A Resource-Constrained Polynomial Regression Approach for Voltage Measurement Compression in Electric Vehicle Battery Packs

Technologies like data-driven methods for battery state estimation, fleet monitoring and cloud-based BMSs are emerging. However, challenges in data compression and storage hinder their widespread adoption. This paper addresses these issues by proposing a novel, efficient lossy voltage data compressi...

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Bibliographic Details
Published in:Batteries (Basel) 2024-09, Vol.10 (9), p.305
Main Authors: Karnehm, Dominic, Neve, Antje
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Ambient temperature
Artificial intelligence
Automobiles, Electric
Bandwidths
Batteries
battery management systems
Cloud computing
cloud-based BMS
Data compression
Data points
data-driven estimation
electric vehicle
Electric vehicles
Electrical measurement
Energy consumption
Fourier transforms
Methods
Polynomials
Real time
Regression models
Reliability (Engineering)
Root-mean-square errors
State estimation
Citations: Items that this one cites
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Summary:Technologies like data-driven methods for battery state estimation, fleet monitoring and cloud-based BMSs are emerging. However, challenges in data compression and storage hinder their widespread adoption. This paper addresses these issues by proposing a novel, efficient lossy voltage data compression method for measurements in electric vehicles. The method is grounded in polynomial regression and enables the use of the adaptive method without the need for parameters or training of the model which, representing an improvement over existing techniques. At a compression rate of 99.75% in an ambient temperature of 25 °C on average across all drive cycles compared, the root mean square error (RMSE) was 5.62 mV. Impressively, at a compression rate of 99%, the RMSE decreased to 3.12 mV. Furthermore, an implementation on a low-power STM32 microcontroller can compress 600 data points in just 35 milliseconds, demonstrating its suitability for real-time applications. These results highlight the potential of our approach to significantly improve the efficiency and accuracy of voltage measurement compression in electric vehicles, paving the way for advancements in electric vehicle technology.
ISSN:2313-0105
2313-0105
DOI:10.3390/batteries10090305