Project Snapshot
Project Type
Energy Storage · ML · IoT
Primary Goal
Repurpose EV batteries for stationary storage
Key Technologies
ML SoH Prediction · IoT Monitoring
Key Metrics
SoH Accuracy: 85% · Microgrid: 50 kW
My Role
Research Engineer
Laboratory
ESTRL · Nepal
Project Overview
The Challenge
The rapid global adoption of Electric Vehicles (EVs) presents a growing challenge: the disposal of batteries at the end of their automotive life. While these batteries may no longer meet the stringent demands for EV propulsion (typically retaining 70–80% of their original capacity), they still possess significant energy storage capabilities. Discarding them contributes to electronic waste and overlooks a valuable resource for stationary energy applications, particularly relevant for grid stability and renewable energy integration in regions like Nepal.
Our Solution
This project at ESTRL develops a robust framework for repurposing these "second-life" EV batteries. Our solution integrates advanced machine learning algorithms to precisely predict battery State-of-Health (SoH) and incorporates IoT technology for real-time monitoring. This approach optimizes performance, extends usable lifespan, and ensures safety in secondary applications, promoting a circular economy and enhancing grid resilience.
Approach & Methodology
Battery Assessment & Selection
Protocols developed to rigorously test and categorize used EV battery modules, identifying suitable candidates for second-life applications based on residual capacity and degradation characteristics.
Machine Learning for SoH Prediction
Advanced ML models (Regression, Neural Networks) trained on extensive battery degradation data to accurately predict remaining State-of-Health and estimate cycle life, enabling optimal deployment and management.
IoT-enabled Monitoring System
Designed and implemented an IoT platform for real-time collection of critical battery parameters (voltage, current, temperature). Data feeds into ML models providing continuous performance and safety insights.
Battery Management System (BMS) Integration
Integrated and customized BMS to ensure safe operation, cell balancing, and protection against overcharge/discharge, crucial for prolonging battery life and preventing hazards.
Pilot Energy Storage System Deployment
Constructed and deployed a pilot-scale stationary energy storage system utilizing repurposed batteries as a real-world testbed for validating overall solution effectiveness and reliability.
Technologies Used
Lessons Learned & Challenges
Variability in Second-Life Batteries
Characterizing and modeling heterogeneous degradation patterns of used EV batteries proved challenging, requiring robust data collection and adaptable ML models.
Accurate SoH Prediction
Achieving high accuracy is complex due to non-linear degradation, temperature effects, and usage patterns, necessitating advanced feature engineering and model tuning.
Scalability and Safety
Scaling from individual modules to full energy storage solutions required careful consideration of thermal management, safety protocols, and robust BMS design.
Data Management for IoT
Handling large volumes of real-time sensor data from multiple battery modules and ensuring reliable transmission for ML analysis was a significant architectural challenge.
Economic Feasibility
Balancing the cost of repurposing, testing, and integrating batteries against the benefits of extending their life and reducing waste is crucial for market adoption.
Future Goals
The next phase involves scaling the system for larger grid applications and commercial deployment, further improving the efficiency and robustness of ML models for more precise SoH and SoL (State-of-Life) prediction. We also aim to explore advanced power electronics integration for seamless grid interaction and establish partnerships with renewable energy providers to deploy this sustainable technology in real-world settings across Nepal and beyond.
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Interested in this work?
Feel free to get in touch or explore other projects in the research portfolio.