WO2016067586A1 - バッテリのパラメータ推定装置 - Google Patents

バッテリのパラメータ推定装置 Download PDF

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WO2016067586A1
WO2016067586A1 PCT/JP2015/005364 JP2015005364W WO2016067586A1 WO 2016067586 A1 WO2016067586 A1 WO 2016067586A1 JP 2015005364 W JP2015005364 W JP 2015005364W WO 2016067586 A1 WO2016067586 A1 WO 2016067586A1
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Prior art keywords
battery
hysteresis
model
estimation device
resistance
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PCT/JP2015/005364
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English (en)
French (fr)
Japanese (ja)
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厚志 馬場
修一 足立
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カルソニックカンセイ株式会社
学校法人慶應義塾
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Publication of WO2016067586A1 publication Critical patent/WO2016067586A1/ja

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • 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
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • 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 a battery parameter estimation device capable of sequentially estimating parameters of a battery equivalent circuit model using a Kalman filter.
  • This conventional battery parameter estimation device detects the charge / discharge current and terminal voltage of the battery, and uses them as inputs, the parameter and internal state quantity of the battery with a Kalman filter using an equivalent circuit model of the battery including resistance and capacity , Estimate (calculate) the open circuit voltage value.
  • the SOC-OCV characteristics of the battery can be represented in the above-described battery equivalent circuit model.
  • a hysteresis phenomenon may occur in which SOC-OCV characteristics differ between after charging and after discharging. In this case, the SOC-OCV characteristics of the battery can not be accurately represented.
  • the hysteresis phenomenon is caused by the material of the electrode, and particularly when lithium phosphate is used, the influence of the hysteresis phenomenon is significant.
  • a model is proposed in which a hysteresis element representing a voltage drop due to hysteresis is added to the equivalent circuit of the battery in order to handle the hysteresis phenomenon of the battery.
  • a battery internal state / parameter estimation device described in Patent Document 2 and Non-Patent Document 1-2 is known.
  • the hysteresis phenomenon of the battery means that, in the fluctuation of the state accompanying charging and discharging of the battery, the equilibrium state of the battery fluctuates due to the fluctuation history.
  • a battery without hysteresis returns to the original equilibrium state by leaving it for a certain period of time regardless of the battery's charge / discharge history, but with a battery with hysteresis, it remains the original regardless of the battery's charge / discharge history. It may not return to equilibrium.
  • An object of the present invention made in view of such circumstances is to provide a battery parameter estimation device capable of handling hysteresis without increasing the hysteresis element in the equivalent circuit of the battery.
  • a parameter estimation device of a battery concerning the 1st viewpoint is:
  • a battery parameter estimation device for sequentially estimating a parameter including the resistance or the capacitance in an equivalent circuit model of the battery based on at least one of a battery voltage and a battery current.
  • the equivalent circuit model of the battery is formed by the maximum range of voltage drop and the resistance related by the characteristics of input current. I assume.
  • the parameter estimating device of the battery concerning the 2nd viewpoint is: A battery parameter estimation device for a battery, wherein the equivalent circuit model of the battery is formed using the maximum range of the voltage drop in the hysteresis model and the capacitance related by the characteristics of the speed of voltage drop in the hysteresis model.
  • the parameter estimating device of the battery concerning the 3rd viewpoint is: Assuming that the maximum range of the voltage drop is M (t) and the input current is u (t), the resistance R h (t) is It is characterized by.
  • a parameter estimation device of a battery concerning the 4th viewpoint is: Assuming that the maximum range of the voltage drop is M (t) and the speed of the voltage drop is ⁇ (t), the capacitance C h (t) is It is characterized by.
  • the parameter estimating device of the battery concerning the 5th viewpoint is:
  • the resistance R h (t) is expressed by It is characterized by.
  • the parameter estimating device of the battery concerning a 6th viewpoint is:
  • the resistance R h (t) is expressed by It is characterized by.
  • the battery parameter estimation device pertaining to the first aspect, it is possible to handle hysteresis without increasing the hysteresis element in the equivalent circuit of the battery.
  • hysteresis can be handled without increasing the hysteresis element in the equivalent circuit of the battery.
  • hysteresis can be handled without increasing the hysteresis element in the equivalent circuit of the battery.
  • hysteresis can be handled without increasing the hysteresis element in the equivalent circuit of the battery.
