Battery pack diagnostic method and diagnostic device

Abstract

To improve calculation accuracy of the energy capacity of a battery pack.SOLUTION: A diagnostic method of a battery pack includes the steps of: obtaining detection data including the current and temperature of a battery pack which has a configuration in which multiple cells are connected in series and the voltage of each cell; obtaining function data of the open circuit voltage and resistance being the function of the charging state for the cell; creating a resistance table for each cell by using the function data and detection data; calculating the charging state of each cell by using the current and temperature and the voltage of each cell; calculating the charge capacity and resistance of each cell and the charging state for each cell; calculating the energy capacity of a battery pack by using the charge capacity, the charging state and resistance for each cell; diagnosing the calculated energy capacity of the battery pack; and recalculating the energy capacity of the battery pack by removing a value of the energy capacity determined to be abnormal by the diagnosis.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This disclosure relates to a method and apparatus for diagnosing battery packs. [Background technology]

[0002] Electric vehicles (EVs) and similar devices contain battery packs composed of multiple cells. Since the energy capacity of the battery pack correlates with the EV's driving range, there is a need for technology to accurately determine the battery pack's energy capacity.

[0003] Patent Document 1 discloses a method for diagnosing a battery pack using a system that obtains detection data including the current and temperature of a battery pack having a configuration in which multiple cells are connected in series, as well as the voltage of each cell. This method calculates the unbalance amount and resistance, which are estimated values ​​of the charge state of each cell when the battery pack is fully charged, using the current and temperature, the voltage of each cell, the open-circuit voltage charge state function and resistance table, and then calculates the energy capacity of the battery pack using the charge capacity, unbalance amount and resistance.

[0004] In this specification, the State of Charge (SOC) of each cell in a battery pack is referred to as "SOC," and the Open Circuit Voltage (OCV) is referred to as "OCV." [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] International Publication No. 2022 / 024885 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] Calculating the energy capacity of a battery pack requires data such as current, temperature, and voltage while the battery is operating (driving and charging). If there are large errors in the input data such as current, temperature, and voltage, the energy capacity of the battery pack diagnosed using the above data will also have a large error, so the accuracy of the data is crucial.

[0007] The battery pack diagnostic method described in Patent Document 1 does not address how to handle situations where there are abnormalities in the data.

[0008] The purpose of this disclosure is to improve the accuracy of calculating the energy capacity of a battery pack. [Means for solving the problem]

[0009] The battery pack diagnostic method of this disclosure includes the steps of: obtaining detection data including the current and temperature of a battery pack having a configuration in which multiple cells are connected in series, and the voltage of each cell; obtaining function data of open-circuit voltage and resistance, which are functions of the charge state of the cells; creating a resistance table for each cell using the function data and detection data; calculating the charge state of each cell using the current and temperature and the voltage of each cell; calculating the charge capacity, resistance and charge state of each cell for each cell; calculating the energy capacity of the battery pack using the charge capacity, charge state and resistance of each cell; diagnosing the calculated energy capacity of the battery pack; and removing the energy capacity values ​​determined to be abnormal by the diagnosis and recalculating the energy capacity of the battery pack. [Effects of the Invention]

[0010] According to this disclosure, the accuracy of calculating the energy capacity of a battery pack can be improved. [Brief explanation of the drawing]

[0011] [Figure 1] This is a configuration diagram showing an example of a battery pack diagnostic system according to the embodiment. [Figure 2]Figure 1 is a diagram showing an example of a battery pack built into the electric vehicle 101. [Figure 3] This table shows an example of communication content from the EV to the server. [Figure 4] Figure 1 is a configuration diagram showing an example of a battery table 105. [Figure 5] Figure 1 is a flowchart showing an example of processing in the SOC calculation means 107. [Figure 6] This is a flowchart showing an example of the processing in the energy capacity calculation means 109 of the battery pack shown in Figure 1. [Figure 7A] This graph shows the voltage of each cell when the battery pack is in an unbalanced state. [Figure 7B] This graph shows the voltage of each cell after the battery pack's imbalance has been resolved. [Figure 8] Figure 1 is a flowchart showing an example of the processing in the energy capacity diagnosis / recalculation means 111 of the battery pack. [Figure 9] Figure 8 shows an example of a graph used to diagnose abnormalities in the energy capacity of the battery pack in step S82. [Figure 10A] This graph shows cases where the battery pack diagnostic results show abnormal values ​​in a specific temperature range. [Figure 10B] This graph shows cases where the battery pack diagnostic results show abnormal values ​​in a specific current range. [Modes for carrying out the invention]

[0012] This disclosure includes a method for transferring data from a battery pack embedded in an EV to a server, and for the server to diagnose the energy capacity and sensor abnormalities of the battery pack.

