CellSage is an advanced battery health modeling, simulation, and analysis (MS&A) software tool that assesses battery condition based on the specific chemistry, usage conditions, and the environment in which it operates. CellSage has been proven to accurately model path dependent aging conditions for lithium-ion batteries that have been studied and tested by over a decade of research in electro-chemistry, physics, and thermodynamics. Under license from the U.S. Department of Energy’s (DOE) Idaho National Laboratory (INL), Ridgetop Group has successfully commercialized and extended CellSage to offer customers advanced software solutions that use data driven, physics-based, and hybrid modeling techniques to determine long term battery performance and aging throughout the entire battery life cycle.
Key Features & Benefits:
Through a user-friendly graphical user interface (GUI), CellSage users are presented a clear picture of how the battery is aging and an accurate projection of how long the battery will last for given conditions. Other key features and benefits include:
- Pre-defined and expandable library of common battery types, including Lithium Ion and NiCd
- Addresses diagnostic and prognostic evaluations of battery systems, including capacity fade
- Optimizes battery life by investigating thermal management, customized operating conditions and string anomalies
- Includes more than 10 environmental and operational parameters, including battery chemistry, temperature and thermal cycling are taken into account
- Powerful analysis covering conditions in 130 U.S. cities
- Advanced modeling framework reduces training time and allows researchers to rapidly employ CellSage to improve battery measurement capability.
- Ability to adjust battery designs to meet actual deployment conditions
- Easily add new or non-standard battery chemistries with the interactive New Chemistry Import Feature
- Provides the capability for “what if” analyses on thermal management system designs as shown in the Battery System Design Document
Originally developed by Dr. Kevin Gering at INL, the CellSage battery health modeling, simulation, and analysis (MS&A) software platform has been designed to support Academic and Industrial Researchers who are responsible for the development of robust battery power sources for diverse applications such as Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs), Unmanned Aerial Systems (UAS), Consumer Electronics, and other crucial applications. Battery researchers in such fields have found incentive in this product by lowering their upfront R&D costs for baseline cycle life testing and optimizing their battery-based design for complex duty cycles and missions.
CellSage Collaboration Opportunities
Ridgetop Group and INL continue to work with industry leaders to adapt CellSage for their own unique battery R&D applications, and we are actively seeking collaboration partners that wish to deploy an embedded version of the technology that will interface with new and existing Battery Management Systems and Integrated Vehicle Health Monitoring (IVHM) systems. An IVHM system is intended to provide the driver and maintenance personnel information on the state-of-health (SoH), and the Remaining Useful Life (RUL) of the vehicle. Ideally, there are situations where a prognostic “signature” that precedes a failure in a given system, can be accurately detected by comparing near-real time battery degradation data against the CellSage model data. This capability, in turn, allows more economical Condition Based Maintenance (CBM) or Prognostic Health Management (PHM) practices to be supported. For an electric vehicle, this would include on-going monitoring of the battery pack, mitigating faults during the lifetime of the vehicle, and guiding maintenance intervals based on actual degradation or wear-out mechanisms.
Contact a Ridgetop Group representative directly if you would like to collaborate on a similar project with your battery powered system.
Related Webinars & Publications
- CellSage Webinar August 2023
- Accelerated Battery Life Predictions Through Synergistic Combination of Physics-based Models and Machine Learning
- Novel Method for Evaluation and Prediction of Capacity Loss Metrics in Li-Ion Electrochemical Cells
- CellSage Webinar August 2020
Contact us for More Information
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