JP5329500B2 - Battery charge rate estimation device - Google Patents

Description

  The present invention relates to a battery charge rate estimation apparatus for estimating a charge rate of a battery used in an electric vehicle or the like.

  For example, in an electric vehicle or a hybrid electric vehicle, electric power is supplied (discharged) to an electric motor used to drive these vehicles, or an electric motor that causes braking energy to function as a generator or on the ground. A rechargeable battery (secondary battery) is used to store electric energy by charging from an installed power source.

In this case, in order to keep the battery in an optimum state for a long period of time, it is necessary to perform battery management by constantly monitoring the state of the battery, particularly the state of charge (SOC). However, when a battery is used, the charge / discharge / storage is due to chemical action, so the state of the battery must be estimated indirectly. In this case, the influence of the temperature change is large, and the state of the battery constantly changes depending on the usage environment and usage history, so it is difficult to estimate the SOC.
On the other hand, a capacitor is known as another means for obtaining the same effect as a battery. However, since a capacitor performs charge / discharge / storage by physical action, if a charge / discharge current, terminal voltage, etc. are measured. The state of the battery can be detected almost certainly. However, since the capacity of the capacitor is smaller than that of the battery, the battery is overwhelmingly used as the main power supply except for some capacitor trolley buses and the like.
Therefore, various methods for estimating the charging rate of various batteries have been proposed.

As a conventional method for detecting the charging rate of a battery, the battery voltage and current are all recorded in time-series data, and the current is time-integrated using these data to determine the current charge, and the battery is charged. A sequential state recording (bookkeeping) method (also referred to as a current integration method or a Coulomb count method) is known in which the SOC is calculated using the initial value of the generated charge and the full charge capacity.
However, with this method, it is necessary to constantly monitor the state of the battery, and once the SOC estimate deviates, it is difficult to return to the original state because errors accumulate thereafter. Another method has been proposed from the fact that it is necessary to obtain.

  Another method is to create a battery model and use the Kalman filter to update the model parameters and the open circuit voltage as the state quantity one by one and estimate the open circuit voltage each time. In other words, it is unnecessary to constantly monitor the battery status, and even if the estimation of the charging rate is deviated, the estimation of the battery status can be estimated without adversely affecting the estimation accuracy of the subsequent charging rate. (For example, refer to Patent Document 1).

Japanese Patent Publication No. 2004-514249

However, the conventional battery charge rate estimation method has the following problems.
That is, in the above-described prior art, a model that performs feedback based on the Kalman gain of the Kalman filter is configured only by the battery model.
On the other hand, a current sensor is used to detect the battery current as an input signal to the battery model and the actual battery. However, in some cases, current detection errors (particularly offset errors) cannot be avoided in the current sensor. In this case, even if the Kalman filter is used, if there is an offset error in the current sensor in the detected current input to the Kalman filter, the estimation accuracy of the battery charge rate using the Kalman filter is increased. Resulting in a negative impact on battery management.

  The present invention has been made paying attention to the above-mentioned problem, and the object of the present invention is to estimate the battery charge rate accuracy due to the offset error even if the current sensor has an offset error of current detection. An object of the present invention is to provide a battery charging rate estimation device capable of suppressing the decrease.

The battery charging rate estimation device of the present invention is
Charge / discharge current detecting means for detecting the charge / discharge current of the battery;
Terminal voltage detecting means for detecting the terminal voltage of the battery;
Charge / discharge current detection means and terminal voltage detection means detected by charge / discharge current detection means based on charge / discharge current detection means / battery model having at least current detection error due to offset of charge / discharge current detection means and battery open voltage as state quantities State estimation means for estimating an open circuit voltage, which is a state quantity, from the terminal voltage detected at
Charging rate estimating means for obtaining the charging rate of the battery from the open circuit voltage estimated by the state estimating means;
It is provided with.

  In the battery charge rate estimation device of the present invention, in addition to the battery model of the prior art having at least an open-circuit voltage as a state quantity, the charge / discharge current having a detection error due to an offset of the charge / discharge current detection means as a state quantity A state estimation means having a charge / discharge current detection means / battery model provided with a detection means model is constructed, and based on the charge / discharge current detection means / battery model, from the charge / discharge current as an input and the terminal voltage as an output The state estimation means estimates the state quantity including at least the open circuit voltage. Then, the charging rate of the battery is obtained by the charging rate estimating unit from the open circuit voltage estimated by the state estimating unit.

