Battery Research

OPTIMIZED BATTERY STATE, CONTROL & MONITORING Steven Wilkins, Bogdan Rosca, Jubin Jacobs, Erik Hoedemakers

ABATTRELIFE: BATTERY THERMAL & AGEING MODELS TNO Project Objectives: • Battery thermal modelling and specifically ageing prediction • Ageing mitigation methodologies and trade-off models and analysis, design and monitoring tools • Online battery state estimators for energy management • Transition to 2nd Life Applications

TNO Multiphysics Model (Electrothermal model) 2

Battery Research and Development

Real-world test validation (based on BMW Mini-E)

TNO New Vehicle Concepts (TRANSFORMERS) Electric Trailer Development

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TNO Role: Assessment on energy management and safety


TNO Inductive Charging (FABRIC)

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TNO Inductive Charging (FABRIC) for Heavy Duty TNO Focus: Feasibility assessment through multi-domain modelling

TNO also active in advising other projects

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AMBER: NEXT GENERATION LIGHT ELECTRIC VEHICLE Objectives: • 40-80 Wh/km energy consumption in real urban driving corresponding to the given weight bracket • At least 150 km pure electric range in real urban driving Compelling acceleration (0 to 100 km/h in 10 s) • EURONCAP-level crash tests, with highly compatible design • TNO torque vectoring, brake blending, state-monitored BMS, crash testing Previous EOS platform

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TNO Vehicle State Estimator

Active State-Based BMS

(realtime model)

(predictive load balancing)


OPTIMISED BATTERY USE WITHIN ELECTRIC VEHICLES Optimal sizing, configuration, chemistry Accurate state estimation (SoC/SoH) and use of range Active balancing systems and balancing algorithms Ageing monitoring/prediction and mitigation techniques Thermal modelling and management Smart-charging strategies Optimised charging and charging technologies OPTIMAL DESIGN

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| Trends Transities TNO

OPTIMAL OPERATION

OPTIMAL CONTROL

WHY DO WE NEED TO ACCURATELY ESTIMATE BATTERY STATES? Battery States : State of Charge (SoC) – definition State of Health (SoH) – definition State of Function (SoF) – definition Thermal State – definition SoF: charge acceptance for regenerative braking and power/energy capability SoH: description of the degradation process of the battery SoC : Pack/vehicle level – range prediction, energy management, etc. Dynamic SoF determination Load balancing algorithms (typically at cell level) 8


STATE ESTIMATION APPLICATION TO CELL BALANCING Cells can differ in capacity and performance Cell capacity can change over time or in re-use applications Ageing can be non-homogeneous Without balancing, the worst performing cell dominates pack performance Especially a problem at low SOC where voltage differences exaggerate Also a problem at high SOC for same reasons

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ARE ALL NEW CELLS THE SAME? Even new cells are different within acceptable batch quality Depending on situation, variation can increase over time or impact others Fast or thorough screening methods available Optimisation on screened cells dependent on series/parallel configuration

BALANCING METHOD AND MANAGEMENT Passive balancing typically used only for charging Charge is normalised through a shunt resistor Wasteful approach – typically slow because of power dissipation Mostly used during final stages of charging Active balancing – charge reallocation via DC-DC converters Unidirectional or bidirectional Cell-to-Pack, Pack-to-Cell, or Cell-to-Cell Several choices of topology, balancing algorithm… Passive balancing topology 10


CHALLENGES FOR ACTIVE BALANCING IMPACT ON OTHER VEHICLE FUNCTIONS The challenge is that SOC cannot be directly measured

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Voltage methods do not fit well with relatively flat profile Coulomb counting methods susceptible to noise, drift, leakage Requires online identification / state-estimation or similar methods Model-based and empirical approaches Active research area – both for cell and pack

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HOW GOOD DOES THE SOC ESTIMATION NEED TO BE FOR CELL BALANCING APPLICATIONS? Case study decision factor for non-dissipative balancing in string of N series connected

battery modules/cells; goal – maximize capacity usage within pack.

