JP2017223536A - Device for estimating battery state and method for estimating battery state - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a device for estimating a battery state and a method for estimating a battery state, capable of accurately estimating a battery state.SOLUTION: A battery state estimation device 100 comprises; an OCV computation unit 120 configured to compute an OCV estimate based on detected current and voltage of a battery and internal resistance during charging and discharging; an SOC computation unit 130 configured to compute an SOC estimate, represented by a function fof the OCV estimate, based on the computed OCV estimate and a cumulative current value; storage means 151 configured to store the computed OCV estimate and SOC estimate; and an SOC-OCV curve computation unit 150 configured to compute an SOC-OCV curve using cumulative SOC estimate data if the OCV estimate data and SOC estimate data are accumulated in the storage means 151 under predetermined conditions.SELECTED DRAWING: Figure 1

Description

The present invention relates to a battery state estimation device and a battery state estimation method.

In electric vehicles such as EV (Electric Vehicle) and HEV (Hybrid Electric Vehicle), it is necessary to accurately estimate the state of the battery from the viewpoint of battery control. Among these, SOC (State Of Charge) is an important parameter. In a high voltage battery such as a Li ion battery used in an electric vehicle, it is known that there is a relationship between OCV (Open Circuit Voltage) and SOC, so OCV is one method for estimating SOC. There is a method of converting to SOC from the above-described relationship between OCV and SOC (hereinafter referred to as SOC-OCV curve).
The SOC-OCV curve changes depending on the deterioration state of the battery and individual differences. Therefore, as a conventional technique, an SOC-OCV curve depending on a deterioration state, a solid difference, and a temperature is stored as a map, and an optimum SOC-OCV curve is selected from the obtained data. In this case, it is general that the SOC-OCV curve is acquired in advance in an ideal state such as a charge / discharge device and vehicle data is set. However, the above-described method cannot cope with variations in the SOC-OCV curve due to mass production or changes in the SOC-OCV curve due to deterioration, leading to deterioration in SOC estimation accuracy.

  Patent Document 1 has an SOC-OCV map data storage unit that stores SOC-OCV map data describing the correspondence between the open circuit voltage of a battery and the state of charge of the battery. There is described a battery control device that outputs a different state of charge according to elapsed time by correcting the correspondence described in the SOC-OCV map data based on the flowing current.

JP 2014-196985 A

  However, in the battery control device described in Patent Document 1, storing the SOC-OCV curve according to the deterioration state, the solid difference, and the temperature as a map is enormous in total, and is actually very Have difficulty. Even if it is implemented, an enormous storage capacity is required to store an enormous map, and the cost increases. Further, if the map is reduced in order to reduce the storage capacity, the accuracy deteriorates. Furthermore, you can search only from the map that you have assumed in advance.

  Then, the subject of this invention is providing the battery state estimation apparatus and battery state estimation method which can estimate a battery state accurately.

  In order to solve the above-described problem, the battery state estimation device according to claim 1 includes a current detection unit that detects a charge / discharge current of a battery, a voltage detection unit that detects a voltage between terminals of the battery, and the detected An estimated OCV calculating means for calculating an estimated OCV based on the battery current and voltage and an internal resistance during charging and discharging, and a function of the estimated OCV based on the calculated estimated OCV and current integrated value. Estimated SOC calculating means for calculating the estimated SOC, storage means for storing the calculated estimated OCV and the estimated SOC, and data of the estimated OCV and the estimated SOC stored in the storage means under predetermined conditions And a SOC-OCV curve calculating means for calculating an SOC-OCV curve using the accumulated estimated SOC data.

  The battery state estimation method according to claim 9 includes a current detection step of detecting a charge / discharge current of the battery, a voltage detection step of detecting a voltage between terminals of the battery, and the detected current and voltage of the battery. An estimated OCV calculating step for calculating an estimated OCV based on the internal resistance during charging and discharging, and an estimated SOC expressed by a function of the estimated OCV based on the calculated estimated OCV and current integrated value An estimated SOC calculation step, a storage step of storing the calculated estimated OCV and the estimated SOC, and the data of the estimated OCV and the estimated SOC stored in the storage means when stored under a predetermined condition. And a SOC-OCV curve calculating step of calculating an SOC-OCV curve using the estimated SOC data.

