JP2021136227A - Battery management device - Google Patents

Abstract

To precisely calculate the parameters of a battery.SOLUTION: The battery management device includes: a controller that calculates indexes for battery based on an electrical circuit model of the battery; and an operation part that estimates parameters of the electrical circuit model using a filter having a variable gain based on the amount of time change of the battery current, which is the current flowing through the battery and battery temperature.SELECTED DRAWING: Figure 3

Description

The present invention relates to a battery management device.

Electric vehicles such as hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs) incorporate a battery pack of multiple cells into a powertrain system. The battery management system manages and controls the cells to maximize the efficiency of the battery pack. The battery management system uses SOC (State of Charge), which is an index indicating the power remaining in the cell, SOH (State of Health), which is an index indicating the degree of deterioration of the cell, the amount of power input to the cell, and the cell. Monitor the amount of output power. These monitors are calculated based on the electrical circuit model that represents the cell.

The parameters of the electrical circuit model are complex. Randall's primary circuit with one polarization voltage branch is defined by four parameters: Ro, Rp, C, and Vp. Since these parameters cannot be measured directly, various methods for calculating them have been proposed. In the online calculation methods known so far, the Kalman filter has been used as an iterative calculation algorithm for estimating ECM parameters in real time using the measured voltage and current. The difficulty of this method lies in the complex dependencies between parameters including not only SOC and temperature, but also cell deterioration and current flowing through the cell. On the other hand, the effect of temperature change on SOC and deterioration is minor, but it is necessary to consider the current for highly accurate calculation. Patent Document 1 discloses a configuration in which the Kalman filter is switched on and off based on the current and voltage flowing through the battery in the battery management system.

US Publication US2014 / 0266059

In the invention described in Patent Document 1, there is room for improvement in the accuracy of parameter calculation.

The battery management device according to the first aspect of the present invention has a controller for calculating an index related to the battery based on an electric circuit model of the battery, a time change amount of the battery current which is a current flowing through the battery, and a temperature of the battery. Based on this, it includes a calculation unit that estimates the parameters of the electric circuit model using a filter having a variable gain.

According to the present invention, the parameters of the battery can be calculated accurately.

Configuration diagram of battery system 100 The figure which shows an example of the primary Randall electric circuit model 200 Configuration diagram of the extended Kalman filter 392 Correlation diagram of current and resistance Nyquist plot of the cell The figure which shows the input in the operation example The figure which shows the calculation result in the operation example

-First Embodiment-
Hereinafter, the first embodiment of the battery system will be described with reference to FIGS. 1 to 7.

FIG. 1 is a configuration diagram of the battery system 100. The battery system 100 includes a battery pack 101, a host controller 110, a battery management device 300, an inverter 120, and a load 107. The battery system 100 converts the chemical energy stored in the battery pack 101 into electrical energy and inputs it to the load 107. The battery system 100 may be part of a hybrid vehicle, in which case the load 107 corresponds to the drive shaft of the vehicle.

The purpose of the battery management device 300 is to monitor the status of the battery pack 101 and to communicate with the host controller 110. The state of the battery pack 101 monitored by the battery management device 300 is SOC or SOH. The battery management device 300 calculates SOC and SOH for each predetermined time step Δt. The host controller 110 controls the output of the battery pack 101 to maximize the efficiency of the inverter 120 while ensuring safety. The battery management device 300 acquires the information obtained by sensing the ammeter 102, the plurality of small voltmeters 103, the thermometer 104, and the voltmeter 105. The name of the small voltmeter 103 merely indicates that the voltage to be measured is smaller than that of the voltmeter 105, and there is no functional difference between the small voltmeter 103 and the voltmeter 105.

The battery management device 300 includes a controller 391 and an extended Kalman filter 392. The extended Kalman filter 392 calculates the parameters described later, and the controller 391 calculates SOC, SOH, and the like using the parameters. Since a known method is used for calculating SOC and the like by the controller 391, it will not be described in particular in this embodiment.

The ammeter 102 measures the current flowing through the battery pack 101. Each of the plurality of small voltmeters 103 measures the voltage of each cell constituting the battery pack 101. The voltmeter 105 measures the voltage across the battery pack 101. The thermometer 104 measures the temperature of the battery pack 101.

