Abstract
This paper develops a framework for estimating the parameters of a combined thermal and electrochemical model of a lithium-ion battery using a single, non-invasive, dynamic experiment. The paper is motivated by the low parameter identifiability suffered by lithium-ion battery models. The existing literature contains a substantial number of studies on battery parameter estimation. However, the existing research focuses either on thermal or electrochemical model parameter estimation. The main goal of this paper, in contrast, is to examine the feasibility of a single, current cycling experiment to identify the parameters of the two models. This study uses equivalent circuit models to simulate both the thermal and electrochemical dynamics of a lithium-ion battery. However, the underlying theoretical framework is extendible to higher order battery representations. The proposed input current trajectory in this study is optimized to maximize the Fisher identifiability of the combined set of parameters (i.e., thermal and electrochemical). We validate the optimal current input trajectory using a Monte Carlo simulation study. The results from the simulation study demonstrate that the optimized current trajectory allows the simultaneous estimation of the combined set of parameters using a single, current cycling, experiment over a 12-hour period of time. The results in this paper are encouraging because they demonstrate that a current input trajectory can be designed to optimize the identifiability of all the parameters of a thermal and electrochemical battery model.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 7314-7320 |
| Number of pages | 7 |
| Journal | IFAC-PapersOnLine |
| Volume | 50 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jul 1 2017 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
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Maximizing Parameter Identifiability of a Combined Thermal and Electrochemical Battery Model Via Periodic Current Input Optimization. / Mendoza, Sergio; Rothenberger, Michael; Liu, Ji; Fathy, Hosam Kadry.
In: IFAC-PapersOnLine, Vol. 50, No. 1, 01.07.2017, p. 7314-7320.Research output: Contribution to journal › Article
TY - JOUR
T1 - Maximizing Parameter Identifiability of a Combined Thermal and Electrochemical Battery Model Via Periodic Current Input Optimization
AU - Mendoza, Sergio
AU - Rothenberger, Michael
AU - Liu, Ji
AU - Fathy, Hosam Kadry
PY - 2017/7/1
Y1 - 2017/7/1
N2 - This paper develops a framework for estimating the parameters of a combined thermal and electrochemical model of a lithium-ion battery using a single, non-invasive, dynamic experiment. The paper is motivated by the low parameter identifiability suffered by lithium-ion battery models. The existing literature contains a substantial number of studies on battery parameter estimation. However, the existing research focuses either on thermal or electrochemical model parameter estimation. The main goal of this paper, in contrast, is to examine the feasibility of a single, current cycling experiment to identify the parameters of the two models. This study uses equivalent circuit models to simulate both the thermal and electrochemical dynamics of a lithium-ion battery. However, the underlying theoretical framework is extendible to higher order battery representations. The proposed input current trajectory in this study is optimized to maximize the Fisher identifiability of the combined set of parameters (i.e., thermal and electrochemical). We validate the optimal current input trajectory using a Monte Carlo simulation study. The results from the simulation study demonstrate that the optimized current trajectory allows the simultaneous estimation of the combined set of parameters using a single, current cycling, experiment over a 12-hour period of time. The results in this paper are encouraging because they demonstrate that a current input trajectory can be designed to optimize the identifiability of all the parameters of a thermal and electrochemical battery model.
AB - This paper develops a framework for estimating the parameters of a combined thermal and electrochemical model of a lithium-ion battery using a single, non-invasive, dynamic experiment. The paper is motivated by the low parameter identifiability suffered by lithium-ion battery models. The existing literature contains a substantial number of studies on battery parameter estimation. However, the existing research focuses either on thermal or electrochemical model parameter estimation. The main goal of this paper, in contrast, is to examine the feasibility of a single, current cycling experiment to identify the parameters of the two models. This study uses equivalent circuit models to simulate both the thermal and electrochemical dynamics of a lithium-ion battery. However, the underlying theoretical framework is extendible to higher order battery representations. The proposed input current trajectory in this study is optimized to maximize the Fisher identifiability of the combined set of parameters (i.e., thermal and electrochemical). We validate the optimal current input trajectory using a Monte Carlo simulation study. The results from the simulation study demonstrate that the optimized current trajectory allows the simultaneous estimation of the combined set of parameters using a single, current cycling, experiment over a 12-hour period of time. The results in this paper are encouraging because they demonstrate that a current input trajectory can be designed to optimize the identifiability of all the parameters of a thermal and electrochemical battery model.
UR - http://www.scopus.com/inward/record.url?scp=85031796769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031796769&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2017.08.1468
DO - 10.1016/j.ifacol.2017.08.1468
M3 - Article
AN - SCOPUS:85031796769
VL - 50
SP - 7314
EP - 7320
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 1
ER -