By doing so, it becomes possible to perform SOC calculation and capacity calculation in consideration of the change due to the production variation of the SOC-OCV curve and the deterioration of the SOC-OCV curve, and the original performance of the battery is further improved. Can be used up. As a result, the vehicle performance can be improved and the number of cells can be reduced.
In addition, since a large number of maps are not prepared, it is possible to cope with various production variations and deterioration states without requiring a huge storage device capacity. In addition, it is possible to easily estimate the state of batteries having different battery characteristics.

  The invention according to claim 2 is characterized in that the predetermined condition is that a predetermined number of data of the estimated OCV near the reference SOC is accumulated.

  According to such a configuration, it is possible to estimate the curve with high accuracy by accumulating a predetermined number of data with respect to the reference SOC.

  According to a third aspect of the present invention, a plurality of the reference SOCs are provided within a range in which the storage battery is used, and the SOC-OCV curve calculating means stores the SOC- after the data storage of each reference SOC. An OCV curve is calculated.

  If learning is partially performed, the SOC-OCV curve mismatch tends to occur. According to this configuration, it is possible to estimate with high accuracy by generating the curve after the data of the entire use range is accumulated.

  According to a fourth aspect of the present invention, the SOC-OCV curve is sequentially estimated based on the estimated OCV and the estimated SOC calculated by the SOC-OCV curve calculating means, and the SOC-OCV curve is automatically learned. Features.

  According to such a configuration, it is possible to automatically learn the SOC-OCV curve using the SOC and OCV calculated by the controller.

  The invention according to claim 5 is characterized in that the SOC-OCV curve calculating means calculates the SOC-OCV curve using a predetermined number or more of the data.

  According to such a configuration, estimation can be performed with high accuracy.

  The invention according to claim 6 is characterized in that the SOC-OCV curve calculating means offsets the calculated SOC-OCV curve with reference to SOC and OCV at full charge.

  According to such a configuration, since the SOC and OCV values at the time of full charge do not change, it is possible to accurately detect the state by offsetting the SOC-OCV curve created based on the SOC and OCV values at the time of full charge. It becomes.

  The invention according to claim 7 includes battery temperature detection means for detecting the temperature of the battery, and the estimated SOC calculation means calculates an estimated SOC expressed by a function of the detected temperature and the estimated OCV. It is characterized by that.

  According to such a configuration, it is possible to calculate the estimated SOC corresponding to each temperature of the battery, and it is possible to estimate the state of charge of the battery more accurately.

  The invention described in claim 8 further includes battery temperature detection means for detecting a temperature of the battery, wherein the SOC-OCV curve calculation means is based on the calculated estimated OCV, the estimated SOC, and the detected temperature. The SOC-OCV curve is calculated.

  According to such a configuration, the SOC-OCV curve corresponding to each temperature of the battery can be automatically learned, and the state of charge of the battery can be estimated more accurately.

  ADVANTAGE OF THE INVENTION According to this invention, the battery state estimation apparatus and battery state estimation method which can estimate a battery state accurately can be provided.

It is a figure which shows the structure of the battery state estimation apparatus which concerns on embodiment of this invention. It is a figure which shows the SOC-OCV curve which the SOC-OCV calculation part of the battery state estimation apparatus which concerns on the said embodiment calculates. It is a figure which shows the measured value of the charging / discharging electric current of a secondary battery when the electric current is input into the secondary battery, and the measured value of the voltage of a secondary battery. It is a figure which shows the equivalent circuit model of the secondary battery of FIG. It is a figure explaining the offset generation | occurrence | production of the SOC-OCV curve of the battery state estimation apparatus which concerns on the said embodiment, (a) is a figure which shows a calibration value and a true value, (b) is a calibration value and a true value. It is a figure which shows an estimated value. It is a figure explaining the offset correction of the SOC-OCV curve of the battery state estimation apparatus which concerns on the said embodiment, (a) is a figure which shows OCV with 100% of SOC, (b) is a calibration value and a true value. (C) is a figure which shows an offset correction | amendment with respect to a true value. It is a figure which shows the data score of estimated OCV and estimated SOC in case the SOC of the center limit theorem of the battery state estimation apparatus which concerns on the said embodiment is 40%. Is a diagram showing the estimated OCV _SOC obtained by integrating the data points in FIG. 7, (a) is a diagram showing an estimated OCV _SOC obtained by integrating the data points, (b) is obtained by integrating the data points in the case of increasing N number It is a figure which shows presumed OCV _SOC . It is a figure which shows OCV _SOC computed for every SOC of the battery state estimation apparatus which concerns on the said embodiment. It is a flowchart which shows the battery state estimation method of the battery state estimation apparatus which concerns on the said embodiment.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
(Embodiment)
FIG. 1 is a diagram showing a configuration of a battery state estimation device according to an embodiment of the present invention. The battery state estimation device of the present embodiment is mounted on a vehicle such as an EV or HEV together with a secondary battery that is estimated by the battery state estimation device. When mounted on the vehicle in this way, the battery state estimation device functions as a battery ECU (Electric Control Unit).
As shown in FIG. 1, the battery state estimation device 100 includes a current detection unit 101 (current detection unit), a voltage detection unit 102 (voltage detection unit), a temperature detection unit 103 (temperature detection unit), a resistance calculation unit 110, and an OCV. A calculation unit 120 (estimated OCV calculation unit), an SOC calculation unit 130 (estimated SOC calculation unit), a capacity calculation unit 140, and an SOC-OCV calculation unit 150 (SOC-OCV curve calculation unit) are provided.
FIG. 2 is a diagram showing an SOC-OCV curve calculated by the SOC-OCV calculation unit 150.

