Battery Condition Monitoring
- 技術優勢
- Fast and precise assessment of battery health and performance conditions.Ability to achieving a more complete and accurate health metric.Support both battery diagnostics and prognostics with embedded predictive data analytics and learning.Adaptable to user needs and preferences for given applications.Amenable to include new and expanded electrochemical definitions for battery health.
- 技術應用
- Plug-in electric vehiclesPortable electronics, such as phones, laptops, and wearablesEnergy storage systems for:MicrogridsUtilitiesCell towersData centersUnmanned Aerial Systems (UAS)Battery infrastructures for energy and weapon systems
- 詳細技術說明
- Researchers at IdahoNational Laboratory have developed a new advanced methodology for real-timebattery health analysis and performance optimization that utilizes embeddeddata analytics and machine learning techniques. The system combines active(e.g. impedance) with passive (e.g. temperature) measurements withcomputationally-efficient data analytics and machine learning to assess realtime health under load operation. The system is capable of estimating severalhealth and performance metrics including State-of-Charge (SOC), State-of-Health(SOH), and Remaining-Useful-Life (RUL). The capability supports the productionof smart battery products with sufficient intelligence to allow for automated,real-time control and optimization.
- *Abstract
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TECHNOLOGYMARKETING SUMMARY:
Batteries are becomingincreasingly important for modern life, powering everything from portableelectronic devices to transportation. Batteries are available in many sizes andchemistries, raising variability and complexity. As electrochemical batteriesare operated, chemical changes occur that effect the performance, effectingcapacity and resistance. Measuring and tracking these changes can help estimateand predict battery health and useful remaining life, as well as optimizeperformance.
Current battery healthmanagement and operation optimization techniques often exhibit largeuncertainties and variation when assessing battery health, driving the adoptionof preventative battery maintenance strategies instead of more efficientmanagement strategies like condition-based predictive maintenance. Thesetraditional techniques often also require hours of measurement time, offloadoperational conditions, and analysis on separate systems, making themcumbersome to employ, especially for approximating real-time health oreffecting real-time optimization. These problems combine to result in asubstantial volume of used batteries with some remaining useful life beingdiscarded and/or disposed of prematurely, as well as unnecessary maintenancebeing performed, greatly increasing battery system operation cost.
- 其他
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LICENSINGOPPORTUNITY:
IdahoNational Laboratory (INL) and its M&O Contractor Battelle Energy Alliance,LLC (BEA) are currently looking for commercialization partner(s) interested inentering into a license agreement for the purpose of commercializing thetechnology described below.
INTELLECTUALPROPERTY STATUS:
This invention has associated intellectual property USPatent Application No. 15/357,322, BEA Docket No. BA-431: “Systems and Methodsfor Estimation and Prediction of Battery Health and Performance,” submitted 21November 2016.
DEVELOPMENTSTATUS:
This technology has been tested and validated at thebench scale. Additional development will be required to demonstrate a pilotscale process.
ADDITIONALINFORMATION:
- Conference:“Long-TermValidation of Rapid Impedance Spectrum Measurements as a BatteryState-of-Health Assessment Technique,” SAE WorldCongress, April 2013, INL/CON-12-27063. Also available on SAE International
- Conference:“AcquiringImpedance Spectra from Diode-Coupled Primary Batteries to Determine Health andState of Charge,” IEEE Aerospace Conference, March 2013. Alsoavailable on IEEExplore.
- Report:“Advanced EnergyStorage Life and Health Prognostics (INL) FY 2012 Annual Progress Report,”INL/EXT-12-27612
- 國家/地區
- 美國
