Battery cell analysis system, battery cell analysis method, and battery cell analysis program

Abstract

A data acquisition unit 111 acquires time-series battery cell data including the voltage and current of secondary battery cells included in a battery pack. A direct-current resistance calculation unit 112 calculates direct-current resistances of the secondary battery cells from the ratio between voltage change and current change when a current change equal to or greater than a certain value has occurred in a predetermined period of time. A representative value calculation unit 114 classifies the calculated direct-current resistances in accordance with the levels of factors defined by conditions at the time of measuring the voltage and current used for calculating the direct-current resistances, and calculates a representative value of the direct-current resistances for each level. A map generation unit 115 generates a direct-current resistance map by plotting the representative value of the direct-current resistances calculated for each of the levels of factors.

Description

Battery analysis system, battery analysis method, and battery analysis program

[0001] The present disclosure relates to a battery analysis system, a battery analysis method, and a battery analysis program that collect and analyze battery data.

[0002] A system is in operation that collects battery data from a wide variety of battery types on a cloud server and estimates the battery state (e.g., direct current resistance (DCR) and state of health (SOH)) of each cell. The battery types for which battery data is collected include lithium iron phosphate batteries (LFP batteries) that use lithium iron phosphate as the positive electrode material.

[0003] The SOC (State Of Charge)-OCV (Open Circuit Voltage) curve of an LFP battery includes a plateau region (flat region). In the plateau region of the SOC-OCV curve, a slight change in OCV causes a large change in SOC, reducing the accuracy of estimating battery states such as SOC and SOH. Therefore, it is desirable to estimate the battery state using the non-plateau region of the SOC-OCV curve. However, if the shape of the SOC-OCV curve changes with battery degradation, it becomes difficult to detect the non-plateau region after battery degradation. If the shape of the SOC-OCV curve changes, a mismatch between the region with a steep OCV slope and the SOC is likely to occur.

[0004] The dV / dQ curve is known as a method for detecting changes in the shape of the SOC-OCV curve due to battery degradation. The non-plateau region can be detected from the inflection points (hereafter referred to as peaks) of the dV / dQ curve (see the bottom of Figure 2). Furthermore, the degree of capacity degradation can be estimated by calculating the rate of capacity loss between the peak and full charge. However, generating a dV / dQ curve requires low-rate charging and discharging. As will be explained in more detail later, increasing the current rate increases the influence of the IR component on the measured cell voltage, making it difficult to detect changes in OCV. Therefore, it is difficult to generate a highly accurate dV / dQ curve from battery data obtained in actual operation, rather than in a laboratory.

[0005] Patent Document 1 discloses a method in which, in an internal resistance measurement step, the internal resistance R1 of a secondary battery at a low charging rate SOC1 and the internal resistance R2 of the secondary battery at a high charging rate SOC2 are measured, and in an estimation step, the difference value ΔR (= R2 - R1) of the internal resistances R is divided by the difference value ΔSOC (= SOC2 - SOC1) of the charging rates SOC to determine an internal resistance change rate R'. If the internal resistance change rate R' is equal to or greater than a reference value RS, it is estimated that neglected deterioration is dominant, and if it is less than the reference value RS, it is estimated that cycle deterioration is dominant (see, for example, Figures 2 and 3 of Patent Document 1). If cycle deterioration is dominant, the SOC-internal resistance map will be relatively flat, and if neglected deterioration is dominant, the SOC-internal resistance map will have a relatively steep slope. However, the method does not focus on the peak position of the SOC-internal resistance map.

[0006] JP 2016-38276 A

[0007] If it were possible to generate a DC resistance map that provides the same level of utility as a dV / dQ curve, regardless of the magnitude of the current rate, it would lead to improved accuracy in estimating the battery state.

[0008] The present disclosure has been made in consideration of these circumstances, and its purpose is to provide a technology for generating a highly accurate DC resistance map.

[0009] In order to solve the above problems, a battery analysis system according to one embodiment of the present disclosure includes a data acquisition unit that acquires time-series battery data including the voltage and current of a secondary battery included in a battery pack; a DC resistance calculation unit that calculates the DC resistance of the secondary battery from the ratio of a voltage change to a current change when a current change of a certain value or more occurs in a predetermined time; a representative value calculation unit that classifies the calculated DC resistance into levels of factors defined by the conditions when the voltage and current used to calculate the DC resistance were measured, and calculates a representative value of the DC resistance for each level; and a map generation unit that plots the representative values ​​of the DC resistance calculated for each level of the factors, to generate a DC resistance map.

[0010] Any combination of the above components, and conversion of the expression of the present disclosure into an apparatus, system, method, computer program, etc., are also valid aspects of the present disclosure.

[0011] According to the present disclosure, a highly accurate DC resistance map can be generated.

[0012] 7(a) and 7(b) are diagrams for explaining a battery analysis system according to an embodiment.

[0022] FIG. 7(a) is a diagram illustrating an example of a remaining capacity-cell voltage curve and a dV / dQ curve when an LFP cell is charged at a low rate (0.05 C).

[0023] FIG. 7(b) is a diagram illustrating an example of a remaining capacity-DCR curve and a dV / dQ curve for an LFP cell.

[0024] FIG. 7(c) is a diagram illustrating an example of the behavior of current and voltage when a constant current pulse is applied to a cell.

[0025] FIG. 7(d) is a diagram illustrating a method for estimating SOH from the remaining capacity-DCR curve of an LFP cell.

[0026] FIG. 7(b) is a diagram illustrating a dV / dQ curve corresponding to the remaining capacity-DCR curve of FIG. 7(a).

[0027] FIG. 7(c) is a diagram illustrating an example of a two-dimensional DCR map using the remaining capacity and SOH of the cell as factors.

[0028] FIG. 7(d) is a diagram illustrating a component breakdown of voltage change when a non-constant current is applied to a cell. 16 is a diagram showing, with specific examples, the behavior of current and voltage and the transition of the calculated value of DC resistance DCR when a cell is charged with a constant current. 17 is a diagram showing, with specific examples, the behavior of current and voltage and the transition of the calculated value of DC resistance DCR when a cell is charged with a non-constant current. 18 is a diagram showing, with specific examples, the behavior of current and voltage and the transition of the calculated value of DC resistance DCR when a cell is charged with a non-constant current. 19 is a diagram showing, with specific examples, experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. 20 is a diagram showing, with specific examples, experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. 21 is a diagram showing, with specific examples, experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. 22 is a diagram showing, with specific examples, experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. 23 is a diagram showing, with specific examples, experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. 24 is a diagram showing, with specific examples, experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. 25 is a diagram showing, with specific examples, experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. 26 is a diagram showing, with specific examples, 19 is a flowchart showing the flow of a DCR map generation process performed by the battery analysis system according to the embodiment.

