Event detail
CEDC seminar: Addressing the Observability Problem in Batteries: Algorithm Design for Electrode-level Charge and Health Estimation
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11:00 AM - 12:15 PM
Room Number: 2507
North Classroom
1200 Larimer Street
Denver, CO
Room Number: 2507
North Classroom
1200 Larimer Street
Denver, CO
Sara Sattarzadeh, Research Assistant, PhD student
Electrical Engineering,University of Colorado Denver
Abstract
Among the available energy storage devices, Lithium-ion Batteries (LIBs) are being considered to be promising for many applications such as renewable power grids, Electrified Vehicles (EVs) and consumer electronics due to their high energy density, long life and lack of memory effect. However, this leading energy storage technology is still suffering from reliability, safety and performance issues. In order to improve the performance and safety of LIBs, we need to estimate the internal condition of battery cells in real-time which is not physically measured in commercial settings. From real-time estimation viewpoint, one of the major barriers in battery estimation arises from weak observability of individual electrode states from terminal voltage measurement. In other words, differential terminal voltage (measured between two electrodes) does not provide enough information on charge and health of each electrodes separately. Nevertheless, such electrode-level information can help expand usable energy and power from the battery cell by enabling electrode-level limit based battery control (as opposed to cell-level limit based control). Furthermore, such electrode-level health monitoring can also increase battery life by enforcing electrode-level health conscious control.
Motivated by these promising improvements, in this seminar, we present a real-time framework for estimating charge and health of individual electrodes. Essentially, the weak observability of the electrodes is addressed by decomposing the overall estimation problem into two sub-problems. Under this framework, each of these sub-problems boils down to designing a sub-estimator based on individual electrode dynamics along with an uncertain terminal voltage model. Finally, these sub-estimators work in a cascaded manner to provide charge and health estimation for individual electrodes. The performance of the proposed scheme is illustrated by using an experimentally identified battery model that considers essential nonlinearities in electrodes' Open Circuit Potential (OCP) functions and resistances as well as dominant Solid Electrolyte Interphase (SEI) aging mechanism.
Bio.
Sara Sattarzadeh received her bachelor’s degree in electrical engineering from Shiraz University in 2011, and master’s degree in control systems from Amirkabir University of Technology, Iran, Tehran. She is currently pursuing a PhD in electrical engineering at University of Colorado Denver. Her technical background in Iran was in the area of robotics and adaptive control. Her current research interests include energy and transportation systems, developing control, estimation and diagnostics algorithms for batteries, electric vehicles, and connected and autonomous vehicles.
Email Address: sara.sattarzadeh@ucdenver.edu
Electrical Engineering,University of Colorado Denver
Abstract
Among the available energy storage devices, Lithium-ion Batteries (LIBs) are being considered to be promising for many applications such as renewable power grids, Electrified Vehicles (EVs) and consumer electronics due to their high energy density, long life and lack of memory effect. However, this leading energy storage technology is still suffering from reliability, safety and performance issues. In order to improve the performance and safety of LIBs, we need to estimate the internal condition of battery cells in real-time which is not physically measured in commercial settings. From real-time estimation viewpoint, one of the major barriers in battery estimation arises from weak observability of individual electrode states from terminal voltage measurement. In other words, differential terminal voltage (measured between two electrodes) does not provide enough information on charge and health of each electrodes separately. Nevertheless, such electrode-level information can help expand usable energy and power from the battery cell by enabling electrode-level limit based battery control (as opposed to cell-level limit based control). Furthermore, such electrode-level health monitoring can also increase battery life by enforcing electrode-level health conscious control.
Motivated by these promising improvements, in this seminar, we present a real-time framework for estimating charge and health of individual electrodes. Essentially, the weak observability of the electrodes is addressed by decomposing the overall estimation problem into two sub-problems. Under this framework, each of these sub-problems boils down to designing a sub-estimator based on individual electrode dynamics along with an uncertain terminal voltage model. Finally, these sub-estimators work in a cascaded manner to provide charge and health estimation for individual electrodes. The performance of the proposed scheme is illustrated by using an experimentally identified battery model that considers essential nonlinearities in electrodes' Open Circuit Potential (OCP) functions and resistances as well as dominant Solid Electrolyte Interphase (SEI) aging mechanism.
Bio.
Sara Sattarzadeh received her bachelor’s degree in electrical engineering from Shiraz University in 2011, and master’s degree in control systems from Amirkabir University of Technology, Iran, Tehran. She is currently pursuing a PhD in electrical engineering at University of Colorado Denver. Her technical background in Iran was in the area of robotics and adaptive control. Her current research interests include energy and transportation systems, developing control, estimation and diagnostics algorithms for batteries, electric vehicles, and connected and autonomous vehicles.
Email Address: sara.sattarzadeh@ucdenver.edu
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