[0013] Furthermore, the device used to implement this diagnostic method can be configured in three ways: one where calculations are performed on a server, one where calculations are performed only on a terminal within the EV, and one where calculations are performed on both the server and the terminal within the EV. These steps may be implemented as a program on the server, a program only on the terminal within the EV, or a program on both the EV terminal and the server.

[0014] However, this disclosure is not limited to the embodiments described below, and includes various modifications and applications within the technical concepts of this disclosure. For example, the battery pack diagnostic system described below can be applied not only to EVs, but also to battery packs for HEMS (Home Energy Management Systems), BEMS (Building Energy Management Systems), FEMS (Factory Energy Management Systems), railways, and construction machinery.

[0015] The embodiments described herein will be described in detail below with reference to the drawings.

[0016] Figure 1 is a configuration diagram showing an example of a battery pack diagnostic system according to the embodiment.

[0017] In Figure 1, the battery pack diagnostic system comprises a server 100 (also called the "processing unit" or "diagnostic device"), multiple electric vehicles 101 (EVs), and communication devices 102 within each EV (for example, mobile terminals such as smartphones, Wi-Fi® routers, and in-vehicle computers such as car navigation systems). The server 100 includes a storage device 103 (first storage device) for storing EV communication data (the types of data will be described later) collected from each EV via the communication device 102, a table setting means 104 (table setting unit), a second storage device for storing a battery table 105, and a battery pack energy calculation means 106 (battery pack energy calculation unit). The battery pack energy calculation means 106 includes a SOC calculation means 107 (SOC calculation unit), a third storage device for storing SOC data group 108, a battery pack energy capacity calculation means 109 (battery pack energy capacity calculation unit), a fourth storage device for storing battery pack energy capacity / voltage / current / temperature data group 110, an energy capacity diagnosis / recalculation means 111 (battery pack energy capacity calculation unit), and a fifth storage device for storing battery pack energy capacity data group 112.

[0018] In this embodiment and in Figure 1, the calculation is performed on a server, but the calculation may also be performed at the edge, such as within the in-vehicle BMU or on an in-vehicle smartphone, or it may be performed on both the server and the edge, such as within the in-vehicle BMU or on an in-vehicle smartphone. When calculations are performed on both the server and the in-vehicle BMU or in-vehicle smartphone, the diagnostic method built and improved from large amounts of data in the cloud may be sent to the edge.

[0019] Figure 2 is a diagram showing an example of a battery pack built into the electric vehicle 101 shown in Figure 1.

[0020] The battery pack has a configuration in which N cell groups 203, each consisting of L cells 201 connected in parallel, are connected in series. Here, each cell group 203 is considered as a single battery cell. One temperature sensor 202 is attached to each of the M selected cell groups 203. In other words, the battery pack has M temperature sensors 202.

[0021] Furthermore, the EV has a built-in battery management unit 205 (BMU) that manages the battery pack. The BMU collects the voltage of each of the parallel-connected cell group 203, the current measured by the ammeter 204 (current sensor), and the temperature measured by M temperature sensors 202 via the EV's internal network 206 (LAN), and calculates the representative SOC of the battery pack. The BMU can then transmit information such as current, voltage, temperature, and the representative SOC of the battery pack (detection data) to a communication device 102 via the LAN. The communication device 102 receives this information and transmits it to a server 100 (Figure 1). This transmission is performed periodically to the server while the EV's ignition is on.

[0022] In Figure 1, the battery pack is shown as being built into the EV, but this disclosure is not limited to this, and the battery pack may be fixed as a stationary energy storage device. Also, the diagnostic device is not limited to being outside the EV, but may be installed inside the EV.

[0023] Next, we will explain the content of the communications sent from the communication device to the server.

[0024] Figure 3 is a table showing an example of the communication content from the EV to the server.