[0013] FIG. 1 is a diagram illustrating a battery analysis system 10 according to an embodiment. The battery analysis system 10 may be constructed, for example, on an in-house server installed in a facility or data center of a business that provides an analysis service for battery packs mounted on electric vehicles 20. The battery analysis system 10 may also be constructed on a cloud server used based on a cloud service. The battery analysis system 10 may also be constructed on multiple servers that are distributed and installed at multiple locations (data centers, company facilities). The multiple servers may be a combination of multiple in-house servers, a combination of multiple cloud servers, or a combination of an in-house server and a cloud server.

[0014] An assembled battery system 21 included in a battery pack mounted on an electric vehicle 20 supplies power to a drive motor (not shown). The assembled battery system 21 includes a plurality of unit cells or a plurality of cell blocks connected in series. A cell block is composed of a plurality of unit cells connected in parallel. The cells may be lithium-ion battery cells, nickel-metal hydride battery cells, lead-acid battery cells, or the like. In the following description, an example using lithium-ion battery cells (nominal voltage: 3.6-3.7 V) is assumed. The number of unit cells or cell blocks connected in series is determined according to the voltage of the drive motor.

[0015] The voltage sensor 22 detects the voltage across each of the series-connected single cells or cell blocks. A shunt resistor is connected in series with the series-connected multiple single cells or multiple cell blocks. The current sensor 23 detects the current flowing through the series-connected single cells or cell blocks based on the voltage across the shunt resistor. Note that a Hall element may be used instead of the shunt resistor. Multiple temperature sensors 24 are installed in the battery pack including the battery pack system 21. For example, a thermistor may be used as the temperature sensor 24. For example, one temperature sensor 24 may be installed for every 6 to 8 single cells or cell blocks.

[0016] The control unit 25 is composed of a BMU (Battery Management Unit) and an ECU (Electronic Control Unit) working together. The BMU estimates the SOC by combining the OCV method and the current integration method. The OCV method is a method for estimating the SOC based on the measured cell OCV and the cell's SOC-OCV curve. The cell's SOC-OCV curve is created in advance by the battery manufacturer based on characteristic tests and is registered in the BMU at the time of shipment.

[0017] The current integration method is a method for estimating the SOC based on the OCV at the start of charging and discharging the cell and the integrated value of the measured current. In the current integration method, current measurement errors accumulate as the charging and discharging time increases. Therefore, it is preferable to use a weighted average of the SOC estimated by the current integration method and the SOC estimated by the OCV method.

[0018] The BMU periodically (e.g., every 10 seconds) transmits battery data, including the voltage, current, temperature, and SOC, of ​​a plurality of single cells or cell blocks to the ECU via an in-vehicle network, so that the ECU samples the battery data in chronological order. The in-vehicle network may be, for example, a Controller Area Network (CAN) or a Local Interconnect Network (LIN).

[0019] The voltages and SOCs of the single cells or cell blocks transmitted from the BMU to the ECU may be the voltages and SOCs of all the single cells or cell blocks included in the battery pack system 21, or may be only the voltages and SOCs of the single cells or cell blocks with the minimum and maximum voltages. Furthermore, the temperatures of the single cells or cell blocks transmitted from the BMU to the ECU may be all the temperatures measured by the multiple temperature sensors 24, or may be only the minimum and maximum temperatures.

[0020] The communication unit 26 has a function of performing communication signal processing with the communication unit 33 of the charging stand 30 and a function of performing wireless signal processing for connecting to the network 5. The communication unit 26 can access the network 5 using, for example, a mobile phone network (cellular network), a wireless LAN, V2I (Vehicle to Infrastructure), V2V (Vehicle to Vehicle), an ETC system (Electronic Toll Collection System), or DSRC (Dedicated Short Range Communications).

[0021] The network 5 is a general term for communication paths such as the Internet, dedicated lines, and VPNs (Virtual Private Networks), and the communication media and protocols are not important. Examples of communication media that can be used include a mobile phone network, a wireless LAN, a wired LAN, an optical fiber network, an ADSL network, and a CATV network. Examples of communication protocols that can be used include TCP (Transmission Control Protocol) / IP (Internet Protocol), UDP (User Datagram Protocol) / IP, and Ethernet (registered trademark).

[0022] The ECU may transmit the sampled battery data to the battery analysis system 10 each time, or may store the sampled battery data in an internal memory and transmit the battery data stored in the memory at a predetermined timing all at once to the battery analysis system 10. Note that when the electric vehicle 20 and the charging stand 30 are connected by a charging cable, the ECU may transmit the battery data stored in the memory to the battery analysis system 10 via the charging stand 30.

[0023] By connecting the electric vehicle 20 to a charging stand 30 via a charging cable, the battery pack system 21 inside the electric vehicle 20 can be charged externally. The charging stand 30 is connected to a commercial power system 2 and charges the battery pack system 21.

[0024] Generally, charging is performed with alternating current (AC) for normal charging and with direct current (DC) for rapid charging. When charging with AC (e.g., single-phase 100 / 200 V), the charging voltage or charging current is controlled by a charger (not shown) inside the electric vehicle 20. When charging with DC, the charging voltage or charging current is controlled by a power supply unit 31 in the charging stand 30. The power supply unit 31 includes a rectifier circuit, a filter, and a DC / DC converter, and generates DC power by full-wave rectifying AC power supplied from the commercial power system 2 using the rectifier circuit and smoothing it using a filter. The DC / DC converter controls the voltage or current of the generated DC power.

[0025] For example, CHAdeMO (registered trademark), ChaoJi, GB / T, and Combo (Combined Charging System) can be used as fast charging standards. CHAdeMO, ChaoJi, and GB / T use CAN as the communication method. Combo uses PLC (Power Line Communication) as the communication method.

[0026] A charging cable that employs the CAN system includes a communication line in addition to a power line. When the charging cable connects the electric vehicle 20 and the charging stand 30, the control unit 25 of the electric vehicle 20 establishes a communication channel with the control unit 32 of the charging stand 30. Note that in a charging cable that employs the PLC system, communication signals are transmitted superimposed on the power line.

[0027] The communication unit 33 of the charging stand 30 has a function of executing communication signal processing with the communication unit 26 of the electric vehicle 20 and a function of executing signal processing for connecting to the network 5. The communication unit 33 can access the network 5 using, for example, a wired LAN, a wireless LAN, or a mobile phone network.

[0028] While the battery pack system 21 is being charged, the control unit 32 of the charging stand 30 may acquire battery data from the control unit 25 of the electric vehicle 20 via the charging cable, and transmit the acquired battery data to the battery analysis system 10 via the network 5 in real time.

[0029] The battery analysis system 10 includes a control unit 11, a storage unit 12, and a communication unit 13. The communication unit 13 is a communication interface (for example, a network interface card (NIC)) for connecting to the network 5 via a wired or wireless connection.

[0030] The control unit 11 includes a data acquisition unit 111, a DC resistance calculation unit 112, a DC resistance selection unit 113, a representative value calculation unit 114, a map generation unit 115, and a battery state estimation unit 116. The functions of the control unit 11 can be realized by a combination of hardware resources and software resources, or by hardware resources alone. Examples of hardware resources that can be used include a CPU, ROM, RAM, GPU (Graphics Processing Unit), NPU (Neural Network Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and other LSIs. Examples of software resources that can be used include an operating system, an application, and other programs.

