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Rasika Kamble,
Shweta Ghadage,
Sayali Ghatule,
Vaibhav Godase,
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
Abstract
Electric-vehicle battery packs require continuous monitoring to maintain performance, prevent unsafe operation, and support reliable estimation of battery state. A battery management system (BMS) measures cell voltage, pack current, and temperature, estimates state of charge (SOC) and state of health (SOH), and coordinates protection actions when abnormal conditions occur. As electric vehicles become more widespread, the demands placed on battery monitoring systems grow in parallel: packs must deliver long range, fast charging, and durable service life across widely varying climates and usage patterns, while remaining safe under fault conditions. Conventional threshold-based monitoring alone is often insufficient to capture sensor faults, gradual degradation, abnormal combinations of signals, or estimation uncertainty, which motivates the integration of intelligent estimation and diagnostic methods alongside deterministic protection logic. This paper presents an intelligent battery monitoring and protection framework that integrates multi-sensor acquisition, signal validation, SOC estimation, anomaly scoring, threshold-based safety logic, and vehicle-level communication. The proposed architecture is organized into five functional layers: sensing and acquisition; signal conditioning and plausibility checks; state estimation and anomaly detection; safety decision and actuation; and vehicle interface and logging. Mathematical foundations are provided for coulomb-counting SOC estimation, a first-order Thevenin equivalent-circuit model, capacity- and resistance-based SOH indicators, and residual-based fault indicators. A transparent anomaly score is described as combines normalized diagnostic features, and a rule-based protection policy is outlined for overvoltage, undervoltage, overcurrent, overtemperature, sensor faults, and communication loss. The proposed architecture is designed to complement not replace independent hardware protection and certified safety processes, preserving a clear separation between interpretable safety rules and higher-level diagnostics.
Keywords: Electric Vehicles; Battery Management System; State of Charge; State of Health; Fault Detection.
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Trends in Electrical Engineering
| Volume | 16 | |
| 03 | ||
| Received | 21/10/2026 | |
| Accepted | 02/11/2026 | |
| Published | 04/11/2026 | |
| Publication Time | 14 Days |