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The pre-prototype development of a simulated rule-based hybrid energy management strategy for a 2019 Chevrolet Blazer RS converted parallel P4 full hybrid is presented. A vehicle simulation model is developed using component bench data and validated using EPA-reported dynamometer fuel economy test data. A combined Willans line model is proposed for the engine and transmission, with hybrid control rules based on efficiency-derived engine power thresholds. Algorithms are proposed for battery state of charge (SOC) management including engine loading and one pedal strategies, with battery SOC maintained within 20% to 80% safe limits and charge balanced behavior achieved. The simulated rule-based hybrid control strategy for the hybrid vehicle has an energy consumption reduction of 20% for the Hot 505, 3.6% for the HwFET, and 12% for the US06 compared to the stock vehicle.

The EcoCAR Mobility Challenge (EMC) is the latest edition of the Advanced Vehicle Technology Competition (AVTC) series sponsored by the US Department of Energy. This competition challenges 11 North American universities to redesign a stock 2019 Chevrolet Blazer into an energy-efficient, SAE level 2-autonomous mild hybrid electric vehicle (mHEV) for use in the Mobility as a Service (MaaS) market. The Mississippi State University (MSU) team designed a P4 electric powertrain with an 85kW (113.99 HP) permanent magnet synchronous machine (PMSM) powered by a custom 5.4 kWh lithium-ion energy storage system. To maximize energy efficiency, Model Based Design concepts were leveraged to optimize the overall gear ratio for the P4 system. To accommodate this optimized ratio in the stock vehicle, a custom offset gearbox was designed that links the PMSM to the rear drive module.

The autonomous collision avoidance problem for multi-trailer vehicle maneuvering is investigated in this paper. Different from conventional vehicle systems that contain one single moving part or multi-parts that can be considered as one rigid body, the interconnection between the tractor and each trailer, and interactions between trailers in the multi-trailer system introduce a high dimensional and highly complex dynamic system for the controller design. The external disturbance and parametric uncertainties further increase the difficulty in system identification and state space formulation. To implement a real time control system for various scenarios where the locations and states of the obstacles are not known beforehand, a supervisory algorithm is designed to convert the control problem to a discrete event system. The model predictive control (MPC) using limited lookahead policy is employed in the proposed algorithm.

Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this work, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two types of synthetic datasets.

There is significant potential for connected and autonomous vehicles to impact vehicle efficiency, fuel economy, and emissions, especially for hybrid-electric vehicles. These improvements could have large-scale impact on oil consumption and air-quality if deployed in large Mobility-as-a-Service or ride-sharing fleets. As part of the US Department of Energy's current Advanced Vehicle Technology Competition (AVCT), EcoCAR: The Mobility Challenge, Mississippi State University’s EcoCAR Team is redesigning and doing the development work necessary to convert a conventional gasoline spark-ignited 2019 Chevy Blazer into a hybrid-electric vehicle with SAE Level 2 autonomy. The target consumer segments for this effort are the Mobility-as-a-Service fleet owners, operators and riders. To accomplish this conversion, the MSU team is implementing a P4 mild hybridization strategy that is expected to result in a 30% increase in fuel economy over the stock Blazer.

In this work, a process-structure-property relationship for additively manufactured Al-Si-Mg alloy was constructed, with the aid of an integrated multi-physics model. Specifically, first, a series of thermal simulations were performed to understand molten pool geometry under different additive manufacturing (AM) operating conditions, including laser beam power, scanning speed, and hatch spacing. The porosity formation was predicted based on thermal simulation results, which yield molten pool dimension information for predicting the lack-of-fusion porosity. Dream.3D was utilized to reconstruct synthetic microstructures with different volume fraction of porosity.

