Smarter Battery Modeling with Machine Learning
Electric vehicle performance heavily depends on accurate battery behavior estimation. VerdOjah’s AI-powered solution enables engineers to model and estimate battery parameters—such as state of charge (SoC), health, degradation, and internal resistance—using real-world operational data and minimal hardware requirements.
Unlike traditional models that rely on complex physics and costly infrastructure, our plug-in solution learns from the actual behavior of batteries in real-time, adapting to varying conditions and chemistries. It integrates seamlessly into your Battery Management System (BMS) or testing environment.
Key Features:
Predicts SoC, SoH, temperature response, and degradation trends
Compatible with various battery chemistries (Li-ion, LFP, etc.)
Minimal dependency on extra sensors
Lightweight and embeddable into BMS architecture
Customizable algorithm tuning for client-specific setups
Use Cases:
BMS development and prototyping
EV range estimation and battery lifecycle studies
Predictive maintenance systems in fleet EVs
Best Benefits of Product
Predictive Accuracy
In this it provides a reliable estimation of SoC, SoH, and degradation over time.
Hardware Efficiency
Minimizes need for additional sensors or complex instrumentation.
Customizable Algorithms
Easily adaptable to various battery chemistries and project requirements.
Product Faq
It is the process of determining critical battery characteristics—such as State of Charge (SoC), State of Health (SoH), internal resistance, and degradation—using real-time data and predictive modeling.
Our system uses advanced Machine Learning (ML) algorithms to analyze real-world battery behavior. It learns from operational data to estimate parameters accurately without requiring complex physical models or extensive sensor setups.
It can estimate SoC, SoH, temperature effects, internal resistance, and degradation trends—helping EV developers and researchers better manage battery performance and safety.
No. One of the key benefits is minimal hardware dependency. Our tool works with standard battery output data and doesn’t require additional sensors or expensive instrumentation.
Yes, it is designed to work with a variety of chemistries including Li-ion, LFP, NMC, and others. The model can be fine-tuned to adapt to specific chemistry and application needs.
Absolutely. Our algorithms can be embedded into existing BMS frameworks or run alongside as a supplementary analytics layer, depending on your system architecture.
Battery health monitoring and prediction
Range estimation and optimization
Accelerated battery R&D and prototyping
Predictive maintenance for EV fleets
Yes. We provide detailed documentation, integration support, and optional training to help your team make the most of the solution.
Let’s Build the Future of Smarter Mobility Together
Partner with us to develop, test, and scale next-gen automotive innovations. From indigenous ECUs to AI-driven validation tools and battery modeling, we offer deep technical expertise, collaborative R&D, and scalable platforms tailored to your needs.