JP6657967B2 - State estimation device and state estimation method - Google Patents

Description

  The present invention relates to a technique for estimating SOC.

  Conventionally, there is a current integration method as one of the techniques for estimating the SOC of a secondary battery. In the current integration method, since the measurement error of the current sensor accumulates, if the current integration is continued for a long period of time, the estimation accuracy of the SOC decreases. Patent Literature 1 below describes that the SOC estimated by the current integration method is corrected by using a battery model and a Kalman filter, thereby improving the SOC estimation accuracy.

Japanese Patent No. 5074830

By the way, an electric storage element includes an element such as a lithium ion secondary battery having a plateau region in which the variation of the OCV with respect to the variation of the SOC is small and the OCV is substantially constant. When the correction using the battery model and the Kalman filter is applied to a storage element having a region where the change amount of the OCV with respect to the change amount of the SOC is small, there is a possibility that the estimation accuracy of the SOC may be reduced.
There is also a similar problem when the SOC estimated by the current integration method is corrected by the battery model and the adaptive digital filter.
The present invention has been completed on the basis of the above-described circumstances, and has an object to highly accurately estimate the SOC even when the storage element has a region where the variation of the OCV with respect to the variation of the SOC is small. I do.

  The state estimating device disclosed in this specification is a state estimating device that estimates the state of a storage element having a low change region and a relatively high change region in which the change in OCV with respect to the change in SOC is relatively low. An area determination unit that determines whether the power storage element belongs to the high-change area, and an SOC estimation unit that estimates an SOC that is one of the internal states of the power storage element, wherein the SOC estimation unit includes: The SOC of the power storage element is estimated by a current integration method for integrating the current of the power storage element, and when the power storage element belongs to a high change region, the SOC estimated by the current integration method is a terminal of the power storage element. A correction process for correcting based on the observed value of the voltage and the terminal voltage predicted by the estimation model for estimating the internal state of the power storage element is performed.

  The state estimation device disclosed in this specification can estimate the SOC with high accuracy even when the storage element has a region where the amount of change in OCV with respect to the amount of change in SOC is small.

FIG. 2 is a schematic diagram illustrating a configuration of a battery pack according to the first embodiment. Graph showing SOC-OCV correlation characteristics of a secondary battery Block diagram of state-space model Circuit diagram showing a circuit model simulating a secondary battery Flow chart showing the outline of the SOC estimation method using the Kalman filter Flow chart showing the flow of SOC estimation processing Graph showing time change of SOC when charging / discharging a battery at a battery temperature of 25 ° C. The figure which expanded the high change area A3 of FIG. 7A. Graph showing time change of SOC when charging / discharging a battery at a battery temperature of 25 ° C. Chart showing root mean square of SOC against true value for each estimation method FIG. 6 is a graph showing a temporal change in SOC when a secondary battery is charged and discharged at a battery temperature of 25 ° C. in Embodiment 2. Graph showing time change of SOC when charging / discharging a battery at a battery temperature of 10 ° C. Graph showing time change of SOC when battery is charged / discharged at battery temperature of 0 ° C Table showing root mean square of corrected SOC with respect to true value for each battery temperature Flow chart showing the flow of SOC estimation processing Flow chart showing the flow of SOC estimation processing FIG. 7 is a graph showing a temporal change in SOC when a secondary battery is charged and discharged at a battery temperature of 25 ° C. in Embodiment 3. Flow chart showing the flow of the SOC estimation method FIG. 7 is a graph showing a temporal change in SOC when a secondary battery is charged and discharged at a battery temperature of 25 ° C. in Embodiment 4. Graph showing time change of SOC when charging / discharging a battery at a battery temperature of 10 ° C. Graph showing time change of SOC when battery is charged / discharged at battery temperature of 0 ° C Chart showing root mean square of corrected SOC with respect to true value for each battery temperature Flow chart showing the flow of SOC estimation processing Flow chart showing the flow of SOC estimation processing

(Overview of this embodiment)
First, an overview of the state estimation device disclosed in the present embodiment will be described. The state estimating device is a state estimating device for estimating the state of the storage element having a relatively low change region and a relatively high change region in which the change amount of the OCV with respect to the change amount of the SOC is relatively low. An area determining unit that determines whether the battery belongs to the high-change area, and an SOC estimating unit that estimates an SOC that is one of the internal states of the power storage device.The SOC estimating unit calculates a current of the power storage element. Estimate the SOC of the power storage element by the current integration method to integrate, when the power storage element belongs to the high change region, the SOC estimated by the current integration method, the observed value of the terminal voltage of the power storage element and the A correction process for correcting based on a terminal voltage predicted by an estimation model for estimating an internal state of the storage element is performed.

  The SOC estimation accuracy when the SOC estimated by the current integration method is corrected based on the observed value of the terminal voltage of the storage element and the terminal voltage predicted by the estimation model for estimating the internal state of the storage element is SOC It depends on the magnitude of the amount of change in OCV with respect to the amount of change in. Specifically, a high change region where the change amount of the OCV is relatively high with respect to the change amount of the SOC has higher SOC estimation accuracy than a low change region where the change amount of the OCV is relatively small.

  In this configuration, the power storage element performs a correction process of correcting the SOC estimated by the current integration method based on the observed value of the terminal voltage of the power storage element and the terminal voltage predicted by the estimation model for estimating the internal state of the power storage element. It is performed when it belongs to the high change area. That is, since the correction process is performed on the high change region where the estimation accuracy of the SOC is expected to be improved by the correction, the power storage element has a low change region where the change amount of the OCV with respect to the change amount of the SOC is small. Even in this case, the SOC can be estimated with high accuracy.

The following configuration is preferable as an embodiment of the state estimation device.
The low change region is a plateau region in which the amount of change in OCV with respect to the amount of change in SOC is smaller than a predetermined value. In the plateau region, the correction process is not performed. In this configuration, since the correction process is not performed in the plateau region, it is possible to suppress a decrease in the SOC estimation accuracy compared to before the correction.

  The correction is a Kalman filter that reduces an estimation error of the internal state of the storage element estimated by the estimation model, based on the observed value of the terminal voltage of the storage element and the terminal voltage predicted by the estimation model. . Since the Kalman filter suppresses the estimation error of the internal state of the storage element, the SOC estimated by the current integration method can be corrected with high accuracy.

  The measurement model is an equivalent circuit model of the power storage device, including an OCV, a conductor resistance, a first impedance simulating short-term polarization of the power storage device, and a second impedance simulating long-term polarization of the power storage device. . It is possible to accurately reproduce the characteristics due to the polarization of the storage element. Therefore, the effect of suppressing the estimation error of the internal state of the storage element is high, and the SOC estimated by the current integration method can be corrected with high accuracy.

