State estimation device for secondary battery

Abstract

The present invention provides a state estimation device for a secondary battery that can accurately estimate the state of the secondary battery. The state estimation device 1 for a secondary battery according to the present invention includes: a state measurement unit 10 that measures state variables of the secondary battery 2 including terminal current and terminal voltage; an internal resistance calculation unit 11 that calculates the internal resistance of the secondary battery 2; a estimated OCV calculation unit 12 that calculates an estimated value of the terminal voltage in an open-circuit state, i.e., estimated OCV; a estimated SOC calculation unit 14 that uses an SOC-OCV characteristic model 13 to calculate an estimated value of the charge rate of the secondary battery 2, i.e., estimated SOC; a differential estimated SOC calculation unit 15 that calculates a differential estimated SOC; an integrated terminal current calculation unit 16 that calculates an integrated terminal current; and an SOH calculation unit 17 that calculates the storage capacity of the secondary battery 2, i.e., SOH; and inputs the SOH calculated by the SOH calculation unit 17 at least in a high SOC state where the estimated OCV is equal to or higher than a first threshold to an SOH estimation model 18 for estimating SOH, and updates the SOH estimation model 18.

Description

Technical Field

[0001] The present invention relates to a state estimation device for a secondary battery. Background Art

[0002] Patent Document 1 describes a lead storage battery used in a natural energy utilization system. The lead storage battery includes: a battery state measurement unit that estimates the state of the lead storage battery; an SOC model that represents the relationship between output factors including current, voltage, and temperature of the lead storage battery and the state of charge of the lead storage battery; and an equalization charge implementation unit that implements equalization charge of the lead storage battery. In addition, the lead storage battery is provided with: an SOC estimation unit that estimates the state of charge of the lead storage battery based on the information measured by the battery state measurement unit and the information of the SOC model; an SOC transition DB that records how the state of charge of the lead storage battery changes; an SOC transition history management unit that records the value of the state of charge estimated by the SOC estimation unit in the SOC transition DB and investigates the transition state of the SOC; a degradation model that represents the relationship between the operating state of the lead storage battery including the state of charge of the lead storage battery and degradation; and an equalization charge optimal planning unit that plans an optimal implementation method of equalization charge based on the SOC transition state from the SOC transition history management unit and the information of the degradation model.

[0003] This lead storage battery is a simplified model with unchanging SOC-OCV characteristics, and estimates changes in SOH and soundness based on changes in SOC obtained from changes in OCV and SOC obtained from current integration. In addition, SOH is estimated only in the SOC region where the change in SOH in the SOC-OCV characteristics is small.

[0004] [Prior Art Documents]

[0005] [Patent Documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-037464 Summary of the Invention

[0007] [Problems to be Solved by the Invention]

[0008] However, in reality, changes in SOH occur at all times, and the SOC-OCV characteristics change, making it difficult to accurately estimate SOH. In addition, SOH cannot be accurately calculated in the SOC region where the influence on SOH estimation is relatively large, and the state of the lead storage battery cannot be known. This problem is not limited to the case of lead storage batteries, but may commonly exist in all secondary batteries.

[0009] The present invention has been made in view of the above problems, and an object thereof is to provide a state estimation device for a secondary battery that can accurately estimate the state of the secondary battery even when the SOC-OCV characteristics are unknown and in a SOC region that has a relatively large influence on SOH estimation.

[0010] [Technical means for solving the problem]

[0011] (1) The state estimation device for a secondary battery of the present invention (for example, the state estimation device 1 for a secondary battery described later) includes: a state measurement unit (for example, the state measurement unit 10 described later) that measures state variables including a terminal current and a terminal voltage of an operating secondary battery (for example, the secondary battery 2 described later) at a predetermined time interval; an internal resistance calculation unit (for example, the internal resistance calculation unit 11 described later) that calculates the internal resistance of the secondary battery using the state variables; a estimated OCV calculation unit (for example, the estimated OCV calculation unit 12 described later) that calculates an estimated value of the terminal voltage in an open circuit state, that is, an estimated OCV, using the state variables and the internal resistance; a estimated SOC calculation unit (for example, the estimated SOC calculation unit 14 described later) that calculates an estimated value of the charge rate of the secondary battery, that is, an estimated SOC, based on the estimated OCV using a SOC-OCV characteristic model (for example, the SOC-OCV characteristic model 13 described later) representing the relationship between the charge rate of the secondary battery, that is, SOC, and the terminal voltage in an open circuit state, that is, OCV; a differential estimated SOC calculation unit (for example, the differential estimated SOC calculation unit 15 described later) that calculates a change amount per unit time of the estimated SOC, that is, a differential estimated SOC, using the estimated SOC; an integrated terminal current calculation unit (for example, the integrated terminal current calculation unit 16 described later) that calculates an integrated amount per unit time of the terminal current, that is, an integrated terminal current, using the state variables; and an SOH calculation unit (for example, the SOH calculation unit 17 described later) that calculates the storage capacity of the secondary battery, that is, SOH, using the differential estimated SOC and the integrated terminal current; and inputs the SOH calculated by the SOH calculation unit at least in a high SOC state where the estimated OCV is equal to or higher than a first threshold to an SOH estimation model (for example, the SOH estimation model 18 described later) for estimating the SOH, and updates the SOH estimation model.

[0012] In a region of a high SOC state where the estimated OCV is equal to or higher than a first threshold set for each type of secondary battery, the deviation of the SOC-OCV characteristic model in various secondary batteries is relatively small. Therefore, according to the state estimation device for a secondary battery of the invention in (1), the SOH can be accurately estimated in a region of a high SOC state with a small deviation in SOC-OCV characteristics, and the SOH estimation model can be accurately updated (learned) using the accurately estimated SOH.

