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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

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Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms.

Obuli Pranav D¹, Preethem S Babu¹, Indragandhi V²

¹School of Electrical Engineering, VIT, Vellore, India.

Scientific Reports

|July 11, 2024

Summary

This summary is machine-generated.

Accurate electric vehicle battery State of Charge (SOC) estimation is crucial. Gaussian Process Regression offers superior SOC prediction accuracy compared to other machine learning methods, enhancing battery management.

Keywords:

Comparative analysis Electric vehicle Gaussian process regression Lithium-ion battery Machine learning State of charge

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Area of Science:

Electric vehicle technology
Battery management systems
Machine learning applications

Background:

Accurate State of Charge (SOC) estimation is critical for electric vehicle (EV) safety and performance.
Existing methods face challenges in accurately predicting SOC under diverse real-world driving conditions.

Purpose of the Study:

To comparatively assess machine learning regression algorithms for battery SOC estimation.
To identify the most accurate algorithm for modeling the complex relationship between driving data and battery SOC.

Main Methods:

Trained and tested Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression models.
Utilized extensive field data from diverse drivers and varying conditions.
Evaluated model performance using statistical metrics on a test set.

Main Results:

Gaussian Process Regression demonstrated superior SOC prediction accuracy.
GPR achieved the lowest prediction errors compared to other tested algorithms.
Case studies confirmed GPR's competence in mimicking real battery behavior.

Conclusions:

Gaussian Process Regression provides a reliable, data-driven framework for precise real-time SOC monitoring.
Advanced analytics, particularly GPR, can significantly enhance EV battery management.
The study offers a robust approach to improving EV operational safety and efficiency.