Work machinery

Abstract

To provide a work machine that can reduce a calculation amount more than before and also can diagnose a sign of an abnormality of a power storage device.SOLUTION: According to an embodiment of the present disclosure, there is provided a work machine having an abnormality sign diagnosis device for diagnosing a sign of an abnormality of a power storage device. The abnormality sign diagnosis device diagnoses a sign of an abnormality of the power storage device by using a first largest deviation ΔVmax(1) which is a deviation of a first largest voltage value Vmax (1) to a first average voltage value Vave (1), a first smallest deviation ΔVmin(1) which is a deviation of a first smallest voltage value Vmin (1) to the first average voltage value Vave (1), a second largest deviation ΔVmax(2) which is a deviation of a second largest voltage value Vmax (2) to a second average voltage value Vave (2), and a second smallest deviation ΔVmin(2) which is a deviation of a second smallest voltage value Vmin (2) to the second average voltage value Vave (2).SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to work machines. [Background technology]

[0002] There has been known a technique for performing a predictive abnormality diagnosis on a power storage device provided in a construction machine. The abstract of Patent Document 1 listed below describes a power storage system characterized by the following configuration.

[0003] The power storage system described in Patent Document 1 includes a first power storage device provided in a construction machine and an abnormality sign diagnosis device that performs abnormality sign diagnosis for the first power storage device. The abnormality sign diagnosis device uses operation information of the first power storage device to calculate a measured deterioration state, which is an index value indicating the deterioration state of the first power storage device, and uses past operation history information of the first power storage device to calculate an estimated deterioration state, which is an index value similar to the measured deterioration state.

[0004] The abnormality sign diagnosis device also calculates a comparison result between the measured deterioration state and the estimated deterioration state for the first power storage device, and calculates a comparison result between the measured deterioration state and the estimated deterioration state for power storage devices provided in each of a plurality of construction machines other than the above construction machine. The abnormality sign diagnosis device diagnoses signs of abnormality in the first power storage device by determining a deviation between these comparison results.

[0005] With this configuration, it is possible to diagnose signs of abnormalities in the power storage system with high accuracy and provide data for preventing unexpected shutdowns of the power storage system, etc. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2020-108283 Summary of the Invention [Problem to be solved by the invention]

[0007] The conventional power storage systems described above have room for improvement in reducing the amount of calculation required when diagnosing signs of abnormality in the power storage device. The present disclosure provides a work machine that is capable of diagnosing signs of abnormality in the power storage device while reducing the amount of calculation required compared to conventional systems. [Means for solving the problem]

[0008] One aspect of the present disclosure is a work machine including a power storage device including a plurality of battery cells, a motor driven by power supplied from the power storage device, and an abnormality sign diagnosis device that diagnoses abnormality signs in the power storage device, wherein the abnormality sign diagnosis device, in a first state of charge of the power storage device, obtains a maximum value and a minimum value from among voltage values ​​of each of the plurality of battery cells as a first maximum voltage value and a first minimum voltage value, respectively, and obtains a first average voltage value that is an average value of the voltages of each of the battery cells, and, in a second state of charge of the power storage device that is higher than the first state of charge, obtains a first average voltage value that is an average value of the voltages of each of the battery cells. and obtains the maximum and minimum values ​​from the voltages of the battery cells as a second maximum voltage value and a second minimum voltage value, respectively, and obtains a second average voltage value that is an average value of the voltages of each battery cell, and diagnoses signs of abnormality in the power storage device using a first maximum value deviation that is a deviation of the first maximum voltage value from the first average voltage value, a first minimum value deviation that is a deviation of the first minimum voltage value from the first average voltage value, a second maximum value deviation that is a deviation of the second maximum voltage value from the second average voltage value, and a second minimum value deviation that is the deviation of the second minimum voltage value from the second average voltage value. [Effects of the Invention]

[0009] According to the above aspect of the present disclosure, it is possible to provide a work machine that is capable of diagnosing signs of abnormality in a power storage device while reducing the amount of calculation compared to conventional methods. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a perspective view showing a first embodiment of a work machine according to the present disclosure. [Figure 2] FIG. 2 is a block diagram showing an example of a battery control system mounted on the work machine of FIG. 1. [Figure 3] FIG. 3 is a functional block diagram of the abnormality sign diagnosis device for the vehicle control unit of FIG. 2. [Figure 4] FIG. 4 is a flowchart illustrating the process flow of the abnormality sign diagnosis device of FIG. 3. [Figure 5] Graph of the normal voltage and SOC of the battery cells of the energy storage device in Figure 2. [Figure 6] Graph of voltage and SOC during abnormal self-discharge of the battery cell of the energy storage device in Figure 2. [Figure 7] 5 is a flow chart corresponding to FIG. 4 for a second embodiment of a work machine according to the present disclosure. [Figure 8] 3 is a graph showing an example of the amount of change in the voltage value of a battery cell of the power storage device of FIG. 2 versus the number of days that have passed. [Figure 9] Graph of voltage and SOC when the capacity of the battery cell of the energy storage device in Figure 2 is abnormally reduced. [Figure 10] Graph of voltage and SOC when the capacity of the battery cell of the energy storage device in Figure 2 is abnormally reduced. [Figure 11] Graph of voltage and SOC when the capacity of the battery cell of the energy storage device in Figure 2 is abnormally reduced. [Figure 12] 5 is a flow chart corresponding to FIG. 4 for a work machine according to a third embodiment of the present disclosure. [Figure 13] Graph of voltage and SOC when the internal resistance of the battery cell of the energy storage device of Figure 2 increases. [Figure 14] Graph of voltage and SOC when the internal resistance of the battery cell of the energy storage device of Figure 2 increases. [Figure 15] 5 is a flow chart corresponding to FIG. 4 for a fourth embodiment of a work machine according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of a work machine according to the present disclosure will be described with reference to the drawings.

[0012] [Embodiment 1] Fig. 1 is a perspective view showing a first embodiment of a work machine according to the present disclosure. Fig. 2 is a block diagram showing an example of a battery control system 200 mounted on the work machine 100 of Fig. 1.

[0013] As shown in Fig. 1, the work machine 100 is, for example, a hydraulic excavator. Note that the work machine according to the present disclosure is not limited to hydraulic excavators, and may be, for example, a wheel loader, a dump truck, or road machinery. The work machine 100 includes, for example, a vehicle body 107 and a work implement 104 shown in Fig. 1, a battery control system 200 shown in Fig. 2, and a hydraulic pump and a control valve (not shown).

[0014] The vehicle body 107 includes, for example, a lower running body 106 equipped with tracks, and an upper rotating body 105 rotatably attached to the lower running body 106. The lower running body 106 has left and right traveling motors 111, which are, for example, hydraulic motors, and these traveling motors 111 rotate the tracks to travel the work machine 100. The upper rotating body 105 is driven, for example, by a swing motor, which is an electric motor (not shown), and rotates relative to the lower running body 106.

[0015] The working device 104 is, for example, an articulated front working machine attached to the front of a vehicle body 107, and includes a boom 101, an arm 102, and a bucket 103. The working device 104 is driven by hydraulic cylinders, namely, a boom cylinder 108, an arm cylinder 109, and a bucket cylinder 110, and performs work such as excavation work and loading work.

[0016] The work machine 100 controls the direction and flow rate of hydraulic oil supplied from the hydraulic pump using 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. The hydraulic pump is driven, for example, by a motor 204 of a battery control system 200 shown in FIG. 2.