[0013] (2) In the state estimation device of the secondary battery in (1), it may include: a differential SOC calculation unit (such as the differential SOC calculation unit 20 described later), which uses the integrated terminal current and the SOH in the SOH estimation model to calculate the change amount per unit time of the charging rate of the secondary battery, that is, the differential SOC; an SOC calculation unit (such as the SOC calculation unit 21 described later), which uses the differential SOC and the estimated SOC before the unit time to calculate the charging rate of the secondary battery, that is, the SOC; an OCV calculation unit (such as the OCV calculation unit 22 described later), which uses the SOC-OCV characteristic model to calculate the terminal voltage in the open circuit state, that is, the OCV, according to the SOC; and an OCV error calculation unit (such as the OCV error calculation unit 23 described later), which uses the estimated OCV and the OCV to calculate the error between the estimated OCV and the COV, that is, the OCV error; and in the case where the estimated OCV is less than the first threshold and above the second threshold in the medium SOC state, the SOC and the OCV error are input to the SOC-OCV characteristic model to update the SOC-OCV characteristic model.

[0014] In the region of the medium SOC state where the estimated OCV is less than the first threshold and above the second threshold, the deviation of the SOC-OCV characteristic model in various secondary batteries is relatively large. Therefore, in the state estimation device of the secondary battery according to the invention in (2), in the region of the medium SOC state, the SOH estimation model is not updated, the differential SOC and SOC are calculated based on the integrated terminal current, and the SOC-OCV characteristic model can be updated based on the error between the OCV calculated according to the calculated SOC and the SOC-OCV characteristic model and the estimated OCV. Thus, the state estimation device of the secondary battery according to the invention in (2) can estimate the SOC with higher accuracy and can update (learn) the unknown SOC-OCV characteristic model.

[0015] (3) In the state estimation device of the secondary battery in (1), the state variable may include the temperature related to the secondary battery. The state estimation device of the secondary battery includes: a heat generation amount calculation unit (such as the heat generation amount calculation unit 30 described later), which uses the state variable to calculate the heat generation amount of the secondary battery; and a lowest limit OCV calculation unit (such as the lowest limit OCV calculation unit 31 described later), which makes the estimated OCV at the time when the heat generation amount reaches a specified threshold be the lowest limit value of the terminal voltage in the open circuit state, that is, the lowest limit OCV; and the lowest limit OCV is input to the SOC-OCV characteristic model to update the SOC-OCV characteristic model.

[0016] The state estimation device for a secondary battery according to the invention of (3) can update the SOC-OCV characteristic model by making the estimated OCV at the time when the heat generation of the secondary battery reaches a specified threshold value be the lowest limit OCV. Thus, the state estimation device for a secondary battery according to the invention of (3) can estimate the SOC with higher accuracy in the entire SOC region and can update (learn) the unknown SOC-OCV characteristic model.

[0017] (4) The state estimation device for a secondary battery according to (3) may include: a plurality of battery packs (for example, battery packs 3, 4, and 5 described later), each of which has at least one of the secondary batteries and is connected in parallel with each other; a plurality of connection switches (for example, connection switches 6, 7, and 8 described later), which are respectively provided corresponding to the plurality of battery packs and switch the connection and disconnection of the power supply from the battery pack; and a control unit (for example, control unit 9 described later), which controls the plurality of connection switches; and the control unit controls the plurality of connection switches to connect only one of them, thereby sequentially updating the SOC-OCV characteristic models of the respective battery packs.

[0018] The state estimation device for a secondary battery according to the invention of (4) can sequentially update the lower limit OCV at which the SOC becomes 0% for each battery pack before the SOC of the entire battery module having a plurality of battery packs becomes 0%, and thus can prevent the SOC of the entire battery module from suddenly becoming 0% during driving.

[0019] (5) In the state estimation device for a secondary battery according to (4), the control unit may control the plurality of connection switches to connect only one of them each time of charging and discharging, thereby sequentially updating the SOC-OCV characteristic models of the respective battery packs.

[0020] The state estimation device for a secondary battery according to the invention of (5) can use the lower limit OCV of the first updated (learned) battery pack for the update (learning) of other battery packs in the case where the SOC-OCV characteristic model is not updated (learned) after the battery pack is replaced. Further, by performing the update (learning) in order, the SOC-OCV characteristic models of all the battery packs can be updated (learned).

[0021] [Advantages of the Invention]

[0022] According to the present invention, a state estimation device for a secondary battery can be provided, which can accurately estimate the state of the secondary battery even when the SOC-OCV characteristic is unknown and in the SOC region where the influence on the SOH estimation is relatively large. Description of the Drawings

[0023] Figure 1It is a block diagram showing the configuration of a moving body equipped with a secondary battery state estimation device according to the first embodiment.

[0024] Figure 2 It is a flowchart explaining the processing flow of the secondary battery state estimation device according to the first embodiment.

[0025] Figure 3 It is a flowchart explaining the processing flow within the high SOC state region and the medium SOC state region.

[0026] Figure 4 It is a graph of the SOC-OCV characteristic model, showing the respective states before and after the processing within the medium SOC state region.

[0027] Figure 5 It is a timing chart showing the time history of each value caused by the discharge of the secondary battery.

[0028] Figure 6 It is a flowchart explaining the processing flow within the low SOC state region.

[0029] Figure 7 It is a graph of the SOC-OCV characteristic model, showing the state before the heat generation of the secondary battery reaches a specified threshold value.

[0030] Figure 8 It is a graph of the SOC-OCV characteristic model, showing the state after the heat generation of the secondary battery reaches a specified threshold value.

[0031] Figure 9 It is a table of the SOC-OCV characteristic model, showing the SOC (SOC_L) corresponding to the estimated OCV (OCV_L) at the time when the heat generation of the secondary battery reaches a specified threshold value.

