Operation support device and operation support method for electric power machinery

Abstract

To provide an operation support device that can suppress battery deterioration in an electrically-driven work machine while extending its life.SOLUTION: An operation support device 1 allocates a plurality of electrically-driven work machines EWM1, EWM2, ..., EWMn to a plurality of operation sites WS1, WS2, ..., WSn, respectively. The operation support device 1 comprises: an information storage unit 11 that stores information on an operation load at each operation site WS; a state acquisition unit 12 that acquires a deterioration state of a battery B incorporated in each electrically-driven work machine EWM; a state prediction unit 13 that calculates a predicted deterioration state of the battery B for a case where each electrically-driven work machine EWM operates in its operation site WS; and an operation planning unit 14. The operation planning unit 14 selects operation sites WS in descending order of operation load on the basis of the information stored by the information storage unit 11, and allocates a specific electrically-driven work machine EWM to the selected operation site WS on the basis of the predicted deterioration state from among the plurality of electrically-driven work machines EWM1, EWM2, ..., EWMn.SELECTED DRAWING: Figure 1A

Description

Technical Field

[0001] The present disclosure relates to an operation support device and an operation support method for a power-operated work machine.

Background Art

[0002] Conventionally, an invention related to a power supply system for a construction machine equipped with a battery-driven electric motor has been known (see Patent Document 1 below). The power supply system for a construction machine described in Patent Document 1 includes a plurality of towed vehicles, at least one construction machine, charging equipment, at least one transport vehicle, and a computer (paragraph 0009 and claim 1).

[0003] Each of the plurality of towed vehicles is equipped with a battery. The at least one construction machine has an electric motor driven by the power of the battery of one of the plurality of towed vehicles that it towes, and a hydraulic pump driven by the electric motor. The charging equipment is equipment for charging the batteries mounted on the plurality of towed vehicles.

[0004] The at least one transport vehicle towes at least one charged towed vehicle charged by the charging equipment to the vicinity of any one of the at least one construction machine, and instead of the charged towed vehicle, it towes the towed vehicle that was being towed by the construction machine to the charging equipment.

[0005] The computer predicts the number of replacements of the towed vehicles to be replaced by the construction machine and the respective replacement times during a predetermined period of the at least one construction machine based on the work amount plan during the predetermined period. Further, the computer calculates the time when the at least one transport vehicle arrives by towing a charged towed vehicle to the vicinity of the construction machine so as to be in time for each replacement time.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

[0007] According to the conventional power supply system for construction machinery described above, the time for battery replacement can be estimated based on the work volume plan of the construction machinery, thereby improving the work efficiency of the construction machinery and the battery utilization efficiency (Patent Document 1, paragraph 0010). However, this conventional system does not take into account the variability in the rate of battery degradation of each construction machine, so there is room for improvement in terms of suppressing battery degradation and extending battery life.

[0008] This disclosure provides an operation support device and operation support method for electric power machinery that can suppress battery degradation and extend the lifespan of electric power machinery batteries. [Means for solving the problem]

[0009] One aspect of the present disclosure is an operation support device for electric work machines, comprising a server for assigning a plurality of electric work machines to a plurality of work sites, wherein the server comprises: an information storage unit for storing information on the workload of each of the plurality of work sites; a state acquisition unit for acquiring the degradation status of batteries mounted on each of the plurality of electric work machines; a state prediction unit for calculating the predicted degradation status of the batteries when each of the plurality of electric work machines works at any of the plurality of work sites; and an operation planning unit for selecting work sites in descending order of workload based on the workload information stored in the information storage unit, and assigning a specific electric work machine from among the plurality of electric work machines in the order of the selected work sites, based on the degradation status of the batteries acquired by the state acquisition unit and the predicted degradation status of the batteries calculated by the state prediction unit. [Effects of the Invention]

[0010] This device provides operational support for electric power machinery, enabling the suppression of battery degradation and extending the lifespan of electric power machinery batteries. [Brief explanation of the drawing]

[0011] [Figure 1A] A schematic diagram showing Embodiment 1 of the operational support device related to this disclosure. [Figure 1B] A flowchart showing an example of the operation of the operational support device shown in Figure 1A. [Figure 2] A flowchart illustrating Embodiment 2 of the driver assistance device relating to this disclosure. [Figure 3] A flowchart illustrating Embodiment 3 of the driver assistance device relating to this disclosure. [Figure 4] A flowchart illustrating Embodiment 4 of the driver assistance device relating to this disclosure. [Figure 5] A flowchart illustrating Embodiment 5 of the driver assistance device relating to this disclosure. [Modes for carrying out the invention]

[0012] Hereinafter, embodiments of the operation support device and operation support method for electric power machinery relating to this disclosure will be described with reference to the drawings.

