WO2006057469A1 - Procede et systeme d'estimation de l'etat et des parametres d'une batterie - Google Patents

Procede et systeme d'estimation de l'etat et des parametres d'une batterie Download PDF

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Publication number
WO2006057469A1
WO2006057469A1 PCT/KR2004/003102 KR2004003102W WO2006057469A1 WO 2006057469 A1 WO2006057469 A1 WO 2006057469A1 KR 2004003102 W KR2004003102 W KR 2004003102W WO 2006057469 A1 WO2006057469 A1 WO 2006057469A1
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Prior art keywords
prediction
augmented
internal
component configured
uncertainty
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PCT/KR2004/003102
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English (en)
Inventor
Gregory L. Plett
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Lg Chem, Ltd.
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Application filed by Lg Chem, Ltd. filed Critical Lg Chem, Ltd.
Priority to BRPI0419141A priority Critical patent/BRPI0419141B8/pt
Priority to TW093136725A priority patent/TWI272737B/zh
Priority to PCT/KR2004/003102 priority patent/WO2006057469A1/fr
Priority to CA2588334A priority patent/CA2588334C/fr
Priority to JP2007542869A priority patent/JP5259190B2/ja
Publication of WO2006057469A1 publication Critical patent/WO2006057469A1/fr

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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M6/00—Primary cells; Manufacture thereof
    • H01M6/50—Methods or arrangements for servicing or maintenance, e.g. for maintaining operating temperature
    • H01M6/5083—Testing apparatus
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/4285—Testing apparatus
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01G—CAPACITORS; CAPACITORS, RECTIFIERS, DETECTORS, SWITCHING DEVICES OR LIGHT-SENSITIVE DEVICES, OF THE ELECTROLYTIC TYPE
    • H01G9/00—Electrolytic capacitors, rectifiers, detectors, switching devices, light-sensitive or temperature-sensitive devices; Processes of their manufacture
    • H01G9/26—Structural combinations of electrolytic capacitors, rectifiers, detectors, switching devices, light-sensitive or temperature-sensitive devices with each other
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/05—Accumulators with non-aqueous electrolyte
    • H01M10/052—Li-accumulators
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/05—Accumulators with non-aqueous electrolyte
    • H01M10/056—Accumulators with non-aqueous electrolyte characterised by the materials used as electrolytes, e.g. mixed inorganic/organic electrolytes
    • H01M10/0564—Accumulators with non-aqueous electrolyte characterised by the materials used as electrolytes, e.g. mixed inorganic/organic electrolytes the electrolyte being constituted of organic materials only
    • H01M10/0565—Polymeric materials, e.g. gel-type or solid-type
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present invention relates to methods and apparatus for estimation of battery pack system state and model parameters using digital filtering techniques.
  • joint Kalman filtering and joint extended Kalman filtering are particularly useful for estimation of battery pack system state and model parameters using digital filtering techniques.
  • HEVs Hybrid Electric Vehicles
  • BEVs Battery Electric Vehicles
  • laptop computer batteries laptop computer batteries
  • portable tool battery packs and the like
  • quickly varying parameters e.g., SOC
  • slowly varying parameters e.g., total capacity
  • SOC quickly varying quantity
  • capacity fade may be calculated if present and initial pack total capacities are known, for example, although other methods may also be used.
  • Power- and capacity-fade are often lumped under the description "state-of-health" (SOH) .
  • SOC is a value, typically reported in percent that indicates the fraction of the cell capacity presently available to do work.
  • a number of different approaches to estimating SOC have been employed: a discharge test, ampere- hour counting (Coulomb counting) , measuring the electrolyte, open-circuit voltage measurement, linear and nonlinear circuit modeling, impedance spectroscopy, measurement of internal resistance, coup de fouet, and some forms of Kalman filtering.
  • the discharge test must completely discharge the cell in order to determine SOC. This test interrupts system function while the test is being performed and can be overly time consuming rendering it not useful for many applications.
  • Ampere-hour counting (Coulomb counting) is an "open loop" methodology whose accuracy degrades over time by accumulated measurement error.
