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Electric vehicles (EVs) are becoming increasingly recognized as an effective solution in the battle against climate change and reducing greenhouse gas emissions. Lithium-ion batteries have become the standard for energy storage in the automobile industry, widely used in EVs due to their superior characteristics compared to other batteries. The growing popularity of the Vehicle-to-grid (V2G) concept can be attributed to its surplus energy storage capacity, positive environmental impact, and the reliability and stability of the power grid. However, the increased utilization of the battery through these integrations can result in faster degradation and the need for replacement. As batteries are one of the most expensive components of EVs, the decision to deploy an EV in V2G operations may be uncertain due to the concerns of battery degradation from the owner’s perspective.

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.

Thermal barrier coatings (TBCs) have long been studied as a potential pathway to achieve higher thermal efficiency in spark ignition engines. Researchers have studied coatings with different thicknesses and thermophysical properties to counteract the volumetric efficiency penalty associated with TBCs in spark ignition. To achieve an efficiency benefit with minimal charge heating during the intake stroke, low thermal inertia coatings characterized by their larger temperature swings are required. To study the impact of low thermal inertia coatings in spark ignition, coatings were applied to the cylinder head, piston crown, intake and exhaust valve faces, and intake and exhaust valve backsides. Tier III EEE E10 certification gasoline was used to keep the experiments relevant to the present on-road vehicles. This study is aimed at analyzing durability of the coatings as well as efficiency and emissions improvements.

Rotary valve technology can provide increased flow area and higher discharge coefficients than conventional poppet valves for internal combustion engines. This increase in intake charging efficiency can improve the power density of four-stroke internal combustion engines, particularly at high engine speeds, where flow is choked through conventional poppet valves. In this work, the valvetrain of a light duty single cylinder spark ignition engine was replaced with a rotary valve train. The impact of this valvetrain conversion on performance and emissions was evaluated by comparing spark timing sweeps with lambda ranging from 0.8 to 1.1 at wide open throttle. The results indicated that the rotary valvetrain increased the amount of air trapped at intake valve closing and resulted in a significantly faster burn duration than the conventional valvetrain.

Catalytic converters, which are commonly used for after-treatment in SI engines, exhibit poor performance at lower temperatures. This is one of the main reasons that tailpipe emissions drastically increase during cold-start periods. Thermal inertia of turbocharger casing prolongs the catalyst warm-up time. Exhaust enthalpy management becomes crucial for a turbocharged direct injection spark ignition (DISI) engine during cold-start periods to quickly heat the catalyst and minimize cold-start emissions. Thermal barrier coatings (TBCs), because of their low thermal inertia, reach higher surface temperatures faster than metal walls, thereby blocking heat transfer and saving enthalpy for the catalyst. The TBCs applied on surfaces that exchange heat with exhaust gases can increase the enthalpy available for the catalyst warm-up.

The application of power electronic converters (PEC) in electric vehicles (EVs) has increased immensely as they provide enhanced controllability and flexibility to these vehicles. Accordingly, the interest in developing innovative and sustainable technologies to ensure safe and reliable operation of PECs has also risen. One of the most difficult challenges experienced during the development of reliable PECs is the design of proper thermal management systems for controlling the junction temperature and reducing the thermal cycling of power semiconductors. The addition of Active Thermal Control (ATC) can mitigate these concerns. Moreover, the performance of the thermal management system can be enhanced further by the integration of active cooling methods. An active cooling system consumes external energy for circulating cooling air or liquid within the PECs.

Image segmentation has historically been a technique for analyzing terrain for military autonomous vehicles. One of the weaknesses of image segmentation from camera data is that it lacks depth information, and it can be affected by environment lighting. Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. The volume of points generated by LiDAR provides the vehicle with distance and spatial data surrounding the vehicle.

In the United States and worldwide, 38,824 and 1.35 million people were killed in vehicle crashes during 2020. These statistics are tragic and indicative of an on-going public health crisis centered on automobiles and other ground transportation solutions. Although the long-term US vehicle fatality rate is slowly declining, it continues to be elevated compared to European countries. The introduction of vehicle safety systems and re-designed roadways has improved survivability and driving environment, but driver behavior has not been fully addressed. A non-confrontational approach is the evaluation of driver behavior using onboard sensors and computer algorithms to determine the vehicle’s “mistrust” level of the given operator and the safety of the individual operating the vehicle. This is an inversion of the classic human-machine trust paradigm in which the human evaluates whether the machine can safely operate in an automated fashion.

