JP2010230469A - Device and method for determining state of health of secondary battery - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a secondary battery state of health determination device and method capable of determining the state of health of a secondary battery with high accuracy, by a short time determination. <P>SOLUTION: The state of health determination device includes: a pseudo white signal generating part 2 for generating a pseudo white binary signal different in frequency component; a voltage sensor 4 for detecting the terminal voltage of the secondary battery when the pseudo white binary signal is input; a parameter estimation part 5 in which a battery model 8 having a charge transfer resistance component of a negative electrode as parameters R1 and C1 is set for the secondary battery 7, and the parameters of the battery model 8 are estimated on the basis of the battery model 8 and the detected voltage; and a state of health determination part 6 for determining the state of health of the secondary battery from the estimated parameter R1 of the charge transfer resistance component of the negative electrode. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention belongs to the technical field of secondary battery deterioration determination apparatus and method.

  Conventionally, the amount of overcharge electricity is calculated from the amount of current accumulated in the secondary battery, and the degree of deterioration of the secondary battery is calculated from the amount of overcharge electricity.

JP 2006-10601 A

  Conventionally, however, accuracy cannot be obtained unless time is required for current integration.

  The present invention has been made paying attention to the above-described problems, and the object of the present invention is to provide a secondary battery deterioration determination device capable of accurately determining the degree of deterioration of the secondary battery with a determination with less time. It is to provide a method.

  In order to achieve the above object, the present invention provides a secondary battery deterioration determination device that determines the deterioration degree of a secondary battery that performs charge and discharge, and generates a pseudo-white binary signal having different frequency components. A pseudo white signal generating means for inputting to the battery; a voltage detecting means for detecting a terminal voltage of the secondary battery when the pseudo white binary signal is inputted; and a charge transfer resistance component of a negative electrode in the secondary battery. Is set as a parameter, parameter estimation means for estimating the parameter of the battery model based on the battery model and the detected voltage, and deterioration of the secondary battery from the parameter of the estimated charge transfer resistance component of the negative electrode Deterioration degree determining means for determining the degree.

  Therefore, in the present invention, it is possible to accurately determine the degree of deterioration of the secondary battery with a determination with less time.

It is a figure which shows the block configuration of the secondary battery deterioration determination apparatus of Example 1. FIG. It is explanatory drawing of the battery model identified in the secondary battery deterioration determination apparatus of Example 1. FIG. FIG. 3 is an explanatory diagram of a pseudo white signal generation unit according to the first embodiment. 6 is a table for explaining the number of shift registers of the M-sequence generation circuit according to the first embodiment. FIG. It is a figure shown in order to demonstrate the example of M series. It is a partially expanded explanatory view of an M-sequence signal. It is explanatory drawing of an M series signal. It is explanatory drawing which shows the block structure of a parameter estimation part. It is explanatory drawing which shows the calculation state of a SOC calculation part. 3 is a graph showing the relationship between SOC and OCV in Example 1. FIG. It is explanatory drawing of the table data of the deterioration degree of a deterioration degree determination part, and the charge transfer resistance of a negative electrode. It is a flowchart which shows the flow of the parameter estimation process performed in a parameter calculation part. It is explanatory drawing of an ARX model. It is explanatory drawing which shows the structure of a secondary battery system.

  Hereinafter, an embodiment for realizing a secondary battery deterioration determination device and method according to the present invention will be described based on Example 1 corresponding to the invention according to claims 1 to 6.

First, the configuration will be described.
FIG. 1 is a block diagram illustrating a secondary battery deterioration determination apparatus according to the first embodiment.
The secondary battery deterioration determination apparatus 1 of the first embodiment includes a pseudo white signal generation unit 2, a current sensor 3, a voltage sensor 4, a parameter estimation unit 5, and a deterioration degree determination unit 6, and a configuration in which a secondary battery 7 is connected. It is. The secondary battery 7 is a battery that can be charged and discharged. Examples include those used for driving electric vehicles (hereinafter abbreviated as EV) and hybrid vehicles (hereinafter abbreviated as HEV). Furthermore, a lithium ion battery is given as a specific example.
The pseudo white signal generation unit 2 creates an M-sequence signal having a predetermined current value and inputs it to the secondary battery 7. Details will be described later.