  The SOC estimating unit executes the correction process when the power storage element is included in a high change area and the power storage element has no current. In this configuration, since the correction process is performed with the current limited, the SOC estimated by the current integration method can be corrected with high accuracy.

  A temperature detecting unit that detects a temperature of the power storage element, wherein the SOC estimating unit executes the correction processing when the temperature of the power storage element is equal to or higher than a first temperature. In this configuration, since the correction process is executed only when the temperature of the storage element is equal to or higher than the first temperature, the SOC estimated by the current integration method can be corrected with high accuracy.

  A temperature detection unit for detecting the temperature of the power storage element is provided, and the SOC estimating unit does not execute the correction processing when the temperature of the power storage element is equal to or lower than a second temperature. In this configuration, when the power storage element is at or below the second temperature, the correction process is not performed, so that it is possible to suppress a decrease in SOC estimation accuracy as compared to before the correction.

  The area determination unit determines whether the power storage element belongs to the high change area based on an SOC value estimated by the SOC estimation unit. By doing so, it is possible to easily determine to which region the power storage element belongs.

  Alternatively, the area determination unit determines whether the power storage element belongs to the low change area or the high change area by comparing a voltage change with respect to a charge / discharge capacity change of the power storage element to a threshold value. I do.

<First embodiment>
Embodiment 1 will be described with reference to FIGS. 1 to 9.
1. Configuration of Battery Pack 20 FIG. 1 is a diagram illustrating a configuration of the battery pack 20 according to the present embodiment. The battery pack 20 according to the present embodiment is mounted on, for example, an electric vehicle or a hybrid vehicle, and supplies electric power to a power source operated by electric energy.

  As shown in FIG. 1, the battery pack 20 includes an assembled battery 30, a current sensor 40, and a battery manager (BM) 50 that manages the assembled battery 30. The battery pack 30 is composed of a plurality of secondary batteries 31 connected in series.

  The secondary battery 31 and the current sensor 40 are connected in series via a wiring 35 and are connected to a charger 10 mounted on an electric vehicle or a load 10 such as a power source provided inside an electric vehicle or the like. Is done.

  The charger 10 has a function of charging the battery pack 30. The current sensor 40 has a function of detecting a current flowing through the secondary battery 31. The current sensor 40 is configured to measure the current value of the secondary battery 31 at a constant cycle and transmit data of the measured current measurement value to the control unit 60.

  The battery manager (hereinafter, BM) 50 includes a control unit 60, a voltage detection circuit 80, and a temperature sensor 95. Note that the secondary battery 31 is an example of a “power storage element”, and the BM 50 is an example of a “state estimation device”.

  The voltage detection circuit 80 is connected to both ends of each of the secondary batteries 31 via a detection line, and has a function of measuring the voltage of each of the secondary batteries 31 in response to an instruction from the control unit 60. The temperature sensor 95 is a contact type or a non-contact type, and has a function of measuring the temperature T [° C.] of the secondary battery 31.

  The control unit 60 includes a central processing unit (hereinafter, CPU) 61, a memory 63, and a communication unit 67. The control unit 60 has a function of determining the area to which the secondary battery 31 belongs (S30 in FIG. 6) and a function of estimating the SOC (S20, S40, S50 in FIG. 6). The control unit 60 is an example of the “region determination unit” and the “SOC estimation unit”.

  The memory 63 includes a program for executing a process of estimating the SOC, data necessary for executing the program, for example, data of the SOC-OCV correlation characteristic shown in FIG. 2 and a region for determining the area to which the SOC belongs. Data is stored. Specifically, for each of the low change regions L1, L2 and the high change regions H1 to H3, the corresponding SOC range is stored. Also, data of the current value of the SOC is stored.

  The communication unit 67 is communicably connected to a vehicle-mounted ECU (Electronic Control Unit) 100 and performs a function of communicating with the vehicle-mounted ECU 100. The battery pack 20 further includes an operation unit (not shown) for receiving an input from a user, and a display unit (not shown) for displaying a state of the secondary battery 31 and the like.

2. SOC-OCV Correlation Characteristics of Secondary Battery 31 The secondary battery 31 is an iron phosphate-based lithium ion battery using lithium iron phosphate (LiFePO4) as a positive electrode active material and graphite as a negative electrode active material.

  FIG. 2 shows SOC-OCV correlation characteristics of the secondary battery 31 in which the horizontal axis is SOC [%] and the vertical axis is OCV [V]. The SOC (charge state) is the ratio of the remaining capacity to the full charge capacity. OCV is the open voltage of the secondary battery 31.

  As shown in FIG. 2, the secondary battery 31 has a plurality of charging regions including a low change region where the change amount of the OCV with respect to the change amount of the SOC is relatively low, and a high change region where the change amount of the OCV is relatively high. .

  Specifically, it has two low change regions L1, L2 and three high change regions H1, H2, H3. As shown in FIG. 2, the low change region L <b> 1 is located in a range of 31 [%] to 62 [%] in SOC value. The low change region L2 is located in a range of 68 [%] to 97 [%] in SOC value. In the low change region L1, the change amount of the OCV with respect to the change amount of the SOC is very small, and the OCV is a substantially constant plateau region at 3.3 [V]. Similarly, the low change region L2 is a substantially constant plateau region with an OCV of 3.34 [V]. Note that the plateau region is a region in which the change amount of OCV with respect to the change amount of SOC is 2 [mV /%] or less.

  The first high change region H1 is in a range of more than 62% and less than 68% in SOC value, and is located between the two low change regions L1 and L2. The second high change area H2 is in a range of less than 31 [%] in SOC value, and is located on the lower SOC side than the low change area L1. The third high change area H3 is in a range of more than 97 [%] in SOC value, and is located on the higher SOC side than the low change area L2. The first to third high change regions H1 to H3 have a relationship in which the OCV change amount (the slope of the graph shown in FIG. 2) with respect to the SOC change amount is relatively higher than the low change regions L1 and L2. ing.

3. SOC estimation using Kalman filter <Description of Kalman filter>
FIG. 3 shows a state space model of a system in which the input is u (k) and the output is y (k). The following Equation 1 shows a state equation of the state space model shown in FIG. 3, and the following Equation 2 shows an output equation of the space state model.

  The Kalman filter is an algorithm that obtains an estimated value of the internal state x (k) of the system based on a time-series observation value {y (i) = 1, 2, 3,. Specifically, the state estimation error, which is the difference between the true internal state x (k) and the estimated value, is evaluated using an evaluation function J (k) based on a mean square error, and the evaluation function J (k) is minimized. The Kalman filter is an algorithm that obtains an estimated value of the internal state x (k) that is optimal in the sense (Equations 3 to 5 below). (^) Attached to the internal state x (k) means an estimated value, and (~) means a state estimation error.

