embeddable state-estimation algorithms for li-s battery … · 2017. 6. 4. · • no embeddable...
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© Cranfield University 2017
www.cranfield.ac.uk
Embeddablestate-estimation algorithmsfor Li-S battery management
Daniel J. Auger, Abbas Fotouhi,Karsten Propp and Stefano Longo
April 26-27, 2017
Confidential [Li-SM³ : London]
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© Cranfield University 2017
Outline
• Context – the REVB project
• Cranfield’s team and facilities
• Li-S state of charge estimation
– Kalman filter derivatives
– ANFIS
• Progress and future directions
• Acknowledgements
• Conclusions
Key references are given on the slides.
Some of the material here has been presented before at the following conferences:
Li-SM3, London, February 2016
EMN Meeting on Batteries, Orlando, February 2016
Hybrid and Electric Industrial Vehicle Technology, Cologne, November 2016
Talk to the Control Group, University of Oxford, February 2017
April 26-27, 2017 Confidential [Li-SM³ : London]
our lithium-sulfur cell modelfor Simulink is free from the
MATLAB File Exchange
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© Cranfield University 2017
’s automotive application
Project aim:
Pack with cells achieving
• 400 Wh/kg
• –10 to + 55°C
• Life of 615 cycles
Our work package:
• BMS algorithms
April 26-27, 2017 Confidential [Li-SM³ : London]
https://www.cranfield.ac.uk/case-studies/research-case-studies/revb
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© Cranfield University 2017
Challenges in Li-S state estimation
no coulomb counting
computational complexityis also an issue!
unhelpful OCV curve
April 26-27, 2017 Confidential [Li-SM³ : London]
Initial charge is unknown:• Capacity depends on usage• Self-discharge occurs
Flat ‘low plateau’ makes itimpossible to estimate remainingcapacity from open-circuit voltagealone.
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Our team
April 26-27, 2017 Confidential [Li-SM³ : London]
Dr Daniel AugerPrincipal Investigator
Dr Stefano LongoCo-Investigator
Dr Abbas FotouhiResearch Fellow
Karsten ProppResearcher
Vaclav KnapVisiting from Aalborg
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PIcontroller
Li-S pouch cell
Copper plates
Heat sink
Heat sink
Power supply12V
FanFan
Peltier elements(combined 280W)
Some of our facilities
Bespoke test rigs:
• Accurate current andtemperature control.
• Cell and pack level.
• Simulate automotiveand other duty cycles
• Two posters to see!
April 26-27, 2017 Confidential [Li-SM³ : London]6
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© Cranfield University 2017
Techniques from control theory (1)
measurementsplant outputs and any
known inputs‘plant’
(system of interest)‘Kalman Filter’ a.k.a.
‘linear quadratic estimator’
measurement noiseunknown
processnoise
unknown
if we know the statistical properties of the process noise and themeasurement noise, then our estimate is optimal in a
(recursive) least squares sense
usually, noise properties are assumed, and ‘tuned’ by trial anderror – this often works, but it is not ‘optimal’ in the
mathematical sense of the word!
April 26-27, 2017 Confidential [Li-SM³ : London]7
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© Cranfield University 2017
Model identification (2)Modified ECN models were fitted using the‘Prediction Error Minimization’ (PEM)algorithm – see Lennart Ljung’s textbook
Basic idea – find model parameters byminimizing
where ‘prediction errors’ are defined by
Using a commercial software tool, PEM wasapplied• The fit was now excellent at high SOCs• Ran very quickly – minutes for a data day
1
1( ) det ( , ) ( , )
NT
N k kk
E t tN
θ ε θ ε θ=
=
∑
1ˆ( , ) ( ) ( ; )k k k kt y t y t tε θ θ−= −
There are two lines here: the fit isalmost perfect at high SOCs.
April 26-27, 2017
Time (min)
doi: 10.1016/j.jpowsour.2016.07.090
Confidential [Li-SM³ : London]8
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© Cranfield University 2017
Model identification (2)Nonlinear model made from all pulses• Covered full state-of-charge range.• Covered multiple temperatures.
Validated using experimental data fromthe New European Driving Cycle.• C-segment vehicle assumed.• Scaled for sensible pack size – chosen
in consultation with cell manufacturerand Tier 1 auto supplier.
Fit was not perfect, but better than thecell manufacturer had ever seen before.• Worst at low states-of-charge
April 26-27, 2017
doi: 10.1016/j.jpowsour.2016.07.090
Confidential [Li-SM³ : London]9
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Techniques from control theory (2)
April 26-27, 2017 Confidential [Li-SM³ : London]
• SOC estimators implemented usingdifferent Kalman filter derivatives.
• extended (EKF)• unscented (UKF)• particle (PF)
• UKF observed to be the most robust.
• Implemented in real-time andapplied to real cells in Cranfield HILrig.
• Following publication,independently implemented byFICOSA on ALISE project – please seetheir poster!☺
Time [104 s]
Time [104 s]
doi: 10.1016/j.jpowsour.2016.12.087
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.
( 1). .