FIG. 2 is a diagram showing an example of a primary Randall electric circuit model 200. The closed-circuit voltage (CCV) 206 of the Randall electrical circuit model 200 is measured by a small voltmeter 103. The closed circuit voltage 206 is defined as the electrical potential between the positive and negative electrodes in each Randall electrical circuit model 200. The closed circuit voltage 206 is calculated by the following equation 1.

CCV = OCV (SOC) + I × R O + V p (I, R p, τ) ··· ( Equation 1)

Here, the OCV in Equation 1 is an open circuit voltage (OCV) 201. The open circuit voltage 201 is the electrical potential between the two terminals when no current is flowing and the polarization voltage Vp202 does not remain in the RC circuit. _{R O} in Equation 1 is a direct-current internal resistance shown by reference numeral 203 in FIG. _{R p} in Equation 1 is the polarization resistance shown by reference numeral 205 in FIG. Τ in Equation 1 is a time constant in the RC circuit and is calculated as the product of _{R p} and C _p. However, C _p is a polarization capacitor shown by reference numeral 204 in FIG. Since the time constant τ is calculated as the product of the polarization resistance Rp of the battery pack 101 and the polarization capacitor Cp, it can also be called the “battery polarization time constant” τ.

In the following, the variable having a code is described by any of a combination of a name and a code, a combination of a variable and a code, and a combination of a name and a variable and a code, all of which have the same meaning. For example, the polarization resistance 205, Rp205, and the polarization resistance Rp205 all have the same meaning.

The open circuit voltage 207 and the current I are measured by the small voltmeter 103 and the ammeter 102 at each time step Δt described above. The open circuit voltage 201 is estimated using a look-up table with SOC as an input. The SOC is obtained as an initial value from the same look-up table in reverse order by using the cell voltage measured by the small voltmeter 103 at the moment the ignition switch is turned on.

The SOC is updated every time a predetermined current flows using the Coulomb counting method. Furthermore, OCV (SOC) = CCV-I
The SOC may be obtained by a look-up table using OCV under the assumption that the equation RO-Vp (I, Rp, τ) holds. Further, weighted averaging of the current integration method and the voltage estimation method can also be used. However, some variables of ECM, such as Vp202 and OCV, can be calculated and measured, but the parameters Ro203, Rp205, and Cp204 cannot be measured and must be estimated by other methods.

The accuracy of SOC and SOH calculated by the battery management device 300 strongly depends on the Randall electric circuit model 200. The Randall electrical circuit model 200 has a strong influence on these parameters. Therefore, the estimation accuracy of Ro203, Rp205, and Cp204 has a strong influence on the entire battery management device 300. The parameters of Ro203, Rp205, and Cp204 are all non-linear functions of cell temperature and SOC. Further, these parameters are also affected by the deterioration of the battery pack 101. Further, Ro203 and Rp205 are also affected by the current I. The combination of these complicates parameter estimation.

FIG. 3 is a block diagram of the extended Kalman filter 392. In FIG. 3, the controller 391 is also shown to show the correlation, but since the controller 391 is not included in the extended Kalman filter 392, it is shown by a broken line. The extended Kalman filter 392 is used to estimate Ro203, Rp205, and the time constant τ. The extended Kalman filter 392 is an iterative algorithm that utilizes the dynamic model 330 of the system, which tends to contain noise, and the measurement result 301 of the sensor to obtain a better estimate of the measure. The extended Kalman filter can be applied to non-linear systems such as lithium-ion battery cells by linearizing the system by time width. The dynamic model 330 is defined by the state equation shown in Equation 2 and the observation equation shown in Equation 3.

^{_{X - k = f (X k}} -1, u k) ··· ( Equation 2)

y _ke = H (X _k ) ... (Formula 3)

In Equation 2, the _{u k} input 302, X to the system based on the current value sensor 102 in the k-th time step outputs ^- _k is a pre-estimated 306 in the k-th time step. In Equation 3, y _ke is the output 303 of the system indicating the battery voltage at the kth time step. In Equation 3, X _k is an ex post facto estimate of 308. The state expression is expressed as follows, for example.

A part of the state equation for estimating the polarization is derived from the physical equation showing the electric circuit model 200.

In Equation 5, I ₁ represents the current flowing through the resistor 205. After performing the Laplace transform, the Vp of the s region is expressed as follows.

