Intelligent Battery Health Monitoring and Fault Detection System Using Real-Time Aggregation and Augmentation Techniques

Abstract

Battery health monitoring has become a critical requirement in modern energy storage
systems, electric vehicles, and industrial applications due to the increasing dependence on
rechargeable batteries. Inefficient monitoring or delayed fault detection can lead to
catastrophic failures, including thermal runaway, reduced lifespan, and unexpected
downtime. This project presents an intelligent battery health monitoring and fault
detection system that integrates real-time data aggregation, feature augmentation, and
anomaly detection techniques to ensure proactive maintenance and operational safety.The
system captures real-time data from individual battery cells within a pack, including
voltage, current, and temperature measurements. A custom-built Aggregation Engine
processes this raw data to derive meaningful statistical and temporal features, such as
average voltage, voltage standard deviation, average temperature, maximum temperature,
and thermal stress derivatives. To enhance fault separability and predictive capability,
these features are further processed using an Augmentation Service, which enriches the
dataset by creating higher-order features and improving the resolution of subtle
anomalies.A dedicated Fault Detection module leverages these augmented features to
identify potential anomalies with associated confidence scores. Detected anomalies are
classified by severity, and fault events are logged for historical analysis and dashboard
visualization. The system supports real-time API endpoints to feed front-end dashboards,
enabling users to monitor battery pack health, visualize trends, and access fault logs
interactively.