[0025] In this figure, No. 1, the vehicle ID, indicates the EV's identification ID. No. 2, the time, indicates the time when the battery current, voltage, and temperature were measured, not the transmission time. No. 3, the vehicle status flag, is a flag indicating whether the EV is charging, stopped, or driving. No. 4, the battery pack current, is the value with the charging side as + in this example, and indicates the current at the time in No. 2. No. 5, the battery pack's SOC, indicates the representative SOC of the battery pack. No. 6, the number of cells in series N within the battery pack, is the number of cells in series shown in Figure 2. No. 7, the number of temperature sensors M within the battery pack, is the number of temperature sensors shown in Figure 2. No. 8, the initial battery pack Ah capacity, indicates the value of the initial Ah of the cell shown in Figure 2 × the number of parallel connections L. No. 9, the initial battery pack energy capacity, is calculated from the value of the initial energy capacity of the cell × the number of cells in series N × the number of parallel connections L. Nos. 10 to No. N+9 indicate the voltage of the cell with the number of parallel connections L shown in Figure 2. Numbers N+10 through N+M+9 show the temperatures from their respective temperature sensors.

[0026] Note that the voltage and temperature are the values ​​measured at time No. 2. Items No. 6 to No. 9 are not necessarily essential for communication. If items No. 6 to No. 9 are missing, it is assumed that the server has the information for each EV's No. 6 to No. 9.

[0027] The storage device 103 shown in Figure 1 stores communication data (EV communication data) at regular time intervals, as shown in Figure 3.

[0028] Figure 4 is a configuration diagram showing an example of the battery table 105 in Figure 1.

[0029] The battery table 105 shown in Figure 4 is a table that is either pre-set or set by the table setting means 104, and is referenced by the energy calculation means 106 of the battery pack. The battery table 105 mainly consists of an OCV table 401, a cell charging resistance table 402, and a cell discharging resistance table 403, which summarize the values ​​of OCV, charging resistance, and discharging resistance for each SOC. Here, the OCV table 401 is a function with a corresponding value for each SOC, and the charging resistance and discharging resistance are functions of SOC and temperature.

[0030] Next, the table setting means 104 will be described.

[0031] OCV Table 401 summarizes the OCV (Operating Condition Value) obtained by repeatedly charging the battery from a fully discharged state (SOC 0%) to 2% of the SOC and waiting for 30 minutes until it reaches a fully charged state (SOC 100%). The voltage after waiting for 30 minutes at each SOC is defined as the OCV.

[0032] The values ​​in the right column of the cell charging resistance table 402 are represented by the following formula (1), and correspond to the charging voltage CCV of each state of charge (SOC) when charged from a fully discharged state to a fully charged state at 25°C. c Therefore, OCVs are the OCVs that correspond to each SOC. c Draw a current I c It is the value obtained by dividing by [a certain factor].

[0033]

number

[0034] Here, any cell temperature T cell Charging resistance R at [°C] c (T cell ) is expressed by the following formula (2).

[0035]

number

[0036] In the formula, R c (25℃) is Tcell = Charging resistance R at 25°C c (T cell ). Also, B is the temperature sensitivity.

[0037] The values in the right column shown in the cell discharge resistance table 403 are the discharge voltages CCV corresponding to each SOC when charging from the fully discharged state to the fully charged state at 25°C, as represented by the following formula (3). d From this, it is the OCV which is the OCV corresponding to each SOC d subtracted by the current I d and divided by the value.

[0038]

Equation

[0039] Here, the charging resistance R cell [°C] at an arbitrary cell temperature T d (T cell ) is represented by the following formula (4).

[0040]

Equation

[0041] In the formula, R d (25°C) is the discharge resistance R cell at T d (T cell ) = 25°C. Also, B is the temperature sensitivity.

[0042] Above, an example of the OCV table, the charging resistance table, and the discharge resistance table has been described, but the method for creating each table is not limited to the above method. Furthermore, the SOC interval of the table is not limited to 2%, and those with any interval can be used.

[0043] Next, a method for diagnosing the deterioration of the battery pack will be described. Here, the diagnosis of deterioration is the process in the energy calculation means 106 of the battery pack in FIG. 1, and briefly, it is to obtain the energy capacity of the battery pack.

[0044] First, the server calculates the State of Charge (SOC) of each cell. Then, it calculates the charge capacity of each cell, the amount of imbalance, the current energy capacity of the battery pack, and the energy capacity when the imbalance is resolved (when the battery pack is fully charged and the SOC of all cells reaches 100%).