  Therefore, in the battery charge rate estimation device of the present invention, even when the current sensor has an offset error of current detection, the state estimation unit has a charge / discharge current detection unit model of the charge / discharge current detection unit having the offset error and the battery The charge / discharge current detection means including the battery model is configured to estimate the open-circuit voltage, which is a state quantity, using the battery model, so the charge rate of the battery is caused by the offset error of the charge / discharge current detection means. It is possible to suppress problems such as a decrease in estimation accuracy.

It is a block diagram which shows the structural relationship of the battery charge rate estimation apparatus of Example 1 of this invention, and the battery to which this apparatus is connected. It is a block diagram which shows the flow of the signal between the charge rate estimation apparatus of the battery of Example 1, and a battery. FIG. 3 is a circuit diagram showing a battery equivalent circuit model in a case where a battery model used in the battery charging rate estimation apparatus of FIG. 2 is represented by a state equation. It is a block diagram which shows the relationship of the state quantity calculation part and Kalman gain calculation part which comprise the state estimation part used with the charging rate estimation apparatus of the battery of FIGS. FIG. 5 is a block diagram showing a configuration of a Kalman gain calculation unit used in the battery charge rate estimation device of FIGS. 1 and 4. It is a block diagram which shows the structure of the state quantity calculation part used with the charging rate estimation apparatus of the battery of FIG. The charge rate estimation results of the battery charge rate estimation device using only the battery model of the prior art and the battery charge rate estimation device of Example 1 using the current sensor model and the battery model were compared. It is a figure, (a) is a figure which shows the comparison result of charging / discharging electric current value, and charging / discharging electric current detection value in the same figure, (b) is a figure which shows the comparison result of charging rate true value and charging rate estimated value (C) is a figure which shows the magnitude | size of estimation error.

    Hereinafter, embodiments of the present invention will be described in detail based on examples shown in the drawings.

FIG. 1 and FIG. 2 show the structural relationship of the charging rate estimation device for the battery 1 and the battery 1 to which this device is connected, and the flow of those signals.
The charging rate estimation device for the battery 1 according to the first embodiment is used for vehicles such as electric vehicles and hybrid electric vehicles. Such a vehicle is equipped with an electric motor (not shown) that drives the vehicle, a battery 1, and a controller (not shown), and when the vehicle is driven, power is supplied (discharged) from the battery 1 to the electric motor, or braking is performed. Sometimes, the electric motor functions as a generator, and the braking energy obtained at that time is collected (charged) into the battery 1 as electric energy, or the battery 1 is charged from a power supply installed on the ground. The charging rate estimation device monitors the charging / discharging current in and out of the battery 1 with the charging rate estimation device of the battery 1 and estimates the charging rate which is one of the states of the battery 1.

First, the overall configuration of the charging rate estimation device for the battery 1 will be described.
As shown in FIGS. 1 and 2, the battery 1 charging rate estimation apparatus according to the first embodiment includes a current sensor 2, a voltage sensor 3, a state estimation unit 4, and a charging rate calculation unit 5. 3 is connected to the battery 1 through 3. In addition, the state estimation part 4 and the charge rate calculation part 5 are comprised by the vehicle-mounted microcomputer in a present Example.

  In this embodiment, the battery 1 uses a rechargeable battery, for example, a lithium ion battery, but is not limited to this, and other types of batteries such as a nickel-hydrogen battery may be used. Needless to say.

The current sensor 2 collects a part of the braking energy by supplying the electric motor from the battery 1 to the electric motor or the like, and makes the electric motor function as a generator at the time of braking. The charging / discharging current value I _S detected there is input to the state detection unit 4 as an input signal.
The voltage sensor 3 detects the voltage between the terminals of the battery 1, and the detected terminal voltage value V is input to the state estimation unit 4, and the state estimation unit 4 outputs the battery 1 for calculating Kalman gain. Used as a signal.
The current sensor 2 and the voltage sensor 3 can be appropriately employed in various structures and types, and correspond to the charge / discharge current detection means and the terminal voltage detection means of the present invention, respectively.

  The state estimation unit 4 includes a Kalman filter KF as an observer. That is, the state estimation unit 4 includes a state quantity calculation unit 4A including a current sensor battery model MSB having the offset detection error of the current sensor 2, the open-circuit voltage of the battery 1, and the terminal voltage as state quantities. A Kalman gain calculation unit 4B for determining the magnitude of the Kalman gain L for correcting the input weight used in the calculation unit 4A is provided.

