International Journal on Science and Technology

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Advanced Fault Detection and Diagnostics in Embedded Control Units for BESS

Author(s) Soujanya Reddy Annapareddy
Country United States
Abstract Battery Energy Storage Systems (BESS) play a critical role in ensuring energy reliability and efficiency in modern power systems. Embedded Control Units (ECUs) in BESS manage essential functions, but they are vulnerable to faults that can lead to system inefficiencies or failures. This research focuses on the development of advanced fault detection and diagnostics (FDD) methodologies tailored for ECUs in BESS. The proposed approach integrates machine learning algorithms, real-time data analysis, and predictive maintenance strategies to enhance fault detection accuracy and diagnostic precision. Key challenges addressed include handling complex fault scenarios, improving response times, and minimizing false positives. The research also examines the implementation of lightweight diagnostic models to ensure compatibility with resource-constrained embedded systems. Simulation results and case studies demonstrate significant improvements in fault detection rates and system resilience, paving the way for more robust and reliable BESS deployments.
Keywords Battery Energy Storage Systems (BESS), Embedded Control Units (ECU), Fault Detection and Diagnostics (FDD), Machine Learning, Predictive Maintenance, Real-time Data Analysis, System Resilience, Lightweight Models
Published In Volume 15, Issue 4, October-December 2024
Published On 2024-11-05
Cite This Advanced Fault Detection and Diagnostics in Embedded Control Units for BESS - Soujanya Reddy Annapareddy - IJSAT Volume 15, Issue 4, October-December 2024. DOI 10.5281/zenodo.14613928
DOI https://doi.org/10.5281/zenodo.14613928
Short DOI https://doi.org/g8x2xp
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