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Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

Advanced engine cooling systems can enhance the combustion environment, increase fuel efficiency, and reduce tailpipe emissions with less parasitic engine load. The introduction of computer controlled electro-mechanical valves, radiator fans, and coolant pumps require mathematic models and real time algorithms to implement intelligent thermal control strategies for prescribed engine temperature tracking. Smart butterfly valves can replace the traditional wax-based thermostat to control the coolant flow based on both engine temperature and operating conditions. The electric water pump and radiator fan replace the mechanically driven components to reduce unnecessary engine loads at high speeds and provide better cooling at low speeds.

This paper describes a computational and experimental effort to document the detailed flow field around a pickup truck. The major objective was to benchmark several different computational approaches through a series of validation simulations performed at Clemson University (CU) and overseen by those performing the experiments at the GM R&D Center. Consequently, no experimental results were shared until after the simulations were completed. This flow represented an excellent test case for turbulence modeling capabilities developed at CU. Computationally, three different turbulence models were employed. One steady simulation used the realizable k-ε model. The second approach was an unsteady RANS simulation, which included a turbulence closure model developed in-house. This simulation captured the unsteady shear layer rollup and breakdown over the front of the hood that was expected and seen in the experiments but unattainable with other off-the-shelf turbulence models.

Automotive and truck manufacturers are introducing electric propulsion systems into their ground vehicles to reduce fossil fuel consumption and harmful tailpipe emissions. The mobility shift to electric motors requires a compact thermal management system that can accommodate heat dissipation demands with minimum energy consumption in a confined space. An innovative cooling system design, emphasizing passive cooling methods coupled with a small liquid system, using a thermal bus architecture has been explored. The laboratory experiment features an emulated electric motor interfaced to a thermal cradle and multiple heat rejection pathways to evaluate the transfer of generated heat to the ambient surroundings. The thermal response of passive (e.g., carbon fiber, high thermal conductivity material, thermosyphon) and active cooling systems are investigated for two operating scenarios.

Enhanced electric motor performance in transportation vehicles can improve system reliability and durability over rigorous operating cycles. The design of innovative heat rejection strategies in electric motors can minimize cooling power consumption and associated noise generation while offering configuration flexibility. This study investigates an innovative electric motor cooling strategy through bench top thermal testing on an emulated electric motor. The system design includes passive (e.g., heat pipes) cooling as the primary heat rejection pathway with supplemental conventional cooling using a variable speed coolant pump and radiator fan(s). The integrated thermal structure, “cradle”, transfers heat from the motor shell towards an end plate for heat dissipation to the ambient surroundings or transmission to an external thermal bus to remote heat exchanger.

Opposed-piston two-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a conventional four-stroke diesel engine. However, the uniflow scavenging process is difficult to control over a wider range of speed and loads due to its sensitivity to pressure dynamics, port timings, and port design. Specifically, the angle of the intake ports can be used to generate swirl which has implications for open and closed cycle effects. This study proposes an analysis of the effects of port angle on the in-cylinder flow distribution and combustion performance of an OP-2S using computational fluid dynamics engine. Large Eddy Simulation (LES) was used to model turbulence given its ability to predict in-cylinder mixing and cyclic variability. A three-cylinder model was validated to experimental data collected by Achates Power and the grid was verified using an LES quality approach from the literature.

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.

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.

In the ever-evolving landscape of automotive technology, the need for robust security measures and dependable vehicle performance has become paramount with connected vehicles and autonomous driving. The Unified Diagnostic Services (UDS) protocol is the diagnostic communication layer between various vehicle components which serves as a critical interface for vehicle servicing and for software updates. Fuzz testing is a dynamic software testing technique that involves the barrage of unexpected and invalid inputs to uncover vulnerabilities and erratic behavior. This paper presents the implementation of fuzz testing methodologies on the UDS layer, revealing the potential vulnerabilities that could be exploited by malicious entities. By employing both open-source and commercial fuzzing tools and techniques, this paper simulates real-world scenarios to assess the UDS layer’s resilience against anomalous data inputs.