[0017] As shown in FIG. 2, the battery control system 200 includes, for example, a battery pack 210, an ammeter 201, a battery management unit (BMU) 202, a charger 203, a motor 204, an inverter 205, and a vehicle control unit (VCU) 206.

[0018] The battery pack 210 includes, for example, a plurality of battery modules 211 connected in series. The plurality of battery modules 211 may be connected in parallel, or may be connected in series and parallel. Each battery module 211 is configured by, for example, a plurality of battery cells 212. Each battery cell 212 is, for example, a lithium-ion secondary battery. That is, the battery pack 210 is a power storage device including the plurality of battery cells 212.

[0019] For example, the ammeter 201 is connected in series to a plurality of battery modules 211 connected in series, and detects the current value I of the current flowing through each battery cell 212. When some series connections of the plurality of battery modules 211 are connected in parallel, one ammeter 201 is provided for each series connection of the plurality of battery modules, and detects the current value I of the current flowing through each battery cell 212.

[0020] The BMU 202 is, for example, a microcontroller equipped with a central processing unit (CPU) and a memory. The BMU 202 is connected to each battery module 211 of the battery pack 210 via wiring, and monitors the state of each battery cell 212. For example, the voltage of each battery cell 212 is input to the BMU 202. The BMU 202 records and manages a data log, which is, for example, the operation history of the battery pack 210.

[0021] The data log of the battery pack 210 recorded in the BMU 202 includes, for example, the time, the temperature and voltage of the battery cells 212 during charging and discharging, etc. The BMU 202 also calculates the state of charge (SOC) and state of health (SOH) of the entire battery pack 210.

[0022] The charger 203 is connected to the battery pack 210 and, for example, is connected to an external power source to charge the battery pack 210. The motor 204 is driven by power supplied from the battery pack 210, which is a power storage device, and, for example, drives a hydraulic pump to generate power for the work machine 100. The inverter 205 converts the DC power supplied from the battery pack 210 into AC power and supplies it to the motor 204.

[0023] The VCU 206 is, for example, a microcontroller equipped with a CPU and memory, and is responsible for the overall management and control of the work machine 100. The VCU 206 is connected to the BMU 202, the charger 203, and the inverter 205, for example, via signal lines. The VCU 206 controls the charger 203 and the inverter 205 based on the voltage and temperature of the battery cells 212 input from the BMU 202, as well as the operation history, SOC, SOH, etc. of the battery pack 210. The VCU 206 also diagnoses, for example, signs of abnormality and abnormality factors of the battery pack 210.

[0024] Fig. 3 is a functional block diagram of an abnormality sign diagnosis device 300 of the VCU 206 of Fig. 2. The abnormality sign diagnosis device 300 represents the functions of the VCU 206 that are realized, for example, by the CPU of the VCU 206 executing a program stored in the memory of the VCU 206. Note that the abnormality sign diagnosis device 300 does not necessarily have to be included in the VCU 206, and may be realized by hardware and software separate from the VCU 206.

[0025] The abnormality sign diagnosis device 300 includes, for example, a data acquisition unit 301, a state determination unit 302, a data extraction unit 303, a voltage value calculation unit 304, and an abnormality diagnosis unit 305, and diagnoses abnormality signs in the battery pack 210, which is a power storage device. The operation of the abnormality sign diagnosis device 300 will be described below.

[0026] Figure 4 is a flow diagram illustrating the processing flow of the abnormality sign diagnosis device 300 of Figure 3. For example, when the start switch of the work machine 100 is turned on, the abnormality sign diagnosis device 300 starts the processing flow shown in Figure 4 and executes process P1 to acquire data on the power storage device. In this process P1, the data acquisition unit 301 of the abnormality sign diagnosis device 300 acquires data on the battery pack 210, which is the power storage device of the work machine 100.

[0027] 3, the data acquisition unit 301 acquires, for example, from the BMU 202, data logs of the current value I flowing through each battery cell 212 of the battery pack 210 and the state of charge SOC of the battery pack 210. In addition, the data acquisition unit 301 acquires, for example, from the BMU 202, data logs of the maximum voltage value Vmax, the minimum voltage value Vmin, and the average voltage value Vave of each of the plurality of battery cells 212.

[0028] Next, the abnormality sign diagnosis device 300 executes process P2 to determine the state of the work machine. In process P2, the state determination unit 302 of the abnormality sign diagnosis device 300 determines whether the state of the work machine 100 is at rest, working, or charging, for example, based on the data log of the battery pack 210 acquired by the data acquisition unit 301.

[0029] More specifically, the state determination unit 302 determines that the work machine 100 is at rest when, for example, the current value I of each battery cell 212 of the battery pack 210 satisfies the following formula (1). Note that the current value I is positive when the battery cell 212 is charging, and negative when the battery cell 212 is discharging.

[0030] I_p_min ≦ I ≦ I_p_max ···(1)

[0031] In the above formula (1), I_p_min and I_p_max are respectively the lower limit and upper limit of the rest determination current of the work machine 100. In other words, the state determination unit 302 determines that the work machine 100 is at rest when the current value I of each battery cell 212 of the battery pack 210 is equal to or greater than the lower limit I_p_min of the rest determination current and equal to or less than the upper limit I_p_max of the rest determination current.

[0032] The state determination unit 302 may determine that the work machine 100 is at rest after a certain time, for example, 10 minutes or more, has elapsed since the above formula (1) is established. Polarization occurs in the battery cells 212 when a current flows through them. Polarization is a deviation in voltage from the open circuit voltage caused by an electrochemical reaction. Therefore, by determining that the work machine 100 is at rest after a certain time has elapsed since the above formula (1) is established, it is possible to avoid erroneous determinations due to the effects of polarization. Furthermore, the time it takes for polarization to be resolved depends on the temperature of the battery cells 212. Therefore, the state determination unit 302 may make the time for determining that the work machine 100 is at rest temperature-dependent.

[0033] Furthermore, the state determination unit 302 determines that the work machine 100 is working, for example, when the current value I of each battery cell 212 of the battery pack 210 satisfies the following formula (2): In other words, the state determination unit 302 determines that the work machine 100 is working when the current value I of each battery cell 212 of the battery pack 210 is smaller than the lower limit value I_p_min of the rest determination current.

[0034] I < I_p_min (2)

[0035] Furthermore, the state determination unit 302 determines that the work machine 100 is charging, for example, when the current value I of each battery cell 212 of the battery pack 210 satisfies the following formula (3): In other words, the state determination unit 302 determines that the work machine 100 is charging when the current value I of each battery cell 212 of the battery pack 210 is greater than the upper limit value I_p_max of the pause determination current.

[0036] I_p_max < I (3)

[0037] The lower limit I_p_min and upper limit I_p_max of the rest determination current described above depend on the operating conditions of the work machine 100, but can be set to a current value of 0.02 C or less, where 1 C is defined as the current magnitude that completely charges or discharges the theoretical capacity of the battery cell 212 in one hour. With such a current value, the effect of voltage drop due to the current can be minimized.

[0038] Furthermore, the current value range may be further limited when determining whether the work machine 100 is in a working or charging state. Specifically, for example, if the current value I of the battery cell 212 is 50 A or greater, it may be determined that the work machine 100 is in a charging state. Furthermore, the states of the work machine 100, such as at rest, in work, and charging, may be determined using an index other than the current value I flowing through the battery cell 212.

