Battery diagnostic system

Abstract

To provide a battery diagnostic system capable of appropriately diagnosing the state of a power storage device while suppressing an increase in the load of arithmetic processing of a controller.SOLUTION: A battery diagnostic system comprises: a controller that acquires operation data on a power storage device having a plurality of secondary battery cells, calculates a battery state value on the basis of the acquired operation data, and diagnoses the state of the power storage device on the basis of the calculated battery state value; and an output device that outputs a result of the diagnosis of the power storage device by the controller. The operation data includes a minimum value and a maximum value of cell voltage of the plurality of secondary battery cells constituting the power storage device. The controller calculates minimum cell capacity or maximum cell resistance as the battery state value on the basis of the minimum value of the cell voltage measured when a charge / discharge state of the power storage device is a first state and the maximum value of the cell voltage measured when the charge / discharge state of the power storage device is a second state.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a battery diagnosis system.

Background Art

[0002] In recent years, efforts have been made to electrify the power sources mounted on work machines such as hydraulic excavators. The electrification of the power source is realized by using a power storage device that stores electric power as a power source and driving an electric motor mounted on the work machine with the electric power supplied from the power storage device. The power storage device includes a plurality of secondary battery cells such as lithium-ion batteries and lead-acid batteries.

[0003] In a work machine equipped with an electrified power source, for the extension of the operating time and the increase in the size of the vehicle body, an increase in the size of the power storage device, that is, a multi-series and multi-parallel arrangement of battery cells is required. Battery cells have differences in degradation rates due to individual differences in performance and differences in usage environments such as temperature. Therefore, when the power storage device is enlarged, the risk of variations in degradation between battery cells and the occurrence of abnormally degraded battery cells increases. If an abnormality occurs in the battery system during the operation of the work machine, such as a decrease in the voltage of the power storage device due to abnormal degradation of the battery cells, the work machine stops and the operation cannot be continued. Therefore, a technology for preventing the occurrence of abnormalities in the battery system is important.

[0004] In a power storage device including a plurality of battery cells, in order to analyze the degradation state of the most degraded battery cell, a method of analyzing the information of all the battery cells constituting the power storage device is effective. However, this method causes an increase in the arithmetic processing of the controller of the battery system. Therefore, it is conceivable to suppress the increase in arithmetic processing by limiting the information used for analysis among the information of battery cells such as cell voltage and cell temperature to the maximum value, minimum value, average value, etc. Patent Document 1 discloses a state-of-charge evaluation system that evaluates the state of a battery system composed of a plurality of battery cells using the maximum value, minimum value, and average value of battery cells.

[0005] The energy storage state evaluation system described in Patent Document 1 includes a memory that stores the voltages of at least two battery cells that are located at different positions in the voltage distribution of the multiple battery cells, and calculates the slope of the voltage with respect to time during the idle period after discharge of at least two battery cells.

[0006] The energy storage state evaluation system described in Patent Document 1 calculates the degradation state of a battery cell based on the slope (time derivative) of the maximum, minimum, and average values ​​of the voltages of two cells in a battery system. The energy storage state evaluation system described in Patent Document 1 calculates the distribution of the degradation state of multiple battery cells based on the degradation state of multiple battery cells and their positions in the voltage distribution of multiple battery cells. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2020-169943 [Overview of the project] [Problems that the invention aims to solve]

[0008] However, in energy storage devices, abnormally degraded battery cells may occur due to large voltage fluctuations caused by charging and discharging. Among multiple battery cells, an abnormally degraded battery cell with large voltage fluctuations may have the lowest voltage before charging and the highest voltage after charging. In this case, a battery diagnostic system that diagnoses the degradation state of the battery cells in an energy storage device based on the time change of the maximum and minimum values ​​of the battery cells in the energy storage device, as in the technology described in Patent Document 1, will underestimate the degradation state of the battery cells. Therefore, the technology described in Patent Document 1 may incorrectly evaluate the energy storage device as normal even though abnormally degraded battery cells with large voltage fluctuations have occurred in the energy storage device.

[0009] The present invention aims to provide a battery diagnostic system that can appropriately diagnose the state of an energy storage device while suppressing an increase in the processing load of the controller. [Means for solving the problem]

[0010] A battery diagnostic system according to one aspect of the present invention involves a plurality of secondary battery cells in series Energy storage devices configured by being connected It is connected in series with the aforementioned energy storage device and is input and output to the energy storage device. The system comprises an ammeter for detecting current, a controller for acquiring operating data of the energy storage device, calculating a battery state value based on the acquired operating data, and diagnosing the state of the energy storage device based on the calculated battery state value, and an output device for outputting the result of the diagnosis of the energy storage device by the controller. The operating data includes the minimum and maximum values ​​of the cell voltages of a plurality of secondary battery cells constituting the energy storage device. The controller determines the minimum value of the cell voltage measured when the energy storage device is in a first state. The minimum value of SOC, SOCmin, obtained from Vmin, The maximum value of the cell voltage measured when the energy storage device is in a second state, which is a more charged state than the first state. The maximum change in SOC, ΔSOCmax, is the difference between Vmax and the maximum value of SOC, SOCmax, and the charge amount ΔQ is obtained by integrating the current values ​​detected by the ammeter between the first state and the second state. Based on, According to the following equation (A) The minimum cell capacity is the cell capacity of the secondary battery cell with the smallest full charge capacity among the multiple secondary battery cells that make up the aforementioned energy storage device. Qmin is calculated using the aforementioned battery state value, or Based on the maximum voltage change ΔVmax, which is the difference between the minimum value Vmin of the cell voltage measured when the energy storage device is in the first state and the maximum value Vmax of the cell voltage measured when the energy storage device is in the second state, and the current difference ΔI between the current detected by the ammeter when the device is in the first state and the current detected by the ammeter when the device is in the second state, the following equation (B) The maximum cell resistance is the cell resistance value of the secondary battery with the highest cell resistance value among the multiple secondary battery cells that make up the energy storage device. R full This is used as the battery state value in the calculation. Qmin=ΔQ / (ΔSOCmax / 100) ···(A) Rmax = ΔVmax / ΔI ... (B) [Effects of the Invention]

[0011] According to the present invention, it is possible to provide a battery diagnostic system that can appropriately diagnose the state of an energy storage device while suppressing an increase in the processing load of the controller. [Brief explanation of the drawing]

[0012] [Figure 1] FIG. 1 is a perspective view of a working machine according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing an example of a battery system mounted on the working machine of FIG. 1. [Figure 3] FIG. 3 is a functional block diagram for explaining the battery diagnosis function of the VCU in FIG. 2. [Figure 4] FIG. 4 is a diagram showing an example of the time change of the SOC of the minimum capacity cell during charging. [Figure 5] FIG. 5 is a flowchart for explaining an example of the flow of battery diagnosis processing executed by the VCU. [Figure 6] FIG. 6 is a diagram for explaining a first case where an incorrect diagnosis is likely to occur. [Figure 7] FIG. 7 is a diagram for explaining a second case where an incorrect diagnosis is likely to occur. [Figure 8] FIG. 8 is a diagram showing an example of the time change of the cell voltage during discharge. [Figure 9] FIG. 9 is a flowchart for explaining an example of the flow of battery diagnosis processing executed by the VCU. [Figure 10] FIG. 10 is a diagram showing an example of the measured value of the battery state value and an approximate formula.

Embodiment for Carrying Out the Invention

[0013] Embodiments of the present invention will be described with reference to the drawings. [[ID=3X]]

[0014] [First Embodiment] [Configuration of Working Machine] FIG. 1 is a perspective view of a working machine 100 according to a first embodiment of the present invention. As shown in FIG. 1, the working machine 100 according to the present embodiment is a hydraulic excavator. Note that the working machine 100 is not limited to a hydraulic excavator, and may be, for example, a wheel loader, a dump truck, or a road machine. As shown in FIG. 1, the working machine 100 includes a vehicle body 107 and a working device 104 attached to the vehicle body 107.

[0015] The vehicle body 107 includes a lower running body 106 equipped with tracks and an upper slewing body 105 that is rotatably mounted relative to the lower running body 106. The lower running body 106 has left and right running motors 111, which are hydraulic motors, and these running motors 111 rotate the tracks to move the work machine 100. The upper slewing body 105 is driven by a slewing motor (not shown), which is an electric motor, and rotates relative to the lower running body 106.

[0016] The working device 104 is a multi-jointed front working machine attached to the front of the vehicle body 107, and includes a boom 101, an arm 102, and a bucket 103. The working device 104 is driven by hydraulic cylinders, which are the boom cylinder 108, the arm cylinder 109, and the bucket cylinder 110, and performs tasks such as excavation and loading.

[0017] The vehicle body 107 is equipped with a hydraulic pump that discharges hydraulic fluid, and a control valve that controls the direction and flow rate of the hydraulic fluid supplied from the hydraulic pump to hydraulic actuators such as hydraulic cylinders and hydraulic motors.

[0018] The direction and flow rate of the hydraulic fluid discharged from the hydraulic pump are controlled by a control valve, thereby controlling the operation of the boom cylinder 108, arm cylinder 109, bucket cylinder 110, and left and right travel motors 111.

[0019] The vehicle body 107 is equipped with an electric motor 204 (see Figure 2) that drives the hydraulic pump, and a battery system 200 (see Figure 2) that supplies power to the electric motor 204 and controls its operation.

[0020] <Battery system configuration> Figure 2 is a block diagram showing an example of a battery system 200 mounted on the work machine 100 shown in Figure 1. As shown in Figure 2, the battery system 200 includes a battery pack 210, an ammeter 201, a BMU (Battery Management Unit) 202, a charger 203, an electric motor 204, an inverter 205, and a VCU (Vehicle Control Unit) 206.

[0021] The battery pack 210 includes a plurality of battery modules 211 connected in series. These battery modules 211 may be connected in parallel, or in a combination of series and parallel. Each battery module 211 is composed of a plurality of secondary battery cells (hereinafter simply referred to as battery cells) 212. These battery cells 212 constituting the battery module 211 are connected in series. Each battery cell 212 is, for example, a lithium-ion battery. In other words, the battery pack 210 is an energy storage device having a plurality of battery cells 212.

[0022] The ammeter 201 is connected in series, for example, to multiple battery modules 211 connected in series, and detects the current value I of the current flowing through each battery cell 212. When multiple battery modules 211 are connected in parallel, one ammeter 201 is installed for each of the multiple battery modules 211, and the current value I of the current flowing through the battery cells 212 that make up each battery module 211 is detected. The ammeter 201 outputs a signal representing the detection result to the BMU 202.

[0023] The BMU202 is a microcontroller equipped with a processing unit 202a and a memory device 202b. The processing unit 202a is, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a DSP (Digital Signal Processor). The memory device 202b is non-volatile memory such as ROM (Read Only Memory), flash memory, or a hard disk drive, as well as volatile memory known as RAM (Random Access Memory).