The current sensor 3 detects a current value input from the pseudo white signal generator 2 to the secondary battery 7.
The voltage sensor 4 detects the voltage between the terminals of the secondary battery 7.
The parameter estimation unit 5 estimates the parameters of the battery model of the secondary battery 7 from the output voltage with respect to the input of the M-sequence signal.
The deterioration degree determination unit 6 determines the deterioration degree using the parameters of the battery model estimated by the parameter estimation unit 5.

Next, the battery model to be identified will be described.
FIG. 2 is an explanatory diagram of a battery model identified by the secondary battery deterioration determination device 1 according to the first embodiment.
As shown in FIG. 2, the battery model 8 includes an open circuit voltage OCV, a resistance R0 of a DC component in the electrolytic solution, resistors R1 and R2 set as charge transfer resistances in the battery, and capacitors C1 and C2.
This charge transfer resistance is set as a component having a frequency component of 2 to 100 kHz. A resistor R1 and a capacitor C1 having a frequency component of 100 to 100 kHz (band with a faster time constant) and a resistor R2 and a capacitor C2 having a frequency component of 2 to 100 Hz (band with a slower time constant) are connected. Make parallel things in series. The resistor R1 and the capacitor C1 are set as negative charge transfer resistors, and the resistor R2 and the capacitor C2 are set as positive charge transfer resistors. In addition, all the frequency bands set this time are assumed to change depending on the battery type, temperature, SOC (capacity), SOH (deterioration degree), and other conditions.

Next, details of the pseudo white signal generation unit 2 will be described.
FIG. 3 is an explanatory diagram of the pseudo white signal generation unit 2 according to the first embodiment. FIG. 4 is a table for explaining the number of shift registers of the M-sequence generation circuit 21 according to the first embodiment.
The pseudo white signal generation unit 2 according to the first embodiment includes an M-sequence generation circuit 21 and a signal adjustment unit 22.
The M series generation circuit 21 includes a D register 211 and an adder 212.
A plurality of D registers are connected in series, and the output of the previous stage is used as the input of the subsequent stage, for example, an operation by D-FF is performed and the result is output.
A plurality of adders 212 are connected in series, add the output results of the respective D registers 211 and the output of the subsequent stage of the D registers 211, and output the result to the adder 212 of the subsequent stage.

Here, the M series will be described.
FIG. 5 is a diagram for explaining an example of the M series.
The M sequence is a 1-bit sequence generated by the following linear recurrence formula.

この式で、各項の値は０か１で、「＋」記号は排他的論理和を示す。そのため、ｎ番目の項はｎ−ｐ番目とｎ−ｑ番目の項とをＸＯＲ演算することによって得られる。但し、ｑは最終段に常にフィードバックされるので、ｑ＝１となる。
一般的に表わすと、Ｍ系列の周期Ｎは、Ｎ＝２ｑ−１で表わされる。
図５は、数式１において、ｐ＝３、ｑ＝１の場合を説明するものである。図５において、範囲Ａの部分は、３ビットのパターンが全部で７種類あり、同じパターンのものはない。つまりＭ系列はｐビットのパターンを全て１回ずつ発生する。各パターンの要素は、「０」と「１」の２通りずつだから、ｐビットで２ｐ通りとなる。ただし、すべてのビットが０となるパターンだけは、信号無発生となるので、除く。

In this expression, the value of each term is 0 or 1, and the “+” symbol indicates exclusive OR. Therefore, the n-th term is obtained by performing an XOR operation on the np-th and n-q-th terms. However, q is always fed back to the final stage, so q = 1.
Generally speaking, the period N of the M sequence is represented by N = 2q-1.
FIG. 5 illustrates the case where p = 3 and q = 1 in Equation 1. In FIG. 5, there are seven types of 3-bit patterns in the range A, and none of the same patterns. That is, the M series generates all p-bit patterns once. Since there are two patterns, “0” and “1”, each pattern has 2 bits in p bits. However, only patterns in which all bits are 0 are excluded because no signal is generated.

  That is, in the M-sequence generation circuit 21 of the first embodiment, the bit values upstream and downstream of the D register 211 in FIG. 3 are values corresponding to X to Xk-n. From FIG. 4, for example, in the first embodiment, 127 patterns are selected, and the number of D registers 211 is set to 7.

