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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.

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.

With the increasing demand for efficient & clean transport solutions, applications such as road transport vehicles, aerospace and marine are seeing a rise in electrification at a significant rate. Irrespective of industries, the main source of power that enables electrification in mobility applications like electric vehicles (EV), electric ships and electrical vertical take-off & landing (e-VTOL) is primarily a battery making it fundamentally a DC system. Fast charging solutions for EVs & e-VTOLs are also found to be DC in nature because of several advantages like ease of integration, higher efficiency, etc. Likewise, the key drivers of the electric grid are resulting in an energy transition towards renewable sources, that are also essentially DC in nature. Overall, these different business trends with their drivers appear to be converging towards DC power systems, making it pertinent.

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 automotive industry has seen accelerating demand for electrified transportation. While the complexity of conventional ICE vehicles has increased, the powertrain still largely consists of a mechanical system. In contrast, vehicle architectures in electrified transportation are a complex integration of power electronics, batteries, control units, and software. This shift in system architecture impacts the entire organization during new product development, with increased focus on high power electronic components, energy management strategies, and complex algorithm development. Additionally, product development impact extends beyond the vehicle and impacts charging networks, electrical infrastructure, and communication protocols. The complex interaction between systems has a significant impact on vehicle safety, development timeline, scope, and cost.

Wet-sump transmissions are widely used in heavy duty and medium duty vehicles. As these transmissions do not have a dedicated forced lubrication system, it is important that the gear train, shafts, and enclosure are designed appropriately so that enough oil splashes to critical locations to ensure sufficient lubrication. The lubrication effectiveness of such transmissions can be studied through detailed tests or numerical simulations. Often, the vehicle, and therefore the transmission, encounters some severe operating conditions, such as climbing on an incline, driving downhill, etc. Studying these conditions through tests is an expensive process and this imposes the need for an analysis first approach. In this paper, the 3D multiphase Volume of Fluid (VOF) method is used to examine two such extreme cases: an 8-degree tilted installation of transmission in a vehicle, and an inclined condition of transmission during a 10-degree uphill climb.

Roots blower is a rotary positive displacement pump which operates by pumping a fluid with a pair of meshing lobes. Recent trends in automotive industry demands high power density solutions for various applications. In comparison with legacy applications, compressors for high power density applications demand continuous operation with harsher duty cycle as well as demand higher pressure ratios. Because of longer duty cycles, it will be subjected to high heat loads which will cause a rise in temperatures of timing gears, bearings, and other components within the assembly. Accurate prediction of thermal performance is critical to design a durable and efficient roots blower for high power density applications. Thermal analysis of an assembly of roots blower involves modelling of multi-physics phenomena. This paper details a coupled CFD analysis approach to predict temperatures of roots blower components and timing gear case oil. Timing gears are lubricated using wet sump lubrication.

The use of electrical contacts in aerospace applications is crucial, particularly in connectors that transmit signal and power. Crimping is a widely preferred method for joining electrical contacts, as it provides a durable connection and can be easily formed. This process involves applying mechanical load to the contact, inducing permanent deformation in the barrel and wire to create a reliable joint with sufficient wire retention force. This study utilizes commercially available Abaqus software to simulate the crimping process using an explicit solver. The methodology developed for this study correlates FEA and testing for critical quality parameters such as structural integrity, mechanical strength, and joint filling percentage. A four-indenter crimping tool CAD model is utilized to form the permanent joint at the barrel-wire contact interfaces, with displacement boundary conditions applied to the jaws of the tool in accordance with MIL-C-22520/1C standard.

With the advent of wide band gap semiconductor devices like SiC based MOSFETs/Diodes, there is a growing demand for utilizing electrical power instead of the conventional fuel-based power generation in both automotive and aerospace industry. In automotive/aerospace industry the focus on electrification has resulted in a need for sub-systems like inverters, power distribution units, motor controllers, DC-DC converters that actively utilize SiC based power electronics devices. To address the growing power density requirements for electronics in next generation product families, more efficient & reliable thermal management solution plays a critical role. The effective thermal management of the power electronics is also critical aspect to ensure overall system reliability. The conventional thermal management system (TMS) optimization targets heat sink/ cold plate design parameters like fin spacing, thickness, height etc. or sizing of the required cooling pump/fan.