[0024] The BMU202 is connected to each battery module 211 of the battery pack 210 via wiring. The BMU202 monitors the status of the battery cells 212 that make up each battery module 211. The BMU202 detects the voltage and temperature of each battery cell 212 and stores them in the storage device 202b. The BMU202 stores and manages a data log (hereinafter also referred to as operation data) that represents the operating history of the battery pack 210. In the following, the voltage of the battery cell 212 will also be referred to as cell voltage, and the temperature of the battery cell 212 will also be referred to as cell temperature.

[0025] The operating data of the battery pack 210 stored in the BMU202 includes, for example, the time, cell voltage, cell temperature, and current value. The BMU202 also calculates the SOC (state of charge) and SOH (state of health) of the entire battery pack 210.

[0026] The charger 203 is connected to the battery pack 210. The charger 203 charges the battery pack 210 by being connected to an external power source (not shown), for example. The inverter 205 converts the DC power supplied from the battery pack 210 into AC power and supplies it to the electric motor 204. The electric motor 204 is driven by the power supplied from the battery pack 210 and drives the hydraulic pump to generate power for the work machine 100.

[0027] The VCU206 is a microcontroller equipped with a processing unit 206a and a memory device 206b. The processing unit 206a is, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a DSP (Digital Signal Processor). The memory device 206b is non-volatile memory such as ROM (Read Only Memory), flash memory, or hard disk drive, as well as volatile memory known as RAM (Random Access Memory).

[0028] The VCU206 manages and controls the entire working machine 100. The VCU206 is connected to the BMU202, charger 203, and inverter 205 via signal lines. The VCU206 acquires operating data (including cell voltage, cell temperature, and current values), SOC, and SOH from the BMU202 of the battery pack 210. Based on the acquired data, the VCU206 controls the charger 203 and inverter 205.

[0029] The VCU206 calculates battery status values ​​based on the minimum and maximum cell voltages included in the acquired operating data. Based on the calculated battery status values, the VCU206 diagnoses the state of the battery pack 210. In other words, the VCU206 according to this embodiment has a battery diagnostic function that diagnoses whether the battery pack 210 is normal or whether an abnormality has occurred in the battery pack 210. The non-volatile memory constituting the storage device 206b stores a program and various thresholds for performing the battery diagnostic function.

[0030] Note that the minimum cell voltage refers to the lowest cell voltage among the multiple battery cells 212 that make up the battery pack 210. The maximum cell voltage refers to the highest cell voltage among the multiple battery cells 212 that make up the battery pack 210. Battery state values ​​include, for example, the minimum cell capacity, as will be described later.

[0031] The VCU206 is connected via signal lines to the operation detection device 221b, the posture detection device 222, the lock lever position detection device 223b, the solenoid valve 241, the display device 231, and the communication device 232. The VCU206 communicates bidirectionally with the server 280, which is installed in a location away from the work site where the work machine 100 is performing its work, via a wide-area network communication line 281.

[0032] The server 280 is, for example, located in the management facility for the work machine 100 and connected to the communication device 282. The communication device 232 located on the work machine 100 and the communication device 282 located in the management facility have a communication interface that enables bidirectional communication via the communication line 281. For example, the communication device 232 mounted on the work machine 100 is a wireless communication device capable of wireless communication with a wireless base station connected to the communication line 281, and has a communication interface that includes a communication antenna with a sensitivity band of the 2.1 GHz band or the like.

[0033] The display device 231 is a liquid crystal display device, an organic EL display device, etc. The display device 231 functions as an output device that outputs information such as the diagnostic results of the battery pack 210 by the VCU 206 as an image. The communication device 232 also functions as an output device that outputs information such as the diagnostic results of the battery pack 210 by the VCU 206 to the server 280 via the communication line 281.

[0034] Server 280 is connected to display device 283, which acts as an output device that outputs information acquired from VCU 206 as an image. Display device 283, like display device 231, is a liquid crystal display device, an organic EL display device, etc.

[0035] The operation detection device 221b detects the operation amount of the operation lever 221a of the hydraulic actuator 243 and outputs a signal representing the detection result to the VCU 206. The hydraulic actuator 243 includes the boom cylinder 108, the arm cylinder 109, the bucket cylinder 110, the travel motor 111, etc. The VCU 206 outputs a control signal corresponding to the operation amount and operation direction of the operation lever 221a to the solenoid valve 241. The solenoid valve 241 generates an operation pressure corresponding to the control signal from the VCU 206 and outputs it to the control valve 242. The control valve 242 is controlled according to the operation pressure output from the solenoid valve 241. By controlling the control valve 242, the flow rate of the hydraulic oil supplied from the hydraulic pump to the hydraulic actuator 243 is controlled, and the operation of the hydraulic actuator 243 is controlled. When the hydraulic actuator 243 operates, the posture of the working device 104 changes or the lower traveling body 106 travels.

[0036] The posture detection device 222 detects the posture of the working device 104 and outputs a signal representing the detection result to the VCU 206. The posture detection device 222 includes, for example, a boom angle sensor that detects the rotation angle of the boom 101 with respect to the upper swing body 105, an arm angle sensor that detects the rotation angle of the arm 102 with respect to the boom 101, and a bucket angle sensor that detects the rotation angle of the bucket 103 with respect to the arm 102.

[0037] The lock lever position detection device 223b detects the operation position of the gate lock lever 223a and outputs a signal representing the detection result to the VCU 206. The gate lock lever 223a is an operating device that can be switched between a lock position that invalidates the operation of the operation lever 221a and an unlock position that validates the operation of the operation lever 221a.

[0038] <Functions of the VCU> FIG. 3 is a functional block diagram for explaining the battery diagnosis function of the VCU 206 in FIG. 2. FIG. 3 shows the functions of the VCU 206 realized by executing the program stored in the storage device 206b by the processing device 206a.

[0039] The VCU206 includes a pre-charge voltage storage unit 301, a post-charge voltage storage unit 302, a pre-discharge voltage storage unit 303, a post-discharge voltage storage unit 304, a voltage acquisition determination unit 310, a diagnosis execution determination unit 311, a maximum cell degradation calculation unit 320, and a state diagnosis unit 330.

[0040] The maximum cell degradation calculation unit 320 calculates the minimum cell capacity as a battery state value that can be used to diagnose the state of the battery pack 210. The minimum cell capacity refers to the cell capacity of the battery cell 212 with the smallest full charge capacity among the multiple battery cells 212 that make up the battery pack 210. The battery cell 212 with the smallest full charge capacity among the multiple battery cells 212 that make up the battery pack 210 is also referred to as the minimum capacity cell.

[0041] Referring to Figure 4, the time evolution of the State of Charge (SOC) of the minimum capacity cell will be explained. Figure 4 shows an example of the time evolution of the SOC of the minimum capacity cell during charging. In Figure 4, a graph showing the time evolution of the SOC of the minimum capacity cell (hereinafter referred to as the SOC graph) is shown along with a graph of the current value (hereinafter referred to as the current graph).

[0042] As shown in Figure 4, the horizontal axis of the SOC graph and current graph represents time [s], with t1 representing the time immediately before the start of charging, i.e., before charging, and t2 representing the time immediately after the end of charging, i.e., after charging. The vertical axis of the SOC graph represents the SOC [%] of battery cell 212.

[0043] The example shown in Figure 4 illustrates the case where, at time t1, the battery cell 212 with the smallest SOC among the multiple battery cells 212 constituting the battery pack 210 is the smallest capacity cell, and at time t2, the battery cell 212 with the largest SOC among the multiple battery cells 212 constituting the battery pack 210 is also the smallest capacity cell.

[0044] In other words, the example shown in Figure 4 illustrates the case where the cell voltage of the minimum capacity cell is at its minimum value before charging and at its maximum value after charging. The state of charge (SOC) of the minimum capacity cell changes within the range indicated by dot hatching in the SOC graph, for example.

[0045] In the SOC graph, the time variation of the maximum SOC (SOCmax) and the time variation of the minimum SOC (SOCmin) are shown as solid lines, and the time variation of the average SOC (SOCave) is shown as a dashed line. The time variation of the maximum SOC (SOCmax) is plotted at each time point, representing the maximum SOC of the multiple battery cells 212 constituting the battery pack 210. The time variation of the minimum SOC (SOCmin) is plotted at each time point, representing the minimum SOC of the multiple battery cells 212 constituting the battery pack 210. The time variation of the average SOC (SOCave) is plotted at each time point, representing the average SOC of the multiple battery cells 212 constituting the battery pack 210.

[0046] Here, if we were to diagnose the state of the battery pack 210 based on the difference between the maximum SOC before charging (time t1) and the maximum SOC after charging (time t2) in the example shown in Figure 4, there is a risk of underestimating the change in SOC before and after charging, leading to a misdiagnosis that the battery pack 210 is normal. The same problem arises when diagnosing the state of the battery pack 210 based on the change in the minimum SOC before and after charging, or the change in the average SOC before and after charging.

[0047] Therefore, assuming a worst-case scenario as shown in Figure 4, this embodiment diagnoses the state of the battery pack 210 based on the difference between the minimum SOC value SOCmin1 before charging (time t1) and the maximum SOC value SOCmax2 after charging (time t2). The method for diagnosing the state of the battery pack 210 according to this embodiment will be described below.

[0048] Before charging (time t1), the lowest cell voltage among the multiple battery cells 212 constituting the battery pack 210 is defined as the minimum cell voltage Vmin1. After charging (time t2), the highest cell voltage among the multiple battery cells 212 constituting the battery pack 210 is defined as the maximum cell voltage Vmax2. In the worst-case scenario, the maximum change in SOC due to charging of the smallest capacity cell, ΔSOCmax, corresponds to the difference between the minimum SOC value SOCmin1 before charging and the maximum SOC value SOCmax2 after charging. The minimum cell capacity Qmin is calculated from the amount of charge ΔQ and the maximum change in SOC ΔSOCmax.

[0049] The maximum change in SOC, ΔSOCmax, is calculated using the following equation (1A). ΔSOCmax=SOCmax2-SOCmin1 (1A) The minimum value of the state of charge (SOC) before charging (time t1), SOCmin1, can be determined from the minimum cell voltage, Vmin1. The maximum value of the state of charge (SOCmax2) after charging (time t2), SOCmax2, can be determined from the maximum cell voltage, Vmax2.

[0050] The minimum cell capacity Qmin [Ah] is calculated using the following formula (2A). Qmin=ΔQc / (ΔSOCmax / 100) ···(2A) Here, ΔQc[Ah] is the amount of charge charged, which is obtained by integrating the current value I of the charging current input to the battery pack 210 from the pre-charging state (time t1) to the post-charging state (time t2).

[0051] By comparing the minimum cell capacity Qmin calculated in this way with a predetermined capacity threshold Qmin0, it is possible to diagnose whether or not an abnormality has occurred in the battery pack 210. The capacity threshold Qmin0 is a threshold used to determine whether or not there are battery cells 212 (hereinafter also referred to as abnormally degraded battery cells) whose full charge capacity does not meet the standard. The capacity threshold Qmin0 is, for example, a value of about 70% of the initial capacity of the battery cell 212 and is pre-stored in the memory device 206b.