  • the battery parameter estimating device since the hysteresis can be accurately handled, it is possible to obtain an accurate estimated value more quickly.
  • the model configuration is simple and the hysteresis phenomenon can be easily handled.
  • FIG. 7 is a diagram showing an n-order Foster RC ladder circuit which approximates a Warburg impedance. It is a figure which shows the n-order Cawell type RC ladder circuit which approximated the Warburg impedance. It is a graph which shows the measurement result of the SOC-OCV characteristic of lithium iron phosphate. It is an enlarged view of the broken-line enclosure part of FIG. 5A.
  • the battery parameter estimation device of the first embodiment is used for a vehicle such as an electric vehicle or a hybrid electric vehicle.
  • a vehicle is equipped with an electric motor for driving the vehicle, a battery, a controller thereof, etc., supply (discharge) of electric power to the electric motor, regeneration of braking energy from the electric motor at the time of braking, ground charging equipment Power recovery (charging) from the battery to
  • charge and discharge current flows into and out of the battery
  • the internal state of the battery changes, and the internal state is monitored while being estimated by the parameter estimation device of the battery, so that the remaining amount of the battery, etc. It has collected the necessary information.
  • the parameter estimation device of the battery 1 includes a voltage sensor (terminal voltage detection unit) 2, a current sensor (charge / discharge current detection unit) 3, an estimation unit 4, and a charge amount calculation unit 5.
  • the charging rate calculating unit 6 and the soundness calculating unit 7 are provided.
  • the estimation unit 4, the charge amount calculation unit 5, the charging rate calculation unit 6, and the soundness calculation unit 7 are configured by, for example, a vehicle-mounted microcomputer.
  • the battery 1 is, for example, a rechargeable battery (secondary battery). Although battery 1 is described as being a lithium ion battery in the present embodiment, other types of batteries may be used.
  • the terminal voltage detection unit 2 is, for example, a voltage sensor, and detects a terminal voltage value v of the battery 1.
  • the terminal voltage detection unit 2 inputs the detected terminal voltage value v to the estimation unit 4.
  • the charge / discharge current detection unit 3 is, for example, a current sensor, and detects a charge / discharge current value i of the battery 1.
  • the charge / discharge current detection unit 3 inputs the detected charge / discharge current value i to the estimation unit 4.
  • the estimation unit 4 includes a battery equivalent circuit model 41 of the battery 1 and a Kalman filter 42.
  • the estimation unit 4 can estimate (calculate) the parameter value of the battery equivalent circuit model 41, the open circuit voltage OCV (Open Circuit Voltage) of the battery 1, and the internal state amount of the battery 1 using the Kalman filter 42. .
  • estimation unit 4 simultaneously estimates and estimates the parameter value and the internal state quantity based on terminal voltage v from terminal voltage detection unit 2 and charge / discharge current i from charge / discharge current detection unit 3.
  • the open circuit voltage OCV is calculated based on the determined parameter value. Details of the estimation / calculation processing performed by the estimation unit 4 will be described later. Further, the estimation unit 4 inputs the calculated open circuit voltage OCV to the charging rate calculation unit 6 and the soundness calculation unit 7.
  • the Foster type RC ladder circuit represented by an approximation of the sum of infinite series, in which a parallel circuit of a resistor and a capacitor is connected, and the resistor connected in series are grounded by a capacitor It consists of a Kawell RC ladder circuit or the like represented by approximation by continuous fraction expansion.
  • the resistor and the capacitor are parameters of the battery equivalent circuit model 41.
  • the Kalman filter 42 designs a model of the target system (in the case of this embodiment, the battery equivalent circuit model 41), inputs the same input signal to this model and the real system, and compares the outputs of that case. If there is an error in them, this error is multiplied by the Kalman gain and fed back to the model to correct the model so as to minimize both errors. By repeating this, the parameters of the model are estimated.
  • the charge amount calculation unit 5 receives the charge / discharge current value i of the battery 1 detected by the charge / discharge current detection unit 3 and sequentially integrates this value to obtain the charge amount in / out of the battery 1.
  • the charge amount calculation unit 5 calculates the charge amount Q that the battery 1 currently has by subtracting the charge amount that has come in and out from the remaining charge amount stored before the successive integration operation.