[0013] [Embodiment 1] Figure 1A is a schematic diagram showing Embodiment 1 of the operation support device for electric power machinery according to the present disclosure. The operation support device 1 of this embodiment is a server connected to a network N such as the Internet, and is composed of a computer equipped with a central processing unit (CPU) and a storage device (not shown). The operation support device 1 is a device that assigns each of the multiple electric power machinery EWM1, EWM2, ..., EWMn to each of the multiple work sites WS1, WS2, ..., WSn.

[0014] Each electric work machine (EWM) is equipped with, for example, a battery B, and generates driving force by rotating a motor using the power stored in battery B. The multiple electric work machines EWM1, EWM2, ..., EWMn include, for example, at least one of an electric hydraulic excavator, an electric dump truck, or an electric material handling machine. The multiple electric work machines EWM1, EWM2, ..., EWMn are on standby at, for example, a machine station MS before being assigned to their respective work sites WS by the operation support device 1.

[0015] Each electric work machine (EWM) includes, for example, a communication device such as a wireless communication device or a satellite communication device (not shown), and a controller (not shown) for calculating the degradation state of battery B. The degradation state of battery B can be expressed as, for example, the "State of Health (SOH)," which is the ratio of the full charge capacity at the time of degradation to the initial full charge capacity of battery B. The degradation state SOH of battery B is expressed as a percentage with the initial full charge capacity set at 100, and decreases as battery B degrades. The degradation state SOH may also be calculated based on, for example, the charge / discharge capacity of battery B during operation of the electric work machine (EWM) or the charge capacity charged to battery B by a charging device.

[0016] The communication device mounted on each electric work machine (EWM) is connected to the operation support device 1 via, for example, a wireless communication line RCL including a wireless base station WBS, or a satellite communication line SCL including a communication satellite CS and satellite base station SBS, and network N. The controller mounted on each electric work machine (EWM) transmits, for example, the degradation status (SOH) of the battery B mounted on each electric work machine (EWM) to the operation support device 1 via the communication device mounted on each electric work machine (EWM).

[0017] The operation support device 1 includes, for example, an information storage unit 11, a state acquisition unit 12, a state prediction unit 13, and an operation plan unit 14. Each part of the operation support device 1 represents each function of the operation support device 1 realized by executing a program stored in the storage device of the operation support device 1 by, for example, the CPU of the operation support device 1. Hereinafter, referring to FIG. 1B, the operation of the operation support device 1 of the present embodiment and the operation support method OSM of the electric working machine EWM of the present embodiment will be described.

[0018] FIG. 1B is a flowchart showing the operation of the operation support device 1 shown in FIG. 1A and is a flowchart for explaining each step of the operation support method OSM of the present embodiment. The operation support method OSM of the present embodiment is, for example, a method of using the operation support device 1 to allocate each of a plurality of electric working machines EWM1, EWM2,..., EWMn to each of a plurality of work sites WS1, WS2,..., WSn.

[0019] When the operation support device 1 starts the operation support method OSM shown in FIG. 1B, first, it executes an information storage step S1. In this step S1, the information storage unit 11 stores, for example, information on the work load of each work site WS. The information on the work load is input, for example, from an information terminal (not shown) owned by the user of the electric working machine EWM to the input / output unit of the operation support device 1 via the network N and is stored in the storage device of the operation support device 1 by the information storage unit 11. An example of the information on the work load of each work site WS stored by the information storage unit 11 is shown in Table 1A below.

[0020]

Table 1A

[0021] In the example shown in Table 1A, the workload information includes, for example, the number of working days, the average temperature during operation of the electric power machine EWM (daytime), the average temperature during non-operation of the electric power machine EWM (nighttime), the amount of charge / discharge per day, the discharge power, and the charging power. Alternatively, the workload information may include, for example, the discharge current and the charging current instead of the discharge power and charging power. Next, the operation support device 1 performs a state acquisition step S2 to acquire the state of deterioration (SOH) of the battery B installed in each electric power machine EWM.