Equation 6 is approximately expressed as follows in the time domain.

Vp is extracted from the Randall electrical circuit model 200, but there is no mathematical formula that describes how Ro203, Rp205, and the time constant τ change over time. Therefore, Equation 4, which is the state expression of the dynamic model 330 of the system, includes a function of specifying three parameters. With reference to the extended Kalman filter 392 shown in FIG. 3, the pre-estimation 306 is equal to the post-estimation 308 in the immediately preceding time step. Ro203, Rp205, and the time constant τ are calculated by the update unit 340 based on the Kalman gain calculated by the Kalman gain calculation unit 350. _{The difference between y km} 301 based on the measurement by the small voltmeter 103 and the estimated y _ke 303 is expressed as follows.

Kalman gain K _k in Equation 8 is calculated as follows.

Here, R _k is the observed noise covariance 309 in the kth time step. C _k is the Jacobian of the measurement function in the kth time step, that is, ∂H (X) / ∂X. P ^- _k is the covariance at the k-th time step estimated in advance. P _k-1 is the covariance at the k-1 th time step by ex post facto estimation. Q _k is the process noise covariance 305 in the kth time step calculated by the covariance calculation unit 304.

The process noise covariance Qk305 and the observed noise covariance Rk309 are indicators of estimation uncertainty. The process noise covariance Qk305 and the observed noise covariance Rk309 are calculated as in Equation 12 and Equation 13, respectively.

Here, _{each of w k} and v _k is a noise vector of the process and measurement in the k-th time step. According to Equations 8 to 10, if the value of the process noise covariance Qk305 is small, the Kalman gain is small and the update in the update unit 340 is small. On the contrary, if the value of the process noise covariance Qk305 is large, the value estimated by the extended Kalman filter algorithm shown in FIG. 3 in the update unit 340 changes significantly. In Patent Document 1, the process noise covariance was a fixed value. In this embodiment, the process noise covariance 305 changes with both temperature and current.

The configuration of the battery management device 300 is inspired by the Butler-Volmer phenomenon, which describes the current affecting the polarization resistor 205. This correlation is shown in FIG. FIG. 4 is a diagram showing a normal correlation 410 and a normalized correlation 420. The normalization correlation 420 is also called a look-up table 420. The normalized correlation 420 is usually created based on the correlation 410. The look-up table 420 is a graph in which the normal correlation 410 is normalized so that the values are the same in the case of a predetermined current value.

In this embodiment, this phenomenon depends only on current and temperature. However, this phenomenon may also depend on SOC. Reference numerals 411 and 422 correspond to a low temperature state, and reference numerals 412 and 421 correspond to a high temperature state. The effect of current is small in all of these. The Butler-Volmer phenomenon is expressed by the following equation.

Here, I ₀ represents the exchange current, R _SEI represents the interfacial resistance of the solid electrolyte, and V ₀ is a constant. All of these depend on temperature and SOC. The Butler-Volmer phenomenon shown in Equation 14 does not apply only to the polarization resistance 205. This is because the internal resistance 203 is a parameter of the material and is not affected by the current value. However, if the discrete time interval of the measurement is long enough, the measured Ro contains an element of Rp. This can be explained by the impedance spectroscopic characteristics shown in the Nyquist diagram shown in FIG.

Each point of reference numeral 601 indicates the impedance of the cell as a complex number for each frequency having a different input current. The internal resistance 602 is measured at a high frequency and corresponds to reference numeral 203 in FIG. The real part of reference numerals 603 and 604 corresponds to the DC resistance at Δt1 and Δt2. However, Δt1 is smaller than Δt2. Therefore, Ro _Δt1 and RO _Δt2 contain a polarization component.

The time step Δt described above may be a sampling rate of 100 ms, that is, 10 Hz. In this case, since the measured internal resistance 203 includes the polarization element Ro _{Δ100 ms} , the Butler-Volmer phenomenon can be applied to the estimation of Ro203 and Rp205. Further, the time constant τ may have a dependence on the current.

