Modeling, Identification, & Fault Diagnostics of Batteries Scott - - PowerPoint PPT Presentation
Modeling, Identification, & Fault Diagnostics of Batteries Scott Moura Assistant Professor | eCAL Director University of California, Berkeley Nuclear Engineering Colloquium Download:
Assistant Professor | eCAL Director University of California, Berkeley
Nuclear Engineering Colloquium
Download: https://ecal.berkeley.edu/pubs/slides/Moura-NE-Batts-Slides.pdf
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 1
Current Researchers
Supporting Researchers
| Reve Ching | Zane Liu | Dylan Kato
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 2
1985 1990 1995 2000 2005 2010 2015
Year
1000 2000 3000
Keyword Search: Battery Systems and Control
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 3
Samsung Galaxy Note Boeing 787 Dreamliner
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 4
Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 5
Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly Some Motivating Facts EV Batts 1000 USD / kWh (2010)∗ 485 USD / kWh (2012)∗ 350 USD / kWh (2015)∗∗ 125 USD / kWh for parity to IC engine Only 50-80% of available capacity is used Range anxiety inhibits adoption Lifetime risks caused by fast charging
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 5
Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 5
Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 5
Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly Some Motivating Facts EV Batts 1000 USD / kWh (2010)∗ 485 USD / kWh (2012)∗ 350 USD / kWh (2015)∗∗ 125 USD / kWh for parity to IC engine Only 50-80% of available capacity is used Range anxiety inhibits adoption Lifetime risks caused by fast charging Two Solutions Design better batteries Make current batteries better (materials science & chemistry) (estimation and control)
∗Source: MIT Technology Review, “The Electric Car is Here to Stay.” (2013) ∗∗Source: Tesla Powerwall. (2015) Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 5
Increase usable energy capacity by 20% Decrease charge times by factor of 5X Increase battery life time by 50% Decrease fault detection time by factor of 10X
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 6
1
BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS
2
ESTIMATION AND CONTROL PROBLEM STATEMENTS
3
ELECTROCHEMICAL MODEL
4
MODEL IDENTIFICATION
5
FAULT DIAGNOSTICS
6
SUMMARY AND OPPORTUNITIES
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 7
Luigi Galvani, 1737-1798, Physicist, Bologna, Italy “Animal electricity” Dubbed “galvanism” First foray into electrophysiology Experiments on frog legs Alessandro Volta, 1745-1827 Physicist, Como, Italy Voltaic Pile Monument to Volta in Como
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 8
Lithium Cobalt Oxide (LiCO2) Lithium Manganese Oxide (LiMn2O4) Lithium Iron Phosphate (LiFePO4) Lithium Nickel Manganese Cobalt Oxide (LiNiMnCoO2) Lithium Nickel Cobalt Aluminum Oxide (LiNiCoAlO2) Lithium Titanate (Li4Ti5O12)
Source: http://batteryuniversity.com/learn/article/types_of_lithium_ion Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 9
1
BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS
2
ESTIMATION AND CONTROL PROBLEM STATEMENTS
3
ELECTROCHEMICAL MODEL
4
MODEL IDENTIFICATION
5
FAULT DIAGNOSTICS
6
SUMMARY AND OPPORTUNITIES
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 10
Equivalent Circuit Model
(a) OCV-R
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 11
Equivalent Circuit Model
(a) (b) (c) OCV-R OCV-R-RC Impedance
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 11
Equivalent Circuit Model
(a) (b) (c) OCV-R OCV-R-RC Impedance
Electrochemical Model
Separator Cathode Anode LixC6 Li1-xMO2 Li+ cs
cs
+(r)
css
+
Electrolyte e- e- ce(x)
+ + sep
L
sep
r r x
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 11
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 12
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 13
1
BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS
2
ESTIMATION AND CONTROL PROBLEM STATEMENTS
3
ELECTROCHEMICAL MODEL
4
MODEL IDENTIFICATION
5
FAULT DIAGNOSTICS
6
SUMMARY AND OPPORTUNITIES
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 14
The Doyle-Fuller-Newman (DFN) Model
Separator Cathode Anode LixC6 Li1-xMO2 Li+ cs
cs
+(r)
css
+
Electrolyte e- e- ce(x)
+ + sep
L
sep
r r x
Key References:
batteries, pp. 345-392.