  • Measuring the electrolyte is only feasible for vented lead-acid batteries, and therefore has limited applicability.
  • Open-circuit voltage measurement may be performed only after extended periods of cell inactivity, and for cells with negligible hysteresis effect and does not work in a dynamic setting.
  • Linear and nonlinear circuit modeling methods do not yield SOC directly; SOC must be inferred from the calculated values.
  • Impedance spectroscopy requires making measurements not always available in a general application. Measurement of internal resistance is very sensitive to measurement error, and requires measurements not available in general applications. Coup de fouet works for lead-acid batteries only. Forms of Kalman filtering that do not use SOC as a filter state do not directly yield error bounds on the estimate.
  • a filter preferably a Kalman filter is used to estimate SOC by employing a known mathematical model of cell dynamics and measurements of cell voltage, current, and temperature. This method directly estimates state values. However, it does not address parameter values.
  • power fade refers to the phenomenon of increasing cell electrical resistance as the cell ages. This increasing resistance causes the power that can be sourced/sunk by the cell to drop.
  • Capacity fade refers to the phenomenon of decreasing cell total capacity as the cell ages. Both the cell's resistance and capacity are time- varying parameters.
  • the prior art uses the following different approaches to estimate SOH: the discharge test, chemistry-dependent methods, Ohmic tests, and partial discharge.
  • the discharge test completely discharges a fully charged cell in order to determine its total capacity. This test interrupts system function and wastes cell energy.
  • Chemistry-dependent methods include measuring the level of plate corrosion, electrolyte density, and "coup de fouet" for lead-acid batteries.
  • Ohmic tests include resistance, conductance and impedance tests, perhaps combined with fuzzy- logic algorithms and/or neural networks. These methods require invasive measurements. Partial discharge and other methods compare cell-under-test to a good cell or model of a good cell.
  • FIGURE 1 is a block diagram illustrating an exemplary system for state .and parameter estimation in accordance with an exemplary embodiment of the invention
  • FIGURE 2 is a block diagram depicting a method of joint filtering, in accordance with an exemplary embodiment of the invention.
  • a first aspect is a method for estimation of the augmented state of an electrochemical cell, the method comprising: making an internal augmented states prediction of the cell where the augmented state comprises at least one internal state value and at least one internal parameter value; making an uncertainty prediction of the internal augmented states prediction; correcting the internal augmented states prediction and the uncertainty prediction; and applying an algorithm that iterates the making an internal augmented states prediction, the making an uncertainty prediction and the correcting to yield an ongoing estimation to the augmented state and an ongoing uncertainty to the augmented state estimation.
  • Another aspect of an exemplary embodiment is an apparatus configured to estimate present augmented state of a cell pack system comprising: a component configured to make an internal augmented states prediction of a cell; a component configured to make an uncertainty prediction of the internal augmented states prediction; a component configured to correct the internal augmented states prediction and the uncertainty prediction; and a component configured to apply an algorithm that iterates steps taken by the component configured to make an internal augmented states prediction, the component configured to make an uncertainty prediction and the component configured to correct to yield an ongoing estimation to the augmented state and an ongoing uncertainty to the augmented state estimation.
  • a system for estimating present augmented state of an electrochemical cell comprising: a means for making an internal augmented states prediction of the cell where the augmented state comprises at least one internal state value and at least one internal parameter value; a means for making an uncertainty prediction of the internal augmented states prediction; a means for correcting the internal augmented states prediction and the uncertainty prediction; and a means for applying an algorithm that iterates the making an internal augmented states prediction, the making an uncertainty prediction and the correcting to yield an ongoing estimation to the augmented state and an ongoing uncertainty to the augmented state estimation.
  • a storage medium encoded with a machine- readable computer program code including instructions for causing a computer to implement the abovementioned method for estimating present augmented states of an electrochemical cell.
  • computer data signal embodied in a computer readable medium. The computer data signal comprises code configured to cause a computer to implement the abovementioned method for estimating present augmented states of an electrochemical cell.
  • a battery or battery pack may include a plurality of cells, where the exemplary embodiments disclosed herein are applied to one or more cells of the plurality.
  • One or more exemplary embodiments of the present invention estimate cell state and parameter values using joint filtering.