The state estimation unit 4 calculates an open-circuit voltage value OCV ^ that is a state quantity based on the current sensor battery model MSB from the charge / discharge current value I and the terminal voltage value V detected by the current sensor 2 and the voltage sensor 3 respectively. The terminal voltage value V ^ is configured to be estimated. The detailed configuration of the state estimation unit 4 will be described later.
The open-circuit voltage value OCV ^ of the battery 1 that is one of the state quantities estimated by the state estimation unit 4 is output to the charge rate calculation unit 5.
The state estimation unit 4 and the current sensor / battery model correspond to the state estimation unit and the charge / discharge current / battery model of the present invention, respectively.

  Since the charge rate calculation unit 5 is less susceptible to temperature and battery deterioration because the relationship between the open circuit voltage value (OCV) and the charge rate (SOC), the results obtained by obtaining these relationships in advance through experiments, etc. For example, it is stored as a table. Based on this table, the charging rate SOC of the battery 1 is estimated from the open-circuit voltage estimated value OCV ^ estimated by the state estimating unit 4.

Next, a detailed configuration of the state estimation unit 4 will be described below.
The state estimation unit 4 includes a Kalman filter KF.
In the Kalman filter, a model of the target system is designed, and the same input signal is input to this model and the actual system. -Applying the gain L and feeding back to the model corrects the model so that the error between them is minimized. By repeating this, the true internal state quantity is estimated.
In the Kalman filter, it is assumed that the observed noise is normal white noise. Therefore, in this case, since the system parameter is a random variable, the true system is a stochastic system. Therefore, the observed values are described in a linear regression model, the sequential parameter estimation problem can be formulated using state space representation, and the time-varying parameter can be estimated without recording the sequential state. In this way, a mathematical model can be created from the measured values of the input / output data of the target dynamic system to explain that it is the same as the target for a predetermined purpose, that is, system identification Is possible.

In designing the Kalman filter, the system to be estimated must be expressed by the following equation of state.
dx / dt = Ax + Bu (Formula 1)
y = Cx + Du (Formula 2)
Here, x is a system state vector (representing a state quantity as a parameter), u is an input vector to the system, y is an output vector, and A, B, C, and D are matrices describing system dynamics. Are a system matrix, an input matrix, an output matrix, and a transfer matrix, respectively, and d / dt is a time derivative.

In this embodiment, the model used in the state estimation unit 4 is set as follows.
That is, in the conventional Kalman filter 6, as shown in FIG. 2, the model of the target system is configured only by the battery model MB, whereas the Kalman filter KF of Example 1 is used. In the model to be used, in addition to the conventional battery model MB, the current sensor model MSB of the current sensor 2 including the offset error is added to the model MB to configure the current sensor battery model MSB.

The battery model MB can be represented by a battery equivalent circuit model. As this equivalent circuit model, a Foster-type RC ladder circuit shown in FIG. 3 (only a primary parallel circuit) is used in this embodiment. That is, in this circuit, a resistance (R ₁ : Faraday impedance) is set in a basque resistance (R ₀ ) for setting a direct current component such as an ohmic resistance due to the electrolyte resistance and connection of the battery 1. A parallel circuit of a capacitor (set as a reaction resistance representing dynamic behavior) and a capacitor (C ₁ : set as a non-Faraday impedance and representing an electric double layer) is connected. Further, in the figure, OCV open circuit voltage value of the capacitor C _OCV representing the open circuit voltage, a terminal voltage value at V, also are respectively displayed overvoltage value generated in the parallel circuit V _1. The terminal voltage value V is equal to the sum of the open-circuit voltage value OCV and the overvoltage V value ₁ .

ただし、状態ベクトルｘ＝［過電圧値 開放電圧値］^Ｔ、入力は充放電電流検出値Ｉ（充電をプラス、放電をマイナスにとる）、出力は端子電圧値Ｖである。なお、上記式中、行列の右上の添え字Ｔは、その行列の転置を意味する。 In this battery model MB, the values of the matrices A, B, C, and D in (Equation 1) and (Equation 2) are as follows.

However, the state vector x = [overvoltage value, open-circuit voltage value] ^T , the input is the charge / discharge current detection value I (plus charge and minus minus discharge), and the output is the terminal voltage value V. In the above formula, the subscript T on the upper right of the matrix means transposition of the matrix.