[0052] The VCU206 calculates the minimum value of SOC, SOCmin1, based on the minimum value of cell voltage Vmin1 measured when the battery pack 210 is in the pre-charge state. The VCU206 also calculates the maximum value of SOC, SOCmax2, based on the maximum value of cell voltage Vmax2 measured when the battery pack 210 is in the post-charge state. Furthermore, the VCU206 calculates the minimum cell capacity Qmin as a battery state value based on the maximum change in SOC ΔSOCmax, which is the difference between the maximum and minimum values ​​of SOC, and the amount of charge ΔQc from the pre-charge state to the post-charge state. Based on the calculated minimum cell capacity Qmin, the VCU206 diagnoses whether or not there are abnormally degraded battery cells in the battery pack 210.

[0053] Although the above describes a method for calculating the minimum cell capacity Qmin using data from charging, it is also possible to calculate the minimum cell capacity Qmin using data from discharging.

[0054] Before discharge, the highest voltage among the multiple battery cells 212 constituting the battery pack 210 is defined as the maximum cell voltage Vmax1. After discharge, the lowest voltage among the multiple battery cells 212 constituting the battery pack 210 is defined as the minimum cell voltage Vmin2. In the worst-case scenario, the maximum change in SOC due to discharge of the smallest capacity cell, ΔSOCmax, corresponds to the difference between the maximum SOC value SOCmax1 before discharge and the minimum SOC value SOCmin2 after discharge. The minimum cell capacity Qmin is calculated from the discharge charge amount ΔQ and the maximum change in SOC ΔSOCmax.

[0055] The maximum change in SOC, ΔSOCmax, is calculated using the following equation (1B). ΔSOCmax=SOCmax1-SOCmin2 (1B) The maximum value of the state of charge (SOC) before discharge, SOCmax1, is determined from the maximum cell voltage, Vmax1. The minimum value of the SOC after discharge, SOCmin2, is determined from the minimum cell voltage, Vmin2.

[0056] The minimum cell capacity Qmin [Ah] is calculated using the following formula (2B). Qmin=ΔQd / (ΔSOCmax / 100) ···(2B) Here, ΔQd[Ah] is the amount of discharged charge obtained by integrating the current value I of the discharge current output from the battery pack 210 from the state before discharge to the state after discharge.

[0057] The VCU206 calculates the maximum value of the State of Charge (SOC), SOCmax1, based on the maximum value of the cell voltage, Vmax1, measured when the battery pack 210 is in the pre-discharge state. The VCU206 also calculates the minimum value of the SOC, SOCmin2, based on the minimum value of the cell voltage, Vmin2, measured when the battery pack 210 is in the post-discharge state. Furthermore, the VCU206 calculates the minimum cell capacity, Qmin, as a battery state value, based on the maximum change in SOC, ΔSOCmax, which is the difference between the maximum and minimum SOC values, and the amount of discharged charge, ΔQd, from the pre-discharge state to the post-discharge state. Based on the calculated minimum cell capacity Qmin, the VCU206 diagnoses whether or not there are abnormally degraded battery cells in the battery pack 210.

[0058] Furthermore, whether the battery pack 210 is charging, discharging, or in a dormant state (neither charging nor discharging) can be determined based on the current value I flowing through the battery pack 210. For example, the VCU 206 determines that the battery pack 210 is in a dormant state, i.e., the work machine 100 is in a dormant state, if the current value I is greater than or equal to the dormant state threshold Ipmin and less than or equal to the dormant state threshold Ipmax. Note that the current value I is positive when the battery cell 212 is charging and negative when the battery cell 212 is discharging.

[0059] The VCU206 determines that the battery pack 210 is discharging, i.e., the work machine 100 is in operation, if the current value I is less than the lower limit of the pause determination current Ipmin. The VCU206 determines that the battery pack 210 is charging, i.e., the work machine 100 is charging, if the current value I is greater than the upper limit of the pause determination current Ipmax.

[0060] Furthermore, the method for determining the charge / discharge state of the battery pack 210, i.e., the state of the work machine 100, is not limited to a method using the current value I. The charge / discharge state of the battery pack 210, i.e., the state of the work machine 100, may be determined by an indicator other than the current value I flowing through the battery cell 212.

[0061] The VCU206 determines whether the following dormancy conditions are met, and if the dormancy conditions are met, it determines that the battery pack 210 is dormant, i.e., the work machine 100 is dormant. Pause conditions: The gate lock lever 223a is operated to the locked position, and the rotational speed of the electric motor 204 is 0 [rpm].

[0062] The VCU206 determines whether the following working conditions are met, and if the working conditions are met, it determines that the battery pack 210 is discharging, i.e., the work machine 100 is working. Operating conditions: The gate lock lever 223a is operated to the unlocked position, and the operating lever 221a is also operated.

[0063] The VCU206 determines whether the following charging conditions are met, and if the charging conditions are met, it determines that the battery pack 210 is charging, i.e., the work machine 100 is charging. Charging conditions: The charging cable is connected to the charger 203, and the charging switch (not shown) is operated to the ON position.

[0064] Furthermore, the VCU 206 may determine that the working conditions are met based on the detection results of the attitude detection device 222, if the working device 104 is in operation.

[0065] <Battery diagnostic process flow> Next, with reference to Figures 3 and 5, an example of the battery diagnostic process flow according to this embodiment will be described. The following example shows the charging process, but it can be carried out similarly in the discharging process.

[0066] Figure 5 is a flowchart illustrating an example of the battery diagnostic process performed by the VCU206. The process shown in the flowchart in Figure 5 is initiated, for example, when the start switch (ignition switch, etc.) of the work machine 100 is turned on, and is repeatedly executed at a predetermined control cycle. Although not shown, the abnormality detection flag is set to off in the initial settings.

[0067] In step S500, the voltage acquisition determination unit 310 determines the timing for acquiring the minimum cell voltage Vmin1 before charging, which is data used for battery diagnostic processing (also referred to as the pre-charging acquisition timing).

[0068] The voltage acquisition determination unit 310 determines the pre-charge acquisition timing based on the cell temperature, current value, and cell voltage acquired from the BMU 202, and sets the pre-charge acquisition flag to ON. The cell temperature used for timing determination is, for example, the minimum or average value of the cell temperature. The voltage acquisition determination unit 310 may also take the State of Charge (SOC) into consideration when determining the pre-charge acquisition timing.

[0069] In this determination process, the voltage acquisition determination unit 310 determines whether the state of the battery pack 210 is an unloaded state (i.e., in a dormant state). Specifically, the voltage acquisition determination unit 310 determines that the battery pack 210 is in an unloaded state when the absolute value of the current value (hereinafter also referred to as the absolute current value) |I| acquired from the BMU 202 is less than or equal to the diagnostic no-load current upper limit (predetermined value) I0 and a predetermined time tp1 has elapsed. If the absolute current value |I| is greater than the diagnostic no-load current upper limit I0, or if the predetermined time tp1 has not elapsed while the absolute current value |I| is less than or equal to the diagnostic no-load current upper limit I0, the voltage acquisition determination unit 310 determines that the battery pack 210 is not in an unloaded state (i.e., is in a loaded state).

[0070] The no-load current upper limit I0 is a threshold value used to determine when the battery pack 210 is under low load, i.e., when neither charging nor discharging is occurring. For example, it is a value of about 5[A]. The predetermined time tp1 corresponds to the time required from when the current flowing through the battery pack 210 falls below the no-load current upper limit I0 until the cell voltage stabilizes to the point where polarization can be ignored. The time required for polarization to be resolved depends on the cell temperature. For this reason, for example, the predetermined time tp1 is calculated from a map of the minimum or average value of the cell temperature. This map is predetermined and stored in the memory device 206b. Alternatively, a map with two axes, cell temperature and SOC, may be stored in the memory device 206b.

[0071] The voltage acquisition determination unit 310 determines the timing at which it is determined that there is no load as the pre-charge acquisition timing.

[0072] In the next step S501, the pre-charge voltage storage unit 301 acquires and stores the minimum cell voltage Vmin1 before charging, if the pre-charge acquisition flag is set to ON in step S500.

[0073] In the next step S502, the maximum cell degradation calculation unit 320 calculates the minimum value of the state of charge (SOC) before charging, SOCmin1, based on the SOC-OCV characteristics of the battery cell 212, from the minimum value of the cell voltage before charging, Vmin1, obtained in step S501. OCV (Open Circuit Voltage) is the terminal voltage in an open state, that is, the voltage of the battery cell 212 when no current is applied.

[0074] In the next step S503, the maximum cell degradation calculation unit 320 calculates the amount of charge ΔQc from the integrated current value during charging.

[0075] The next step, S504, is the same process as step S500. In step S504, the voltage acquisition determination unit 310 determines the timing (also referred to as the post-charge acquisition timing) for acquiring the maximum cell voltage Vmax2 after charging, which is data used for battery diagnostic processing.

[0076] The voltage acquisition determination unit 310 determines the post-charge acquisition timing based on the cell temperature, current value, and cell voltage acquired from the BMU 202, and sets the post-charge acquisition flag to ON. The cell temperature used for timing determination is, for example, the minimum or average value of the cell temperature. The voltage acquisition determination unit 310 may also take the State of Charge (SOC) into consideration when determining the post-charge acquisition timing.

[0077] In this determination process, the voltage acquisition determination unit 310 determines whether the state of the battery pack 210 is an unloaded state (i.e., in a dormant state). Specifically, the voltage acquisition determination unit 310 determines that the battery pack 210 is in an unloaded state when a predetermined time tp2 has elapsed while the absolute value of the current |I| is less than or equal to the diagnostic no-load current upper limit (predetermined value) I0. If the absolute value of the current |I| is greater than the diagnostic no-load current upper limit I0, or if a predetermined time tp2 has not elapsed while the absolute value of the current |I| is less than or equal to the diagnostic no-load current upper limit I0, the voltage acquisition determination unit 310 determines that the battery pack 210 is not in an unloaded state (i.e., is in a loaded state).

[0078] The no-load current upper limit I0 is a threshold value used to determine when the load on the battery pack 210 is low, i.e., when neither charging nor discharging is occurring. For example, it is a value of about 5[A]. The predetermined time tp2 corresponds to the time required from when the current flowing through the battery pack 210 falls below the no-load current upper limit I0 until the cell voltage stabilizes to the point where polarization can be ignored. The predetermined time tp2 is calculated from a map of the minimum or average value of the cell temperature. This map is predetermined and stored in the memory device 206b. Alternatively, a map with two axes, cell temperature and SOC, may be stored in the memory device 206b.

[0079] The voltage acquisition determination unit 310 determines the timing at which it is determined that there is no load as the post-charge acquisition timing.

