Volt-Guardian XAI is an Explainable AI (XAI) project designed to monitor and predict the State of Health (SoH) for Electric Vehicle batteries. This project was developed as part of the 3rd Year Computer Engineering curriculum.
The project combines:
- Genetic Algorithm (GA) Hyperparameter Optimization
- XGBoost Regression
- SHAP (SHapley Additive exPlanations)
- Feature Engineering
- Comparative Model Benchmarking
The primary objective is not only to predict battery degradation with high accuracy but also to provide transparent explanations for each prediction.
A Genetic Algorithm is used to optimize XGBoost hyperparameters automatically, resulting in improved predictive performance.
SHAP is integrated to:
- Explain global feature importance
- Analyze local predictions
- Generate waterfall and beeswarm visualizations
- Increase model transparency
The framework introduces custom battery stress indicators:
- Thermal Stress Index
- Monthly Charge Intensity
- Power Stress
These engineered features help the model better capture battery degradation behavior.
The dataset contains EV battery operational parameters such as:
- Battery Capacity
- Vehicle Age
- Charging Cycles
- Fast Charging Ratio
- Average Temperature
- Internal Resistance
- Driving Style
- Battery Chemistry
Target Variable:
State of Health (SoH %)
| Metric | Score |
|---|---|
| R² | 0.9891 |
| Adjusted R² | 0.9890 |
| MAE | 0.2650 |
| RMSE | 0.3404 |
| MAPE | 0.28% |
| Metric | Value |
|---|---|
| Mean R² | 98.83% |
| R² Std | ±0.08 |
| Mean MAE | 0.27 |
| MAE Std | ±0.01 |
The framework evaluates multiple machine learning models:
- GA-XGBoost (Hybrid Champion)
- Random Forest
- Standard XGBoost
- Gradient Boosting
- Decision Tree
- Linear Regression
GA-XGBoost achieved the best overall performance.
The project generates:
- SHAP Global Beeswarm Plot
- SHAP Waterfall Plot
- Actual vs Predicted Analysis
- Residual Distribution Analysis
- Feature Correlation Heatmap
These visualizations help researchers understand the decision-making process of the model.
- Python
- XGBoost
- SHAP
- Scikit-Learn
- sklearn-genetic-opt
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Google Colab
- Real-time EV battery monitoring
- Web-based dashboard deployment
- Integration with Battery Management Systems (BMS)
- Deep Learning based SoH estimation
- Edge AI deployment for embedded systems
Volt-Guardian XAI, elektrikli araç (EV) bataryalarının Sağlık Durumu (State of Health - SoH) tahmini ve yorumlanması amacıyla geliştirilmiş bir Açıklanabilir Yapay Zeka (XAI) sistemidir.
Bu proje;
- Genetik Algoritma (GA)
- XGBoost
- SHAP
- Özellik Mühendisliği
- Model Karşılaştırma Analizleri
yaklaşımlarını bir araya getirerek yüksek doğrulukta tahminler üretmektedir.
- Hibrit GA-XGBoost mimarisi
- SHAP tabanlı açıklanabilir yapay zeka
- Gelişmiş özellik mühendisliği
- Çoklu model karşılaştırmaları
- Akademik düzeyde performans değerlendirmesi
- R²: 0.9891
- MAE: 0.2650
- RMSE: 0.3404
- MAPE: %0.28
- Python
- XGBoost
- SHAP
- Scikit-Learn
- sklearn-genetic-opt
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Google Colab
| Name |
|---|
| Eren OĞAN |
| Enes GÜZEL |
| Esengül VELET |
| Yiğit Mert YILMAZ |
If you use this work in academic research, please cite the associated publication and repository.
Volt-Guardian XAI — Making EV Battery Intelligence Explainable