Skip to main content
arc logo
Automotive Research Center
hero image

2020 ARC Research Seminars - Winter Series

January 24, Friday, 9:30-11:00am eastern time

University of Michigan, North Campus, GG Brown Lab. room 2540 (Grand Conference Room)

Manned and Autonomous Vehicles: Design for Terrain Mobility, Maneuver, and Energy Efficiency

Introducing new ARC Partner School University of Alabama, Birmingham

PI: Prof. Vladimir Vantsevich, Mechanical Engineering, U. of Alabama at Birmingham

This seminar discusses research directions in manned and autonomous ground vehicle dynamics and vehicle design that provide necessary conditions for creating vehicle transformative technologies:

  1. Vehicle operational properties and vehicle system design
  2. Coupled and interactive dynamics of vehicle systems
  3. Vehicle mechatronics and intelligent physical systems
  4. Open architecture system design
  5. Agile tire and vehicle dynamics and mobility.

Wheel-power-distribution optimization and control ensured by mechanical and mechatronic driveline systems and by hybrid and electrical virtual/distributed driveline systems is presented as a key foundation of the above-listed research directions.

The effectiveness of the proposed analytical methods and engineered systems is illustrated by including a new hybrid-electric power-transmitting unit, a new wheel rotational kinematics sensor, various limited slip and controllable differentials, and wheel power management control algorithms for fully electric vehicles. The seminar presents and discusses results on fundamental improvements of terrain mobility, vehicle energy efficiency, and maneuver of terrain vehicles.

The seminar introduces the Autonomous Vehicle Mobility Institute (AVMI) as a new initiative of UAB in collaboration with the U.S. Army GVSC. There are three primary initiatives that makeup the AVMI:

  1. The Simulator of Autonomous Mobility (SAM) and five research laboratories
  2. The Master’s of Science Program in High-Mobility Vehicle Engineering (MS-HMVE)
  3. The NATO Center of Excellence for Vehicle Transformative Technologies (NATO COE-VTT)

AVMI will support the U.S. Army GVSC with basic and applied research that is directly integrated with vehicle system and vehicle platform prototyping and engineering design. AVMI will serve as the research lab hub for the collaboration between the U.S. Army GVSC, ARC universities, and other U.S. research agencies, international professional and intergovernmental organizations, including NATO, and private industry.

February 28, Friday, 9:30-11:00am eastern time

University of Michigan, North Campus, GG Brown Lab. room 2540 (Grand Conference Room)

Computational Discovery of Materials for Energy Storage: High Throughput Screening and Machine Learning

PI: Prof. Donald Siegel, Mechanical Engineering, U of M

Link to project

Abstract: Salt hydrates demonstrate promise as heat storage materials as they possess high energy densities and reversibility at moderate temperatures. Despite their promise, a great number of salt hydrate compositions have not been explored. The goal of this work is to identify new salt hydrates that can outperform known materials in terms of energy density and are predicted to be thermodynamically stable. A total of 5,292 hypothetical salt hydrates were generated by systematically substituting cations and halides into 76 salt hydrate crystal structures mined from the Inorganic Crystal Structure Database. These hypothetical hydrates were characterized according to their energy densities, thermodynamic stability, and operating temperature using high-throughput density functional theory calculations. Several promising hypothetical hydrates were identified with higher energy densities than experimentally-known salt hydrates. Several machine learning algorithms were then trained on the dataset in order to create models that could effectively predict salt hydrate performance based on simple ionic and structural properties of the salt hydrate. These models were then explored to identify property- performance relationships in heat-storing salt hydrates.

Sonar-Based Sensors for Autonomous Vehicles Using Passive and Active Metamaterials

PI: Prof. Bogdan Popa, Prof. Bogdan Epureanu, Mechanical Engineering, U of M

Link to project

Abstract: To operate autonomously in dynamic and uncertain environments, autonomous vehicles (AVs) must construct and continuously update a world model. The most prominent sensor systems (LIDAR, radar, video cameras) operate with electromagnetic waves and thus lose efficiency in adverse weather such as rain, snow and fog. Moreover, they typically scan the environment with beams that travel long distances (LIDAR, radar) and thus are an impediment for stealthy operation. In contrast, ultrasound (US) sensor systems maintain efficiency in any kind of weather and are stealthy because sound is rapidly absorbed in air and does not travel too far. However, the current generation of US sensors have significant limitations such as low resolution and range that makes them unsuitable for most military applications. In this presentation we will show how to create US sensors that have significantly larger ranges, and which can create high-resolution images, employ a single transducer element and thus are low power and low cost, are stealthy, and operate for long periods of time in any weather. In our approach, the sound produced by a single transducer is manipulated by a metamaterial lens to form focused beams used to scan the environment. Furthermore, we will show that sub-millimeter deformations of the lens can send the beams to prescribed directions without rotating the entire device. We will discuss the power required by our sensing system and contrast it with existing state-of-the art beam-formers. Finally, we will discuss future work regarding strategies used to leverage the sensor in order to map the surroundings.

March 20, Friday, 9:30-11:00am eastern time

This seminar is postponed to the Fall semester in response to the ongoing COVID-19 situation.

Novel Data-Driven Algorithms for Autonomous Vehicle Path Planning Problems with Uncertain Data Parameters
PIs: Prof. Saravanan Venkatachalam, Industrial and Systems Engineering, Wayne State U.; Prof. Manish Bansal, Industrial and Systems Engineering, Virginia Tech
Link to project

Despite the numerous advantages of ground vehicles (GVs), their size and limited payload capacity lead to fuel constraints and therefore, they are required to make one or more refueling stops in a long mission. Moreover, these operations encounter unknown terrain or obstacles, resulting in uncertainty in the fuel (or time) required to travel among different points of interests (POIs); for example, in a hostile terrain with improvised explosive devices (IEDs), conducting anti-IED sweeps and explosive ordinance disposal can lead to unexpected delays for GVs. In many applications, even the locations of the POIs are not precisely known (uncertain) due to inaccurate a-priori map or imperfect and noisy exteroceptive sensory information or perturbations. This research focuses on the following fundamental question that arises during the planning and execution/operational stages of deploying a team of ground vehicles: How to guarantee a set of paths for heterogeneous GVs with stochasticity in their availability and environment? Specific focus will be on missions with differing unmanned ground vehicle (UGV) and manned ground vehicle (MGV) interactions, and the tasks where the team of UGVs and MGVs need to visit a collection of POIs in the presence of uncertain fuel/time/distance/information required to travel between POIs or reach refueling station.

Resilient Teaming: Fleet organization and decision making in heterogeneous vehicle teams
PI: Prof. Kira Barton, Mechanical Engineering, U of M
Link to project

As autonomy becomes more pervasive, the capabilities of different vehicles may become more pronounced as resources are designed to maximize mission performance, while taking into consideration other requirements such as minimizing energy costs. This introduces an important research problem: How does the configuration of heterogeneous vehicle teams change as autonomy becomes more pervasive in uncertain environments? Methods to define the unique requirements of the fleet across a mixture of vehicles must be developed. Once the make‐up of the team has been determined, a dynamic understanding of the team’s capacity to make real‐time decisions based on the team’s current state, environmental conditions, and uncertainties must be derived. This talk aims to highlight our approach for addressing these needs through resilient teaming: a fleet organization and decision‐making strategy for designing heterogeneous teams to meet these objectives.