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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.

The application of virtual engineering methods can streamline the product design process through improved collaboration opportunities among the technical staff and facilitate additive manufacturing processes. A product digital twin can be created using the available computer-aided design and analytical mathematical models to numerically explore the current and future system performance based on operating cycles. The strategic decision to implement a digital twin is of interest to companies, whether the required financial and workforce resources will be worthwhile. In this paper, two metrics are introduced to assist management teams in evaluating the technology potential. The usefulness and time savings metrics will be presented with accompanying definitions. A case study highlights the usefulness metric for the “Deep Orange” prototype vehicle, an innovative off-road hybrid vehicle designed and fabricated at Clemson University.

Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track.

Tradespace exploration (TSE) is an important aspect of the early stages of the design process, in which stakeholders search for the most optimal solutions within a design variable-bounded solution space. This decision-making process requires stakeholders to understand the trade-offs and compromises that may be required to choose a solution. In order for stakeholders to make these decisions appropriately, information must be presented in an efficient manner and should ensure that the trade-offs between solutions are clearly visible. Existing visualizations often struggle to elucidate these trade-offs, and can rapidly become difficult to understand as the dimensionality of the tradespace increases. In this paper, the benefits and drawbacks to these existing methods will be discussed. In addition, this paper will explore potential methods to improve information presentation for TSE, including framing, visual steering, and visualization options.

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.

Spark ignition knock is highly sensitive to changes in intake air temperature. Hot surface temperatures due to ceramic thermal barrier coatings increase knock propensity by elevating the incoming air temperature, thus mitigating the positive impacts of low heat transfer losses by requiring spark retard to avoid knock. Low thermal inertia coatings (i.e. Temperature swing coatings) have been proposed as a means of reducing or eliminating the open cycle charge heating penalty of traditional TBCs through a combination of low thermal conductivity and low volumetric heat capacity materials. However, in order to achieve a meaningful gain in efficiency, a significant fraction of the combustion chamber must be coated. In this study, a coated piston and intake and exhaust valves with coated combustion faces, backsides, and stems are installed in a single-cylinder research engine to evaluate the effect of high coated fractions of the combustion chamber in a knock-sensitive architecture.

In military applications, diesel engines are required to achieve high power outputs and therefore must operate at high loads. This high load operation leads to high piston component temperatures and heat rejection rates limiting the packaged power density of the powertrain. To help predict and understand these constraints, as well as their effects on performance, a thermodynamic engine model coupled to a finite element heat conduction solver is proposed and validated in this work. The finite element solver is used to calculate crank angle resolved, spatially averaged piston temperatures from in-cylinder heat transfer calculations. The calculated piston temperatures refine the heat transfer predictions as well requiring iteration between the thermodynamic model and finite element solver.

A single radiator cooling system architecture has been widely applied in ground vehicles for safe equipment (e.g., engine block, electronics, and motors) temperature control. The introduction of multiple smaller heat exchangers provides additional energy management features and alternate pathways for continued operation in case of critical subsystem failure. Although cooling performance is often designed for maximum thermal loads, systems typically operate at a fraction of the peak values for most of their life cycle. In this project, a two-radiator configuration with variable flow rates and valve positions has been mathematically modelled and experimentally validated to study its performance feasibility. A multi-node resistance-capacitance thermal model was derived using the ε−NTU approach with accompanying convective and conductive heat transfer pathways within the system.

Thermal barrier coatings (TBCs) have been of interest since the 1970s for application in internal combustion (IC) engines. Thin TBCs exhibit a temperature swing phenomenon wherein wall temperatures dynamically respond to the transient working-gas temperature throughout the engine cycle, thus reducing the temperature difference driving the heat transfer. Determining these varying wall temperatures is necessary to evaluate and study the effect of coatings on wall heat transfer. This study focuses on developing a 3D computational fluid dynamics (CFD)-finite element analysis (FEA) coupled simulation, or co-simulation, routine to determine the wall temperatures of a piston coated with a thin TBC layer subject to spark ignition combustion heat flux. A CONVERGE 3D-CFD model was used to simulate the combustion process in a single-cylinder, light-duty experimental spark ignition (SI) engine.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

Determining the validity of the design space early in the conceptualization of a project can make the difference between project success and failure. Early assessment of technical feasibility, project risk, technical readiness and realistic performance expectations based on models with different levels of fidelity, uncertainty, and technical robustness is a challenging mission critical task for large procurement projects. Tradespace exploration uses model-based engineering analysis, design exploration methods, and multi-objective optimization techniques to enable project stakeholders to make informed decisions and tradeoffs concerning the scope, schedule, budget, performance and risk profile of a project. As the intersection with a number of project stakeholders, tradespace studies can provide a significant impact upon the direction and decision-making in a project.