[0022] In step S2, the status acquisition unit 12 of the operation support device 1 acquires the state of deterioration (SOH) of the battery B installed in each electric work machine EWM. The controller of each electric work machine EWM acquires the state of deterioration (SOH) of the battery B installed in each electric work machine EWM at predetermined intervals, for example, and transmits it to the operation support device 1 via a communication device, a wireless communication line RCL or a satellite communication line SCL, and the network N. The status acquisition unit 12 acquires the state of deterioration (SOH) of the battery B of each electric work machine EWM transmitted from each electric work machine EWM via the input / output unit of the operation support device 1.

[0023] Next, the operation support device 1 executes a work feasibility prediction step S3, which predicts whether each electric work machine EWM can operate. In this step S3, the state prediction unit 13 predicts whether each electric work machine EWM can operate at each work site WS based on, for example, the workload information of each work site WS stored by the information storage unit 11.

[0024] More specifically, the state prediction unit 13 retrieves, for example, the specifications of the battery B of each electric power machine EWM stored in the memory device by the information storage unit 11, and compares them with the daily charge / discharge amount and discharge power required at each work site WS. As a result, if the battery B of a certain electric power machine EWM cannot satisfy the daily charge / discharge amount and discharge power required at a certain work site WS, the unit records that the electric power machine EWM is not compatible with that work site WS.

[0025] Next, the operation support device 1 executes a state prediction step S4 to calculate the predicted degradation state ESOH of the battery B. In this step S4, the state prediction unit 13 of the operation support device 1 calculates the predicted degradation state ESOH of the battery B when each electric work machine EWM performs work at each work site WS. The predicted degradation state ESOH is the predicted value of the degradation state SOH of the battery B of each electric work machine EWM after each electric work machine EWM has performed predetermined work at each work site WS.

[0026] The state prediction unit 13 predicts the predicted degradation state ESOH of the battery B of each electric power machine EWM, using all or some of the work load items shown in Table 1A above as degradation factors. More specifically, the state prediction unit 13 calculates the predicted degradation state ESOH using, for example, the following equations (1) to (3).

[0027]

number

[0028]

number

[0029]

number

[0030] In equation (1) above, a_st is the storage degradation rate of battery B. 0.5 ] (d: day). Also, in equation (1), T_st is the conserved absolute temperature [K], SOC_st is the conserved SOC (0 ≤ SOC_st < 1), a0 to a2 are coefficients, and a3 is a constant.

[0031] Furthermore, in equation (2) above, a_cyc is the degradation rate of battery B [ / d]. Also, on the right side of equation (2), T_op is the average operating temperature [°C], D_day is the daily discharge amount, Dp is the discharge power, and Cp is the charging power. Also, Q_bat is the battery capacity of battery B, a4 to a7 are coefficients, and a8 is a constant.

[0032] Furthermore, in equation (3) above, ESOH is the predicted degradation state [%]. Also, a_st on the right side of equation (3) is the storage degradation rate of battery B obtained in equation (1) above [ / d 0.5 ], where a_cyc is the degradation rate of battery B obtained in equation (2) above [ / day], and d is the number of operating days of the electric power machine EWM.

[0033] In other words, the predicted degradation state ESOH is calculated based on the storage degradation rate a_st, which is the rate of degradation of battery B during storage or inactivity without charging or discharging, and the degradation rate a_cyc, which is the rate of cycle degradation of battery B due to charging and discharging. Note that the coefficients a0, a1, a3 to a7, and constants a3 and a8 in equations (1) and (2) differ depending on the battery B. Therefore, these coefficients and constants for each individual battery B are stored in a memory device in advance by, for example, the information storage unit 11 of the operation support device 1.

[0034] Next, the operation support device 1 executes the operation plan creation process S5, which assigns electric work machines (EWMs) to each work site WS. In this process S5, the operation planning unit 14 of the operation support device 1 first selects work sites WS in descending order of workload based on the workload information for each work site WS stored in the storage device (see Table 1A).

[0035] In this step S5, the operation planning unit 14 further assigns a specific electric power machine EWM to the selected work site WS based on the predicted deterioration state ESOH from among multiple electric power machine EWMs. An example of the procedure by which the operation planning unit 14 assigns each electric power machine EWM to each work site WS will be explained with reference to Table 1B below.

[0036] [Table 1B]

[0037] In Table 1B, Machine No. is, for example, the identification number of each of the multiple electric power machines EWM1, EWM2, ..., EWMn. Battery Manufacturer, Battery Capacity, and SOH are, for example, the manufacturer, battery capacity, and current state of degradation (SOH) of Battery B installed in each electric power machine EWM.

