National Aeronautics and Space Administration Prognostics ...• A set of Li-ion cells • Aging...
Transcript of National Aeronautics and Space Administration Prognostics ...• A set of Li-ion cells • Aging...
Prognostics TestbedPrognostics TestbedPrognostics TestbedPrognostics TestbedBhaskar Saha and Kai Goebel
National Aeronautics and Space Administration
Problem
Bhaskar Saha and Kai Goebel Prognostics Center of Excellence, NASA ARC
Problem
Motivation
To facilitate research in prognostics, it is imperative to have
Requirements
The testbed shall:To facilitate research in prognostics, it is imperative to have
a hardware testbed that mimics the complexities and
issues encountered for a real system.
Such a system will support
• Algorithm development
• Testing and validation of prognostic tools
• Resemble a system that has real-world relevance
• Allow for repeated run-to-failure of components
• Perform run-to-failure in reasonable time
• Support monitoring of ground truth
• Collect data for state assessment• Testing and validation of prognostic tools
• Benchmarking of different approaches
• Development of metrics for prognostics
• Collection and dissemination of run-to-failure data
Goal
• Demonstrate ability to distinguish between components
• Collect data for state assessment
• Support demonstration of prognostic solutions
• Allow control of several operational and/or environmental
variables
• Allow quantification of uncertainty sources
• Support repeated run-to-failure within a finite budget
System
• Demonstrate ability to distinguish between components
at different health states having similar external
observables and then to predict the end of life
• Support repeated run-to-failure within a finite budget
• Support automated data collection during the aging
SystemTestbed – Data
CollectionExperimental setup
• A set of Li-ion cells
• Aging dynamics slow enough to be observable and fast enough for reasonable run-to-failure
times (~1 month)
• Low cost
BHM
• Low cost
• May be aged either inside or outside an environmental chamber
• Programmable Charger and Electronic Load
• EIS equipment for battery health monitoring (BHM)
• Sensor suite – Voltage, Current, Temperature
• Custom switching circuitry
• Data acquisition system
• Computer for control and analysis
Experimental Plan • Cells are cycled through charge and discharge under
different load and environmental conditions set by
the electronic load and environmental chamber
respectively
• Periodically EIS measurements are taken to monitor
the internal condition of the battery
Aging
• Computer for control and analysis
the internal condition of the battery
• DAQ system collects externally observable
parameters from the sensors
• Switching circuitry enables cells to be in the charge,
discharge or EIS health monitoring state as dictated
by the aging regime
Results
Algorithm Development
The algorithms considered so far include both
model-based as well as data-driven algorithms, for
Sample Results
Results
example
• Relevance vector machines (RVM)
• Gaussian Process Regression (GPR)
• Particle Filters (PF, RBPF)
• Neural Networks (NN)
• Random Forest Regression
• ARIMA models
• Kalman Filters
Further algorithms will be explored and results will
be published to disseminate findings on be published to disseminate findings on
advantages and disadvantages of each one.
www.nasa.gov
Battery PrognosticsBattery PrognosticsBattery PrognosticsBattery PrognosticsBhaskar Saha and Kai Goebel
Problem
Bhaskar Saha and Kai Goebel Prognostics Center of Excellence, NASA ARC
Problem
(AFRL, Artist's depiction)
Electric Propulsion Space Experiment (AFRL)
•Ammonia Arcjet onboard ARGOS
(1999)
•Gases released from electrolyte
Questions to be Answered
Can the current mission be completed?
• Given the health of the battery, is there enough charge left
for anticipated load profile (within allowable uncertainty
Beech A200 (Reg # N258AG)
•Gases released from electrolyte
decomposition resulted in a breach of
the battery case, releasing
superheated gas into the unit
•Onboard generators failed to activate as
starter was still engaged after ignition
(Courtesy: AFRL-PR-ED-TR-2001-0027)
for anticipated load profile (within allowable uncertainty
bounds)?
• Dominant metrics: state of charge (SOC), state of health
(SOH)
Can future missions be completed?
• Given the health of the battery, at what point can typical
future missions not be met?
(NASA/JPL, Artist's concept) Mars Global Surveyor
starter was still engaged after ignition
•Battery completely dischargedresulting in total electrical failure
disabling normal landing gear extension
capability
•Landing gear failed & the plane crashed (Courtesy: NTSB, ID #SEA00LA066)
•The MGS failed Nov 2006
future missions not be met?
