A Physical-Based Electro-Thermal Model for a Prismatic LFP Lithium-Ion Cell Thermal Analysis

This article presents an electro-thermal model of a prismatic lithium-ion cell, integrating physics-based models for capacity and resistance estimation. A 100 Ah prismatic cell with LFP-based chemistry was selected for analysis. A comprehensive experimental campaign was conducted to determine electrical parameters and assess their dependencies on temperature and C-rate. Capacity tests were conducted to characterize the cell’s capacity, while an OCV test was used to evaluate its open circuit voltage. Additionally, Hybrid Pulse Power Characterization tests were performed to determine the cell’s internal resistive-capacitive parameters. To describe the temperature dependence of the cell’s capacity, a physics-based Galushkin model is proposed. An Arrhenius model is used to represent the temperature dependence of resistances. The integration of physics-based models significantly reduces the required test matrix for model calibration, as temperature-dependent behavior is effectively predicted. The electrical response is represented using a first-order equivalent circuit model, while thermal behavior is described through a nodal network thermal model. Model validation was conducted under real driving emissions cycles at various temperatures, achieving a root mean square error below 1% in all cases. Furthermore, a comparative study of different cell cooling strategies is presented to identify the most effective approach for temperature control during ultra-fast charging. The results show that side cooling achieves a 36% lower temperature at the end of the charging process compared to base cooling.

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Publication Details

Journal
Energies
Published
2025-03-05
DOI
https://doi.org/10.3390/en18051281
Citations
1
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.68

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article

A Physical-Based Electro-Thermal Model for a Prismatic LFP Lithium-Ion Cell Thermal Analysis

Alberto Broatch, X. Margot, Pablo Olmeda, Luca Agizza
1 citations
Energies
Advanced Battery Technologies Research
0.68
article

A Physical-Based Electro-Thermal Model for a Prismatic LFP Lithium-Ion Cell Thermal Analysis

Alberto Broatch, X. Margot, Pablo Olmeda, Luca Agizza
article en
1 citations

Abstract

This article presents an electro-thermal model of a prismatic lithium-ion cell, integrating physics-based models for capacity and resistance estimation. A 100 Ah prismatic cell with LFP-based chemistry was selected for analysis. A comprehensive experimental campaign was conducted to determine electrical parameters and assess their dependencies on temperature and C-rate. Capacity tests were conducted to characterize the cell’s capacity, while an OCV test was used to evaluate its open circuit voltage. Additionally, Hybrid Pulse Power Characterization tests were performed to determine the cell’s internal resistive-capacitive parameters. To describe the temperature dependence of the cell’s capacity, a physics-based Galushkin model is proposed. An Arrhenius model is used to represent the temperature dependence of resistances. The integration of physics-based models significantly reduces the required test matrix for model calibration, as temperature-dependent behavior is effectively predicted. The electrical response is represented using a first-order equivalent circuit model, while thermal behavior is described through a nodal network thermal model. Model validation was conducted under real driving emissions cycles at various temperatures, achieving a root mean square error below 1% in all cases. Furthermore, a comparative study of different cell cooling strategies is presented to identify the most effective approach for temperature control during ultra-fast charging. The results show that side cooling achieves a 36% lower temperature at the end of the charging process compared to base cooling.

EnergiesVol. 18(5)
Universitat Politècnica de València (ES)
Generalitat Valenciana
Openalex Percentile: Top 33%
Advanced Battery Technologies Research
0.68
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