[0038] ESOH is the predicted degradation state of battery B after each electric power machine EWM has performed a predetermined task at each work site WS. The predicted degradation state ESOH is calculated for each combination of multiple work sites WS1, WS2, ..., WSn, selected from left to right in order of increasing workload, and each combination of multiple electric power machines EWM1, EWM2, ..., EWMn.

[0039] ΔSOH is the amount of battery degradation calculated based on the current degradation state SOH and predicted degradation state ESOH of the battery B of each electric power machine EWM. Specifically, the battery degradation amount ΔSOH is the change in degradation state SOH from the current degradation state SOH of the battery B of each electric power machine EWM to the predicted degradation state ESOH when each electric power machine EWM operates at each work site WS.

[0040] In other words, the operation planning unit 14 calculates the amount of battery degradation ΔSOH when each electric work machine EWM operates at each work site WS, based on the degradation state SOH and predicted degradation state ESOH of battery B. Furthermore, the operation planning unit 14 selects, for example, the work site WS1 with the highest workload, and assigns to that work site WS1 the electric work machine EWM2 from among the multiple electric work machines EWM1, EWM2, ..., EWMn, which has a battery degradation amount ΔSOH of -5[%] and the smallest decrease in the degradation state SOH.

[0041] In the example shown in Table 1B, the electric power machine EWM3, machine No. 3, is predicted to be unsuitable (NA) for work at work site WS1 in step S3, which predicts the feasibility of the electric power machine EWM. In this case, the operation planning unit 14 does not assign the electric power machine EWM3 to work site WS1.

[0042] After the completion of step S5, the operation planning unit 14 executes step S6, for example, to determine whether the assignment of all electric power tools (EWMs) to the work sites (WSs) has been completed. In step S6, if the operation planning unit 14 determines that the assignment of at least one electric power tool (EWM) has not been completed (NO), it repeats step S5 to assign each electric power tool (EWM) to each work site (WS).

[0043] In the second step S5, the operation planning unit 14 selects the work site WS2 with the second highest workload. The operation planning unit 14 then assigns to the selected work site WS2 the electric work machine EWMn from among several electric work machines EWM1, EWM3, ..., EWMn, excluding the electric work machine EWM2 assigned to work site WS1, the electric work machine EWMn with a battery degradation amount ΔSOH of -5[%] and the smallest decrease in the degradation state SOH.

[0044] Similarly, in the third step S5, the electric power machine EWM1 is assigned to the work site WS3, which has the third highest workload, and in the fourth step S5, the electric power machine EWM3 is assigned to the work site WS4, which has the fourth highest workload. Subsequently, in step S6, when the operation planning unit 14 determines that the assignment of electric power machines EWM to all work sites WS has been completed (YES), it terminates the operation support method OSM shown in Figure 1B.

[0045] The operation planning unit 14 creates an operation plan for each electric power work machine (EWM), including information on each work site WS assigned to each EWM, and transmits it to the user's information terminal for the EWM via the network N. The operation planning unit 14 also transmits information on each work site WS assigned to each EWM to each EWM via the network N and the satellite communication line SCL or wireless communication line RCL.

[0046] The operation of the operation support device 1 and operation support method OSM of the electric work machine EWM of this embodiment will be described below.

[0047] The operation support device 1 of this embodiment is a device equipped with a server that assigns multiple electric power machines EWM1, EWM2, ..., EWMn to multiple work sites WS1, WS2, ..., WSn. The server comprises an information storage unit 11, a status acquisition unit 12, a status prediction unit 13, and an operation planning unit 14. The information storage unit 11 stores information on the workload of each of the multiple work sites WS1, WS2, ..., WSn. The status acquisition unit 12 acquires the degradation status of the batteries B installed in each of the multiple electric power machines EWM1, EWM2, ..., EWMn. The status prediction unit 13 calculates the predicted degradation status ESOH of the batteries B when each of the multiple electric power machines EWM1, EWM2, ..., EWMn works at any of the multiple work sites WS1, WS2, ..., WSn. The operation planning unit 14 selects work sites WS in descending order of workload based on the workload information stored in the information storage unit 11, and assigns a specific electric work machine EWM from among multiple electric work machines EWM1, EWM2, ..., EWMn in the order of the selected work sites WS, based on the degradation status of battery B acquired by the status acquisition unit 12 and the predicted degradation status ESOH of battery B calculated by the status prediction unit 13.

