Research and Education re.Modeling and Control ofModeling and Control of

Lithium Ion Battery Systems

Guest Lecture forECEN 5017: Power Electronics for Electric Drive Vehicles

28 September 2012

Dr. Gregory L. PlettDepartment of Electrical and Computer EngineeringDepartment of Electrical and Computer Engineering

University of Colorado Colorado Springs

Introducing the Teamg Gregory L. Plett, Professor of Electrical Engineering PhD in EE (adaptive controls), Stanford University Performing battery controls research since 2001

M. Scott Trimboli, Asst. Prof. of Electrical Engineering PhD in Control Systems Engineering Oxford University PhD in Control Systems Engineering, Oxford University Applying optimal and model predictive controls to battery cells

Graduate students: L. Aldrich, M. Aldrich, K. Karami, M. Kraska, J. Moore, K. Stetzel, M. Xavier (seeking three more PhD students)

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PART 1PART 1

B i f B tt M t T t i lBrief Battery Management Tutorial

BMS must produce two critical estimatesp Electric drive vehicles need to know two things about their battery:

How much energy is available in the battery pack How much energy is available in the battery pack How much power is available in the immediate future

An estimate of energy is most important for EV An estimate of energy is most important for EV An estimate of power is most important for HEV Both are important for E-REV/PHEV

Power tells me if I

p

Power tells me if I can accelerate or

accept regen

Energy tells me how far I can

drive

3

Inputs and outputs To estimate energy, must know all cell SOCs, capacities Q To estimate power must know (at least) all cell SOCs resistances

p p

To estimate power, must know (at least) all cell SOCs, resistances But, cannot directly measure cell SOC, resistance, capacity Must estimate these quantities as well

Available inputs include all cell voltages, pack current, and temperatures of cells or modules

SOCSOCQSOCSOCV

Model EnergyPack

SOCSOCSOC

SOCSOCR

I

SOCSOCT

Based Estimators

Pack Calculations

Power

4

SOCSOC

Estimation algorithm requirementsg qKey on:

InitializationSampleVk, Tk, I

EstimateSOC, R, Q

Calculatepack energy

Calculatepack power

Equalizecells

Key off:Store data

The BMS must estimate quantities that (1) describe the present battery pack condition, but (2) may not be directly measured “States” are quantities that change quickly (e.g., state-of-

charge, diffusion voltage, hysteresis voltage) “Parameters” are quantities that change slowly (e g cell Parameters are quantities that change slowly (e.g., cell

capacities, resistances, aging effects)

xEV batteries are subject to very dynamic power cycling hence rarely in electro-chemical equilibrium Contributing factors: diffusion voltage, hysteresis

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Contributing factors: diffusion voltage, hysteresis

Importance of diffusion voltagep g Cell dynamics include diffusion voltages Slow time constants resisting fast changes in terminal voltage Slow time constants resisting fast changes in terminal voltage Seen clearly during rest intervals in the example shown

Voltage does not instantly flatten out, but smoothly approaches a steady-state value

Ignoring diffusion

Cell voltage showing diffusion voltage

Ignoring diffusion voltages leads to very high SOC estimate errors in 10s of %in 10s of %

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Importance of hysteresis voltagep y g The cell voltage lags the predicted voltage in some sense A change in the direction of SOC (or current) leads to the voltage A change in the direction of SOC (or current) leads to the voltage

failing to retrace the path it passed in the forward direction Especially pronounced at low temperatures

Ignoring hysteresis leads to SOC estimation errors in 10s of %

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Kalman filters: An optimal state estimatorp

Kalman filter uses “known” mathematical model of system Same input propagated through true system and model

M d d di t d t t d d t d t Measured and predicted outputs compared; error used to update model’s estimate of the true state Output error due to: state, measurement, model errors

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Update must be done carefully to account for all of these

Models for Kalman filteringg

The dynamic system model has two equationsTh “ t t ” ti hi h d ib th d i l ti The “state” equation, which describes the dynamic evolution of the state we wish to estimate

