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ARL Penn State NDIA Tri-Service Expo on Power Management 15 July, 2003 Norfolk, Va. Battery Prognostics for Enhanced Combat Vehicle Readiness and The Next Step Reduction of Total Ownership Costs Dr. James Kozlowski Complex Systems Monitoring The Pennsylvania State University Applied Research Laboratory (814) 863-3849 firstname.lastname@example.org USMC Light Armored Vehicle LAV-25 ARL Penn State Presentation Outline • Needs for Battery Monitoring • Available Technology Comparison • Model-Based Battery Prognostics • Operational Implementation • Operational Risk Management • Impact to Life Cycle Costs ARL New Capability Available to the Warfighter Penn State • Will the battery crank the engine? How Many more times? • Before I shutdown, is the battery ok? Prognostics • I’m on silent watch, how much longer Information to: can I go and be sure I can restart? •Operator • This battery has been in storage, is it •Maintainer still good and charged? •Log/supply • What’s the electrical problem- the •PM alternator or battery? Which battery? •Command/Ctrl ARL Penn State Battery Health Monitoring Failure Modes as a Percentage of Present Solutions: Total Automotive Battery Failures • Carry backup or reserve Serviceability batteries 11% Wear Out Damaged • Over-design batteries to 16% 2% reduce use and time Open Circuit between failures 12% • Heavy, costly Corrosion New Alternative 32% Use an online battery Short Circuit 27% monitoring system to detect and predict impending faults and assess available power “Batteries for Automotive Use”, P. Reasbeck and J.G. Smith and usage time ARL Why not just use off-the-shelf Penn State battery monitoring products? • Open-Circuit Voltage : accuracy • Discharge Test : time and equipment • Coulomb Counting: need full discharge • Temperature: harsh environment • Specific Gravity: SOC only, sealed? ARL Penn State Technology Comparison Standard Technologies: Impedance Interrogation Technology: Commercial State of Charge (SOC) technologies Uses patented complex impedance measurement use a very simple measurements of voltage, to estimate SOC, SOH and SOL on-line. current, temperature and internal impedance. • Voltage Monitoring: Compares voltage to • Impedance measurement covers broad range SOC table. of frequencies. – Measurements must be made off-line – High resolution impedance image creates and drop off voltages are difficult to accurate model for analysis. measure accurately. – SOC prediction: 1% to 2% error • Coulomb Counter: Measures total amount of • Fuzzy Logic, Neural Network and ARMA current in/out of battery. models with decision fusion algorithm. – Low accuracy due to battery self- – Multiple predictions provides a higher discharge and temperature variations. level of performance and increased • Internal Impedance: Measurements are based confidence. on impedance values at a few frequencies. • SOH provides classification of the failure mode. – Models are highly frequency dependant – Improves prognostic capability so SOC estimates have 10% – 20% error. • SOL - remaining useful cycles prediction is dependant upon accurate failure mode • Limited State of Health (SOH) and State of identification. Life (SOL) information available with these methods. ARL Penn State Impedance Interrogation Excitation IN Response OUT Characteristics of Internal Condition and Activity ARL Penn State Isn’t impedance-based technology available in off-the-shelf products? Yes, but… There is a lack in performance for both measurement techniques and processing of the information And therefore… There is a perception that impedance- based technology cannot effectively assess the condition and health of a battery ARL Penn State Battery Prognostic Processing Architecture SOC, SOH, and SOL Estimators LAV Feature Vector Files ARMA Installation Fuzzy Logic Impedance Processing Electrochemical Model Identification Ex, Neural Network Sn SOC, SOH, SOL Estimation FIles User Interface User Info File DataDecision Fusion Fusion Workbench History if,...then Knowledge S History Knowledge ARL Penn State Example from SOC Testing Results 20% Train / 80% Test Training and Testing Results from SLI Lead-Acid Battery Set (-10 to 50 degrees C) Test ARMA N.N. Fuzzy error error error No Load 7.08% 3.41% 7.90% ISOC 3.07% 3.15% 3.52% CSOC 1.52% 2.96% 2% ARL Tactical Use of Battery Prognostics Penn State •Gives broadest range of tactical information before, during and after mission: -State of charge: ready to start -State of life: condition during use -State of Health: readiness for future mission(s) •Applies to huge range of battery types, sizes and uses •Example of True Prognostics Capability • Fast, reliable predictions of State-of-Charge, State-of- Health and State-of-Life with performance errors <5% • A low power system (<1/2 watt) that is co-located with battery ARL Penn State MANAGING OPERATIONAL RISK WITH BATTERY PROGNOSTICS User interface