2010.
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 15
well, some of them | Open Source Matlab CODE: github.com/scott-moura/fastDFN Description Equation Solid phase Li concentration
∂c±
s
∂t (x, r, t) =
1 r2
∂ ∂r
s (c± s ) · r2 ∂c±
s
∂r (x, r, t)
concentration
εe ∂ce
∂t (x, t) = ∂ ∂x
e (ce) ∂ce
∂x (x, t) +
1−t0
c
F
i±
e (x, t)
potential
σeff,± ∂φ±
s
∂x (x, t) = i±
e (x, t) − I(t)
Electrolyte potential
κeff(ce) ∂φe
∂x (x, t) = −ie±(x, t) +
2RT(1−t0
c )κeff(ce)
F
d ln fc/a d ln ce
∂x
(x, t)
Electrolyte ionic current
∂ie± ∂x (x, t) = a±
s Fjn±(x, t)
Molar flux btw phases j±
n (x, t) = 1 F i± 0 (x, t)
αaF RT η±(x,t) − e− αcF RT η±(x,t)
Temperature
ρcP
dT dt (t) = h
0+
0− asFjn(x, t)∆T(x, t)dx
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 16
well, some of them | Open Source Matlab CODE: github.com/scott-moura/fastDFN Description Equation Solid phase Li concentration
∂c±
s
∂t (x, r, t) =
1 r2
∂ ∂r
s (c± s ) · r2 ∂c±
s
∂r (x, r, t)
Electrolyte Li concentration
εe ∂ce
∂t (x, t) = ∂ ∂x
e (ce) ∂ce
∂x (x, t) +
1−t0
c
F
i±
e (x, t)
Solid potential
σeff,± ∂φ±
s
∂x (x, t) = i±
e (x, t) − I(t)
Electrolyte potential
κeff(ce) ∂φe
∂x (x, t) = −ie±(x, t) +
2RT(1−t0
c )κeff(ce)
F
d ln fc/a d ln ce
∂x
(x, t)
Electrolyte ionic current
∂ie± ∂x (x, t) = a±
s Fjn±(x, t)
Molar flux btw phases j±
n (x, t) = 1 F i± 0 (x, t)
αaF RT η±(x,t) − e− αcF RT η±(x,t)
Temperature
ρcP
dT dt (t) = h
0+
0− asFjn(x, t)∆T(x, t)dx
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 16
well, some of them | Open Source Matlab CODE: github.com/scott-moura/fastDFN Description Equation Solid phase Li concentration
∂c±
s
∂t (x, r, t) =
1 r2
∂ ∂r
s (c± s ) · r2 ∂c±
s
∂r (x, r, t)
concentration
εe ∂ce
∂t (x, t) = ∂ ∂x
e (ce) ∂ce
∂x (x, t) +
1−t0
c
F
i±
e (x, t)
potential
σeff,± ∂φ±
s
∂x (x, t) = i±
e (x, t) − I(t) (ODE in x)
Electrolyte potential
κeff(ce) ∂φe
∂x (x, t) = −ie±(x, t) +
2RT(1−t0
c )κeff(ce)
F
d ln fc/a d ln ce
∂x
(x, t) (ODE in x)
Electrolyte ionic current
∂ie± ∂x (x, t) = a±
s Fjn±(x, t) (ODE in x)
Molar flux btw phases j±
n (x, t) = 1 F i± 0 (x, t)
αaF RT η±(x,t) − e− αcF RT η±(x,t)
Temperature
ρcP
dT dt (t) = h
0+
0− asFjn(x, t)∆T(x, t)dx
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 16
well, some of them | Open Source Matlab CODE: github.com/scott-moura/fastDFN Description Equation Solid phase Li concentration
∂c±
s
∂t (x, r, t) =
1 r2
∂ ∂r
s (c± s ) · r2 ∂c±
s
∂r (x, r, t)
concentration
εe ∂ce
∂t (x, t) = ∂ ∂x
e (ce) ∂ce
∂x (x, t) +
1−t0
c
F
i±
e (x, t)
potential
σeff,± ∂φ±
s
∂x (x, t) = i±
e (x, t) − I(t)
Electrolyte potential
κeff(ce) ∂φe
∂x (x, t) = −ie±(x, t) +
2RT(1−t0
c )κeff(ce)
F
d ln fc/a d ln ce
∂x
(x, t)
Electrolyte ionic current
∂ie± ∂x (x, t) = a±
s Fjn±(x, t)
Molar flux btw phases j±
n (x, t) = 1 F i± 0 (x, t)
αaF RT η±(x,t) − e− αcF RT η±(x,t)
(nonlinear AE) Temperature
ρcP
dT dt (t) = h
0+
0− asFjn(x, t)∆T(x, t)dx
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 16
