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Title: A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models

Abstract

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [1]
+ Show Author Affiliations
  1. Univ. of Texas, Austin, TX (United States)
  2. Univ. of Washington, Seattle, WA (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  4. Indian Inst. of Technology Bombay, Maharashtra (India)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Electricity (OE); USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1872016
Report Number(s):
SAND2022-6046J
Journal ID: ISSN 0013-4651; 706317
Grant/Contract Number:  
NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Journal of the Electrochemical Society
Additional Journal Information:
Journal Volume: 169; Journal Issue: 5; Journal ID: ISSN 0013-4651
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE

Citation Formats

Kolluri, Suryanarayana, Mittal, Prateek, Subramaniam, Akshay, Preger, Yuliya, De Angelis, Valerio, Ramadesigan, Venkatasailanathan, and Subramanian, Venkat R. A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models. United States: N. p., 2022. Web. doi:10.1149/1945-7111/ac6b5d.
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Kolluri, Suryanarayana, Mittal, Prateek, Subramaniam, Akshay, Preger, Yuliya, De Angelis, Valerio, Ramadesigan, Venkatasailanathan, & Subramanian, Venkat R. A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models. United States. https://doi.org/10.1149/1945-7111/ac6b5d
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Kolluri, Suryanarayana, Mittal, Prateek, Subramaniam, Akshay, Preger, Yuliya, De Angelis, Valerio, Ramadesigan, Venkatasailanathan, and Subramanian, Venkat R. Mon . "A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models". United States. https://doi.org/10.1149/1945-7111/ac6b5d. https://www.osti.gov/servlets/purl/1872016.
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@article{osti_1872016,
title = {A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models},
author = {Kolluri, Suryanarayana and Mittal, Prateek and Subramaniam, Akshay and Preger, Yuliya and De Angelis, Valerio and Ramadesigan, Venkatasailanathan and Subramanian, Venkat R.},
abstractNote = {Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.},
doi = {10.1149/1945-7111/ac6b5d},
journal = {Journal of the Electrochemical Society},
number = 5,
volume = 169,
place = {United States},
year = {Mon May 16 00:00:00 EDT 2022},
month = {Mon May 16 00:00:00 EDT 2022}
}
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Publisher's Version of Record
https://doi.org/10.1149/1945-7111/ac6b5d
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