Mechanical and thermal properties of the rubber compounds of a tire play an important role in the overall performance of the tire when it is in contact with the terrain. Although there are many studies conducted on the properties of the rubber compounds of the tire to improve some of the tire characteristics such as the wear of the tread, there is a limited number of studies that focused on the performance of the tire when it is in contact with ice. This study is a part of a more comprehensive project looking into tire-ice performance and modeling. A significant part of this study is the experimental investigation of the effect of rubber compounds on tire performance in contact with ice. For this, four tires have been selected for testing. Three of them are completely identical in all tire parameters (such as tire dimensions), except for the rubber compounds. Several tests were conducted for the chosen tires in three modes: free rolling, braking, and traction.

Recent developments in the area of autonomous vehicle navigation have emphasized algorithm development for the characterization of LiDAR 3D point-cloud data. The LiDAR sensor data provides a detailed understanding of the environment surrounding the vehicle for safe navigation. However, LiDAR point cloud datasets need point-level labels which require a significant amount of annotation effort. We present a framework which generates simulated labeled point cloud data. The simulated LiDAR data was generated by a physics-based platform, the Mississippi State University Autonomous Vehicle Simulator (MAVS). In this work, we use the simulation framework and labeled LiDAR data to develop and test algorithms for autonomous ground vehicle off-road navigation. The MAVS framework generates 3D point clouds for off-road environments that include trails and trees.

Vehicle headlamps and roadway lighting are the major sources of illumination at night. These sources affect contrast - defined as the luminance difference of an object from its background - which drives visibility at night. However, the combined effect of vehicle headlamps and intersection lighting on object contrast has not been reported previously. In this study, the interactive effects of vehicle headlamps and overhead lighting on object contrast were explored based on earlier work that examined drivers’ visibility under three intersection lighting designs (illuminated approach, illuminated box, and illuminated approach + box). The goals of this study were to: 1) quantify object luminance and contrast as a function of a vehicle’s headlamps and its distance to an intersection using the three lighting designs; and, 2) to assess whether contrast influences visual performance and perceived visibility in a highly dynamic intersection environment.

An unconventional conceptual design of truck chassis with multi-functional cross-members is proposed, and an optimization framework is developed to optimize its structure to minimize mass while satisfying stiffness and modal frequency constraints. The side rails are C-sectional channels of variable height and were divided into six sections, each with different thickness distribution for the flanges and the web. The gearbox cross-member and the intermediate cross-members are compressed-air cylinders, and hence they act as multi-functional components. The dimensions and thickness of the side rails and the air-tank cross members are defined by a set of parameters which are considered as design variables in the optimization problem. The structure consists of three additional fixed cross-members which are modeled using beam elements. The limits of the design variables are decided while considering manufacturing limits.

The moisture/water absorption and microvoids/cracks progression are two well-understood mechanisms that have significant degradation effects on the mechanical properties/behaviors of the polymer-based composites. To theoretically investigate the effects of above two mechanisms, we develop a simple fiber reinforced polymer composites model by employing the internal state variable (ISV) theory. The water content and the anisotropically distributed damage of the composites are considered as two ISVs (the water content is described by a scalar variable and the damage is defined as a second order tensor) whose histories are governed by two specific physically-based evolution equations. The proposed model can be easily cast into a general theoretical framework to capture more polymer composites behaviors such as viscoelasticity, viscoplasticity and the thermal effect.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

A common interaction between a penetrator and a target has been the use of copper and nickel materials. However, a multiscale analysis has not been performed on such a system. Compared to steels, aluminum alloys, titanium alloys and other metallic materials, a description of the mechanical behavior of pure ductile metals such as Cu struck by a penetrator comprises nickel under the high strain rate at different multiscale still remains unknown. In this research, Modified Embedded Atom Method (MEAM) Potential is utilized to study this system and the molecular dynamics simulation is employed in order to provide structure property evolution information for plasticity and shearing mechanisms.

This paper presents the design of two cleaning systems following systems engineering design approach. An in situ cleaning system was designed for removing engine oil stains and metal swarf and shavings that adhere to rollers of conveying lines which convey cylinder head as well as other heavy engine components. The other system was to clear and collect metal debris accumulated in the grooves of an engine block internal assembly line. Prototypes were fabricated for the designed cleaning equipment for further testing and assessment. In the system engineering design process, preliminary, intermediate, and detailed design were conducted following an identification of the design problem, within that process a sequence of tasks such as synthesis, analysis, prototyping, and assessment were completed.