[0039] More specifically, the abnormality sign diagnosis device 300 acquires, for example, the on / off state of the gate lock lever of the work machine 100, the rotation speed of the motor 204, the on / off state of the operation lever of the work machine 100, whether the charging cable is connected, and the on / off state of the charging switch using the data acquisition unit 301. In this case, the state determination unit 302 determines that the work machine 100 is at rest, working, or charging when the following rest condition, working condition, or charging condition is satisfied, respectively.

[0040] Pause condition: The gate lock lever is on and the rotation speed of the motor 204 is 0 rpm. Working conditions: Gate lock lever is off and operation lever is on. Charging conditions: The charging cable is connected and the charging switch is on.

[0041] Next, the abnormality sign diagnosis device 300 executes process P3 to extract data during work suspension. In this process P3, the data extraction unit 303 of the abnormality sign diagnosis device 300 extracts data logs for the work machine 100 during suspension determined by the state determination unit 302 from the data logs of the battery pack 210 acquired by the data acquisition unit 301. Note that this process P3 is executed periodically, for example, after a period of approximately 6 to 24 hours has passed, which is sufficient to collect data logs of the battery pack 210. Next, the abnormality sign diagnosis device 300 executes process P4 to calculate a voltage value in the first state of charge.

[0042] Fig. 5 is a graph showing the relationship between the voltage of the battery cell 212 and the SOC of the battery pack 210 when all the battery cells 212 in the battery pack 210 in Fig. 2 are normal. Fig. 6 is a graph showing the relationship between the voltage of the battery cell 212 and the SOC of the battery pack 210 when a self-discharge abnormality occurs in one of the battery cells 212 in the battery pack 210 in Fig. 2. In Figs. 5 and 6, the data of the battery cell 212 with the maximum voltage value Vmax is shown by a two-dot chain line, the data of the battery cell 212 with the minimum voltage value Vmin is shown by a single-dot chain line, and the data of the average voltage value Vave of all the battery cells 212 is shown by a dotted line.

[0043] The voltage value calculation unit 304 of the abnormality sign diagnosis device 300 executes the following process in the voltage value calculation P4 for the first state of charge. As shown in Fig. 5 or 6, the voltage value calculation unit 304 acquires the maximum and minimum values, as a first maximum voltage value Vmax(1) and a first minimum voltage value Vmin(1), from among the voltage values ​​of the multiple battery cells 212 in the first state of charge SOC(1) of the battery pack 210, which is the power storage device. Furthermore, the voltage value calculation unit 304 acquires a first average voltage value Vave(1), which is the average value of the voltage values ​​of the individual battery cells 212.

[0044] Next, the abnormality sign diagnosis device 300 executes a second state-of-charge voltage value calculation P5. In this process P5, the voltage value calculation unit 304 of the abnormality sign diagnosis device 300 executes the following process. As shown in FIGS. 5 and 6, the voltage value calculation unit 304 acquires the maximum and minimum voltage values ​​of each battery cell 212 as a second maximum voltage value Vmax(2) and a second minimum voltage value Vmin(2), respectively, from among the voltage values ​​of each battery cell 212 in a second state of charge SOC(2) that is higher than the first state of charge SOC(1) of the battery pack 210, which is the power storage device. In addition, the voltage value calculation unit 304 acquires a second average voltage value Vave(2), which is the average value of the voltages of each battery cell 212.

[0045] In this embodiment, in the above-mentioned process P1, the data acquisition unit 301 acquires from the BMU 202 data logs of the maximum voltage value Vmax, the minimum voltage value Vmin, and the average voltage value Vave of each of the plurality of battery cells 212 shown in Fig. 5 or 6. Therefore, in the above-mentioned processes P4 and P5, the voltage value calculation unit 304 can easily acquire the first maximum voltage value Vmax(1), the second maximum voltage value Vmax(2), the first minimum voltage value Vmin(1), the second minimum voltage value Vmin(2), the first average voltage value Vave(1), and the second average voltage value Vave(2).

[0046] Furthermore, the first state of charge SOC(1) and second state of charge SOC(2) of the battery pack 210 can be set, for example, based on the usage range of the state of charge of the battery pack 210 in the work machine 100. The first state of charge SOC(1) is set, for example, to a value slightly higher than the lower limit of the usage range, and the second state of charge SOC(2) is set, for example, to a value slightly lower than the upper limit of the usage range. From the perspective of accurately determining self-discharge abnormality in the battery cell 212, for example, when the usage range is a state of charge from 20% to 80%, the first state of charge SOC(1) can be set to around 25% and the second state of charge SOC(2) can be set to around 75%.

[0047] Next, the abnormality sign diagnosis device 300 executes processes P6, P7, and P8 to diagnose abnormality signs in the battery pack 210, which is an electricity storage device, using the first maximum value deviation, the first minimum value deviation, the second maximum value deviation, and the second minimum value deviation. More specifically, the abnormality sign diagnosis device 300 first executes determination process P6 in the first state of charge.

[0048] In this determination process P6, the voltage value calculation unit 304 of the abnormality sign diagnosis device 300 first calculates a first maximum value deviation ΔVmax(1), which is the deviation of the first maximum voltage value Vmax(1) from the first average voltage value Vave(1), and a first minimum value deviation ΔVmin(1), which is the deviation of the first minimum voltage value Vmin(1) from the first average voltage value Vave(1). Next, the voltage value calculation unit 304 determines whether the value obtained by subtracting the first maximum value deviation ΔVmax(1) from the first minimum value deviation ΔVmin(1) is greater than the first voltage threshold value Vth(1).

[0049] For example, as shown in FIG. 5, when all the battery cells 212 in the battery pack 210 are normal, the voltage values ​​of the battery cells 212 are distributed approximately uniformly, so the first minimum value deviation ΔVmin(1) and the first maximum value deviation ΔVmax(1) are relatively small, and the difference between them is also small.

[0050] In such a case, in determination process P6, the voltage value calculation unit 304 determines that the difference obtained by subtracting the first maximum value deviation ΔVmax(1) from the first minimum value deviation ΔVmin(1) is equal to or less than the first voltage threshold Vth(1) (NO). Then, the abnormality sign diagnosis device 300 ends the processing flow shown in Fig. 4 without diagnosing an abnormality sign in the battery pack 210. Note that the first voltage threshold Vth(1) depends on the specifications of the battery cells 212, but can be set to, for example, about 30 mV, which corresponds to a 5% difference in the state of charge in the open circuit voltage (OCV) of the battery cells 212.

[0051] 6, when a self-discharge abnormality occurs in one battery cell 212 of the battery pack 210, the voltage of that battery cell 212 drops significantly below the voltages of the other battery cells 212, resulting in a significant drop in the first minimum voltage value Vmin(1). However, the voltages of the other battery cells 212 in the battery pack 210 do not drop significantly, so the first maximum voltage value Vmax(1) does not change significantly from the normal state shown in FIG. 5, and the drop in the first average voltage value Vave(1) is also small.

[0052] In such a case, in the judgment process P6, the voltage value calculation unit 304 determines that the value obtained by subtracting the first maximum value deviation ΔVmax(1) from the first minimum value deviation ΔVmin(1) is greater than the first voltage threshold value Vth(1) (YES), and then executes the next judgment process P7 for the second charging state.

[0053] In this determination process P7, the voltage value calculation unit 304 of the abnormality sign diagnosis device 300 first calculates a second maximum value deviation ΔVmax(2), which is the deviation of the second maximum voltage value Vmax(2) from the second average voltage value Vave(2), and a second minimum value deviation ΔVmin(2), which is the deviation of the second minimum voltage value Vmin(2) from the second average voltage value Vave(2). Next, the voltage value calculation unit 304 determines whether the value obtained by subtracting the second maximum value deviation ΔVmax(2) from the second minimum value deviation ΔVmin(2) is greater than the second voltage threshold value Vth(2).