• Dominant metrics: end of life (EOL), state of health (SOH)
end of charge ?
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Time (Long term)
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end of charge ?end of charge ?end of charge ?end of charge ?end of charge ?end of charge ?
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Time (Short term)Time (Short term)Time (Short term)Time (Short term)Time (Short term)Time (Short term)
Time (Long term)Time (Long term)Time (Long term)Time (Long term)Time (Long term)Time (Long term)
SO
HS
OH
SO
HS
OH
SO
HS
OH
Approach
•The MGS failed Nov 2006
"We think that the failure was due to a
software load ... The radiator for the
battery pointed at the sun, the
temperature went up, and battery
failed...”John McNameeMars Exploration Program, NASA
Time (Long term)Time (Long term)Time (Long term)Time (Long term)Time (Long term)Time (Long term)Time (Long term)
GOAL: Develop a model that makes a prediction of
end-of-charge and end-of-life based on rapid
state of health (SOH) assessment
ApproachBattery Schematic
Lumped Parameter Model
Impedance = Resistance + Reactance
CDL
RCT RW
RE
CDL
RCT RW
RE+– +
–
–+
+–
e- e-II
Relevance Vector Machine
– State of the art in nonlinear
probabilistic regression
– Data driven learning
– Learn degradation mode
Particle Filter
RVM
Impedance = Resistance + Reactance
•The External Voltage (E) of a battery
is less than its Open Circuit Potential
(Eo) whenever it is in use.
•Losses are due to:
• Ro: Internal Resistance (IR drop),
• Rp: Polarization Resistance
DISCHARGE CHARGE
Electrochemical Impedance Spectroscopy (EIS)
•Carry out a frequency sweep
•Plot Capacitive (1/ωC) v/s Resistive (R)
component of the Reactance
•Response is different in presence of
passivation and corrosion, providing a
diagnostic for the health state of battery
– State of the art for nonlinear
non-Gaussian state estimation
– Uses model to predict and data
to correct prediction
– Sequential Monte-Carlo simulations
with Importance Sampling for
p-step ahead predictions
PF
• Rp: Polarization Resistance
• Rc: Concentration Polarization
•These losses tend to increase as the
current drawn from the battery
increases.
Concentration
polarization
IR drop
Activation polarization
Eo
Incre
asin
g V
olta
ge
E
diagnostic for the health state of battery
Re(Z)
Im(Z
)
Increasing Ohmic Resistance
0
0
Increasing Ohmic Resistance
Electrolyte Weakening
Feature
Extraction
RVM
Regression
Model
Identification
EIS
Data
Feature
Extraction
PF
Tracking
PF
EIS
Data
Results
Increasing Current I
Incre
asin
g V
olta
ge
Polarization Curve
Ohmic resistance (Ro) and polarization resistance (Rp) of a Li-ion battery gradually increase with aging
Re(Z)
Im(Z
)
Increasing Charge Transfer Resistance
0
0
Increasing Charge Transfer Resistance
Plate Sulphation
PF
Prediction
Impedance-
Capacity Mapping
Cause-Effect
Analysis
SOH SOC SOL
Results
0.015
0.02
0.025Particle Filter Output
E, R
CT (Ω
)
PF Tracking
PF Prediction
RE
Particle Filter Prediction α-λ Metric ( α = 0.2, λ = 0.5)
0 10 20 30 40 50 60 700
0.005
0.01
Time (weeks)
RE
Fault d
ecla
red
Measurements
RCT
0.75
0.8
0.85
0.9
0.95
1Particle Filter Prediction
C/1
ca
pa
city (
mA
h);
R
UL
pd
f (b
iase
d b
y 0
.7)
Real data
15
20
25
30
35
40
α-λ Metric ( α = 0.2, λ = 0.5)
RU
L
POC: Bhaskar Saha, (650)604-4379, [email protected]
0 10 20 30 40 50 60 700.6
0.65
0.7
0.75
Time (weeks)
C/1
ca
pa
city (
mA
h);
R
UL
pd
f (b
iase
d b
y 0
.7)
RUL threshold
RUL pdfs
30 35 40 45 50 55 60 650
5
10
Time Index ( i)