[0080] The predetermined time tp2 used in the process of determining the timing of acquisition after charging (step S504) may be calculated using the same map as the predetermined time tp1 used in the process of determining the timing of acquisition before charging (step S500), or it may be calculated using a different map.

[0081] In the next step S505, the post-charge voltage storage unit 302 acquires and stores the maximum cell voltage Vmax2 after charging, if the post-charge acquisition flag is set to ON in step S504.

[0082] In the next step S506, the maximum cell degradation calculation unit 320 calculates the maximum value of the state of charge (SOC) after charging, SOCmax2, based on the SOC-OCV characteristics of the battery cell 212, from the maximum value of the cell voltage Vmax2 after charging obtained in step S505.

[0083] In the next step, S507, the diagnostic execution determination unit 311 determines whether the diagnostic execution conditions are met based on the maximum, minimum, and average values ​​of the cell voltage, the maximum, minimum, and average values ​​of the cell temperature, the current value, and the SOC obtained from the BMU202. The diagnostic execution conditions are defined to prevent misdiagnosis. Details of the diagnostic execution conditions will be described later.

[0084] If it is determined in step S507 that the diagnostic conditions are met, the process proceeds to step S508. If it is determined in step S507 that the diagnostic conditions are not met, the process shown in the flowchart of Figure 5 is terminated without diagnosing the state of the battery pack 210 based on the battery state value (minimum cell capacity Qmin).

[0085] In step S508, the maximum cell degradation calculation unit 320 calculates the maximum change in SOC due to charging of the minimum capacity cell in the worst case, ΔSOCmax, using equation (1A), based on the minimum value of SOC before charging, SOCmin1, calculated in step S502, and the maximum value of SOC after charging, SOCmax2, calculated in step S506.

[0086] Furthermore, in step S508, the maximum cell degradation calculation unit 320 calculates the minimum cell capacity Qmin using equation (2A) based on the maximum SOC change amount ΔSOCmax and the charge amount ΔQc calculated in step S503.

[0087] In the next step, S509, the status diagnosis unit 330 determines whether the minimum cell capacity Qmin calculated in step S508 is less than the capacity threshold Qmin0. If it is determined in step S509 that the minimum cell capacity Qmin is less than the capacity threshold Qmin0, the process proceeds to step S510. If it is determined in step S509 that the minimum cell capacity Qmin is greater than or equal to the capacity threshold Qmin0, the process shown in the flowchart of Figure 5 is terminated.

[0088] In step S510, the status diagnosis unit 330 determines that there are abnormally degraded battery cells in the battery pack 210 whose cell capacity has abnormally decreased. In step S510, the status diagnosis unit 330 sets the abnormality determination flag, which indicates that there are abnormally degraded battery cells (i.e., abnormal degradation of the battery pack 210 has occurred), to ON, and terminates the process shown in the flowchart of Figure 5. Although not shown, if a negative determination is made in step S509, the abnormality determination flag is kept in its initial state of OFF.

[0089] Referring to Figures 3 and 5, an example of the battery diagnostic process flow during charging has been described, but the process flow during discharge is similar. In this case, at the pre-discharge acquisition timing determined by the voltage acquisition determination unit 310, the pre-discharge voltage storage unit 303 acquires and stores the maximum cell voltage Vmax1 before discharge. Also, at the post-discharge acquisition timing determined by the voltage acquisition determination unit 310, the post-discharge voltage storage unit 304 acquires and stores the minimum cell voltage Vmin2 after discharge. The maximum cell degradation calculation unit 320 calculates the minimum cell capacity Qmin based on the maximum cell voltage Vmax1 before discharge and the minimum cell voltage Vmin2 after discharge.

[0090] As described above, if the battery diagnostic process determines that an abnormality has occurred in the battery pack 210, the abnormality detection flag is set to ON. When the abnormality detection flag is set to ON, the VCU 206 outputs a control signal to the display device 231 located in the operator's cab of the work machine 100, and displays an image indicating that an abnormality has occurred on the display screen of the display device 231. In addition, when the abnormality detection flag is set to ON, the VCU 206 outputs a control signal to the communication device 232 and sends error information to the server 280 via the communication device 232 to inform it that an abnormality has occurred. This error information includes an identification ID to identify the work machine 100 and the time when the abnormality flag was set to ON.

[0091] <Conditions for conducting the diagnosis> Next, specific examples of diagnostic execution conditions used in the judgment process (step S507) executed by the diagnostic execution judgment unit 311 will be described.

[0092] Generally, when the cell temperature is low, the accuracy of the SOC calculation deteriorates compared to when the cell temperature is high. For this reason, it is preferable that the diagnostic execution conditions include the following first condition (temperature condition). Condition 1: The cell temperature T is equal to or greater than the lower temperature limit T0. The lower temperature limit (threshold) T0 is, for example, around 10°C.

[0093] Furthermore, if there is a large variation in the cell temperature within the battery pack 210, variations will occur in the calculation accuracy of the State of Charge (SOC), which may prevent accurate diagnosis of the battery pack 210's condition. For this reason, it is preferable that the diagnostic conditions include the following second condition (temperature variation condition). Second condition: The difference ΔT between the maximum and minimum cell temperatures of the multiple battery cells 212 constituting the battery pack 210 is less than the upper temperature difference limit ΔT0. The upper limit of the temperature difference (threshold) ΔT0 is, for example, about 10°C.

[0094] The State of Charge (SOC) of battery cell 212 depends on the SOC-OCV characteristics, but there is an SOC range where the gradient is small and the accuracy of calculating the SOC from the cell voltage is poor. For this reason, it is preferable that the diagnostic procedure includes the following third condition (SOC condition). Third condition: The total SOC of the battery pack 210 before charging is less than the first SOC threshold SOCt1, and the total SOC of the battery pack 210 after charging is greater than the second SOC threshold SOCt2. The first SOC threshold SOCt1 and the second SOC threshold SOCt2 are determined to avoid the above SOC range.

[0095] Alternatively, instead of using the State of Charge (SOC) of the battery pack 210, the voltage of the battery pack 210 may be used to determine whether the SOC of the entire battery pack 210 is within the above SOC range.

[0096] Next, with reference to Figures 6 and 7, examples of cases in which erroneous diagnoses are likely to occur in the battery diagnostic method of this embodiment will be described. Figure 6 is a diagram illustrating the first example of a case in which an erroneous diagnosis is likely to occur. As with Figure 4, Figure 6 shows the SOC graph and the current graph. As shown in Figure 6, the first example differs from the worst case in that the SOC of each battery cell 212 changes in parallel over time. In this embodiment, the minimum cell capacity Qmin is calculated from the maximum change in SOC ΔSOCmax and the charge amount ΔQc using equations (1A) and (2A).

[0097] In the case shown in Figure 6, the maximum change in SOC, ΔSOCmax, is larger than the actual maximum change in SOC of an abnormally degraded battery cell, ΔSOCreal, due to the SOC variation caused by the cell voltage variation within the battery pack 210. As a result, the minimum cell capacity Qmin is calculated to be smaller than it actually is, and the battery is evaluated as being more degraded than it actually is.

[0098] In this case, the larger the value of the actual maximum SOC change amount ΔSOCreal for abnormally degraded battery cells, the smaller the influence of SOC variation due to the cell voltage variation described above. However, in this embodiment, the VCU206 does not calculate the cell voltage of all battery cells 212, but only the minimum, average, and maximum values ​​of the cell voltage, making it difficult to calculate the actual maximum SOC change amount ΔSOCreal for abnormally degraded battery cells. Therefore, the change amount ΔSOCave, which is the change in the average SOC, is used as a parameter to determine whether or not to perform the battery diagnostic process. In other words, it is preferable that the diagnostic execution conditions include the following fourth condition (SOC variation condition). Condition 4: The change in the mean SOC value, ΔSOCave, must be greater than or equal to the change threshold ΔSOCave 0. The threshold for change, ΔSOCave0, is, for example, around 40%.

[0099] The change in the mean SOC value, ΔSOCave, is calculated using the following equation (3). ΔSOCave=SOCave2-SOCave1 ···(3) SOCave1 is the average value of the State of Charge (SOC) before charging, and is calculated from the average value of the cell voltage before charging. SOCave2 is the average value of the SOC after charging, and is calculated from the average value of the cell voltage after charging.

[0100] Figure 7 illustrates a second case that is prone to misdiagnosis. Like Figure 4, Figure 7 shows the SOC graph and current graph. As shown in Figure 7, the second case involves a self-discharge abnormality occurring in some of the battery cells 212 within the battery pack 210. Generally, self-discharge abnormalities manifest locally within the battery pack 210.

[0101] When all battery cells 212 in the battery pack 210 are functioning correctly, the voltage values ​​of each battery cell 212 are distributed fairly uniformly. Therefore, the deviation ΔSOCamin from the average of the minimum SOC values, and the deviation ΔSOCamax from the average of the maximum SOC values, are relatively small. Also, the difference between the deviation ΔSOCamin and the deviation ΔSOCamax is relatively small.

[0102] In contrast, as shown in the example in Figure 7, if a self-discharge abnormality occurs in one of the battery cells 212 of the battery pack 210, the voltage of that battery cell 212 drops significantly lower than the voltage of the other battery cells 212, causing the minimum cell voltage and minimum SOC value (SOCmin) to drop significantly compared to normal conditions. On the other hand, the voltages of the other battery cells 212 of the battery pack 210 do not drop significantly. In other words, when a self-discharge abnormality occurs, the maximum cell voltage and maximum SOC value (SOCmax) do not change significantly from normal conditions. Similarly, the average cell voltage and average SOC value (SOCave) do not change significantly from normal conditions.

[0103] Thus, when a self-discharge abnormality occurs, the minimum value of SOC (SOCmin) and the minimum cell voltage tend to decrease. Therefore, if the difference between the deviation of the minimum SOC value from the average value (ΔSOCamin) and the deviation of the maximum SOC value from the average value (ΔSOCamin - ΔSOCamax) is greater than a predetermined value, it can be said that a self-discharge abnormality has occurred.

[0104] In this case, the maximum SOC change ΔSOCmax becomes larger than the maximum SOC change ΔSOCreal for the battery cell 212 where the self-discharge abnormality is actually occurring. As a result, the minimum cell capacity is calculated to be smaller than it actually is, and the battery is evaluated as being more degraded than it actually is. For this reason, it is preferable to perform the battery diagnostic process only when no self-discharge abnormality is occurring.

[0105] Whether or not a self-discharge abnormality is occurring can be determined, for example, based on the cell voltage. The decrease in the average cell voltage due to a self-discharge abnormality is smaller than the decrease in the minimum cell voltage because it is averaged out by the number of cells in series. Therefore, the deviation of the minimum cell voltage from the average cell voltage is larger than the deviation of the maximum cell voltage from the average cell voltage.

[0106] Therefore, it is preferable that the diagnostic conditions include the following fifth condition (voltage deviation condition). Condition 5: The difference ΔVd between the deviation ΔVamin from the average value of the minimum cell voltage and ΔVamax from the average value of the maximum cell voltage is less than the deviation threshold ΔVd0.