[0031] The storage unit 12 includes a non-volatile recording medium such as an HDD or SSD, and stores various data. The storage unit 12 includes a battery data holding unit 121 and a DC resistance map generation unit 122. The data acquisition unit 111 acquires battery data (time-series data including at least the voltage and current of a single cell or a cell block) of a battery pack including the battery pack system 21 from the electric vehicle 20 or the charging stand 30 via the network 5, and stores the acquired battery data in the battery data holding unit 121.

[0032] When creating a DC resistance map (hereinafter referred to as a DCR map) of a target cell, the DC resistance calculation unit 112 reads battery data of the cell from the battery data storage unit 121. The DC resistance calculation unit 112 calculates the DC resistance of the cell from the ratio of voltage change to current change when a current change of a certain value or more occurs in a predetermined time. More specifically, the DC resistance calculation unit 112 calculates the voltage change ΔV / current change ΔI over a relatively long time period Δt (e.g., 30 seconds or more) (hereinafter referred to as a calculation reference time Δt) to calculate the time-series DC resistance DCR.

[0033] For example, if the sampling period of the battery data is 10 seconds and the calculation reference time Δt is 60 seconds, the DC resistance calculation unit 112 calculates the DC resistance DCR based on the difference ΔV between the voltage data at the target time point and the voltage data six points prior to the target time point, and the difference ΔI between the current data at the target time point and the current data six points prior to the target time point. The DC resistance calculation unit 112 calculates the DC resistance DCR every 10 seconds.

[0034] Note that if there is no current change ΔI during the calculation reference time Δt, the DC resistance DCR cannot be calculated. Furthermore, if the current change |ΔI| during the calculation reference time Δt is small (for example, |ΔI|<0.2 C), the denominator value is small, and the DC resistance DCR fluctuates significantly. The DC resistance calculation unit 112 invalidates DC resistance DCRs for which the current change |ΔI| during the calculation reference time Δt is less than a certain value.

[0035] Furthermore, if the polarities of the voltage change ΔV and the current change ΔI during the calculation reference time Δt do not match (0<ΔV×ΔI), the DC resistance DCR will have a negative value. This is likely to indicate that the current and voltage data of the battery are out of synchronization with each other over time. The DC resistance calculation unit 112 invalidates the DC resistance DCR when the polarities of the voltage change ΔV and the current change ΔI during the calculation reference time Δt do not match.

[0036] The representative value calculation unit 114 classifies the DC resistance DCR calculated by the DC resistance calculation unit 112 into levels of factors defined by the conditions at the time of measuring the voltage and current used to calculate the DC resistance DCR, and calculates a representative value of the DC resistance DCR for each level. The factors include the remaining capacity of the cell. The remaining capacity level may be set in increments of 1 Ah, for example. The remaining capacity of the cell can be measured from the current accumulated from a fully discharged state or from a full charge capacity (FCC). The remaining capacity of the cell can also be estimated by multiplying the initial FCC of the cell by the SOH and SOC. A specific method for estimating the SOH will be described later.

[0037] The representative value calculation unit 114 calculates the average or median of the multiple DC resistances DCR classified by factor level as the representative value of DC resistance DCR for each factor level. The map generation unit 115 plots the representative values ​​of DC resistance DCR calculated for each factor level to generate a DCR map. The map generation unit 115 stores the generated DCR map in the DC resistance map generation unit 122.

[0038] FIG. 2 shows an example of the remaining capacity vs. cell voltage curve and dV / dQ curve when an LFP cell is charged at a low rate (0.05 C). Hereinafter, the remaining capacity of the cell on the horizontal axis is assumed to be 0 Ah at full discharge. FIG. 2 also shows the remaining capacity vs. cell voltage curve and dV / dQ curve for a brand new (BOL) LFP cell and an LFP cell with an SOH of 96%. The dV / dQ curve peaks near SOC = 60% in SOC terms, allowing detection of a non-plateau region with a large OCV slope. However, generating a highly accurate dV / dQ curve requires charging and discharging at a low rate. Therefore, in this embodiment, a remaining capacity vs. DCR curve is generated as an alternative to the dV / dQ curve.

[0039] 3 is a diagram showing an example of the remaining capacity-DCR curve and dV / dQ curve of an LFP cell. Figure 3 shows three remaining capacity-DCR curves: a remaining capacity-DCR curve based on a time-series DC resistance DCR calculated when the calculation reference time Δt is 1 s, a remaining capacity-DCR curve based on a time-series DC resistance DCR calculated when the calculation reference time Δt is 10 s, and a remaining capacity-DCR curve based on a time-series DC resistance DCR calculated when the calculation reference time Δt is 60 s. It can be seen that when the calculation reference time Δt is a relatively long time, 60 s, the peak of the remaining capacity-DCR curve and the peak of the dV / dQ curve closely match.

[0040] The battery state estimation unit 116 detects a peak in the remaining capacity-DCR curve based on the DCR map, and detects a non-plateau region in the SOC-OCV curve based on the detected peak position. For example, the battery state estimation unit 116 determines that a region including a DCR equal to or greater than a set peak determination threshold is a non-plateau region in the SOC-OCV curve.

[0041] Fig. 4 is a diagram showing an example of the behavior of current and voltage when a constant current pulse is applied to a cell. Fig. 5 is a diagram showing a schematic breakdown of the components of the voltage change when a constant current pulse is applied to the cell shown in Fig. 4. Fig. 6 is a diagram showing specific experimental results showing the transition of the components of the voltage change when a constant current pulse is applied. Hereinafter, in this specification, charging is referred to as positive and discharging is referred to as negative.

[0042] At the initial rise of the constant current pulse, the polarization voltage due to the ohmic resistance component becomes dominant. The ohmic resistance component has low SOC dependency, and in particular, it has similar values ​​in SOC ranges other than full charge and full discharge. During constant current application, the IR becomes almost constant. The remaining capacity-DCR curve based on the time series DC resistance DCR calculated with a calculation reference time Δt of 1 s shown in Figure 3 is flat with respect to the remaining capacity because the ohmic resistance component, which has low SOC dependency, is dominant.

[0043] As time passes from the rising edge of the constant current pulse, the polarization voltage due to non-ohmic resistance components such as diffusion increases exponentially, and the OCV increases linearly. As described above, the ohmic resistance components have low SOC dependency and therefore do not generally affect the peak of the dV / dQ curve. Therefore, the peak of the dV / dQ curve is caused by the polarization voltage due to the non-ohmic resistance components or the steep slope of the OCV.