<Detection means>
The current detection unit 101 detects at least one current (hereinafter also referred to as charge / discharge current) I of the charge current to the secondary battery 1 and the discharge current from the secondary battery.
The voltage detection unit 102 detects the inter-terminal voltage V of the secondary battery 1.
The temperature detection unit 103 detects the temperature T of the secondary battery 1.

<Resistance calculation unit>
The resistance calculation unit 110 calculates the resistance R from the detected current of the secondary battery 1 and the inter-terminal voltage. Specifically, the resistance calculation unit 110 includes a differential value dI of a current (hereinafter also referred to as an actual current) detected by the current detection unit 101 and a voltage (hereinafter also referred to as an actual voltage) detected by the voltage detection unit 102. The resistance R is calculated from the differential value dV according to the following equation (1).
R = dV / dI (1)

Here, a method for identifying the temporary internal resistance r performed by the resistance calculation unit 110 will be described.
3 is a diagram showing measured values of charge / discharge current of the secondary battery and measured values of voltage of the secondary battery when current is input to the secondary battery, and FIG. 4 is an equivalent circuit of the secondary battery of FIG. It is a figure which shows a model (a capacitor component shows only a primary component).
When obtaining the internal resistance and SOC from the measured values of the voltage and current of the secondary battery, it is assumed that the following relational expression (formula (2)) holds.
V (voltage measurement value) = OCV (open circuit voltage) −K (internal resistance) × I (current measurement value)
... (2)
In order to obtain the internal resistance K, parameter estimation including parameters of internal resistance is performed using the primary expression of the expression (2) as a simple model of a secondary battery. Although the successive least square method is known as an approximation method of the primary equation, the internal resistance of the secondary battery cannot be accurately estimated only by this method.

  The characteristics of the secondary battery are not completely linear, but include a non-linear part as shown in FIG. FIG. 3 shows the measured value of the charge / discharge current of the secondary battery and the measured value of the voltage of the secondary battery when current is input to the secondary battery. In FIG. 3, after the input of the current is started at time t1, the voltage gradually rises behind the current. Then, after the input of current is terminated at time t2, the voltage gradually decreases behind the current. The reason why the voltage fluctuates behind the current in this way is that the secondary battery includes a capacitor component (C component, component C1 in FIG. 4) as shown in FIG. Actually, after the input of current to the secondary battery is stopped, a primary delay and a secondary delay occur as voltage delays.

  By the way, in the secondary battery 1 shown in FIG. 1, so-called polarization occurs in which the electrode potential when the current flows is different from the potential (equilibrium potential) when the current does not flow. If polarization occurs in the secondary battery, the estimation accuracy of the SOC may be reduced due to the fluctuation of polarization. Therefore, after confirming that the polarization of the secondary battery is eliminated, the SOC of the secondary battery is estimated. For example, when a predetermined time has elapsed since the ignition key was switched off, it is determined that the polarization of the secondary battery has been eliminated.

<OCV calculation part>
The OCV calculation unit 120 calculates an OCV (estimated OCV) from the detected value (current I, terminal voltage CCV) and the calculated resistance (internal resistance during charging and discharging) according to the following equation (3).
OCV = CCV + IR (3)
Here, since OCV (estimated OCV) is calculated by OCV = CCV + IR as shown in Expression (3), the SOC-OCV curve information is not necessary for estimation.