Renewable fuels, such as the alcohols, ammonia, and hydrogen, have a high autoignition resistance. Therefore, to enable these fuels in compression ignition, some modifications to existing engine architectures is required, including increasing compression ratio, adding insulation, and/or using hot internal residuals. The opposed-piston two-stroke (OP2S) engine architecture is unique in that, unlike conventional four-stroke engines, the OP2S can control the amount of trapped residuals over a wide range through its scavenging process. As such, the OP2S engine architecture is well suited to achieve compression ignition of high autoignition resistance fuels. In this work, compression ignition with wet ethanol 80 (80% ethanol, 20% water by mass) on a 3-cylinder OP2S engine is experimentally demonstrated. A load sweep is performed from idle to nearly full load of the engine, with comparisons made to diesel at each operating condition.

Formal verification plays an important role in proving the safety of autonomous vehicles (AV). It is crucial to find errors in the AV system model to ensure safety critical features are not compromised. Model checking is a formal verification method which checks if the finite state machine (FSM) model meets system requirements. These requirements can be expressed as linear Temporal logic (LTL) formulae to describe a sequence of states with linear Temporal properties to be satisfied. NuSMV is a dedicated software for performing model checking based on Temporal logic formulae on FSM models. However, NuSMV does not provide model-based design. On the other hand, Stateflow in MATLAB/SIMULINK is a powerful tool for designing the model and offers an interactive Graphical User Interface (GUI) for the user/verifier but is not as efficient as NuSMV in model checking.

Testing any new safety technology of Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) requires simulation-based validation and verification. The specific scenarios used for testing, outline incidences of accidents or near-miss events. In order to simulate these scenarios, specific values for all the above parameters are required including the ego vehicle model. The ‘criticality’ of a scenario is defined in terms of the difficulty level of the safety maneuver. A scenario could be over-critical, critical, or under-critical. In over-critical scenarios, it is impossible to avoid a crash whereas, for under-critical scenarios, no action may be required to avoid a crash. The criticality of the scenario depends on various parameters e.g. speeds, distances, road/tire parameters, etc. In this paper, we propose a definition of criticality metric and identify the parameters such that a scenario becomes critical.

As powertrain hybridization technologies are becoming popular, their application for heavy-duty military vehicles is drawing attention. An intelligent design and operation of the energy management system (EMS) is important to ensure that hybrid military vehicles can operate efficiently, simultaneously maximize fuel economy and minimize monetary cost, while successfully completing mission tasks. Furthermore, an integrated EMS framework is vital to ensure a functional vehicle power system (VPS) to survive through critical missions in a highly stochastic environment, when needed. This calls for situational awareness and dynamic system reconfiguration capabilities on-board of the military vehicle. This paper presents a new energy management and control (EMC) framework based on holistic situational awareness (SA) and dynamic reconfiguration of the VPS.

Tradespace analysis is used to define the characteristics of the solution space for a vehicle design problem enabling decision-makers (DMs) to evaluate the risk-benefit posture of a vehicle design program. The tradespace itself is defined by a set of functional objectives defined by vehicle simulations and evaluating the performance of individual design solutions that are modeled by a set of input variables. Of special interest are efficient design solutions because their perfomance is Pareto meaning that none of their functional objective values can be improved without decaying the value of another objective. The functional objectives are derived from a combination of simulations to determine vehicle performance metrics and direct calculations using vehicle characteristics. The vehicle characteristics represent vendor specifications of vehicle subsystems representing various technologies.

The integration of sensors, actuators, and real-time control in transportation systems enables intelligent system operation to minimize energy consumption and maximize occupant safety and vehicle reliability. The operating cycle of military ground vehicles can be on- and off-road in harsh weather and adversarial environments, which demands continuous subsystem functionality to fulfill missions. Onboard diagnostic systems can alert the operator of a degraded operation once established fault thresholds are exceeded. An opportunity exists to estimate vehicle maintenance needs using model-based predicted trends and eventually compiled information from fleet operating databases. A digital twin, created to virtually describe the dynamic behavior of a physical system using computer-mathematical models, can estimate the system behavior based on current and future operating scenarios while accounting for past effects.