[0107] The diagnostic execution determination unit 311 calculates the deviation ΔVamin from the average value of the minimum cell voltage by subtracting the minimum cell voltage from the average value of the cell voltage. The diagnostic execution determination unit 311 calculates the deviation ΔVamax from the average value of the maximum cell voltage by subtracting the average value of the cell voltage from the maximum cell voltage. The diagnostic execution determination unit 311 calculates the difference in deviations ΔVd by subtracting the deviation ΔVamax from the deviation ΔVamin.

[0108] The diagnostic execution and determination unit 311 determines that a self-discharge abnormality has occurred if the difference in deviations ΔVd is greater than or equal to the deviation threshold ΔVd0. The diagnostic execution and determination unit 311 determines that a self-discharge abnormality has not occurred if the difference in deviations ΔVd is less than the deviation threshold ΔVd0.

[0109] The diagnostic implementation determination unit 311 determines that the diagnostic implementation conditions have been met if all of the first to fifth conditions are met, and determines that the diagnostic implementation conditions have not been met if at least one of the first to fifth conditions is not met.

[0110] According to the above-described embodiment, the following effects are achieved.

[0111] The battery diagnostic system 230 includes a VCU (controller) 206 that acquires operational data of a battery pack (energy storage device) 210 having multiple battery cells (secondary battery cells) 212, calculates battery state values ​​based on the acquired operational data, and diagnoses the state of the battery pack 210 based on the calculated battery state values, and an output device (display device 231 and communication device 232) that outputs the results of the diagnosis of the battery pack 210 by the VCU 206. The operational data includes the minimum and maximum values ​​of the cell voltages of the multiple battery cells 212 that make up the battery pack 210. The VCU 206 calculates the minimum cell capacity Qmin as a battery state value based on the minimum value of the cell voltage measured when the charge / discharge state of the battery pack 210 is in the first state and the maximum value of the cell voltage measured when the charge / discharge state of the battery pack 210 is in the second state.

[0112] The VCU206 can calculate battery status values ​​based on the minimum and maximum values ​​of cell voltage included in the operating data. Since the VCU206 does not need to analyze the cell voltage of each of the multiple battery cells 212 that make up the battery pack 210, it can suppress an increase in the computational load.

[0113] The VCU206 calculates the minimum SOC value, SOCmin1, based on the minimum cell voltage Vmin1 measured when the battery is in the pre-charge state (first state). The VCU206 calculates the maximum SOC value, SOCmax2, based on the maximum cell voltage Vmax2 measured when the battery is in the post-charge state (second state). The VCU206 calculates the minimum cell capacity, Qmin, based on the maximum SOC change ΔSOCmax, which is the difference between the maximum and minimum SOC values, and the amount of charge ΔQc from the pre-charge state to the post-charge state. The VCU206 diagnoses the state of the battery pack 210 based on the minimum cell capacity, Qmin.

[0114] With this configuration, if there is a battery cell 212 among the multiple battery cells 212 that has the lowest cell voltage before charging and the highest cell voltage after charging, i.e., an abnormally degraded battery cell 212 with a large change in cell voltage, that battery cell 212 can be detected. Therefore, it is possible to prevent the battery pack 210 from being mistakenly judged as normal when there is an abnormally degraded battery cell with a large change in voltage.

[0115] Furthermore, the VCU206 calculates the maximum SOC value, SOCmax1, based on the maximum cell voltage Vmax1 measured when the battery is in the pre-discharge state (second state). In addition, the VCU206 calculates the minimum SOC value, SOCmin2, based on the minimum cell voltage Vmin2 measured when the battery is in the post-discharge state (first state). The VCU206 calculates the minimum cell capacity, Qmin, based on the maximum SOC change ΔSOCmax, which is the difference between the maximum and minimum SOC values, and the discharge charge amount ΔQd from the pre-discharge state to the post-discharge state. Based on the minimum cell capacity, the VCU206 diagnoses the state of the battery pack 210.

[0116] With this configuration, if there is a battery cell 212 among the multiple battery cells 212 that has the highest cell voltage before discharge and the lowest cell voltage after discharge, i.e., an abnormally degraded battery cell 212 with a large change in cell voltage, that battery cell 212 can be detected. Therefore, it is possible to prevent the battery pack 210 from being mistakenly judged as normal when there is an abnormally degraded battery cell with a large change in voltage.

[0117] As described above, according to this embodiment, it is possible to provide a battery diagnostic system 230 that can appropriately diagnose the state of the battery pack 210 while suppressing an increase in the processing load of the VCU 206.

[0118] The maximum and minimum values ​​of the cell voltage used in calculating the battery state value are measured after a predetermined time has elapsed while the current value flowing through the battery cell 212 is below a predetermined value. Polarization occurs in the battery cell 212 when current flows through it. Polarization is a voltage deviation from the open-circuit voltage caused by an electrochemical reaction. Therefore, when polarization occurs and the cell voltage is unstable, the accuracy of the battery diagnosis may decrease. In this embodiment, the maximum and minimum values ​​of the cell voltage are measured after a predetermined time has elapsed while the current value flowing through the battery cell 212 is below a predetermined value, thereby preventing misdiagnosis due to the effects of polarization.

[0119] [Modification 1 of the First Embodiment] In the first embodiment described above, it was diagnosed that abnormally degraded battery cells had occurred in the battery pack 210 when the minimum cell capacity Qmin was less than the capacity threshold Qmin0. However, the battery diagnostic method is not limited to this. For example, the VCU 206 may diagnose the state of the battery pack 210 based on the minimum cell capacity and the average cell capacity. The battery diagnostic method based on the minimum cell capacity and the average cell capacity will be described in detail below.

[0120] The condition diagnosis unit 330 determines that abnormally degraded battery cells have occurred in the battery pack 210 if the capacity ratio Rq, which is the ratio of the minimum cell capacity to the average cell capacity, is less than the capacity ratio threshold Rq0.

[0121] The capacity ratio Rq is calculated by the maximum cell degradation calculation unit 320 using the following equation (4). Rq = Qmin / Qave ... (4) Qave[Ah] is the average cell capacity and is calculated using the following formula (5). Qave=ΔQc,d / (ΔSOCave / 100) ···(5) ΔSOCave is the change in the average value of SOC, and is calculated, for example, by equation (3) above. ΔQc,d[Ah] is the amount of charge being charged or the amount of charge being discharged.

[0122] The capacity ratio Rq indicates the degree of deviation from the average cell capacity used for battery control (such as the SOC usage range) in a typical battery system. Even if the minimum cell capacity Qmin is less than the capacity threshold Qmin0, if the capacity ratio Rq is large, there may be cases where abnormal degradation has not occurred. Assuming such cases, the state diagnosis unit 330 in this modified example determines that abnormal degradation has occurred if the capacity ratio Rq is less than the capacity ratio threshold Rq0. The state diagnosis unit 330 determines that abnormal degradation has not occurred if the capacity ratio Rq is equal to or greater than the capacity ratio threshold Rq0.

[0123] The capacity ratio threshold Rq0 is determined based on a value that poses a risk of abnormalities (e.g., abnormal cell voltage) occurring in the battery system 200 due to variations in cell capacity within the battery pack 210. The capacity ratio threshold Rq0 is, for example, around 0.9 and is pre-stored in the memory device 206b.

[0124] According to this modified version, the condition of the battery pack 210 can be diagnosed more accurately based on the minimum cell capacity and the average cell capacity.

[0125] [Modification 2 of the First Embodiment] In the first embodiment described above, it was diagnosed that abnormally degraded battery cells had occurred in the battery pack 210 when the minimum cell capacity Qmin was less than the capacity threshold Qmin0, but the battery diagnostic method is not limited to this. For example, the VCU206 may diagnose the state of the battery pack 210 based on the minimum cell capacity degradation rate SOHQmin.

[0126] The minimum cell capacity degradation rate SOHQmin[%] is calculated by the maximum cell degradation calculation unit 320 using the following formula (6). SOHQmin=Qmin / Qi (6) Qmin[Ah] is the minimum cell capacity, and Qi[Ah] is the initial capacity of the battery cell 212. The initial capacity Qi of the battery cell 212 is pre-stored in the memory device 206b.

[0127] The condition diagnosis unit 330 in this modified example determines that abnormal degradation has occurred if the minimum capacity degradation rate SOHQmin is less than the threshold SOHQmin0. The condition diagnosis unit 330 determines that abnormal degradation has not occurred if the minimum capacity degradation rate SOHQmin is equal to or greater than the threshold SOHQmin0. The threshold SOHQmin0 is, for example, about 70%, and is pre-stored in the storage device 206b.

[0128] According to this modified example, the same effects as those described in the first embodiment can be obtained.

[0129] [Second Embodiment] The battery diagnostic system 230 according to the second embodiment of the present invention will be described mainly with reference to Figures 8 and 9. Components identical or equivalent to those described in the first embodiment will be given the same reference numerals, and the differences will be described primarily.

[0130] In the first embodiment, the VCU 206 of the battery diagnostic system 230 was configured to diagnose the state of the battery pack 210 based on the minimum cell capacity Qmin as a battery state value. In contrast, the VCU 206 of the battery diagnostic system 230 in the second embodiment is configured to diagnose the state of the battery pack 210 based on the maximum cell resistance Rmax as a battery state value.

[0131] In the following, among the multiple battery cells 212 that make up the battery pack 210, the battery cell 212 with the highest cell resistance value will also be referred to as the maximum resistance cell. Maximum cell resistance Rmax refers to the cell resistance value of the maximum resistance cell.

[0132] Figure 8 shows an example of the time variation of the cell voltage during discharge. The example shown in Figure 8 assumes a worst-case scenario in which the cell voltage of the cell with the highest resistance switches from the maximum value to the minimum value of the cell voltage in the battery pack 210 before and after the discharge current output.

[0133] The maximum cell degradation calculation unit 320 calculates the maximum cell resistance Rmax of the battery pack 210 and, based on the calculation result, determines whether or not there are abnormally degraded battery cells in the battery pack 210 with abnormally increased cell resistance values. The method of this determination will be explained in detail below.

[0134] The maximum cell degradation calculation unit 320 calculates the maximum cell resistance Rmax based on the maximum voltage change ΔVmax before and after the discharge current output from the battery pack 210 and the current difference ΔI before and after the discharge current output from the battery pack 210. The maximum cell resistance Rmax is calculated by the following equation (7). Rmax = ΔVmax / ΔI ... (7) The maximum voltage change ΔVmax before and after the discharge current output from the battery pack 210 is calculated by the following equation (8). ΔVmax=V2(Vmin)-V1(Vmax) (8) Here, the maximum cell voltage V1 (Vmax) is the maximum value of the cell voltage V1 before the output of the discharge current from the battery pack 210, and the minimum cell voltage V2 (Vmin) is the minimum value of the cell voltage V2 after the output of the discharge current from the battery pack 210 (during discharge).

