Development of a physics-based electrochemical and thermal model of Li-ion batteries for microcontroller applications

Enabling fast-charging procedures is one of the key challenges of Li-ion battery applications, from automotive topower tools. A promising strategy for realizing reliable fast-charging algorithms in battery systems involves employingmodel-based estimations of internal cell states and integrating these predictions into the charging control.Utilizing this approach to simulate cells during charging reduces the risk of plating, thereby enhancing safety as wellas cycle stability. However, achieving accurate model predictions requires substantial computational effort and extensiveparameterization, making onboard application on a simple battery management system (BMS) challenging. Toaddress this gap, a reduced physicochemical and thermal model is developed capable of running on a basic microcontroller(MCU). This model comprises the electrochemical processes, including the anode potential, which enablesthe detection of lithium plating. Combined with the thermal model, it also considers the cell’s thermal behavior andtemperature-driven parameter changes.

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Publication Details

Journal
Journal of Energy Storage
Published
2026-08-20
DOI
https://doi.org/10.1016/j.est.2026.123997
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
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article

Development of a physics-based electrochemical and thermal model of Li-ion batteries for microcontroller applications

Daniel Sauerteig, Alexander Fill, Kai Peter Birke, Pascal Willer
Journal of Energy Storage
Advanced Battery Technologies Research
article

Development of a physics-based electrochemical and thermal model of Li-ion batteries for microcontroller applications

Daniel Sauerteig, Alexander Fill, Kai Peter Birke, Pascal Willer
article en

Abstract

Enabling fast-charging procedures is one of the key challenges of Li-ion battery applications, from automotive topower tools. A promising strategy for realizing reliable fast-charging algorithms in battery systems involves employingmodel-based estimations of internal cell states and integrating these predictions into the charging control.Utilizing this approach to simulate cells during charging reduces the risk of plating, thereby enhancing safety as wellas cycle stability. However, achieving accurate model predictions requires substantial computational effort and extensiveparameterization, making onboard application on a simple battery management system (BMS) challenging. Toaddress this gap, a reduced physicochemical and thermal model is developed capable of running on a basic microcontroller(MCU). This model comprises the electrochemical processes, including the anode potential, which enablesthe detection of lithium plating. Combined with the thermal model, it also considers the cell’s thermal behavior andtemperature-driven parameter changes.

Journal of Energy StorageVol. 180
University of Stuttgart (DE), Stihl (Germany) (DE)
Affordable and clean energy
Openalex Percentile: Top 83%
Advanced Battery Technologies Research
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