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10

50

inaccuracy [%]

CAPACITY UTILIZATION SENSITIVITY TO SOC ESTIMATION ACCURACY Goal: determine sensitivity of capacity usage as a percentage of the total installed value on SoC estimation accuracy Assumptions: • Parallel series battery pack configuration • 10 cells in parallel form a module • 4 modules in series for a battery pack • Self-balancing behaviour of parallel cell • Deviation included in module total capacity and internal resistance of cells • Capacity of modules (% of nominal value) – [-10% 5%] spread • Active balancing based on SoC at module level • Bias included in real SoC to mimic SoC estimation inaccuracies • SoC estimation offset for each cell – random values from a normal distribution – within predefined limits • 100 tests for each predefined limit – e.g. for an 1% estimation offset, 100 simulations are performed, in each of them the SoC estimation offset for each cell being a random value between 0 and 1% • Vehicle specific test profile – discharging a full battery until empty

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CAPACITY UTILIZATION SENSITIVITY TO SOC ESTIMATION ACCURACY 110

Available capacity usage [%]

100

90

80 Monte Carlo analysis individual results Monte Carlo analysis mean results Monte Carlo analysis median results usable capacity with passive balancing maximum usable capacity

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50 14

0


5

10 SoC estimation error [%]

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ADAPTIVE BATTERY MODEL State of Charge (SoC) • the fraction of the total capacity available in the battery at a certain time: • indication of the battery state: ‘fuel gauge’ of an EV

SoC =

Cavailable [ −] Cmax

Phenomenological battery model • different parameters for charging and discharging • SoC dependent internal resistance - Ro(SoC) • SoC dependent ideal voltage source - Voc(SoC) ALL model parameters identified using regression methods (online and offline)

f i f i

h t i w

f i f i

h t i w

 Ri+ , I bat ≥ 0 Ri =  − , R , I < 0 bat  i

Ci+ , I bat ≥ 0 Ci =  − , C I , < 0 bat  i 15


i = 0:2

i = 1: 2

BATTERY MODEL IDENTIFICATION    ∆t       SoCk −1   Cn  0 0  SoCk   1      ∆t   ∆t 0  V1,k  =  0 1 −  V1,k −1  +   I bat ,k −1 R C 1 1 V     C1   V  2, k   ∆t  2,k −1   ∆t  0 0 1 −  C  R2C2    2 Vbat ,k = Voc ( SoCk ) + V1,k + V2,k + R0 ( SoCk ) I bat ,k : l e d o m y r e t t a b c i m a n y D

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n

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C : y t i c a p a c y r e t t a b l a n i m o n

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t V 2Δ , : V 1e , m i tC t a bo tV S g a: : n Ib i t s l : u t e p p t u t m a a p u t n o s s i

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OBSERVER FOR SOC ESTIMATION EXTENDED KALMAN FILTER Model update Model update Observer correction

SoˆCk = SoˆCk −1 +

∆t I k −1 + ∆SoCk Cn

Increased robustness : adaptive gain

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Typical results:

SoC [-]

SoCEKF 0.5

0 0 17


SoCreal

5000

10000 time [s]

15000

BATTERY CAPACITY ESTIMATION Battery ageing decrease

capacity

1

V

C 1→ 2 =

t2

∫I

bat

( t ) dt

t1

oc1

Voc method

2

V

oc2

Voc (SoC) can be assumed to remain unchanged as the battery ages

SoC

Observer based method

SoC

1

2

Caged < Cn

ˆ k = SoC ˆ k −1 + SoC

Model update 18


∆t I bat ,k −1 + ∆SoCk Cn

∆SoCk ≅ f (Caged , Cn )

Observer correction

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BATTERY THERMAL BEHAVIOUR Model validation - voltage prediction ambient temperature: 0°C 30

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voltage [V]

Voc [V]

26 24 22 20 1 0.5 0

SoC [-]

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-20

CHARGING

0.08

15

0.1 R0 [Ω]

R0 [Ω]