[0045] The energy calculation means 106 of the battery pack shown in Figure 1 will be described in detail below.

[0046] First, the SOC calculation means 107 calculates the SOC of each cell and stores the SOC data in the database (DB) of the SOC data group 108. Then, the battery pack energy capacity calculation means 109 calculates the energy capacity of the battery pack using the SOC data group 108. The battery pack energy calculation means 106 performs the calculation based on the data in the battery table 105 and the storage device 103.

[0047] The SOC calculation means 107 and the battery pack energy capacity calculation means 109 will be described below.

[0048] The SOC calculation means 107 uses data delimiters for each drive or each charge if the time interval of the communication data is short (for example, 1 s or less). On the other hand, if the time interval of the communication data is long, it uses data delimiters for when the vehicle is charging at the charger.

[0049] Figure 5 is a flowchart showing an example of the processing in the SOC calculation means 107 of Figure 1.

[0050] In Figure 5, the processing in the SOC calculation means 107 includes the charging data extraction step S51, the charging number initial setting step S52, the cell number initial setting step S53, the SOC solution step S54, the cell loop termination determination step S56, the cell number increment step S57, the charging number termination determination step S58, and the charging number increment step S59.

[0051] As shown in this figure, in step S51, the charger acquires data (current, each cell voltage, temperature) during charging. These data are represented as I(t,k) and V, respectively. j Let (t,k) and Temp(t,k) be (j=1,...,N). Here, t is the time since charging started with the charger, k is the number of times the charger has been used, and j is the cell number. Since there are M temperature sensors, Temp may be the average value of the M sensors.

[0052] Next, in step S52, the oldest charge number (data from while charging with the charger), k, is set to 1. Then, in step S53, the cell number j is set to 1.

[0053] In step S54, the following equation (5) is used, and the estimated voltage Vest of cell j is calculated using the following equations (6), (7), and (8). j (t) and the actual voltage of cell j Vreal j The parameter P that minimizes the sum of squares of the differences with (t). j (k), Q j (k), SOCI(j,k), Q max Find (j,k).

[0054]

number

[0055]

number

[0056]

number

[0057]

number

[0058] Here, P j (k) is the coefficient of the charging resistance of each cell. Qj (k) is the coefficient of the charging resistance of each cell. SOCI(j,k) is the initial value of the state of charge (SOC) of cell j after k charges. Q max (j,k) is the charge capacitance (Ah capacitance) of cell j. fc (SOC) is the standard resistance function at 25°C on the charging side. c is the charging resistance of each cell. In equation (5) above, argmin represents a function that finds the parameter that gives the minimum value. The actual method for finding argmin may be the quasi-Newton method.

[0059] Next, in step S55, the unbalance amount of cell j (the SOC of each cell when the battery pack is fully charged) (hereinafter referred to as SOC) u Let (j,k) be written as such. Calculate ( ).

[0060] The charge (charge) of cell j when it is fully charged is expressed by the following formula: SOCI j This is the initial SOC of cell j, and Qmax j This is the charge capacity (Ah capacity) of cell j.

[0061] (1-SOCI j (÷100) × Qmax j For a battery pack, if any one cell is fully charged, the battery pack is considered fully charged, and the charge Q is expressed by the following formula. f The battery pack becomes fully charged when it is charged.

[0062] Q f =min [(1-SOCI j (÷100) × Qmax j ] Therefore, the State of Charge (SOC) of each cell when the battery pack is fully charged can be expressed by the following formula.

[0063] SOC=SOCI j +100 × Q f / Qmax j The SOC of cell j at this time is the amount of imbalance (SOC uThis is denoted as (j). When the imbalance is resolved, the amount of imbalance becomes 100% in all cells. This means that the battery pack's energy capacity is at its maximum when the amount of imbalance in all cells is 100%.

[0064] Therefore, this calculation is expressed by the following equations (9) and (10): SOC (Solving the imbalance of cell j) u (j,k) are the State of Charge (SOC) of each cell when the battery pack is fully charged. Q f (k) is the charge (Ah) required for the battery pack to be fully charged, that is, for any cell to be fully charged.