Binder jetting of sand molds and cores for metal casting provides a scalable and efficient means of producing metal components with complex geometric features made possible only by Additive Manufacturing. Topology optimization software that can mathematically determine the optimum placement of material for a given set of design requirements has been available for quite some time. However, the optimized designs are often not manufacturable using standard metal casting processes due to undercuts, backdraft and other issues. With the advent of binder-based 3D printing technology, sand molds and cores can be produced to make these optimized designs as metal castings.

A robot mining system was developed by the State Space Robotic undergraduate student design team from Mississippi State University (MSU) for the 2016 NASA Robotic Mining Competition. The mining robot was designed to traverse the Martian chaotic terrain, excavate a minimum of 10 kg of Martian regolith and deposit the regolith into a collector bin within 10 minutes as part of the competition. A Systems Engineering approach was followed in proceeding with this design project. The designed mining robot consisted of two major components: (1) mechanical system and (2) control system. This paper mainly focuses on the design and assessment process of the mechanical system but will also briefly mention the control system so as to evaluate the designed robotic system in its entirety. The final designed robot consisted of an aluminum frame driven by four motors and wheels. It utilized a scoop and lifting arm subsystem for collecting and depositing Martian regolith.

In modern aircraft power systems, active power converters are promising replacements for transformer rectifier units concerning efficiency and weight. To assess the benefits of active power converters, converter design and optimization should be carefully done under the operation requirements of aircraft applications: electromagnetic interference (EMI) standards, power quality standards, etc. Moreover, certain applications may have strict limits on other converter specifications: weight, size, converter loss, etc. This paper presents the methodology for performance optimization of different active power converters (active front-ends, isolated DC/DC converters and three-phase isolated converters) for aircraft applications. Key methods for power converter component (e.g. inductors, semiconductor devices, etc.) performance optimization and loss calculation are introduced along with the converter optimization procedure.

The ability to quickly and automatically evaluate vehicle designs is a critical tool in an efficient vehicle design process. This paper presents techniques for vehicle parameter estimation using automatic intelligent simulations. These techniques enable the efficient and automatic evaluation of many important aspects of vehicle designs. The effectiveness of this approach is demonstrated by using vehicle tests that are commonly performed on military ground vehicles. Our simulation techniques are able to determine the relevant vehicle performance characteristics in a much more efficient manner than could be done previously. This is done automatically, once the user has specified the type of test to be performed. A terrain sample is automatically generated and the vehicle’s behavior on each terrain instance is evaluated until the specified test conditions are met.

1 Most traditional methods and equations for estimating the structural and nonstructural weights and aerodynamics used at the aircraft conceptual design phase are empirical relations developed for conventional tube-and-wing aircraft. In a computation-heavy design process, such as Multidisciplinary Design and Optimization (MDO) simplicity of calculation is paramount, and for conventional configurations the aforementioned approaches work well enough for conceptual design. But, for non-traditional designs such as strut-braced winged aircraft, empirical data is generally not available and the usual methods can no longer apply. One solution to this is a movement toward generalized physics-based methods that can apply equally well to conventional or non-traditional configurations.

The vertical force generated from terrain-tire interaction has long been of interest for vehicle dynamic simulations and chassis development. To improve simulation efficiency while still providing reliable load prediction, a terrain pre-filtering technique using a constraint mode tire model is developed. The wheel is assumed to convey one quarter of the vehicle load constantly. At each location along the tire's path, the wheel center height is adjusted until the spindle load reaches the pre-designated load. The resultant vertical trajectory of the wheel center can be used as an equivalent terrain profile input to a simplified tire model. During iterative simulations, the filtered terrain profile, coupled with a simple point follower tire model is used to predict the spindle force. The same vehicle dynamic simulation system coupled with constraint mode tire model is built to generate reference forces.