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The safety and reliability of ground vehicles is a motivating factor for periodic maintenance which includes fluids, lubrication, cleaning, repairs, and general observation of key subsystems. The scheduling of maintenance activities can occur at different rates such as daily, weekly, or perhaps operating time based on collected historical data and general guidelines. The availability of a digital twin (DT), which offers a virtual representation of the vehicle behavior, enables virtual system simulations for different operating cycles to explore the dynamic behavior. When field operating fleet data can be integrated with the digital twin estimates, then this supplemental information can be combined with the existing maintenance plan to provide a more comprehensive approach. In this paper, a digital twin with a statistical based predictive maintenance strategy is investigated for a wheeled military ground vehicle.

Digital twin technology has become impactful in Industry 4.0 as it enables engineers to design, simulate, and analyze complex systems and products. As a result of the synergy between physical and virtual realms, innovation in the “real twin” or actual product is more effectively fostered. The availability of verified computer models that describe the target system is important for realistic simulations that provide operating behaviors that can be leveraged for future design studies or predictive maintenance algorithms. In this paper, a digital twin is created for an offroad tracked vehicle that can operate in either autonomous or remote-control modes. Mathematical models are presented and implemented to describe the twin track and vehicle chassis governing dynamics. These components are interfaced through the nonlinear suspension elements and distributed bogies.

The design of transportation vehicles, whether passenger or commercial, typically involves a lengthy process from concept to prototype and eventual manufacture. To improve competitiveness, original equipment manufacturers are continually exploring ways to shorten the design process. The application of digital tools such as computer-aided-design and computer-aided-engineering, as well as model-based computer simulation enable team members to virtually design and evaluate ideas within realistic operating environments. Recent advances in machine learning (ML)/artificial intelligence (AI) can be integrated into this paradigm to shorten the initial design sequence through the creation of digital agents. A digital agent can intelligently explore the design space to identify promising component features which can be collectively assessed within a virtual vehicle simulation.

Design of internal combustion (IC) engine pistons is dependent on accurate prediction of the temperature field in the component. Experimental temperature measurements can be taken but are costly and typically limited to a few select locations. High-fidelity computer simulations can be used to predict the temperature at any number of locations within the model, but the models must be calibrated for the predictions to be accurate. The largest barrier to calibration of piston thermal models is estimating the backside boundary conditions, as there is not much literature available for these boundary conditions. Bayesian model calibration is a common choice for model calibration in literature, but little research is available applying this method to piston thermal models. Neural networks have been shown in literature to be effective for calibration of piston thermal models.

Opposed piston two-stroke (OP2S) diesel engines have demonstrated a reduction in engine-out emissions and increased efficiency compared to conventional four-stroke diesel engines. Due to the higher stroke-to-bore ratio and the absence of a cylinder head, the heat transfer loss to the coolant is lower near ‘Top Dead Center.’ The selection and design of the air path is critical to realizing the benefits of the OP2S engine architecture. Like any two-stroke diesel engine, the scavenging process and the composition of the internal residuals are predominantly governed by the pressure differential between the intake and the exhaust ports. Without dedicated pumping strokes, the two-stroke engine architecture requires external devices to breathe.

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.

A major concern for a high-power density, heavy-duty engine is the durability of its components, which are subjected to high thermal loads from combustion. The thermal loads from combustion are unsteady and exhibit strong spatial gradients. Experimental techniques to characterize these thermal loads at high load conditions on a moving component such as the piston are challenging and expensive due to mechanical limitations. High performance computing has improved the capability of numerical techniques to predict these thermal loads with considerable accuracy. High-fidelity simulation techniques such as three-dimensional computational fluid dynamics and finite element thermal analysis were coupled offline and iterated by exchanging boundary conditions to predict the crank angle-resolved convective heat flux and surface temperature distribution on the piston of a heavy-duty diesel engine.

Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles.

Robust communications are crucial for autonomous military fleets. Ground vehicles function as mobile local area networks utilizing Controller Area Network (CAN) backbones. Fleet coordination between autonomous platforms relies on the Robot Operating System (ROS) publish/subscribe robotic middleware for effective operation. To bridge communications between the CAN and ROS network segments, the CAN2ROS bridge software supports bidirectional data flow with message mapping and node translation. Fuzzing, a software testing technique, involves injecting randomized data inputs into the target system. This method plays a pivotal role in identifying vulnerabilities. It has proven effective in discovering vulnerabilities in online systems, such as the integrated CAN/ROS system. In our study, we consider ROS implementing zero-trust access control policies, running on a Gazebo test-bed connected to a CAN bus.

Semiconductor devices in electric vehicle (EV) motor drive systems are considered the most fragile components with a high occurrence rate for open circuit fault (OCF). Various signal-based and model-based methods with explicit mathematical models have been previously published for OCF diagnosis. However, this proposed work presents a model-free machine learning (ML) approach for a single-switch OCF detection and localization (DaL) for a two-level, three-phase inverter. Compared to already available ML models with complex feature extraction methods in the literature, a new and simple way to extract OCF feature data with sufficient classification accuracy is proposed. In this regard, the inherent property of active thermal management (ATM) based model predictive control (MPC) to quantify the conduction losses for each semiconductor device in a power converter is integrated with an ML network.

