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The Advanced Electrolyte Model (AEM) is an advanced simulation tool designed to revolutionize the optimization of electrolyte chemistry. Created by Dr. Kevin Gering at Idaho National Laboratory and distributed by Ridgetop Group, this molecular simulation tool acts as a virtual laboratory, enabling comprehensive exploration of electrolyte properties at a genome level. Using advanced theoretical models like Nonprimitive, Nonrestricted Associated Mean Spherical Approximation and an ion-solvation equation of state, AEM offers precise predictions with an impressive average deviation within only 5-10% from lab data. AEM's robust database of over 50 solvents and 30 salts offers diverse options for users wanting to explore electrolyte properties. Its impact goes beyond providing scientific insights; it accelerates the shift to electric transportation and broader adoption of grid-scale battery systems by enhancing battery research and development. The goal is to achieve a cleaner, sustainable energy future, all at reduced costs and time investments. AEM ultimately serves as a catalyst for change, allowing the international battery research community to drive forward innovative solutions in green energy storage.
CellSage is a robust tool for battery health modeling, simulation, and analysis, delivering precision insights into lithium-ion battery performance under real-world conditions. Results from a decade of research supported by the Idaho National Laboratory, this platform adopts hybrid modeling techniques that combine data-driven and physics-based approaches. Users get a comprehensive suite for analyzing aspects like capacity fade, thermal management, and operational parameters impacting battery life. CellSage is essential for sectors needing reliable battery management, helping reduce upfront R&D costs and accelerate development timelines. It further supports optimal battery management, enhancing safety and efficiency. Designed for academic and industrial researchers, CellSage facilitates dynamic interaction and integration with Battery Management Systems (BMS) and Integrated Vehicle Health Monitoring systems. It aids in ensuring precise state-of-health assessments and RUL predictions, thus supporting Condition-Based Maintenance. This capability not only improves operational continuity but also extends the battery lifespan, enabling forward-thinking battery technology.
Sentinel Motion is an innovative IoT-based sensor system specifically engineered for monitoring critical equipment in rail applications. Originally developed for the aerospace sector, it is tailored for railroads, merging the functions of conventional wayside detection systems into a singular, cost-effective solution. Utilizing a network of RotoSense smart sensors and Sentinel Gateway, this system enables continuous remote monitoring and generates alerts based on anomalies found in temperature or vibration data. Deployed directly onto the axles, it offers rigorous real-time diagnostics that help mitigate costly maintenance operations, addressing bearing and wheel failures effectively. Sentinel Motion's introduction allows the railroad industry to optimize maintenance strategies and significantly cut costs associated with bearing and wheel failures, which are major financial burdens. It enhances safety and reliability in operations while reducing spend on reactive maintenance. This comprehensive platform contributes substantially to condition-based maintenance implementations, promoting proactive management of rail infrastructure health.
ARULE, or Adaptive Remaining Useful Life Estimator, is an essential predictive tool for prognostic health management of complex systems. By estimating the Remaining Useful Life (RUL), State-of-Health (SoH), and Prognostic Horizon (PH), ARULE assists in predicting when maintenance should occur, minimizing downtime and optimizing service schedules. The system operates using condition-based data, applying Extended Kalman Filtering to enhance the accuracy of its predictions. As conditions evolve, ARULE dynamically adjusts its predictions, providing highly accurate results in near real-time. ARULE is particularly useful in various applications, including power supply and battery management systems, actuator control, and industrial automation systems. Its intuitive graphical user interface simplifies the process of integrating and analyzing condition-based data. This versatility makes it a valuable component of Ridgetop’s Sentinel Suite, offering comprehensive insights into systems' operational health. By anticipating system failures, ARULE helps reduce wasteful maintenance practices and improve overall reliability and efficiency in operational settings.
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