Cloud-Based Battery Analysis Platform
A comprehensive solution revolutionizing battery testing and health analysis through advanced ML and automated grading.
BatteryDB was developed at ReJoule to transform how the industry approaches battery testing, analysis, and management. Starting as a core member of the development team in 2021, I grew into the lead engineering role, guiding the project's evolution and expansion. Working with a dynamic team that scaled from 1 to 4 engineers, we built an innovative solution that combines battery testing, health estimation, and device management in a single unified platform.
The platform serves diverse stakeholders in the battery industry, including used battery traders, second-life battery deployment companies, research institutions, and OEM electric vehicle manufacturers. Our multi-tenant architecture ensures secure and efficient data management for each client while maintaining data isolation and customized access controls.
What sets BatteryDB apart is its unique integration of multiple testing methodologies - EIS (Electrochemical Impedance Spectroscopy), cycling tests, and BMS (Battery Management System) data analysis - all unified in a single cloud platform. This comprehensive approach is enhanced by our cloud-deployed machine learning models that provide automatic inference and analysis, making it the only solution in the market offering such a complete testing and analysis ecosystem.
Technical Implementation
BatteryDB employs a modern, scalable cloud architecture leveraging industry-standard technologies:
Cloud Infrastructure
- Infrastructure as Code using Terraform for reliable and repeatable deployments
- Containerized applications orchestrated by Kubernetes
- Digital Ocean cloud platform for compute and storage
Application Stack
- Django backend framework for robust API development
- PostgreSQL database for structured data storage
- Redis for caching and task management
- S3-compatible storage for large file management
Machine Learning Pipeline
The platform features a sophisticated machine learning infrastructure that enables real-time battery health analysis:
- Automated inference pipeline triggered upon test completion
- Containerized ML environment for consistent model execution
- High-precision State of Health (SOH) predictions with less than 2% RMSE
- Cloud-based model storage and versioning
Features & Capabilities
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📊 Data Management
Comprehensive storage and organization of battery test information with secure multi-tenant architecture.
- Secure data isolation between clients
- Automated data processing and validation
- Historical test data tracking and comparison
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🔮 Predictive Analytics
Advanced ML models for battery health estimation and performance prediction.
- Real-time SOH predictions
- Performance degradation analysis
- Anomaly detection and safety warning
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🔍 Health Classification
Automated categorization of batteries into health buckets based on comprehensive analysis.
- Multi-factor grading system
- Automated report generation
- Comparative market value assessment
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⚡ Device Management
Integrated ReJoule device management and monitoring system.
- Real-time device status monitoring
- Test scheduling and queue management
- Diagnostic and maintenance tools
Impact & Results
BatteryDB has made significant contributions to the battery recycling and second-life market:
- Successfully analyzed and graded over 10,000 batteries
- Enabled third-party sellers to unlock additional value through accurate health grading
- Streamlined battery testing and certification processes
- Enhanced confidence in used battery transactions through standardized testing and grading
Key Achievement
Achieved industry-leading accuracy in battery health estimation with less than 2% RMSE in State of Health (SOH) predictions, enabling precise grading and valuation of used batteries.