Adaptive Compressing Electric Vehicle Battery Pack Measurements using Polynomial Coding

This manuscript introduces an adaptive compression technique for electric vehicle battery pack measurements predicated on polynomial coding. The proposed methodology employs a lossy algorithm for voltage compression and reconstruction. The algorithm leverages the interdependence of current, time, and voltage through an adaptive time window governed by voltage thresholds. This approach facilitates the adjustment of voltage thresholds to accommodate the high dynamics of battery systems. At a Rate of Compression (RoC) of 99.2% for the LA92 drive cycle at an ambient temperature of 25°C, the root mean square error (RMSE) and mean absolute error (MAE) are 2.4 mV and 1.6 mV, respectively. The method stands out for its simplicity and effectiveness and surpasses the accuracy of current state-of-the-art compression algorithms by a factor of 20% to 160% at comparable RoC.

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Published
2024-09-16
DOI
https://doi.org/10.1109/sm63044.2024.10733471
Primary Topic
Advanced Battery Technologies Research
Type
article
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article

Adaptive Compressing Electric Vehicle Battery Pack Measurements using Polynomial Coding

Antje Neve, Dominic Karnehm
Advanced Battery Technologies Research
article

Adaptive Compressing Electric Vehicle Battery Pack Measurements using Polynomial Coding

Antje Neve, Dominic Karnehm
article en

Abstract

This manuscript introduces an adaptive compression technique for electric vehicle battery pack measurements predicated on polynomial coding. The proposed methodology employs a lossy algorithm for voltage compression and reconstruction. The algorithm leverages the interdependence of current, time, and voltage through an adaptive time window governed by voltage thresholds. This approach facilitates the adjustment of voltage thresholds to accommodate the high dynamics of battery systems. At a Rate of Compression (RoC) of 99.2% for the LA92 drive cycle at an ambient temperature of 25°C, the root mean square error (RMSE) and mean absolute error (MAE) are 2.4 mV and 1.6 mV, respectively. The method stands out for its simplicity and effectiveness and surpasses the accuracy of current state-of-the-art compression algorithms by a factor of 20% to 160% at comparable RoC.

Universität der Bundeswehr München (DE)
Affordable and clean energy
Openalex Percentile: Top 80%
Advanced Battery Technologies Research
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