  • the charge amount Q is output to the soundness level calculation unit 7.
  • the charging rate calculation unit 6 uses relationship data obtained by obtaining these relationships in advance by experiment etc. I remember. Then, based on this characteristic table, the state of charge SOC (State of Charge) at that time is estimated from the open circuit voltage estimation value estimated by the estimation unit 4. The charging rate SOC is used for battery management of the battery 1.
  • the health level calculation unit 7 has a characteristic table that represents the relationship between the charge amount Q and the open circuit voltage OCV for each of the health levels SOH (State of Health) divided into predetermined widths.
  • the details of the characteristic table are disclosed, for example, in Japanese Patent Application Laid-Open No. 2012-57956 filed by the present applicant.
  • the openness voltage OCV estimated by the estimation unit 4 and the charge amount Q calculated by the charge amount calculation unit 5 are input to the soundness calculation unit 7, and these fall within any soundness SOH range of the above characteristic table. Is calculated, and the applicable soundness level SOH is output.
  • the electrode reaction of a battery includes a charge transfer process at the interface between the electrolyte and the active material, and a diffusion process of ions in the electrolyte or the active material.
  • a non-faradaic process battery such as a lithium ion battery, ie, a battery in which the diffusion phenomenon is dominant
  • the influence of the Warburg impedance which is an impedance resulting from the diffusion process becomes dominant.
  • the open circuit voltage OCV is a non-linear function of the charging rate SOC as shown in FIG.
  • the charging rate SOC is expressed by equation (1) using a charge / discharge current value i and a full charge capacity FCC (Full Charge Capacity).
  • the transfer function of the Warburg impedance Z w is expressed by equation (2).
  • s is the Laplace operator
  • the diffusion resistance R d is the low frequency limit ( ⁇ ⁇ 0) of Z w (s).
  • the diffusion time constant ⁇ d means the speed of the diffusion reaction.
  • the diffusion capacitance C d is defined by equation (3) using the diffusion resistance R d and the diffusion time constant ⁇ d .
  • Equation (2) it is difficult to convert the Warburg impedance Z w into the time domain as it is because there is a square root of the Laplace operator s. For this reason, an approximation of the Warburg impedance Z w is considered.
  • the Warburg impedance Z w can be approximated by, for example, a sum of infinite series, or an approximation by continued fraction expansion.
  • the Warburg impedance Z w can be expressed as a sum of infinite series, as shown in equation (4). However, It is. If the above-mentioned approximate expression is expressed in a circuit diagram, it is an n-order Foster type circuit in which n parallel circuits of a resistor and a capacitor are connected in series (see FIG. 4A). As apparent from the equations (5) and (6), according to the n-order Foster-type equivalent circuit model approximating the Warburg impedance Z w , the diffusion capacitance C d and the diffusion resistance R d are used to obtain an equivalent circuit. Other parameters (resistor R n , capacitor C n ) can be calculated.
  • the Warburg impedance Z w can be represented by a continued fraction expansion as shown in equation (7). However, It is. If the above-mentioned approximate expression is expressed in a circuit diagram, it is an n-order Kawell type circuit in which each of n resistors R connected in parallel is connected between n capacitors C connected in series (see FIG. 4B). ). As apparent from the equations (8) and (9), according to the n-th order Kawell-type equivalent circuit model approximating the Warburg impedance Z w , the diffusion capacitance C d and the diffusion resistance R d are used to generate the other circuit. Parameters (resistance R n , capacitor C n ) can be calculated.
  • the estimation unit 4 simultaneously estimates the internal state quantity and the parameter value of the battery using the Kalman filter 42 in the battery equivalent circuit model 41 of either the Foster type or the Cawell type.
  • the internal state quantity of the battery includes the SOC of the battery
  • the parameter value includes at least one of the diffusion capacitance C d or the diffusion resistance R d .
  • an unscented Kalman filter (UKF: Unscented Kalman Filter) is used as the Kalman filter 42, but another filter may be used. UKF uses weighted sample points called sigma points to approximate the probability distribution and calculate each weighted transition.
  • the average value and variance after transition are calculated for each sigma point, and they are added according to the weight. By doing this, it is possible to approximate the probability distribution after the transition closer to the true value and without increasing the amount of calculation too much. Also, since the probability distribution is approximated by sigma points instead of approximating the system, there is no restriction on the nonlinearity of the system.
