This project investigates the behaviour of electric vehicle battery systems under varying operating and environmental conditions using data-driven analytical approaches.
The research focuses on analysing battery performance behaviour, degradation sensitivity, and operational trends through regression-based system modelling and explainable machine learning techniques.
Experimental battery datasets and operational parameter analysis were integrated to evaluate the influence of temperature, load variation, and dynamic operating conditions on battery efficiency and lifecycle behaviour.
This repository presents the project architecture, analytical workflow, and interpretability framework developed during academic research.
- Multi-operating condition battery behaviour analysis
- Regression-based system performance modelling
- SHAP-based model interpretability and sensitivity analysis
- Dynamic operational parameter evaluation
- Battery degradation trend assessment under varying conditions
- MATLAB
- Regression & Statistical Modelling
- SHAP-Based Explainable AI Analysis
- Experimental Battery Datasets
- Data Processing & Sensitivity Analysis
This project demonstrates how interpretable machine learning and operational condition analysis can support battery performance assessment, degradation understanding, and lifecycle optimization in electric vehicle battery systems.
The implementation used in this project was developed as part of academic research and related publications.
To maintain research integrity and comply with institutional and publication-related guidelines, the full source code and analytical configurations are not publicly released.
This repository is intended to present the project concept, modelling framework, and technical methodology.
Code access may be shared upon request for academic collaboration or research discussion.