• New state function of old state, measurable input, unmeasurable process noise

The “observation” or “output” equation, which describes how sensor measurements are related to the statesensor measurements are related to the state

• Output function of present state, measurable input, unmeasurable sensor noise

xk f (xk1,uk1,wk1)yk h(xk ,uk ,vk )

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yk ( k , k , k )

Enhanced self-correcting (ESC) cell modelg ( ) State-of-charge captured in state equation as

Diffusion voltages captured in state equation as

Hysteresis captured in state equation as

Overall state is

Output equation blends all terms, including OCV and ohmic loss

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Using Kalman filter to estimate cell stateg Now we have the ESC model defined use it in a Kalman filter to

make estimates of cell states and parametersp Two basic steps in every variety of Kalman filter:

Prediction of state, cell voltage using model and past state

Correction of state using measurements from real system

Kalman gain L optimum blending factor KF propagates state expected value and uncertainty

Uses current for short-term SOC dynamics Uses voltage measurement for long-term SOC dynamics

KF ti ll bi th t i b t SOC ti t

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KF optimally combines these to give best SOC estimates For nonlinear dynamics, we prefer “sigma point Kalman filter”

Examples of SPKF SOC estimationp On left, estimation error over time for properly initialized filter On right estimation error over time for improperly initialized filter On right, estimation error over time for improperly initialized filter Notice also the filter’s self-knowledge of error bounds on the

estimate (a feature common to all Kalman filters) Very useful when computing maximum available power, or a

conservative estimate of pack total energy

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Pack power and energy estimationp gy

Want to know how much dis/charge power vehicle can command at a constant level for the next T secondsat a constant level for the next T seconds Use present state estimate and simulate cell model T

seconds into the future with a constant current factor ik Bisect on ik to maximize power within voltage/SOC limits SPKF bounds on SOC, R, and Q give conservative estimates

Want to know how much discharge energy is available For a single cell, Can store integrated OCV in a table Can use knowledge of individual cell Q, SOC, and

configuration to determine the pack total available energy

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configuration to determine the pack total available energy

Pack power resultsp Some results of “desktop validation” to test power prediction

capability with a 10-second prediction horizonp y p Estimate tracks truth very well—generally somewhat conservative

to account for error bounds in SOC estimateA di ti i d t d h d i l ( ll) As power prediction is updated each second, occasional (small) overestimates can be corrected before damage is done

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Great future potential for BMS algorithmsp g The SOC, resistance, capacity estimation problems are “solved”

SPKF can be adapted to estimate cell resistances also SPKF can be adapted to estimate cell resistances also A somewhat different method, based on total least squares,

may be used for optimal capacity estimates Improvements can still be made by improving cell model used

Energy is a deterministic computation based on these estimates

However, power is not yet being computed optimally Power estimates are presently computed based on the

premise that certain voltage limits are not to be exceededp g Voltage limits are not the issue: Cell degradation is the issue Physics-based models of degradation must be formed

P t b t d t i i i d d ti

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Power must be computed to minimize degradation

Big picture and summary of Part 1g p yKey on:

InitializationSampleVk, Tk, I

EstimateSOC, R, Q

Calculatepack energy

Calculatepack power

Equalizecells

Key off:Store data

Accurate BMS algorithms are vital: impact battery life, battery size (therefore, battery cost) and perceived “driveability”

Need precise estimates of available power and available energy, which themselves need precise estimates of SOC, R, Q

KF based methods supported by volumes of theory and practical KF-based methods supported by volumes of theory and practical experience they work in practice, not just in theory SPKF directly produces cell state estimates, including SOC Other model-based estimation can produce R, Q estimates

SOC, R, Q estimates used to estimate power/energy Great future potential for optimizing pack size by computing

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Great future potential for optimizing pack size by computing power to minimize degradation

PART 2PART 2

P t R h/Ed ti P j tPresent Research/Education Projects

Ongoing BMS algorithm workg g gKey on:

