Abstract :

The Rapid Adoption Of Electric Vehicles (EVs) Has Increased The Demand For Efficient Battery Management Systems To Ensure Safety, Performance, And Longevity. This Paper Presents An AIassisted Framework For Real-time Monitoring And Analysis Of Critical EV Battery Parameters Such As Voltage, Current, Temperature, And State Of Charge (SoC). The Proposed System Integrates Internet Of Things (IoT) Sensors With Advanced Machine Learning Algorithms To Collect, Process, And Predict Battery Behavior Under Varying Operating Conditions. The Framework Leverages Data-driven Models To Detect Anomalies, Estimate Battery Health, And Provide Predictive Maintenance Insights. By Using Artificial Intelligence Techniques, The System Enhances Accuracy In Parameter Estimation And Enables Early Fault Detection, Reducing The Risk Of Battery Failure And Improving Overall Efficiency. Experimental Results Demonstrate That The Proposed Approach Significantly Improves Monitoring Precision And Reliability Compared To Conventional Battery Management Systems. This Framework Contributes To The Development Of Smarter And Safer EV Ecosystems.

Published:

21-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2764-2768

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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