CellNostix™

An integrated battery diagnostics, modeling, and prognostics platform that transforms test data into predictive insight.

CellNostix™ is Ridgetop Group’s next-generation Battery Diagnostic and Prognostic System (BDPS), purpose-built to bridge empirical battery testing with physics-informed predictive modeling and prognostics. Developed through U.S. Department of Energy–supported research and refined using Ridgetop’s rigorous Systems Engineering Process, CellNostix enables battery researchers to move beyond raw test data toward explainable insights into degradation mechanisms, performance limits, and remaining useful life.

At its core, CellNostix™ integrates hardware-in-the-loop (HIL) testing, multi-physics simulation, and model-informed prognostic reasoning within a single, unified platform. Users can execute real cycle-life experiments, synchronize high-resolution telemetry with validated battery models, and quantitatively compare measured behavior against physics-based predictions. This closed-loop workflow supports rapid hypothesis testing, accelerated model validation, and confident, data-driven decision making across the battery R&D lifecycle.

Designed in accordance with the IEEE 1856-2017 Standard Framework for Prognostics and Health Management of Electronic SystemsCellNostix™ provides a scalable foundation for laboratory research, manufacturing validation, and advanced battery health assessment. Whether evaluating new chemistries, optimizing duty cycles, or forecasting end-of-life behavior, CellNostix delivers a unified environment for understanding why batteries degrade—and how to act on that insight.

Key Features & Benefits

  • Integrated Test-to-Model Workflow
    Seamlessly connect real cycle-life testing data with physics-based simulations, enabling direct, quantitative comparison between empirical measurements and predicted battery behavior.
  • Standards-Based PHM Architecture (IEEE 1856-2017)
    Implements the full Sense–Acquire–Analyze–Advise–Act operational framework, providing traceable, auditable diagnostics and prognostics workflows aligned with industry best practices. 
  • Multi-Physics Digital Twin Capability
    Combine electrolyte physics (AEM), electrochemical cell modeling (DFN), and path-dependent aging models (CellSage) to create high-fidelity, physics-informed battery digital twins. 
  • Advanced Prognostics with ARULE™
    Generate real-time estimates of State of Health (SOH), Remaining Useful Life (RUL), and Prognostic Horizon (PH) using adaptive, model-informed prognostic reasoning (ARULE™). 
  • Hardware-in-the-Loop (HIL) Battery Testing Integration
    Interface directly with commercial battery test systems to acquire synchronized voltage, current, temperature, and impedance data for model validation and diagnostics.
  • Accelerated Battery R&D and Risk Reduction
    Reduce experimental burden, shorten development timelines, and increase confidence in performance and lifetime predictions before committing to long-duration testing campaigns.

Ready to optimize your battery R&D workflow?

Contact us today to start the conversation.