[0065]

number

[0066]

number

[0067] Next, in step S56, it is determined whether cell number j has reached the number of cells in series N. If the cell number has reached N, the process moves to step S58. Otherwise, in step S57, the cell number is incremented by one, and step S54 is repeated. In step S58, it is determined whether the charge number is the latest charge. If yes, the process moves to step S59, and then to step S53. Otherwise, the process ends.

[0068] The process shown in Figure 5 is performed periodically, for example, daily. Charging data that has already been processed is excluded. This figure illustrates the process for a single vehicle; however, if there are multiple vehicles, the process should be performed for each vehicle.

[0069] After processing as shown in this figure, not only the SOC time series of each cell, but also the initial value of the SOC for each charge, and the resistance coefficients Pj(k), Qj(k), and Q are obtained. max(j,k) is determined. Pj(k) and Qj(k) are constants in equation (4) above. This information is stored in the DB of SOC data group 108.

[0070] Next, when discharged at a constant current, the state of discharge (SOC) at which each cell j stops discharging (SOC) e (j) is denoted as (j). Calculate the value of (j). Note that the discharge of the battery pack is complete when any one cell reaches the lowest voltage V m This occurs when the following nonlinear equation is solved, and SOC e (j) is required.

[0071] V m =OCV(SOC e (j))-I d ×R d (SOC e (j)) In the formula, I d R is the discharge current, d This represents the current discharge resistance and is a function of the State of Charge (SOC). In other words, R d This is obtained by multiplying the standard value of the discharge resistance table stored in battery table 105 by the aforementioned resistance coefficient Pj and adding Qj. The initial value during discharge is SOC u (j) Then SOC e (j) = SOC u (j)-100×Q / Qmax j Therefore, the discharge charge Q of cell j is as follows: d (j) is expressed by the following formula:

[0072] Q d (j) = {SOC u (j)-SOC e (j) × Qmax j ÷100 When any one cell reaches its lowest voltage, the battery pack stops discharging, hence Q is expressed by the following equation. d Once the battery is discharged, the battery pack will stop discharging.

[0073] Q d =min[{SOC u (j)-SOCe (j) × Qmax j [÷100] Therefore, the range of SOC for cell j is SOC u (j) From SOC u (j)-100×Q d / Qmax j This is the extent of the integration range of SOC, and the voltage v of cell j. j (t) Mean value Va j This is expressed by the following formula (11).

[0074]

number

[0075] This increases the energy capacity P of the battery pack. max This can be calculated using the following formula (12).

[0076]

number

[0077] The above calculation results will be taken as the current battery pack energy capacity. And, SOC u The same calculation is performed with (j) set to 100 to calculate the battery pack energy capacity after the imbalance is resolved. These values ​​can be calculated and notified to the user.

[0078] Based on the above principles, Figure 6 shows a flowchart illustrating an example of the processing in the energy capacity calculation means 109 of the battery pack in Figure 1.

[0079] The processing shown in this figure does not need to be synchronized with the processing shown in Figure 5; the calculation can be performed whenever the user wants to know the energy capacity of the battery pack, or it can be performed once a day or once a week.

[0080] The process shown in this figure includes the moving average step in S61, the charge number initial setting step in S62, the cell number initial setting step in S63, the current battery pack energy capacity calculation step in S64, the energy capacity calculation step after balancing is resolved in S65, the cell loop termination step in S66, and the cell number up step in S67.

[0081] First, in step S61, the Q data stored in the SOC data group 108 max The moving average of (j,k) with respect to k is calculated. In this case, equations (13) and (14) below are the ratio of "variance of SOC × number of data". The reason for using the ratio of "variance of SOC × number of data" is that data with small SOC changes or a small number of data will have large errors, so the weight of these data with large errors w k This is to reduce the size. Also, step S61 may be omitted.

[0082]

number

[0083]

number

[0084] In step S61, P j (k), Q j (k) and SOC u For (j), the moving average is calculated using the same method as in equations (13) and (14) above.

[0085] Next, in step S62, set the charge number k to 1, and in step S63, P j (k) and Q j From (k), determine the ratio of the current charging resistance of each cell at 25°C and 50% SOC to the standard charging resistance at 25°C and 50% SOC. This ratio is considered to be the same as the ratio of the discharge resistance and is used when determining the energy capacity of the battery pack during discharge.