[0048] In other words, the operation support device 1 of this embodiment is a server that assigns each of the multiple electric power machines EWM1, EWM2, ..., EWMn to each of the multiple work sites WS1, WS2, ..., WSn, as described above. The operation support device 1 comprises an information storage unit 11, a status acquisition unit 12, a status prediction unit 13, and an operation planning unit 14. The information storage unit 11 stores information on the workload of each work site WS. The status acquisition unit 12 acquires the degradation state SOH of the battery B installed in each electric power machine EWM. The status prediction unit 13 calculates the predicted degradation state ESOH of the battery B when each electric power machine EWM works at each work site WS. The operation planning unit 14 selects work sites WS in order of highest workload based on the information stored by the information storage unit 11, and assigns a specific work site WS to the selected work site WS from among the multiple electric power machines EWM based on the predicted degradation state ESOH.

[0049] With this configuration, the operation support device 1 of this embodiment can sequentially assign an appropriate electric power machine EWM to a plurality of work sites WS1, WS2, ..., WSn, from among a plurality of electric power machines EWM1, EWM2, ..., EWMn, based on the predicted degradation state ESOH of the battery B, from work sites WS with high workload to work sites WS with low workload. This prevents the degradation state SOH of the battery B from dropping drastically in each electric power machine EWM, thereby suppressing battery B degradation and extending its lifespan.

[0050] Furthermore, in the operation support device 1 of this embodiment, the operation planning unit 14 calculates the battery degradation amount ΔSOH when multiple electric work machines EWM1, EWM2, ..., EWMn each work at multiple work sites WS1, WS2, ..., WSn, based on the degradation state SOH of battery B and the predicted degradation state ESOH of battery B. The operation planning unit 14 also selects the electric work machine EWM with the smallest battery degradation amount ΔSOH from among the multiple electric work machines EWM1, EWM2, ..., EWMn as a specific electric work machine WS.

[0051] In other words, in the operation support device 1 of this embodiment, the operation planning unit 14 calculates the battery degradation amount ΔSOH when each electric work machine EWM works at each work site WS, based on the degradation state SOH and predicted degradation state ESOH of the battery B, for example as shown in Table 1B. Furthermore, the operation planning unit 14 assigns the electric work machine EWM with the smallest battery degradation amount ΔSOH from among a plurality of electric work machines EWM1, EWM2, ..., EWMn to the work sites WS selected in order of highest workload.

[0052] With this configuration, the operation support device 1 of this embodiment can minimize the decrease in the state of deterioration (SOH) of the battery B after each electric work machine EWM has performed work at each work site WS. As a result, the deterioration of multiple batteries B installed in multiple electric work machines EWM1, EWM2, ..., EWMn can be suppressed overall, and the overall lifespan of these batteries B can be extended.

[0053] Furthermore, the OSM operation support method of this embodiment is a method for assigning multiple electric work machines EWM1, EWM2, ..., EWMn to multiple work sites WS1, WS2, ..., WSn. As described above, the OSM operation support method of this embodiment includes an information storage step S1, a state acquisition step S2, a state prediction step S4, and an operation plan creation step S5. The information storage step S1 is a step of storing information on the workload of each of the multiple work sites WS1, WS2, ..., WSn. The state acquisition step S2 is a step of acquiring the state of deterioration (SOH) of the batteries B installed in each of the multiple electric work machines EWM1, EWM2, ..., EWMn. The state prediction step S4 is a step of calculating the predicted state of deterioration (ESOH) of the batteries B when each of the multiple electric work machines EWM1, EWM2, ..., EWMn works at any of the multiple work sites WS1, WS2, ..., WSn. The operation plan creation process S5 is a process in which, based on the workload information stored in the information storage process S1, work sites WS are selected in descending order of workload, and then, in the order of the selected work sites WS, a specific electric work machine EWM is assigned from among multiple electric work machines EWM1, EWM2, ..., EWMn based on the degradation state SOH of battery B obtained in the state acquisition process S2 and the predicted degradation state ESOH of battery B calculated in the state prediction process S4.

[0054] In other words, the OSM operation support method of this embodiment is a method of assigning each of the multiple electric work machines EWM1, EWM2, ..., EWMn to each of the multiple work sites WS1, WS2, ..., WSn. As described above, the OSM operation support method of this embodiment has an information storage step S1, a state acquisition step S2, a state prediction step S4, and an operation plan creation step S5. The information storage step S1 is a step of storing information on the workload of each work site WS. The state acquisition step S2 is a step of acquiring the deterioration state SOH of the battery B mounted on each electric work machine EWM. The state prediction step S4 is a step of calculating the predicted deterioration state ESOH of the battery B when each electric work machine EWM works at each work site WS. The operation plan creation process S5 is a process in which work sites WS are selected in order of highest workload based on the information stored in the information storage process S1, and a specific electric work machine EWM is assigned to the selected work sites WS from among multiple electric work machines EWM1, EWM2, ..., EWMn based on the predicted degradation state ESOH of battery B.