[0032] Figure 10 It is a table of the SOC-OCV characteristic model, showing the calculation formula for updating the SOC in each grid point data.

[0033] Figure 11 It is a graph of the SOC-OCV characteristic model, showing the respective states before and after the heat generation of the secondary battery reaches a specified threshold value.

[0034] Figure 12 It is a timing chart showing the time history of each value caused by the discharge of the secondary battery.

[0035] Figure 13 It is a block diagram showing the configuration of a moving body equipped with a secondary battery state estimation device according to the second embodiment.

[0036] Figure 14This is a flowchart showing the processing flow of the state estimation device for a secondary battery according to the second embodiment. Detailed Embodiment

[0037] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the description of the second embodiment, the same reference numerals are given to the components, operations, and effects common to the first embodiment, and the description thereof will be appropriately omitted.

[0038] [First Embodiment]

[0039] First, use Figure 1 The configuration of a moving body V such as an electric vehicle equipped with the state estimation device 1 for a secondary battery according to the first embodiment of the present invention will be described. Figure 1 This is a block diagram showing the configuration of the moving body V equipped with the state estimation device 1 for a secondary battery.

[0040] As Figure 1 shown, the moving body V includes a state estimation device 1 for a secondary battery, a secondary battery 2, etc. The secondary battery 2 can be replaced for the moving body V, and the SOC-OCV characteristic model 13 is different for each type of the secondary battery 2. The charge rate, i.e., SOC [%], of the secondary battery 2 changes due to discharge and charge, and it deteriorates due to discharge and charge, resulting in a decrease in the storage capacity, i.e., SOH. Therefore, the state estimation device 1 for a secondary battery includes a general SOC-OCV characteristic model 13, updates the SOC-OCV characteristic model 13 to the SOC-OCV characteristic model of the secondary battery 2 through learning, and updates the SOH estimation model 18 for estimating the storage capacity, i.e., SOH. Thereby, the state of the secondary battery 2 is accurately estimated.

[0041] Specifically, the state estimation device 1 for a secondary battery includes: a state measurement unit 10, an internal resistance calculation unit 11, an estimated OCV calculation unit 12, an SOC-OCV characteristic model 13, an estimated SOC calculation unit 14, a differential estimated SOC calculation unit 15, an integrated terminal current calculation unit 16, an SOH calculation unit 17, an SOH estimation model 18, a differential SOC calculation unit 20, an SOC calculation unit 21, an OCV calculation unit 22, an OCV error calculation unit 23, a heat generation amount calculation unit 30, and a lowest limit OCV calculation unit 31, etc.

[0042] The state measurement unit 10 measures state variables including the terminal current and terminal voltage of the operating secondary battery 2 and the temperature related to the operating secondary battery 2 (for example, the temperature of the secondary battery 2 itself or the coolant temperature of the secondary battery 2) at a prescribed time interval.

[0043] The internal resistance calculation unit 11 calculates the internal resistance of the secondary battery 2 using the state variables measured by the state measurement unit 10.

[0044] The estimated OCV calculation unit 12 calculates the estimated value of the terminal voltage in the open-circuit state, i.e., the estimated OCV, using the state variables measured by the state measurement unit 10 and the internal resistance calculated by the internal resistance calculation unit 11. That is, the estimated OCV calculation unit 12 calculates the estimated value of the terminal voltage in the open-circuit state, i.e., the estimated OCV, using the terminal current and terminal voltage measured by the state measurement unit 10 and the internal resistance calculated by the internal resistance calculation unit 11.

[0045] The SOC-OCV characteristic model 13 is a set of multiple lattice point data representing the relationship between the state of charge of the secondary battery 2, i.e., SOC [%], and the terminal voltage in the open-circuit state, i.e., OCV [V], and the set of the multiple lattice point data forms a curve (see Figure 4 ). In the region of the high SOC state where OCV [V] is equal to or higher than the first threshold (e.g., 3.8 [V]), the deviation of SOC [%] is small. In the region of the medium SOC state where OCV [V] is less than the first threshold and higher than the second threshold (e.g., 3.5 [V]) which is less than the first threshold, the deviation of SOC [%] is large. In the region of the low SOC state where OCV [V] is less than the second threshold, the deviation of SOC [%] is small.

[0046] The SOC-OCV characteristic model 13 is updated by learning. Specifically, in the case of the medium SOC state where the estimated OCV [V] calculated by the estimated OCV calculation unit 12 is less than the first threshold (e.g., 3.8 [V]) and higher than the second threshold (e.g., 3.5 [V]) which is less than the first threshold, the SOC-OCV characteristic model 13 is updated by inputting the SOC [%] calculated by the input SOC calculation unit 21 and the OCV error calculated by the OCV error calculation unit 23. More specifically, in the case of the medium SOC state, the SOC-OCV characteristic model 13 is updated using the smoothing coefficient k2 (0 < k2 < 1) (see Figure 4 ). The OCV [V] in the updated SOC-OCV characteristic model 13 is represented by OCV [V] = OCV [previous] + ΔOCV × k2.

[0047] In addition, when the heat generation amount of the secondary battery 2 reaches a specified threshold, the SOC-OCV characteristic model 13 is updated by inputting the lowest limit OCV calculated by the lowest limit OCV calculation unit 31 into a specified conversion formula (see Figure 9 and Figure 10 ).

[0048] The estimated SOC calculation unit 14 uses the SOC-OCV characteristic model 13 to calculate the estimated value of the charge rate of the secondary battery 2, that is, the estimated SOC, based on the estimated OCV calculated by the estimated OCV calculation unit 12.

[0049] The differential estimated SOC calculation unit 15 uses the estimated SOC calculated by the estimated SOC calculation unit 14 to calculate the change amount per unit time T [sec] of the estimated SOC, that is, the differential estimated SOC. The differential estimated SOC is represented by ΔSOC_v [%]=SOC [before T [sec]] - SOC [current].