In this embodiment, the process noise covariance Qk305 is calculated in consideration of the current and temperature due to the resistor. Based on these explanations, the process noise covariance 305 of the extended Kalman filter 392 is calculated by the covariance calculation unit 304 as follows. First, in the k-th time step, the current measurement value I _k 423 in the k-th time step and the current measurement value I _{k- in the k-1th time step are used by using the predetermined data in the lookup table 420. 1} 425, the temperature measurement in the kth time step, the resistance value B _k 424 in the normalized kth time step, and the resistance value B _k-1 426 in the normalized k-1st time step. Calculated. The resistance value Bk is calculated according to the current and temperature in the kth time step using the equation shown in Equation 14.

Pulse experiments at different temperature and current states shown in the look-up table 420 of FIG. 4 are used to find the optimum values for the parameters _{Vo, Io and R SEI} used to calculate the resistance value Bk. When extracting the Ro for each experiment, the combination of parameters that Vo, Io and R _SEI at each temperature are identified by matching the value of Ro corresponding to formula 14. _{Then, Vo, Io and R SEI} used for calculating the resistance value Bk are estimated using the values obtained by the fitting process. Since Ro is sufficient for creating the look-up table 420, the time required for developing the battery management device 300 can be shortened as compared with the conventional method for creating a map.

In order to create the look-up table 400 for calculating the parameters of Vo, Io and R _{SEI, Rp205 and the time constant τ are not required in this embodiment.} If only Ro is needed, a very short pulse test will suffice. Therefore, in the present embodiment, the time required for production can be shortened as compared with the conventional method.

Since the non-diagonal components in the process noise covariance Qk305 do not significantly affect the result and only increase the calculation load, they may be set to zero in order to ignore them. In this embodiment, only diagonal components are considered. This diagonal component shows Vp, Ro, Rp, and τ, which are vector parameters representing the noise covariance. The calculation of the process noise covariance Qk305 is implemented in the covariance calculation unit 304 as follows.

That is, in the process noise covariance Qk305, only the (2,2) component, the (3,3) component, and the (4,4) component are not zero, and the others are zero. Here, Ro _ave and R p _ave are moving average values of the estimated value 307 output by the extended Kalman filter 392. Further, α is a fixed value having a very small value.

First, the values of the (2,2) component and the (3,3) component will be described. As shown in FIG. 4 (b), the normalized Ro is used for fitting and the process noise covariance corresponds to the absolute value of the estimated error. The product of Ro- _ave and (B _k- B _k-1 ), and Rp- _ave and (B _k- B _k-1 ), respectively, are the differences between the actual value and the estimated value in the k-1 th time step. This is considered to be process noise in this embodiment. According to Equations 12 and 13, the process noise covariance Qk is composed of the square of the process noise, so these equations are squared.

The values of the (1,1) component and the (4,4) component will be described. Since the matter of interest in this embodiment is the estimation of Ro203, Rp205, and the time constant τ, the Vp noise is set to zero. This causes the extended Kalman filter to follow Equation 7 without the updater 340 changing the value of Vp. The covariance is set to a very small fixed value, "α", to prevent the estimation of the time constant τ from changing prematurely.

The principle of this embodiment is as follows. In order to limit the update of Ro203 and Rp205 when the change in current is small, in other words, the process noise covariance is set to a small value so that the values do not change significantly. Similarly, when the current changes significantly, the process noise covariance value is set high so that Ro203 and Rp205 converge to their true values early. The Butler-Volmer phenomenon is the reason why the value of the process noise covariance changes according to the amount of change in the current. The effect of this embodiment is that the estimated behavior can be corrected from the viewpoint of electrochemical.

(Operation example)
With reference to FIGS. 6 and 7, the battery system 100 of the present embodiment in which the process noise covariance is appropriately changed is compared with the system of the comparative example in which the process noise covariance is a fixed value. There are two comparative examples, Comparative Example 1 in which the process noise covariance is set to a relatively small value, and Comparative Example 2 in which the process noise covariance is set to a relatively large value. That is, in this operation example, the operations of a total of three systems will be described. Both systems are initialized with incorrect Ro203 and Rp205 values, indicating that behavioral and presumed convergence works well.

FIG. 6 is a schematic diagram of a pulse wave input to each system in an operation example. FIG. 7 is a diagram showing time-series changes in the process noise covariance Qk, Ro, and Rp in the operation example. In FIG. 7, the process noise covariance Qk is shown in the first stage, Ro is shown in the second stage, and Rp is shown in the third stage. Of the nine graphs shown in FIG. 7, the three on the left show Comparative Example 1, the three in the center show Comparative Example 2, and the three on the right show Examples. In FIG. 7, the vertical axis is shown only at the left end for convenience of drawing.

