scholarly journals Reduction of high fidelity lithium-ion battery model via data-driven system identification

2013 ◽  
Author(s):  
M. Sumislawska ◽  
K.J. Burnham ◽  
M.M. Marinescu ◽  
N. Phillip
Keyword(s):  
System Identification ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Data Driven ◽  
High Fidelity ◽  
Battery Model ◽  
Driven System

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

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

2021 ◽  
pp. 014233122110579
Author(s):  
Tao Chen ◽  
Ciwei Gao ◽  
Hongxun Hui ◽  
Qiushi Cui ◽  
Huan Long
Keyword(s):  
Energy Storage ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Additive Models ◽  
Data Driven ◽  
Local Effects ◽  
Interaction Terms ◽  
Battery Capacity ◽  
Formulation Parameters ◽  
Capacity Prediction

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

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