[0050] The integrated terminal current calculation unit 16 uses the state variables measured by the state measurement unit 10 to calculate the integrated amount per unit time T [sec] of the terminal current, that is, the integrated terminal current. That is, the integrated terminal current calculation unit 16 uses the terminal current measured by the state measurement unit 10 to calculate the integrated amount per unit time T [sec] of the terminal current, that is, the integrated terminal current. The integrated terminal current is represented by ΔAh = A [last time]+A [the time before last time]+···+A [before T [sec]].

[0051] The SOH calculation unit 17 uses the differential estimated SOC calculated by the differential estimated SOC calculation unit 15 and the integrated terminal current calculated by the integrated terminal current calculation unit 16 to calculate the storage capacity of the secondary battery 2, that is, the SOH. The storage capacity of the secondary battery 2 calculated by the SOH calculation unit 17 is represented by SOH_0 = ΔAh / ΔSOC_v×100.

[0052] The SOH estimation model 18 is a model for estimating the storage capacity of the secondary battery 2, that is, the SOH. At least in the case of a high SOC state where the estimated OCV [V] calculated by the estimated OCV calculation unit 12 is equal to or higher than the first threshold value (for example, 3.8 [V]), the SOH estimation model 18 is updated by inputting the SOH calculated by the SOH calculation unit 17.

[0053] Specifically, when the estimated OCV [V] calculated in the estimated OCV calculation unit 12 is in a high SOC state equal to or higher than the first threshold (for example, 3.8 [V]), and when the estimated OCV [V] calculated in the estimated OCV calculation unit 12 is in a low SOC state less than the second threshold (for example, 3.5 [V]) that is less than the first threshold, the SOH estimation model 18 is updated using the smoothing coefficient k1 (0 < k1 < 1). In this case, the SOH in the SOH estimation model 18 is represented by SOH_s = SOH_s[previous]×(1 - k1) + SOH_0×k1. Additionally, when the estimated OCV [V] calculated in the estimated OCV calculation unit 12 is in a medium SOC state less than the first threshold and equal to or higher than the second threshold (for example, 3.5 [V]) that is less than the first threshold, the SOH estimation model 18 is updated using the smoothing coefficient k0 (0 ≤ k0 < k1). In this case, the SOH in the SOH estimation model 18 is represented by SOH_s = SOH_s[previous]×(1 - k0) + SOH_0×k0. However, when the smoothing coefficient k0 is set to zero, the SOH estimation model 18 is not updated, and the SOH in this SOH estimation model 18 is represented by SOH_s = SOH_s[previous].

[0054] Additionally, when the heat generation amount of the secondary battery 2 reaches a specified threshold, the SOH estimation model 18 is updated using the SOC [%] corresponding to OCV_L [V], that is, SOC_L [%]. In this case, the SOH in the SOH estimation model 18 is represented by SOH_s = SOH_s[latest]×(100 [%] - SOC_L [%]) / 100 [%].

[0055] The differential SOC calculation unit 20 calculates the change amount per unit time T [sec] of the charging rate of the secondary battery 2, that is, the differential SOC, using the integrated terminal current calculated in the integrated terminal current calculation unit 16 and the SOH in the SOH estimation model 18. The differential SOC is represented by ΔSOC_Ah [%] = ΔAh / SOH_s.

[0056] The SOC calculation unit 21 calculates the charging rate of the secondary battery 2, that is, SOC [%], using the differential SOC calculated in the differential SOC calculation unit 20 and the estimated SOC before the unit time T [sec] calculated in the estimated SOC calculation unit 14. The charging rate of the secondary battery 2, that is, SOC [%], is represented by SOC [%] = SOC[T [sec] before] + ΔSOC_Ah.

[0057] The OCV calculation unit 22 calculates the terminal voltage in the open circuit state, that is, OCV, using the SOC-OCV characteristic model 13 based on the SOC [%] calculated in the SOC calculation unit 21.

[0058] The OCV error calculation unit 23 calculates the OCV error, which is the error between the estimated OCV calculated by the estimated OCV calculation unit 12 and the OCV [V] calculated by the OCV calculation unit 22. The OCV error is represented by ΔOCV [V]=OCV_Ah - OCV. However, in this formula, OCV_Ah [V] represents the OCV calculated by the OCV calculation unit 22, and OCV [V] represents the estimated OCV.

[0059] The calorific value calculation unit 30 calculates the calorific value of the secondary battery 2 using the state variables measured by the state measurement unit 10. That is, the calorific value calculation unit 30 calculates the calorific value of the secondary battery 2 using the temperature related to the secondary battery 2 measured by the state measurement unit 10.

[0060] The lowest limit OCV calculation unit 31 sets the estimated OCV calculated by the estimated OCV calculation unit 12 at the time when the calorific value of the secondary battery 2 reaches a specified threshold value as the lowest limit value of the terminal voltage in the open circuit state, that is, the lowest limit OCV. The lowest limit OCV is represented by OCV_L [V].

[0061] Next, Figure 2 The processing flow of the state estimation device 1 for the secondary battery will be described. Figure 2 It is a flowchart for explaining the processing flow of the state estimation device 1 for the secondary battery.

[0062] As Figure 2 shown, step S100 is a step of updating the SOH estimation model 18 at least when the estimated OCV [V] calculated by the estimated OCV calculation unit 12 is in a high SOC state equal to or higher than the first threshold value (for example, 3.8 [V]). In addition, step S100 is a step of updating the SOC-OCV characteristic model 13 when the estimated OCV [V] calculated by the estimated OCV calculation unit 12 is in a medium SOC state less than the first threshold value (for example, 3.8 [V]) and equal to or higher than the second threshold value (for example, 3.5 [V]) less than the first threshold value (refer to Figure 4 ).