  Also, the estimated value of the internal state x (k) by the Kalman filter is expressed by a pre-estimated value of the internal state x (k) (the first term on the right side of Equation 6) and a correction term (the second term on the right side of Equation 6). Can be done.

  The pre-estimated value of the internal state x (k) is a predicted estimated value of the internal state x (k) at the time k based on data available until the time k-1. The correction term is for correcting the predicted estimated value of the first term, and can be represented by the product of “Kalman gain g (k)” and “prediction error of output y (k)”. The Kalman gain g (k) is a value that minimizes the covariance with respect to the estimation error of the internal state x (k) (error of the estimated value with respect to the true value), and can be calculated by using the principle of orthogonality or the like. (See Equation 7 below).

<Systematization of secondary batteries>
The secondary battery 31 is a current I input, output can be thought of as a system for the terminal voltage U _L, it can be described by the output equation and the state equation of Equation 1 and Equation 2. In the present embodiment, the secondary battery 31 is systemized using an equivalent circuit model of the secondary battery 31 shown in FIG. Specifically, the secondary battery 31 is defined by an OCV representing an electromotive force (a DC voltage source), a conductor resistance _R0 representing a resistance in a current collector or an electrolyte, a first impedance Z1, and a second impedance Z1. Z2.

  OCV varies with SOC and can be expressed as a function of SOC. The first impedance Z1 is a parallel circuit of a first resistor R1 and a first capacitor C1. The second impedance Z2 is a parallel circuit of a second resistor R2 and a second capacitor C2.

  The first impedance Z1 and the second impedance Z2 have different time constants (τ = R × C). Specifically, the number of times τ1 of the first impedance Z1 is smaller than the time constant τ2 of the second impedance Z2. The first impedance Z1 is an impedance simulating a fast response portion of the secondary battery 31, that is, a short-term polarization voltage of the secondary battery 31. The second impedance Z2 is a slow response portion, that is, an impedance simulating a long-term polarization voltage. Note that the equivalent circuit model M is a model for estimating the internal state (SOC, U1, U2, R0) of the secondary battery 31, and is an example of the “estimated model” of the present invention.

  When the above equivalent circuit model M is mathematically solved, Equation 9 is obtained.

Then, when Expression 9 is discretized, the state equation shown in Expression 11 is obtained for the internal state x (k) = SOC (k), U ₁ (k), U ₂ (k), and R ₀ (k) of the secondary battery 31. Is obtained, and the output equation of Expression 12 is obtained.

Then, an estimated value of SOC can be obtained by applying a Kalman filter, specifically, an extended Kalman filter to the described system based on the state equation shown in Equation 11 and the output equation shown in Equation 12. That is, as shown in Equation 13, with respect to pre-estimate the SOC, by adding the product of "prediction error of the terminal voltage U _L (k)""Kalman gain g (k)" and pre-estimation of the SOC The value can be corrected.

  Note that the pre-estimated value of SOC (the first term on the right side of Expression 13) is calculated by changing the previous estimated value of SOC (the first term on the right side of Expression 14) by the current integration method as shown in the following Expression 14. Since the value is the value obtained by adding the minute (the second term on the right side of Equation 14), the Kalman filter performs a process of correcting the SOC estimated by the current integration method so as to reduce the estimation error.

  If the outline of the SOC estimation process by the Kalman filter is shown in a flowchart, it can be represented by three processes S1 to S3 as shown in FIG.

  In S1, the internal state (SOC, U1, U2, R0) is newly estimated from the previously estimated internal state (SOC, U1, U2, R0) and the current I detected by the current sensor 40. At the same time, the error information of the internal state (SOC, U1, U2, R0) is newly estimated from the error information of the internal state (SOC, U1, U2, R0) and the error information of the current I previously estimated.

Newly estimated internal state in S2 in S1 (SOC, U1, U2, R0) predicts the terminal voltage U _L of the secondary battery 31 from. Specifically, the OCV is calculated by referring to the estimated SOC to the SOC-OCV correlation characteristic shown in FIG. 2, and the terminal voltage _UL is predicted from the OCV and the sum of U0, U1, and U2.

Then, from the error information of the results were compared observations of the detected terminal voltage U _L at the terminal voltage U _L and the voltage detection circuit 80 that is predicted and the voltage detection circuit 80, the internal state (SOC newly estimated in S1 , U1, U2, R0) to generate the Kalman gain g (k) that minimizes the error information.

  In S3, error information between the internal state (SOC, U1, U2, R0) newly estimated in S1 and the internal state (SOC, U1, U2, R0) is corrected using the Kalman gain g (k). In S1, since the SOC is estimated based on the current integration method, the estimated value is corrected by the Kalman gain g (k).

Thus, the Kalman filter, while uptake data for the terminal voltage U _L and the current I from the voltage detection circuit 80 and the current sensor 40 the secondary battery 31, by repeating the three processing S1~S3 described above, the state estimation This is a process for estimating the internal state (SOC, U1, U2, R0) of the secondary battery 31 with the error minimized.

  However, the secondary battery 31 has low change regions L1 and L2 in the SOC-OCV correlation characteristics. In the low change regions L1 and L2, the change amount of the OCV with respect to the change amount of the SOC is very small. Therefore, a small error in the estimated value of the OCV appears in the SOC error. Therefore, the influence on the SOC estimation accuracy is greater than in the high change regions H1, H2, and H3, and the SOC estimation accuracy may be reduced. That is, when the estimated value of the SOC is corrected from the Kalman gain g (k) generated in S2, the accuracy of the estimated SOC may be significantly reduced.

4. Restriction on Correction by Kalman Filter Therefore, the BM 50 performs the correction by the Kalman filter while limiting the SOC of the secondary battery 31 to any of the high change regions H1, H2, and H3, Does not belong to the high change regions H1, H2, H3, that is, does not belong to the low change regions L1, L2, the correction by the Kalman filter is not performed.

  FIG. 6 is a flowchart of the SOC estimating process, which includes six processes of S10 to S60. The SOC estimation process shown in FIG. 6 is executed by the control unit 60 at the same time that the BM 50 starts up and starts monitoring the battery pack 30, for example.

  When the process starts, the control unit 60 accesses a memory or the like and acquires the current value of the SOC (S10).