[0044] FIG. 6 shows the time progression of the cell voltage, OCV, polarization voltage due to OCV + ohmic resistance, and polarization voltage due to non-ohmic resistance after application of a constant current pulse. In the example shown in FIG. 6, constant current pulses are intermittently applied to the cell using GITT (Galvanostatic Intermittent Titration Technique). The cell voltage is an actual measurement. The OCV is a value estimated from the actual measurement of the cell capacity. The polarization voltage due to the ohmic resistance is calculated using the time-series DC resistance DCR calculated with the calculation reference time Δt set to 1 s. As shown in FIG. 3, the time-series DC resistance DCR calculated with the calculation reference time Δt set to 1 s has low SOC dependency. The polarization voltage due to the non-ohmic resistance is calculated as cell voltage - (OCV + polarization voltage due to ohmic resistance).

[0045] The left axis of Figure 6 shows the cell voltage [V], and the right axis shows the polarization voltage [V] due to the non-ohmic resistance component, but the numerical scale is the same. The OCV and (OCV + polarization voltage due to the ohmic resistance component) draw almost the same curve. In other words, it can be said that the polarization voltage due to the ohmic resistance component is almost constant.

[0046] The non-ohmic resistance component has low SOC dependence and is relatively flat. At around 60% SOC, the slope of the OCV changes more than the slope of the polarization voltage due to the non-ohmic resistance component. Therefore, it can be seen that the peak of the dV / dQ curve in this cell is caused by the slope of the OCV.

[0047] The battery state estimation unit 116 detects the peak of the remaining capacity-DCR curve based on the DCR map, and can estimate the degradation state of the cell based on the change in the capacity difference between the remaining capacity at the detected peak position and the fully charged capacity.

[0048] FIG. 7(a) is a diagram illustrating a method for estimating the SOH from the remaining capacity-DCR curve of an LFP cell. FIG. 7(b) is a diagram illustrating the dV / dQ curve corresponding to the remaining capacity-DCR curve of FIG. 7(a). FIGS. 7(a) and 7(b) show the remaining capacity-DCR curve and dV / dQ curve of a brand new (BOL) LFP cell and an LFP cell with an SOH of 96%, respectively. The remaining capacity-DCR curve shown in FIG. 7(a) was generated based on a time series of DC resistance DCR calculated with a calculation reference time Δt of 60 seconds.

[0049] As shown in Figure 7(a), when the remaining capacity at full discharge is set to 0, it was confirmed that the remaining capacity in the non-plateau region other than when fully charged is almost the same as that of a new cell (BOL) at SOH = 96%. From the above, as shown in the following (Equation 1) and (Equation 2), for example, the SOH can be estimated based on the rated capacity of the cell and the decrease in the full charge-to-peak capacity ΔQ from the full charge capacity on the remaining capacity-DCR curve to the capacity in the non-plateau region around SOC = 60%.

[0050] FCC = 125Ah (rated capacity) - 4.6Ah (decrease in full charge - peak-to-peak capacity ΔQ) = 120.4Ah (Equation 1) Estimated SOH value = current FCC / initial FCC = 120.4Ah / 125Ah × 100% = 96.32% (Equation 2) True SOH value = 120.0Ah / 124.6Ah × 100% = 96.30% (Equation 3)

[0051] The estimated SOH values ​​calculated from the above formulas (1) and (2) and the true SOH value calculated from the above formula (3) are almost identical. The 124.6 Ah in the above formula (3) is the value measured by charging a brand new (BOL) cell from a fully discharged state to a fully charged state. The 120.0 Ah in the above formula (3) is the value measured by charging a cell after a certain period of use from a fully discharged state to a fully charged state.

[0052] The 4.6 Ah (decrease in the full charge-peak capacity ΔQ) in the above (Equation 1) is the capacity (48.6 Ah = 124.6 Ah - 76.0 Ah) measured by discharging a new (BOL) cell from a fully charged state to the peak of the remaining capacity-DCR curve, minus the capacity (44.0 Ah = 120.0 Ah - 76.0 Ah) measured by discharging a cell from a fully charged state to the peak of the remaining capacity-DCR curve after use for a certain period of time.

[0053] In this way, if the rated capacity of the cell and the decrease in the cell's full charge-peak capacity ΔQ are known, the cell's SOH can be estimated with high accuracy. With this method, since it is only necessary to know the full charge-peak capacity ΔQ, the SOH can be estimated with high accuracy even without battery data in the low SOC region. In other words, the SOH of cells that have not been used to a deep depth of discharge can also be estimated with high accuracy.

[0054] This SOH estimation method utilizes the phenomenon that when a cell deteriorates, the low capacity side of the remaining capacity vs. DCR curve hardly shrinks, and the fully charged side mainly shrinks. Similarly, when a cell deteriorates, the low capacity side of the dV / dQ curve hardly shrinks, and the fully charged side mainly shrinks, as shown in Figure 7(b).

[0055] The above-mentioned method of estimating the SOH of a cell from the rated capacity of the cell and the decrease in the cell's full charge-peak capacity ΔQ can be used with either the remaining capacity-DCR curve or the dV / dQ curve, but as mentioned above, generating a highly accurate dV / dQ curve requires charging and discharging at a low rate.In contrast, the remaining capacity-DCR curve can be generated with high accuracy without being restricted by the current rate.

[0056] The above description has been given of an example of generating a one-dimensional DCR map using the remaining capacity of the cell as a factor. However, at least one of temperature and SOH may also be added as a factor of the DCR map. For example, the representative value calculation unit 114 can classify the DC resistance DCR calculated by the DC resistance calculation unit 112 by the level of the combination of capacity and SOH when the voltage and current used to calculate the DC resistance DCR were measured, and calculate a representative value of the DC resistance DCR for each level. In this case, the map generation unit 115 plots the representative value of the DC resistance DCR calculated for each level of the combination of capacity and SOH to generate a two-dimensional DCR map.

[0057] Furthermore, the representative value calculation unit 114 classifies the DC resistance DCR calculated by the DC resistance calculation unit 112 into levels of the combination of capacity and temperature when the voltage and current used to calculate the DC resistance DCR were measured, and calculates a representative value of the DC resistance DCR for each level. In this case, the map generation unit 115 plots the representative value of the DC resistance DCR calculated for each level of the combination of capacity and temperature to generate a two-dimensional DCR map.

[0058] Furthermore, the representative value calculation unit 114 classifies the DC resistance DCR calculated by the DC resistance calculation unit 112 into levels of the combination of capacity, temperature, and SOH when the voltage and current used to calculate the DC resistance DCR were measured, and calculates a representative value of the DC resistance DCR for each level. In this case, the map generation unit 115 plots the representative value of the DC resistance DCR calculated for each level of the combination of capacity, temperature, and SOH to generate a three-dimensional DCR map.

[0059] 8 is a diagram showing an example of a two-dimensional DCR map using the remaining capacity and SOH of a cell as factors. In the example shown in Fig. 8, the remaining capacity level is set in increments of 1 Ah, and the SOH level is set in increments of 5%.

[0060] A notification unit (not shown) of the battery analysis system 10 can transmit the n-dimensional DCR map (n is a natural number) generated by the map generation unit 115 to the control unit 25 of the electric vehicle 20 via the network 5. The notification unit can also transmit the SOH estimated by the battery state estimation unit 116 to the control unit 25 via the network 5.

