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Predictive Analytics and Self-Healing Mechanism for Lithium-Ion IoT-Based BMS

Sumit D.Thakre1, Dr. Vaijayanti Deshpande2,

1 Sumit D.Thakre

Zeal College Of Engineering & Research, Pune

2 Dr. Vaijayanti Deshpande

Zeal College Of Engineering & Research, Pune

Abstract - This paper presents an AI-driven predictive and self-healing Battery Management System (BMS) framework for lithium-ion batteries used in electric vehicles and renewable energy storage. The proposed model employs predictive analytics algorithms—combining time-series forecasting and anomaly detection—to continuously monitor critical parameters such as State of Health (SOH), State of Charge (SOC), and temperature distribution.

A machine learning-based diagnostic layer predicts degradation trends and early fault conditions. Once an anomaly or imbalance is detected, the self-healing mechanism dynamically adjusts charge/discharge rates and redistributes load across cells to mitigate failure risks.

The system was validated on a real-time EV battery testbed, achieving:

~18% improvement in lifespan prediction accuracy,

22% reduction in charge imbalance events, and

measurable enhancements in thermal stability and cycle life.

The authors conclude that predictive AI and adaptive control enable next-generation BMS to evolve toward autonomous, self-correcting architectures, reducing downtime and enhancing overall energy reliability.