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A numerical investigation of a six-stroke direct injection compression ignition engine operation in a low temperature combustion (LTC) regime is presented. The fuel employed is a gasoline-like oxygenated fuel consisting of 90% isobutanol and 10% diethyl ether (DEE) by volume to match the reactivity of conventional gasoline with octane number 87. The computational simulations of the in-cylinder processes were performed using a high-fidelity multidimensional in-house 3D CFD code (MTU-MRNT) with improved spray-sub models and CHEMKIN library. The combustion chemistry was described using a two-component (isobutanol and DEE) fuel model whose oxidation pathways were given by a reaction mechanism with 177 species and 796 reactions.

Eco Approach and Departure (Eco-AnD) is a Connected Automated Vehicle (CAV) technology aiming to reduce energy consumption for crossing a signalized intersection or set of intersections in a corridor that features vehicle-to-infrastructure (V2I) communication capability. This research focuses on developing a Dynamic Programming (DP) based algorithm for a PHEV operating in Charge Depleting mode. The algorithm used the Reduced Order Energy Model (ROM) to capture the vehicle powertrain characteristics and road grade to capture the road dynamics. The simulation results are presented for a real-world intersection and 20-25% energy benefits are shown by comparing against a simulated human driver speed profile. The vehicle-level validation of the developed algorithm is carried out by performing closed-course track testing of the optimized speed solutions on a real CAV vehicle.

The AutoDrive Challenge competition sponsored by General Motors and SAE gives undergraduate and graduate students an opportunity to get hands-on experience with autonomous vehicle technology and development as they work towards their degree. Michigan Technological University has participated in the AutoDrive Challenge since its inception in 2017 with students participating through MTU’s Robotic System Enterprise. The MathWorks Simulation Challenge has been a component of the competition since its second year, tasking students with the development of perception, control and testing algorithms using MathWorks software products. This paper presents the pedagogical approach graduate student mentors used to enable students to build their understanding of autonomous vehicle concepts using familiar tools. This approach gives undergraduate students a productive experience with these systems that they may not have encountered in coursework within their academic program.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

This paper introduces a novel approach to modeling Torque Converter (TC) in conventional and hybrid vehicles, aiming to enhance torque delivery accuracy and efficiency. Traditionally, the TC is modelled by estimating impeller and turbine torque using the classical Kotwicki’s set of equations for torque multiplication and coupling regions or a generic lookup table based on dynamometer (dyno) data in an electronic control unit (ECU) which can be calibration intensive, and it is susceptible to inaccurate estimations of impeller and turbine torque due to engine torque accuracy, transmission oil temperature, hardware variation, etc. In our proposed method, we leverage an understanding of the TC inertia – torque dynamics and the knowledge of the polynomial relationship between slip speed and fluid path torque. We establish a mathematical model to represent the polynomial relationship between turbine torque and slip speed.

Brake assemblies are an essential part of any vehicle, and their effective functioning is critical for the safety and comfort of passengers. The surface roughness of brake components plays a vital role in figuring out their tribological and NVH (Noise, Vibration, and Harshness) behavior. It is essential to understand the impact of surface roughness on brake performance to ensure efficient braking and it has been a topic of interest in the automotive industry. In this study, the influence of surface roughness on the wear, and noise characteristics of a brake assembly has been investigated. The study also provides insights into the relationship between surface roughness, frictional behavior, and NVH performance, which can be used to improve the design and manufacturing of brake assemblies. The brake assembly includes of a disc, caliper, and brake pads, which work together to convert the kinetic energy of the vehicle into heat energy, has been considered in this study.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

This paper focuses on an inherent problems of active damping control prevalent in contemporary hybrid torque controls. Oftentimes, a supervisory torque controller utilizes simplified system models with minimal system states representation within the optimization problem, often not accounting for nonlinearities and stiffness. This is motivated by enabling the generation of the optimum torque commands with minimum computational burden. When inherent lash and stiffness of the driveline are not considered, the resulting command can lead to vibrations and oscillations in the powertrain, reducing performance and comfort. The paper proposes a Linear Quadratic Integral (LQI)-based compensator to be integrated downstream the torque supervisory algorithm, which role is to shape transient electric machine torques, compensating for the stiffness and backlash present in the vehicle while delivering the driver-requested wheel torque.

During input tracking, closed-loop performance is strongly influenced by the dynamic of the system under control. Internal and external delays, such as actuation and measurement delays, have a detrimental effect on the bandwidth and stability. Additionally, production controllers are discrete in nature and the sampling time selection is another critical factor to be considered. In this paper we analyze the impact of both transported delay and controller sampling time on tracking performance using an electric machine speed-control problem as an example. A simple linear PI controller is used for this exercise. Furthermore, we show how the PI parameters can be adjusted to maintain a certain level of performance as the delays and sampling times are modified. This is achieved through an optimization algorithm that minimizes a specifically designed cost function.

A multiple-injection combustion strategy has been developed for heated gasoline direct injection compression ignition (HGCI). Gasoline was injected into a 0.4L single cylinder engine at a fuel pressure of 300bar. Fuel temperature was increased from 25degC to a temperature of 280degC by means of electric injector heater. This approach has the potential of improving fuel efficiency, reducing harmful CO and UHC as well as particulate emissions, and reducing pressure rise rates. Moreover, the approach has the potential of reducing fuel system cost compared to high pressure (>500bar) gasoline direct injection fuel systems available in the market for GDI SI engines that are used to reduce particulate matter. In this study, a multiple injection strategy was developed using electric heating of the fuel prior to direct fuel injection at engine speed of 1500rpm and load of 12.3bar IMEP.