[0055] With this configuration, according to the OSM operation support method of this embodiment, for multiple work sites WS1, WS2, ..., WSn, an appropriate electric work machine EWM1, EWM2, ..., EWMn can be sequentially assigned to work sites WS with high workloads, from work sites WS with low workloads, based on the predicted degradation state ESOH of the battery B. This prevents the degradation state SOH of the battery B from dropping drastically in each electric work machine EWM, thereby suppressing battery B degradation and extending its lifespan.

[0056] As described above, according to this embodiment, it is possible to provide an operation support device 1 and operation support method OSM for an electric power machine that can suppress the degradation of the battery B of the electric power machine EWM and extend its lifespan.

[0057] [Embodiment 2] Hereinafter, with reference to Figure 1A, Figure 2, and Table 2, Embodiment 2 of the operation support device and operation support method for electric power machinery according to this disclosure will be described. Figure 2 is a flowchart illustrating the operation of the operation support device 1 of this embodiment, and a flowchart illustrating each step of the operation support method OSM2 of this embodiment.

[0058] The operation support device 1 and operation support method OSM2 of this embodiment differ from the operation support device 1 and operation support method OSM of Embodiment 1 in the operation plan creation process S5a. Other aspects of the operation support device 1 and operation support method OSM2 of this embodiment are the same as those of the operation support device 1 and operation support method OSM of Embodiment 1, so the same reference numerals are used for the same parts and their description is omitted.

[0059] In this embodiment, when the operation support device 1 starts the operation support method OSM2 shown in Figure 2, it executes the information storage process S1 to the state prediction process S4, similar to the operation support method OSM described above. As a result, as shown in Table 2, the predicted degradation state ESOH and battery degradation amount ΔSOH are calculated for each of the multiple electric work machines EWM1, EWM2, ..., EWMn when they are working at each of the multiple work sites WS1, WS2, ..., WSn.

[0060] [Table 2]

[0061] Subsequently, the operation planning unit 14 of the operation support device 1 executes the operation plan creation process S5a. In the operation support method OSM2 of this embodiment, the operation plan creation process S5a includes, for example, the following steps S51a to S55a. In step S51a, the operation planning unit 14 determines whether there is an electric work machine EWM whose predicted degradation state ESOH of battery B exceeds a predetermined value when each of the multiple electric work machines EWM1, EWM2, ..., EWMn is working at the selected work site WS.

[0062] Here, the operation planning unit 14 sets a predetermined value used for the determination in process S51a to, for example, 80%. Then, in process S51, the operation planning unit 14 determines, as shown in Table 2, whether there is an electric power machine EWM whose predicted deterioration state ESOH exceeds a predetermined value when each of the multiple electric power machines EWM1, EWM2, ..., EWMn is working at the initially selected work site WS1.

[0063] At work site WS1, the predicted degradation state ESOH of batteries B of electric power machines EWM1 and EWM4 of machine No. 1 and 4 is 80% or higher, which is a predetermined value. Therefore, in process S51a, the operation planning unit 14 determines that there are electric power machines EWMs whose predicted degradation state ESOH is ≥ the predetermined value (YES), and executes the next process S52a.

[0064] In step S52a, the operation planning unit 14 determines whether there are multiple electric power machines EWMs whose predicted degradation state ESOH is above a predetermined value. At work site WS1, the predicted degradation state ESOH of batteries B of electric power machines EWM1 and EWM4 of machines No. 1 and 4 is above the predetermined value of 80%. Therefore, in step S52a, the operation planning unit 14 determines that there are multiple electric power machines EWMs whose predicted degradation state ESOH is above a predetermined value (YES), and executes the next step S53a.

[0065] In step S53a, the operation planning unit 14 assigns a specific electric work machine EWM to the selected work site WS1 from among multiple electric work machines EWM1, EWM2, ..., EWMn, based on the predicted degradation state ESOH of the battery B when each electric work machine EWM operates at work site WS1. More specifically, as shown in Table 2, the electric work machine EWM4 with the smallest battery degradation amount ΔSOH, calculated based on the degradation state SOH and predicted degradation state ESOH of the battery B of each electric work machine EWM, is assigned to work site WS1.