well, some of them | Open Source Matlab CODE: github.com/scott-moura/fastDFN Description Equation Solid phase Li concentration
∂c±
s
∂t (x, r, t) =
1 r2
∂ ∂r
s (c± s ) · r2 ∂c±
s
∂r (x, r, t)
concentration
εe ∂ce
∂t (x, t) = ∂ ∂x
e (ce) ∂ce
∂x (x, t) +
1−t0
c
F
i±
e (x, t)
potential
σeff,± ∂φ±
s
∂x (x, t) = i±
e (x, t) − I(t)
Electrolyte potential
κeff(ce) ∂φe
∂x (x, t) = −ie±(x, t) +
2RT(1−t0
c )κeff(ce)
F
d ln fc/a d ln ce
∂x
(x, t)
Electrolyte ionic current
∂ie± ∂x (x, t) = a±
s Fjn±(x, t)
Molar flux btw phases j±
n (x, t) = 1 F i± 0 (x, t)
αaF RT η±(x,t) − e− αcF RT η±(x,t)
Temperature
ρcP
dT dt (t) = h
0+
0− asFjn(x, t)∆T(x, t)dx (ODE in t)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 16
Space, r
s (x, r, t)/c− s,max
Center Surface Space, r
s (x, r, t)/c+ s,max 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.5 1 1.5 2
Space, x/L
0.45 0.5 0.55 0.6 0.65 0.7 0.75
Cathode Separator Anode
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 17
1
BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS
2
ESTIMATION AND CONTROL PROBLEM STATEMENTS
3
ELECTROCHEMICAL MODEL
4
MODEL IDENTIFICATION
5
FAULT DIAGNOSTICS
6
SUMMARY AND OPPORTUNITIES
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 18
Given measurements of current I(t), voltage V(t), and temperature T(t), identify unknown/uncertain parameters. Challenges: How to design the experiments? How to optimally fit the parameters?
Space, r
c−
s (x, r, t)/c− s,max Center Surface Space, r
c+
s (x, r, t)/c+ s,max 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.5 1 1.5 2 Space, x/L
ce(x, t)
0.45 0.5 0.55 0.6 0.65 0.7 0.75 Cathode Separator Anode
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 19
Literature Review (Schmidt 2010) Fisher info, param grouping (Forman 2012) Fisher info, genetic algorithm (Marcicki 2013) SPMe, heuristic approach (Arenas 2014) ANOVA confidence intervals (Zhang 2015) Multi-obj GA (voltage & temp) (Alavi 2015) identifiability analysis (Rothenberger 2015) ECM, sinusoidal input (Liu 2016) ECM & SPM, sinusoidal input and more?
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 20
Literature Review (Schmidt 2010) Fisher info, param grouping (Forman 2012) Fisher info, genetic algorithm (Marcicki 2013) SPMe, heuristic approach (Arenas 2014) ANOVA confidence intervals (Zhang 2015) Multi-obj GA (voltage & temp) (Alavi 2015) identifiability analysis (Rothenberger 2015) ECM, sinusoidal input (Liu 2016) ECM & SPM, sinusoidal input and more?
Experiments are not optimized for parameter identifiability
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 20
minimize
t=t0
L(x, u)dt + Φ(x(tf)) (1) subject to: d dt x(t) = f(x, u); x(t0) = x0 (2) xmin ≤ x(t) ≤ xmax (3) umin ≤ u(t) ≤ umax (4) x(t): state; u(t): controlled input
d dt x(t) = f(x, u);
x(t0) = x0
Dynamic programming Quasilinearization Direct shooting Spectral methods Collocation methods many more...