The steering assembly is a part of an automotive suspension system that provides control and stability. It provides control of direction, stability, and control over placement of the car. Optimization of the vehicle in weight results in enhanced performance and low fuel consumption, more so for an all-terrain race car. Optimization in this paper loosely refers to weight reduction and achieving the optimum stiffness to weight ratio of each component. This research encompasses various aspects linked to conceptualizing, designing, analysing, optimizing, and finally manufacturing the steering sub-system. Analytical calculations for mechanical design were performed using data from various experiments and jigs. CAD was developed using SolidWorks, and various analyses were performed using Altair HyperWorks. Finite Element Analysis (FEA) was primarily used to build stress plots and locate weak spots aiding optimization.

Increasing regulatory demand to reduce CO2 emissions has led to an industry focus on electrified vehicles while limiting the development of conventional internal combustion engine (ICE) and hybrid powertrains. Hybrid electric vehicle (HEV) powertrains rely on conventional SI mode IC engines that are optimized for a narrow operating range. Advanced combustion strategies such as Gasoline Compression Ignition (GCI) have been demonstrated by several others including the authors to improve brake thermal efficiency compared to both gasoline SI and Diesel CI modes. Soot and NOx emissions are also reduced significantly by using gasoline instead of diesel in GCI engines due to differences in composition, fuel properties, and reactivity. In this work, an HEV system was proposed utilizing a multi-mode GCI based ICE combined with a HEV components (e-motor, battery, and invertor).

One of the building blocks of the Stellantis hybrid powertrain embedded control software computes the maximum and minimum values of objective functions, such as output torque, as a function of engine torque, hybrid motor torque and other variables. To test such embedded software, an offline reference function was created. The reference function calculates the ideal minimum and maximum values to be compared with the output of the embedded software. This article presents the offline reference function with an emphasis on mathematical novelties. The reference function computes the minimum and maximum points of a linear objective function as a function of six independent variables, subject to 42 linear and two nonlinear constraints. Concave domains, curved surfaces, disjoint domains and multiple local extremum points challenge the algorithm. As a theorem, the conditions and methods for running trigonometric calculations in 6D Euclidean space are presented.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.

The Brake judder is a low-level vibration caused due to Disc Thickness Variation (DTV), Temperature, Brake Torque Variation (BTV), thermal degradation, hotspot etc. which is a major concern for the past decades in automobile manufacturers. To predict the judder performance, the modelling methods are proposed in terms of frequency and BTV respectively. In this study, a mathematical model is constructed by considering full brake assembly, tie rod, coupling rod, steering column, and steering wheel as a spring mass system for identifying judder frequency. Simulation is also performed to predict the occurrence of brake judder and those results are validated with theoretical results. Similarly, for calculating BTV a separate methodology is proposed in CAE and validated with experimental and theoretical results.

Water injection has been used to reduce the charge temperature and mitigate knocking due to its higher latent heat of vaporization compared to gasoline fuel. When water is injected into the intake manifold or into the cylinder, it evaporates by absorbing heat energy from the surrounding and results in charge cooling. However, the effect of detailed evaporation process on the combustion characteristics under gasoline direct injection relevant conditions still needs to be investigated. Therefore, spray study was firstly conducted using a multi-hole injector by injecting pure water and water-methanol mixture into constant volume combustion chamber (CVCC) at naturally aspirated and boosted engine conditions. The target water-fuel ratio was fixed at 0.5. Mie-scattering and schlieren images of sprays were analyzed to study spray characteristics, and evaluate the amount of water vaporization.

The medium and heavy-duty powertrain industry trend is to reduce reliance on diesel fuel and is aligned with continued efforts of achieving ultra-low emissions and high brake efficiencies. Compression Ignition (CI) of late cycle Directly Injected (DI) Natural Gas (NG) shows the potential to match diesel performance in terms of brake efficiency and power density, with the benefit of utilizing a lower carbon content fuel. A primary challenge is to achieve stable ignition of directly injected NG over a wide engine speed and load range without the need for a separate ignition source. This project aims to demonstrate the CI of DI NG through experimental studies with a Single Cylinder Research Engine (SCRE), leading to the development of a mono-fueled NG engine with equivalent performance to that of current diesel technology, 25% lower CO2 emissions, and low engine out methane emissions.

Gasoline knock resistance is characterized by the Research and Motor Octane Number (RON and MON), which are rated on the CFR octane rating engine at naturally aspirated conditions. However, modern automotive downsized boosted spark ignition (SI) engines generally operate at higher cylinder pressures and lower temperatures relative to the RON and MON tests. Using the naturally aspirated RON and MON ratings, the octane index (OI) characterizes the knock resistance of gasolines under boosted operation by linearly extrapolating into boosted “beyond RON” conditions via RON, MON, and a linear regression K factor. Using OI solely based on naturally aspirated RON and MON tests to extrapolate into boosted conditions can lead to significant errors in predicting boosted knock resistance between gasolines due to non-linear changes in autoignition and knocking characteristics with increasing pressure conditions.

The increase in regulatory demand to reduce CO2 emissions resulted in a focus on the development of novel combustion modes such as gasoline compression ignition (GCI). It has been shown by others that GCI can improve the overall engine efficiency while achieving soot and NOx emissions targets. In comparison with diesel fuel, gasoline has a higher volatility and has more resistance to autoignition, therefore, it has a longer ignition delay time which facilitates better mixing of the air-fuel charge before ignition. In this study, a GCI combustion system has been tested using a 2.2L compression ignition engine as part of a US Department of Energy funded project. For this purpose, a multiple injection strategy was developed to improve the pressure rise rates and soot emission levels for the same engine out NOx emissions.