  • the SOC-OCV characteristics of the battery can be represented in the above-described battery equivalent circuit model.
  • a hysteresis phenomenon may occur in which the SOC-OCV characteristics differ between after charging and after discharging. In this case, the SOC-OCV characteristics of the battery can not be accurately represented.
  • the hysteresis phenomenon is caused by the material of the electrode, and particularly when lithium phosphate is used, the influence of the hysteresis phenomenon is significant.
  • FIG. 5A shows the measurement results of the SOC-OCV characteristics of a lithium iron phosphate battery. According to FIG. 5A, it can be seen that there is a difference in OCV between the characteristics during charging and the characteristics during discharging. Moreover, in FIG. 5B which expanded the broken-line enclosure part of FIG. 5A, even if it is made to discharge at SOC about 30%, it turns out that a hysteresis characteristic is shown.
  • the above-described battery equivalent circuit model can not accurately handle the SOC-OCV characteristics of the battery in which the hysteresis phenomenon occurs.
  • a hysteresis model by Plett which is one of models representing such a hysteresis phenomenon, is represented by an equivalent circuit of FIG.
  • the element V H is an element that represents a hysteresis voltage.
  • This hysteresis model is expressed by the following equation (10).
  • v h (t) is the hysteresis voltage
  • ⁇ (t) is the voltage drop speed of the hysteresis model (corresponding to the slope of the SOC-OCV curve)
  • M (t) is the maximum range of the voltage drop of the hysteresis model
  • u (t) is a parameter representing the input current.
  • the hysteresis of a battery appears as a result of an electrochemical reaction inside the battery, and is closely related to the charge transfer process inside the battery and the diffusion process of ions.
  • the hysteresis model by Plett expresses v h (t) by adding a reaction independent of the charge transfer process and the diffusion process of ions. Therefore, when estimating the battery state based on the equation (10), it is necessary to estimate ⁇ (t) and M (t) representing the hysteresis voltage, in addition to the estimation of the resistance and capacity of the RC parallel circuit. That is, the parameters to be estimated are increased by two ( ⁇ and M) as compared with the estimation of the battery model not considering the hysteresis.
  • equation (10) representing the hysteresis model can be rewritten as the following equation (13). This can be interpreted as being equivalent to the equation representing an RC parallel circuit configured by the variable resistor R h and the variable capacitor C h shown in FIG.
  • the resistance of the model is a variable resistance which is variable according to the magnitude of the current.
  • a variable resistor and a variable capacitance hysteresis model of the first embodiment can be applied to the RC parallel circuit of the charge transfer resistance R ct and the electric double layer capacity C dl models the charge transfer process.
  • the Plett hysteresis model is represented by an equivalent circuit in which a hysteresis element is added to the RC parallel circuit as shown in the left of FIG.
  • the hysteresis model of the present embodiment it is represented by a parallel circuit of variable resistance and variable capacitance as shown in the right of FIG.
  • variable resistor R ct, h (t) and the variable capacitance C dl, h (t) are expressed as the following equations (14) and (15).
  • M ct (t) indicates the maximum range of the hysteresis voltage drop generated by the charge transfer process
  • ⁇ ct (t) is the speed of the hysteresis voltage drop generated by the charge transfer process (corresponding to the slope of the SOC-OCV characteristic)
  • the hysteresis phenomenon can be handled by estimating the same number of parameters (resistance and capacity) as the battery model estimation that does not consider hysteresis.
  • the parameter since it is possible to obtain the parameter by integrating the hysteresis phenomenon into the resistance and capacity of the battery model which does not consider the hysteresis within the time constant matched to the charge transfer process and the diffusion process of ions, accuracy improves. .
  • the model in which the denominator of the equation representing the variable resistance R h is the absolute value
  • variable resistance and the variable capacitance of the hysteresis model of the first embodiment can be applied to the Foster type circuit representing the diffusion process of ions.
  • the Plett hysteresis model is represented by an equivalent circuit in which a hysteresis element is added to the n-order Foster type circuit as shown in FIG.
  • the hysteresis model of this embodiment can be represented by an equivalent circuit in which an n-order Foster circuit as shown in the lower part of FIG. 9 is configured by a variable resistor and a variable capacitor.
  • the circuit parameters of the Foster type circuit FIG.
