber-physical vehicle network, with minimal performance impact measured as timing, bandwidth, and processing power. The automotive ZTA was tested using a software-in-the-loop vehicle simulation paired with resource constrained hardware that closely emulated a production vehicle network.

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.

Multiple areas in the U.S. continue to struggle with achieving National Ambient Air Quality Standards for ozone. These continued issues highlight the need for further reductions in NOX emission standards in multiple industry sectors, with heavy-duty on-highway engines being one of the most important areas to be addressed. Starting in 2014, CARB initiated a series of technical demonstration programs aimed at examining the feasibility of achieving up to a 90% reduction in tailpipe NOX, while at the same time maintaining a path towards GHG reductions that will be required as part of the Heavy-Duty Phase 2 GHG program. These programs culminated in the Stage 3 Low NOX program, which demonstrated low NOX emissions while maintaining GHG emissions at levels comparable to the baseline engine.

Off-road diesel engines remain one of the most significant contributors to the overall oxides of nitrogen (NOX) inventory and the California Air Resources Board (CARB) has indicated that reductions of up to 90% from current standards may be necessary to achieve its air quality goals. In recognition of this, CARB has funded a program aimed at demonstrating emission control technologies for off-road engines. This program builds on previous efforts to demonstrate Low NOX technologies for on-road engines. The objective was to demonstrate technologies to reduce tailpipe NOX and particulate matter (PM) emissions by 90 and 75%, respectively, from the current Tier 4 Final standards. In addition, the emission reductions were to be achieved while also demonstrating a 5 to 8.6% carbon dioxide (CO2) reduction and remaining Greenhouse Gas (GHG) neutral with respect to nitrous oxide (N2O) and methane (CH4).

This work represents an advanced engineering research project partially funded by the U.S. Department of Energy (DOE). Ford Motor Company, FEV North America, and Oak Ridge National Laboratory collaborated to develop a next generation boosted spark ignited engine concept. The project goals, specified by the DOE, were 23% improved fuel economy and 15% reduced weight relative to a 2015 or newer light-duty vehicle. The fuel economy goal was achieved by designing an engine incorporating high geometric compression ratio, high dilution tolerance, low pumping work, and low friction. The increased tendency for knock with high compression ratio was addressed using early intake valve closing (EIVC), cooled exhaust gas recirculation (EGR), an active pre-chamber ignition system, and careful management of the fresh charge temperature.

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.

According to the Annual Energy Outlook 2022 (AEO2022) report, almost 30% of the transport sector will still use internal combustion engines (ICE) until 2050. The transportation sector has been actively seeking different methods to reduce the CO2 emissions footprint of fossil fuels. The use of lower carbon-intensity fuels such as Renewable Diesel (RD) can enable a pathway to decarbonize the transport industry. This suggests the need for experimental or advanced numerical studies of RD to gain an understanding of its combustion and emissions performance. This work presents a numerical modeling approach to study the combustion and emissions of RD. The numerical model utilized the development of a reduced chemical kinetic mechanism for RD’s fuel chemistry. The final reduced mechanism for RD consists of 139 species and 721 reactions, which significantly shortened the computational time from using the detailed mechanism.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

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.

Upcoming regulations from CARB and EPA will require diesel engine manufacturers to validate aftertreatment durability with full useful life aged components. To this end, the Diesel Aftertreatment Accelerated Aging Cycle (DAAAC) protocol was developed to accelerate aftertreatment aging by accounting for hydrothermal aging, sulfur, and oil poisoning deterioration mechanisms. Two aftertreatment systems aged with the DAAAC protocol, one on an engine and the other on a burner system, were directly compared to a reference system that was aged to full useful life using conventional service accumulation. After on-engine emission testing of the fully aged components, DOC and SCR catalyst samples were extracted from the aftertreatment systems to compare the elemental distribution of contaminants between systems. In addition, benchtop reactor testing was conducted to measure differences in catalyst performance.

The capacity of a lithium-ion battery decreases during cycling. This capacity loss or fade occurs due to several different mechanisms associated with unwanted side reactions that occur in these batteries. The same reactions occur during overcharge and cause electrolyte decomposition, passive film formation, active material dissolution, and other phenomena. As the battery ages the accuracy of state of charge prediction decreases and vulnerability to persistent overcharge increases. Moreover, as the battery ages, its tolerance to such unintended overcharge changes. This tolerance depends on the nature of the history of cycle and calendar aging. A map of this tolerance in the BMS can provide awareness of the factor of safety due to overcharge as battery ages. Signatures of early warning signs of incipient thermal runaway due to overcharge can also be very useful features in a BMS.

Simulation and testing are often done by different engineers in different departments of a company. This can lead to disconnects and unrealistic predictions, especially if the person doing simulations does not have an experimental background. On the other hand, experimental results can also include errors that result in misleading answers. It is important for the engineer doing either testing or simulation to have a good understanding for what results are plausible and what results might be suspect. This paper will provide examples where error crept into testing or simulation that could have been caught and corrected early if a good feel for “reasonable” results had been in place. The importance of understanding how a software package is analyzing the data will be explained, since settings buried deep within a menu structure can drive misleading results.