In this paper, we review the literature related to the reuse of computer-based simulation models in the context of systems design. Models are used to capture aspects of existing or envisioned systems and are simulated to predict the behavior of these systems. However, developing such models from scratch requires significant time and effort. Researchers have recognized that the time and effort can be reduced if existing models or model components are reused, leading to the study of model reusability. In this paper, we review the tasks necessary to retrieve and reuse model components from repositories, and to prepare new models and model components such that they are more amenable for future reuse. Model reuse can be significantly enhanced by carefully characterizing the model, and capturing its meaning and intent so that potential users can determine whether the model meets their needs.

Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. Operating a practical engine with SACI combustion is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. This work describes the process and results of developing a fast-running control-oriented model for the autoignition phase of SACI combustion. A data-driven model is selected, specifically artificial neural networks (ANNs).

In this work, two blends of isopropanol, n-butanol, and ethanol (IBE) that can be produced by metabolically engineered clostridium acetobutylicum are studied experimentally in advanced compression ignition (ACI). This is done to determine whether these fuel blends have the right fuel properties to enable thermally stratified compression ignition, a stratified ACI strategy that using the cooling potential of single stage ignition fuels to control the heat release process. The first microorganism, ATCC824, produces a blend of 34.5% isopropanol, 60.1% n-butanol, and 5.4% ethanol, by mass. The second microorganism, BKM19, produces a blend of 12.3% isopropanol, 54.0% n-butanol, and 33.7% ethanol, by mass. The sensitivity of both IBE blends to intake pressure, intake temperature, and cylinder energy content (fueling rate) is characterized and compared to that of its neat constituents. Both IBE blends behaved similarly with a reactivity level between that of ethanol and n-butanol.

Multi-material design is one of the trending methods for automakers to achieve lightweighting cost-efficiently and meet stringent regulations and fuel efficiency concerns. Motivated by this trend, the hybrid single-shot (HSS) process has been recently introduced to manufacture thermoset-thermoplastic composites in one single integrated operation. Although this integration is beneficial in terms of reducing the cycle time, production cost, and manufacturing limitations associated with such hybrid structures, it increases the process complexity due to the simultaneous filling, forming, curing, and bonding actions occurring during the process. To overcome this complexity and have a better understanding on the interaction of these physical events, a quick yet accurate simulation of the HSS process based on an experimentally calibrated numerical approach is presented here to elucidate the effect of different process settings on the final geometry of the hybrid part.

Military ground vehicles operate in off-road environments traversing different terrains under various environmental conditions. There has been an increasing interest towards autonomous off-road vehicle navigation, leading to the needs of terrain traversability assessment through sensing. These methods utilized data-driven approaches on classical robotic perception sensing modalities (RGB cameras, Lidar, and depth cameras) positioned in front of ground vehicles in order to observe approaching terrain. Classical robotic sensing modalities, though effective for describing environment geometry and object detection and tracking, aren’t able to directly observe features related to compaction and moisture content which have significant effects on the moduli properties governing terrain mechanics. These methods then become very specialized to specific regions and environmental conditions which are inevitably subject to change.

This research proposes to leverage the fast response time of Permanent Magnet Synchronous Motors (PMSMs) to compensate for crank angle resolved engine torque variations caused by cycle-by-cycle combustion variations. This method reduces powertrain vibration and enables engine calibrations with high combustion variation that produces low fuel consumption. This research integrates a Field Oriented Control (FOC) strategy with an Explicit Model Predictive Control (EMPC) to trace previewed current references. The previewed current references are computed from the engine torque difference between predicted nominal operation and the measured torque output. This research reveals that the MPC can track a d-q current reference without overshoot, rendering current magnitude constraints unnecessary in the MPC formulation. A control rate penalty is used to tune the aggressiveness of transient voltage demand and meet with the DC voltage limit.