[0054] 5, when all the battery cells 212 in the battery pack 210 are normal, the voltage values ​​of the battery cells 212 are distributed uniformly, so the second minimum value deviation ΔVmin(2) and the second maximum value deviation ΔVmax(2) are relatively small, and the difference between them is also small. In such a case, in determination process P7, the voltage value calculation unit 304 determines that the value obtained by subtracting the second maximum value deviation ΔVmax(2) from the second minimum value deviation ΔVmin(2) is equal to or smaller than the second voltage threshold value Vth(2) (NO), and ends the processing flow shown in FIG. 4 without diagnosing the battery pack 210 for signs of abnormality.

[0055] 6, when a self-discharge abnormality occurs in one battery cell 212 of the battery pack 210, the voltage of that battery cell 212 drops significantly below the voltages of the other battery cells 212, resulting in a significant drop in the second minimum voltage value Vmin(2). On the other hand, the voltages of the other battery cells 212 in the battery pack 210 do not drop, so the second maximum voltage value Vmax(2) does not change from the normal state shown in FIG. 5, and the drop in the second average voltage value Vave(2) is also small.

[0056] In such a case, in determination process P7, the voltage value calculation unit 304 determines that the value obtained by subtracting the second maximum value deviation ΔVmax(2) from the second minimum value deviation ΔVmin(2) is greater than the second voltage threshold value Vth(2) (YES). In this case, the abnormality sign diagnosis device 300 executes process P8 to diagnose the next abnormality sign. Note that the first voltage threshold value Vth(1) and the second voltage threshold value Vth(2) may be the same value or different values, but by making them dependent on changes in the state of charge, the accuracy of determining self-discharge abnormality in the battery cell 212 can be improved.

[0057] In process P8 for diagnosing an abnormality sign, the abnormality sign diagnosis device 300, for example, uses the abnormality diagnosis unit 305 to diagnose that a self-discharge abnormality has occurred in one battery cell 212 of the battery pack 210. The VCU 206 notifies the operator or manager of the work machine 100 of the diagnosis result of the abnormality sign diagnosis device 300, for example, by displaying it on a display device in the cabin of the work machine 100, turning on an indicator lamp, outputting sound from a speaker, or transmitting it to the outside via a wireless communication device.

[0058] As described above, the work machine 100 of this embodiment includes the battery pack 210, which is a power storage device including a plurality of battery cells 212, the motor 204 driven by power supplied from the battery pack 210, and an abnormality sign diagnosis device 300 that diagnoses abnormality signs in the battery pack 210. When the battery pack 210 has a first state of charge SOC(1), the abnormality sign diagnosis device 300 obtains the maximum and minimum values ​​from among the voltage values ​​of the plurality of battery cells 212 as a first maximum voltage value Vmax(1) and a first minimum voltage value Vmin(1), respectively, and also obtains a first average voltage value Vave(1), which is the average value of the voltages of the respective battery cells 212. In addition, the abnormality precursor diagnosis device 300 obtains the maximum and minimum values ​​from the voltage values ​​of each battery cell 212 as a second maximum voltage value Vmax(2) and a second minimum voltage value Vmin(2), respectively, when the battery pack 210 is in a second state of charge SOC(2) that is higher than the first state of charge SOC(1), and also obtains a second average voltage value Vave(2), which is the average value of the voltages of each battery cell 212. Furthermore, the abnormality precursor diagnosis device 300 diagnoses abnormality precursors in the battery pack 210 using a first maximum value deviation ΔVmax(1) which is the deviation of the first maximum voltage value Vmax(1) from the first average voltage value Vave(1), a first minimum value deviation ΔVmin(1) which is the deviation of the first minimum voltage value Vmin(1) from the first average voltage value Vave(1), a second maximum value deviation ΔVmax(2) which is the deviation of the second maximum voltage value Vmax(2) from the second average voltage value Vave(2), and a second minimum value deviation ΔVmin(2) which is the deviation of the second minimum voltage value Vmin(2) from the second average voltage value Vave(2).

[0059] With this configuration, the work machine 100 of this embodiment can diagnose signs of abnormality in the battery pack 210, which is a power storage device, based on the first minimum voltage value Vmin(1), the first maximum voltage value Vmax(1), the first average voltage value Vave(1), the second minimum voltage value Vmin(2), the second maximum voltage value Vmax(2), and the second average voltage value Vave(2). Therefore, according to this embodiment, it is possible to provide a work machine 100 that can diagnose signs of abnormality in the battery pack 210 while reducing the amount of calculation compared to conventional methods.

[0060] Furthermore, in the work machine 100 of this embodiment, the abnormality sign diagnosis device 300 diagnoses a self-discharge abnormality in one of the multiple battery cells 212 as an abnormality sign when the value obtained by subtracting the first maximum value deviation ΔVmax(1) from the first minimum value deviation ΔVmin(1) is greater than the first voltage threshold Vth(1) and the value obtained by subtracting the second maximum value deviation ΔVmax(2) from the second minimum value deviation ΔVmin(2) is greater than the second voltage threshold Vth(2).

[0061] With this configuration, the work machine 100 of this embodiment can diagnose a self-discharge abnormality in one of the multiple battery cells 212 in the battery pack 210 as a sign of an abnormality in the battery pack 210 while reducing the amount of calculation compared to conventional methods.

[0062] Furthermore, in the work machine 100 of this embodiment, the abnormality sign diagnosis device 300 extracts a data log of the power storage device, i.e., the battery pack 210, when the work machine 100 is at rest. Furthermore, the abnormality sign diagnosis device 300 acquires the first minimum voltage value Vmin(1), the first maximum voltage value Vmax(1), the first average voltage value Vave(1), the second minimum voltage value Vmin(2), the second maximum voltage value Vmax(2), and the second average voltage value Vave(2) based on the extracted data log.

[0063] With this configuration, the work machine 100 of this embodiment can diagnose signs of abnormality in the battery pack 210 based on the data log of the battery pack 210 when the work machine 100 is at rest, when the current value I of the battery cell 212 is small and the impact of voltage drop is small. This can improve the accuracy of diagnosing signs of abnormality in the battery pack 210. More specifically, when the work machine 100 is working or charging, the current value I of the battery cell 212 becomes larger than when the work machine 100 is at rest, causing a voltage drop expressed as the product of the internal resistance value of the battery cell 212 and the current value I, and causing the voltage of the battery cell 212 to deviate from the open circuit voltage. Therefore, by diagnosing signs of abnormality in the battery pack 210 based on the data log of the battery pack 210 when the work machine 100 is at rest, when the voltage drop of the battery cell 212 is small, the accuracy of diagnosing signs of abnormality can be improved.

[0064] [Embodiment 2] A second embodiment of a work machine according to the present disclosure will be described below with reference to Figures 1 to 3, 5 and 6 of the first embodiment described above, and with reference to Figure 7. Figure 7 is a flow diagram illustrating the processing flow of the abnormality sign diagnosis device 300 of the work machine 100 according to this embodiment.

[0065] The work machine 100 of this embodiment differs from the work machine 100 of the first embodiment described above in the processing performed by the abnormality sign diagnosis device 300. The other configuration of the work machine 100 of this embodiment is similar to that of the work machine 100 of the first embodiment described above, so similar parts are given the same reference numerals and description thereof will be omitted. This embodiment is effective, for example, when a sufficient data log of the battery pack 210 in the range from the first state of charge SOC(1) to the second state of charge SOC(2) cannot be obtained when the work machine 100 is at rest.