[0063] Step S200 is a step of updating the SOC-OCV characteristic model 13 when the calorific value of the secondary battery 2 reaches a specified threshold value (refer to Figures 7 to 11 ).

[0064] Next, Figure 3 The processing flow in the high SOC state region and the medium SOC state region will be described. Figure 3 It is a flowchart for explaining the processing flow in the high SOC state region and the medium SOC state region.

[0065] AsFigure 3 As shown, step S101 is a step of determining whether the estimated OCV calculated in the estimated OCV calculation unit 12 falls within the range of 0[%] ≤ SOC ≤ 100[%] (for example, 3.3[V] ≤ OCV ≤ 4.1[V]). If it is determined to be YES in step S101, the process proceeds to step S102. If it is determined to be NO in step S101, the process ends.

[0066] Step S102 is a step of calculating the differential estimated SOC. In step S102, the internal resistance calculation unit 11 calculates the internal resistance of the secondary battery 2. The estimated OCV calculation unit 12 calculates the estimated OCV. The estimated SOC calculation unit 14 calculates the estimated SOC. The differential estimated SOC calculation unit 15 calculates the differential estimated SOC. After step S102, the process proceeds to step S103.

[0067] Step S103 is a step of calculating the integrated terminal current. In step S103, the integrated terminal current calculation unit 16 calculates the integrated terminal current. After step S103, the process proceeds to step S104.

[0068] Step S104 is a step of calculating the state of health (SOH) of the secondary battery 2, which is the storage capacity. In step S104, the SOH calculation unit 17 calculates the SOH. After step S104, the process proceeds to step S105.

[0069] Step S105 is a step of determining whether the estimated OCV [V] calculated in the estimated OCV calculation unit 12 is less than the first threshold (for example, 3.8[V]) and above the second threshold (for example, 3.5[V]) that is less than the first threshold, in the medium SOC state. If it is determined to be YES in step S105, that is, in the medium SOC state, the process proceeds to step S107. If it is determined to be NO in step S105, that is, in the high SOC state or the low SOC state, the process proceeds to step S106.

[0070] Step S106 is a step of updating the SOH estimation model 18 using the smoothing coefficient k1. After step S106, the process ends.

[0071] Step S107 is a step of updating the SOH estimation model 18 using the smoothing coefficient k0. However, when the smoothing coefficient k0 is set to zero, the SOH estimation model 18 is not updated. After step S107, the process proceeds to step S108.

[0072] Step S108 is a step of calculating the differential SOC. In step S108, the differential SOC calculation unit 20 calculates the differential SOC. After step S108, the process proceeds to step S109.

[0073] Step S109 is the step of calculating OCV [V]. In step S109, the SOC calculation unit 21 calculates SOC [%]. The OCV calculation unit 22 calculates OCV [V]. After step S109, the process proceeds to step S110.

[0074] Step S110 is the step of calculating the OCV error. In step S110, the OCV error calculation unit 23 calculates the OCV error. After step S110, the process proceeds to step S111.

[0075] Step S111 is the step of updating the SOC-OCV characteristic model 13 using the smoothing coefficient k2. After step S111, the process ends.

[0076] Next, Figure 4 Step S111 will be described. Figure 4 FIG. is a diagram of the SOC-OCV characteristic model 13, and is a diagram showing the respective states before and after the processing in the region of the middle SOC state.

[0077] Figure 4 In, as shown by the arrow, in the present embodiment, the SOC-OCV characteristic model 13 is updated in the region of the middle SOC state. Thus, in the region of the middle SOC state where the deviation of SOC [%] is large, the deviation of SOC [%] becomes smaller.

[0078] Next, Figure 5 The time variation of each value due to the discharge of the secondary battery 2 will be described. Figure 5 FIG. is a timing chart showing the time history of each value due to the discharge of the secondary battery 2.

[0079] As Figure 5 shown, the terminal voltage in the open circuit state, i.e., OCV [V], decreases with time. The state of charge, i.e., SOC [%], decreases with time.

[0080] In the case of the high SOC state where OCV [V] is equal to or higher than the first threshold value (for example, 3.8 [V]), and in the case of the low SOC state where OCV [V] is less than the second threshold value (for example, 3.5 [V]), the SOH estimation model 18 is updated using the smoothing coefficient k1. That is, in the case of the high SOC state where OCV [V] is equal to or higher than the first threshold value (for example, 3.8 [V]), and in the case of the low SOC state where OCV [V] is less than the second threshold value (for example, 3.5 [V]), the SOH estimation model 18 is in the learning state.

[0081] On the other hand, in the medium SOC state where the OCV [V] is less than the first threshold (e.g., 3.8 [V]) and above the second threshold (e.g., 3.5 [V]), the SOH estimation model 18 is not updated or is updated slowly using the smoothing coefficient k0. That is, in the medium SOC state where the OCV [V] is less than the first threshold (e.g., 3.8 [V]) and above the second threshold (e.g., 3.5 [V]), the SOH estimation model 18 stops learning or is in the process of slow learning.

[0082] In the case of a high SOC state where the OCV [V] is above the first threshold (e.g., 3.8 [V]) and in the case of a low SOC state where the OCV [V] is less than the second threshold (e.g., 3.5 [V]), the SOC-OCV characteristic model 13 is not updated.

[0083] On the other hand, in the medium SOC state where the OCV [V] is less than the first threshold (e.g., 3.8 [V]) and above the second threshold (e.g., 3.5 [V]), the SOC-OCV characteristic model 13 is being updated.

[0084] Next, use Figure 6 To describe the processing flow in the low SOC state region. Figure 6 It is a flowchart for describing the processing flow in the low SOC state region.