Here, in the present embodiment, since the current sensor model MS having an offset error is added before the battery model MB, the state vector x is the conventional [overvoltage value open voltage value] ^T and I _OS. (Detection error due to offset). In this embodiment, it is assumed that the detection error I _OS due to the offset is constant. The input and output are the same as described above.

Returning to the block diagram of FIG. 2, in the current sensor model MS, the charge / discharge current detection value I _S from the current sensor 2 (the detection error i due to the offset in the current sensor 2 to the actual charge / discharge current value I of the battery 1). _And the offset error I _OS set by the current sensor model MS is input to the subtractor 40, and the latter is subtracted from the former. Since the offset error I _OS is assumed to be constant in this embodiment, the offset error I _OS is obtained by a step function that starts with an initial value 0 and integrates with 1 / s (s is a Labruss operator). Needless to say, this integration may be multiplied by an integral constant.

On the other hand, in the battery model MB, the current value (I _S −I _OS ) output from the subtracter of the current sensor model MS is multiplied by the matrix B and input to the adder 41.
Separately, the current value (I _S −I _OS ) output from the subtracter of the current sensor model MS is multiplied by the matrix D and input to the adder 42.
The output y from the adder 42 is an estimated terminal voltage value V ^ estimated by the current sensor / battery model MSB, and is input to the subtractor 43, where the output voltage y is detected from the terminal voltage value V detected by the voltage sensor 3. The estimated terminal voltage value V ^ is subtracted. As a result, the terminal voltage value V of the battery 1, the estimated terminal voltage value V ^ in the model, and the error ε are obtained, and this error ε is multiplied by the Kalman gain L and input to the adder 41 . Note that the symbol ^ represents estimation, and in the present specification, for the sake of convenience, it is shifted to the right, unlike the notation in the figure.

The adder 41 further receives a third signal, which will be described later, and adds and outputs these three inputs. This output is integrated into a state quantity x (= estimated open-circuit voltage value OCV ^), and this value is further multiplied by a matrix A to become a third signal. Separately, the value obtained by multiplying the state quantity x by the matrix C and the value obtained by multiplying the current value (I _S −I _OS ) by the matrix D are added by the adder 43, and the output y is obtained. To get.

ここで、このモデルでの状態ベクトルｘは、従来のｘ（＝［過電圧値 開放電圧値］^Ｔ）とＩ_ＯＳ（オフセット誤差）で構成されるとしている。 Therefore, if you enter the current detection value I _S to the current sensor Battery model MSB, the equation of state of the battery, A, B, each matrix of C is (Formula 1), changes from (equation 2) (matrix D Can be expressed as follows:

Here, the state vector x in this model is assumed to be composed of conventional x (= [overvoltage value, open voltage value] ^T ) and I _OS (offset error).

を用いることになる。 Thus, in this embodiment, in the following equation, A, B, C, and D are expressed as follows, using the new matrix A, B, C, and D in (Equation 7), and the Kalman filter KF: To design.
That is,

Will be used.

The above equation of state of the Kalman filter KF explained above has been explained. Since (Equation 1) and (Equation 2) are described in a continuous system, the sampling time is T as follows. Discretizes with 0th-order hold.
In the equation below, the subscript k is the order of the sampling number, u _k is the input data in the k-th (detected current value I _S in this _embodiment), y _k is the output data (the example in the k-th terminal voltage estimated value ^{V ^), Σ - x ~} , k is pre-estimated error covariance value when the k-th, sigma ⁺ x ^~, k is the estimated error covariance value after a time in the k-th, in L _k is the k-th Kalman gain, Σ _V is process noise, Σ _W is observation noise, ^ is an estimated value, ⁻ is an estimation before time, + is an estimation after time, ε _k is an error between the detection output and the estimation output at the k th (ie, In this embodiment, an error between the terminal voltage detection value and the terminal voltage prediction value is expressed respectively. However, when using the Kalman filter KF, ε _k is an average value of 0, normal white noise, and process noise and observation noise are assumed to be independent from each other. In addition, in the above symbols, the symbols ^, ^˜ , ⁻ , + are described at the positions shifted to the right in the specification, unlike the use in the drawings.

The state equation of the discrete Kalman filter KF can be expressed as follows.