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 21
minimize
t=t0
det
3(t)Q−1S3(t)
(5) subject to: d dt x(t) = f(x, z, u; θ); x(t0) = x0(θ) d dt S1(t) = ∂f
(6) 0 = g(x, z, u; θ) 0 = ∂g
(7) y = h(x, z, u; θ) S3(t) = ∂h
(8)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 22
minimize
t=t0
det
3(t)Q−1S3(t)
(5) subject to: d dt x(t) = f(x, z, u; θ); x(t0) = x0(θ) d dt S1(t) = ∂f
(6) 0 = g(x, z, u; θ) 0 = ∂g
(7) y = h(x, z, u; θ) S3(t) = ∂h
(8) Elegant formulation! However, the OCP proved to be computationally intractable: 2 weeks to generate 100 sec of optimized input signals
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 22
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 23
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 23
Figure: Fixed menu of L inputs, index by j
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 24
Figure: Fixed menu of L inputs, index by j
Pre-compute all sensitivities Sj on menu minimizeη log det
L
j
−1
subject to:
L
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 24
Figure: Fixed menu of L inputs, index by j
Pre-compute all sensitivities Sj on menu minimizeη log det
L
j
−1
subject to:
L
Convex program → polynomial complexity Optimize 750 input profiles in 20 seconds
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 24
Note: 10-bit A/D converter + 10V ref ⇒ 10mV resolution
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 25
1
BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS
2
ESTIMATION AND CONTROL PROBLEM STATEMENTS
3
ELECTROCHEMICAL MODEL
4
MODEL IDENTIFICATION
5
FAULT DIAGNOSTICS
6
SUMMARY AND OPPORTUNITIES
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 26
Samsung Galaxy Note Boeing 787 Dreamliner
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 27
Sensor Faults Thermal Faults Electrochemical Faults Voltage sensor Thermal Runaway Mechanical deformation Current sensor Convective cooling failure Current collector corrosion
Internal thermal failure Gassing Separator failure Electrode fracture
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 28
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 29
Detect fault Estimate fault size Boeing 787 Dreamliner
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 30
Detect fault Estimate fault size
Few measurements Uncertainty Boeing 787 Dreamliner
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 30
Detect fault Estimate fault size
Few measurements Uncertainty
Industry: Limit check measurements Published Literature: Sensor faults,
Boeing 787 Dreamliner
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 30
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 31
Figure: Radial heat transfer model of cylindrical cell
r
k
Q(t) (9)
(10)
h k [T∞ − T(R, t)] (11)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 32
Figure: Radial heat transfer model of cylindrical cell
r
k
Q(t)+∆Q (9)
(10)
h k [T∞ − T(R, t)] (11)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 32
Detect and estimate thermal fault size Robust Observer: Estimates dis- tributed temperature, under faulty & healthy conditions Diagnostic Observer: Detects and es- timates fault size
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 33
System Model:
k
Q(t) + θ · ψ(x, t) Thermal Fault (12)
0;
k [T∞ − T(1, t)] (13)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 34
System Model:
k
Q(t) + θ · ψ(x, t) Thermal Fault (12)
0;
k [T∞ − T(1, t)] (13) Robust Observer:
T1
T1
k
Q(t) + p1(x)
T1(1, t)
T1
0;
T1
k [T∞ − T(1, t)] + p10
T1(1, t)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 34
System Model:
k
Q(t) + θ · ψ(x, t) Thermal Fault (12)
0;
k [T∞ − T(1, t)] (13) Robust Observer:
T1
T1
k
Q(t) + p1(x)
T1(1, t)
T1
0;
T1
k [T∞ − T(1, t)] + p10
T1(1, t)
Diagnostic Observer:
T2
T2
k
Q(t) + ˆ
T1(x, t) − ˆ T2(x, t)
T2
0;
T2
k [T∞ − T(1, t)] (17) d dt
p3
T1(x, t) − ˆ T2(x, t)
(18)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 34
1
Backstepping transformation to get target error system
2
Analyze the target error system using Lyapunov stability theory
3
