International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 3
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Correlating Environmental Factors and Charging Patterns with EV Battery Health using Machine Learning

Author(s) Ms. Alisha Khwaja, Mr. Jai Shankar Pandey, Ms. Ananya Agrawal, Prof. Dr. Yogesh Kumar Rathore
Country India
Abstract Electric vehicle (EV) battery degradation is strongly influenced by environmental conditions and charging behavior, making accurate State of Health (SoH) prediction a critical challenge for modern battery management systems. This research presents a machine learning-based framework that correlates temperature variations, charging patterns, and operational telemetry with battery health degradation. The proposed approach combines controlled laboratory battery datasets from NASA’s Prognostics Center of Excellence with more than 175,000 real-world fleet charging records to capture both accelerated aging behavior and practical driving conditions.

A Bidirectional Long Short-Term Memory (Bi-LSTM) network is then employed to model temporal dependencies in battery degradation by learning from both past and future sequence patterns. The model is further evaluated against traditional machine learning and deep learning approaches, including Linear Regression, Random Forest, XGBoost, and standard LSTM architectures.
Keywords Electric Vehicles (EVs), Battery State of Health (SoH), Lithium-ion Batteries, Battery Degradation, Machine Learning, Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Learning, Charging Patterns, Environmental Factors, Thermal Effects, Predictive Maintenance, Battery Prognostics, Fleet Telemetry, Time-Series Analysis, Feature Extraction, Remaining Useful Life (RUL), EV Battery Management Systems (BMS), Data-Driven Modeling
Field Computer > Data / Information
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-13
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E-ISSN 2582-2160
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