なお、上記（式10）、（式11）中におけるｅ^ＡＴは、状態遷移マトリクスである。ここで、ｅは自然数、Ｔは入出力信号のサンプリング周期（行列やベクトルの転置を表す上付き添え字Ｔとは異なる）である。 A _k , B _k , C _k , and D _k in the above (formula 8) and (formula 9) are as follows.

The above equation (10), ^{e AT} in the equation (11) is a state transition matrix. Here, e is a natural number, and T is a sampling period of the input / output signal (different from the superscript T representing the transposition of a matrix or a vector).

これらの式(式14)〜(式17)は状態量を推定するための式である。
これらの式により、状態推定部４は、図４のブロック線図にて表わすことができる。また、状態推定部４の状態量算出部４Ａは、図６のブロック線図にて表すことができる。これらのブロック線図については後で説明する。 Therefore, the state equation of the Kalman filter KF is expressed as follows.

These formulas (formula 14) to (formula 17) are formulas for estimating the state quantity.
From these equations, the state estimation unit 4 can be represented by the block diagram of FIG. Further, the state quantity calculation unit 4A of the state estimation unit 4 can be represented by the block diagram of FIG. These block diagrams will be described later.

これらの式（式18）〜(式20)により、カルマン・ゲイン算出部４Ｂのブロック線図は、図５のように表すことができる。このブロック線図については後で説明する。 At this time, the estimation error covariance value and the Kalman gain in the k-th prior estimation and post-estimation are expressed by the following equations.

From these equations (Equation 18) to (Equation 20), the block diagram of the Kalman gain calculation unit 4B can be expressed as shown in FIG. This block diagram will be described later.

  As described above, the estimation of the state quantity using the Kalman filter KF is performed by the state estimation unit 4 (state quantity) based on (Equation 14) to (Equation 20) and the state equation (Equation 7) of the battery equivalent model of FIG. The calculation unit 4A and the Kalman gain calculation unit 4B are provided), and this block diagram is shown in FIG.

As shown in FIG. 4, in the state quantity calculation unit 4, a Kalman gain L _k is calculated from a Kalman gain calculation unit 4 _< / b> B, which will be described later, and is output to the multiplier 8.
An error ε _k obtained by subtracting the terminal voltage estimated value V ^ estimated by the state quantity calculation unit 4A from the terminal voltage detected value V detected by the voltage sensor 3 is input to the multiplier 8, and the Kalman gain L _k is multiplied and input to the state quantity calculation unit 4A.
The Kalman gain calculation unit 4B will be described later with reference to FIG.

On the other hand, the state quantity calculation unit 4A, and the charge and discharge current detection value I _S detected by the current sensor 2, the error epsilon _k Kalman gain L between the terminal voltage detection value and the terminal voltage estimation value obtained in the multiplier 8 _The integrated value multiplied by _k is input, and the open circuit voltage estimated value OCV ^ and the terminal voltage estimated value V ^ are estimated by the calculation method described later with reference to FIG.

Next, the estimation of the Kalman gain L _k in the Kalman gain calculation unit 4B is performed by (Equation 18) to (Equation 20), and its block diagram is shown in FIG.
As shown in the figure, the adder 10 includes the process noise Σ _V and the kth previous (k−1) th estimated error covariance value Σ _x ^˜ _{, k,} which is the output of the delay unit 12. A value _obtained by multiplying ₋₁ ⁺ by coefficient multipliers 13 and 14 by A _k and A _k ^T is added and output as estimated error covariance values Σ _x ^to _{, k} ⁻ as the k-th previous estimation ( Equation 18).
In the multiplier 11, the estimated error output from the adder 10 is subtracted by the subtractor 15 obtained by subtracting the accumulated value _obtained by multiplying the Kalman gain L _k by the matrix C _k from the charge / discharge current detection value I _S. The covariance values Σ _x ^˜ _{, k} ⁻ are multiplied to obtain an estimation error covariance value Σ _x ^˜ _{, k} ⁺ as the k-th post-estimation (Equation 19). The estimated error covariance value _x sigma ^~, _k ⁺ is (multiplied by Z ^-1) delay unit 12 by Z conversion k-th previous (k-1 th) estimation error covariance value sigma _x ^~ _{a, k-1} ⁻ is obtained. The estimated error covariance values Σ _x ^˜ _{, k−1} ⁻ are multiplied by the matrix A _k and the transposed matrix A _k ^T respectively by the coefficient multipliers 13 and 14 as described above, and then the multiplied value is added. Input to the device 10.