describing state of battery health, life and charge to the operator. Information presented before operation (availability) and during operation (maintaining op tempo and managing operational risk) Fault failure information provided to maintainer ARL Tactical Combat Vehicle Penn State System Layout Condition/Health Vehicle Data • Battery Health and State of Charge Bus/Networking • Engine Health Information • Power Train Health Information Wired, wireless or Condition Monitoring SneakerNet Intelligent Nodes Performance/Status • Warnings • Advisories • Status, levels Readiness Intelligent Node Diagnostic Monitoring Location Unit /Info • Vehicle identification Server Smart Maintainter • Location (GPS) • Time-of-day Asset Visibility Intelligent Node ARL Platform Status Information Penn State Exchange Do I have assets for ……… When do I need to buy the upcoming mission? material for systems that are predicted to Logistician fail? What is the status of OPS Supply all my platform Cs? Planner Give battery health data for all of PM platform type As? LCMS SME CMMS = Computerized Maintenance Management System GCSS = Global Combat Support System CMMS MC ISEA = In-Service Engineering Agent What is going to LCMS = Life-Cycle Management System break on any of MC = Maintenance Controller Condition Interface Engine: platform Bs? OPS = Operations PM = Program Manager We need to define the requirements for SME = Subject Matter Expert the interface between vehicles and corporate systems and select What is broken or about information standards to implement. to break on this vehicle? I want to report a problem in to CMMS. Vehicle Vehicle Vehicle Vehicle Vehicle Type AA Vehicle Vehicle Type AA Vehicle Vehicle Vehicle Type AA Vehicle Smart Type A Type A Type A Type Block 1 Block 11 Type Block 1 B Type Block 11 Type Block 1 C Type Block 11 Maintainer Block Block 1 Block Block 1 Block Vehicle Vehicle Vehicle Vehicle Vehicle Vehicle Vehicle Type AA Type AB Vehicle Type AA Transfer Vehicle Type A Type Vehicle Type A Vehicle Type Block 11 A Block 12 Vehicle Type Block 11 A Device Type Block 2 A Block Type Block 2 C Type Block Type Block Block 22 Block Operator Block 22 Block ARL Benefits of Battery Penn State Prognostics Benefit Category Impact of Battery Prognostics Benefits to All Vehicles Using 6TL Battery Types (P/A) Operational Availability -confirms state-of-charge and state-of-health 513,488 lost hours (out- -Eliminates unanticipated failures- prior to attempted start of- service time) to-start -provides state-of-charge and remaining -Enables management of battery useful life during silent watch power during silent watch Maintenance -Stops unintended replacement of good $2.698M per year - Isolates fault to a specific battery batteries -Confirms good condition of battery -Stops the practice of replacing battery farm (visa electrical system problem) when only one is bad -Prevents unnecessary battery removal as part of electrical system diagnosis -Identifies battery mode of failure and indicates cause Log/Supply -Extended life of batteries $5.565M -Reduces number of batteries in -A priori determination of need for battery inventory replacement -Provides anticipatory needs for battery replacement ARL Cost/Benefits Top Level- Penn State AAV RAM/RS Studies Prognostics/CBM Effect on LCC Benefits $1,400,000,000 increase as $1,300,000,000 service life is LCC Without extended $1,200,000,000 Prognostics Total Life Cycle Cost $1,100,000,000 $1,000,000,000 $900,000,000 3-4 yr. $800,000,000 payback LCC With $700,000,000 Prognostics $600,000,000 “s” shape effect due to $500,000,000 deferred depot overhauls $400,000,000 5 7.5 10 12.5 15 17.5 20 22.5 Length of Estimate (Years) Adjusted Prognostics ARL Top Level- EFV (AAAV) Increased Penn State Operational Availability AAV RAM/RS Data (Hours) W/O Prog W/Prog Mean Time Between Failures 64 73.6 Increase in Mean Time To Repair 0.87 Operational 0.87 Availability As a result Mean Logistics Delay Time of CBM+ 5.4 2.7675 AAV RAM/RS Calculations W/O Prog W/Prog Forecasted Op Availability 91.08% 95.29% Increase in Op Avail w/Prog 4.21% Increased AAVs Mission Capable w/Prog 29 Total LCC Costs per AAV w/Prog $973,504 Operational Availability Opportunity Benefit of Prognostics $27,890,754 Benefits can either be: increased Ao; decreased life cycle cost or reduced number of assets for same total operational availability ARL SUMMARY Applied Research Penn State Laboratory • Battery Prognostics is Real PennState University • High accuracy over present techniques • High value added to the warfighter • Enabling capability to manage operational risk, increase operational readiness and reduce life cycle costs ARL Penn State P.O. Box 30 State College, Pennsylvania 16804 www.arl.psu.edu (814) 865-6343 This work was supported by the Office of Naval Research and Dr. Philip Abraham, Code 331, under ONR Grant N00014-98-1-0795.
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