[0066] In step S53a, the operation planning unit 14, similar to Embodiment 1 described above, excludes work site WS3, which was determined to be unsuitable for work at work site WS1 in step S3 (NA), from the multiple electric work machines EWM1, EWM2, ..., EWMn that will be assigned to work site WS1. Next, the operation planning unit 14 executes step S6, similar to Embodiment 1 described above, and if it determines that the assignment of electric work machines EWM to all work sites WS has not been completed (NO), it repeats the steps from step S51a onwards.

[0067] In the second step S51a, the operation planning unit 14 selects the work site WS2 with the second highest workload, for example, as shown in Table 2. It then determines whether there are any electric power machines EWM1, EWM2, ..., EWMn among the multiple electric power machines EWM1, EWM2, ..., EWMn, excluding the electric power machine EWM4 already assigned to another work site WS1, whose predicted deterioration state ESOH when working at work site WS2 is equal to or greater than a predetermined value.

[0068] Here, as shown in Table 2, for example, if the predetermined value is set to 80%, then only electric work machine EWM1, excluding electric work machine EWM4, has a predicted deterioration state ESOH of 80% or higher when working at the work site WS2. Therefore, in this second step S51a, the operation planning unit 14 determines that there are electric work machines EWMs whose predicted deterioration state ESOH is 80% or higher (YES), and further determines in the second step S52a that there are not multiple electric work machines EWMs whose predicted deterioration state ESOH is 80% or higher (NO).

[0069] In this case, the operation planning unit 14 executes step S54a, assigning an electric power machine EWM1 to the selected work site WS2 whose predicted deterioration state ESOH is equal to or greater than a predetermined value. It then executes a second step S6 to determine that the assignment of electric power machines EWM to all work sites WS is not yet complete (NO), and executes a third step S51a. In this third step S51a, the operation planning unit 14 selects work site WS3, which has the third highest workload.

[0070] Here, as shown in Table 2, if, for example, the predetermined value is set to 80%, then, excluding the electric power machines EWM4 and EWM1 already assigned to other work sites WS1 and WS2, there are no electric power machines EWM whose predicted degradation state ESOH of battery B after work at work site WS3 is above the predetermined value. Therefore, in this third step S51a, the operation planning unit 14 determines that there are no electric power machines EWM whose predicted degradation state ESOH is above the predetermined value (NO), and executes the next step S55a.

[0071] In step S55a, the operation planning unit 14 assigns to the selected work site WS3 the electric work machine EWM3, which has the highest predicted degradation state ESOH of battery B, from among multiple electric work machines EWM2, EWM3, ..., EWMn, excluding the electric work machines EWM4 and EWM1 assigned to the other work sites WS1 and WS2. Subsequently, the operation planning unit 14 executes the second step S55a after going through steps S6 and the fourth step S51a.

[0072] In this second step S55a, the operation planning unit 14 selects the work site WS4, which has the fourth largest workload. The operation planning unit 14 also excludes the electric power machines EWM4, EWM1, and EM3, which are assigned to the other work sites WS1, WS2, and WS3, from the candidates for the electric power machine EWM to be assigned to the selected work site WS4. Furthermore, the operation planning unit 14 assigns to the selected work site WS4 the electric power machine EWMn with the highest predicted degradation state ESOH of battery B from among the multiple electric power machines EWM2, ..., EWMn.

[0073] Subsequently, in step S6, when the operation planning unit 14 determines that the allocation of electric work machines (EWMs) to all work sites (WSs) has been completed (YES), the operation support device 1 terminates the operation support method OSM2 shown in Figure 2.

[0074] As described above, in the operation support device 1 of this embodiment, the operation planning unit 14 determines whether there is an electric work machine EWM among the multiple electric work machines EWM1, EWM2, ..., EWMn in which the predicted degradation state ESOH of the battery B is equal to or greater than a predetermined value. Furthermore, if such an electric work machine EWM exists, the operation planning unit 14 selects an electric work machine EWM4 from among the multiple electric work machines EWM1, EWM2, ..., EWMn as a specific electric work machine EWM, in which the predicted degradation state ESOH of the battery B is equal to or greater than a predetermined value and the battery degradation amount ΔSOH is the smallest.

