Advances in HEV Battery Management...

CONVERGENCECONVERGENCE 20062006

Advances in HEV BatteryManagement Systems

Martin KleinCompact Power, Inc. (a subsidiary of LGChem)

Gregory L. PlettUniversity of Colorado at Colorado Springs

2CONVERGENCECONVERGENCE 20062006 Paper 2006-21-0060

Outline

Importance of Battery Management Systems in HEVsψ HEV Battery Pack Overviewψ BMS Functionsψ State-of-Charge Estimation

BMS and State-of-Charge Estimationψ Battery Pack Dynamics and Cell State Estimationψ Sigma-Point Kalman Filter Applicationψ Results of Testing

Conclusion


HEV Battery Pack Overview

The Battery Pack is theheaviest and costliestcomponent of an HEVPropulsion System

PackHousing

BMS

JunctionModule

Cooling System

High VoltageDC/DC Converter

HCU(HEV Control Unit)

Vehicle (Mounting)

Physical

Material

Data*

Energy*•Data and Energy includesPhysical Input and output ofcable and connector

Cell

Cell

Cell

Cell

Cell

Cell

Cell

Environment

. . .


Battery Management System: Functions

Manages delivery/acceptance of energy from/to cells Manages the cooling system Hi-voltage relay control; pre-charge circuit control Data conditioning Diagnostics Battery-to-host vehicle communications Cell-state monitoring (V, I, T) State-of-Charge and State-of-Health estimating


Importance of SOC, SOH Estimationaccura

te

Optimal & smooth blending of battery power with ICEngine

Maximum battery life depends on SOC, Temperaturecontrol, and SOH

Optimized battery size (# of cells)ψ Cell quantity directly translates to $, weightψ Also affects volume, reliability

HEVs typically operate in SOC range of 20% - 80%:what happens if SOC is not accurate?


If SOC is too optimistic

SOC reports available charge > reality:ψ Propulsion system may demand power > available

power/energy of the cells8 Pack discharges deeper than expected8 Leaves battery with insufficient energy to start the vehicle

ψ BMS may detect over-current condition and abruptly reducepower (perception of poor drivability)

ψ Potential cell damage if no over-discharge protection available

Designers may simply add cells for “headroom” to compensate


If SOC is too pessimistic

SOC reports available charge < reality:ψ Propulsion system may unnecessarily limit power demand

8 Excessive reliance on ICE -> lower fuel economy8 Customer dissatisfaction

ψ During deceleration/regen events, BMS may allow cells to“accept” energy in excess of true available capacity8 Potential cell damage

ψ BMS may detect over-current condition and abruptly reducepower (perception of poor drivability)

ψ Potential cell damage if no over-discharge protection available

Designers may simply add cells for “headroom” to compensate


Importance of SOC, SOH Estimationaccura

te

One final thought:ψ Good SOC estimation is important for Pure EV,

PHEV, and HEV,BUTψ Most critical for HEVs, which do not have a plug-in

feature that can “reset” SOC frequently


Battery Pack Dynamics andCell State Estimation

The BMS must estimate quantities thatψ Describe the present battery pack condition butψ May not be directly measured

“States” are quantities that change quickly (e.g., SOC, cell & packvoltages, currents, temp)

“Parameters” are quantities that change slowly (e.g., cell capacities,aging effects)

HEV batteries are subject to very dynamic power cycling hence rarely inelectro-chemical equilibrium

Noise Factors: Hysteresis, Polarization, Time Constants


SOC Estimation Methods: Survey Voltage vs. Voltage/Power Curve

ψ Measure V, then report SOC perpoint on curve

ψ Issues:8 misses effects of IxR losses,

hysteresis;8 Wide flat areas of curve

difficult to estimate

“Tino” method:ψ SOC ≈ [V – I x R]/OCVψ Better, but discounts effects of high R at low T or at very low SOC

Cuolomb Countingψ Keeps track of energy in, out of cellsψ OK for short periods of operation when initial conditions are known or can be

frequently “reset”ψ Subject to drift due to current sensors fluctuations, other losses


Kalman Filtering

Kalman Filter: Excellent estimator for linear systems

Extended Kalman Filter (EKF) – common approach for state estimation ofnonlinear systemsψ Linearizes equations at sample points using Taylor Series expansionsψ Must compute derivatives

Sigma-Point Kalman Filter (SPKF): improves on EKF for superiorstate estimation of non-linear systems.ψ Better approximation of covariancesψ Use current for short term SOC dynamicsψ Use voltage measurement for longer-term SOC dynamicsψ Kalman Filter optimally combines these to give best SOC estimates


SPKF: State-Space Model

Where: Equation (1) is the State Equation Equation (2) is the Output Equation f( ) and g( ) are functions specified by the cell model

And: xk is the state vector at time index k uk is the system input vector, typically containing

ψ ik: instantaneous cell currentψ Tk: cell Temperatureψ Ck: nominal Cell Capacity and/or Rk: internal cell resistance estimate

wk and vk are Gaussian random process (model sensor noise)

!

xk+1 = f(xk,uk ,wk)

yk = g(xk ,uk ,vk) (2)

(1)

State-Space model of the cell’s dynamics:


SPKF: Cell Model

Per the previous slide we learned f( ) and g( ) are functions specified by the cell model xk is the state vector and uk is the system input vector

Therefore a cell model must be defined!

We have developed an “Enhanced Self-Correcting” (ESC) Cell Modelthat includes in the state vector:

Voltage, Current, Temperature Polarization, Hysteresis SOC, Ohmic Losses

!

xk+1 = f(xk ,uk ,wk)

yk = g(xk,uk ,vk) (2)

(1)


SPKF-Based SOC Estimation Test Results

“Proof of Concept” testing was performed using actual packψ 40 high-power Li-ion battery cells (3.7V/cell; 4.7Ah)ψ Cell were characterized prior to test (parameters)

Cells were exercised using Aerovironment ABC 150 high-powercyclerψ 1C Constant-current chargingψ Dynamic drive cycleψ Rest periods

Raw input and output data was collected throughout the test, thenpost-processed per several estimation methodsψ Two Coulomb counting methodsψ SPKF-Based (BMS)ψ C-code PC-basedψ Tino


Results:ψ Tino performed poorly during transients, OK at steady-stateψ Coulomb-counting showed divergenceψ SPKF method did very well during transients; consistently convergedψ At room temp, SPKF’s RMS estimation error for SOC approx. 2%.

SPKF-Based SOC Estimation Test Results


Additional Work

Initial Proof-of-Concept testing very favorable, but limited in rangeof testing

Further algorithm testing pushing the bounds of operation (V, I,T, charge, discharge rates, deliberately poor SOC initialization)also showed excellent convergence1

CPI is establishing hardware-in-the-loop validation models andsystems to evaluate actual BMS, cells and the SOC algorithm inreal time

1 Paper to be published this year.


Conclusion

Accurate real-time battery management is critical inψ Optimizing battery size, weight, cost, and reliabilityψ Providing acceptable, transparent vehicle performance

State-of-Charge estimation is the most critical BMS function

Many SOC algorithms have been considered, but few can handlethe wide dynamic range of a non-plug-in HEV

An SOC algorithm based on Sigma-Point Kalman Filtering hasbeen developed which demonstrates superior estimation underdynamic conditions

Further evaluation is in progress to further evaluate this algorithmusing HIL techniques

Advances in HEV Battery Management...

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