Next, the signal adjustment unit 22 will be described.
FIG. 6 is a partially enlarged explanatory view of an M-sequence signal. FIG. 7 is an explanatory diagram of an M-sequence signal.
As shown in FIG. 6, the signal adjustment unit 22 sets the minimum unit of the M-sequence signal generated by the M-sequence generation circuit 21 to the minimum time width, that is, the clock cycle determined from the configuration. Then, as shown in FIG. 7, for example, the M-sequence signal is adjusted to be a square wave that repeats ON and OFF between +1 (A) and −1 (A), and the output of the pseudo white signal generation unit 2 Is output to the secondary battery 7.

  This M-sequence signal is a pseudo whiteness binary signal, and the sum of + (ON) and-(OFF) is the same signal. The ON width of this signal is one set (one cycle) having 127 ON widths.

Next, details of the parameter estimation unit 5 will be described.
FIG. 8 is an explanatory diagram showing a block configuration of the parameter estimation unit 5.
The parameter estimation unit 5 includes an SOC calculation unit 51, an open-circuit voltage detection unit 52, an overvoltage calculation unit 53, and a parameter calculation unit 54.
FIG. 9 is an explanatory diagram showing a calculation state of the SOC calculation unit.
The SOC calculation unit 51 calculates the SOC by integrating the current detected by the current sensor 2. Note that SOC (State of charge, hereinafter abbreviated as SOC) is a battery capacity. Specifically, as shown in FIG. 9, integration is performed with the charge / discharge repetition time and the current value. In the first embodiment, this charge / discharge is intentionally performed in the base station, and the SOC is calculated in a short time. In addition, you may make it calculate SOC by charging / discharging performed during driving | running | working of a vehicle.

FIG. 10 is a graph showing the relationship between SOC and OCV in the first embodiment.
The open circuit voltage detector 52 measures the relationship between the SOC (%) and the open circuit voltage OCV (V) in advance as shown in FIG. 10 and prepares it as data, and the OCV (V) is obtained from the SOC (%) from the SOC calculator 51. ) Is calculated.
The overvoltage calculation unit 53 calculates an overvoltage η by subtracting (subtracting) the open-circuit voltage detected by the open-circuit voltage detection unit 52 from the battery terminal voltage measured by the voltage sensor 4 when the M-sequence signal is input. To do.
The parameter calculation unit 54 estimates the parameters of the DC resistance R0, the negative charge transfer resistance R1, the positive charge transfer resistance R2, and the capacitor capacitances C1 and C2 from the overvoltage change with respect to the M-sequence signal and the current value.

Next, the deterioration degree determination unit 6 will be described.
FIG. 11 is an explanatory diagram of table data of the deterioration degree of the deterioration degree determination unit and the charge transfer resistance of the negative electrode.
The degradation degree determination unit 6 obtains table data in advance from the parameter estimation unit 5 to obtain the relationship between the degradation level SOH (State of Health: SOH) and the negative charge transfer resistance R1 shown in FIG. To determine the degradation degree SOH.

The operation will be described.
[Parameter estimation process]
FIG. 12 is a flowchart showing the flow of parameter estimation processing executed by the parameter calculation unit 54. Each step will be described below.

  In step S <b> 1, the parameter calculation unit 54 collects identification data from the output voltage of the battery with respect to the M-sequence signals having different clock cycles, which is given by the pseudo white signal generation unit 2.

In step S2, system identification is performed based on the collected identification data.
An example using the ARX model is given.
Here, the ARX model (Auto-Regressive eXogeneous model) will be described.
FIG. 13 is an explanatory diagram of the ARX model.
First, consider the difference equation as shown below.

