embeddable state-estimation algorithms for li-s battery … · 2017. 6. 4. · • no embeddable...

© 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]


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

2


’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


https://www.cranfield.ac.uk/case-studies/research-case-studies/revb

3


Challenges in Li-S state estimation

no coulomb counting

computational complexityis also an issue!

unhelpful OCV curve


Initial charge is unknown:• Capacity depends on usage• Self-discharge occurs

Flat ‘low plateau’ makes itimpossible to estimate remainingcapacity from open-circuit voltagealone.

4


Our team


Dr Daniel AugerPrincipal Investigator

Dr Stefano LongoCo-Investigator

Dr Abbas FotouhiResearch Fellow

Karsten ProppResearcher

Vaclav KnapVisiting from Aalborg

5


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


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!



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


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


Techniques from control theory (2)


• 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

10


.

( 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


.t i i O L PV a SOC b R I V= + − −

1

10

i

P P

aC

OCA

R C

− = =

[ ]

11

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

11

© 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

12

© 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.

13


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.



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



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




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!☺





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!

18


Acknowledgements

Funding

Grant no. EP/L505286/1

This presentation’s co-authors

Other colleagues/collaborators



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

[email protected]


https://tinyurl.com/lithium-sulfur-postdoc-2462

our lithium-sulfur cell modelfor Simulink is free from the

MATLAB File Exchange

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embeddable state-estimation algorithms for li-s battery … · 2017. 6. 4. · • no embeddable...

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