InitializationSampleVk, Tk, I

EstimateSOC, R, Q

Calculatepack energy

Calculatepack power

Equalizecells

Key off:Store data

Our past focus has been to investigate algorithms to estimate cell SOC, resistance, capacity and so forth: can then compute energy

We have used nonlinear Kalman filters with equivalent-circuit We have used nonlinear Kalman filters with equivalent circuit models of cells – worked very well for that application

Ongoing projects related to BMS algorithms include cell characterization, evaluation and analysis of algorithms for SOC estimation, SOH estimation, balancing, charging, etc

Sponsors/collaborators include LGCPI, AEV, Linear Technology, Sponsors/collaborators include LGCPI, AEV, Linear Technology, Fairchild Semiconductor, and TARDEC/Creare

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Modeling, optimized controlsg, p To estimate power to give maximum performance while

maximizing life as well Must understand how cells age, and What dynamic implications that has on voltage and current

limits over time as the cells agelimits over time as the cells age Our current main research focus is on reduced-order controls-

oriented modeling of ideal-cell and degradation dynamics

Ultimate goal: Be able to extend life through active control

Sponsors include: UM/GM, DOE, TI/NSC, NSFp

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DOE: GATE Center of Excellence, IDEATE, GATE Center of Excellence in Innovative Drivetrains in Electric

Automotive Technology Education (IDEATE) IDEATE is a five-year DOE-sponsored project that:

1. Establishes a graduate certificate in electric drivetrain technology2. Establishes graduate degree options to educate a future workforceg g p3. Supports fundamental research to develop new enabling technology4. Has local appeal but national impact

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IDEATE Course: Battery Modelingy g Multi-scale modeling of the dynamics of lithium-ion cells Brief introduction to microscopic models (mostly out of course scope) Considerable attention given to deriving meso-scale single-phase models

of the solid, electrolyte, thermodynamics and kinetics of the cell. Volume-averaging techniques introduced to create continuum models via g g q

porous-electrode theory. Cutting edge (just published) methods for automatically converting

continuum models to reduced-order models for controls purposes will be p pinvestigated in detail

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IDEATE Course: Battery Controly Focus on developing model-based estimation algorithms for estimating

state-of-charge, state-of-health, and state-of-life of a battery pack. We will emphasize methods based on nonlinear Kalman filtering

Figure shows the information flows: raw voltage, current, and temperature measurements are used by model-based estimators to produce estimates of cell capacity, state of charge, and resistance.

These estimates are then used by nonlinear optimization strategies to compute battery pack available power and energy, which are the two quantities required by a vehicle controller.

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IDEATE Course: Pwr Electronics for EDV Design and control of power converters applied to the unique

requirements and challenges found in electric drive vehicles. Course includes an overview of system architectures and covers system-

level dynamic modeling and control at levels appropriate to determine requirements and validate performance of vehicle power converters

Develops details required for practical design and control of power converters with emphasis on the unique requirements and challenges in the major applications such as motor drives, energy storage and battery hchargers.

Electric Drive Vehicles• System Architecture: EV, HEV, PHEV, …, xEV; Series, parallel, complex• Electric system control and dynamics; Simulink modeling and simulation

Power Convertersfor Motor Drives

• Bi-directional DC-AC• High power density

Power Converters for Battery Chargers• Level I, II, III; charger

requirements

Power Converters for Energy Storage

• Energy storage cells, battery management

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• High power density, high current, high temperature

• Sensing and digital control

requirements• Bi-directional AC-DC,

DC-DC• Control and coordination

with BMS

battery management system electronics

• Bi-directional DC-DC• Sensing and digital

controls

IDEATE Course: Adj. Speed AC Drivesj p Existing course updated to become a direct companion to the Power

electronics for electric drive vehicles course. Focus on motor operation and control: Begins with basic principles for

analysis of electric machines and reference frame theory, and then develops in detail the operation and control algorithms for symmetrical i d ti hi d t t h hiinduction machines and permanent magnet synchronous machines.

Course updated to include an emphasis on electric drive vehicles with specific examples of practical designs and commercial solutions.