[0135] By comparing the maximum cell resistance Rmax calculated in this way with a predetermined resistance threshold Rmax0, it is possible to determine whether or not an abnormality has occurred in the battery pack 210. The resistance threshold Rmax0 is, for example, a value of about 200% of the initial value of the cell resistance of the battery cell 212 (hereinafter also referred to as initial resistance), and is stored in advance in the memory device 206b.

[0136] The VCU206 calculates the maximum voltage change ΔVmax, which is the difference between the maximum cell voltage V1 (Vmax) measured when the battery pack 210 is in the pre-discharge state (second state) and the minimum cell voltage V2 (Vmin) measured when the battery pack 210 is in the discharge state (first state). The VCU206 also calculates the current difference ΔI between the pre-discharge state and the discharge state. Based on the calculated maximum voltage change ΔVmax and current difference ΔI, the VCU206 calculates the maximum cell resistance Rmax as a battery state value. Based on the calculated maximum cell resistance Rmax, the VCU206 diagnoses whether or not there are abnormally degraded battery cells in the battery pack 210.

[0137] Although the above describes how to calculate the maximum cell resistance Rmax using data from the discharge phase, it is also possible to calculate the maximum cell resistance Rmax using data from the charging phase.

[0138] In this case, the VCU206 calculates the maximum voltage change ΔVmax, which is the difference between the minimum cell voltage V1 (Vmin) measured when the battery pack 210 is in the pre-charging state (first state) and the maximum cell voltage V2 (Vmax) measured when the battery pack 210 is in the charging state (second state). The VCU206 also calculates the current difference ΔI between the pre-charging state and the charging state. Based on the calculated maximum voltage change ΔVmax and current difference ΔI, the VCU206 calculates the maximum cell resistance Rmax as a battery state value. Based on the calculated maximum cell resistance Rmax, the VCU206 diagnoses whether or not there are abnormally degraded battery cells in the battery pack 210.

[0139] Next, with reference to Figures 3 and 9, an example of the battery diagnostic process flow according to this embodiment will be described. The following example shows the discharge scenario, but it can be performed similarly in the charging scenario.

[0140] Figure 9 is a flowchart illustrating an example of the battery diagnostic process performed by the VCU206. The process shown in the flowchart in Figure 9 is initiated, for example, when the start switch (ignition switch, etc.) of the work machine 100 is turned on, and is repeatedly executed at a predetermined control cycle. Although not shown, in the initial settings, the resistance diagnostic preparation flag and the abnormality determination flag are set to off.

[0141] Generally, when the cell temperature is low, the calculation accuracy of the maximum cell resistance is worse compared to when the cell temperature is high. Also, if there is a large variation in the cell temperature within the battery pack 210, the calculation accuracy of the maximum cell resistance will also vary, and there is a risk that the condition of the battery pack 210 cannot be accurately diagnosed. For this reason, the diagnostic execution determination unit 311 performs a diagnosis to determine whether or not there are abnormally degraded battery cells whose cell resistance value exceeds the standard value, only when the cell temperature satisfies the temperature diagnostic execution conditions for performing the diagnosis.

[0142] As shown in Figure 9, in step S900, the diagnostic execution determination unit 311 determines whether the temperature diagnostic execution conditions are met based on the cell temperature. The temperature diagnostic execution conditions are met when both the first and second conditions described in the first embodiment are satisfied, and are not met when at least one of the first and second conditions is not satisfied.

[0143] In step S900, the diagnostic execution determination unit 311 determines that if the temperature diagnostic execution conditions are met, it is possible to perform a battery diagnostic based on the maximum cell resistance, and proceeds to step S910. In step S900, if the temperature diagnostic conditions are not met, the diagnostic execution determination unit 311 determines that it is impossible to perform a battery diagnostic based on the maximum cell resistance, and terminates the process shown in the flowchart of Figure 9.

[0144] In step S910, the voltage acquisition determination unit 310 determines whether the battery pack 210 is in an unloaded state. The processing in step S910 is the same as that in steps S500 and S504 in Figure 5. Based on the cell temperature, current value, and cell voltage acquired from the BMU 202, the voltage acquisition determination unit 310 determines the pre-discharge acquisition timing and sets the acquisition flag to ON. The cell temperature used to determine the timing is, for example, the minimum or average value of the cell temperature.

[0145] Specifically, the voltage acquisition determination unit 310 determines that the battery pack 210 is in an unloaded state when a predetermined time tp3 has elapsed while the absolute value of the current |I| is less than or equal to the diagnostic no-load current upper limit (predetermined value) I0. The voltage acquisition determination unit 310 determines that the battery pack 210 is not in an unloaded state (i.e., it is in a loaded state) if the absolute value of the current |I| is greater than the diagnostic no-load current upper limit I0, or if a predetermined time tp3 has not elapsed while the absolute value of the current |I| is less than or equal to the diagnostic no-load current upper limit I0.

[0146] The no-load current upper limit I0 is a threshold value used to determine that the battery pack 210 is under low load, i.e., neither charging nor discharging is occurring. For example, it is a value of about 5[A]. The predetermined time tp3 corresponds to the time required from when the current flowing through the battery pack 210 falls below the no-load current upper limit I0 until the cell voltage stabilizes to the point where polarization can be ignored. The predetermined time tp3 is calculated from a map of the minimum or average value of the cell temperature. This map is predetermined and stored in the storage device 206b. Note that a map with two axes, cell temperature and SOC, may also be stored in the storage device 206b. Furthermore, the predetermined time tp3 used in the process of determining the pre-discharge acquisition timing (step S910) may be calculated using the same map as the one used to calculate the predetermined times tp1 and tp2 described in the first embodiment, or it may be calculated using a different map.

[0147] If it is determined in step S910 that the battery pack 210 is in an unloaded state, the process proceeds to step S911. If it is determined in step S910 that the battery pack 210 is not in an unloaded state, the process proceeds to step S920.

[0148] If there is a large variation in the cell voltage within the battery pack 210, the maximum cell resistance may be calculated to be higher than the actual resistance value, potentially leading to an assessment that the battery is more degraded than it actually is. For this reason, the diagnostic execution determination unit 311 performs a diagnosis of the state of the battery pack 210 based on the battery state value only if the cell voltage satisfies the voltage variation condition, which is a condition for performing the diagnosis.

[0149] The voltage variation within the battery pack 210 can be expressed as the voltage difference ΔV (=Vmax - Vmin), which is obtained by subtracting the minimum cell voltage Vmin from the maximum cell voltage Vmax. The voltage variation condition is met when the voltage difference ΔV is less than or equal to a predetermined diagnostic voltage difference upper limit (threshold) Vu0. The voltage variation condition is not met when the voltage difference ΔV is greater than the voltage difference upper limit Vu0. The voltage difference upper limit Vu0 is set to a value that is not judged as abnormal when there is no degradation variation, for example, 20mV.

[0150] In step S911, the diagnostic execution determination unit 311 determines whether the voltage variation condition, which is a condition for performing a diagnosis, is met. In step S911, if the voltage variation condition is met, the diagnostic execution determination unit 311 determines that it is possible to perform a battery diagnosis based on the maximum cell resistance and proceeds to step S912. In step S911, if the voltage variation condition is not met, the diagnostic execution determination unit 311 determines that it is impossible to perform a battery diagnosis based on the maximum cell resistance and terminates the process shown in the flowchart of Figure 9.

[0151] In step S912, the pre-discharge voltage storage unit 303 acquires and stores the minimum cell voltage Vmin at that time as the pre-discharge minimum cell voltage V1(Vmin), and also acquires and stores the maximum cell voltage Vmax at that time as the pre-discharge maximum cell voltage V1(Vmax).

[0152] In the next step S913, the maximum cell degradation calculation unit 320 sets the resistance diagnosis preparation flag to ON and terminates the process shown in the flowchart of Figure 9.

[0153] When the work machine 100 starts working or charging begins, the absolute value of the current |I| becomes greater than the diagnostic no-load current upper limit I0. In this case, in step S910, it is determined that the battery pack 210 is under load, and the process proceeds to step S920.

[0154] In step S920, the voltage acquisition determination unit 310 determines whether the resistance diagnosis preparation flag is set to ON. If it is determined in step S920 that the resistance diagnosis preparation flag is set to ON, the voltage acquisition determination unit 310 counts the elapsed time since the load state was determined (hereinafter also referred to as load elapsed time ti). If it is determined in step S920 that the resistance diagnosis preparation flag is set to ON, the process proceeds to step S921. If it is determined in step S920 that the resistance diagnosis preparation flag is set to OFF, the process shown in the flowchart of Figure 9 ends.

[0155] In step S921, the diagnostic execution determination unit 311 determines whether the load elapsed time ti is less than or equal to the diagnostic requirement time ti0. If it is determined in step S921 that the load elapsed time ti is less than or equal to the diagnostic requirement time ti0, the process proceeds to step S922. If it is determined in step S921 that the load elapsed time ti is greater than the diagnostic requirement time ti0, the process proceeds to step 930.

[0156] In step S922, the diagnostic execution determination unit 311 determines whether the current condition and the SOC condition, which are conditions for performing a diagnosis, are met. The current condition is met when the absolute value |I| of the current flowing through the multiple battery cells 212 constituting the battery pack 210 is greater than or equal to the diagnostic current lower limit Imin0 and less than or equal to the diagnostic current upper limit Imax0, as shown in the following equation (9). Imin0 ≤ |I| ≤ Imax0 ···(9) The current lower limit Imin0 and the current upper limit Imax are predetermined threshold values ​​and are stored in the memory device 206b.

[0157] The reason for setting current conditions as diagnostic requirements is that if the current is not above a certain value, the maximum voltage change ΔVmax will be small, increasing the risk of errors due to voltage sensor errors. The lower limit of the diagnostic current Imin0 is set to, for example, 50[A], taking sensor accuracy into consideration. Furthermore, the calculated maximum cell resistance is evaluated for abnormalities by comparing it with the initial resistance. For this reason, it is preferable to set the upper limit of the diagnostic current Imax0 in accordance with the evaluation conditions for the initial resistance. The upper limit of the diagnostic current Imax0 is set to, for example, 90[A].

[0158] The SOC condition is met when the total SOC of the battery pack 210 is greater than or equal to the diagnostic SOC lower limit SOC min0 and less than or equal to the diagnostic SOC upper limit SOC max0, as shown in equation (10) below. SOCmin0 ≤ SOC ≤ SOCmax0 ... (10) The lower limit of SOC, SOCmin0, and the upper limit of SOC, SOCmax0, are predetermined thresholds and are stored in the memory device 206b.

[0159] The reason for setting SOC conditions as diagnostic requirements is that, in the low SOC range (for example, when the SOC is 20% or less) and the high SOC range (for example, when the SOC is 80% or more), some batteries experience a sharp change in the resistance value of the battery cell, making them prone to errors within that SOC range.

