ENRGX Advanced Energy Technologies Private Limited
AI-powered platform for predicting battery health and remaining useful life (RUL)
CIN: U72100AS2025PTC029433
IIT Guwahati
Supported By
IIT Guwahati
MeitY Startup Hub
IITG - TIC
FICCI, New Delhi
The Battery Crisis
Unexpected Failures
EV and energy systems face sudden battery breakdowns, causing operational disruptions and safety hazards.
High Costs
Maintenance expenses soar due to reactive approaches and inefficient energy usage patterns.
Lack of Prediction
Current BMS are reactive, lacking real-time predictive accuracy and scalability for dynamic conditions.
Second-Life Gaps
Recycled batteries lack credible health verification, leading to reuse inefficiencies and market uncertainty.
Market Dynamics
EV Battery Failure Impact
Critical reliability concerns highlight the urgent need for predictive battery health systems.
Li-ion Market Growth
Exponential market expansion drives significant economic impact and investment opportunities.
Second-Life Grading
Battery grading systems face development challenges, creating market opportunities for innovation.
Sustainability Pressures
Environmental demands drive market need for efficient, long-lasting battery solutions.
Our Solution
AI-Powered Battery Intelligence
ENRGX delivers real-time prediction of battery State of Health (SoH) and Remaining Useful Life (RUL) through advanced AI analytics.
AI/ML Prediction
Advanced models trained on real-world datasets deliver accurate SoH and RUL forecasting.
Early Warning System
Proactive alerts reduce failure risks and prevent costly unexpected breakdowns.
Interactive Dashboard
Comprehensive analytics platform visualizes battery health metrics in real-time.
Battery Optimization
Extend lifespan and reduce maintenance through intelligent usage recommendations.
Second-Life Grading
Enable circular economy through credible health verification for battery reuse.
Target Market
EV Manufacturers
OEMs seeking advanced battery management and predictive maintenance capabilities.
Battery Producers
Suppliers requiring quality assurance and lifecycle optimization tools.
Fleet Operators
Logistics and transit companies managing large-scale EV deployments.
Energy Storage
Renewable energy firms optimizing battery performance and longevity.
Aerospace & Defense
Critical applications demanding highest reliability and safety standards.
Battery Swapping
Leasing and swapping networks requiring real-time health verification.
Competitive Advantage
ENRGX differentiates through AI-powered predictive analytics, offering intelligent forecasting where competitors provide only basic monitoring.
Business Model Canvas
Key Partners
Battery research labs, EV OEMs, energy storage firms, cloud providers (AWS/GCP), startup accelerators (NEICP, DST).
Key Activities
Data collection and preprocessing, predictive model development, dashboard and API building, OEM validation, IP protection.
Key Resources
Battery datasets, testing infrastructure, cloud hosting, talented team (data scientists, battery engineers), ML models and IP.
Value Propositions
Accurate SoH and RUL prediction, early warning system, optimized battery lifecycle, reduced maintenance costs, seamless API integration.
Customer Segments
EV manufacturers, battery producers, renewable energy companies, fleet operators, defense and aerospace, battery leasing services, research labs.
Channels
Direct B2B sales, pilot projects with fleets and OEMs, online demo platform, EV ecosystem partnerships.
Customer Relationships
Dedicated account managers for B2B clients, long-term contracts, subscription support, pilot trials transitioning to licensing, feedback integration.
Revenue Streams
Pay-per-use API access, customized enterprise analytics, consulting packages, OEM licensing, subscription models.
Cost Structure
Cloud hosting and storage, team salaries, hardware and testing tools, legal and IP protection, marketing and pilot deployment.
Traction & Roadmap
Current Status
01
Prototype Developed
Initial version validated on public datasets with promising SoH and RUL accuracy.
02
Lab Testing
Integrating controlled experimental data to improve model robustness.
03
Early Discussions
Conversations initiated with EV OEMs, manufacturers, and academic labs.
04
Pilot Programs
Applied to NEICP for support, mentorship, and field validation.
05
Beta-Phase Ready
Open to onboarding select partners for real-world testing.
Future Milestones
1
Q2 2026
Complete pilot tests and collect real-world feedback from partner deployments.
2
Q3-Q4 2026
Launch Beta version with partner fleet operators across multiple use cases.
3
2027
Commercial launch and scale across EV and energy storage sectors.
4
2028+
Expand to global markets and support multiple battery chemistries.
Our Team
Led by IIT Madras expertise, our multidisciplinary team combines AI innovation, battery engineering, and strategic business acumen.
Aman Malik
CEO and Director
Musfika Sultana
Director
Prof. Bishnupada Mandal
Faculty Advisor
Subjrajit Mishra
Chief Operating Officer
Chirag Varshney
Strategic Advisor
Cecilia
HR Manager
Akhil Raj
Head of Corporate Affairs
Dr. S.Suhail Mohammad
Advisor
Dr. Farha Sultana
Advisor