これは、現在の出力y(t)を有限個の過去の出力データy(t-k)と入力データu(t-k)に関連付けるものである。
このように、構造は、３つの整数na,nb,nkによって定義され、引数naは極の数、nb-1は零点の数になる。一方、nkはシステムの純粋な時間遅れ（むだ時間）である。サンプル値制御のシステムに対して、むだ時間がなければ、一般的にはnkは１になる。複数入力システムに対して、nbとnkは、行ベクトルになる。ここでi番目の入力に関した次数／遅れになる。
ここで、パラメータベクトルを、以下のように定義する。

This associates the current output y (t) with a finite number of past output data y (tk) and input data u (tk).
Thus, the structure is defined by three integers na, nb, nk, the argument na is the number of poles, and nb-1 is the number of zeros. On the other hand, nk is the pure time delay (dead time) of the system. Generally, nk is 1 if there is no dead time for a sample value control system. For multiple input systems, nb and nk are row vectors. Here is the order / lag for the i-th input.
Here, the parameter vector is defined as follows.

そして、データベクトルを、以下のように定義する。

The data vector is defined as follows.

すると、出力y(k)は次の式で表わすことができる。ここで、ωを白色雑音とする。

Then, the output y (k) can be expressed by the following equation. Here, ω is white noise.

また、２つの多項式を定義する。

Two polynomials are defined.

そして、以下のように記述し、これをＡＲＸモデルとする。

And it describes as follows and makes this an ARX model.

  (Equation 8)

  A (q) y (k) = B (q) u + ω (k)

  In step S3, the discrete time LTI identification model obtained in step S2 is converted into a continuous time LTI model. The LTI model is a linear time-invariant model. Thereby, it is possible to perform characteristic evaluation with respect to a continuous time axis.

  In step S4, the frequency characteristic of the continuous time LTI model obtained in step S3 is evaluated. In the first embodiment, evaluation using a Bode diagram and evaluation using a Nyquist diagram are performed.

  In step S5, the two frequency characteristic diagrams obtained from the result of step S4 are synthesized in a reliable frequency band.

  In step S6, parameter estimation is performed by curve fitting from the synthesized frequency characteristics. The content of this process is the same as that of obtaining the reaction resistances R1 to R3 and the capacitor capacities C1 to C3 as AC circuit constants from the plotted waveforms of the real and imaginary axis components of impedance in Japanese Unexamined Patent Application Publication No. 2007-178215. It is.

[Deterioration judgment function]
In the first embodiment, when the secondary battery 7 is stored at a base station or the like or charged at the base station or the like, the secondary battery deterioration determination device 1 determines the degree of deterioration of the secondary battery 7.
During traveling, the battery state changes sequentially due to charge / discharge and load. For this reason, it is difficult to accurately measure the degree of deterioration, but in Example 1, measurement is performed under constant conditions by measuring at the base station or the like during storage or charging.
When the secondary battery deterioration determination device 1 and the secondary battery 7 are electrically connected, the M-sequence signal created by the pseudo white signal generator 2 is input to the secondary battery 7, and the parameter estimation unit 5 performs the processing of steps S1 to S6 to estimate the parameters.
The M series signal generated by the pseudo white signal generation unit 2 and input to the secondary battery 7 is a rectangular signal in which the current value is the same as the actual value plus or minus. For example, the rectangular waves of +1 (A) and -1 (A) shown in FIG. As a result, the actual state of the battery can be estimated (in one cycle).

Further, the M-sequence signal of the first embodiment is measured with, for example, 127 ON widths, that is, frequencies, and the value of this frequency can be easily grasped by obtaining the value of each part of the M-sequence generation circuit 21. is there. Therefore, processing becomes easy, and one way of measurement desired to be measured can be easily performed.
Thereby, a Bode diagram and a Nyquist diagram can be easily obtained.
If necessary, the type of frequency may be increased or decreased with reference to FIG.

The M-sequence signal is a pseudo-whiteness binary signal, and since the sum of the positive value and the negative value is the same, the secondary battery 7 to which this signal is input discharges with the current to be charged. The currents are the same, and the SOC value does not change in one cycle of 127 ON widths, for example.
Although the internal resistance value of the secondary battery 7 varies depending on the SOC value of the secondary battery 7, in Example 1, the measurement is performed with the SOC value unchanged. Thereby, the good battery model 8 is identified.

In Example 1, as shown in FIG. 2, as the battery model 8, a direct current component resistance R0, a negative charge transfer resistance R1, and a positive charge transfer resistance R2 are set, and the open circuit voltage OCV and the overvoltage are separated. Estimate the parameters of resistance and capacitor.
The overvoltage η is obtained from the following relationship. That is, open circuit voltage OCV = terminal voltage V + overvoltage η. Since the secondary battery 7 has chemical reactions including various elementary process reactions and can be separated, the frequency characteristic is subdivided to construct a detailed equivalent circuit model.