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 work, a novel opposed piston architecture is proposed where one crankshaft rotates at twice the speed of the other. This results in one piston creating a 2-stroke profile and another with a 4-stroke profile. In this configuration, the slower piston operates in the 2-stroke CAD domain, while the faster piston completes 2 reciprocating cycles in the same amount of time (4-stroke). The key benefit of this cycle is that the 4-stroke piston increases the rate of compression and expansion (dV/dθ), which lowers the combustion-induced pressure rise rate after top dead center (crank angle location of minimum volume). Additionally, it lowers in-cylinder temperatures and pressures more rapidly, resulting in a lower residence time at high temperatures, which reduces residence time for thermal NOx formation and reduces the temperature differential between the gas and the wall, thereby reducing heat transfer.

The integration of electric motors into ground vehicle propulsion systems requires the effective removal of heat from the motor shell. As the torque demand varies based on operating cycles, the generated heat from the motor windings and stator slots must be rejected to the surroundings to ensure electric machine reliability. In this paper, an electric motor cooling system design will be optimized for a light duty autonomous vehicle. The design variables include the motor cradle volume, the number of heat pipes, the coolant reservoir dimensions, and the heat exchanger size while the cost function represents the system weight, overall size, and performance. The imposed requirements include the required heat transfer per operating cycle (6, 9, 12kW) and vehicle size, component durability requirement, and material selection. The application of a nonlinear optimization package enabled the cooling system design to be optimized.

There are many methods developed over the past decade to solve the problem of energy management control for hybrid electric vehicles. A novel method is introduced in this paper to address the same problem which reduces the problem to a set of physical equations and maps. In simple terms, this method directly calculates the actual cost or savings in fuel energy from the generation or usage of electric energy. It also calculates the local optimum electric power that yields higher electric fuel savings (EFS) or lower electric fuel cost (EFC) in the fuel energy that is spent for driving the vehicle (which in general does not take the system to the lowest engine Brake Specific Fuel Consumption (BSFC)). Based on this approach, a control algorithm is developed which attempts to approach the global optimum over a drive cycle.

Thermal barrier coatings with low conductivity and low heat capacity have been shown to improve the performance of homogeneous charge compression ignition (HCCI) engines. These coatings improve the combustion process by reducing heat transfer during the hot portion of the engine cycle without the penalty thicker coatings typically have on volumetric efficiency. Computational fluid dynamic simulations with conjugate heat transfer between the in-cylinder fluid and solid piston of a single cylinder HCCI engine with exhaust valve rebreathing are carried out to further understand the impacts of these coatings on the combustion process. For the HCCI engine studied with exhaust valve rebreathing, it is shown that simulations needed to be run for multiple engine cycles for the results to converge given how sensitive the rebreathing process is to the residual gas state.

The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system is a challenge for real time application. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. This paper proposes a look-ahead control strategy to explore the potential of increased power recovery from a simulated WHR system. In the look-ahead control, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model.

This paper presents the transient power optimization of an organic Rankine cycle waste heat recovery (ORC-WHR) system operating on a heavy-duty diesel (HDD). The optimization process is carried on an experimentally validated, physics-based, high fidelity ORC-WHR model, which consists of parallel tail pipe and EGR evaporators, a high pressure working fluid pump, a turbine expander, etc. Three different ORC-WHR mixed vapor temperature (MVT) operational strategies are evaluated to optimize the ORC system net power: (i) constant MVT; (ii) constant superheat temperature; (iii) fuzzy logic superheat temperature based on waste power level. Transient engine conditions are considered in the optimization. Optimization results reveal that adaptation of the vapor temperature setpoint based on evaporation pressure strategy (ii) provides 1.1% mean net power (MNP) improvement relative to a fixed setpoint strategy (i).

This paper develops a methodology to optimize the supervisory controller for a heavy-duty series hybrid electric vehicle, with consideration of battery aging and cooling loss. Electrochemistrybased battery aging model is integrated into vehicle model. The side reaction, reductive electrolyte decomposition, is modeled to determine battery aging rate, and the thermal effect on this reaction rate is considered by Arrhenius Law. The resulting capacity and power fading is included in the system-level study. Sensitivity analysis shows that battery aging could cause fuel economy loss by 5.9%, and increasing temperature could improve fuel economy at any given state-of-health, while accelerating battery aging. Stochastic dynamic programming algorithm is applied to a modeled system to handle the tradeoff between two objectives: maximizing fuel economy and minimizing battery aging.