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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
jdk173@psu.edu
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.