  Thereafter, the control unit 60 performs a process of estimating the SOC by the current integration method (S20). That is, the current I output from the current sensor 40 is integrated to calculate the accumulated charge / discharge amount. Then, the SOC at the next time point is estimated by adding the change amount of the SOC calculated from the accumulated charge / discharge amount to the current value of the SOC read from the memory 63. The first term of Equation 15 indicates the current value of SOC, and the second term indicates the amount of SOC change from the current value.

  Thereafter, the control unit 60 executes a process of determining whether or not the estimated value of the SOC estimated by the current integration method belongs to any of the high change regions H1, H2, and H3 (S30). Specifically, the determination is made by comparing the SOC range corresponding to each of the high change regions H1, H2, and H3 with the estimated value of the SOC by the current integration method.

  When the estimated value of the SOC estimated by the current integration method belongs to any of the high change regions H1, H2, and H3, the control unit 60 performs a correction process of correcting the SOC estimated by the current integration method with a Kalman filter. Perform (S40).

Specifically, the control unit 60, the terminal voltage U _L of the secondary battery 31 detected by the voltage detection circuit 80, on the basis of the terminal voltage U _L which is predicted by the equivalent circuit model M, an equivalent circuit model M A Kalman gain g (k) for minimizing the error information of the estimated internal state (SOC, U1, U2, R0) of the secondary battery 31 is generated.

Thereafter, the control unit 60, the SOC estimated by current integration method is corrected based on the prediction error of the Kalman gain g (k) and terminal voltage U _L (see Equation 13). Then, the corrected SOC is set as the SOC at the next time, that is, the latest value of the SOC.

  On the other hand, when the current value of the SOC does not belong to the high change regions H1, H2, and H3, that is, when it belongs to any of the low change regions L1 and L2, the control unit 60 does not perform the Kalman filter correction. The SOC estimated by the current integration method is set as the SOC at the next time point, that is, the latest value of the SOC.

  When the processing in S40 or S50 is executed, the control unit 60 accesses the memory 63 and updates the SOC value each time. Therefore, the latest value of the SOC is always stored in the memory 63.

  After performing either of the processes in S40 and S50, it is determined whether or not the monitoring of the secondary battery 31 is to be terminated (S60).

  When the monitoring of the secondary battery 31 is continued, the process returns to S20, and the control unit 60 performs the process of estimating the SOC based on the current integration method again. That is, this time, the SOC obtained in S40 or S50 is set as the current value, and the amount of change in the SOC from the current value is calculated by current integration. Then, the SOC at the next time point is estimated by adding the amount of change of the SOC to the current value of the SOC.

  After that, the control unit 60 executes the process of S30, and determines whether the SOC estimated by the current integration method belongs to any of the high change regions H1, H2, and H3. Only when the SOC estimated by the current integration method belongs to any of the high change regions H1, H2, and H3, the SOC estimated by the current integration method is corrected by a Kalman filter, and the corrected SOC is The SOC at that time is set (S40).

  On the other hand, when the SOC estimated by the current integration method does not belong to the high change regions H1, H2, and H3, the control unit 60 sets the SOC estimated by the current integration method as the SOC at the next time (S50).

  Thereafter, when monitoring of the secondary battery 31 is continued, the process returns to S20, and the same process is repeatedly performed.

  As described above, while monitoring the secondary battery 31, the control unit 60 always estimates the SOC by the current integration method regardless of the SOC area. That is, the SOC is always estimated by the current integration method not only when the SOC belongs to the low change regions L1 and L2, but also when it belongs to the high change regions H1, H2, and H3. Then, the control unit 60 corrects the SOC estimated by the current integration method with the Kalman filter only when the SOC estimated by the current integration method belongs to any of the high change regions H1, H2, and H3. If the estimated SOC does not belong to the high change regions H1, H2, H3, that is, if it belongs to any of the low change regions L1, L2, the Kalman filter is not corrected.

  Therefore, for example, when charging is performed from the point P shown in FIG. 2 to the full charge, the period after the start of charging, during which the estimated value of the SOC is smaller than 31% (the period when the SOC belongs to the high change region H2), The SOC estimated by the integration method is corrected by the Kalman filter, and the corrected SOC is set as the latest SOC value. Thereafter, during a period in which the estimated value of the SOC is 31% to 62% (a period in which the SOC belongs to the low change region L1), the correction by the Kalman filter is not performed, and the SOC estimated by the current integration method is replaced by the SOC of the SOC. Use the latest value.

  During a period in which the estimated value of the SOC is larger than 62% and smaller than 68% (a period in which the SOC belongs to the high change region H1), the SOC estimated by the current integration method is corrected by the Kalman filter, and the corrected SOC is used. Is the latest SOC value.

  Thereafter, during a period in which the estimated value of SOC is 68% to 97% (a period in which the SOC belongs to the low change region L2), the Kalman filter is not corrected, and the SOC estimated by the current integration method is replaced by the SOC Use the latest value. During a period in which the estimated value of SOC is greater than 97% (a period in which the SOC belongs to the high change region H3), the SOC estimated by the current integration method is corrected by the Kalman filter, and the corrected SOC is replaced with the SOC of the SOC. Use the latest value.

  As described above, in the present embodiment, the SOC estimation accuracy can be improved by restricting the correction by the Kalman filter to the high change regions H1 to H3 in which the correction is expected to improve the SOC estimation accuracy. I can do it.

  In other words, the SOC estimation accuracy can be improved in the low change regions L1 and L2 in which the accuracy may be degraded by the correction by not performing the correction by the Kalman filter.

  Further, a test for charging and discharging the battery pack 30 was performed under the condition that the battery temperature T was 25 ° C. Then, the SOC estimated by the current integration method is corrected by applying the Kalman filter to the entire range of the SOC (0% to 100%), and when the SOC is limited to the high change regions H1, H2, and H3. Then, the difference in the effect was verified.

  In FIGS. 7A and 8, a “dot-dash line” indicates a true value of SOC. The “dashed line” indicates the SOC estimated by the current integration method. That is, during the test, the SOC is obtained by measuring the current flowing through the battery pack 30 with the current sensor 40 and integrating the obtained measurement values. In this example, the current measurement error of the current sensor 40 is about 20 mA on the discharge side, and the SOC estimation error at the time of current integration increases with time.

  The “solid line” illustrated in FIG. 7A indicates the SOC when the SOC estimated by the current integration method is corrected by the Kalman filter for “the entire range of SOC (0% to 100%)”.

  In FIG. 7A, as shown by the “solid line”, the SOC corrected by the Kalman filter is closer to the true value than the SOC estimated by the current integration method in A1 to A3 (high change region). It can be understood that in the change region, the SOC estimation accuracy is higher when the correction is performed by the Kalman filter than in the current integration method. FIG. 7B is an enlarged view of the high change area A3 in FIG. 7A.