[0160] Furthermore, the SOC range, where errors are likely to occur, differs between charging and discharging. Therefore, it is preferable to set these two thresholds (lower SOC limit and upper SOC limit) to different values ​​for charging and discharging.

[0161] In step S922, the diagnostic execution determination unit 311 determines that if both the current condition and the SOC condition are met, it is possible to perform a battery diagnosis based on the maximum cell resistance, and proceeds to step S923. In step S922, if at least one of the current condition and the SOC condition is not met, the diagnostic execution determination unit 311 determines that it is not possible to perform a battery diagnosis based on the maximum cell resistance, and terminates the process shown in the flowchart of Figure 9.

[0162] In step S923, the post-discharge voltage storage unit 304 acquires and stores the minimum cell voltage Vmin at that time as the minimum cell voltage V2(Vmin) during discharge, and also acquires and stores the maximum cell voltage Vmax at that time as the maximum cell voltage V2(Vmax) during discharge. Note that "during discharge" refers to the stage after the discharge current output has finished but before the discharge current output has finished. Once the voltage acquisition process in step S923 is completed, the process shown in the flowchart of Figure 9 is completed.

[0163] If the work by the machine 100 continues and the load elapsed time ti exceeds the diagnostic time ti0, or if charging continues and the load elapsed time ti exceeds the diagnostic time ti0, a negative determination is made in step S921 and the process proceeds to step 930.

[0164] In S930, the maximum cell degradation calculation unit 320 sets the resistance diagnostic preparation flag to off. In other words, the resistance diagnostic preparation flag returns to its initial state.

[0165] In the next step S931, the maximum cell degradation calculation unit 320 calculates the maximum cell resistance Rmax using equations (7) and (8) from the maximum cell voltage V1 (Vmax) before discharge and the minimum cell voltage V2 (Vmin) during discharge.

[0166] In the next step S932, the state diagnosis unit 330 determines whether the maximum cell resistance Rmax calculated in step S931 is greater than the resistance threshold Rmax0. If it is determined in step S932 that the maximum cell resistance Rmax is greater than the resistance threshold Rmax0, the process proceeds to step S933. If it is determined in step S932 that the maximum cell resistance Rmax is less than or equal to the resistance threshold Rmax0, the process shown in the flowchart of Figure 9 is terminated.

[0167] In step S933, the status diagnosis unit 330 determines that there is an abnormally degraded battery cell in the battery pack 210 with an abnormally increased cell resistance value. In step S933, the status diagnosis unit 330 sets the abnormality determination flag, which indicates that an abnormally degraded battery cell has occurred (i.e., abnormal degradation of the battery pack 210 has occurred), to ON, and terminates the process shown in the flowchart of Figure 9. Although not shown, if a negative determination is made in step S932, the abnormality determination flag is kept in its initial state of OFF.

[0168] Referring to Figures 3 and 9, an example of the battery diagnostic process flow during the discharge phase has been described, but the battery diagnostic process flow during the charging phase is similar. In this case, at the pre-charging acquisition timing determined by the voltage acquisition determination unit 310, the pre-charging voltage storage unit 301 acquires and stores the minimum cell voltage V1 (Vmin) before charging. Also, at the post-charging acquisition timing determined by the voltage acquisition determination unit 310, the post-charging voltage storage unit 302 acquires and stores the maximum cell voltage V2 (Vmax) during charging (after charging current input). The maximum cell degradation calculation unit 320 calculates the maximum cell resistance Rmax based on the minimum cell voltage V1 (Vmin) before charging and the maximum cell voltage V2 (Vmax) during charging (after charging current input).

[0169] As described above, if the battery diagnostic process determines that an abnormality has occurred in the battery pack 210, the abnormality detection flag is set to ON. When the abnormality detection flag is set to ON, the VCU 206 outputs a control signal to the display device 231 located in the operator's cab of the work machine 100, and displays an image indicating that an abnormality has occurred on the display screen of the display device 231. In addition, when the abnormality detection flag is set to ON, the VCU 206 outputs a control signal to the communication device 232 and sends error information to the server 280 via the communication device 232 to inform it that an abnormality has occurred. This error information includes an identification ID to identify the work machine 100 and the time when the abnormality flag was set to ON.

[0170] According to this second embodiment, the same effects and advantages as in the first embodiment can be obtained.

[0171] [Modification 1 of the second embodiment] In the second embodiment described above, it was diagnosed that an abnormally degraded battery cell had occurred in the battery pack 210 when the maximum cell resistance Rmax was greater than the resistance threshold Rmax0, but the battery diagnostic method is not limited to this. For example, the VCU206 may diagnose the state of the battery pack 210 based on the maximum cell resistance and the average cell resistance. The battery diagnostic method based on the maximum cell resistance and the average cell resistance will be described in detail below.

[0172] The VCU206 determines that abnormally degraded battery cells have occurred in the battery pack 210 when the resistance ratio Rr, which is the ratio of the maximum cell resistance to the average cell resistance, is greater than the resistance ratio threshold Rr0.

[0173] The resistance ratio Rr is calculated using the following equation (11). Rr = Rmax / Rave ... (11) Rave is the average cell resistance (the average value of the cell resistance), and is calculated using the following formula (12). Rave = ΔVave / ΔI ... (12) ΔVave is the change in the average value of the cell voltage and is calculated by the following equation (13). ΔVave=V2(Vave)-V1(Vave) ···(13) V1(Vave) is the average value of the cell voltage before discharge, and V2(Vave) is the average value of the cell voltage during discharge. The average value of the cell voltage before discharge, V1(Vave), is acquired in step S912 by the pre-discharge voltage storage unit 303 at the same timing as the minimum value V1(Vmin) and maximum value V1(Vmax) of the cell voltage. The average value of the cell voltage during discharge, V2(Vave), is acquired in step S923 by the post-discharge voltage storage unit 304 at the same timing as the minimum value V2(Vmin) and maximum value V2(Vmax) of the cell voltage.

[0174] The resistance ratio Rr indicates the degree of deviation from the average cell resistance used for battery control (such as the maximum allowable discharge current value) in a typical battery system. Even if the maximum cell resistance Rmax is greater than the resistance threshold Rmax0, if the resistance ratio Rr is small, there are cases where abnormal degradation has not occurred. Assuming such cases, the state diagnosis unit 330 in this modified example determines that abnormal degradation has occurred if the resistance ratio Rr is greater than the resistance ratio threshold Rr0. The state diagnosis unit 330 determines that abnormal degradation has not occurred if the resistance ratio Rr is less than or equal to the resistance ratio threshold Rr0.

[0175] The resistance ratio threshold Rr0 is determined based on a value that poses a risk of abnormality (e.g., abnormal cell voltage) occurring in the battery system 200 due to variations in cell resistance values ​​within the battery pack 210. The resistance ratio threshold Rr0 is, for example, around 1.2 and is pre-stored in the memory device 206b.

[0176] According to this modified example, the condition of the battery pack 210 can be more accurately diagnosed based on the maximum cell resistance and the average cell resistance.

[0177] [Modification 2 of the second embodiment] In the second embodiment described above, it was diagnosed that an abnormally degraded battery cell had occurred in the battery pack 210 when the maximum cell resistance Rmax was greater than the resistance threshold Rmax0, but the battery diagnostic method is not limited to this. For example, the VCU206 may diagnose the state of the battery pack 210 based on the maximum cell resistance degradation rate SOHRmax.

[0178] The maximum cell resistance degradation rate SOHRmax[%] is calculated by the maximum cell degradation calculation unit 320 using the following formula (14). SOHRmax = Rmax / Ri ... (14) Rmax [mΩ] is the maximum cell resistance, and Ri [mΩ] is the initial resistance of the battery cell 212. The initial resistance Ri of the battery cell 212 is pre-stored in the memory device 206b.

[0179] The condition diagnosis unit 330 in this modified example determines that abnormal degradation has occurred if the maximum resistance degradation rate SOHRmax is greater than or equal to the threshold SOHRmax0. The condition diagnosis unit 330 determines that abnormal degradation has not occurred if the maximum resistance degradation rate SOHRmax is less than the threshold SOHRmax0. The threshold SOHRmax0 is, for example, about 200%, and is pre-stored in the memory device 206b.

[0180] According to this modified example, the same effects as those described in the second embodiment can be obtained.

[0181] [Third Embodiment] A battery diagnostic system 230 according to the third embodiment of the present invention will be described with reference to Figures 2 and 10. Components that are the same as or equivalent to those described in the first and second embodiments will be given the same reference numerals, and the differences will be primarily described.

[0182] The battery diagnostic system 230 according to the third embodiment includes a controller (e.g., VCU 206) provided on the work machine 100 and a server 280 that communicates with the VCU 206. The VCU 206 transmits time-series data of battery status values ​​calculated in the battery diagnostic process to the server 280 via a communication device 232. The server 280 calculates an approximate formula for the battery status values ​​with time as a variable by analyzing the time-series data of battery status values ​​received from the VCU 206. The battery status values ​​include the minimum cell capacity Qmin, maximum cell resistance Rmax, capacity ratio Rq, minimum capacity degradation rate SOHQmin, resistance ratio Rr, and maximum resistance degradation rate SOHRmax, as described above. The server 280 predicts the timing of abnormality in the battery pack 210 using the calculated approximate formula for battery status values.

[0183] Figure 10 shows an example of measured battery state values ​​and an approximation formula. In Figure 10, an example is shown where the battery state value is the minimum cell capacity Qmin. Server 280 analyzes the time-series data of the battery state values ​​(measured values) shown by the black circles in the figure and creates an approximation formula fd(t) for the elapsed time t in the following formula (15). fd(t) = ΔD × t + a ... (15) ΔD is the linear coefficient, and a is the constant term.

[0184] Although the above example of the approximation formula fd(t) is a linear expression, it can also be a polynomial of degree two or higher, an exponential function, or any other expression.

[0185] Server 280 predicts the time of abnormality occurrence te using the approximate formula fd(t) for the time change of the battery state value. The predicted time of abnormality occurrence te is the time when the battery state value reaches the judgment threshold. The judgment threshold is, for example, the capacity threshold Qmin0 when the battery state value is the minimum cell capacity Qmin, and the resistance threshold Rmax0 when the battery state value is the maximum cell resistance Rmax.

[0186] Server 280 issues a warning before an abnormality occurs if the current operation of the work machine 100 continues. Specifically, Server 280 calculates the period from the predicted abnormality occurrence time te to the present, i.e., the period ta until the judgment threshold is reached. Server 280 determines whether this period ta is less than the period threshold ta0. If the period ta is less than the period threshold ta0, Server 280 determines that an abnormality in the battery pack 210 of the work machine 100 is imminent.

[0187] In this case, the server 280 displays a warning image representing the judgment result on the display screen of the display device 283. The server 280 may also transmit warning information representing the judgment result to the work machine 100 via the communication device 282. Upon receiving the warning information, the VCU 206 of the work machine 100 displays a warning image on the display screen of the display device 231 indicating that an abnormality is imminent in the battery pack 210.