[0061] The control unit 25 of the electric vehicle 20 can identify a non-plateau region in the SOC-OCV curve by referring to the n-dimensional DCR map received from the battery analysis system 10. If the n-dimensional DCR map is a two-dimensional DCR map in which capacity and temperature are factors, the control unit 25 identifies the remaining capacity-DCR curve that corresponds to the temperature measured by the temperature sensor 24 that is closest to the target cell, and identifies the non-plateau region in the SOC-OCV curve from the peak position of the identified remaining capacity-DCR curve.

[0062] When the n-dimensional DCR map is a three-dimensional DCR map with capacity, temperature, and SOH as factors, the control unit 25 identifies the remaining capacity-DCR curve corresponding to the combination of the temperature measured by the temperature sensor 24 closest to the target cell and the SOH of the cell received from the battery analysis system 10, and identifies the non-plateau region in the SOC-OCV curve from the peak position of the identified remaining capacity-DCR curve.

[0063] The control unit 25 estimates the SOC of the cell using the OCV method only when the remaining capacity of the cell is near the full charge capacity or near the peak position of the remaining capacity-DCR curve. This eliminates the need to estimate the SOC from the OCV in the plateau region of the SOC-OCV curve, thereby improving the accuracy of SOC estimation.

[0064] The control unit 25 can estimate the OCV of the cell during charging and discharging using the following (Equation 4). The control unit 25 refers to an n-dimensional DCR map to identify a DCR that meets the conditions, and estimates the OCV of the cell based on the voltage (CCV(t)) and current I(t) measured at time t of the cell. OCV = CCV(t) - I(t) * DCR (Equation 4)

[0065] In order to reduce the amount of communication, the battery analysis system 10 may not notify the control unit 25 of the electric vehicle 20 of the n-dimensional DCR map. In this case, the notification unit of the battery analysis system 10 transmits the remaining capacity in the non-plateau region estimated by the battery state estimation unit 116 to the control unit 25 of the electric vehicle 20. The control unit 25 of the electric vehicle 20 estimates the SOC of a target cell using the OCV method only when the remaining capacity of the target cell is near the full charge capacity or near the remaining capacity in the plateau region received from the battery analysis system 10.

[0066] The DCR map generation method described above can generate a highly accurate DCR map when battery data obtained when intermittent constant current pulses are applied is used as the basis. However, the quality of the DCR map decreases when battery data obtained when a non-constant current is applied, in which the current varies irregularly, is used as the basis. It is difficult to collect battery data obtained when intermittent constant current pulses are applied from actual operation data of the electric vehicle 20, and it is difficult to complete an n-dimensional DCR map.

[0067] In view of the above, the control unit 11 of the battery analysis system 10 includes a DC resistance selection unit 113. The DC resistance selection unit 113 narrows down the DC resistances DCR calculated by the DC resistance calculation unit 112 using predetermined conditions, and selects valid DC resistances DCR.

[0068] 9 is a diagram showing a breakdown of the components of the voltage change when a non-constant current is applied to a cell. Compared to the breakdown of the components of the voltage change when a constant current is applied to a cell shown in FIG. 5, the proportions of the polarization voltage due to ohmic resistance, polarization voltage due to non-ohmic resistance, and OCV that make up the cell voltage change dynamically. Therefore, the variation in the DC resistance DCR calculated over time increases.

[0069] The DC resistance selection unit 113 narrows down the DC resistances DCR calculated by the DC resistance calculation unit 112 using the four conditions (1) to (4), thereby selecting a high-quality DC resistance DCR.

[0070] The DC resistance selection unit 113 determines, as condition (1), the DC resistance DCR valid if the charging duration or discharging duration of the cell falls within the effective duration time Δt±α, which is obtained by adding a predetermined margin α before and after the calculation reference time Δt, and determines, as invalid, the DC resistance DCR valid if the charging duration or discharging duration of the cell falls outside the effective duration time Δt±α.

[0071] When charging and discharging are repeated within the calculation reference time Δt, the diffusion resistance component is canceled out. When charging and discharging are equal, the diffusion resistance component becomes almost zero. Since it is desirable for the DC resistance DCR to also include a non-ohmic resistance component due to diffusion, the condition for effective DC resistance DCR is that charging or discharging continues for a time equivalent to the calculation reference time Δt. Note that condition (1) is automatically satisfied when a constant current pulse is applied.

[0072] The DC resistance selection unit 113 validates the DC resistance DCR when the cell capacitance that has changed during the calculation reference time Δt falls within a predetermined capacitance range ΔQ±β as condition (2), and invalidates the DC resistance DCR when the cell capacitance falls outside the capacitance range ΔQ±β. The cell capacitance that has changed during the calculation reference time Δt is the charge capacity during the calculation reference time Δt if the calculation reference time Δt is a charge period, and is the discharge capacity during the calculation reference time Δt if the calculation reference time Δt is a discharge period.

[0073] When the calculation reference time Δt is the discharging period, ΔQ can be calculated, for example, by multiplying the average discharging current in an average running state of the electric vehicle 20 by the calculation reference time Δt. When the calculation reference time Δt is the charging period, ΔQ can be calculated, for example, by multiplying the average charging current during normal charging of the electric vehicle 20 by the calculation reference time Δt.

[0074] If the change in the amount of electricity during the calculation reference time Δt is too small, the non-ohmic resistance component due to diffusion included in the DC resistance DCR will be too small. Conversely, if the change in the amount of electricity during the calculation reference time Δt is too large, the non-ohmic resistance component due to diffusion included in the DC resistance DCR will be too large. Since it is desirable for the DC resistance DCR to contain an appropriate amount of non-ohmic resistance component due to diffusion, a condition for a valid DC resistance DCR is that the change in capacitance during the calculation reference time Δt falls within a predetermined capacitance range ΔQ±β.

[0075] The DC resistance selection unit 113 determines, as condition (3), that the DC resistance DCR is valid when the absolute value of the current change ΔI during the calculation reference time Δt is increasing, and invalid when the absolute value of the DC resistance DCR is decreasing. When the calculation reference time Δt is a charging period, the DC resistance DCR is valid when the current change ΔI is positive, and invalid when the current change ΔI is negative. When the calculation reference time Δt is a discharging period, the DC resistance DCR is valid when the current change ΔI is negative, and invalid when the current change ΔI is positive.

[0076] For example, if the current suddenly drops during high-rate charging, the rate of voltage drop will be smaller than the rate of current drop because the non-ohmic resistance component due to diffusion increases during high-rate charging. In this case, the DC resistance DCR will be too small. To exclude DC resistance DCR that is too small, the condition for valid DC resistance DCR is that the current change ΔI during the calculation reference time Δt is increasing in absolute value.

[0077] According to the conditions (1) to (3), among the DC resistances DCR calculated by the DC resistance calculation unit 112, DC resistances DCR that are too large or too small are excluded.

[0078] When DC resistances DCR satisfying the conditions (1) to (3) are consecutive at a unit sampling interval, the DC resistance selection unit 113 determines, as a condition (4), the largest DC resistance DCR among the consecutive DC resistances DCR satisfying the conditions, as valid, and determines the other DC resistances DCR as invalid. In this embodiment, the unit sampling interval is set to 10 seconds.