On the other hand, the estimation error covariance values Σ _x ^˜ _{, k} ⁻ as the k-th pre-estimation output from the adder 10 are multiplied by the transposed matrix C _k ^T by the coefficient multiplier 16 and input to the divider 17. In addition, the integrated value obtained by multiplying the matrix C _k by the coefficient multiplier 18 is input to the adder 19. The integrated value are summed and observation noise sigma _W by the adder 19, the added value is inputted to the divider 17. The divider 17 divides the output from the coefficient multiplier 16 by the output from the adder 19 and outputs the result as a Kalman gain L _k (Equation 20). The Kalman gain L _k is multiplied by the matrix C _k by the coefficient multiplier 20 and input to the subtractor 15 as described above.

Next, the state quantity calculation in the state quantity calculation unit 4A is performed by the state quantities (Expression 14) to (Expression 17), and a block diagram thereof is shown in FIG.
In the figure, the input u _k (= charge / discharge current detection value I _S ) is multiplied by Z ⁻¹ by the delay unit 22 by Z conversion to obtain the (k−1) th previous input u of kth. _k-1 is obtained. The input u _k−1 is input to the multiplier 23, where B _k u _k−1 is obtained by multiplying the input u _k−1 by the matrix B _k . This B _k u _k−1 is input to the adder 24.

The adder 24 further receives and adds the estimated value A _k x _k−1 ^ ^+, which is the output from the coefficient multiplier 26, and obtains the _k- th pre-estimated state quantity x _k ^ ⁻ ( Equation 14). The estimated value A _k x _k−1 ^ ⁺ is converted into the k-th post-estimated state quantity x _k ^ ⁺ (= open-circuit voltage estimated value OCV ^) output from the adder 27 by the delay unit 25. It is obtained by multiplying the previous state quantity x _k-1 ^ ⁺ obtained by multiplying by ⁻¹ by the matrix A _k by the coefficient multiplier 26 as described above.

The estimated state quantity x _k ^ ⁻ in the adder 24 is added to the accumulated value L _k · ε _k obtained by the Kalman gain calculating unit 4B and the multiplier 8 in the adder 27 to obtain the estimated state quantity x _k ^ ⁺ (= open-circuit voltage estimated value OCV ^) is obtained (Expression 15).

On the other hand, the output x _k ^ ⁻ from the adder 24 is also input to the coefficient multiplier 28 and multiplied by the matrix C _k to obtain C _k x _k ^ ⁻ . This C _k x _k ^ ⁻ is input to the adder 30. Further, D _k u _k obtained by multiplying the input u _k (= charge / discharge current detection value I _S ) by the matrix D _k by the coefficient multiplier 29 is input to the adder 30 and added, and the state quantity is added. ^{_{C k x k ^ - + D}} k u k, namely _{y k} ^ (= terminal voltage estimation value V ^) is obtained (equation 17).

In order to confirm the effect of the battery charge rate estimation apparatus configured as described above, the result of simulation performed in the following manner is shown in FIG. Here, the true current value is too expensive to be mounted on the vehicle, but is obtained by measuring using a current sensor with very high measurement accuracy.
In this simulation, the current sensor 2 has an offset error current of 0.2 A [ampere]. As a result, the detection current value _{I S} at the current sensor 2 becomes the current true value + 0.2 A. In FIG. 7, the result obtained by the conventional Kalman filter (using only the battery model MB) is shown on the left side, and the Kalman filter KF of this embodiment is shown on the right side (a model obtained by adding a current sensor model to the battery model). The results obtained with MSB are shown.

  The figure (a) shows the result of having compared the true value (shown with a dotted line) and detection value (shown with a continuous line) of charging / discharging current. The horizontal axis represents time, and the vertical axis represents the current value. The simulation is performed using the same input (the left and right figures are the same) for both the conventional apparatus and the apparatus of the present embodiment.

  FIG. 6B compares the true value (indicated by the dotted line) and the estimated value (indicated by the solid line) of the charging rate. The horizontal axis represents time, and the vertical axis represents the charging rate. In the case of using the conventional Kalman filter, the estimated charging rate is lower than the true value. In this case, there is a possibility that the battery 1 may be damaged due to overcharging as a result of being determined to be insufficient even though the charging is completed. On the other hand, in the charging rate estimation apparatus of the present embodiment, the estimated value is higher than the true value, so there is no concern that overcharging will be performed by determining that charging is insufficient.