  • variable resistor R d, h and the variable capacitance C d may be estimated h incorporating a hysteresis phenomenon.
  • variable resistance and variable capacitance of the hysteresis model of the first embodiment can also be applied to a Kawell-type circuit that represents the diffusion process of ions.
  • the circuit parameters may be replaced with the values of the variable resistor and the variable capacitance.
  • the resistance and the capacitance of the equivalent circuit are simply replaced with the variable resistance and the variable capacitance.
  • a hysteresis model can be applied. Thus, hysteresis can be handled without increasing the hysteresis element in the equivalent circuit of the battery.
  • the hysteresis phenomenon can be handled by estimating the same number of parameters (resistance and capacity) as the battery model estimation that does not consider hysteresis.
  • the parameter since it is possible to obtain the parameter by integrating the hysteresis phenomenon into the resistance and capacity of the battery model which does not consider the hysteresis within the time constant matched to the charge transfer process and the diffusion process of ions, accuracy improves. .
  • variable resistor R h of the hysteresis model is represented by a function having the absolute value
  • of the input current u (t)
  • ) is defined as a function of the input current u (t).
  • the function f (x) represents the output for the input x, and the relationship between the input x and the output f (x) is arbitrarily determined.
  • the resistance of the model is a variable resistance which is variable according to the magnitude of the current.
  • f (x) ⁇ x + ⁇
  • ⁇ and ⁇ are constants.
  • hysteresis can be handled without increasing the hysteresis element in the equivalent circuit of the battery, and accurate estimated values can be obtained by selecting coefficients as a model conforming to the hysteresis characteristics.
  • the battery parameter estimation device sequentially estimates a parameter including resistance or capacity in the equivalent circuit model of the battery based on at least one of the battery voltage and the battery current.
  • the resistance R h (t) M (t) / f (
  • the battery parameter estimation device by replacing the resistance and capacitance of the equivalent circuit with a variable resistance and a variable capacitance, it is possible to handle hysteresis without increasing the hysteresis element in the battery equivalent circuit. .
  • the battery parameter estimation device is a battery parameter estimation device that sequentially estimates a parameter including resistance or capacity in an equivalent circuit model of the battery based on at least one of a battery voltage and a battery current.
  • the battery parameter estimation device is a battery parameter estimation device that sequentially estimates a parameter including resistance or capacity in an equivalent circuit model of the battery based on at least one of a battery voltage and a battery current.
  • the model configuration is simple and the hysteresis phenomenon can be easily handled.
  • each component, each function included in each step, etc. can be rearranged so as not to be logically contradictory, and it is possible to combine or divide a plurality of components and steps into one. It is.
  • the Warburg impedance Z w is approximated by the infinite series expansion or the continued fraction expansion, but may be approximated by any method. For example, approximation using an infinite product expansion can be considered.

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CN112083335A (zh) * 2020-09-28 2020-12-15 国联汽车动力电池研究院有限责任公司 一种车用蓄电池的快充方法及系统
CN112731160A (zh) * 2020-12-25 2021-04-30 东莞新能安科技有限公司 电池滞回模型训练方法、估算电池soc的方法和装置
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JP6540588B2 (ja) 2016-04-28 2019-07-10 トヨタ自動車株式会社 車両のバッテリ搭載構造
JP6869697B2 (ja) 2016-11-02 2021-05-12 マレリ株式会社 オブザーバゲインの設定方法
JP6945485B2 (ja) * 2018-04-04 2021-10-06 三菱電機株式会社 蓄電池のヒステリシス電圧推定装置、およびこれを用いた蓄電池の残量推定装置、蓄電池の管理システム
WO2020090429A1 (ja) * 2018-10-30 2020-05-07 住友電気工業株式会社 パラメータ推定システム、パラメータ推定装置、車両、コンピュータプログラム及びパラメータ推定方法

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CN109874354A (zh) * 2016-09-23 2019-06-11 古河电气工业株式会社 二次电池状态检测装置和二次电池状态检测方法
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CN112083335A (zh) * 2020-09-28 2020-12-15 国联汽车动力电池研究院有限责任公司 一种车用蓄电池的快充方法及系统
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CN112731160A (zh) * 2020-12-25 2021-04-30 东莞新能安科技有限公司 电池滞回模型训练方法、估算电池soc的方法和装置

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