<SOC calculation part>
The SOC calculation unit 130 has an SOC-OCV map 130a. The SOC-OCV map 130a is used when referring to the initial value.
The SOC calculation unit 130 calculates the SOC (estimated SOC) according to the following equation (4) using the calculated OCV (estimated OCV) and the current integrated value. The SOC (estimated SOC) is expressed by a function f _SOC of temperature and OCV (estimated OCV) as shown in Equation (5).

SOC = f _SOC (T, OCV) (5)

<Capacity calculation part>
The capacity calculation unit 140 calculates the capacity Cap from the differential value dAh of Ah (current amount) and the differential value dSOC of SOC (estimated SOC) according to the following equation (6).
Cap = dAh / dSOC (6)

<SOC-OCV calculation unit 150>
Based on the calculated OCV (estimated OCV), SOC (estimated SOC), and detected temperature T, SOC-OCV calculation unit 150 calculates an SOC-OCV curve according to the following equation (7). A specific method for calculating the SOC-OCV curve will be described later.
OCV = f (SOC, T) (7)
In addition, the SOC-OCV calculation unit 150 includes a storage unit 151 that stores the calculated estimated OCV and estimated SOC.

The SOC-OCV calculation unit 150 calculates the SOC-OCV curve using the estimated OCV and the estimated SOC data when the predetermined condition (data of the entire use range) is accumulated. The predetermined condition is that a predetermined number of data in the entire use range (particularly, data in the vicinity of the reference SOC) is accumulated. Here, as the number of data points N increases, the average value converges to the average value of the original distribution (central limit theorem). For this reason, by increasing the number of data points N, it is possible to increase the calculation accuracy of the estimated OCV _SOC described later.
A plurality of the reference SOCs may be provided within a range where the secondary battery 1 is used. In this case, the SOC-OCV calculation unit 150 calculates the SOC-OCV curve after data storage of each reference SOC.
The SOC-OCV calculation unit 150 offsets the calculated SOC-OCV curve with reference to the SOC and OCV when fully charged (see FIG. 5 below).

As described above, the SOC-OCV calculation unit 150 automatically learns the SOC-OCV curve by sequentially estimating the SOC-OCV curve from the calculated OCV (estimated OCV) and SOC (estimated SOC). Further, the estimated SOC-OCV curve shown in FIG. 2 is calculated for each predetermined temperature.
The SOC-OCV calculation unit 150 learns the SOC-OCV curve by sequentially estimating the SOC-OCV curve (see FIG. 2) from the calculated OCV (estimated OCV) and SOC (estimated SOC). Here, since the estimated OCV is calculated by CCV-IR, the SOC-OCV curve information is not necessary for the estimation. Further, since the estimated SOC is calculated based on current integration, the influence of the SOC-OCV curve is small (the initial capacity is known). By using the estimated OCV and the estimated SOC, the SOC-OCV curve can be sequentially estimated.

In this embodiment, the control configuration is based on the assumption that the initial SOC-OCV curve and the initial capacity are known.
Further, the SOC-OCV curve and the capacity value need to be controlled and set so as to slowly and slowly change from the initial value over a long period of time. Incidentally, when the SOC-OCV curve has hysteresis, in principle, the OCV mode value including hysteresis is estimated for each SOC.

[Offset correction of SOC-OCV curve]
Next, occurrence of offset of the SOC-OCV curve and correction thereof will be described.
As the estimated SOC, a current integration-based SOC is used. However, the initial value needs to be estimated from the SOC-OCV curve. In principle, the SOC-OCV curve is offset in the SOC-OCV curve.
FIG. 5 is a diagram for explaining the offset generation of the SOC-OCV curve, in which the vertical axis represents OCV and the horizontal axis represents SOC. FIG. 5A shows the calibration value and the true value, and FIG. 5B further shows the estimated value in a superimposed manner.
When the calibration value of the SOC-OCV curve is different from the true SOC-OCV curve, an error due to the difference between the two SOC-OCV curves occurs in the initial estimated SOC. If this error is left as it is, the SOC-OCV curve will be learned during the learning period of the SOC-OCV curve. Therefore, the SOC-OCV curve after learning is compared with the true SOC-OCV curve. Thus, an offset error has occurred. Therefore, it is necessary to correct this offset error after learning the SOC-OCV curve.