In the present embodiment, as described in Equation 15, the value of the process noise covariance corresponding to Ro203 and Rp205 is not a constant value, but changes in time series as shown on the right side of the first stage of FIG. ing. On the other hand, in Comparative Example 1, the process noise covariance is fixed at a low value, and in Comparative Example 2, the process noise covariance is fixed at a high value.

Reference numeral 521 shown in the second row of FIG. 7 and reference numeral 531 shown in the third row of FIG. 7 indicate true values of the parameters. In the second and third stages of FIG. 7, it is shown that the present embodiment in which the process noise covariance is appropriately changed is superior to the prior art in terms of both speed of convergence and stability. When the current suddenly increases from 0A as shown by reference numeral 501 in FIG. 6, the process noise covariance calculated by the equation 15 increases as shown in reference numeral 513 in the present embodiment, and the extended Kalman filter 392 is shown in FIG. It is possible to greatly change the estimated values shown in the second and third stages on the right side.

According to Equation 8, when the process noise covariance 305 is set to a small fixed value as shown by reference numeral 511 in the update unit 340, the convergence of the estimated values of Ro203 and Rp205 is very large as shown by reference numeral 522 and reference numeral 532. Become slow. When a high current is applied, it becomes a constant value, as shown by reference numeral 502. Under the assumption that there is no change in temperature and SOC, Ro203 and Rp205 are constant values because the current is constant.

In Comparative Example 1, since the process noise covariance 305 is set to a small fixed value, the change is very gradual and does not reach the true value as shown by reference numerals 522 and 532. In Comparative Example 2, the estimated value of Ro converges early as shown by reference numeral 523, but has a tendency of overshoot. In Comparative Example 2, the estimated value of Rp tends to undershoot as shown by reference numeral 533. That is, in Comparative Example 2, the estimation of both resistances becomes inaccurate.

In the present embodiment in which the process noise covariance 305 is variable, the process noise covariance 305 is set to a small value by Equation 15 so as to prevent changes in Ro203 and Rp205 when the change in current is small. As a result, the estimation is stable when the current value is constant. By comparing the results of this operation example, it was confirmed that the method according to the present embodiment is more effective than the conventional extended Kalman filter.

According to the first embodiment described above, the following effects can be obtained.
(1) The battery management device 300 is based on the electric circuit model of the battery shown in FIG. 2, and has a controller 391 that outputs indicators related to the battery, such as SOC and SOH, and a filter having a variable gain for the parameters of the electric circuit model. It is provided with an update unit 340 that estimates based on the amount of time change of the battery current, which is the current flowing through the battery, and the temperature of the cell. It should be noted that the fact that the update unit 340 uses the time change amount of the battery current is specifically shown in the point of evaluating the difference in Bk in Equation 15 and the point in FIG. 4 that Bk corresponds to the current. Further, the fact that the update unit 340 uses the temperature of the cell is shown in FIG. 4 at the point where which curve is used is determined by the temperature. Therefore, as shown in the operation example of FIG. 7, the battery management device 300 can accurately calculate the battery parameters.

(2) the battery management system 300 includes an input u _k 302 based on the battery current, as shown in FIG. 3, the output y _{ke 303} based on the battery voltage, the battery management system used in the calculation of the function of a battery management system It comprises an extended Kalman filter 392 with a ^{state index X-} _k.

(3) The extended Kalman filter 392 has at least one internal resistance Ro, at least one polarization resistance Rp, and at least one battery polarization time constant τ, as shown in FIG.

(4) The extended Kalman filter 392 includes a covariance calculation unit 304 that calculates the process noise covariance Qk based on the amount of time change of the cell current and the cell temperature.

(5) The covariance calculation unit 304 reflects the look-up table 420, which is a database of current and temperature that reflects the Butler-Volmer phenomenon obtained by the experiment. Since the look-up table 420 is easy to create, the lead time for creating the battery management device 300 can be reduced as compared with a known method such as creating a correspondence map between OCV and SOC for each temperature. Furthermore, the amount of memory required for execution is smaller than that of a known method, for example, a method using a map corresponding to OCV and SOC for each temperature.