[0085] As Figure 6 Shown, step S201 is a step of determining whether the estimated OCV calculated by the estimated OCV calculation unit 12 is in a low SOC state where it is less than the second threshold (e.g., 3.5 [V]). If it is determined to be yes in step S201, that is, in the case of a low SOC state, the process proceeds to step S202. If it is determined to be no in step S201, that is, in the case of a non-low SOC state, the process ends.

[0086] Step S202 is a step of calculating the heat generation amount of the secondary battery 2. In step S202, the heat generation amount calculation unit 30 calculates the heat generation amount of the secondary battery 2. After step S202, the process proceeds to step S203.

[0087] Step S203 is a step of determining whether the heat generation amount of the secondary battery 2 reaches a specified threshold. If it is determined to be yes in step S203, that is, in the case where the heat generation amount of the secondary battery 2 reaches the specified threshold, the process proceeds to step S204. If it is determined to be no in step S203, that is, in the case where the heat generation amount of the secondary battery 2 does not reach the specified threshold, the process ends.

[0088] Step S204 is a step of making the estimated OCV at the time when the heat generation amount of the secondary battery 2 reaches a specified threshold value be the lowest-limit OCV. In step S204, the lowest-limit OCV calculation unit 31 calculates the lowest-limit OCV. After step S204, the process proceeds to step S205.

[0089] Step S205 is a step of using the SOC-OCV characteristic model 13 to calculate the SOC [%] (i.e., SOC_L [%]) corresponding to the OCV_L [V], which is the lowest-limit OCV, based on the OCV_L [V] (refer to Figure 9 ). After step S205, the process proceeds to step S206.

[0090] Step S206 is a step of updating the SOC-OCV characteristic model 13 using a specified conversion formula (refer to Figure 10 and Figure 11 ). For example, the grid point data with SOC [%] being -20 [%] uses the conversion formula of 100 [%] / (100 [%] - SOC_L [%]) × (-20 [%] - SOC_L [%]). The grid point data with SOC [%] being 0 [%] uses the conversion formula of 100 [%] / (100 [%] - SOC_L [%]) × (0 [%] - SOC_L [%]). The grid point data with SOC [%] being 20 [%] uses the conversion formula of 100 [%] / (100 [%] - SOC_L [%]) × (20 [%] - SOC_L [%]). The grid point data with SOC [%] being 100 [%] uses the conversion formula of 100 [%] / (100 [%] - SOC_L [%]) × (100 [%] - SOC_L [%]). After step S206, the process proceeds to step S207.

[0091] Step S207 is a step of updating the SOH estimation model 18 using the conversion formula of SOH_s = SOH_s (latest) × (100% - SOC_L) / 100%. After step S207, the process ends.

[0092] Next, the processing for the case where the heat generation amount of the secondary battery 2 reaches the specified threshold value is described using Figures 7 to 11 . Figure 7 is a diagram of the SOC-OCV characteristic model 13 and is a diagram showing the state before the heat generation amount of the secondary battery 2 reaches the specified threshold value. Figure 8 is a diagram of the SOC-OCV characteristic model 13 and is a diagram showing the state after the heat generation amount of the secondary battery 2 reaches the specified threshold value. Figure 9 is a table of the SOC-OCV characteristic model 13 and is a diagram showing the SOC (SOC_L) corresponding to the estimated OCV (OCV_L) at the time when the heat generation amount of the secondary battery 2 reaches the specified threshold value. Figure 10It is a table of the SOC-OCV characteristic model and a graph showing the calculation formula of SOC under the condition of updating the data of each grid point. Figure 11 It is a graph of the SOC-OCV characteristic model 13 and a graph showing the respective states before and after the heat generation of the secondary battery 2 reaches a specified threshold value.

[0093] As Figure 7 shown, when the OCV of the SOC-OCV characteristic model 13 is 3.3 [V], the SOC becomes 0 [%]. However, when the time point at which the heat generation of the secondary battery 2 reaches the specified threshold value is regarded as SOC = 0 [%], as Figure 8 shown, when the OCV of the SOC-OCV characteristic model 13 is 2.8 [V], the SOC becomes 0 [%]. In this case, as Figure 9 shown, the SOC (SOC_L) corresponding to the estimated OCV (OCV_L) at the time point when the heat generation of the secondary battery 2 reaches the specified threshold value is calculated. Then, using the Figure 10 shown conversion formulas, the SOC under the data of each grid point is updated in the form of changing the scale. As a result, as Figure 11 shown, the SOC-OCV characteristic model 13 is updated.

[0094] Next, Figure 12 is used to explain the change over time of each value caused by the discharge of the secondary battery 2. Figure 12 It is a timing chart showing the time history of each value caused by the discharge of the secondary battery 2. In addition, the description Figure 5 the same as is omitted.

[0095] The time point at which the state of charge (SOC) [%] of the actual secondary battery 2 becomes zero is different from the time point at which the SOC [%] in the SOC-OCV characteristic model 13 becomes zero. Here, it is assumed that the time point at which the state of charge (SOC) [%] of the actual secondary battery 2 becomes zero is after the time point at which the SOC [%] in the SOC-OCV characteristic model 13 becomes negative.

[0096] The time point at which the state of charge (SOC) [%] of the actual secondary battery 2 becomes zero is the time point at which the heat generation of the secondary battery 2 reaches the specified threshold value. That is, the time point at which the heat generation of the secondary battery 2 reaches the specified threshold value is after the time point at which the SOC [%] in the SOC-OCV characteristic model 13 becomes negative.

[0097] The SOH estimation model 18 stops learning when the SOC [V] in the SOC-OCV characteristic model 13 reaches zero. Then, the SOH estimation model 18 is updated after the heat generation of the secondary battery 2 reaches the specified threshold value.

[0098] The SOC-OCV characteristic model 13 is updated after the calorific value of the secondary battery 2 reaches a specified threshold value.