  On the other hand, in B1 to B4 (low-change areas), the SOC corrected by the Kalman filter is a value farther from the true value than the SOC estimated by the current integration method, and in the low-change areas L1 and L2, It can be understood that, when the SOC estimated by the equation (1) is corrected by the Kalman filter, the accuracy of the estimation of the SOC decreases.

  On the other hand, the “solid line” shown in FIG. 8 indicates that the Kalman filter corrects the SOC estimated by the current integration method to the high change regions H1 to H3 and does not perform the Kalman filter correction in the low change regions L1 and L2. Shows the SOC.

  As shown by the “solid line” in FIG. 8, the SOC when the correction by the Kalman filter is limited to the high change regions H1 to H3 is closer to the true value in the whole range than the SOC estimated by the current integration method. It can be understood that the estimation accuracy of the SOC is improved.

  FIG. 9 is a table showing the root mean square (RMS) of “SOC” with respect to “true value” for each method. In the case of the current integration method, the root mean square of the “estimated SOC” with respect to the “true value” is “4.738”. When the Kalman filter correction is performed in the entire range of the SOC, the root mean square of the “corrected SOC” to be “true value” is “6.619”, and when the Kalman filter correction is limited to the high change regions H1 to H3, “ The “corrected SOC” root mean square to be “true value” is “4.406”. The “estimated SOC” is the SOC estimated by the current integration method, and is the SOC indicated by the broken line in FIGS. “Corrected SOC” is the SOC corrected by the Kalman filter, and is the SOC indicated by a solid line in FIGS.

  As described above, it can be understood that the root mean square when the correction of the Kalman filter is limited to the high change regions H1 to H3 is the smallest, and the estimation accuracy of the SOC is improved.

<Embodiment 2>
Embodiment 2 will be described with reference to FIGS. Like the battery pack 20 of the first embodiment, the battery pack 20 of the second embodiment includes an assembled battery 30, a current sensor 40, and a battery manager 50 that manages the assembled battery 30.

  In the second embodiment, a test for charging and discharging the assembled battery 30 (a test similar to that of the first embodiment) was performed by changing the battery temperature T, and the relationship between the battery temperature T and the effect of the correction by the Kalman filter was verified.

  FIG. 10 is a graph showing a time transition of the SOC when the battery temperature T is 40 ° C. FIG. 11 is a graph showing a time transition of the SOC when the battery temperature T is 10 ° C. FIG. 12 is a graph showing a temperature transition of a graph showing a time transition of the SOC when the battery temperature T is 0 ° C. Note that the “solid line” in FIGS. 10 to 12 indicates the SOC when the SOC estimated by the current integration method is corrected by the Kalman filter to the high change regions H1, H2, and H3. .

  Time transition of the SOC when the battery temperature T is 40 ° C. and when the battery temperature T is 25 ° C. and the correction is performed by the Kalman filter while limiting to the high change regions H1, H2, and H3 (solid lines in FIGS. 10 and 8). Can be understood that the corrected SOC is closer to the true value when the battery temperature is 40 ° C. than the case where the battery temperature is 25 ° C., and that the effect of the correction by the Kalman filter is higher. .

  Similarly, in the case where the battery temperature T is 25 ° C. and the case where the battery temperature T is 10 ° C., transition of the SOC when the Kalman filter is performed by limiting the high change region to H1, H2, and H3 (FIG. 8, FIG. Comparing (solid line in FIG. 11), when the battery temperature is 10 ° C., the SOC after correction is far from the true value when the battery temperature is 25 ° C., and the effect of the correction by the Kalman filter is lower. I can understand.

  Then, in the case where the battery temperature T is 10 ° C. and the case where the battery temperature T is 0 ° C., transition of the SOC when the correction by the Kalman filter is performed by limiting to the high change regions H1, H2, and H3 (FIGS. 11 and 12). When the battery temperature T is 0 ° C., the corrected SOC is far from the true value when the battery temperature T is 0 ° C., and the effect of the correction by the Kalman filter is further improved. It can be understood that it is low.

  FIG. 13 shows the root mean square (RMS) of the “estimated SOC” for the “true value” and the root mean square (RMS) of the “corrected SOC” for the “true value” for each battery temperature T. It is a table. The “estimated SOC” is the SOC estimated by the current integration method, and is the SOC indicated by a broken line in FIGS. 8, 10, 11, and 12. The “corrected SOC” is the SOC corrected by the Kalman filter, and is the SOC indicated by a solid line in FIGS. 8, 10, 11, and 12.

  The root mean square of the “corrected SOC” for the “true value” is “3.879” when the battery temperature T is 40 ° C. and “4.406” when the battery temperature T is 25 ° C. When the battery temperature T is 10 ° C., “9.804”, and when the battery temperature T is 0 ° C., the root mean square is “12.038”.

  As described above, the root mean square of the “corrected SOC” with respect to the “true value” is lower as the battery temperature T is higher. Therefore, the higher the battery temperature T, the higher the effect of the correction by the Kalman filter. In particular, the root mean square of the “corrected SOC” for the “true value” is “4.406” when the battery temperature T is 25 ° C. and “3.879” when the battery temperature T is 40 ° C. Of the estimated root-mean-squares "4.738" and "4.515" respectively.

  Therefore, by limiting the battery temperature T to 25 ° C. or higher and correcting the SOC estimated by the current integration method with the Kalman filter, the SOC estimation accuracy can be increased.

The higher the battery temperature T, the higher the SOC estimation accuracy is as follows. That is, in general, the impedance of the secondary battery 31 decreases as the battery temperature T increases, so that the voltage change when a current flows decreases. Therefore, since the better the terminal voltage U _L of accuracy predicted by estimating the model M, accordingly, believed that the estimation accuracy of the SOC is high. On the other hand, when the battery temperature T is low, since the impedance is high, the voltage change when the current flows increases. Therefore, since the terminal voltage U _L of accuracy predicted by the estimation model M is deteriorated, accordingly, the estimation accuracy of the SOC is considered to be low.

  FIG. 14 is a flowchart in which the battery temperature T is added to the condition for determining whether or not the SOC estimated by the current integration method is to be corrected by the Kalman filter. Has been added. That is, a process in which the control unit 60 determines whether the battery temperature T detected by the temperature sensor 95 is equal to or higher than the first temperature (25 ° C. in this example) is added.

