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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68411
Volume:
10 |
10 |
Oct 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics and telecommunication engineering
Dr. Ashok Gujar Technical Institute’s, Dr. Daulatrao Aher College of Engineering, Karad.
Electronics and telecommunication engineering
Dr. Ashok Gujar Technical Institute’s, Dr. Daulatrao Aher College of Engineering, Karad.
Electronics and telecommunication engineering
Dr. Ashok Gujar Technical Institute’s, Dr. Daulatrao Aher College of Engineering, Karad.
Electronics and telecommunication engineering
Dr. Ashok Gujar Technical Institute’s, Dr. Daulatrao Aher College of Engineering, Karad.
The Predictive EV Battery Health and Fire Detection System is designed to monitor the health and safety of electric vehicle batteries. Lithium-ion batteries are widely used in electric vehicles, but overheating, battery ageing, and abnormal electrical conditions can reduce battery performance and cause fire hazards. The proposed system monitors battery parameters such as temperature, voltage, and current using suitable sensors. A gas or smoke sensor can also be used to detect abnormal conditions. The collected data is processed by a microcontroller to identify potential battery faults. When an abnormal condition is detected, the system generates an alarm to provide an early warning. A relay can be used to disconnect the battery or load during critical conditions. The system aims to support battery health monitoring, improve safety, and reduce the risk of battery damage. This low-cost system can be useful for electric vehicles and battery monitoring applications.
KEYWORDS
The Predictive EV Battery Health and Fire Detection System provides a practical approach to monitoring the health and safety of electric vehicle batteries. The system monitors important battery parameters such as temperature, voltage, and current using suitable sensors. The collected data is processed by a microcontroller to identify abnormal battery conditions. A gas or smoke sensor can also help detect possible warning signs of battery failure. When a dangerous condition is detected, an alarm provides an early warning, and a relay can disconnect the battery or load during critical conditions. The system aims to improve battery safety, support battery health estimation, and reduce the risk of overheating and fire hazards. In the future, IoT and AI/ML techniques can be incorporated for remote monitoring and improved battery health prediction.
1. D. Li et al., “Battery Thermal Runaway Fault Prognosis in Electric Vehicles Based on Abnormal Heat Generation and Deep Learning Algorithms,” IEEE Transactions on Power Electronics, 2022. 2. “State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory,” IEEE, 2019. 3. Zhang et al., “Multi-dimension Fault Diagnosis of Battery System in Electric Vehicles Based on Real-world Thermal Runaway Vehicle Data,” IEEE iSPEC, 2019. 4. Feng et al., “Thermal Runaway Mechanism of Lithium-Ion Battery for Electric Vehicles: A Review,” Energy Storage Materials, 2018. 5. Hong, Z. Wang, and Y. Yao, “Fault Prognosis of Battery System Based on Accurate Voltage Abnormity Prognosis Using Long Short-Term Memory Neural Networks,” Applied Energy, 2019. 6. Wang et al., “Thermal Runaway Caused Fire and Explosion of Lithium Ion Battery,” Journal of Power Sources, 2012. 7. IEEE Xplore Digital Library — Research papers on EV battery health monitoring and fire detection. 8. ScienceDirect — Research articles on lithium-ion battery safety and thermal runaway.
Publication Date
10/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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