[0066] The abnormality sign diagnosis device 300 of the first embodiment described above calculates the voltage values ​​of the first state of charge SOC(1) and the second state of charge SOC(2) of the battery pack 210 in steps P4 and P5 after steps P1 to P3 shown in Fig. 4, and diagnoses an abnormality sign of the battery pack 210 in steps P6 to P8. In contrast, in the processing flow of the abnormality sign diagnosis device 300 of the present embodiment shown in Fig. 7, steps P4a to P7a are different from the processing flow of the abnormality sign diagnosis device 300 of the first embodiment shown in Fig. 4.

[0067] In this embodiment, the abnormality sign diagnosis device 300 executes processes P1 to P3 similar to those of the abnormality sign diagnosis device 300 of the above-described embodiment 1, and then executes process P4a to determine whether the range of the state of charge of the battery pack 210 is appropriate for performing abnormality sign detection. In process P4a, the voltage value calculation unit 304 of the abnormality sign diagnosis device 300 determines whether the value obtained by subtracting the minimum value SOCmin from the maximum value SOCmax of the state of charge of the battery pack 210 extracted in the previous process P3 is equal to or greater than the state of charge threshold value SOCth.

[0068] Here, the state of charge threshold value SOCth depends on the open circuit voltage of the battery cell 212, but can be set to, for example, 5% or more. This makes it possible to suppress the influence of voltage sensor errors on the open circuit voltage of the battery cell 212 to a negligible level using the maximum value SOCmax and minimum value SOCmin of the state of charge of the battery pack 210. In process P4a, if the voltage value calculation unit 304 determines that the value obtained by subtracting the minimum value SOCmin from the maximum value SOCmax of the state of charge of the battery pack 210 is smaller than the state of charge threshold value SOCth (NO), it ends the process shown in Fig. 7 because it is difficult to ensure the accuracy of the abnormality prediction.

[0069] On the other hand, if in process P4a the voltage value calculation unit 304 determines that the value obtained by subtracting the minimum value SOCmin from the maximum value SOCmax of the state of charge of the battery pack 210 is equal to or greater than the state of charge threshold value SOCth (YES), it executes process P5a to calculate an approximate equation for voltage value changes. In process P5a, the voltage value calculation unit 304 calculates approximate equations fvmax, fvmin, and fvave for the maximum voltage value Vmax, the minimum voltage value Vmin, and the average voltage value Vave, respectively, as shown in Figures 5 and 6. Here, the approximate equations fvmax, fvmin, and fvave can be approximate equations based on linear approximation or polynomial approximation, and an approximation method such as regression analysis using the least squares method can be used.

[0070] Next, the voltage value calculation unit 304 executes a voltage value calculation P6a for a specific state of charge. Here, the specific state of charge is, for example, an arbitrarily selected state of charge SOC(s) as shown in Figures 5 and 6, but from the viewpoint of improving the accuracy of diagnosing abnormality signs, it is preferable that the specific state of charge is within or close to the range of the state of charge of the battery pack 210 used for analysis. In this process P6a, the voltage value calculation unit 304 first calculates the specific minimum voltage value Vmin(s), the specific maximum voltage value Vmax(s), and the specific average voltage value Vave(s) for the selected state of charge SOC(s) using the approximate formulas fvmax, fvmin, and fvave.

[0071] Next, the voltage value calculation unit 304 executes a determination process P7a for the specific state of charge. In this determination process P7a, the voltage value calculation unit 304 first calculates a specific maximum value deviation ΔVmax(s), which is the deviation of the specific maximum voltage value Vmax(s) from the specific average voltage value Vave(s), and a specific minimum value deviation ΔVmin(s), which is the deviation of the specific minimum voltage value Vmin(s) from the specific average voltage value Vave(s). Next, the voltage value calculation unit 304 determines whether the value obtained by subtracting the specific maximum value deviation ΔVmax(s) from the specific minimum value deviation ΔVmin(s) is greater than a specific voltage threshold value Vth(s).

[0072] For example, as shown in FIG. 5, when all the battery cells 212 in the battery pack 210 are normal, the voltage values ​​of the battery cells 212 are distributed approximately uniformly, so the specific minimum value deviation ΔVmin(s) and the specific maximum value deviation ΔVmax(s) are relatively small, and the difference between them is also small.

[0073] In such a case, in the determination process P7a, the voltage value calculation unit 304 determines that the difference obtained by subtracting the specific maximum value deviation ΔVmax(s) from the specific minimum value deviation ΔVmin(s) is equal to or less than the specific voltage threshold Vth(s) (NO). Then, the abnormality sign diagnosis device 300 ends the process flow shown in Fig. 7 without diagnosing the battery pack 210 for an abnormality sign. Note that the specific voltage threshold Vth(s) can be set in the same way as the first voltage threshold Vth(1).

[0074] 6, when a self-discharge abnormality occurs in one battery cell 212 of the battery pack 210, the voltage of that battery cell 212 drops significantly below the voltages of the other battery cells 212, resulting in a significant drop in the minimum voltage value Vmin. However, the voltages of the other battery cells 212 in the battery pack 210 do not drop as significantly, so the maximum voltage value Vmax does not change significantly from the normal state shown in FIG. 5, and the drop in the average voltage value Vave is also small.

[0075] In such a case, in determination process P7a, the voltage value calculation unit 304 determines that the value obtained by subtracting the specific maximum value deviation ΔVmax(s) from the specific minimum value deviation ΔVmin(s) is greater than the specific voltage threshold value Vth(s) (YES), and executes process P8 to diagnose the next abnormality sign. In this process P8, the abnormality sign diagnosis device 300 diagnoses that a self-discharge abnormality has occurred in one battery cell 212 of the battery pack 210, as in the above-mentioned first embodiment, and notifies, for example, an operator or manager of the work machine 100.

[0076] As described above, the work machine 100 of this embodiment, like the work machine 100 of embodiment 1, is equipped with a battery pack 210 which is a power storage device including a plurality of battery cells 212, a motor 204 which is driven by power supplied from the battery pack 210, and an abnormality sign diagnosis device 300 which diagnoses an abnormality sign of the battery pack 210. The abnormality sign diagnosis device 300 obtains the maximum and minimum values ​​from among the voltage values ​​of the plurality of battery cells 212 as a maximum voltage value Vmax and a minimum voltage value Vmin, respectively, in a plurality of states of charge of the battery pack 210, and also obtains an average voltage value Vave which is the average value of the voltages of the battery cells 212. The abnormality sign diagnosis device 300 calculates approximate expressions fvmax, fvmin, and fvave which indicate the relationship between the state of charge of the battery pack 210 and each of the maximum voltage value Vmax, minimum voltage value Vmin, and average voltage value Vave. The abnormality sign diagnosis device 300 uses approximate expressions fvmax, fvmin, and fvave to calculate a specific maximum voltage value Vmax(s), a specific minimum voltage value Vmin(s), and a specific average voltage value Vave(s), which are the maximum voltage value Vmax, the minimum voltage value Vmin, and the average voltage value Vave at a specific state of charge SOC(s) of the battery pack 210. When the value obtained by subtracting the specific maximum value deviation ΔVmax(s), which is the deviation of the specific maximum voltage value Vmax(s) from the specific average voltage value Vave(s), from the specific minimum value deviation ΔVmin(s), which is the deviation of the specific minimum voltage value Vmin(s) from the specific average voltage value Vave(s), is greater than a specific voltage threshold value Vth(s), the abnormality sign diagnosis device 300 diagnoses a self-discharge abnormality in one of the multiple battery cells 212 as an abnormality sign.