  • One or more exemplary embodiments of the present invention estimate cell state and parameter values using joint Kalman filtering.
  • Some embodiments of the present invention estimate cell state and parameter values using joint extended Kalman filtering.
  • Some embodiments simultaneously estimate SOC, power- and/or capacity-fade, while others estimate additional cell state values and/or additional time-varying parameter values.
  • filtering is employed for description and illustration of the exemplary embodiments, the terminology is intended to include methodologies of recursive prediction and correction commonly denoted as filtering, including but not limited to Kalman filtering and/or extended Kalman filtering.
  • FIG. 1 shows the components of the parameter estimator system 10 according an embodiment of the present invention.
  • Electrochemical cell pack 20 comprising a plurality of cells 22, e.g., battery is connected to a load circuit 30.
  • load circuit 30 could be a motor in an Electric Vehicle (EV) or a Hybrid Electric Vehicle (HEV) .
  • An apparatus for measuring various cell characteristics and properties is provided as 40.
  • the measurement apparatus 40 may include but not be limited to a device for measurement of cell terminal voltage such as a voltage sensor 42, e.g. a voltmeter and the like, while measurements of cell current are made with a current sensing device 44, e.g., an ammeter and the like.
  • measurements of cell temperature are made with a temperature sensor 46, e.g., a thermometer and the like.
  • Additional cell properties such as internal pressure or impedance, may be measured using (for example) pressure sensors and/or impedance sensors 48 and may be employed for selected types of cells 22 of cell pack 20.
  • Various sensors may be employed as needed to evaluate the characteristics and properties of the cell(s) 22.
  • Voltage, current, and optionally temperature and cell-property measurements are processed with an arithmetic circuit 50, e.g., processor or computer, which estimates the parameters of the cell(s) 22.
  • the system may also include a storage medium 52 comprising any computer usable storage medium known to one of ordinary skill in the art.
  • the storage medium is in operable communication with arithmetic circuit 50 employing various means, including, but not limited to a propagated signal 54. It should be appreciated that no instrument is required to take measurements from the internal chemical components of the cell 22 although such instrumentation may be used with this invention. Also note that all measurements may be non-invasive; that is, no signal must be injected into the system that might interfere with the proper operation of load circuit 30.
  • arithmetic circuit 50 may include, but not be limited to, a processor (s) , gate array(s), custom logic, computer(s), memory, storage, register (s), timing, interrupt (s), communication interfaces, and input/output signal interfaces, as well as combinations comprising at least one of the foregoing.
  • Arithmetic circuit 50 may also include inputs and input signal filtering and the like, to enable accurate sampling and conversion or acquisitions of signals from communications interfaces and inputs. Additional features of arithmetic circuit 50 and certain processes therein are thoroughly discussed at a later point herein.
  • One or more embodiments of the invention may be implemented as new or updated firmware and software executed in arithmetic circuit 50 and/or other processing controllers.
  • Software functions include, but are not limited to firmware and may be implemented in hardware, software, or a combination thereof.
  • Arithmetic circuit 50 uses a mathematical model of the cell 22 that includes indicia of a dynamic system state.
  • a discrete-time model is used.
  • An exemplary model in a (possibly nonlinear) discrete-time state-space form has the form:
  • ⁇ # ( * * > « * A )+v * >
  • x k is the system state
  • ⁇ k is the set of time varying model parameters
  • u k is the exogenous input
  • y k is the system output
  • w k and v k are "noise" inputs—all quantities may be scalars or vectors.
  • /(-,•,-) and g(-, v ) are functions defined by the cell model being used. Non-time-varying numeric values required by the model may be embedded within /(-,-,•) and g(- >y ) , and are not included in ⁇ k .
  • the system state includes, at least, a minimum amount of information, together with the present input and a mathematical model of the cell 22, needed to predict the present output.
  • the state might include: SOC, polarization voltage levels with respect to different time constants, and hysteresis levels, for example.