Utilize Lyapunov-based adaptive observer design to estimate θ
Asymptotically, estimation errors T(x, t) − ˆ T1(x, t)H1 → ǫ1,
Bounds ǫ1, ǫ2 can be made arbitrarily small by choosing p1(x), p10, p2, p3 appropriately
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 35
Commercial LiFeO4 battery cell (A123 26650)
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 36
Robust Observer estimates surface temperature under all conditions [estimation error within 0.2 deg C] Diagnostic Observer detects and estimates the fault [estimation error within 15%] Fault detection time 5 sec
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 37
1
BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS
2
ESTIMATION AND CONTROL PROBLEM STATEMENTS
3
ELECTROCHEMICAL MODEL
4
MODEL IDENTIFICATION
5
FAULT DIAGNOSTICS
6
SUMMARY AND OPPORTUNITIES
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 38
Background & Electrochemistry Fundamentals Electrochemical-based models can enhance monitoring & performance! Optimal Experiment Design for Parameter Identifiability (Dim Sum) Thermal Fault Diagnostics
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 39
Background & Electrochemistry Fundamentals Electrochemical-based models can enhance monitoring & performance! Optimal Experiment Design for Parameter Identifiability (Dim Sum) Thermal Fault Diagnostics Applicable control theoretic tools: PDE Control State estimation System identification Nonlinear and adaptive systems Optimal & constrained control
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 39
Background & Electrochemistry Fundamentals Prob Defns: SOC Estimation, SOH Estimation, Charge/Discharge Control Electrochemical Modeling SOC Estimation Optimally Fast-Safe Charging
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 40
Background & Electrochemistry Fundamentals Prob Defns: SOC Estimation, SOH Estimation, Charge/Discharge Control Electrochemical Modeling SOC Estimation Optimally Fast-Safe Charging Research Topics NOT discussed: Model Reduction State-of-Charge Estimation State-of-Health Estimation Optimal Fast-Safe Charging
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 40
SJM and H. Perez, “Better Batteries through Electrochemistry and Controls,” ASME Dynamic Systems and Control Magazine, July 2014.
battery-management systems,” IEEE Control Systems Magazine, 2010.
Reference Governors & Electrochemical Models,” IEEE/ASME Transactions on Mechatronics, Aug 2015. SJM, N. A. Chaturvedi, M. Krstic, “Adaptive PDE Observer for Battery SOC/SOH Estimation via an Electrochemical Model,” ASME Journal of Dynamic Systems, Measurement, and Control, Oct 2013.
Control Conference, Chicago, IL, 2015. O. Hugo Schuck Best Paper & ACC Best Student Paper. SJM, F . Bribiesca Argomedo, R. Klein, A. Mirtabatabaei, M. Krstic, “Battery State Estimation for a Single Particle Model with Electrolyte Dynamics,” IEEE Transactions on Control Systems Technology, Mar 2017
and Thermal Dynamics,” Journal of the Electrochemical Society. DOI: 10.1149/2.1301707jes
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 41
Download: https://ecal.berkeley.edu/pubs/slides/Moura-NE-Batts-Slides.pdf
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 42
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 43
Methods in literature: Spectral methods Residue grouping Quasilinearization & Padé approx. Principle orthogonal decomposition Single particle model variants and much, much more Very popular and saturated topic Will not discuss further
ACCURACY SIMPLICITY Integrator Atomistic ECT SPMeT SPMe SPM ECM
Anode Separator Cathode
I(t) cs
r Li+ Rs
r Li+ I(t) Rs
+ Electrolyte Solid Li+ Solid ElectrolyteV(t) x ce(x,t) I(t) I(t) Li+ Li+ Li+ 0+ 0- L- 0sepLsepL+ T(t)
Cell 𝑊(𝑢) = ℎ(𝑑𝑡 − (𝑆𝑡 −, 𝑢), 𝑑𝑡 +(𝑆𝑡 +, 𝑢), 𝑑𝑓 −(𝑦, 𝑢), 𝑑𝑓 𝑡𝑓𝑞(𝑦, 𝑢), 𝑑𝑓 +(𝑦, 𝑢), 𝑈(𝑢), 𝐽(𝑢))
V(t) cs
r cs
+(r,t)
r Li+ Li+ Anode Separator Cathode Li
+
I(t) I(t) Rs
+
Solid Electrolyte
(b) OCV-R-RC
1/s
Power Energy
Atomistic ECT SPMe SPM ECM Integrator
Scott Moura | UC Berkeley Battery Modeling, ID, Fault Diagnostics September 18, 2017 | Slide 44