[0077] The work machine 100 of this embodiment can achieve the same effects as the work machine 100 of the above-described embodiment 1. Furthermore, according to this embodiment, even if it is not possible to acquire sufficient data logs of the battery pack 210 when the work machine 100 is in a dormant state, it is possible to diagnose a self-discharge abnormality in the battery cell 212 as a sign of an abnormality in the battery pack 210 by using the approximate expressions fvmax, fvmin, and fvave.

[0078] Furthermore, in the work machine 100 of this embodiment, when the abnormality sign diagnosis device 300 determines that a self-discharge abnormality has occurred in a battery cell 212 that constitutes the battery pack 210, the abnormality sign diagnosis device 300 may calculate the self-discharge rate of the battery cell 212 in which the self-discharge abnormality has occurred. More specifically, the voltage value calculation unit 304 of the abnormality sign diagnosis device 300 calculates the approximate expressions fvmax, fvmin, and fvave every day from the day the work machine 100 started to be used, for example, using daily log data of the battery pack 210.

[0079] Furthermore, the abnormality sign diagnosis device 300 uses the calculated approximate expressions fvmax, fvmin, fvave to calculate the specific maximum voltage value Vmax(s), the specific minimum voltage value Vmin(s), and the specific average voltage value Vave(s) at a specific state of charge SOC(s) of the battery pack 210. Furthermore, the abnormality sign diagnosis device 300 calculates the amount of change in the specific maximum voltage value Vmax(s), the specific minimum voltage value Vmin(s), and the specific average voltage value Vave(s) from the day the work machine 100 started to be used every day.

[0080] FIG. 8 is a graph showing an example of the number of days elapsed since the start of use of the work machine 100 and the amount of change in the specific maximum voltage value Vmax(s), the specific minimum voltage value Vmin(s), and the specific average voltage value Vave(s). In the example shown in FIG. 8, the specific minimum voltage value Vmin(s) is significantly lower than the specific maximum voltage value Vmax(s) and the specific average voltage value Vave(s). The voltage value calculation unit 304 can, for example, calculate the gradient of the specific minimum voltage value Vmin(s) in the graph shown in FIG. 8 as the self-discharge rate of the battery cell 212. Note that the abnormality sign diagnosis device 300 may use the self-discharge rate of the battery cell 212 to determine whether a self-discharge abnormality exists in the battery cell 212.

[0081] [Embodiment 3] Hereinafter, a third embodiment of a work machine according to the present disclosure will be described with reference to Figures 1 to 3 and 5 of the first embodiment described above, and with reference to Figures 9 to 12. Figures 9 to 11 are graphs of voltage and SOC when an abnormality occurs in the capacity decrease of the battery cell 212 of the battery pack 210 of Figure 2.

[0082] The work machine 100 of this embodiment differs from the work machine 100 of the previously described first embodiment in the processing performed by the abnormality sign diagnosis device 300. The other configuration of the work machine 100 of this embodiment is similar to that of the work machine 100 of the previously described first embodiment, so similar parts are given the same reference numerals and description thereof will be omitted. In the work machine 100 of this embodiment, the abnormality sign diagnosis device 300 diagnoses a capacity decrease abnormality in the battery cell 212 as a sign of an abnormality in the battery pack 210.

[0083] When a capacity drop abnormality occurs in one or more of the multiple battery cells 212 constituting the battery pack 210, the voltage change of the abnormal battery cell 212 in response to a change in the state of charge of the battery pack 210 becomes larger than the voltage change of the other normal battery cells 212. As a result, the maximum and minimum voltage values ​​Vmax and Vmin of the multiple battery cells 212 change from the normal state shown in Fig. 5 to, for example, as shown in Figs. 9 to 11.

[0084] Figure 12 is a flow diagram illustrating the flow of processing by the abnormality sign diagnosis device 300 in the work machine 100 of this embodiment. Processes P1 to P5 of the abnormality sign diagnosis device 300 of this embodiment are the same as processes P1 to P5 of the abnormality sign diagnosis device 300 of the above-described first embodiment shown in Figure 4. The abnormality sign diagnosis device 300 of this embodiment differs from the abnormality sign diagnosis device 300 of the above-described first embodiment in that it executes determination process P6b in the first and second states of charge after process P5.

[0085] In this determination process P6b, the voltage value calculation unit 304 determines whether the value obtained by subtracting the sum of the first maximum value deviation ΔVmax(1) and the second minimum value deviation ΔVmin(2) from the sum of the first minimum value deviation ΔVmin(1) and the second maximum value deviation ΔVmax(2) is greater than the voltage threshold value Vth. For example, as shown in FIG. 5, if no abnormality has occurred in the battery pack 210, the voltage value calculation unit 304 determines in process P6b shown in FIG. 12 that the value on the left side of the inequality is equal to or less than the voltage threshold value Vth (NO). In this case, the abnormality sign diagnosis device 300 ends the process flow shown in FIG. 12 without diagnosing the battery pack 210 for an abnormality sign.

[0086] 9 to 11, when a capacity drop abnormality has occurred in one or more battery cells 212 of the battery pack 210, the voltage value calculation unit 304 determines in process P6b shown in FIG. 12 that the value of the left side of the inequality is greater than the voltage threshold value Vth (YES). In this case, the abnormality sign diagnosis device 300 executes process P8 for diagnosing an abnormality sign. In process P8, the abnormality diagnosis unit 305 of the abnormality sign diagnosis device 300 diagnoses that a capacity drop abnormality has occurred in one or more battery cells 212 of the battery pack 210, as in the first embodiment. The voltage threshold value Vth can be determined in the same way as the first voltage threshold value Vth(1) and the second voltage threshold value Vth(2) in the first embodiment.

[0087] As described above, in the work machine 100 of this embodiment, the abnormality sign diagnosis device 300 diagnoses a capacity decrease abnormality in one or more of the multiple battery cells 212 as an abnormality sign when the value obtained by subtracting the sum of the first maximum value deviation ΔVmax(1) and the second minimum value deviation ΔVmin(2) from the sum of the first minimum value deviation ΔVmin(1) and the second maximum value deviation ΔVmax(2) is greater than the voltage threshold value Vth. Therefore, according to this embodiment, it is possible to provide a work machine 100 that is capable of diagnosing a capacity decrease abnormality in a battery cell 212 as an abnormality sign in the battery pack 210 while reducing the amount of calculation compared to conventional methods.

[0088] The abnormality sign diagnosis device 300 can also diagnose a capacity decrease abnormality in the battery cell 212 using, for example, the approximate expressions fvmax, fvmin, and fvave described in the above-mentioned embodiment 2. In this case, the abnormality sign diagnosis device 300 diagnoses a capacity decrease abnormality in the battery cell 212 when the gradients Svmax, Svmin, and Svave of the approximate expressions fvmax, fvmin, and fvave of the maximum voltage value Vmax, the minimum voltage value Vmin, and the average voltage value Vave, respectively, satisfy the following expression (4):

[0089] (Svmax+Svmin)-2×Svave>Sth ···(4)

[0090] In the above formula (4), Sth is a threshold value for determining a capacity decrease abnormality, and is calculated by the following formula (5).