  In the SOC estimation process shown in FIG. 14, when the current value of the SOC belongs to any of the high change regions H1, H2, and H3 and the battery temperature T is equal to or higher than 25 ° C. (S30: YES, S37A: YES) Next, the control unit 60 executes a process of correcting the SOC estimated by the current integration method with the Kalman filter (S40). Then, the control unit 60 sets the SOC after the correction to the SOC at the next time point, that is, the latest value of the SOC.

  On the other hand, when the current value of the SOC does not belong to the high change regions H1, H2, and H3 (S30: NO) and when the battery temperature T is less than 25 ° C. (S37A: NO), the control unit 60 corrects the Kalman filter. Is not performed, the SOC estimated by the current integration method is set as the SOC at the next time, that is, the latest value of the SOC.

  As shown in FIG. 13, the root mean square of the “corrected SOC” for the “true value” is “9.804” when the battery temperature T is 10 ° C. and “12.038” when the battery temperature T is 0 ° C. Yes, larger than the root-mean-squares "4.440" and "4.427" of the "estimated SOC" estimated using the current integration method.

  As described above, when the battery temperature T is equal to or lower than 10 ° C., when the correction is performed by the Kalman filter, the accuracy of estimating the SOC tends to decrease. Therefore, when the battery temperature T is 10 ° C. or lower, the SOC estimated by the current integration method is not corrected by the Kalman filter, so that it is possible to suppress a decrease in the SOC estimation accuracy.

  FIG. 15 is a flowchart in which the battery temperature T is added to the condition for determining whether or not the SOC estimated by the current integration method is corrected by the Kalman filter. Has been added. That is, a process in which the control unit 60 determines whether the battery temperature T detected by the temperature sensor 95 is equal to or lower than the second temperature (10 ° C. in this example) is added.

  In the SOC estimation process shown in FIG. 15, when the current value of the SOC belongs to any of the high change regions H1, H2, and H3 and the battery temperature T is higher than 10 ° C. (S30: YES, S37B: NO) Next, the control unit 60 executes a process of correcting the SOC estimated by the current integration method with the Kalman filter (S40). Then, the control unit 60 sets the SOC after the correction to the SOC at the next time point, that is, the latest value of the SOC.

  On the other hand, when the current value of the SOC does not belong to the high change regions H1, H2, and H3 (S30: NO) and when the battery temperature T is 10 ° C. or less (S37B: YES), the control unit 60 corrects the Kalman filter. Is not performed, the SOC estimated by the current integration method is set as the SOC at the next time, that is, the latest value of the SOC.

<Embodiment 3>
Embodiment 3 will be described with reference to FIGS. Similar to the battery pack 20 of the first embodiment, the battery pack 20 of the third embodiment includes an assembled battery 30, a current sensor 40, and a battery manager 50 that manages the assembled battery 30.

  In the third embodiment, a test for charging and discharging the battery pack 30 under the condition that the battery temperature T is 25 ° C. was performed. Then, the difference in the effect between the case where "no current" was added to the execution condition of the correction by the Kalman filter and the case where it was not added was verified. Note that "no current" is a state in which the current flowing through the battery pack 30 is equal to or less than a predetermined value (for example, 100 mA).

  In FIG. 16, the “dot-dash line” indicates the true value of SOC. The “dashed line” is the SOC estimated by the current integration method. That is, during the test, the SOC is obtained by measuring the current flowing through the battery pack 30 with the current sensor 40 and integrating the obtained measurement values.

  Further, the “solid line” indicates the SOC when the SOC estimated by the current integration method is corrected by the Kalman filter when the SOC belongs to the high change regions H1 to H3 and the battery pack 30 has no current. I have.

  When “no current” is added to the execution condition of the correction by the Kalman filter (solid line in FIG. 16), the value is closer to the true value than when it is not added (solid line in FIG. 8). It can be understood that is improved.

  When the correction of the Kalman filter is limited to the high change regions H1 to H3 and no current, the root mean square (RMS) of the “corrected SOC” with respect to the “true value” is “2.563”. On the other hand, when there is no limitation when there is no current (in the case of the first embodiment), the value is smaller than the root mean square “4.406” of the “corrected SOC” with respect to the “true value”. The “corrected SOC” is the SOC corrected by the Kalman filter.

  As described above, the correction of the Kalman filter is more effective when the correction is performed during “no current”. Therefore, in the third embodiment, the Kalman filter is applied when the following two conditions (a) and (b) are satisfied.

(A) SOC belongs to any of the high change regions H1, H2, and H3.
(B) The secondary battery has no current.

  FIG. 17 is a flowchart of the SOC estimation process applied to the third embodiment, and the process of S35 is added to the SOC estimation process (FIG. 6) applied to the first embodiment. In S35, the control unit 60 determines whether there is no current in the secondary battery 31 by comparing the detection value of the current sensor 40 with a threshold value. In this example, when the current is equal to or less than the predetermined value, it is determined that there is no current.

  In the SOC estimation process shown in FIG. 17, the condition of (a) is determined in S30, and the condition of (b) is determined in S35. When the two conditions are satisfied, the SOC estimated by the current integration method is calculated. The correction is performed by the Kalman filter (S40). Then, the SOC after the correction by the Kalman filter is set as the SOC at the next time, that is, the latest value of the SOC. In other cases, the value estimated by the current integration method without using the Kalman filter is used as the SOC at the next time, that is, the latest value of the SOC (S50).

  As described above, in the third embodiment, the correction by the Kalman filter is performed while the SOC belongs to any of the high change regions H1, H2, and H3, and is limited to when there is no current. Therefore, the SOC estimation accuracy can be further improved as compared with the first embodiment.

<Embodiment 4>
Embodiment 4 will be described with reference to FIGS. Similar to the battery pack 20 of the first embodiment, the battery pack 20 of the fourth embodiment includes a battery pack 30, a current sensor 40, and a battery manager 50 that manages the battery pack 30.

  In the fourth embodiment, a test for charging and discharging the assembled battery 30 (a test similar to that of the third embodiment) is performed by changing the battery temperature T, and the relationship between the battery temperature T and the effect of the correction by the Kalman filter is verified.

  FIG. 18 is a graph showing a time transition of the SOC when the battery temperature T is 40 ° C. FIG. 19 is a graph showing a time transition of the SOC when the battery temperature T is 10 ° C. FIG. 20 is a graph showing the temperature transition of the graph showing the time transition of the SOC when the battery temperature T is 0 ° C. Note that the “solid line” in FIGS. 18 to 20 shows the SOC when the Kalman filter is used to correct the SOC estimated by the current integration method while limiting it to the high-change regions H1 to H3 and no current. ing.