  • the system exogenous input u k includes at minimum the present cell current i k , and may, optionally, include cell temperature
  • the system parameters ⁇ k are the values that change only slowly with time, in such a way that they may not be directly determined with knowledge of the system measured input and output. These might include, but not be limited to: cell capacity(ies) , resistance (s) , polarization voltage time constant (s), polarization voltage blending factor (s), hysteresis blending factor(s) , hysteresis rate constant (s), efficiency factor(s), and so forth.
  • the model output y k corresponds to physically measurable cell guantities or those directly computable from measured quantities—at minimum, the cell voltage under load.
  • An exemplary model has the form:
  • a procedure of joint filtering is applied.
  • a joint Kalman filter 100 may be employed, or a joint extended Kalman filter 100.
  • Table 1 is an exemplary implementation of the methodology and system utilizing joint extended Kalman filtering. The procedure is initialized by setting the augmented state estimate ⁇ 0 to the best guess of the true augmented state by setting the top portion to E[x Q ] and the bottom portion to E[ ⁇ 0 ] . The estimation-error covariance matrix ⁇ is also initialized.
  • Table 1 Joint extended Kalman filter for state and weight update. State-space models:
  • Time update Xk F( ⁇ + -) >%-i)
  • the augmented state estimate ⁇ is propagated forward in time, through the function F .
  • the augmented state vector uncertainty is also updated.
  • the table gives only one example.
  • a measurement of the cell output is made, and compared to the predicted output based on the augmented state estimate, ⁇ ; the difference is used to update the values of ⁇ .
  • the steps outlined in the table may be performed in a variety of orders. While the table lists an exemplary ordering for the purposes of illustration, those skilled in the art will be able to identify many equivalent ordered sets of equations.
  • FIG. 2 an exemplary implementation of an exemplary embodiment of the invention is depicted.
  • a single filter 100 jointly updates the state and parameter estimates.
  • the filter has a time update or prediction 101 aspect and a measurement update or correction 102 aspect.
  • Time update/prediction block 101 receives as input the previous exogenous input « A _, (which might include cell current and/or temperature, for example) along with the previously estimated augmented state value ⁇ _ ⁇ and augmented state uncertainty estimate ⁇ ,4 _i •
  • the time update/prediction block 101 provides predicted augmented state ⁇ j t and predicted augmented state uncertainty ⁇ ⁇ output to augmented state measurement update/correction block 102.
  • State measurement update/correction block 102 also receives the predicted augmented state ⁇ k ⁇ and predicted augmented state uncertainty ⁇ iit , as well as the exogenous input u k , and the system output y k , while providing current system augmented state estimate and augmented state uncertainty estimate ⁇ jt .
  • a minus notation denotes that the vector is the result of the prediction components 101 of the filter 100
  • the plus notation denotes that the vector is the result of the correction component 102 of the filter 100.
  • An exemplary embodiment uses a cell model that includes effects due to one or more of the open-circuit-voltage (OCV) for the cell 22, internal resistance, voltage polarization time constants, and a hysteresis level.
  • parameter values including, but not limited to: an efficiency factor(s) such as Coulombic efficiency, denoted ⁇ iJc ; cell capacity (ies) , denoted C k ; polarization voltage time constant (s), denoted a i ,k '--- a n k ' polarization voltage blending factor (s), denoted
  • SOC is captured by one state of the model. This equation to address SOC is:
  • ⁇ i **-07 ⁇ /C:*)'* (3)
  • ⁇ .t represents the inter-sample period (in seconds)
  • C k represents the cell capacity (in ampere-seconds)
  • z k is the cell SOC 22 at time index k
  • i k is the cell current 22
  • ⁇ ih is the Coulombic efficiency of a cell 22 at current level h •
  • the matrix A j ⁇ Ia may be a diagonal matrix with real-
  • the vector B ⁇ & t ?H f may simply be set to ti j - "l"s.
  • the entries of B f are not critical as long as they are non-zero.
  • the value of n f entries in the A f matrix are chosen as part of the system identification procedure to best fit the model parameters to measured cell data.
  • the A j - , and B f matrices may vary with time and other factors pertinent to the present battery pack operating condition.
  • the hysteresis level is captured by a single state
  • ⁇ k is the hysteresis rate constant, again found by system identification .