Very large scale, 3D, viscous, turbulent flow simulations, involving 840,000 finite volume cells and the complete form of the time-averaged Navier-Stokes equations, were conducted to study the mechanisms responsible for total pressure losses in the entire intake system (inlet duct, plenum, ports, valves, and cylinder) of a straight-six diesel engine. A unique feature of this paper is the inclusion of physical mechanisms responsible for cylinder-to-cylinder variation of flows between different cylinders, namely, the end-cylinder (#1) and the middle cylinder (#3) that is in-line with the inlet duct. Present results are compared with cylinder #2 simulations documented in a recent paper by the Clemson group, Taylor, et al. (1997). A validated comprehensive computational methodology was used to generate grid independent and fully convergent results.

Fatigue analysis of pistons is reliant on an accurate representation of the high temperatures to which they are exposed. It can be difficult to represent this accurately, because instrumented tests to validate piston thermal models typically include only measurements near the piston crown and there are many unknown backside heat transfer coefficients (HTCs). Previously, a methodology was proposed to aid in the estimation of HTCs for backside convection boundary conditions of a stratified charge compression ignition (SCCI) piston. This methodology relies on Bayesian inference of backside HTC using a co-simulation between computational fluid dynamics (CFD) and finite element analysis (FEA) solvers. Although this methodology primarily utilizes the more computationally efficient FEA model for the iterations in the calibration, this can still be a computationally expensive process.

This paper describes the development of test equipment for determining the wear viability of various lunar wheel tread materials with service lives of up to ten years and 10,000 km. The problem is defined, and concepts are proposed, evaluated, and selected. An abrasive turntable is chosen for simplicity and accuracy of modeling the original wheel configuration. Additionally, the limitations of the test are identified, such as the sensitivity to off-vertical loading, and future work is projected in order to more effectively continue testing. Finally, this paper presents the challenges of collaborative research effort between an undergraduate research team and industry, with government lab representatives as customers

The development of next-generation ground vehicle systems relies on modeling and simulation to predict vehicle performance and conduct trade studies in the design and acquisition process. In this paper, we describe the development of an ontology suite to support modeling and simulation of next generation military ground vehicles. The ontology suite is intended to address model reuse challenges and increase the shared understanding of ground vehicle system simulations. The ontology suite consists of four domain ontologies: Vehicle operations (VehOps), Operational environment (Env), Ground vehicle architecture (VehArch), and Simulation model ontology (SimMod) and one integration ontology. The separate domain ontologies allow for extensibility, while the integration ontology establishes semantic relationships across the domains ontologies.

This paper is a review of turbulence models and computational methods that have been produced at Clemson University's Advanced Computational Research Laboratory. The goal of the turbulence model development has been to create physics-based models that are economically feasible and can be used in a competitive environment, where turnaround time is a critical factor. Given this goal, all of the work has been focused on Reynolds-Averaged Navier-Stokes (RANS) simulations in the eddy-viscosity framework with the majority of the turbulence models having three transport equations in addition to mass, momentum, and energy. Several areas have been targeted for improvement in turbulence modeling for complex flows such as those found in motorsports aerodynamics: the effects of streamline curvature and rotation on the turbulence field, laminar-turbulent transition, and separated shear layer rollup and breakdown.

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.

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.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

Since 1989, ISO has published procedures for developing and testing public information symbols (ISO 9186), while the SAE standard for in-vehicle icon comprehension testing (SAE J2830) was first published in 2008. Neither testing method was designed to evaluate the comprehension of symbols in modern vehicles that offer digital instrument cluster interfaces that afford new levels of flexibility to further improve drivers’ understanding of symbols. Using a driving simulator equipped with an eye tracker, this study investigated drivers’ understanding of six automotive symbols presented on in-vehicle displays. Participants included 24 teens, 24 adults, and 24 senior drivers. Symbols were presented in a symbol-only, symbol + short text descriptions, and symbol + long text description conditions. Participants’ symbol comprehension, driving performance, reaction times, and eye glance times were measured.