Battery Management

System(BMS)

Electronics

Bidi ti l

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BidirectionalDC-DC

converter

AC motor drive

Sensing and controlEnergy

Storage System

Sensing and control

Vehicle communication and control

Overcoming Barriers to IDEATEgDISTANCE: All IDEATE courses will be lecture-captured to enable remote students All lab exercises will also be available remotely, some via internet All existing ECE controls courses will be revisited and lecture captured

COST:COST: “GATE Fellowships” available: A total of $355,000 will be distributed

from DOE grant to students over five years at the two CU campuses Additionally, UCCS Chancellor Dr. Shockley-Zalabak has allocated

$40,000 to GATE Fellowships to be made available to UCCS students

TIMING AND CAPACITY: Fast progress: First cohort of students will enter Fall 2012, first cert.

grads in Spring of 2013, first MSEE-BC option grads in Spring of 2014. Predict a sustained headcount and graduation rate of forty students per Predict a sustained headcount and graduation rate of forty students per

year when steady state achieved25

IDEATE Industry Collaborationy IDEATE is an excellent opportunity to transfer technology to industry,

accelerating vehicle electrification Relationship with sixteen

companies/labs: Equipment manufacturers, Battery cell design/

manufacturers, Semiconductor companies, Service on the Industry

Ad i B d (IAB)p ,

Battery pack design, Vehicle companies, National Renewable

Advisory Board (IAB), giving guest lectures, PhD qualified personnelto sit on student thesis/ National Renewable

Energy Lab (NREL). to sit on student thesis/dissertation committees.

27-50 paid internships for IDEATE students

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p pwho have completed at least one semester of coursework, hiring program 10+ graduates.

10/25/2011 State of Life Estimation for Lithium Ion Battery Cells 27

Admission to MSEE-BC/cert. Primary admission requirements for MSEE-BC are same as for MSEE. Admission to the Battery Controls option via written request at time of

application to MSEE, or subsequent to admission MSEE, but prior to conferral of the degree.

Admission to the Battery Controls option requires, in addition to standardAdmission to the Battery Controls option requires, in addition to standard MSEE requirements, evidence of academic maturity equivalent to completion with a grade of B or better of the following: Mathematics: Calculus III, Linear Algebra, Differential Equations Mathematics: Calculus III, Linear Algebra, Differential Equations Science: Physics III – calculus based, General Chemistry I Engineering: Circuits II, Electronics I, Linear Systems

Provisional admission may be granted to students who do not meet all Provisional admission may be granted to students who do not meet all these requirements.

C ifi i h GPA d i i l Certificate course requirements the same; GPA admit requirement lower

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ECE Courses Offered in Control SystemsyECE 2610: 

Introduction to Signals and 

Systems

ECE 3610: Engineering Probability and Statistics

ECE 4510: Feedback Control Systems

UndergraduateEngineering Curriculum

ECE 4530: Feedback Control 

LaboratorySystems and Statistics

ECE 5510: Feedback Control 

Systems

ECE 5520: Multivariable 

Control 

ECE 5530: Multivariable 

Control S II

Regularly offeredgraduate controls and

Laboratory

MSEE-BCcore

FallSystems

ECE 5540: Digital

Fall (odd)Systems I

Spring (even)Systems IIestimation courses

Oth d tECE 5550: Applied

ECE 5560: S

coreCertificate

Digital Control Systems

Other graduate controls and estimation courses: offered on demand or 

Applied Kalman Filtering

System Identification

ECE 5570: ECE 5590: M d l

ECE 5580: M lti i bl

ECEN 5017: Power 

Electronics 

as independent study

ECE 5500:ECE 5500:

Methods of Optimization

Model Predictive Control

/ECE 5710:  ECE 5720: 

Multivariable Control 

Systems III

for EDV

ECEN 5737: Adjustable Speed AC

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ECE 5500: Nonlinear Control

ECE 5500: Nonlinear Control

Proposed/related courses

Model., Sim., Ident. Battery Dynamics

Control of Battery Dynamics

Speed AC Drives