[0079] 10 is a diagram showing, by way of example, the behavior of current and voltage and the transition of the calculated value of DC resistance DCR when a cell is charged at a constant current. A prerequisite for calculating DC resistance DCR is that the current change |ΔI| during the calculation reference time Δt must be 0.2 C or more. Therefore, DC resistance DCR is not calculated in the constant current section where no current change occurs during the calculation reference time Δt.

[0080] 10 and 5, at the start of charging, the current rises instantaneously, whereas the voltage rises instantaneously due to the polarization voltage caused by the ohmic resistance component, but rises gradually due to the polarization voltage caused by the non-ohmic resistance component. Since the denominator of ΔV / ΔI is constant and the numerator gradually increases, the DC resistance DCR increases monotonically.

[0081] In Figure 10, the first DC resistor DCR from the left does not satisfy condition (1) and is therefore invalid. The second DC resistor DCR satisfies condition (1) but does not satisfy condition (2), and is therefore invalid. The third DC resistor DCR from the left is valid because it satisfies conditions (1) to (4). The fourth to seventh DC resistors DCR from the left are invalid because they do not satisfy condition (1).

[0082] Even if the second DC resistance DCR from the left satisfies conditions (1) to (3), it is invalid due to condition (4). In constant current charging, the DC resistance DCR monotonically increases from the start of charging, so if the last DC resistance DCR in the series that monotonically increases is selected, the DC resistance DCR with the largest DC resistance will always be selected. The last DC resistance DCR in the series contains the largest amount of non-ohmic resistance components, so it can be said to be the DC resistance DCR with the highest quality among the multiple DC resistance DCRs in the series. Excluding DC resistance DCRs other than the highest quality DC resistance DCR contributes to improving the accuracy of the DCR map.

[0083] Figure 11 is a diagram showing specific examples of the behavior of current and voltage and the transition of the calculated value of DC resistance DCR when a cell is charged at a non-constant current. In Figure 11, the first DC resistance DCR from the left does not satisfy condition (1) and is therefore invalid. The second to fourth DC resistances DCR from the left satisfy condition (1) but do not satisfy condition (2), and are therefore invalid. The fifth and sixth DC resistances DCR from the left do not satisfy condition (1), and are therefore invalid.

[0084] The seventh DC resistor DCR from the left satisfies conditions (1)-(3), but does not satisfy condition (4), making it invalid. The eighth DC resistor DCR from the left satisfies conditions (1)-(4), making it valid. The ninth to fourteenth DC resistors DCR from the left do not satisfy condition (1), making them invalid. Note that the ninth DC resistor DCR from the left has a negative ΔI during charging, and does not satisfy condition (3). The ninth DC resistor DCR from the left is calculated from ΔV / ΔI at Δt, which is the third resistor from the left in Figure 11, but due to the influence of non-ohmic resistance components, the decrease in ΔV is relatively small compared to ΔI.

[0085] By providing condition (4), in the case of constant current charging, it is possible to select the last DC resistance DCR from among a plurality of consecutive DC resistances DCR that satisfy conditions (1) to (3) from the start of charging, and it is possible to select one DC resistance DCR with the greatest value from one continuous charging interval. Note that in the case of constant current charging, one DC resistance DCR is basically selected from one continuous charging interval, but if no DC resistance DCR that satisfies conditions (1) to (3) occurs, no DC resistance DCR is selected from one continuous charging interval.

[0086] In the case of non-constant current charging, due to fluctuations in current, there are cases where the consecutive DC resistances DCR that satisfy conditions (1) to (3) do not monotonically increase. By setting condition (4), it is possible to select the largest DC resistance DCR from the consecutive DC resistances DCR that satisfy conditions (1) to (3). This makes it possible to generate a DCR map from battery data during non-constant current charging that is close in accuracy to a DCR map based on battery data during constant current charging.

[0087] In the case of non-constant current charging, if no DC resistance DCR that satisfies conditions (1) to (3) occurs, no DC resistance DCR is selected from one continuous charging interval. In the case of non-constant current charging, multiple groups of consecutive DC resistance DCRs that satisfy conditions (1) to (3) may occur in one continuous charging interval, and in such cases, multiple DC resistance DCRs may be selected from one continuous charging interval.

[0088] Figure 12 shows experimental data (part 1) evaluating the accuracy of DC resistance (DCR) estimation calculated from battery data when a non-constant current is applied to a cell. In this experiment, a nickel-based lithium-ion battery using nickel, cobalt, and aluminum as the positive electrode material was used. The calculation reference time Δt was set to 30 seconds. In this experiment, a one-dimensional DCR map was generated using the cell's SOC as a factor. The SOC level was set in 10% increments.

[0089] The true value of DC resistance DCR is a DCR map based on actual measured values ​​when a constant current is applied. The estimated value of DC resistance DCR (no condition) is a DCR map estimated using all DC resistances DCR calculated by the DC resistance calculation unit 112. The estimated value of DC resistance DCR (condition (1)) is a DCR map estimated using the selected DC resistances DCR that satisfy condition (1) from the DC resistances DCR calculated by the DC resistance calculation unit 112. The right axis of the graph indicates the error rate between the true value of DC resistance DCR and the estimated value of DC resistance DCR (condition (1)) for each SOC level.

[0090] It can be seen that the estimated value of DC resistance DCR (condition (1)) is closer to the true value of DC resistance DCR than the estimated value of DC resistance DCR (no condition). The error between the estimated value of DC resistance DCR (no condition) and the true value of DC resistance DCR, and the error between the estimated value of DC resistance DCR (condition (1)) and the true value of DC resistance DCR were each evaluated using MAPE (Mean Absolute Percentage Error). The MAPE between the estimated value of DC resistance DCR (no condition) and the true value of DC resistance DCR was calculated to be 28.4%, and the MAPE between the estimated value of DC resistance DCR (condition (1)) and the true value of DC resistance DCR was calculated to be 19.2%.

[0091] 13 is a diagram (part 2) showing experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. The estimated DC resistance DCR (conditions (1) and (2)) is a DCR map estimated using the DC resistance DCR selected from the DC resistances DCR calculated by the DC resistance calculation unit 112 that satisfy both conditions (1) and (2). The MAPE between the estimated DC resistance DCR (conditions (1) and (2)) and the true value of DC resistance DCR was calculated to be 11.0%.

[0092] 14 is a diagram (part 3) showing experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. The estimated DC resistance DCR (conditions (1) and (3)) is a DCR map estimated using the DC resistance DCR selected from the DC resistances DCR calculated by the DC resistance calculation unit 112 that satisfy both conditions (1) and (3). The MAPE between the estimated DC resistance DCR (conditions (1) and (3)) and the true value of DC resistance DCR was calculated to be 18.5%.

