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Proceedings Papers

Battery State of Health Monitoring by Estimation of the Number of Cyclable Li-Ions

Xin Zhou, Jeffrey L. Stein, Tulga Ersal
Proc. ASME. DSCC2016, Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation, V001T08A001, October 12–14, 2016
Paper No: DSCC2016-9730
https://doi.org/10.1115/DSCC2016-9730
View Paper titled, Battery State of Health Monitoring by Estimation of the Number of Cyclable Li-Ions
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Topics: Batteries, Ions, Algorithms, Electric vehicles, Engineering simulation, Errors, Simulation, Cities, Cycles, Dynamometers
Proceedings Papers

Preliminary Investigation of Provability of Li-Ion Macroscale Models Subject to Capacity Fade

Harikesh Arunachalam, Ilenia Battiato, Simona Onori
Proc. ASME. DSCC2016, Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation, V001T08A002, October 12–14, 2016
Paper No: DSCC2016-9736
https://doi.org/10.1115/DSCC2016-9736
View Paper titled, Preliminary Investigation of Provability of Li-Ion Macroscale Models Subject to Capacity Fade
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Topics: Automotive industry, Batteries, Chemistry, Durability, Dynamics (Mechanics), Energy storage, Lithium, Lithium-ion batteries, Microscale devices, Phase diagrams
Proceedings Papers

Auto-Tuning Tub-Level Control of Blender in Hydraulic Fracturing

Zheng Chen, Leslie Cargill, Brent Naizer
Proc. ASME. DSCC2016, Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation, V001T08A003, October 12–14, 2016
Paper No: DSCC2016-9848
https://doi.org/10.1115/DSCC2016-9848
View Paper titled, Auto-Tuning Tub-Level Control of Blender in Hydraulic Fracturing
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Topics: Hydraulic fracturing, Control systems, Density, Feedback, Fracture (Process), Rocks, Sands, Shales, Water
Proceedings Papers

State Estimation for an Electrochemical Model of Multiple-Material Lithium-Ion Batteries

Leobardo Camacho-Solorio, Miroslav Krstic, Reinhardt Klein, Anahita Mirtabatabaei, Scott J. Moura
Proc. ASME. DSCC2016, Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation, V001T08A004, October 12–14, 2016
Paper No: DSCC2016-9877
https://doi.org/10.1115/DSCC2016-9877
View Paper titled, State Estimation for an Electrochemical Model of Multiple-Material Lithium-Ion Batteries
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Topics: Lithium-ion batteries, State estimation, Active materials, Electrodes, Batteries, Bessel functions, Diffusion (Physics), Manufacturing, Partial differential equations, Particulate matter
Proceedings Papers

Early Kick Detection Using Real-Time Data Analysis With a Lumped Parameter Model

M. A. Ojinnaka, J. J. Beaman, S. Fish
Proc. ASME. DSCC2016, Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation, V001T08A005, October 12–14, 2016
Paper No: DSCC2016-9931
https://doi.org/10.1115/DSCC2016-9931
View Paper titled, Early Kick Detection Using Real-Time Data Analysis With a Lumped Parameter Model
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Topics: Drilling, Hinges, Lumped parameter models, Pressure, Risk, Safety, Water