[0091] Sth=Vth / {SOC(2)-SOC(1)} ···(5)

[0092] As described above, in the work machine 100 of this embodiment, the abnormality sign diagnosis device 300 can also diagnose a capacity decrease abnormality in one of the multiple battery cells 212 as a sign of an abnormality when the value obtained by subtracting twice the gradient Svave of the approximate equation fvave of the average voltage value Vave from the sum of the gradient Svmax of the approximate equation fvmax of the maximum voltage value Vmax and the gradient Svmin of the approximate equation fvmin of the minimum voltage value Vmin is greater than the threshold value Sth. Therefore, according to this embodiment, it is possible to provide a work machine 100 that is capable of diagnosing a capacity decrease abnormality in a battery cell 212 as a sign of an abnormality in the battery pack 210 while reducing the amount of calculation compared to conventional methods.

[0093] [Embodiment 4] Hereinafter, a fourth embodiment of a work machine according to the present disclosure will be described with reference to Figures 1 to 3 and 5 of the first embodiment described above, and with reference to Figures 13 to 15. Figures 13 and 14 are graphs of voltage and SOC when an abnormal increase in internal resistance occurs in the battery cell 212 of the battery pack 210 of Figure 2.

[0094] The work machine 100 of this embodiment differs from the work machine 100 of the previously described first embodiment in the processing performed by the abnormality sign diagnosis device 300. The other configuration of the work machine 100 of this embodiment is similar to that of the work machine 100 of the previously described first embodiment, so similar parts are given the same reference numerals and description thereof will be omitted. In the work machine 100 of this embodiment, the abnormality sign diagnosis device 300 diagnoses an increase in internal resistance of the battery cell 212 as a sign of an abnormality in the battery pack 210.

[0095] Suppose an internal resistance increase abnormality occurs in one battery cell 212 among the multiple battery cells 212 that make up the battery pack 210. Then, while the work machine 100 is working, as shown in Fig. 13, the voltage of the abnormal battery cell 212 in response to changes in the state of charge of the battery pack 210 drops significantly more than the voltage of the other normal battery cells 212. Furthermore, while the battery pack 210 is being charged, as shown in Fig. 14, the voltage of the abnormal battery cell 212 in response to changes in the state of charge of the battery pack 210 increases significantly more than the voltage of the other normal battery cells 212. This tendency becomes more pronounced as the current value I of the battery cell 212 increases.

[0096] Figure 15 is a flow diagram illustrating the flow of processing by the abnormality sign diagnosis device 300 in the work machine 100 of this embodiment. Processes P1, P2, P4, and P5 of the abnormality sign diagnosis device 300 of this embodiment are similar to processes P1, P2, P4, and P5 of the abnormality sign diagnosis device 300 of the above-described first embodiment shown in Figure 4. The abnormality sign diagnosis device 300 of this embodiment differs from processes P3, P6, and P7 of the abnormality sign diagnosis device 300 of the above-described first embodiment in data extraction process P3c after process P2 and determination processes P6c and P7c after process P5.

[0097] In process P3c shown in FIG. 15, the data extraction unit 303 extracts, for example, a data log of the battery pack 210 while the work machine 100 is working, and a data log of the battery pack 210 while it is being charged. As described above, the larger the current value I of the battery cell 212 in which an internal resistance increase abnormality has occurred, the greater the drop in voltage of the battery cell 212 while the work machine 100 is working, and the greater the increase in voltage of the battery cell 212 while the battery pack 210 is being charged. For this reason, the data extraction unit 303 may, for example, set an upper limit value of the current value I for extracting data while the work machine 100 is working, and a lower limit value of the current value I for extracting data while the battery pack 210 is being charged. This can improve the accuracy of determining an internal resistance increase abnormality in the battery cell 212.

[0098] 15, the voltage value calculation unit 304 uses the data log of the battery pack 210 while the work machine 100 is working to determine whether the following first and second conditions are met. The first condition is that the value obtained by subtracting the first maximum value deviation ΔVmax(1) from the first minimum value deviation ΔVmin(1) and dividing this value by the product of the absolute value of the current value I of the battery cell 212 and the internal resistance value Ri of the battery cell 212 is greater than the threshold value Qth. The second condition is that the value obtained by subtracting the second maximum value deviation ΔVmax(2) from the second minimum value deviation ΔVmin(2) and dividing this value by the product of the absolute value of the current value I of the battery cell 212 and the internal resistance value Ri of the battery cell 212 is greater than the threshold value Qth.

[0099] The threshold value Qth is, for example, a ratio that deviates from the variation in the internal resistance values ​​Ri of the multiple battery cells 212, and is set to, for example, a ratio of about 10%. The internal resistance value Ri of the battery cells 212 is a variable, and is calculated based on an internal resistance value map. The internal resistance value map is prepared in advance based on the temperature of the battery cells 212 and the state of charge of the battery pack 210, and the internal resistance value Ri of the battery cells 212 can be obtained from the temperature of the battery cells 212 and the state of charge of the battery pack 210. The voltage value calculation unit 304 may also perform the determination in process P6c on the data of all SOCs during work of the work machine 100 extracted in the above-mentioned process P3c.

[0100] In process P6c shown in Fig. 15, if the voltage value calculation unit 304 determines that the condition is not met (NO), the abnormality sign diagnosis device 300 ends the process flow shown in Fig. 15 without diagnosing an abnormality sign. On the other hand, in process P6c, if the voltage value calculation unit 304 determines that the condition is met (YES), the abnormality sign diagnosis device 300 executes the next process P7c.

[0101] 15, the voltage value calculation unit 304 uses the data log during charging of the battery pack 210 to determine whether the following first and second conditions are met. The first condition is that the value obtained by subtracting the first minimum deviation ΔVmin(1) from the first maximum deviation ΔVmax(1) and dividing the result by the product of the absolute value of the current value I of the battery cell 212 and the internal resistance value Ri of the battery cell 212 is greater than a threshold value Qth. The second condition is that the value obtained by subtracting the second minimum deviation ΔVmin(2) from the second maximum deviation ΔVmax(2) and dividing the result by the product of the absolute value of the current value I of the battery cell 212 and the internal resistance value Ri of the battery cell 212 is greater than a threshold value Qth. The voltage value calculation unit 304 may also perform the determination in process P7c on the data of all SOCs during charging of the battery pack 210 extracted in the above-mentioned process P3c.

[0102] In process P7c shown in Fig. 15, if the voltage value calculation unit 304 determines that the condition is not met (NO), the abnormality sign diagnosis device 300 ends the process flow shown in Fig. 15 without diagnosing an abnormality sign. On the other hand, in process P7c, if the voltage value calculation unit 304 determines that the condition is met (YES), the abnormality sign diagnosis device 300 executes the next process P8. In process P8, the abnormality diagnosis unit 305 of the abnormality sign diagnosis device 300 diagnoses that an internal resistance increase abnormality has occurred in one battery cell 212 of the battery pack 210, as in the first embodiment described above.