[0086] This uses the standard resistance table R f (SOC) at 25°C and P j (k) + Q j (k) / R f (SOC50%) and can be obtained. Although it is set as SOC50%, it is not necessarily required to be exactly SOC50%. Any value of SOC can be used as long as the discharge resistance and the charge resistance match.

[0087] Next, the current battery pack energy capacity is calculated in step S64. This is when each cell discharges at temperature Temp and constant discharge I d and the SOC (hereinafter referred to as SOC m (j)) when cell j reaches the lowest voltage V e is obtained. Note that SOC e can be obtained from SOC u . Specifically, when discharging each cell, the SOC of each cell is obtained as SOC u (j,k) - 100Q / Q(j,k). Here, Q is the discharge Ah capacity of the battery pack. And the voltage of each cell is OCV (SOC of cell j) - I × discharge resistance (temperature, SOC of cell j), so an equation where this becomes V m can be solved. The discharge resistance in this case is the standard resistance table at 25°C multiplied by the magnification of the charge resistance described above.

[0088] And the sum of the product of Q e (j) and the integrated cell voltage from SOC u (j) to SOC max (j) is used.

[0089] Here, the calculation of the battery pack charging charge will be explained.

[0090] Figure 7A is a graph showing the voltage of each cell in a state where an imbalance state has occurred in the battery pack. The horizontal axis shows the discharge amount of the entire battery pack, and the vertical axis shows the voltage of each cell.

[0091] In this figure, the curve SOC of each cell j (j=1,…,N) e (j) is shown. Typically, the curve SOC e In (j), the left and right ends of each curve do not coincide. This is an unbalanced state.

[0092] As shown in this figure, an imbalance occurs in each cell, so when the battery pack is fully charged, the SOC of each cell u These are not the same, but different values. And the SOC of each cell when the battery pack is empty is SOC e Therefore, the voltage of each cell is set to SOC. e From SOC u The integral value up to this point (the gray-shaded area in this diagram) represents the energy capacity of each cell. The total energy capacity of the battery pack is calculated as the sum of the energy capacities of each cell.

[0093] In step S65 of Figure 6, the energy capacity after the imbalance is resolved is calculated. This is the (SOC) when the imbalance state is resolved. u (j)100%) is calculated similarly for the battery pack energy capacity.

[0094] Figure 7B is a graph showing the voltage of each cell after the imbalance in the battery pack has been resolved.

[0095] As shown in this figure, when the imbalance is resolved, the voltage of each cell becomes SOC e From SOC u As the integral value up to that point increases, the overall usable energy capacity of the battery pack increases.

[0096] In step S66 of Figure 6, it is determined whether the charge is the latest. If not, the charge number is incremented by one in step S67, and the process moves to step S63. When the charge number is the latest, the process ends.

[0097] Figure 8 is a flowchart showing an example of the processing in the energy capacity diagnosis / recalculation means 111 of the battery pack shown in Figure 1.

[0098] S81 is a process to check the relationship between the calculated energy capacity (Wh capacity) and the total driving distance, and to verify that there are no abnormalities in the calculated energy capacity. In S82, the results from S81 are checked to see if there are any results that deviate from the overall trend. If there are no deviations, the process ends. If there are deviations, in S83 the relationship between the battery pack's energy capacity and the input data is examined, and any abnormalities at specific temperatures and currents are checked, and the data causing the identified abnormalities is deleted. In S84, the abnormal data is deleted, and the battery pack's energy capacity is recalculated using only the normal data.

[0099] Figure 9 is an example of a graph used to diagnose the energy capacity of the battery pack in S82 of Figure 8. The horizontal axis represents the total mileage of the EV, and the vertical axis represents the energy capacity of the battery pack. ○ marks indicate normal values, and △ marks indicate abnormal values.

[0100] One method for diagnosing the calculated energy capacity of a battery pack is to draw an approximation curve (obtained using all data) showing the relationship between total mileage and energy capacity within a specific range, as indicated by the solid curve in the graph shown in Figure 9. Here, the specific range can be specified as a period (up to one year ago from the present) or total mileage (up to 10,000 km ago from the current mileage). If the energy capacity of the battery calculated for a certain mileage deviates from the energy capacity on the approximation curve by more than a predetermined tolerance (δ), the calculation result is considered abnormal. Subsequently, the results of recalculation excluding the detected abnormal value are output.