[0188] Thus, in this third embodiment, the server 280 predicts the time te when an abnormality will occur in the battery pack 210 based on the time-series data of the battery status value received from the VCU 206. The server 280 can warn the operator or manager of the work machine 100 by outputting warning information when the predicted time te approaches. As a result, the operator or manager of the work machine 100 can appropriately plan maintenance work such as replacing the battery pack 210.

[0189] The following modifications are also within the scope of the present invention, and it is possible to combine the configurations shown in the modifications with the configurations described in the embodiments described above, or to combine the configurations described in the different embodiments described above, or to combine the configurations described in the following different modifications.

[0190] <Example 1> For example, the diagnostic conditions described in the second embodiment (e.g., voltage variation conditions) may be included in the diagnostic conditions of the first embodiment.

[0191] Furthermore, in the above embodiment, an example was described in which the VCU206 determines whether or not the diagnostic execution conditions are met, calculates a battery state value if the diagnostic execution conditions are met, and diagnoses the state of the battery pack 210 based on the calculated battery state value, and does not diagnose the state of the battery pack 210 based on the battery state value if the diagnostic execution conditions are not met. However, the present invention is not limited thereto. The process of determining whether or not the diagnostic execution conditions are met may be omitted.

[0192] Preferably, the diagnostic conditions include at least one of the following: a temperature condition that is met when the temperature of the battery cell 212 is above the lower temperature limit; a temperature variation condition that is met when the difference between the maximum and minimum temperatures of multiple battery cells 212 is less than the upper temperature difference limit; a voltage variation condition that is met when the difference between the maximum and minimum cell voltages is less than or equal to the upper voltage difference limit; a voltage deviation condition that is met when the difference between the deviation of the minimum cell voltage from the average value and the deviation of the maximum cell voltage from the average value is less than the deviation threshold; and a current condition that is met when the absolute value of the current of the battery cell 212 is above the lower current limit and below the upper current limit. By setting such diagnostic conditions, it is possible to effectively prevent misdiagnosis.

[0193] <Modification 2> In the above embodiment, an example was described in which the VCU206 has a battery diagnostic function, but the present invention is not limited to this. Part of the battery diagnostic function may be provided by another controller (for example, the BMU202). In other words, the battery diagnostic function may be implemented by multiple controllers.

[0194] Furthermore, the server 280 may have some or all of the battery diagnostic functions described in the first and second embodiments. For example, the VCU 206 acquires and stores time-series changing operational data of the battery pack 210 at multiple time intervals, and transmits the stored operational data to the server 280 via the communication device 232. The server 280 acquires the operational data of the battery pack 210 from the work machine 100, calculates a battery status value based on the acquired operational data, and diagnoses the battery pack 210 based on the calculated battery status value.

[0195] This configuration allows for easy management of the status of battery packs 210 for multiple work machines 100 at the management facility.

[0196] <Variation 3> In the above embodiment, an example was described in which the battery cell 212 is a lithium-ion battery, but the present invention is not limited thereto. The battery cell 212 may also be a lead-acid battery, a nickel-metal hydride battery, or the like.

[0197] Although embodiments of the present invention have been described above, these embodiments only represent a part of the application examples of the present invention, and are not intended to limit the technical scope of the present invention to the specific configurations of the embodiments described above. For example, the embodiments described above are explained in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add a configuration from another embodiment to the configuration of one embodiment. In addition, it is possible to add, delete, or replace a part of the configuration of each embodiment with a configuration from another embodiment.

[0198] Furthermore, some or all of the above configurations and functions may be implemented in hardware, for example, by designing them as integrated circuits. Alternatively, the above configurations and functions may be implemented in software by having the processor interpret and execute programs that realize each function. Information such as programs, tables, and files that realize each function can be stored in memory, storage devices such as hard disks and SSDs (Solid State Drives), or recording media such as IC cards, SD cards, and DVDs. Also, the control lines and information lines shown are those deemed necessary for explanation and do not necessarily represent all control lines and information lines in the actual product. In practice, it can be assumed that almost all configurations are interconnected. [Explanation of symbols]

[0199] 100...Working machine, 104...Working device, 105...Upper rotating body, 106...Lower traveling body, 107...Vehicle body, 200...Battery system, 201...Ammeter, 203...Charger, 204...Electric motor, 205...Inverter, 206...VCU (controller), 206a...Processing device, 206b...Memory device, 210...Battery pack (energy storage device), 211...Battery module, 212...Battery cell (secondary battery cell), 221a...Operating lever, 221b...Operation detection device, 222...Attitude detection device, 223a...Gate lock lever, 223b...Lock lever position detection device, 223b...Gate lock lever, 230...Battery diagnostic system, 231...Display device (output device) 232...Communication device (output device), 241...Solenoid valve, 242...Control valve, 243...Hydraulic actuator, 280...Server, 281...Communication line, 282...Communication device (output device), 283...Display device (output device), 301...Pre-charge voltage storage unit, 302...Post-charge voltage storage unit, 303...Pre-discharge voltage storage unit, 304...Post-discharge voltage storage unit, 310...Voltage acquisition determination unit, 311...Diagnosis execution determination unit, 320...Maximum cell degradation calculation unit, 330...Status diagnosis unit, Qmin...Minimum cell capacity, Qmin0...Capacity threshold, Rmax...Maximum cell resistance, Rmax0...Resistance threshold, Rq...Capacity ratio, Rq0...Capacity ratio threshold, Rr...Resistance ratio, Rr0...Resistance ratio threshold

Claims

1. An ammeter connected in series to an energy storage device configured by connecting multiple secondary battery cells in series, and which detects the current input and output to the energy storage device, A controller that acquires operational data of the energy storage device, calculates a battery status value based on the acquired operational data, and diagnoses the status of the energy storage device based on the calculated battery status value, A battery diagnostic system comprising an output device that outputs the results of a diagnosis of the energy storage device by the controller, The aforementioned operating data includes the minimum and maximum values ​​of the cell voltages of the multiple secondary battery cells constituting the energy storage device. The aforementioned controller, Based on the following equation (A), the maximum change in SOC ΔSOCmax is calculated by comparing the minimum value of SOC (SOCmin), which is the difference between the minimum value of SOC (SOCmin) obtained from the minimum value of the cell voltage (Vmin) measured when the energy storage device is in a first state, and the maximum value of SOC (SOCmax), which is the difference between the maximum value of SOC (SOCmax) obtained from the maximum value of the cell voltage (Vmax) measured when the energy storage device is in a second state, which is a more charged state than the first state, and the charge amount ΔQ, which is obtained by integrating the current values ​​detected by the ammeter between the first state and the second state, the cell capacity of the secondary battery cell with the smallest fully charged capacity among the multiple secondary battery cells constituting the energy storage device is calculated by the following equation (A). The small cell capacity Qmin is calculated as the battery state value, or, based on the maximum voltage change ΔVmax, which is the difference between the minimum value Vmin of the cell voltage measured when the energy storage device is in the first state and the maximum value Vmax of the cell voltage measured when the energy storage device is in the second state, and the current difference ΔI between the current detected by the ammeter when it is in the first state and the current detected by the ammeter when it is in the second state, the maximum cell resistance Rmax, which is the cell resistance value of the secondary battery with the largest cell resistance value among the multiple secondary battery cells constituting the energy storage device, is calculated as the battery state value using the following formula (B). A battery diagnostic system characterized by the following features. Qmin=ΔQ / (ΔSOCmax / 100)...(A) Rmax=ΔVmax / ΔI...(B)

2. In the battery diagnostic system according to claim 1, The first state is the state before charging, The second state described above is the state after charging. The aforementioned controller, Based on the maximum SOC change amount ΔSOCmax and the charge amount ΔQ, which is obtained by integrating the current values ​​detected by the ammeter from the first state to the second state, the minimum cell capacity Qmin is calculated using equation (A). Based on the minimum cell capacity Qmin, the state of the energy storage device is diagnosed. A battery diagnostic system characterized by the following features.

3. In the battery diagnostic system according to claim 1, The first state described above is the state after discharge. The second state is the state before discharge, The aforementioned controller, Based on the maximum SOC change amount ΔSOCmax and the discharge charge amount ΔQ, which is obtained by integrating the current values ​​detected by the ammeter from the second state to the first state, the minimum cell capacity Qmin is calculated using equation (A). Based on the minimum cell capacity Qmin, the state of the energy storage device is diagnosed. A battery diagnostic system characterized by the following features.

4. In the battery diagnostic system according to claim 1, The first state is the state before charging, The second state described above is the charging state. The aforementioned controller, Based on the maximum voltage change ΔVmax and the current difference ΔI, the maximum cell resistance Rmax is calculated using equation (B). The state of the energy storage device is diagnosed based on the maximum cell resistance Rmax. A battery diagnostic system characterized by the following features.

5. In the battery diagnostic system according to claim 1, The first state described above is the state during discharge. The second state is the state before discharge, The aforementioned controller, Based on the maximum voltage change ΔVmax and the current difference ΔI, the maximum cell resistance Rmax is calculated using equation (B). The state of the energy storage device is diagnosed based on the maximum cell resistance Rmax. A battery diagnostic system characterized by the following features.

6. In the battery diagnostic system according to claim 1, At least one of the maximum and minimum values ​​of the cell voltage used in calculating the battery state value is a value measured when a predetermined time has elapsed while the current value detected by the ammeter is less than or equal to a predetermined value. A battery diagnostic system characterized by the following features.

7. In the battery diagnostic system according to claim 1, The aforementioned controller, Determine whether the conditions for performing the diagnosis are met. If the above diagnostic conditions are met, the state of the energy storage device is diagnosed based on the battery state value. If the aforementioned diagnostic conditions are not met, the diagnosis of the state of the energy storage device based on the battery state value will not be performed. The aforementioned diagnostic conditions are: The temperature conditions that are met when the temperature of the secondary battery cell is above the lower temperature limit, The temperature variation condition that is met when the difference between the maximum and minimum temperatures of the aforementioned multiple secondary battery cells is less than the upper limit of the temperature difference, Voltage variation condition that is met when the difference between the maximum and minimum values ​​of the cell voltage is less than or equal to the upper limit of the voltage difference, A voltage deviation condition that is met when the difference between the deviation of the minimum value of the cell voltage from the average value and the deviation of the maximum value of the cell voltage from the average value is less than the deviation threshold, and The current conditions include at least one of the current conditions that are met when the absolute value of the current detected by the ammeter is greater than or equal to the lower current limit and less than or equal to the upper current limit. A battery diagnostic system characterized by the following features.

8. In the battery diagnostic system according to claim 1, The system includes a server that communicates with the aforementioned controller, The controller transmits the time-series data of the battery status value to the server. The server predicts the timing of an abnormality in the energy storage device based on the time-series data of the battery status values ​​received from the controller. A battery diagnostic system characterized by the following features.

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