  FIG. 2C shows the charging rate estimation error (shown by a solid line). The horizontal axis represents time, and the vertical axis represents estimation error. In the case of using the conventional Kalman filter, the estimation error is a negative value. This is because the estimated value of the charging rate is smaller than the true value as shown in FIG. On the other hand, in the present example, although the first several tens of seconds have a negative value, the time after 50 seconds is a positive value. This is because the estimated value of the charging rate exceeds the true value.

  In addition, in the same figure (b) and (c), for the first tens of seconds after the start of estimation, the estimated value and estimation error of the charging rate start from 0 and then rise rapidly, but this short interval is ignored and the charging rate is ignored. Not used for guessing. Of course, it is needless to say that there is no problem in practical use at such a time. The reason why the charging rate, the estimated value, and the estimation error start from 0 in this way is that the initial value is set to 0 in the detection error estimation portion in the battery model MB.

As described above, in the battery charge rate estimation apparatus according to the first embodiment, the following effects can be obtained.
(1) In the battery charge rate estimation apparatus of this embodiment, a current sensor model MS that models the current sensor 2 having an offset error in addition to the battery model MB in order to use the Kalman filter KF. Since the battery sensor / battery model MSB provided with is used, even if the current sensor 2 has an offset error in current detection, the estimation accuracy of the charging rate of the battery 1 is prevented from being lowered due to the offset error. be able to.

(2) Further, since the charging rate is estimated using the Kalman filter KF, the estimation accuracy of the open circuit voltage can be easily increased at low cost, and as a result, the estimation accuracy of the charging rate can be improved.

  The present invention has been described based on the above embodiments. However, the present invention is not limited to these embodiments, and is included in the present invention even when there is a design change or the like without departing from the gist of the present invention. .

  For example, the battery model is not limited to the model of the embodiment, and uses a Foster-type RC ladder circuit that represents the dynamic behavior in the diffusion process, and uses a parallel connection of multiple stages of resistors and capacitors in series. Also good. Alternatively, a battery model different from the Foster type RC ladder circuit, such as a Cowell type ladder circuit, may be used.

  The battery charging rate estimation device of the present invention is not limited to a lithium ion battery, and can be applied to other types of batteries. The battery is not limited to a vehicle such as an electric vehicle or a hybrid electric vehicle. It can also be used to estimate the charging rate of batteries used in and structures.

KF Kalman Filter MSB Current Sensor Battery Model (Charge / Discharge Current Detection Means Battery Model)
MB battery model MS current sensor model 1 battery 2 current sensor (charge / discharge current detection means)
3 Voltage sensor (terminal voltage detection means)
4 State detection unit (state detection means)
4A state quantity calculation unit 4B Kalman gain calculation unit 5 charge rate calculation unit (charge rate calculation means)
8, 11, 23 Multiplier 9, 15 Subtractor 10, 19, 24, 27, 30 Adder 12, 22, 25 Delayer 17 Divider 13, 14, 16, 18, 20, 26, 28, 29 Coefficient multiplication vessel

Claims (3)

Charge / discharge current detecting means for detecting the charge / discharge current of the battery;
Terminal voltage detecting means for detecting the terminal voltage of the battery;
Charge / discharge current detected by the charge / discharge current detection means based on a charge / discharge current detection means / battery model having at least a detection error due to an offset of the charge / discharge current detection means and an open voltage of the battery as a state quantity and the terminal voltage State estimation means for estimating the open circuit voltage, which is the state quantity, from the terminal voltage detected by the detection means;
Charging rate estimating means for obtaining the charging rate of the battery from the open circuit voltage estimated by the state estimating means;
A battery charge rate estimation device comprising: The battery charge rate estimation apparatus according to claim 1,
The state estimating means is a Kalman filter.
An apparatus for estimating a charging rate of a battery. In the battery charging rate estimation device according to claim 2,
The Kalman filter receives a charge / discharge current including an offset error of the charge / discharge current detection means detected by the charge / discharge current detection means, and a terminal voltage detected by the terminal voltage detection means , a value obtained by multiplying the Kalman gain to the error between the estimated terminal voltage with the terminal voltage and the discharge current detection unit battery model is fed back to the calculation of the state quantity, is the state quantity Estimate the open circuit voltage ,
An apparatus for estimating a charging rate of a battery.

Images