• Understand battery aging, degradation, SOH, and RUL concepts.
• Process and analyze battery cycling datasets.
• Extract useful battery-health indicators from voltage, current, capacity, and temperature data.
• Build ML models for SOH prediction.
• Apply Explainable AI using SHAP.
• Develop time-series models for degradation forecasting.
• Estimate battery Remaining Useful Life using AI-based approaches.

Day 1: Battery Aging Data, Health Indicators & SOH Fundamentals

• Battery Health Fundamentals: Capacity, cycle life, degradation, State of Health (SOH), End-of-Life (EOL), and Remaining Useful Life (RUL).
• Battery Aging Mechanisms: Capacity fade, internal resistance growth, temperature effects, and cycling degradation.
• Battery Cycling Data: Understanding voltage, current, capacity, temperature, charge/discharge cycles, and timestamps.
• Battery Datasets: Introduction to publicly available battery-aging datasets such as NASA, CALCE, and Oxford datasets.
• Data Preparation: Cleaning, restructuring, handling missing data, and cycle-wise battery-data organization using Python.
• Health Indicator Extraction: Capacity retention, voltage characteristics, charge/discharge duration, temperature statistics, and cycle-based indicators.
• SOH Calculation: Creating SOH labels from battery capacity and degradation data.
• Exploratory Analysis: Visualizing capacity fade and identifying battery-aging patterns.

Hands-On

Build a Battery Aging & SOH Dataset

Participants will load a real battery-cycling dataset, extract degradation indicators, calculate SOH, and visualize battery capacity fade across charge-discharge cycles.

Tools: Python, Google Colab, Pandas, NumPy, Matplotlib

Day 2: Machine Learning & Explainable AI for SOH Prediction

• SOH Prediction as an AI Problem: Converting battery degradation data into supervised machine-learning datasets.
• Feature Engineering: Creating predictive features from voltage, current, temperature, capacity, and cycle information.
• Feature Selection: Identifying the most informative battery-health indicators.
• Baseline Models: Linear Regression and Random Forest for SOH estimation.
• Advanced Machine Learning: XGBoost-based battery-health prediction.
• Model Training & Testing: Train-test splitting and cross-validation for battery datasets.
• Model Evaluation: MAE, RMSE, R², and prediction-error interpretation.
• Predicted vs Actual SOH: Evaluating model reliability across battery cycles.
• Explainable AI: Using SHAP to identify which battery parameters drive SOH predictions.
• Model Generalization: Challenges when applying an AI model across different batteries, chemistries, temperatures, and operating conditions.

Hands-On

Develop an Explainable AI Model for Battery SOH Prediction

Participants will train Random Forest/XGBoost models, predict battery SOH, evaluate prediction accuracy, and use SHAP to interpret important degradation indicators.

Tools: Scikit-learn, XGBoost, SHAP, Pandas, Google Colab

Day 3: Deep Learning, Degradation Forecasting & Remaining Useful Life Prediction

• From SOH to RUL: Understanding how degradation trajectories are converted into remaining-life estimates.
• Time-Series Battery Data: Sequential behavior of capacity and degradation measurements.
• Degradation Trend Modeling: Learning battery-aging trajectories from historical cycles.
• Deep Learning for Battery Prognostics: Introduction to LSTM networks for sequential battery data.
• LSTM Data Preparation: Time windows, sequence generation, scaling, and training datasets.
• Battery Degradation Forecasting: Predicting future SOH and capacity decline.
• End-of-Life Threshold: Defining battery EOL using capacity-retention criteria.
• RUL Estimation: Predicting the number of remaining operating cycles before EOL.
• ML vs Deep Learning: Comparing conventional machine-learning and LSTM-based approaches.
• Prediction Uncertainty: Understanding why RUL predictions change across batteries and operating conditions.
• Research Interpretation: Translating AI predictions into battery-management, EV, and energy-storage applications.

Hands-On

Build an AI Workflow for Battery RUL Prediction

Participants will prepare sequential battery data, train an LSTM-based degradation model, forecast future SOH, identify the predicted EOL point, and estimate Remaining Useful Life.

• Prepare AI-ready battery degradation datasets.
• Analyze capacity fade and battery-aging trends.
• Estimate and predict battery SOH.
• Build and evaluate Random Forest and XGBoost models.
• Interpret battery-health predictions using SHAP.
• Develop LSTM-based battery degradation models.
• Forecast End-of-Life and Remaining Useful Life.
• Design an end-to-end AI workflow for battery health monitoring and prognostics.

• Students: Undergraduate and postgraduate students in Electrical, Electronics, Mechanical, Chemical, Materials, Energy, EV Technology, AI/ML, or related disciplines.
• Ph.D. Scholars / Researchers: Researchers working in batteries, BMS, energy storage, EVs, battery degradation, predictive maintenance, or AI/ML.
• Academicians / Faculty: Faculty members interested in battery analytics, AI-driven energy research, and advanced teaching/research applications.
• Industry Professionals: Professionals from EV, battery manufacturing, BMS, energy storage, battery testing, renewable energy, and predictive analytics sectors.