  • the overall model state is
  • the state equation for the model is formed by combining all of the individual equations identified above.
  • the output equation that combines the state values to predict cell voltage is
  • G k may be constrained such that the dc-gain from ⁇ fc to G k f k is zero.
  • the parameters are Q, a,ya k , g lk ---g, u , ⁇ k , R k , M k f ⁇
  • the augmented state vector ⁇ k is formed by joining the state vector (or combined state vector e.g. Equation (6)) and the parameter vector e.g., Equation (7) into one vector. For example,
  • ⁇ k comprise all the details required to compute the equations for /(•,-,•) (e.g., Equations (3) -(5)) and g(-,y) (e.g., Equation 7) .
  • the joint filter 100 will adapt a state estimate and a parameter estimate so that a model input-output relationship matches the measured input-output data as closely as possible. This does not guarantee that the model augmented state converges to physical augmented state values.
  • the cell model used for joint filtering may be further supplemented by appending the cell model with a secondary cell model that includes as outputs those augmented states that must converge to their correct values.
  • An exemplary embodiment takes extra steps to ensure that one model augmented state converges to SOC:
  • the supplemented model output is compared to a measured output in the joint filter 100.
  • a measured value for SOC may be approximated using z k derived as y k « OCV ⁇ z k ) -R k i k
  • this example computes a noisy estimate of SOC, z k .
  • One or more embodiments use a Kalman filter 100. Some embodiments use an extended Kalman filter 100. Further, some embodiments include a mechanism to force convergence of state-of-charge. The present invention is applicable to a broad range of applications, and cell electrochemistries .
  • the disclosed method may be embodied in the form of computer-implemented processes and apparatuses for practicing those processes.
  • the method can also be embodied in the form of computer program code containing instructions embodied in tangible media 52, such as floppy diskettes, CD-ROMs, hard drives, or ' any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus capable of executing the method.
  • the present method can also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or as data signal 54 transmitted whether a modulated carrier wave or not, over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus capable of executing the method.
  • the computer program code segments configure the microprocessor to create specific logic circuits.

Abstract

L'invention concerne un procédé et un appareil d'estimation de l'état de recharge d'une cellule électrochimique. Ledit procédé consiste à prévoir l'état de recharge interne de la cellule, l'état de recharge comprenant au moins une valeur d'état interne et au moins une valeur de paramètre interne, à effectuer une prévision d'incertitude de la prévison d'état de recharge interne, à corriger la prévision de l'état de recharge interne et la prévision d'incertitude, et à appliquer un algorithme d'itération de la prévision d'état de recharge interne, de la prévision d'incertitude, et de la correction afin d'obtenir une estimation continue de l'état de recharge et une incertitude continue de l'estimation de l'état de recharge.
PCT/KR2004/003102 2004-11-23 2004-11-29 Procede et systeme d'estimation de l'etat et des parametres d'une batterie WO2006057469A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
BRPI0419141A BRPI0419141B8 (pt) 2004-11-29 2004-11-29 Método para estimar um presente estado aumentado de um sistema de células eletroquímicas usando filtragem conjunta, aparato configurado para estimar o presente estado aumentado de um pacote de células usando filtragem conjunta e sistema para estimar o presente estado aumentado de uma célula eletroquímica usando filtragem conjunta
TW093136725A TWI272737B (en) 2004-11-23 2004-11-29 Method and system for joint battery state and parameter estimation
PCT/KR2004/003102 WO2006057469A1 (fr) 2004-11-29 2004-11-29 Procede et systeme d'estimation de l'etat et des parametres d'une batterie
CA2588334A CA2588334C (fr) 2004-11-29 2004-11-29 Procede et systeme d'estimation de l'etat et des parametres d'une batterie
JP2007542869A JP5259190B2 (ja) 2004-11-29 2004-11-29 ジョイントバッテリー状態とパラメーター推定システム及び方法

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PCT/KR2004/003102 WO2006057469A1 (fr) 2004-11-29 2004-11-29 Procede et systeme d'estimation de l'etat et des parametres d'une batterie

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CA2588334A1 (fr) 2006-06-01
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CA2588334C (fr) 2011-09-06

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