  When the battery temperature T is 40 ° C. and the battery temperature T is 25 ° C., the time change of the SOC when the correction by the Kalman filter is performed (solid lines in FIGS. 18 and 16) is compared. In comparison, when the battery temperature is 40 ° C., the corrected SOC is closer to the true value, and it can be understood that the effect of the correction by the Kalman filter is higher.

  Similarly, when the battery temperature T is 25 ° C. and when the battery temperature T is 10 ° C., the transition of the SOC when the Kalman filter is corrected (solid lines in FIGS. 16 and 19) is compared. It can be understood that the corrected SOC is far from the true value when the battery temperature is 10 ° C., and the effect of the correction by the Kalman filter is lower than when the battery temperature is 10 ° C.

  Then, when the battery temperature T is 10 ° C. and the battery temperature T is 0 ° C., the transition of the SOC when the correction by the Kalman filter is performed (solid lines in FIGS. 19 and 20) is compared. It can be understood that the corrected SOC is far from the true value when the battery temperature T is 0 ° C., and the effect of the correction by the Kalman filter is further lower than when the battery temperature T is 0 ° C.

  FIG. 21 shows the root mean square (RMS) of the “estimated SOC” for the “true value” and the root mean square (RMS) of the “corrected SOC” for the “true value” for each battery temperature T. It is a table. The “estimated SOC” is the SOC estimated by the current integration method, and is the SOC indicated by a broken line in FIGS. 16, 18, 19, and 20. The “corrected SOC” is the SOC corrected by the Kalman filter, and is the SOC indicated by a solid line in FIGS. 16, 18, 19, and 20.

  The root mean square of the “corrected SOC” for the “true value” is “2.598” when the battery temperature T is 40 ° C. and “2.621” when the battery temperature T is 25 ° C. When the battery temperature T is 10 ° C., the value is “6.461”, and when the battery temperature T is 0 ° C., the value is “8.522”.

  As described above, the root mean square of the “corrected SOC” with respect to the “true value” is lower as the battery temperature T is higher. Therefore, the higher the battery temperature T, the higher the effect of the correction by the Kalman filter. In particular, the root mean square of the “corrected SOC” for the “true value” is “2.621” when the battery temperature T is 25 ° C. and “2.598” when the battery temperature T is 40 ° C. Of the estimated root-mean-squares "4.738" and "4.515" respectively.

  Therefore, by limiting the battery temperature T to 25 ° C. or higher and correcting the SOC estimated by the current integration method with the Kalman filter, the SOC estimation accuracy can be increased.

  FIG. 22 is a flowchart in which the battery temperature T is added to the condition for determining whether or not the SOC estimated by the current integration method is to be corrected by the Kalman filter. Has been added. That is, the control unit 60 determines whether the battery temperature T detected by the temperature sensor 95 is equal to or higher than the first temperature (25 ° C. in this example).

  In the SOC estimation process shown in FIG. 22, when the current value of the SOC belongs to any of the high change regions H1, H2, and H3, there is no current, and the battery temperature T is 25 ° C. or higher (S30: YES, (S35: YES, S37A: YES), the control unit 60 executes a process of correcting the SOC estimated by the current integration method with the Kalman filter (S40). Then, the control unit 60 sets the SOC after correction as the SOC at the next time, that is, the latest value of the SOC.

  On the other hand, when the current value of the SOC does not belong to the high change regions H1, H2, and H3 (S30: NO), when the current is not a current (S35: NO), and when the battery temperature T is lower than 25 ° C. (S37A: NO), the control unit 60 does not perform the Kalman filter correction, and sets the SOC estimated by the current integration method as the SOC at the next time point, that is, the latest value of the SOC.

  As shown in FIG. 21, the root mean square of the “corrected SOC” for the “true value” is “6.461” when the battery temperature T is 10 ° C. and “8.522” when the battery temperature T is 0 ° C. Yes, it is larger than the root mean square “4.440” or “4.427” of the “estimated SOC” estimated by the current integration method.

  As described above, when the battery temperature T is equal to or lower than 10 ° C., when the correction is performed by the Kalman filter, the accuracy of estimating the SOC tends to decrease. Therefore, when the battery temperature T is 10 ° C. or lower, the SOC estimated by the current integration method is not corrected by the Kalman filter, so that it is possible to suppress a decrease in the SOC estimation accuracy.

  FIG. 23 is a flowchart in which the battery temperature T is added to the condition for determining whether or not the SOC estimated by the current integration method is to be corrected by the Kalman filter. The processing for estimating the SOC shown in FIG. Has been added. That is, a process in which the control unit 60 determines whether the battery temperature T detected by the temperature sensor 95 is equal to or lower than the second temperature (10 ° C. in this example) is added.

  In the SOC estimation process shown in FIG. 23, when the current value of the SOC belongs to any of the high change regions H1, H2, and H3, there is no current, and the battery temperature T is higher than 10 ° C. (S30YES, S35: (YES, S37B: NO), the control unit 60 executes a process of correcting the SOC estimated by the current integration method with the Kalman filter (S40). Then, the control unit 60 sets the SOC after the correction to the SOC at the next time point, that is, the latest value of the SOC.

  On the other hand, when the current value of the SOC does not belong to the high change regions H1, H2, and H3 (S30: NO), when the secondary battery 31 has no current (S35: YES), the battery temperature T is 10 ° C. or less. In the case of (S37B: YES), the control unit 60 does not perform the correction of the Kalman filter and sets the SOC estimated by the current integration method as the SOC at the next time point, that is, the latest value of the SOC.

<Other embodiments>
The present invention is not limited to the embodiments described with reference to the above description and drawings. For example, the following embodiments are also included in the technical scope of the present invention.

  (1) In the first to fourth embodiments, the lithium ion secondary battery 31 is illustrated as an example of the electric storage element. The storage element may be other than a lithium ion battery as long as it has a low change region where the rate of change of OCV with respect to SOC is relatively low, and a high change area where the rate of change of OCV with respect to SOC is relatively high. .

  (2) In the first to fourth embodiments, whether the secondary battery 31 belongs to the high change regions H1 to H3 is determined based on the estimated value of the SOC. The area may be determined by comparing a voltage change (ΔV / ΔAh) with respect to a change in charge / discharge capacity of the secondary battery 31 with a threshold. That is, the charge / discharge capacity change ΔAh and the voltage change ΔV per unit time are calculated from the outputs of the current sensor 40 and the voltage detection circuit 80, and the area is determined by comparing the ratio (ΔV / ΔAh) with the threshold value. You may do so.

