Search Results

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

Flexibility and backlash of vehicle drivelines typically cause unwanted oscillations and noise, known as shuffle and clunk, during tip-in and tip-out events. Computationally efficient and accurate driveline models are necessary for the design and evaluation of torque shaping strategies to mitigate this shuffle and clunk. To accomplish these goals, this paper develops a full-order physics-based model and uses this model to develop a reduced-order model (ROM), which captures the main dynamics that influence the shuffle and clunk phenomena. The full-order model (FOM) comprises several components, including the engine as a torque generator, backlash elements as discontinuities, and propeller and axle shafts as compliant elements. This model is experimentally validated using the data collected from a Ford vehicle. The validation results indicate less than 1% error between the model and measured shuffle oscillation frequencies.

While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain.

Ducted fuel injection (DFI) has been shown to be an effective method to significantly reduce soot formation in mixing controlled compression ignition (MCCI) diesel combustion. This reduction has been demonstrated in both combustion vessels and in an optical engine. The mechanisms driving the soot reduction are to date not fully understood. Optimal duct configurations are also not immediately evident. The objective of this study is to show the effects of two geometric variables, namely distance from fuel injector orifice exit to duct inlet (0.1-6 mm) for a 2x14 mm duct, and duct length variation (8-14 mm) at a given stand-off distance of 0.1 mm. A 138 μm on-axis single-orifice injector operated at 100-250 MPa was used in a heated, continuous flow, constant pressure vessel with optical access.

A three phase catalytic mathematical model was developed for analysis and optimization of the volatile reactor assembly (VRA) used on International Space Station (ISS) Water Processor. The Langmuir-Hinshelwood Hougen-Watson (L-H) expression was used to describe the surface reaction rate. Small column experiments were used to determine the L-H rate parameters. The test components used in the experiments were acetic acid, acetone, ethanol, 1-propanol, 2-propanol and propionic acid. These compounds are the most prevalent ones found in the influent to the VRA reactor. The VRA model was able to predict performance of small column data and experimental data from the VRA flight experiment.

Surrogate targets are used throughout the automotive industry to safely and repeatably test Advanced Driver Assistance Systems (ADAS) and will likely find similar applications in tests of Automated Driving Systems. For those test results to be applicable to real-world scenarios, the surrogate targets must be representative of the real-world objects that they emulate. Early target development efforts were generally divided into those that relied on sophisticated radar measurement facilities and those that relied on ad-hoc measurements using automotive grade equipment. This situation made communication and interpretation of results between research groups, target developers and target users difficult. SAE J3122, “Test Target Correlation - Radar Characteristics”, was developed by the SAE Active Safety Systems Standards Committee to address this and other challenges associated with target development and use. J3122 addresses four topics.

Nitrites have long been added to heavy-duty coolant to inhibit iron cylinder liner corrosion initiated by cavitation. However, in heavy-duty use, nitrites deplete from the coolant, which then must be refortified using supplemental coolant additives (SCA's). Recently, carboxylates have also been found to provide excellent cylinder liner protection in heavy-duty application. Unlike nitrites, carboxylate inhibitors deplete slowly and thus do not require continual refortification with SCA's. In the present paper laboratory aging experiments shed light on the mechanism of cylinder liner protection by these inhibitors. The performance of carboxylates, nitrites and mixtures of the two inhibitors are compared. Results correlate well with previously published fleet data. Specifically, rapid nitrite and slow carboxylate depletion are observed. More importantly, when nitrite and carboxylates are used in combination, nitrite depletion is repressed while carboxylates deplete at a very slow rate.

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 presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV). The objective of the optimization is to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile. The optimization reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The optimization method splits drive cycles into constant distance segments and then uses a reduced-order model to sort the segments by the best use of battery energy vs. fuel energy. The PHEV used in this investigation is the Stellantis Pacifica. Results support energy savings up to 20% which depend on the route and initial battery State of Charge (SOC). Initial optimization takes 1 second for 38 km and 3 seconds for 154 km.

Vehicle navigation in off-road environments is challenging due to terrain uncertainty. Various approaches that account for factors such as terrain trafficability, vehicle dynamics, and energy utilization have been investigated. However, these are not sufficient to ensure safe navigation of optionally manned ground vehicles that are prone to detection using thermal infrared (IR) seekers in combat missions. This work is directed towards the development of a vehicle IR signature aware navigation stack comprised of global and local planner modules to realize safe navigation for optionally manned ground vehicles. The global planner used A* search heuristics designed to find the optimal path that minimizes the vehicle thermal signature metric on the map of terrain’s apparent temperature. The local planner used a model-predictive control (MPC) algorithm to achieve integrated motion planning and control of the vehicle to follow the path waypoints provided by the global planner.