[0103] As described above, in the work machine 100 of this embodiment, the abnormality sign diagnosis device 300 extracts data logs of the battery pack 210 when the work machine 100 is working and when the battery pack 210, which is a power storage device, is being charged. The abnormality sign diagnosis device 300 diagnoses an internal resistance increase abnormality in one of the multiple battery cells 212 when all of the following first to fourth conditions are met. The first condition is that, in the data log during work, the value obtained by subtracting the first maximum value deviation ΔVmax(1) from the first minimum value deviation ΔVmin(1) and dividing the value by the product of the absolute value of the current value I of the multiple battery cells 212 and the internal resistance value Ri of one of the multiple battery cells 212 is greater than the threshold value Qth. The second condition is that, in the data log during work, the value obtained by subtracting the second maximum value deviation ΔVmax(2) from the second minimum value deviation ΔVmin(2) and dividing the value by the product of the absolute value of the current value I and the internal resistance value Ri is greater than the threshold value Qth. The third condition is that, in a data log during charging, the value obtained by subtracting the first minimum deviation ΔVmin(1) from the first maximum deviation ΔVmax(1) and dividing the value by the product of the absolute value of the current value I and the internal resistance value Ri is greater than the threshold value Qth. The fourth condition is that, in a data log during charging, the value obtained by subtracting the second minimum deviation ΔVmin(2) from the second maximum deviation ΔVmax(2) and dividing the value by the product of the absolute value of the current value I and the internal resistance value Ri is greater than the threshold value Qth.

[0104] According to this embodiment, it is possible to provide a work machine 100 that can diagnose an abnormal increase in the internal resistance of a battery cell 212 as a sign of an abnormality in the battery pack 210 while reducing the amount of calculation compared to conventional methods.

[0105] The above has described in detail an embodiment of a work machine according to the present disclosure using the drawings, but the specific configuration is not limited to this embodiment, and even if there are design changes and the like within the scope that does not deviate from the gist of the present disclosure, they are also included in the present disclosure. [Explanation of symbols]

[0106] 100 Work Machinery 204 Motor 210 Battery pack (electricity storage device) 212 battery cells 300 Abnormality Prediction Diagnostic Device fvmax approximation formula fvmin approximation formula fvave approximation formula I Current value Qth Threshold Ri Internal resistance value SOC(1) 1st State of Charge SOC(2) Second State of Charge Sth threshold Svmax gradient Svmin gradient Svave gradient Vave(1) First average voltage value Vave(2) Second average voltage value Vave(s) Specific average voltage value Vmax(1) First maximum voltage value Vmax(2) Second maximum voltage value Vmax(s) Specific maximum voltage value Vmin(1) First minimum voltage value Vmin(2) Second minimum voltage value Vmin(s) Specific minimum voltage value Vth voltage threshold Vth(1) First voltage threshold Vth(2) Second voltage threshold Vth(s) Specific voltage threshold ΔVmax(1) First maximum value deviation ΔVmax(2) Second maximum deviation ΔVmax(s) Deviation from specific maximum value ΔVmin(1) First minimum deviation ΔVmin(2) Second minimum deviation ΔVmin(s) Specific minimum deviation

Claims

1. A work machine comprising: a power storage device including a plurality of battery cells; a motor driven by electric power supplied from the power storage device; and an abnormality sign diagnosis device that diagnoses abnormality signs in the power storage device, The abnormality sign diagnosis device includes: In a first charging state of the power storage device, a maximum value and a minimum value are obtained from among the voltage values ​​of the plurality of battery cells as a first maximum voltage value and a first minimum voltage value, respectively, and a first average voltage value is obtained as an average value of the voltages of the battery cells; in a second state of charge of the power storage device that is higher than the first state of charge, acquiring a maximum value and a minimum value from among the voltage values ​​of each of the battery cells as a second maximum voltage value and a second minimum voltage value, respectively, and acquiring a second average voltage value that is an average value of the voltages of each of the battery cells; a first maximum value deviation which is a deviation of the first maximum voltage value from the first average voltage value, a first minimum value deviation which is a deviation of the first minimum voltage value from the first average voltage value, a second maximum value deviation which is a deviation of the second maximum voltage value from the second average voltage value, and a second minimum value deviation which is a deviation of the second minimum voltage value from the second average voltage value.

2. 2. The work machine according to claim 1, wherein the abnormality sign diagnosis device diagnoses a self-discharge abnormality in one of the plurality of battery cells as the abnormality sign when a value obtained by subtracting the first maximum deviation from the first minimum deviation is greater than a first voltage threshold value, and a value obtained by subtracting the second maximum deviation from the second minimum deviation is greater than a second voltage threshold value.

3. 3. The work machine according to claim 2, wherein the abnormality sign diagnosis device extracts a data log of the power storage device when the work machine is at rest, and acquires the first minimum voltage value, the first maximum voltage value, the first average voltage value, the second minimum voltage value, the second maximum voltage value, and the second average voltage value based on the extracted data log.

4. 2. The work machine according to claim 1, wherein the abnormality sign diagnosis device diagnoses a capacity decrease abnormality in one or more of the plurality of battery cells as the abnormality sign when a value obtained by subtracting the sum of the first maximum value deviation and the second minimum value deviation from the sum of the first minimum value deviation and the second maximum value deviation is greater than a voltage threshold value.

5. The abnormality sign diagnosis device includes: extracting a data log of the power storage device when the work machine is operating and when the power storage device is being charged; In the data log during the operation, a value obtained by subtracting the first maximum deviation from the first minimum deviation and dividing the value by the product of the absolute value of the current values ​​of the plurality of battery cells and the internal resistance value of one of the plurality of battery cells is greater than a threshold value, and a value obtained by subtracting the second maximum deviation from the second minimum deviation and dividing the value by the product of the absolute value of the current values ​​and the internal resistance value is greater than the threshold value, In the data log during charging, if a value obtained by subtracting the first minimum deviation from the first maximum deviation and dividing the value by the product of the absolute value of the current value and the internal resistance value is greater than the threshold value, and a value obtained by subtracting the second minimum deviation from the second maximum deviation and dividing the value by the product of the absolute value of the current value and the internal resistance value is greater than the threshold value, 2. The work machine according to claim 1, wherein the one battery cell is diagnosed for an abnormal increase in internal resistance.

6. A work machine comprising: a power storage device including a plurality of battery cells; a motor driven by electric power supplied from the power storage device; and an abnormality sign diagnosis device that diagnoses abnormality signs in the power storage device, The abnormality sign diagnosis device includes: a maximum voltage value and a minimum voltage value are respectively obtained from the voltage values ​​of the plurality of battery cells in a plurality of charging states of the power storage device, and an average voltage value is obtained which is an average value of the voltages of the respective battery cells; calculating an approximate expression indicating a relationship between the state of charge of the power storage device and each of the maximum voltage value, the minimum voltage value, and the average voltage value; A work machine characterized in that the approximate expression is used to diagnose signs of abnormality in the power storage device.

7. the abnormality sign diagnosis device calculates a specific maximum voltage value, a specific minimum voltage value, and a specific average voltage value, which are the maximum voltage value, the minimum voltage value, and the average voltage value in a specific state of charge of the power storage device, using the approximation formula; 7. The working machine according to claim 6, wherein when a value obtained by subtracting a specific maximum value deviation, which is the deviation of the specific maximum voltage value from the specific average voltage value, from a specific minimum value deviation, which is the deviation of the specific minimum voltage value from the specific average voltage value, is greater than a specific voltage threshold value, a self-discharge abnormality of one of the plurality of battery cells is diagnosed as a sign of an abnormality.

8. 7. The work machine according to claim 6, wherein the abnormality sign diagnosis device diagnoses a capacity decrease abnormality in one of the plurality of battery cells as an abnormality sign when a value obtained by subtracting twice the gradient of the gradient of the approximation equation for the average voltage value from the sum of the gradient of the approximation equation for the maximum voltage value and the gradient of the approximation equation for the minimum voltage value is greater than a threshold value.

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