[0101] Furthermore, the method of diagnosing energy capacity calculated using the relationship between total mileage and energy capacity is included in the method of diagnosing the presence or absence of abnormal values ​​in energy capacity using time-series data of the battery pack's energy capacity.

[0102] Figure 10A is an example of a graph plotting abnormal and normal values ​​classified as shown in Figure 9 against the battery pack temperature conditions at the time of diagnosis. The horizontal axis shows the battery pack temperature, and the vertical axis shows the battery pack energy capacity. ○ marks represent normal values, and △ marks represent abnormal values. In Figure 10A, if abnormal values ​​are concentrated below a specific temperature range, the temperature range in which the probability of abnormal values ​​occurring is above a predetermined probability threshold (α) is defined as the condition for a diagnostic abnormality, and all results diagnosed from data in this temperature range are considered abnormal. Subsequently, the results of recalculation excluding the detected abnormal values ​​are output.

[0103] Figure 10B is an example of a graph plotting abnormal and normal values ​​classified as shown in Figure 9, against the current conditions at the time of diagnosis. The horizontal axis shows the current at the time of diagnosis, and the vertical axis shows the energy capacity of the battery pack. ○ marks represent normal values, and △ marks represent abnormal values. In Figure 10B, if abnormal values ​​are concentrated below a specific current range, the current range in which the probability of abnormal values ​​occurring is above a predetermined probability threshold (β) is defined as the condition for a diagnostic abnormality, and all results diagnosed from the data in this current range are considered abnormal. Subsequently, the results of recalculation excluding the detected abnormal values ​​are output.

[0104] The following describes preferred embodiments of this disclosure.

[0105] In the battery pack diagnostic method, abnormalities in the battery pack are diagnosed by checking for abnormal values ​​in the battery pack's energy capacity using time-series data of the battery pack's energy capacity.

[0106] The battery pack diagnostic method further includes the steps of comparing the relationship between the change in the battery pack's energy capacity over time and the voltage data of each cell, and detecting an abnormality in the voltage sensor based on the diagnosed abnormal value of the battery pack's energy capacity.

[0107] The battery pack diagnostic method further includes the steps of comparing the relationship between the change in the battery pack's energy capacity over time and current data, and detecting an abnormality in the current sensor based on the diagnosed abnormal value of the battery pack's energy capacity.

[0108] The battery pack diagnostic method further includes the steps of comparing the relationship between the change in the battery pack's energy capacity over time and temperature data, and detecting an abnormality in the temperature sensor based on the diagnosed abnormal value of the battery pack's energy capacity.

[0109] The battery pack is installed in the vehicle, and any abnormalities in the battery pack are diagnosed based on a formula relating the battery pack's energy capacity to the vehicle's total mileage.

[0110] The battery pack diagnostic method further includes a step of notifying the battery pack user of the diagnostic results regarding the presence or absence of abnormalities.

[0111] The battery pack diagnostic device obtains detection data including the current and temperature of a battery pack having a configuration in which multiple cells are connected in series, as well as the voltage of each cell. It obtains function data for OCV (Optical Capacity Value), which is a function of SOC (State of Charge), and resistance for each cell. Using the function data and detection data, it creates a resistance table for each cell. Using the current, temperature, and voltage of each cell, it calculates the SOC of each cell. It calculates the difference in charge capacity, resistance, and SOC for each cell. Using the difference in charge capacity, SOC, and resistance, it calculates the energy capacity of the battery pack. It diagnoses abnormalities in the battery pack, removes data diagnosed as abnormal, and recalculates the energy capacity of the battery pack.

[0112] In a battery pack diagnostic device, the battery pack is installed outside the diagnostic device, and the battery pack and the diagnostic device are connected in a way that allows them to communicate with each other. In this case, the means of communication may be wired, wireless, or may use the internet.

[0113] The following summarizes the effects that can be obtained from this disclosure.