[0099] In the region where the estimated OCV is in a high SOC state of the first threshold value or more, the deviation of the SOC-OCV characteristic model 13 in various secondary batteries 2 is relatively small. Therefore, according to the state estimation device 1 of the secondary battery, the relearning of the SOH estimation model 18 can be performed with high accuracy in the region of the high SOC state.

[0100] In the region where the estimated OCV is in a medium SOC state of less than the first threshold value and at least the second threshold value or more, the deviation of the SOC-OCV characteristic model in various secondary batteries is relatively large. Therefore, according to the state estimation device 1 of the secondary battery, the relearning of the SOH estimation model 18 is not performed in the region of the medium SOC state, and the processes of steps S107 to S111 are performed, so that the learning of the unknown SOC-OCV characteristic model 13 can be performed.

[0101] In addition, according to the state estimation device 1 of the secondary battery, the learning of the unknown SOC-OCV characteristic model can be performed based on the estimated OCV at the time when the calorific value of the secondary battery 2 reaches a specified threshold value.

[0102] [Second Embodiment]

[0103] Next, use Figure 13 The configuration of the moving body V of the state estimation device 1 of the secondary battery according to the second embodiment of the present invention will be described. Figure 13 It is a block diagram showing the configuration of the moving body V provided with the state estimation device 1 of the secondary battery.

[0104] As Figure 13 shown, the moving body V includes the state estimation device 1 of the secondary battery, and a plurality of battery packs 3, 4, 5, etc. having built-in secondary batteries (not shown). In the state estimation device 1 of the secondary battery, in addition to the same configuration as that of the first embodiment, it further includes a plurality of connection switches 6, 7, 8, and a control unit 9, etc.

[0105] Each of the plurality of battery packs 3, 4, 5 has at least one secondary battery (not shown) that is the same as the secondary battery 2 of the first embodiment (refer to Figure 1 ), and they are connected in parallel to each other.

[0106] A plurality of connection switches 6, 7, 8 are provided corresponding to the plurality of battery packs 3, 4, 5 respectively. The connection switch 6 switches the connection and disconnection of the power supply from the battery pack 3. The connection switch 7 switches the connection and disconnection of the power supply from the battery pack 4. The connection switch 8 switches the connection and disconnection of the power supply from the battery pack 5.

[0107] The control unit 9 controls a plurality of connection switches 6, 7, and 8. Specifically, when the required output of the moving body V is equal to or less than a specified value, the control unit 9 connects the plurality of connection switches 6, 7, and 8 one by one in sequence, and sequentially updates the SOC-OCV characteristic models 13 (refer to Figure 1 ) of the respective battery packs 3, 4, and 5. In addition, when the required output of the moving body V is equal to or less than a specified value, the control unit 9 connects the plurality of connection switches 6, 7, and 8 one by one in sequence during each charge and discharge. The control of the plurality of connection switches 6, 7, and 8 by the control unit 9 is performed based on the determination of a flag.

[0108] Next, the processing flow of the state estimation device 1 for the secondary battery will be described using Figure 14 . Figure 14 FIG. is a flowchart for explaining the processing flow of the state estimation device 1 for the secondary battery.

[0109] As shown in Figure 14 , step S301 is a step of determining whether the required output of the moving body V is equal to or less than a specified value (for example, 10% of the maximum output). If it is determined to be yes in step S301, the process proceeds to step S302. If it is determined to be no in step S301, the process ends.

[0110] Step S302 determines whether the flag of "F_1st learning after charging completed" is cleared to 0. The flag of "F_1st learning after charging completed" is cleared to 0 by charging the entire battery packs 3, 4, and 5 by a specified value or more (for example, 50%). If it is determined to be yes in step S302, the process proceeds to step S303. If it is determined to be no in step S302, the process ends.

[0111] Step S303 determines whether the flag of "F_BP1 learning completed" is cleared to 0. If it is determined to be yes in step S303, the process proceeds to step S304. If it is determined to be no in step S303, the process proceeds to step S307.

[0112] Step S304 is a step of disconnecting the connection switches 7 and 8 corresponding to the battery packs 4 and 5. In step S304, only the connection switch 6 is connected. After step S304, the process proceeds to step S305.

[0113] Step S305 is a step of performing learning on the battery pack 3. In step S305, learning of the SOC-OCV characteristic model 13 (refer to Figure 1 ) of the battery pack 3 is performed. After step S305, the process proceeds to step S306.

[0114] Step S306 is a step of raising the flag of "F_BP1 learning completed" to 1 and raising the flag of "F_1st learning after charging completed" to 1. After step S306, the process ends.

[0115] Step S307 determines whether the flag of "F_BP2 learning completed" is cleared to 0. If it is determined to be yes in step S307, the process proceeds to step S308. If it is determined to be no in step S307, the process proceeds to step S311.

[0116] Step S308 is a step of disconnecting the connection switches 8 and 6 corresponding to the battery packs 5 and 3. In step S308, only the connection switch 7 is connected. After step S308, the process proceeds to step S309.

[0117] Step S309 is a step of performing learning on battery pack 4. In step S309, learning of the SOC-OCV characteristic model 13 of battery pack 4 (refer to Figure 1 ) is performed. After step S309, the process proceeds to step S310.

[0118] Step S310 is a step of raising the flag of "F_BP2 learning completed" to 1 and raising the flag of "F_1st learning after charging completed" to 1. After step S310, the process ends.

[0119] Step S311 determines whether the flag of "F_BP3 learning completed" is cleared to 0. If it is determined to be yes in step S311, the process proceeds to step S312. If it is determined to be no in step S311, the process proceeds to step S315.

[0120] Step S312 is a step of disconnecting the connection switches 6 and 7 corresponding to the battery packs 3 and 4. In step S312, only the connection switch 8 is connected. After step S312, the process proceeds to step S313.