  The parameters of the battery model 8 (equivalent circuit) are obtained as follows. As a response of the voltage of the secondary battery 7 to the M-sequence signal, system identification is performed using the ARX model from the collected data (step S1) (step S2). Since the system identification obtained in step S2 is a discrete system, it is converted to a continuous system in order to obtain a Bode diagram and a Nyquist diagram (step S3), and a Bode diagram and a Nyquist line are evaluated as frequency characteristics. A diagram is generated (step S4). Preferably, a reliable frequency band is grasped from the two diagrams, and the frequency characteristics are synthesized at this portion (step S5), and parameters R1, R2, and B2 of the battery model 8 (equivalent circuit) are synthesized from the synthesized frequency characteristics. C1 and C2 are estimated. The DC resistance component R0 can be easily obtained from the direct term of the frequency characteristic result (the value of the real part when the imaginary part of the complex plane = 0).

As described above, in Example 1, the DC resistance component in the electrolytic solution, the negative charge transfer resistance composed of the parallel circuit of the resistor and the capacitor, and the positive charge transfer resistance are set as the battery model 8 and parameters are estimated.
Battery degradation can be broadly divided into cycle degradation and storage degradation. When the cycle deteriorates, a change mainly appears in the charge transfer resistance of the positive electrode. Further, when storage deteriorates, a change mainly appears in the charge transfer resistance of the negative electrode.
In Example 1, an equivalent circuit model having a positive electrode and a negative electrode is constructed and used as a battery model 8 whose parameters are estimated.
Further, the deterioration of the battery is mainly governed by the deterioration of the negative electrode. As the negative electrode of the battery progresses, the electrolytic movement resistance at the interface between the negative electrode and the electrolyte, that is, the resistance R1 on the equivalent circuit model increases. In Example 1, the deterioration of the battery is accurately determined by determining the degree of deterioration based on the resistance R1 that is the parameter of the obtained negative electrode.

In order to clarify the operation of the first embodiment, the following description is added.
In order to determine the degree of deterioration of the secondary battery, it is conceivable to obtain the full charge capacity by current integration and determine the degree of deterioration based on the ratio.
However, it takes time to obtain the full charge capacity by current integration. In addition, it is conceivable to determine the degree of deterioration using a DC resistance. However, since the DC resistance is the sum of the bulk resistance, charge transfer resistance, and diffusion resistance of the battery electrolyte, it is difficult to accurately determine the degree of deterioration.
In the first embodiment, the deterioration determination is performed by setting the charge transfer resistances of the positive electrode and the negative electrode by using the fact that changes in the charge transfer resistances of the positive electrode and the negative electrode are mainly caused by the deterioration of the battery. The equivalent circuit model is used.

  And, using a pseudo-whiteness signal having a signal component in the band corresponding to the charge transfer resistance, system identification is performed from the output obtained by inputting to the battery, and parameter estimation is performed, so that it has a strong influence on deterioration. The parameter is calculated, and the battery deterioration determination is performed accurately and without taking time from the relationship between the parameter and the deterioration degree (see FIG. 11).

The battery exhibits its performance through scientific reactions including various elementary processes. Each battery chemical reaction has a unique response time. By the AC impedance measurement method, chemical reactions including respective elementary process reactions can be separated, and as a result, the battery can be represented by the equivalent circuit model shown in FIG. Each element can be expressed by chemical factors such as the bulk resistance of the electrolytic solution, the electric double layer, and the charge transfer resistance.
The following can be cited as the cause of the change in the charge transfer process due to the deterioration of the battery. By repeating charge and discharge, the capacity decreases due to the destruction and formation of the SEI film (solid electrolyte film), the capacity decreases due to the formation of the SEI film due to storage, and the formation of an inert substance at the positive electrode is increased.