[0114] By using sensors installed in the EV to detect battery voltage, current, and temperature data, and by using a separately created algorithm and table, the energy capacity of the battery pack can be calculated. Furthermore, by diagnosing the time-series data of the calculated battery pack energy capacity, the presence or absence of anomalies in the input data can be diagnosed, and if anomalies are found, the anomalous data is removed and recalculated, thereby enabling the calculation of the battery pack's energy capacity with high accuracy. [Explanation of Symbols]

[0115] 100: Server, 101: Electric vehicle, 102: Communication device, 103: Storage device, 104: Table setting means, 105: Battery table, 106: Battery pack energy calculation means, 107: SOC calculation means, 108: SOC data group, 109: Battery pack energy capacity calculation means, 110: Battery pack energy capacity / voltage / current / temperature data group, 111: Energy capacity diagnosis / recalculation means, 112: Battery pack energy capacity data group, 201: Cell, 202: Temperature sensor, 203: Cell group, 204: Ammeter, 205: Battery management unit, 206: Network, 401: OCV table, 402: Cell charging Resistance table, 403: Cell discharge resistance table, S51: Charging data extraction step, S52: Charging number initial setting step, S53: Cell number initial setting step, S54: SOC calculation step, S56: Cell loop termination determination step, S57: Cell number up step, S58: Charging number termination determination step, S59: Charging number up step, S61: Moving average step, S62: Charging number initial setting step, S63: Cell number initial setting step, S64: Current battery pack energy capacity calculation step, S65: Energy capacity calculation step after balancing resolution, S66: Cell loop termination step, S67: Cell number up step, S81: Energy capacity and mileage relationship confirmation step, S82: Abnormal diagnosis result determination step, S83: Abnormal data deletion step, S84: Battery pack energy capacity recalculation step.

Claims

1. A step of obtaining detection data including the current and temperature of a battery pack having a configuration in which multiple cells are connected in series, and the voltage of each of the cells, A step of obtaining function data of open-circuit voltage and resistance, which are functions of the charge state of the cell, A step of creating a resistance table for each of the cells using the function data and the detection data, A step of calculating the charge state of each cell using the current, the temperature, and the voltage of each of the cells, A step of calculating the charge capacity, resistance, and charge state of each of the aforementioned cells, A step of calculating the energy capacity of the battery pack using the aforementioned charge capacity, the charge state of each cell, and the resistance, A step of diagnosing the calculated value of the energy capacity of the battery pack, The process involves removing the energy capacity value determined to be abnormal by the diagnosis and recalculating the energy capacity of the battery pack. A step of comparing the relationship between the change in the energy capacity of the battery pack over time and the current data, The process includes a step of determining the current range of the data used for diagnosis based on the abnormal value of the energy capacity of the diagnosed battery pack, The aforementioned abnormality is a battery pack diagnostic method that diagnoses whether or not there is an abnormality in the newly calculated energy capacity using the energy capacity value calculated within a specific range.

2. A step of comparing the relationship between the change in the energy capacity of the battery pack over time and the temperature data, The diagnostic method according to claim 1, further comprising the step of determining the temperature range of data to be used for diagnosis based on the abnormal value of the energy capacity of the diagnosed battery pack.

3. The aforementioned battery pack is installed in the vehicle. The diagnostic method according to claim 1, wherein the abnormality of the battery pack is diagnosed based on a relationship formula between the energy capacity of the battery pack and the total mileage of the vehicle.

4. The diagnostic method according to claim 1, further comprising the step of notifying the user of the battery pack of the diagnostic results regarding the presence or absence of the aforementioned abnormality.

5. Detection data is obtained including the current and temperature of a battery pack having a configuration in which multiple cells are connected in series, as well as the voltage of each of the cells. Obtain function data of open-circuit voltage and resistance, which are functions of the charge state of the aforementioned cell. Using the function data and the detection data, a resistance table for each of the cells is created. Using the current, temperature, and voltage of each of the cells, the charge state of each of the cells is calculated. The charge capacity, resistance, and charge state of each cell are calculated. Using the aforementioned charge capacity, the charge state of each cell, and the resistance, the energy capacity of the battery pack is calculated. The calculated value of the energy capacity of the battery pack is diagnosed, The energy capacity value determined to be abnormal by the above diagnosis is removed, and the energy capacity of the battery pack is recalculated. The relationship between the change in the energy capacity of the battery pack over time and the current data is compared, Based on the diagnosed abnormal value of the energy capacity of the battery pack, the current range of the data used for diagnosis is determined. The aforementioned abnormality is a battery pack diagnostic device that diagnoses whether or not there is an abnormality in the newly calculated energy capacity using the energy capacity value calculated within a specific range.

6. The battery pack is installed outside the diagnostic device, The diagnostic device according to claim 5, wherein the battery pack and the diagnostic device are connected to each other in a manner that enables communication.

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