[0121] Step S313 is a step of performing learning on battery pack 5. In step S313, learning of the SOC-OCV characteristic model 13 of battery pack 5 (refer to Figure 1 ) is performed. After step S313, the process proceeds to step S314.

[0122] Step S314 is a step of raising the flag of "F_BP3 learning completed" to 1 and raising the flag of "F_1st learning after charging completed" to 1. After step S314, the process ends.

[0123] Step S315 is a step of clearing the flag of "F_BP1 learning completed" to 0. After step S315, the process proceeds to step S316.

[0124] Step S316 is the step of clearing the flag of "F_BP2 learning completed" to 0. After step S316, it proceeds to step S317.

[0125] Step S317 is the step of clearing the flag of "F_BP3 learning completed" to 0. After step S317, the process ends.

[0126] According to the state estimation device 1 of the secondary battery, before the overall SOC of the multiple battery packs 3, 4, and 5 reaches 0%, for each of the battery packs 3, 4, and 5, the OCV at which the SOC becomes 0% is sequentially updated, so that it is possible to prevent the overall SOC of the multiple battery packs 3, 4, and 5 from reaching 0%.

[0127] In addition, the present invention is not limited to the above-described embodiments, and variations, improvements, etc. within the scope that can achieve the object of the present invention are included in the present invention.

[0128] Reference Numerals

[0129] 1: State estimation device of secondary battery

[0130] 2: Secondary battery

[0131] 3, 4, 5: Battery packs

[0132] 6, 7, 8: Connection switches

[0133] 9: Control unit

[0134] 10: State measurement unit

[0135] 11: Internal resistance calculation unit

[0136] 12: Estimated OCV calculation unit

[0137] 13: SOC-OCV characteristic model

[0138] 14: Estimated SOC calculation unit

[0139] 15: Differential estimated SOC calculation unit

[0140] 16: Integrated terminal current calculation unit

[0141] 17: SOH calculation unit

[0142] 18: SOH estimation model

[0143] 20: Differential SOC calculation unit

[0144] 21: SOC calculation unit

[0145] 22: OCV calculation unit

[0146] 23: OCV Error Calculation Unit

[0147] 30: Heat Generation Calculation Unit

[0148] 31: Lowest Limit OCV Calculation Unit

[0149] V: Moving Body

Claims

1. A state estimation device for a secondary battery, comprising: A state measurement unit that measures state variables including terminal current and terminal voltage of the secondary battery in operation at a prescribed time interval; An internal resistance calculation unit that calculates the internal resistance of the secondary battery using the state variables; A estimated OCV calculation unit that calculates an estimated value of the terminal voltage in an open circuit state, i.e., estimated OCV, using the state variables and the internal resistance; A estimated SOC calculation unit that calculates an estimated value of the charge rate of the secondary battery, i.e., estimated SOC, based on the estimated OCV using a SOC-OCV characteristic model representing the relationship between the charge rate of the secondary battery, i.e., SOC, and the terminal voltage in an open circuit state, i.e., OCV; A differential estimated SOC calculation unit that calculates a change amount per unit time of the estimated SOC, i.e., differential estimated SOC, using the estimated SOC; An integrated terminal current calculation unit that calculates an integrated amount per unit time of the terminal current, i.e., integrated terminal current, using the state variables; and An SOH calculation unit that calculates the storage capacity of the secondary battery, i.e., SOH, using the differential estimated SOC and the integrated terminal current; and Input the SOH calculated by the SOH calculation unit at least in a high SOC state where the estimated OCV is equal to or higher than a first threshold into an SOH estimation model for estimating the SOH, and update the SOH estimation model.

2. The state estimation device for a secondary battery according to claim 1, comprising: A differential SOC calculation unit that calculates a change amount per unit time of the charge rate of the secondary battery, i.e., differential SOC, using the integrated terminal current and the SOH in the SOH estimation model; A SOC calculation unit that calculates the charge rate of the secondary battery, i.e., SOC, using the differential SOC and the estimated SOC from one unit time ago; An OCV calculation unit that calculates the terminal voltage in an open circuit state, i.e., OCV, based on the SOC using the SOC-OCV characteristic model; and An OCV error calculation unit that calculates an error between the estimated OCV and the OCV, i.e., OCV error, using the estimated OCV and the OCV; and In a medium SOC state where the estimated OCV is less than the first threshold and equal to or higher than a second threshold smaller than the first threshold, input the SOC and the OCV error into the SOC-OCV characteristic model and update the SOC-OCV characteristic model.

3. The state estimation device for a secondary battery according to claim 1, wherein the state variables include the temperature related to the secondary battery, The state estimation device for the secondary battery comprises: A heat generation amount calculation unit that calculates the heat generation amount of the secondary battery using the state variables; and A lowest limit OCV calculation unit that makes the estimated OCV at the time when the heat generation amount reaches a prescribed threshold be the lowest limit value of the terminal voltage in an open circuit state, i.e., lowest limit OCV; and Input the lowest OCV to the SOC-OCV characteristic model to update the SOC-OCV characteristic model.

4. The state estimation device for a secondary battery according to claim 3, comprising: A plurality of battery packs, each having at least one of the secondary batteries and being connected in parallel with each other; A plurality of connection switches, each corresponding to one of the plurality of battery packs and switching the supply connection and disconnection of power from the battery pack; and A control unit that controls the plurality of connection switches; and The control unit controls the plurality of connection switches to connect only one of them, thereby sequentially updating the SOC-OCV characteristic models of the respective ones of the plurality of battery packs.

5. The state estimation device for a secondary battery according to claim 4, wherein the control unit controls the plurality of connection switches to connect only one of them each time of charging and discharging, thereby sequentially updating the SOC-OCV characteristic models of the respective ones of the plurality of battery packs.

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