ARC Collaborative Research Seminar Series
Winter 2019

ARC seminars are free and open to the general public. Center members can download the presentation files on our password-access online portal iARC. Non-ARC attendees please email with your requests.

Parking & directions inquires: Contact by 2:00 p.m. the day before the seminar.

Remote attendance via tele/video conference: Contact William Lim

Refreshments will be served 9:15-9:30am. The talks will begin at 9:30 a.m. sharp.

February 1, Friday (9:30a.m. - 11a.m.)
University of Michigan, North Campus, Duderstadt Center, room 1180

The MSU Autonomous Vehicle Simulator (MAVS)
Dr. Daniel Carruth, Associate Director
Dr. Chris Goodin, Assistant Research Professor
Center for Advanced Vehicular Systems, Mississippi State University

Abstract & Biography         While the prevalence of autonomous and automated driving has increased for on-road vehicles in recent years, off-road autonomous navigation faces many challenges beyond those encountered in on-road driving. Mississippi State University is developing a realistic simulator for off-road autonomous navigation that will assist in the development and testing of autonomous off-road vehicles. This talk will briefly discuss the history and current state of off-road autonomy and how emerging trends in autonomous navigation necessitate the use of simulation. We will give a brief overview of the MAVS and discuss potential use-cases and planned future developments.
Christopher T. Goodin received his B.S. in mathematics and physics from Mississippi College in 2004, M.S. in physics from Vanderbilt University in 2006, and Ph.D. in physics from Vanderbilt in 2008. From 2008-2017, Dr. Goodin worked with the U.S. Army Engineer Research and Development Center (ERDC) in Vicksburg, MS, developing physics-based simulations of ground vehicles, sensors, and robotics. Dr. Goodin is currently an Assistant Research Professor with the Center for Advanced Vehicular Systems, Mississippi State University, developing simulation tools for off-road autonomy.

February 22, Friday (9:30a.m. - 11a.m.)
University of Michigan, North Campus, Duderstadt Center, room 1180

ARC Project: Novel Hybrid Electric Powertrains Enabled by Models of Electro-Magnetic-Structural Dynamics
Chenyu Yi, Grad. Student Research Assistant
Dr. Heath Hofmann, Prof. Electrical Engineering
Dr. Bogdan Epureanu, Prof. Mechanical Engineering

University of Michigan

Abstract         Vehicle electrification requires the application of electromagnetic devices such as electric machines. The distributed electromagnetic forces that exist in such devices can cause structural deformations, especially for devices with high power densities. The deformations may in turn lead to changes in the electromagnetic forces. As a consequence, mechanical and electromagnetic behavior is coupled, leading to electromagnetic-structural (EMS) phenomena. The AC current input, periodic forces generated by permanent magnets, and other disturbances to the electromagnetic device can lead to the excitation of specific structural resonances due to the EMS coupling. Previous studies have not focused on capturing the feedback coupling between electromagnetic forces and structural deformations. In this project, a novel hybrid electric powertrain architecture was designed, modeled, and optimized. The EMS coupling was then modeled to investigate potential structural parametric excitations. This presentation will review the construction of the multi-physics model, the study of the stability of the system under parametric resonance, and a demonstration of parametric resonance using a proof-of-concept design.

ARC Project: Advanced Battery Diagnostics: Decode the information in Electrode Swelling
Dr. Anna Stefanopoulou, Prof. Mechanical Engineering, University of Michigan

Abstract         The battery state of health (SOH) is currently estimated by determining capacity (cyclable energy) and cell resistance (power capability). These parameters can save your vehicle or your robot from getting stranded with empty or weakened batteries so they are critical. Unfortunately, estimating these parameters with high confidence can only be done under certain discharge patterns. This uncertainty is masked by overly conservative estimations of the vehicle range, especially under cold temperatures. Knowing the true state of the battery such as the Loss of Active Material (LAM) in the anode and cathode along with the Loss of Lithium Inventory (LLI) allows us to push the cells to their true limit instead of a conservative terminal voltage limit, without inducing more degradation or hazardous shorts from lithium plating. In this presentation, we’ll take you from the hypothesis of identifying the electrode-specific SOH parameters by observing the cell expansion via a force measurement (year 1), to the intrinsic shift of the phase transition of the battery material as it ages (year 2). This shift creates an “aging signature” like a wrinkle that we can observe in the measured force more clearly than in the measured voltage, especially at relevant discharge ranges and rates. In year three, we will collect data from aged cells and at higher discharge rates to further address the critical real-world considerations and document the evolution of the electrode “age wrinkles” in the electrical and mechanical domains.

March 29, Friday (9:30a.m. - 11a.m.)
University of Michigan, North Campus, G.G. Brown Laboratory, 2540 Grand conference room

Trust-based Control and Scheduling for UGV Platoon under Cyber Attacks
Dr. Yue Wang, Warren H. Owen - Duke Energy Assoc. Prof. of Engineering, Clemson University

Abstract         Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. Human experience and onboard intelligence may help mitigate such cases. However, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise multiple UGVs simultaneously. The human operator needs to trust the UGVs properly in these risky scenarios. In this talk, we consider the scenario where a connected UGV platoon is under cyber-attacks, which may lead to safety disruption and performance degradation. Each UGV generates both internal and external evaluations based on the platoon’s performance metrics. A cloud-based trust-based information management system is designed to collect these evaluations to detect abnormal UGV platoon behaviors. To deal with inaccurate information due to vehicle to cloud (V2C) cyber-attacks, the RoboTrust algorithm is designed to analyze vehicle trustworthiness and eliminate information with low credit. Finally, a human operator scheduling algorithm is proposed for the teaming of the operator and the UGVs when the number of abnormal UGVs exceeds the limit of what the human operators can handle concurrently.

ARC Project: Mutually-Adaptive Shared Control between Human Operators and Autonomy in Ground Vehicles
Dr. Tulga Ersal, Asst. Research Scientist, Mechanical Engineering, University of Michigan

Abstract         Successful shared control between a human operator and autonomy in ground vehicles critically relies on a mutual understanding and adaptation. Various shared control schemes have been presented in the literature for arbitrating the control authority between the human operator and autonomy. However, most of the existing schemes are not adaptive to the human’s needs and capabilities, and the few adaptive ones do not consider an important human factor, namely, mental workload.
        This collaborative project is based on the hypothesis that estimating the mental workload of the human operator in real-time and adapting the shared control scheme accordingly could lead to a more seamless and successful shared control. This presentation will summarize our progress to date to explore this hypothesis. In particular, the presentation will comprise two parts. In the first part we will focus on the challenge of estimating mental workload in real-time and present a novel Bayesian inference based approach that leverages two computational models to estimate human’s workload from two different physiological measurements — gaze trajectory and pupil size. A pilot user study will be reported that shows that the proposed method can estimate human’s workload with only a 4 second-time window and achieve 70% accuracy. In the second part we will present the design of an adaptive shared control scheme. Preliminary human subject studies show that the adaptive scheme can achieve similar vehicle performance as the non-adaptive one with less control effort for the human in a controlled moderate mental workload condition.

ARC members can download the presentation files on our password-access online portal iARC.
Non-ARC members please email with your requests.