TY - JOUR
T1 - From Battery Cell to Electrodes
T2 - Real-Time Estimation of Charge and Health of Individual Battery Electrodes
AU - Dey, Satadru
AU - Shi, Ying
AU - Smith, Kandler
AU - Colclasure, Andrew
AU - Li, Xuemin
N1 - Funding Information:
Manuscript received August 1, 2018; revised October 25, 2018, January 16, 2019, and February 13, 2019; accepted February 25, 2019. Date of publication April 1, 2019; date of current version October 31, 2019. This work was supported in part by the U.S. Department of Energy (DOE) under Contract DE-AC36-08GO28308, authored by the Alliance for Sustainable Energy, LLC, the manager, and operator of the National Renewable Energy Laboratory, in part by the University of Colorado Denver Office of Research Services and in part by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office Energy Storage Program. (Corresponding author: Satadru Dey.) S. Dey is with the Department of Electrical Engineering, University of Colorado Denver, Denver, CO 80204 USA (e-mail:, satadru.dey@ ucdenver.edu).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - Accurate information of battery internal variables is crucial for health-conscious and optimal battery management. Due to lack of measurements, advanced battery management systems rely heavily on estimation algorithms that provide such internal information. Although algorithms for cell-level charge and health estimation have been widely explored in the literature, algorithms for electrode-level quantities are almost nonexistent. The main obstacle in electrode-level estimation is the observability problem where the individual electrode states are not observable from terminal voltage output. However, if available, real-time feedback of electrode-level charge and health can be highly beneficial in maximizing energy utilization and battery life. Motivated by this scenario, in this paper we propose a real-time algorithm that estimates the available charge and health of individual electrodes. We circumvent the aforementioned observability problem by proposing an uncertain model-based cascaded estimation framework. The design and analysis of the proposed scheme are aided by a combination of Lyapunov's stability theory, adaptive observer theory, and interconnected systems theory. Finally, we illustrate the effectiveness of the estimation scheme by performing extensive simulation and experimental studies.
AB - Accurate information of battery internal variables is crucial for health-conscious and optimal battery management. Due to lack of measurements, advanced battery management systems rely heavily on estimation algorithms that provide such internal information. Although algorithms for cell-level charge and health estimation have been widely explored in the literature, algorithms for electrode-level quantities are almost nonexistent. The main obstacle in electrode-level estimation is the observability problem where the individual electrode states are not observable from terminal voltage output. However, if available, real-time feedback of electrode-level charge and health can be highly beneficial in maximizing energy utilization and battery life. Motivated by this scenario, in this paper we propose a real-time algorithm that estimates the available charge and health of individual electrodes. We circumvent the aforementioned observability problem by proposing an uncertain model-based cascaded estimation framework. The design and analysis of the proposed scheme are aided by a combination of Lyapunov's stability theory, adaptive observer theory, and interconnected systems theory. Finally, we illustrate the effectiveness of the estimation scheme by performing extensive simulation and experimental studies.
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U2 - 10.1109/TIE.2019.2907514
DO - 10.1109/TIE.2019.2907514
M3 - Article
AN - SCOPUS:85074724027
VL - 67
SP - 2167
EP - 2175
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 3
M1 - 8678658
ER -