Explain the effect. The secondary battery deterioration determination device of Example 1 has the effects listed below.
(1) A secondary battery deterioration determination device 1 that determines the deterioration degree of a secondary battery 7 that is charged and discharged, and generates pseudo white binary signals having different frequency components and inputs them to the secondary battery 7 The signal generator 2, the voltage sensor 4 for detecting the terminal voltage of the secondary battery 7 when the pseudo white binary signal is input, and the charge transfer resistance of the negative electrode of the secondary battery 7 as parameters R1 and C1 The battery model 8 (FIG. 2) is set, and based on the battery model 8 and the detected voltage, the parameter estimation unit 5 that estimates the parameters of the battery model 8 and the estimated negative-electrode charge transfer resistance component parameter R1 Since the deterioration degree determination unit 6 that determines the deterioration degree of the battery is provided, the degree of deterioration of the secondary battery can be accurately determined with a determination with less time.

  (2) In the above (1), the battery model 8 (FIG. 2) is set with the negative charge transfer component parameters R1 and C1 and the positive charge transfer component parameters R2 and C2. , The charge transfer resistance of the positive electrode that changes mainly due to cycle deterioration and the charge transfer resistance of the negative electrode that changes mainly due to storage deterioration are calculated as parameters of the battery model 8, and the charge of the negative electrode that is dominant in battery deterioration is calculated. Since the degree of deterioration is determined from the movement resistance, the degree of deterioration of the secondary battery can be determined with higher accuracy.

  (3) In the above (1) or (2), the pseudo white signal generation unit 2 includes a plurality of frequency components, is composed of a binary value of a positive value and a negative value, and the total current value of the one set. In order to generate an M-sequence signal whose change is 0 as a pseudo white binary signal, it is possible to input signals having different frequencies without changing the state of the battery, and to accurately estimate the parameters of the battery model 8, Thereby, it is possible to accurately determine the degree of deterioration of the secondary battery with a little time determination.

  (4) In the above (1) to (3), the parameter estimation unit 5 includes an overvoltage calculation unit 53 for calculating an overvoltage change of the secondary battery when the pseudo white binary signal is input, and a parameter from the overvoltage change. Since the parameter calculation unit 54 for calculating is provided, the output of the pseudo white binary signal input to the secondary battery is accurately captured by an overvoltage change with good frequency response, so that the parameters of the battery model 8 can be accurately determined from the characteristics of a plurality of frequencies. Estimation can be performed.

  (5) In the above (4), the parameter calculation unit 54 performs system identification from the overvoltage change, calculates the discrete time model (step S1), and converts the discrete time model obtained from the system identification result to continuous time. Model conversion processing (step S3) for conversion to a model, frequency characteristic evaluation processing (step S4) for calculating the frequency characteristics of the continuous time model, and parameter determination processing (step S6) for determining parameters so as to match the frequency characteristics Therefore, it is possible to calculate the parameter from the overvoltage change in a systematic manner while suppressing the processing load, and thereby it is possible to accurately determine the degree of deterioration of the secondary battery with a little time.

  (6) A secondary battery deterioration determination method for determining the deterioration degree of the secondary battery 7 to be charged / discharged, wherein the secondary battery 7 has negative charge transfer resistance components as parameters R1 and C1 (see FIG. 2), a pseudo white binary signal having a different frequency component is generated by the pseudo white signal generator 2 and input to the secondary battery 7, and the secondary battery 7 when the pseudo white binary signal is input is set. The terminal voltage is detected by the voltage sensor 4, and the parameters R1 and C1 of the battery model 8 are estimated by the parameter estimation unit 5 based on the battery model 8 and the detected terminal voltage, and the degree of deterioration of the secondary battery 7 is determined from the parameter R1. Since the deterioration degree determination unit 6 makes the determination, it is possible to accurately determine the degree of deterioration of the secondary battery with a determination that requires less time.

As described above, the battery model identification method of the present invention has been described based on the first embodiment. However, the specific configuration is not limited to these embodiments, and the invention according to each claim of the claims is described. Design changes and additions are allowed without departing from the gist.
For example, in Example 1, both the Bode diagram and the Nyquist diagram were evaluated and combined, but either one may be used.
For example, although the ARX model is used as the model in the first embodiment, other models may be used.

For example, the battery model identification method of Example 1 may be used in the configuration shown in FIG.
FIG. 14 is an explanatory diagram showing the configuration of the secondary battery system.

