Abstract :

Battery Reliability Is A Critical Determinant Of Performance, Safety, And Consumer Acceptance In Electric Vehicles (EVs) [3], [4]. As Modern Transportation Shifts Toward Sustainable Mobility, Ensuring Long Battery Life, Stable Operation, And Predictable Degradation Has Become A Primary Research Priority [1], [2]. This Study Presents A Comprehensive State-of-the-art Review And Proposes An Advanced Reliability Assessment Framework That Integrates Machine Learning– Based Prediction Models With Diagnostic And Prognostic Algorithms To Evaluate Battery Health, Aging Patterns, And Failure Modes [6], [7], [12]. The Proposed Approach Emphasizes Real-time Data Analysis, Thermal Stability Evaluation, And Charge–discharge Behavior Modeling, Building On Existing Findings Related To Low-temperature Behavior, Cycling Aging, And Degradation Dynamics [5], [10], [11], [13]. A Detailed Comparison Between Existing Techniques And The Proposed System Highlights Significant Improvements In Reliability Prediction Accuracy, Early-warning Capabilities, And Lifecycle Optimization [14], [15], [17]. The Findings Contribute To Enhancing EV Safety, Reducing Maintenance Costs, And Supporting The Development Of Next-generation Battery Management Systems (BMS) [9]. Keywords: Battery Reliability, Electric Vehicles (EVs), Machine Learning, Prognostics And Health Management (PHM), Battery Health Monitoring, State-of-Health (SOH) Prediction

Published:

29-11-2025

Issue:

Vol. 25 No. 11 (2025)

Page Nos:

280-285

Section:

Articles

License:

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

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