A hybrid lithium-ion battery model for system-level analyses

2016 ◽  
Vol 40 (11) ◽  
pp. 1576-1592 ◽  
Author(s):  
Juuso Lindgren ◽  
Imran Asghar ◽  
Peter D. Lund
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
System Level ◽  
Battery Model

scholarly journals Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3791
Author(s):  
Yong Li ◽  
Jue Yang ◽  
Wei Long Liu ◽  
Cheng Lin Liao
Keyword(s):  
Lithium Ion Battery ◽  
High Precision ◽  
Tracking Error ◽  
Lithium Ion ◽  
Data Driven ◽  
System Level ◽  
Identification Algorithm ◽  
Identification Methods ◽  
Battery Model ◽  
Non Linear System

The lithium-ion battery is a complicated non-linear system with multi electrochemical processes including mass and charge conservations as well as electrochemical kinetics. The calculation process of the electrochemical model depends on an in-depth understanding of the physicochemical characteristics and parameters, which can be costly and time-consuming. We investigated the electrochemical modeling, reduction, and identification methods of the lithium-ion battery from the electrode-level to the system-level. A reduced 9th order linear model was proposed using electrode-level physicochemical modeling and the cell-level mathematical reduction method. The data-driven predictor-based subspace identification algorithm was presented for the estimation of lithium-ion battery model in the system-level. The effectiveness of the proposed modeling and identification methods was validated in an experimental study based on LiFePO4 cells. The accuracy and dynamic characteristics of the identified model were found to be much more likely related to the operating State of Charge (SOC) range. Experimental results showed that the proposed methods perform well with high precision and good robustness in the SOC range of 90% to 10%, and the tracking error increases significantly within higher (100–90%) or lower (10–0%) SOC ranges. Moreover, to achieve an optimal balance between high-precision and low complexity, statistical analysis revealed that the 6th, 3rd, and 5th order battery model is the optimal choice in the SOC range of 90% to 100%, 90% to 10%, and 10% to 0%, respectively.

Optimal parameter identification strategy applied to lithium‐ion battery model

2021 ◽  
Author(s):  
Seydali Ferahtia ◽  
Ali Djeroui ◽  
Hegazy Rezk ◽  
Aissa Chouder ◽  
Azeddine Houari ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Battery ◽  
Optimal Parameter ◽  
Lithium Ion ◽  
Battery Model ◽  
Identification Strategy

scholarly journals Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model

2019 ◽  
Vol 27 (5) ◽  
pp. 1862-1877 ◽  
Author(s):  
Adrien M. Bizeray ◽  
Jin-Ho Kim ◽  
Stephen R. Duncan ◽  
David A. Howey
Keyword(s):  
Parameter Estimation ◽  
Lithium Ion Battery ◽  
Single Particle ◽  
Lithium Ion ◽  
Battery Model

Thermal component for an electrochemical lithium-Ion battery model: Impact and variation on the battery performance

2020 ◽  
Author(s):  
M.I. Ardani ◽  
M. Ab. Wahid ◽  
M.H. Ab. Talib ◽  
Z.H. Che Daud ◽  
Z. Asus ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Thermal Component ◽  
Battery Model
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