Tradespace exploration (TSE) describes the activity occurring early in the design process through which stakeholders explore a broad solution space in search of more-optimal alternatives. In doing so, these stakeholders attempt to maximize the utility inherent in the chosen solution while understanding the tradeoffs and compromises that may be required to find an acceptable solution. In the field of vehicle design, tradespaces are often comprised of vast amounts of alternatives which increases the complexity of the decision-making process. Additionally, the number of stakeholders has grown, as decision-makers seek to include more variety in both perspectives and expertise. As such, decision-making stakeholders can often find themselves working at odds and attempting to maximize vastly different objectives in the process. One way to rectify these contrasting viewpoints can be to intentionally introduce a group framing prior to the start of decision making.

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.

The joint real-time co-simulation, which involves the virtual integration of laboratories located in different locations, is met with challenges, especially the communication latency or delay, which significantly affects co-simulation accuracy and system stability. The real-time power system co-simulation is particularly susceptible to these delays and could lose synchronism, which affects the simulation fidelity and limits dynamic and transient studies. This paper proposes a model-free framework for predicting and compensating delays in the virtual integration of real-time co-simulators through the damped impedance interface method to address this issue. The framework includes an improved co-simulation interface algorithm called the Damping Impedance Method (DIM) and a model-free predictor system designed to predict and compensate for delays without decomposing and reconstructing signals at coupling points.

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.

High compression ratios are critical to increasing the efficiency of spark ignition engines, but the trend in downsized and down sped configurations has brought attention to the nominally low compression ratios used to avoid knock. Knock is an abnormal combustion event defined by the acoustic sound caused by end-gas auto-ignition ahead of the flame front. In order to avoid engine-damaging levels of knock, low compression ratios and retarded combustion phasing at high loads are used, both of which lower efficiency. Low carbon alternative fuels such as ethanol or water-based alcohol fuels combine strong chemical auto-ignition resistance with large charge cooling characteristics that can suppress knock and enable optimal combustion phasing, thus allowing an increase in the compression ratio.

Microgrids are a topic of interest in recent years, largely due to their compatibility with the integration of distributed renewable resources, capability for bidirectional power flow, and ability to reconfigure to mitigate the effects of faults. Fault diagnosis algorithms are a foundational technology for microgrids. These algorithms must have two primary capabilities. First, faults must be detectable; it is known when the fault occurs. Second, faults must be isolable; the type and location of detected faults can be determined. However, most fault handling research considering microgrids has focused on the protection algorithm. Protection algorithms seek to quickly extinguish dangerous faults which can damage components. However, these algorithms may not sufficiently capture less severe faults, or provide comprehensive monitoring for the microgrid. This is particularly relevant when considering applications involving fault tolerant control or dynamic grid reconfiguration.

The occurrence of imbalance conditions within the cells of a battery pack can be reduced or mitigated by an active cell equalization circuit integrated in the Battery Management System (BMS), which transfers energy between the most charged cells and the least charged ones in the pack. However, incomplete knowledge on the performances and range of operability in real-world scenarios is still limiting the adoption of active equalizers for lithium-ion battery systems in different fields of application. In this paper, among the different architectures presented in literature for active cell equalizers, the multi-inductor configuration has been investigated. For the generalized category of inductor-based configuration, an analytical model has been developed by taking into account the static and dynamic parasitic parameters of the components of the equalization circuit as well as the operating conditions of the cells.

Battery packs used in automotive application experience high-power demands, fast charging, and varied operating conditions, resulting in temperature imbalances that hasten degradation, reduce cycle life, and pose safety risks. The development of proper simulation tools capable of capturing both the cell electrical and thermal response including, predicting the cell’s temperature rise and distribution, is critical to design efficient and reliable battery packs. This paper presents a co-simulation model framework capable of predicting voltage, 2-D heat generation and temperature distribution throughout a cell. To capture the terminal voltage and 2-D heat generation across the cell, the simulation framework employs a high-fidelity electrical model paired with a charge balance model based on the Poisson equation. The 2-D volumetric heat generation provided by the charge balance model is used to predict the temperature distribution across the cell surface using CFD software.

A novel advanced combustion strategy that employs the kinetically controlled compression ignition of gasoline whose autoignition is sensitive to fuel concentration is termed Low Temperature Gasoline Combustion. The LTGC method can achieve high thermal efficiency with a commercially available fuel while generating ultra-low soot and NOx emissions relative to the conventional combustion modes. At high loads, a double direct injection (D-DI) strategy is used where the first injection generates a background premixed charge while a second compression stroke injection controls the level of fuel stratification on a cycle-to-cycle basis to manage the heat release rates. With lower loads, this combustion performance of this D-DI strategy decreases as the background charge becomes increasingly lean. Instead, a single direct injection (S-DI) is used at lower loads to maintain an adequate combustion efficiency.