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 this work, a novel bearing test rig was used to evaluate the impact of oil viscoelasticity on friction torque and oil film thickness in a hydrodynamic journal bearing. The test rig used an electric motor to rotate a test journal, while a hydraulic actuator applied radial load to the connecting rod bearing. Lubrication of the journal bearing was accomplished via a series of axial and radial drillings in the test shaft and journal, replicating oil delivery in a conventional engine crankshaft. Journal bearing inserts from a commercial, medium duty diesel engine (Cummins ISB) were used. Oil film thickness was measured using high precision eddy current sensors. Oil film thickness measurements were taken at two locations, allowing for calculation of minimum oil film thickness. A high-precision, in-line torque meter was used to measure friction torque. Four test oils were prepared and evaluated.

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.

Trained human raters have been used by organizations such as the Coordinating Research Council (CRC) to assess the vehicle driveability performance effect of fuel volatility. CRC conducts workshops to test fuel effects and their impact on vehicle driveability. CRC commissioned Southwest Research Institute (SwRI) to develop a “Trick Car” vehicle that could trigger malfunctions on-demand that mimic driveability events. This vehicle has been used to train novice personnel on the CRC Driveability Procedure E-28-94. While largely effective, even well-trained human raters can be inconsistent with other raters. Further, CRC rater workshop programs used to train and calibrate raters are infrequent, and there are a limited number of available trained raters. The goal of this program was to augment or substitute human raters with an electronic driveability sensing system.

Eco-driving algorithms enabled by Vehicle to Everything (V2X) communications in Connected and Automated Vehicles (CAVs) can improve fuel economy by generating an energy-efficient velocity trajectory for vehicles to follow in real time. Southwest Research Institute (SwRI) demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks using SwRI’s eco-driving algorithms. However, the impact of these schemes on vehicle emissions is not well understood. This paper details the effort of using data from SwRI’s on-road vehicle tests to measure and evaluate how eco-driving could impact emissions. Two engine and aftertreatment configurations were evaluated: a production system that meets current NOX standards and a system with advanced aftertreatment and engine technologies designed to meet low NOX 2031+ emissions standards.

A physics-based spark ignition model was developed and integrated into a commercial CFD code. The model predicted the spark discharge process based on the electrical parameters of the secondary ignition circuit, tracked the spark motion as it was stretched by in-cylinder gas motion, and determined the resulting energy deposition to the gas. In concert with the existing kinetic solver in the CFD code, the resulting ignition and flame propagation processes were simulated. The model results have been validated against both imaging rig experiments of the spark in moving air and against engine experimental data. The model was able to replicate the key features of the spark and to capture the cyclic variability of high-dilution combustion when multiple engine cycles were simulated.

This program involved the detailed evaluation of a novel laser-based in-exhaust ammonia sensor using a diesel fuel-based burner platform integrated with an ammonia injection system. Test matrix included both steady-state modes and transient operation of the burner platform. Steady-state performance evaluation included tests that examined impact of exhaust gas temperature, gas velocity and ammonia levels on sensor response. Furthermore, cross sensitivity of the sensor was examined at different levels of NOX and water vapor. Transient tests included simulation of the FTP test cycles at different ammonia and NOX levels. A Fourier transform infrared (FTIR) spectrometer as well as NIST traceable ammonia gas bottles (introduced into the exhaust stream via a calibrated flow controller) served as references for ammonia measurement.

Despite considerable progress towards clean air in previous decades, parts of the United States continue to struggle with the challenge of meeting the ambient air quality targets for smog-forming ozone mandated by the U.S. EPA, with some of the most significant challenges being seen in California. These continuing issues have highlighted the need for further reductions in emissions of NOX, which is a precursor for ozone formation, from a number of key sectors including the commercial vehicle sector. In response, the California Air Resources Board (CARB) embarked on a regulatory effort culminating in the adoption of the California Heavy-Duty Low NOX Omnibus regulation.[1] This regulatory effort was supported by a series of technical programs conducted at Southwest Research Institute (SwRI).