FIG. 6 is a diagram for explaining offset correction of the SOC-OCV curve, where the vertical axis represents OCV and the horizontal axis represents SOC. FIG. 6A shows an OCV with an SOC of 100%. The inventors of the present invention focused on the fact that an OCV with 100% SOC does not change due to deterioration by definition. Therefore, as shown in FIG. 6A, offset correction is performed using an OCV with an SOC of 100% and an OCV with an SOC of 100%.
FIG. 6B is a reproduction of FIG. 5B for convenience of explanation. As shown in FIG. 6B, the SOC-OCV curve offset occurs with respect to the initial value. For this offset generation, offset correction is performed using an OCV with 100% SOC. Specifically, the offset correction is performed based on the OCV with the estimated SOC of 100% in FIG. Thereby, as shown in FIG.6 (c), an estimated value is offset-corrected with respect to a true value.

[Details of SOC-OCV curve estimation]
<Central limit theorem>
7 and 8 are diagrams for explaining the outline of the central limit theorem. FIG. 7 is a diagram illustrating the estimated OCV and the estimated SOC data score when the SOC is 40%, and FIG. 8 is a diagram illustrating the estimated OCV _SOC obtained by integrating the data score of FIG.
In FIG. 7, ± α is, for example, 0.5%. The SOC-OCV calculation unit 150 (see FIG. 1) processes data with an SOC of 40% ± α as data with an SOC of 40%.
The SOC-OCV calculation unit 150 integrates the estimated data points and the OCV value. Here, SOC-OCV calculation unit 150 accumulates estimated data points N ₄₀ and OCV value OCV ₄₀ according to the following equation (8) from the data points shown in FIG.

SUM_N ₄₀ = ΣN ₄₀
SUM_OCV ₄₀ = ΣOCV ₄₀ (8)

As shown in FIGS. 8A and 8B, when the number of data points N increases, the accuracy of calculating the estimated OCV _SOC increases by the central limit theorem.
Here, how many N numbers the OCV value converges is determined from a test executed in advance, and is a specification that can be set as a calibration value. In addition, the gradient descent method (gradient descent method) is used for updating the OCV value.

<Gradient descent method>
An example of a mathematical formula based on the gradient descent method is represented by the following formula (9). According to the gradient descent method, it moves greatly when the difference between the calibration value and the learning value is large, and moves small when the difference is small.
OCV _SOC = OCV _SOC- α (OCV _SOC -estimated OCV _SOC ) (9)

<Final SOC-OCV curve estimation>
The SOC-OCV calculation unit 150 (see FIG. 1) calculates the OCV _SOC for each SOC.
FIG. 9 is a diagram showing the OCV _SOC calculated for each SOC.
As shown in FIG. 9, the SOC-OCV calculation unit 150 estimates a final SOC-OCV curve. The above-described SOC-OCV curve offset correction is performed at this time.

Next, the operation of the battery state estimation device 100 will be described.
FIG. 10 is a flowchart showing a battery state estimation method of the battery state estimation apparatus 100 (see FIG. 1).
First, in step S11, each detection unit obtains current information I, voltage information CCV, and temperature information T. That is, the current detection unit 101 acquires current information I, the voltage detection unit 102 acquires voltage information CCV, and the temperature detection unit 103 acquires temperature information T.
In step S12, the OCV calculation unit 120 calculates an OCV (estimated OCV) from the detected value (current I, terminal voltage CCV) and the calculated resistance according to the following equation (2).
In step S13, the SOC calculation unit 130 calculates the SOC (estimated SOC) using the calculated OCV (estimated OCV) and the current integrated value.
In step S14, the SOC-OCV calculating unit 150 acquires the calculated OCV (estimated OCV) and SOC (estimated SOC).
At the loop end of step S15, the SOC-OCV calculation unit 150 loops until a predetermined number of OCVs in the used SOC range are obtained.

In step S16, the SOC-OCV calculation unit 150 determines whether or not the SOC is within a specified range.
When the SOC is within the specified range (step S16: YES), in step S17, the SOC-OCV calculation unit 150 accumulates the OCV _SOC for the _SOC in the storage unit 151 (see FIG. 1).
When the SOC is out of the specified range (step S16: NO), in step S18, the SOC-OCV calculation unit 150 determines whether the SOC is within the specified range.
When the SOC is within the specified range (step S18: YES), the SOC-OCV calculation unit 150 accumulates the OCV _SOC for the _SOC in the storage unit 151 in step S19.
When the SOC is out of the specified range (step S18: NO), in step S20, the SOC-OCV calculation unit 150 accumulates the OCV _SOC for the _SOC in the storage unit 151 for the SOC in the usage range.