[0093] 15 is a diagram (part 4) showing experimental data evaluating the estimation accuracy of DC resistance DCR calculated from battery data when a non-constant current is applied to a cell. The estimated DC resistance DCR (conditions (1)-(4)) is a DCR map estimated using the DC resistance DCR selected from the DC resistances DCR calculated by the DC resistance calculation unit 112 that satisfy all of conditions (1)-(4). The MAPE between the estimated DC resistance DCR (conditions (1)-(4)) and the true value of DC resistance DCR was calculated to be 3.7%.

[0094] In this way, it was found that by using the DC resistance DCR narrowed down by conditions (1)-(4) from the DC resistance DCR calculated by the DC resistance calculation unit 112, a DCR map approximating the DCR map generated from the true value can be obtained.

[0095] The MAPE between the estimated value of DC resistance DCR (conditions (1)-(3)) and the true value of DC resistance DCR was omitted because it was almost the same as the MAPE between the estimated value of DC resistance DCR (conditions (1) and (2)) and the true value of DC resistance DCR.

[0096] 16 is a diagram showing an example of a record of effective DC resistance DCR that satisfies conditions (1) to (4). The example shown in FIG. 16 shows an example of a record of DC resistance DCR when generating a three-dimensional DCR map using the cell SOH, SOC, and temperature as factors.

[0097] FIG. 17 is a diagram illustrating an example of a three-dimensional DCR map generated from the record of the effective DC resistance DCR shown in FIG. 16 . The representative value calculation unit 114 classifies the effective DC resistance DCR by the combination of SOH, SOC, and temperature at the time of measuring the voltage and current used to calculate the DC resistance DCR, and calculates a representative value of the DC resistance DCR for each level. The map generation unit 115 plots the representative value of the DC resistance DCR calculated for each combination of SOH, SOC, and temperature to generate a three-dimensional DCR map. Note that remaining capacity may be used instead of SOC. For simplification, a two-dimensional DCR map may be generated by omitting the SOH or temperature, or a one-dimensional DCR map may be generated by omitting the SOH and temperature.

[0098] 18 is a flowchart showing the flow of a DCR map generation process performed by the battery analysis system 10 according to the embodiment. The data acquisition unit 111 acquires battery data from the electric vehicle 20 via the network 5 and stores the acquired battery data in the battery data holding unit 121 (S10). The DC resistance calculation unit 112 reads the battery data of the target cell from the battery data holding unit 121, calculates the voltage change ΔV / current change ΔI over the calculation reference time Δt, and calculates the DC resistance DCR (S20).

[0099] The DC resistance selection unit 113 narrows down the DC resistances DCR calculated by the DC resistance calculation unit 112 using predetermined conditions to select effective DC resistances DCR (S30). The representative value calculation unit 114 classifies the effective DC resistances DCR selected by the DC resistance selection unit 113 by factor level and calculates a representative value of the DC resistance DCR for each level. The map generation unit 115 plots the representative values ​​of the DC resistance DCR calculated for each factor level to generate a DCR map (S40).

[0100] 19 is a flowchart of a subroutine showing the specific processing of the DCR narrowing down process in step S30 of the flowchart shown in FIG. 18. The DC resistance selection unit 113 determines whether the DC resistance DCR satisfies the condition that the charging duration or discharging duration of the cell falls within the valid duration time period Δt±α (S31). If the DC resistance DCR does not fall within the valid duration time period Δt±α (N in S31), the DC resistance selection unit 113 invalidates the DC resistance DCR (S36).

[0101] If the DC resistance DCR satisfies the condition of the effective duration time period Δt±α (Y in S31), the DC resistance selection unit 113 determines whether the DC resistance DCR satisfies the condition of the cell capacitance that has changed during the calculation reference time Δt falls within the capacitance range ΔQ±β (S32).If the DC resistance DCR satisfies the condition of the cell capacitance that has changed during the calculation reference time Δt falls within the capacitance range ΔQ±β (N in S32), the DC resistance selection unit 113 invalidates the DC resistance DCR (S36).

[0102] If the DC resistance DCR is within the capacitance range ΔQ±β (Y in S32), the DC resistance selection unit 113 determines whether the current change ΔI during the calculation reference time Δt is increasing in absolute value for the DC resistance DCR (S33).If the current change ΔI is not increasing in absolute value for the DC resistance DCR (N in S33), the DC resistance selection unit 113 invalidates the DC resistance DCR (S36).

[0103] If the DC resistance DCR satisfies the condition that the current change ΔI is increasing in absolute value (Y in S33), the DC resistance selection unit 113 determines whether the DC resistance DCR is continuous at the unit sampling interval (S34).If the DC resistance DCR is not continuous at the unit sampling interval (N in S34), the DC resistance selection unit 113 validates the DC resistance DCR (S37).

[0104] If the DC resistance DCR is continuous at unit sampling intervals (Y in S34), the DC resistance selection unit 113 determines whether the DC resistance DCR to be determined is the largest among the multiple DC resistances DCR that are continuous at unit sampling intervals (S35). If it is not the largest (N in S35), the DC resistance selection unit 113 invalidates the DC resistance DCR to be determined (S36). If it is the largest (Y in S35), the DC resistance selection unit 113 validates the DC resistance DCR to be determined (S37).

[0105] As described above, according to this embodiment, a highly accurate DCR map can be generated. When a DCR map that includes the remaining capacity of the cell as a factor is generated, the remaining capacity-DCR curve based on this DCR map can achieve the same level of utility as a dV / dQ curve, regardless of the magnitude of the current rate. In other words, the non-plateau region of the SOC-OCV curve can be detected with high accuracy from the peak of the remaining capacity-DCR curve. By utilizing the non-plateau region of the SOC-OCV curve, battery conditions such as SOC and SOH can be estimated with high accuracy. The remaining capacity-DCR curve can be generated without the constraint of requiring battery data during low-rate charge and discharge, as is the case with the dV / dQ curve. The technique of detecting the non-plateau region of the SOC-OCV curve from the peak of the remaining capacity-DCR curve is particularly effective for LFP batteries, which contain a large amount of plateau region.

[0106] Furthermore, by narrowing down the calculated DC resistance DCR using the above-mentioned conditions (1) to (4), a highly accurate DCR map can be generated. A DCR map with accuracy similar to that of a DCR map generated from battery data when a constant current is applied can also be generated from battery data when a non-constant current is applied. Note that simply adopting at least one of conditions (1) to (4) has the effect of approaching the DCR map generated from battery data when a constant current is applied. Furthermore, the effect of approaching the DCR map generated from battery data when a constant current is applied is independent of the battery type, and can be obtained for both ternary lithium-ion batteries and LFP batteries.

[0107] The present disclosure has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of the respective components and processing steps, and that such modifications are also within the scope of the present disclosure.

[0108] In the above embodiment, the electric vehicle 20 is assumed to be a four-wheeled electric vehicle. In this regard, it may also be an electric motorcycle (electric scooter), an electric bicycle, or an electric kick scooter. Furthermore, electric vehicles include not only full-scale electric vehicles but also low-speed electric vehicles such as golf carts and land cars. Furthermore, the device in which the assembled battery system 21 is installed is not limited to the electric vehicle 20. Devices in which the assembled battery system 21 is installed include electric vehicles such as electric ships, railcars, and multicopters (drones), stationary power storage systems, and consumer electronic devices (smartphones, notebook PCs, etc.).