(6) The covariance calculation unit 304 increases the process noise covariance Qk when the change in current is large. Therefore, it is possible to allow a large change in the estimated value and shorten the time for convergence to the true value.

(7) The covariance calculation unit 304 reduces the process noise covariance Qk when the change in current is small. Therefore, the estimated value can be stabilized.

(8) The covariance calculation unit 304 sets the process noise covariance Qk to a low value when the temperature is high. Therefore, the estimated value can be stabilized.
(9) The covariance calculation unit 304 sets the process noise covariance Qk to a high value when the temperature is low. Therefore, it is possible to allow a large change in the estimated value and shorten the time for convergence to the true value.

(Modification example 1)
The process noise covariance Qk may be changed by other input methods that change the SOC or by the temperature per hour.

(Modification 2)
Different parameters of the extended Kalman filter 392, such as the observed noise covariance 309 or the Kalman gain calculated by the Kalman gain calculation unit 350, may be changed under the influence of current and temperature.

(Modification example 3)
In the confirmation by the operation pattern, the estimated result cannot be directly compared with the new value because the true value is unknown. The estimated parameters may be used to calculate the SOC based on the voltage value. Compared with the conventional method that uses a look-up table without using an extended Kalman filter, the accuracy is improved by calculating the SOC using the estimated parameters.

In each of the above-described embodiments and modifications, the configuration of the functional block is only an example. Several functional configurations shown as separate functional blocks may be integrally configured, or the configuration represented by one functional block diagram may be divided into two or more functions. Further, a part of the functions possessed by each functional block may be provided in the other functional blocks.

Each of the above-described embodiments and modifications may be combined. Although various embodiments and modifications have been described above, the present invention is not limited to these contents. Other aspects conceivable within the scope of the technical idea of the present invention are also included within the scope of the present invention.

100 ... Battery system 101 ... Battery pack 102 ... Current meter 103 ... Small voltage meter 104 ... Thermometer 105 ... Voltage meter 200 ... Randall electric circuit model 201 ... Open circuit voltage 202, Vp ... Polarization voltage 203, Ro ... Internal resistance 204 ... Polarization capacitor 205, Rp ... polarization resistance 206 ... closed circuit voltage 207 ... open voltage 300 ... battery control unit 304 ... covariance calculation unit 302, u k _... input 305, Qk ... process noise covariance 306 ... pre estimated 307 ... estimates 308 ... Ex-post estimation 309 ... Observation noise covariance 330 ... Dynamic model 340 ... Update unit 350 ... Kalman gain calculation unit 391 ... Controller 392 ... Extended Kalman filter 420 ... Lookup table Kk ... Kalman gain τ ... Time constant

Claims (9)

A controller that calculates an index related to the battery based on the electric circuit model of the battery, and
A battery management device including a calculation unit that estimates parameters of the electric circuit model using a filter having a variable gain based on the amount of time change of the battery current, which is the current flowing through the battery, and the temperature of the battery. In the battery management device according to claim 1,
A battery management device further comprising an extended Kalman filter comprising an input based on battery current, an output based on battery voltage, and a state index of the battery used to calculate the index by the controller. In the battery management device according to claim 2.
The extended Kalman filter is a battery management device having at least one internal resistance, at least one polarization resistance, and at least one battery polarization time constant. In the battery management device according to claim 2.
The extended Kalman filter
A battery management device further comprising a covariance calculation unit that calculates the process noise covariance based on the time change amount of the battery current and the temperature of the battery. In the battery management device according to claim 4.
The covariance calculation unit is a battery management device that reflects a database of current and temperature that reflects the Butler-Volmer phenomenon obtained by experiments. In the battery management device according to claim 4.
The covariance calculation unit is a battery management device that increases the process noise covariance when the change in current is large. In the battery management device according to claim 4.
The covariance calculation unit is a battery management device that reduces the process noise covariance when the change in current is small. In the battery management device according to claim 4.
The covariance calculation unit is a battery management device that sets the process noise covariance to a low value when the temperature is high. In the battery management device according to claim 4.
The covariance calculation unit is a battery management device that sets the process noise covariance to a high value when the temperature is low.

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