  By looping until a predetermined number of OCVs in the used SOC range is obtained by the above loop processing, the value of the OCV for the SOC is stocked in the storage unit 151. The value of OCV with respect to the SOC is stocked, and when a certain amount is accumulated, learning is performed. If learning is partially performed, the SOC-OCV curve is inconsistent. Therefore, learning is not performed until data in the entire use range is collected.

In step S <b> 21, the SOC-OCV calculation unit 150 performs an SOC-OCV curve generation process.
In step S22, the SOC-OCV calculation unit 150 resets the data points and ends the processing of this flow.

As described above, the battery state estimation device 100 (see FIG. 1) according to the present embodiment calculates the estimated OCV based on the detected battery current and voltage, and the internal resistance during charging and discharging. OCV calculation unit 120, SOC calculation unit 130 that calculates an estimated SOC represented by a function f _SOC of the estimated OCV based on the calculated estimated OCV and current integrated value, and a memory that stores the calculated estimated OCV and estimated SOC When the estimated OCV and estimated SOC data are stored in the means 151 and the storage means 151 under a predetermined condition, the SOC-OCV curve calculation unit 150 that calculates the SOC-OCV curve using the stored estimated SOC data. And prepare.

In the battery state estimation method according to the present embodiment, the OCV calculation step of calculating the OCV based on the detected current and voltage of the secondary battery 1 and the internal resistance during charging and discharging, the calculated estimated OCV, and An SOC calculation step for calculating an estimated SOC represented by a function f _SOC of the estimated OCV based on the integrated current value, a storage step for storing the calculated estimated OCV and the estimated SOC, and a storage step for the estimated OCV and the estimated SOC. When the data is accumulated under a predetermined condition, an SOC-OCV curve calculation step of calculating an SOC-OCV curve using the accumulated estimated SOC data is executed.

By doing so, it becomes possible to perform SOC calculation and capacity calculation in consideration of the change due to the production variation of the SOC-OCV curve and the deterioration of the SOC-OCV curve, and the original performance of the battery is further improved. Can be used up. As a result, the vehicle performance can be improved and the number of cells can be reduced.
In addition, since a large number of maps are not prepared, it is possible to cope with various production variations and deterioration states without requiring a huge storage device capacity. In addition, it is possible to easily estimate the state of batteries having different battery characteristics.

  Further, in the present embodiment, since a predetermined number of estimated SOC data in the vicinity of the reference SOC is accumulated, it is possible to accurately estimate the curve by accumulating the predetermined number of data with respect to the reference SOC.

  Further, in the present embodiment, the SOC-OCV curve calculation unit 150 calculates the SOC-OCV curve after accumulating data of each reference SOC, thereby avoiding partial learning and causing inconsistencies in the SOC-OCV curve. Therefore, accurate estimation is possible.

  In the present embodiment, the SOC-OCV curve calculation means 15 offsets the calculated SOC-OCV curve with reference to the SOC and OCV at the time of full charge. Since the SOC and OCV values at the time of full charge do not change, it is possible to accurately detect the state by offsetting the SOC-OCV curve created based on the SOC and OCV values at the time of full charge.

Further, in the present embodiment, the SOC calculation unit 120 calculates the estimated SOC represented by the detected temperature and the function f _SOC of the estimated OCV, so that it is possible to calculate the estimated SOC corresponding to each temperature of the battery, and more accurately. In addition, the state of charge of the battery can be estimated. Similarly, the SOC-OCV curve calculation unit 150 calculates an SOC-OCV curve based on the calculated estimated OCV, estimated SOC, and detected temperature, and automatically learns an SOC-OCV curve corresponding to each temperature of the battery. Thus, the state of charge of the battery can be estimated more accurately.

The present invention is not limited to the above-described embodiments, and includes other modifications and application examples without departing from the gist of the present invention described in the claims.
For example, the battery characteristic estimation method, the battery characteristic estimation device, and the program may be hardware with independent calculation functions or software in the battery system. Therefore, an ASIC (Application Specific Integrated Circuit) or the like may be used for the battery characteristic estimation method, the battery characteristic estimation apparatus, the program calculation, and the arithmetic processing, instead of the computer program.