0.06 0.04

0 -20

0.05 1

1 0

0.5 20

40

temperature [°C]

60 0

SoC [-]


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temperature [oC]

DISCHARGING

0.02

measurement model including thermal effects model without thermal effects

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40

20

25

0 -20

0.5 0

20

40

temperature [°C]

60 0

SoC [-]

0

2000

4000 6000 time [s]

8000

10000

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CAPTURING BATTERY THERMAL BEHAVIOUR Model validation - voltage prediction ambient temperature: 0°C

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Experimental validation

voltage error [%]

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voltage [V]

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Model validation - voltage prediction error ambient temperature: 0°C model including thermal effects model without thermal effects

0

0.2

0.4

0.6 SoC [-]


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1

measurement model including thermal effects model without thermal effects

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-10 0

25

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2000

4000 6000 time [s]

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10000

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MOVING FROM LOW LEVEL TO HIGH LEVEL FUNCTION SoC estimation 1

Battery Model

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^ Ubat

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ε

0.6 0.4 0.2 0

^ SOC

Gain Factor Battery SoC Estimator

SoC [-]

Battery

estimated measured

Ubat

2000

4000

6000

8000 10000 12000 14000 16000 18000 time [s]

30 SoC estimation error [%]

Ibat

SoC estimation error upper error bound for E-range prediction application lower error bound for E-range prediction application

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Initial SoC estimation 0.4

0.5

0.6

0.7

0.8

0.9

1

SoC[-]

Requirement from range application


APPLICATION TO MULTI-LEVEL SOC DETERMINATION FOR SOF FOR PACK Module 1: ∆C bat=-4%; ∆Rint ~ 23%; SoCinit = 94% Module 2: ∆C bat=-5%; ∆Rint ~ 20%; SoCinit = 94% Module 3: ∆C bat=4%; ∆Rint ~ 25%; SoCinit = 86%

If all cells are similar and SoC consistent throughout the pack, battery level SoC estimation is reduced to cell level SoC estimation.

Module 4: ∆C bat=1%; ∆Rint ~ 27%; SoCinit = 94% Module 5: ∆C bat=2%; ∆Rint ~ 30%; SoCinit = 88% Module 6: ∆C bat=-8%; ∆Rint ~ 23%; SoCinit = 91%

Differences between the battery modules are most challenging for SoC estimation.

Assessment results (right): • Both highest (relevant for maximum regen power) and lowest SoC (needed for maximum motoring power) are correctly estimated. Computational Design • Optimised time-efficient algorithm 22


0.9 0.85 0.8 SoC [-]

Assumptions: • Different module capacities at throughout the series string – manufacturing tolerance • Different module internal resistances throughout the series string– manufacturing tolerance • Various initial SoC values throughout the string – unbalanced battery pack

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0.75 0.7 Pack Estimate Lower Limit Pack Estimate Upper Limit Module 1 Module 2 Module 3 Module 4 Module 5 Module 6

0.65 0.6 0.55 0.5 0

500

1000

1500

2000 2500 time [s]

3000

3500

4000

4500

MULTI-LEVEL ESTIMATION FOR SOF – TRACTION AND REGEN CONTROL Real-time control is required on a computationally limited platform. Robustness and safety required. Motivation: • Module capabilities need to take into account dynamic variations for optimised energy use • Thermal state also part of the assessment • Overall motor and driveline control optimisation Dynamic SOF envelope (right): • Accommodates small variations in multi-level states • Function is input to motor controller Vehicle Design • Functionally robust; coupled within integrated system • Total system includes link to TNO safety state estimation and driveline control (ESC, brake blending for optimal regen)

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COMMENTS AND CONCLUSIONS • Battery research comprises many aspects which can require optimisation • New chemistries and technologies support and aid developments in this area • Practical tools for requirements, design, implementation are critical • Battery functions and control influences higher level functions and energy management • TNO works alongside industry and academia to provide new solutions; both for large and small companies – both within NL and international

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