  (3) The estimation accuracy of the SOC when the correction is performed by applying the Kalman filter depends on the estimation accuracy of the internal state of the secondary battery 31 using the equivalent circuit model M. For example, when the current value is large, the equivalent circuit The accuracy of estimating the internal state by the model M is likely to decrease. In the first embodiment, an example has been described in which the SOC estimated by the current integration method is corrected using a Kalman filter by limiting the case where the secondary battery 31 belongs to the high change regions H1 to H3. In addition to this, when the estimation accuracy of the internal state is apt to decrease, such as when the current value is large, the correction by the Kalman filter is limited even when the secondary battery 31 belongs to the high change regions H1 to H3. May not be executed.

  (4) In the first embodiment, the equivalent circuit model M is illustrated as an example of the estimation model for estimating the internal state (SOC, U1, U2, R0) of the secondary battery 31. The estimation model M may be a model other than the equivalent circuit model as long as the model estimates the internal state (SOC, U1, U2, R0) of the secondary battery 31. Further, even when the equivalent circuit model is applied, a model different from the equivalent circuit model M exemplified in the first embodiment is used, such as setting the number of terms of the impedance Z simulating the polarization voltage of the secondary battery 31 to other than two. It is also possible.

  (5) In the first embodiment, the example in which the SOC estimated by the current integration method is corrected using the Kalman filter has been described. However, for example, the SOC may be corrected using an adaptive digital filter. That is, if the SOC estimated by the current integration method is corrected based on the observed value of the terminal voltage of the storage element and the terminal voltage predicted by the estimation model for estimating the internal state of the storage element, the adaptive digital filter For example, correction using a filter other than the Kalman filter is possible.

20 ... Battery pack 30 ... Battery pack 31 ... Secondary battery (corresponding to "storage element" of the present invention)
40 ... current sensor 50 ... battery manager (corresponding to "state estimation device" of the present invention)
60 ... Control unit (corresponding to "region determination unit" and "SOC estimation unit" of the present invention)
61 ... CPU
63 ... memory 80 ... voltage detection circuit

Claims (8)

A state estimating device for estimating the state of the storage element having a relatively low change region and a relatively high change region in which the change amount of the OCV with respect to the change amount of the SOC,
An area determination unit that determines whether the power storage element belongs to the high change area,
A temperature detector for detecting the temperature of the storage element,
An SOC estimating unit that estimates an SOC that is one of the internal states of the power storage element,
The SOC estimator,
Estimate the SOC of the storage element by a current integration method of integrating the current of the storage element,
When the power storage element belongs to the high change region, and the temperature of the power storage element is 25 ° C. or more, the SOC estimated by the current integration method, the observed value of the terminal voltage of the power storage element and the power storage element A state estimating device that performs a correction process for correcting based on a terminal voltage predicted by an estimation model for estimating an internal state. The state estimation device according to claim 1,
The state estimation device, wherein the SOC estimation unit does not execute the correction processing when the temperature of the power storage element is lower than 25 ° C. The state estimation device according to claim 1 or 2, wherein:
The SOC estimating unit estimates a state in which the correction process is performed when the power storage element belongs to a high change region, and the temperature of the power storage element is equal to or higher than 25 ° C., and the power storage element has no current. apparatus. A state estimating device for estimating the state of the storage element having a relatively low change region and a relatively high change region in which the change amount of the OCV with respect to the change amount of the SOC,
An area determination unit that determines whether the power storage element belongs to the high change area,
A temperature detector for detecting the temperature of the storage element,
An SOC estimating unit that estimates an SOC that is one of the internal states of the power storage element,
The SOC estimator,
Estimate the SOC of the storage element by a current integration method of integrating the current of the storage element,
When the temperature of the storage element is equal to or less than 10 ° C., the SOC estimated by the current integration method is changed to a terminal voltage predicted by an observation value of the terminal voltage of the storage element and an estimation model for estimating an internal state of the storage element. Without performing the correction process to correct based on
A state estimating device that executes the correction processing when the power storage element belongs to a high change region and the temperature of the power storage element is higher than 10 ° C. A state estimating device for estimating the state of the storage element having a relatively low change region and a relatively high change region in which the change amount of the OCV relative to the change amount of the SOC,
An area determination unit that determines whether the power storage element belongs to the high change area,
A temperature detector for detecting the temperature of the storage element,
An SOC estimating unit that estimates an SOC that is one of the internal states of the power storage element,
The SOC estimator,
Estimate the SOC of the storage element by a current integration method of integrating the current of the storage element,
When the temperature of the storage element is equal to or less than 10 ° C., the SOC estimated by the current integration method is changed to a terminal voltage predicted by an observation value of the terminal voltage of the storage element and an estimation model for estimating an internal state of the storage element. Without performing the correction process to correct based on
Before SL electric storage element belongs to the high change region and higher than the temperature is 10 ° C. of the electric storage device, further, when the electric storage device is currentless, state estimation apparatus for executing the correction process. A state estimation method for estimating the state of a power storage element having a relatively low change region and a relatively high change region in which the change in OCV with respect to the change in SOC is relatively low,
An area determination step of determining whether the power storage element belongs to the high change area,
An SOC estimation step of estimating the SOC that is one of the internal states of the power storage element,
In the SOC estimation step,
Estimate the SOC of the storage element by a current integration method of integrating the current of the storage element,
When the power storage element belongs to the high change region, and the temperature of the power storage element is 25 ° C. or more, the SOC estimated by the current integration method, the observed value of the terminal voltage of the power storage element and the power storage element A state estimating method for correcting based on a terminal voltage predicted by an estimation model for estimating an internal state. The state estimation method according to claim 6 , wherein
In the SOC estimation step, when the temperature of the storage element is less than 25 ° C., the SOC estimated by the current integration method is an estimation model for estimating the observed value of the terminal voltage of the storage element and the internal state of the storage element. A state estimation method that does not perform correction based on the terminal voltage predicted by A state estimation method for estimating the state of a power storage element having a relatively low change region and a relatively high change region in which the change in OCV with respect to the change in SOC is relatively low,
An area determination step of determining whether the power storage element belongs to the high change area,
An SOC estimation step of estimating the SOC that is one of the internal states of the power storage element,
In the SOC estimation step,
Estimate the SOC of the storage element by a current integration method of integrating the current of the storage element,
When the temperature of the storage element is equal to or less than 10 ° C., the SOC estimated by the current integration method is changed to a terminal voltage predicted by an observed value of the terminal voltage of the storage element and an estimation model for estimating an internal state of the storage element. Without correction based on
When the power storage element belongs to the high change region, and when the temperature of the power storage element is higher than 10 ° C., the SOC estimated by the current integration method, the observed value of the terminal voltage of the power storage element and the power storage element A state estimating method for correcting based on a terminal voltage predicted by an estimation model for estimating an internal state .

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