OC i i i
SOC i SOC
V a SOC b
where i SOC i
= +
− ∆ ≤ ≤ ∆
Techniques from computer scienceLi-S Cell SOC Observability Analysis
April 26-27, 2017 Confidential [Li-SM³ : London]
.t i i O L PV a SOC b R I V= + − −
1
10
i
P P
aC
OCA
R C
− = =
[ ]
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0
0 0
1
P
PPP P L
t
P
t i i O L
dVCVdt R C I
SOCdSOC
Cdt
VV b a R I
SOC
η
− = +
− = − −
observability matrix
a = 0 is possible for Li-S cell.
Li-S cell SOC is not observable using OCV curve.
Classical methods in the literature may not be applicable for Li-S battery SOC estimation.
A generic framework is proposed in REVB project for battery SOC and SOH estimation.
Fotouhi et al, PEMD 2016, Glasgow
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© Cranfield University 2017April 26-27, 2017 Confidential [Li-SM³ : London]
Identifying instantaneousECN parameters
Identifying instantaneousECN parameters
BatteryState
Estimator(ANFIS)
BatteryState
Estimator(ANFIS)
Current
Voltage
SOC
Battery MeasurementsBattery Measurements
Ba
tte
ryP
ara
me
ters
(P
1,P
2,…
,Pn
)
Temperature
( , , , )OC O P PV R R Cθ =
1
1( ) det ( , ) ( , )
NT
N k kk
E t tN
θ ε θ ε θ=
=
∑
1ˆ( , ) ( ) ( ; )k k k kt y t y t tε θ θ−= −
Fotouhi et al, IEEE Transactions on Systems Man and Cybernetics: Systems, & Fotouhi et al, PEMD 2016, Glasgow
Techniques from computer scienceLi-S Cell SOC Estimation based on real-time identification and using ANFIS
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© Cranfield University 2017April 26-27, 2017 Confidential [Li-SM³ : London]
Advantages:(i) It can start from any initial SOC value and no initial condition data is needed,(ii) The whole battery capacity is not needed for SOC calculation,(iii) Small convergence time,(iv) The proposed method is simple and fast enough to be used in real-time.
Techniques from computer scienceLi-S Cell SOC Estimation over UDDS using ANFIS
Using the proposed method, a Li-S cell’s SOC is estimatedwith a mean error of 4% and maximum error of 7% underreal driving condition.
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Where were we last year?
Before REVB project
• No embeddable modelsof any kind.
• No estimators.
At Li-SM³ in February 2016
• PEM-based ECN modeltechniques developed, but stillin peer review.
• Initial Kalman filter results,paper in preparation.
• Early ANFIS estimatorsdeveloped and, but still in peerreview.
April 26-27, 2017 Confidential [Li-SM³ : London]14
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Where are we now?
Before REVB project• No embeddable models
of any kind.
• No estimators
At Li-SM³ in April 2017 (now)• PEM-based ECN models
published and available fromMATLAB File Exchange.
• Kalman filter techniquesimplemented published – andsuccessfully replicated by athird party.
• More advanced version ofthese in peer review.
• ANFIS estimators improved. Inpeer review
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Future directions (1)
ECN Models
Early Zero-DimensionalElectrochemical Models
Spatially DistributedElectrochemical Models
ECN-Based EKF/UKF
Mature 0D Model StateEstimators
Spatially DistributedState Estimators
Mature Zero-DimensionalElectrochemical Models
Prototype 0D Model StateEstimators
Time
Now
Work by collaborators
April 26-27, 2017 Confidential [Li-SM³ : London]16
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© Cranfield University 2017
Future directions (2)
Degradation modelling with ECN parameter changes• Working with Aalborg University, Denmark• Uses ECN model as a basis• Estimates increases in resistance/reductions in capacity• Early results gave some success.• Journal paper in preparation• Key researcher: Vaclav Knap – look out for his poster!☺
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Future directions (3)
April 26-27, 2017 Confidential [Li-SM³ : London]
source: https://airbusdefenceandspace.com/our-portfolio/military-aircraft/uav/zephyr/
• Applications in other domains,beyond automotive.
• About to begin working in aconsortium with Airbus.
• We have an exciting newopportunity as a post-docresearch fellow – seehttps://tinyurl.com/lithium-sulfur-postdoc-2462
• Continuing to look atautomotive applications, too!
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Acknowledgements
Funding
Grant no. EP/L505286/1
This presentation’s co-authors
Other colleagues/collaborators
April 26-27, 2017 Confidential [Li-SM³ : London]19
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© Cranfield University 2017
Conclusions – thanks for listening!
• Two practical methods of SOCestimation have been developed.
– Kalman filter derivatives
– ANFIS
• Both methods have beenimplemented in real-time and onpractical hardware.
• Several directions of future work.
• Exciting new research fellowship:
For further information contact:
Dr Daniel J. [email protected]
Dr Abbas Fotouhi
April 26-27, 2017 Confidential [Li-SM³ : London]
https://tinyurl.com/lithium-sulfur-postdoc-2462
our lithium-sulfur cell modelfor Simulink is free from the
MATLAB File Exchange
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