dr. cairo lúcio nascimento júnior eng. prof.m. josé affonso moreira penna eng. m.sc. leonardo...
TRANSCRIPT
Health Monitoring and Remaining Useful Life
Estimation of Lithium-Ion Aeronautical Batteries
Dr. Cairo Lúcio Nascimento JúniorEng. Prof.M. José Affonso Moreira PennaEng. M.Sc. Leonardo Ramos Rodrigues
Instituto Tecnológico de Aeronáutica
1
Introduction
PHM (Prognostics and Health Management)
Lithium-Ion Battery
Capacity Model
Health Monitoring Model
Model Simulations
Estimating Remaining Useful Life
Case Study
Conclusions
Summary
2
•Study of lithium-ion’s battery•Analyses of
experimental data
Models
•Simulation of battery discharges
through the life cycle
Monitoring • Study and application of techniques
for PHM
RUL Estimation
Motivation Reducing costs of operation and maintenance;
Flight safety improvement;
Development of techniques for prognosis.
Objective Develop methodology for estimating the Remaining Useful Life (RUL) of
lithium-ion’s aeronautical battery.
Methodology
Introduction
3
Present scenario:
◦ MTBF (Mean Time Between Failures); Maintenance tasks are assigned based on hard times; Decrease in the dispatch of the aircraft; Insufficient data to predict failure; Possible degradation in flight safety;
Current proposals :
◦ Data-Driven Methods;◦ Model-Based Methods.
PHM (Prognostics and Health Management)
4
Fundamental concepts:
1. All electromechanical systems age as a function of use, passage of time, and environmental conditions;
2. Component aging and damage accumulation is a monotonic process that manifests itself in the physical and chemical composition of the component;
3. Signs of aging (either direct or indirect) are detectable prior to overt failure of the component (i.e., loss of function);
4. It is possible to correlate signs of aging with a model of component aging and thereby estimate remaining useful life of individual components.
PHM (Prognostics and Health Management)
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Data-driven methods
◦ Capture and analyze multi-dimensional and noisy data containing a large number of variables related to component degradation;
◦ Management of uncertainty;
PHM (Prognostics and Health Management)
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Model-based methods
◦ Development of first-principles models of component use and damage accumulation;
◦ Use operational data to fine-tune model parameters;
◦ Model-based prognostics typically result in more accurate and precise RUL estimation;
◦ Advantages in validation, verification, and certification since the model response can be correlated with laws of nature.
PHM (Prognostics and Health Management)
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Why study this type of battery?
◦ Increasing application in the aerospace industry (Boeing 787, Airbus A380);
◦ Higher energy density, low self-discharge, long life in stock;◦ Available experimental data at NASA Ames Prognostics Data Repository.
Lithium-Ion Batteries
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Failure modes
◦ Over-voltage;◦ Under-voltage;◦ Low temperature operation;◦ High temperature operation; ◦ Mechanical fatigue; Life Cycle
Lithium-Ion Batteries
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Data Repository
Source: NASA Ames Prognostics Data Repository; 34 lithium-ion batteries (Cnominal=2Ah); Repetitive cycles of discharge, recharge, and impedance
measurement; Archives ”.mat”.
Discharge and Capacity Model
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◦ Data treatment Extrapolation of discharge curve.
◦ Effect of degradation over the life cycle Reduced time of discharge; Reduction of voltage.
Discharge and Capacity Model
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Data repository
◦ Battery capacity (C) calculated by
◦ State of Charge (SoC) calculated by
Discharge and Capacity Model
finalt
dttIC0
)(
1
0
)(1
)()( 01
t
t
dttIC
tSoCtSoC
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Discharge model
◦ (PAATERO, 1997) e (SPERANDIO, 2010);
◦ Voltage U (I,T,SoC) calculated by
Discharge and Capacity Model
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Discharge model
◦ Determination of parameters x1...x17:
First discharge curve of each selected batteries; FMINSEARCH (MATLAB®) minimizing square
error; Error mean=0,0565 V (<1.8%); Error variance= 0,0058 V2.
Discharge and Capacity Model
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Capacity model
◦ Linear model Capacity = f (T, I, nc)
◦ Determination of the parameters c0 and c1:
Selected five batteries with different discharge profiles; Selected c0 and c1 models; fminsearch (MATLAB®) minimizing square error; Error mean=0,0324 Ah (<2,2%); Error variance=0,0035 (Ah)2
Discharge and Capacity Model
),(),(),,( 01 TIcncTIcncTIC
16
Capacity model
◦ Capacity x electrical current
Low electrical current:
Higher initial capacity C0; Faster loss of capacity.
◦ Capacity x temperature
High temperature:
Higher initial capacity C0; Faster loss of capacity.
Discharge and Capacity Model
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◦ State of Health (SoH)
◦ Delta Health
◦ nc(C=0)
◦ Capacidade @SoH
◦ Relative Number of Cycles (ncr)
◦ Remaining Useful Life (RUL)
Health Monitoring Model
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◦ Example of Simulation
Example of the evaluation of SoH, delta health and nrc at determinate operation profile throughout the life of the battery. At the cycle 210 the discharge profile change from I=4A and T=43°C to I=2A and T=24°C.
Model Simulations
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Estimating Remaining Useful Life
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Method proposed to estimate the remaining useful life (RULmin and RULmax):
1. A linear regression of the SoH data available to date using the function REGRESS (Matlab R2010b);
2. Evaluation of the cycle number at which the battery reaches the minimum threshold of SoH (SoHmin) by extrapolating the line obtained by linear regression;
3. Addition of the uncertainty of the model and of the future operating profile to be performed.
Estimating Remaining Useful Life
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1. A linear regression:
2. Evaluation of ncfailure and RUL::
3. Addition of the uncertainty:
Case Study
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◦ Case A
electrical starting of the engines; 15 minutes discharge; I=4A (exponential decay); T=43ºC.
Simulation failure at cycle 495
Even if the battery can execute the starting profile until cycle number 770, the battery cannot comply with the emergency requirement after cycle 495, as shown in Figure 20. In this case the failure of the battery is declared on cycle 495.
Case Study
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◦ Case B
Nominal operation of I=4A and T=24ºC; Non-anticipated degradation; Increase on the ambient temperature
(T=43ºC) during 25 cycles.
Simulation failure antecipated from cycle 1130 to cycle 1059
Case Study
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◦ Case B
RUL estimation good response and accuracy even with a
dynamical change; good precision (approximately 79 cycles).
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Contact:
Prof. Dr. Cairo Lúcio Nascimento Jú[email protected]
Eng. M.Sc. José Affonso Moreira [email protected]
Eng. M.Sc. Leonardo Ramos [email protected]
Instituto Tecnológico
de Aeronáutica