Further, the above-described exemplary embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of an embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of an embodiment. . Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each exemplary embodiment.
Each of the above-described configurations, functions, processing units, processing means, and the like may be realized by hardware by designing a part or all of them with, for example, an integrated circuit. Further, as shown in FIGS. 1 and 4, each of the above-described configurations, functions, and the like may be realized by software for interpreting and executing a program that realizes each function by the processor. Information such as programs, tables, and files for realizing each function is stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), an IC (Integrated Circuit) card, an SD (Secure Digital) card, an optical disk, etc. It can be held on a recording medium. Further, in this specification, the processing steps describing time-series processing are not limited to processing performed in time series according to the described order, but are not necessarily performed in time series, either in parallel or individually. The processing (for example, parallel processing or object processing) is also included.
In addition, the control lines and information lines are those that are considered necessary for the explanation, and not all the control lines and information lines on the product are necessarily shown. Actually, it may be considered that almost all the components are connected to each other.

DESCRIPTION OF SYMBOLS 100 Battery state estimation apparatus 101 Current detection part (current detection means)
102 Voltage detection unit (voltage detection means)
103 Temperature detection part (temperature detection means)
110 resistance calculator 120 OCV calculator (estimated OCV calculator)
130 SOC calculator (estimated SOC calculator)
130a SOC-OCV map 140 Capacity calculation unit 150 SOC-OCV calculation unit (SOC-OCV curve calculation means)
151 Storage means

Claims (9)

Current detection means for detecting the charge / discharge current of the battery;
Voltage detection means for detecting the voltage between the terminals of the battery;
Estimated OCV calculating means for calculating an estimated OCV based on the detected current and voltage of the battery and internal resistance during charging and discharging;
Estimated SOC calculating means for calculating an estimated SOC represented by a function of the estimated OCV based on the calculated estimated OCV and the integrated current value;
Storage means for storing the calculated estimated OCV and the estimated SOC;
SOC-OCV curve calculating means for calculating an SOC-OCV curve using the stored estimated SOC data when the estimated OCV and the estimated SOC data are accumulated in the storage means under a predetermined condition;
A battery state estimation device comprising: The predetermined condition is:
The battery state estimation apparatus according to claim 1, wherein a predetermined number of data of the estimated OCV in the vicinity of a reference SOC is accumulated. A plurality of the reference SOCs are provided in a range where the battery is used,
The SOC-OCV curve calculating means is:
The battery state estimation device according to claim 2, wherein the SOC-OCV curve is calculated after data storage of each reference SOC. The SOC-OCV curve calculating means is:
The battery state estimation apparatus according to claim 1, wherein the SOC-OCV curve is sequentially estimated based on the calculated estimated OCV and estimated SOC, and the SOC-OCV curve is automatically learned. The SOC-OCV curve calculating means is:
The battery state estimation device according to claim 1, wherein the SOC-OCV curve is calculated using a predetermined number or more of the data. The SOC-OCV curve calculating means is:
The battery state estimation device according to claim 1, wherein the calculated SOC-OCV curve is offset with reference to SOC and OCV at full charge. Battery temperature detecting means for detecting the temperature of the battery,
The estimated SOC calculation means includes
The battery state estimation device according to claim 1, wherein an estimated SOC expressed by a function of the detected temperature and the estimated OCV is calculated. Battery temperature detecting means for detecting the temperature of the battery,
The SOC-OCV curve calculating means is:
The battery state estimation device according to claim 1, wherein the SOC-OCV curve is calculated based on the calculated estimated OCV, the estimated SOC, and the detected temperature. A current detection step for detecting the charge / discharge current of the battery;
A voltage detection step for detecting the voltage between the terminals of the battery;
An estimated OCV calculating step of calculating an estimated OCV based on the detected current and voltage of the battery and internal resistance during charging and discharging;
An estimated SOC calculation step of calculating an estimated SOC represented by a function of the estimated OCV based on the calculated estimated OCV and the integrated current value;
Storing the calculated estimated OCV and the estimated SOC;
An SOC-OCV curve calculating step of calculating an SOC-OCV curve using the stored estimated SOC data when the estimated OCV and the estimated SOC data are stored in the storage unit under a predetermined condition;
A battery state estimation method comprising:

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