[0109] In the above embodiment, an example has been described in which the DCR map is generated by the battery analysis system 10 implemented on a cloud server. In this regard, a battery analysis system having a DCR map generation function may be implemented on an edge device. For example, the battery analysis system may be implemented in the control unit 25 of the electric vehicle 20 (more specifically, in the BMU in the battery pack).

[0110] The embodiment may be specified by the following items.

[0111] [Item 1] A battery analysis system (10) comprising: a data acquisition unit (111) that acquires time-series battery data including the voltage and current of a secondary battery (21) included in a battery pack; a DC resistance calculation unit (112) that calculates the DC resistance of the secondary battery (21) from the ratio of a voltage change to a current change when a current change of a certain value or more occurs over a predetermined time period; a representative value calculation unit (114) that classifies the calculated DC resistance into levels of factors defined by conditions when measuring the voltage and current used to calculate the DC resistance and calculates a representative value of the DC resistance for each level; and a map generation unit (115) that plots the representative values ​​of the DC resistance calculated for each level of the factors to generate a DC resistance map. This makes it possible to generate a highly accurate DC resistance map. [Item 2] The battery analysis system (10) according to Item 1, wherein the factors include the capacity of the secondary battery (21), and the battery analysis system (10) further includes a battery state estimation unit (116) that detects a peak in a capacity-DC resistance curve based on the DC resistance map and detects a non-plateau region in an SOC-OCV curve based on the peak position. This contributes to highly accurate SOC estimation. [Item 3] The battery analysis system (10) according to Item 1, wherein the factors include the capacity of the secondary battery (21), and the battery analysis system (10) further includes a battery state estimation unit (116) that detects a peak in a capacity-DC resistance curve based on the DC resistance map and estimates a degradation state of the secondary battery (21) based on a change in the capacity difference between the capacity at the peak position and the full charge capacity. This allows the degradation state of the secondary battery (21) to be estimated without discharging to a deep depth of discharge. [Item 4] The battery analysis system (10) according to Item 1, wherein the factors include a capacity and a temperature of the secondary battery (21), a capacity and a State Of Health (SOH) of the secondary battery (21), or a capacity, a temperature, and a SOH of the secondary battery (21). According to this, by generating a DC resistance map having a plurality of factors, it is possible to generate a DC resistance map with higher accuracy.[Item 5] A battery analysis method comprising the steps of: acquiring time-series battery data including the voltage and current of a secondary battery (21) included in a battery pack; calculating a DC resistance of the secondary battery (21) from a ratio of a voltage change to a current change when a current change of a certain value or more occurs in a predetermined time; classifying the calculated DC resistance into levels of factors defined by conditions when measuring the voltage and current used to calculate the DC resistance, and calculating a representative value of the DC resistance for each level; and plotting the representative values ​​of the DC resistance calculated for each level of the factors to generate a DC resistance map. This allows for the generation of a highly accurate DC resistance map. [Item 6] A battery analysis program that causes a computer to execute the following processes: a process of acquiring time-series battery data including the voltage and current of a secondary battery (21) included in a battery pack; a process of calculating the DC resistance of the secondary battery (21) from the ratio of a voltage change to a current change when a current change of a certain value or more occurs in a predetermined time; a process of classifying the calculated DC resistance into levels of factors defined by the conditions when the voltage and current used to calculate the DC resistance were measured, and calculating a representative value of the DC resistance for each level; and a process of plotting the representative value of the DC resistance calculated for each level of the factors to generate a DC resistance map. This enables the generation of a highly accurate DC resistance map.

[0112] The present disclosure can be used to create a DC resistance map of a secondary battery.

[0113] 2 Commercial power system, 5 Network, 10 Battery analysis system, 11 Control unit, 12 Memory unit, 13 Communication unit, 20 Electric vehicle, 21 Battery pack system, 22 Voltage sensor, 23 Current sensor, 24 Temperature sensor, 25 Control unit, 26 Communication unit, 30 Charging stand, 31 Power supply unit, 32 Control unit, 33 Communication unit, 111 Data acquisition unit, 112 DC resistance calculation unit, 113 DC resistance selection unit, 114 Representative value calculation unit, 115 Map generation unit, 116 Battery state estimation unit, 121 Battery data storage unit, 122 DC resistance map generation unit.

Claims

1. A battery analysis system comprising: a data acquisition unit that acquires time-series battery data including the voltage and current of a secondary battery included in a battery pack; a DC resistance calculation unit that calculates the DC resistance of the secondary battery from the ratio of voltage change to current change when a current change of a certain value or more occurs in a predetermined time; a representative value calculation unit that classifies the calculated DC resistance into levels of factors defined by the conditions when the voltage and current used to calculate the DC resistance were measured, and calculates a representative value of the DC resistance for each level; and a map generation unit that plots the representative value of the DC resistance calculated for each level of the factors, to generate a DC resistance map.

2. The battery analysis system according to claim 1, wherein the factors include the capacity of the secondary battery, and the battery analysis system further comprises a battery state estimation unit that detects a peak in a capacity-DC resistance curve based on the DC resistance map and detects a non-plateau region in an SOC (State Of Charge)-OCV (Open Circuit Voltage) curve based on the peak position.

3. The battery analysis system according to claim 1, wherein the factors include the capacity of the secondary battery, and the battery analysis system further comprises a battery state estimation unit that detects a peak in a capacity-DC resistance curve based on the DC resistance map and estimates a degradation state of the secondary battery based on a change in the difference in capacity between the capacity at the peak position and the fully charged capacity.

4. The battery analysis system according to claim 1, wherein the factors include the capacity and temperature of the secondary battery, the capacity and SOH (State Of Health) of the secondary battery, or the capacity, temperature, and SOH of the secondary battery.

5. A battery analysis method comprising the steps of: acquiring time-series battery data including the voltage and current of a secondary battery included in a battery pack; calculating the DC resistance of the secondary battery from the ratio of voltage change to current change when a current change of a certain value or more occurs in a predetermined time; classifying the calculated DC resistance into levels of factors defined by the conditions when the voltage and current used to calculate the DC resistance were measured, and calculating a representative value of the DC resistance for each level; and plotting the representative value of the DC resistance calculated for each level of the factors to generate a DC resistance map.

6. A battery analysis program that causes a computer to perform the following processes: acquiring time-series battery data including the voltage and current of a secondary battery included in a battery pack; calculating the DC resistance of the secondary battery from the ratio of the voltage change to the current change when a current change of a certain value or more occurs within a predetermined time; classifying the calculated DC resistance into levels of factors defined by the conditions when the voltage and current used to calculate the DC resistance were measured, and calculating a representative value of the DC resistance for each level; and plotting the representative values ​​of the DC resistance calculated for each level of the factors to generate a DC resistance map.

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