HPPC Characterisation
Extract high-fidelity battery dynamics from Hybrid Pulse Power Characterisation (HPPC) tests to build robust equivalent circuit models and enable accurate parameter identification.
Reduce estimation errors, accelerate battery characterization, and improve utilization with physics-informed intelligence built for existing BMS platforms.
Transform raw battery measurements into production-ready SoC algorithms with physics-informed modeling, robust state estimation, and seamless deployment on existing BMS infrastructure.
Generate engineering-grade equivalent circuit models from battery characterization data with optimized parameter fitting for reliable downstream estimation.
An all-in-one customer service platform that helps you balance everything your customers need to be happy.
Move from validated models to production-ready SoC estimation that integrates directly with your existing Battery Management System.
Transform HPPC characterization data into validated, deployable SoC algorithms with physics-informed precision and engineering-grade reliability.
Extract high-fidelity battery dynamics from Hybrid Pulse Power Characterisation (HPPC) tests to build robust equivalent circuit models and enable accurate parameter identification.
Validate SoC performance across real operating conditions using ECM-based simulation and Extended Kalman Filtering, ensuring stable estimation throughout dynamic charge and discharge cycles.
Hear first-hand from our incredible community of customers.
The validated framework accurately captured dynamic battery behaviour, strengthened SoC estimation, and identified hidden usable capacity that conventional BMS algorithms failed to detect.
International Battery Company (IBC)
Canada
By combining ECM modeling, Kalman filtering, and real-world validation, we built deployable SoC algorithms that improve estimation reliability while integrating seamlessly with existing BMS platforms.
Avkalan Labs
India
Start for free, scale with advanced modeling capabilities, and unlock enterprise-grade workflows as your battery engineering needs grow.
Free Plan
Best for: Students, researchers, and evaluation
Professional Plan
Best for: Individual engineers and small R&D teams
Enterprise Plan
Best for: OEMs, battery manufacturers, and enterprise teams
Everything you need to know about the product and billing. Can't find the answer you're looking for? Please chat to our friendly team.
eigenbatteries combines physics-informed ECM modeling with advanced state estimation techniques to reduce drift, improve low-SoC visibility, and deliver reliable battery intelligence from real-world data.
No. The platform is designed to work with existing BMS architectures and battery data, enabling advanced SoC algorithms without requiring additional infrastructure.
Tell us what you're building, from SoC estimation and ECM modeling to IoT or BMS integration's and we'll use your feedback to guide future development.