  The secondary battery system shown in FIG. 14 includes a controller 91, a voltage sensor 92, a current sensor 93, a temperature sensor 94, a secondary battery 95, and a load 96, and the controller 91 determines the secondary battery based on the value detected by each sensor. Charging / discharging of the battery 95 is controlled. The controller 91 calculates the degree of deterioration of the secondary battery capacity, and performs control based on the degree of deterioration or a value using an SOC related to the degree of deterioration. However, it is difficult to directly measure these values calculated from the sensor values. For example, when charging / discharging is repeated depending on the state of the vehicle, such as HEV, it is particularly difficult to measure the state of the secondary battery with high accuracy. At that time, as in Example 1, it is possible to operate the secondary battery more efficiently when the vehicle is used by performing measurement with high accuracy at the time of charging at the base station or the like. become.

  The present invention can be used for secondary batteries used for other mobile bodies in addition to EV and HEV secondary batteries.

DESCRIPTION OF SYMBOLS 1 Secondary battery deterioration determination apparatus 2 Pseudo white signal generation part 21 M series generation circuit 211 Register 212 Adder 22 Signal adjustment part 3 Current sensor 4 Voltage sensor 5 Parameter estimation part 51 SOC calculation part 52 Open-circuit voltage detection part 53 Overvoltage calculation part 54 Parameter calculation unit 6 Deterioration degree determination unit 7 Secondary battery 8 Battery model 91 Controller 92 Voltage sensor 93 Current sensor 94 Temperature sensor 95 Secondary battery 96 Load OCV Open-circuit voltage R0 DC resistance component R1 (Negative) charge transfer resistance component R2 Charge transfer resistance component C1 (positive charge transfer resistance) capacitor component C2 (positive charge transfer resistance) capacitor component (positive charge transfer resistance)

Claims (6)

A secondary battery deterioration determination device that determines the deterioration degree of a secondary battery that performs charge and discharge,
Pseudo white signal generating means for generating pseudo white binary signals having different frequency components and inputting the signals to the secondary battery;
Voltage detection means for detecting a terminal voltage of the secondary battery when the pseudo white binary signal is input;
A battery model having a charge transfer resistance component of a negative electrode as a parameter is set in the secondary battery, and based on the battery model and a detection voltage, parameter estimation means for estimating a parameter of the battery model;
Deterioration degree determining means for determining the deterioration degree of the secondary battery from the estimated parameter of the charge transfer resistance component of the negative electrode,
A secondary battery deterioration determination device comprising: The secondary battery deterioration determination device according to claim 1,
In the battery model, the charge transfer component of the negative electrode and the charge transfer component of the positive electrode are set as parameters.
A secondary battery deterioration determining device. In the secondary battery deterioration determination device according to claim 1 or 2,
The pseudo white signal generation means includes a plurality of frequency components, is configured with binary values of a positive value and a negative value, and outputs an M-sequence signal in which the total current value change of one set is 0 as a pseudo white binary value. As a signal,
A secondary battery deterioration determining device. In the secondary battery deterioration determination apparatus according to any one of claims 1 to 3,
The parameter estimation means includes
Overvoltage calculating means for calculating an overvoltage change of the secondary battery when the pseudo white binary signal is input;
Parameter calculating means for calculating a parameter from the overvoltage change;
A secondary battery deterioration determination device comprising: In the secondary battery degradation determination device according to claim 4,
The parameter calculation means includes
System identification means for performing system identification from the overvoltage change and calculating a discrete time model;
Model conversion means for converting a discrete time model obtained from the system identification result to a continuous time model,
A frequency characteristic evaluation means for calculating the frequency characteristic of the continuous time model;
Parameter determining means for determining parameters so as to match the frequency characteristics;
A secondary battery deterioration determination device comprising: A secondary battery deterioration determination method for determining a deterioration degree of a secondary battery that performs charge and discharge,
A battery model having the charge transfer resistance component of the negative electrode as a parameter for the secondary battery is set,
A pseudo white binary signal having different frequency components is input to the secondary battery,
Detecting the terminal voltage of the secondary battery when the pseudo white binary signal is input,
Based on the battery model and the detected terminal voltage, estimate the parameters of the battery model,
Determining the degree of deterioration of the secondary battery from the parameters,
A method for determining deterioration of a secondary battery.

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