[0075] In other words, in the operation support device 1 of this embodiment, the operation planning unit 14 determines whether there are multiple electric work machines EWM1, EWM2, ..., EWMn whose predicted degradation state ESOH is equal to or greater than a predetermined value. Furthermore, if such multiple electric work machines EWM exist, the operation planning unit 14 assigns to the selected work site WS1 an electric work machine EWM4 from among the multiple electric work machines EWM1, EWM2, ..., EWMn whose predicted degradation state ESOH of battery B is equal to or greater than a predetermined value and whose battery degradation amount ΔSOH is the smallest.

[0076] With this configuration, the operation support device 1 of this embodiment can assign the electric work machine EWM4, in which the predicted degradation state ESOH of battery B is above a predetermined value and the battery degradation amount ΔSOH is the smallest, to work site WS1, which has a higher workload than other work sites WS2, WS3, ..., Wsn. Therefore, the operation support device 1 of this embodiment makes it possible to suppress the degradation and extend the lifespan of each battery B of multiple electric work machines EWM1, EWM2, ..., EWMn, compared to assigning electric work machines EWM1, EWM2, ..., EWMn, which are equipped with batteries B that have relatively low predicted degradation states ESOH and large battery degradation amounts ΔSOH, to work site WS1, which has a relatively high workload.

[0077] Furthermore, in the operation support device 1 of this embodiment, if the operation planning unit 14 determines that there is only one electric work machine EWM1 among the multiple electric work machines EWM1, EWM2, ..., EWMn whose predicted deterioration state ESOH is equal to or greater than a predetermined value, it assigns that electric work machine EWM1 whose predicted deterioration state ESOH is equal to or greater than a predetermined value to the selected work site WS2.

[0078] With this configuration, the operation support device 1 of this embodiment can assign an electric power machine EWM1, whose battery B's predicted degradation state ESOH is above a predetermined value, to work site WS2, which has a higher workload than other work sites WS3 and WS4. Therefore, the operation support device 1 of this embodiment makes it possible to suppress the degradation and extend the lifespan of each battery B of multiple electric power machines EWM2, EWM3, ..., EWMn, compared to assigning electric power machines EWM2, EWM3, ..., EWMn, which are equipped with batteries B with relatively low predicted degradation states ESOH, to work site WS2, which has a relatively high workload.

[0079] Furthermore, in the operation support device 1 of this embodiment, if there is no electric work machine EWM among the multiple electric work machines EWM2, EWM3, ..., EWMn in which the predicted degradation state ESOH of battery B is equal to or greater than a predetermined value, the operation planning unit 14 selects the electric work machine EWM2, EWM3, ..., EWMn in which the predicted degradation state ESOH of battery B is the largest as a specific electric work machine EWM.

[0080] In other words, in the operation support device 1 of this embodiment, the operation planning unit 14 assigns the selected work site WS3 to the electric work machine EWM3, which has the highest predicted deterioration state ESOH, from among the multiple electric work machines EWM2, EWM3, ..., EWMn. Similarly, the operation planning unit 14 assigns the selected work site WS4 to the electric work machine EWMn, which has the highest predicted deterioration state ESOH, from among the multiple electric work machines EWM2, ..., EWMn.

[0081] With this configuration, the operation support device 1 of this embodiment can assign the electric work machine EWM3, which has the highest predicted degradation state ESOH of battery B, to work site WS3, which has a higher workload than other work sites WS4. Similarly, the electric work machine EWMn, which has the highest predicted degradation state ESOH of battery B, can be assigned to work site WS4, which has a higher workload than other work sites WS. Therefore, the operation support device 1 of this embodiment makes it possible to suppress the degradation and extend the lifespan of each battery B of multiple electric work machines EWM2, EWM3, ..., EWMn.

[0082] As described above, according to this embodiment, similar to Embodiment 1 described above, it is possible to provide an electric power machine operation support device 1 and an operation support method OSM2 that can suppress the degradation of the battery B of the electric power machine EWM and extend its lifespan.

[0083] [Embodiment 3] Hereinafter, with reference to Figure 1A, Figure 3, Table 3A, and Table 3B, Embodiment 3 of the operation support device and operation support method for electric work machines according to this disclosure will be described. Figure 3 is a flowchart illustrating the operation of the operation support device 1 of this embodiment, and a flowchart illustrating each step of the operation support method OSM3 of this embodiment.

[0084] The operation support device 1 and operation support method OSM3 of this embodiment differ from the operation support device 1 and operation support method OSM of Embodiment 1 described above in that the operation planning unit 14 executes the sales assessment process S7, and the operation of the operation planning unit 14 in the subsequent operation plan creation process S5b. Other aspects of the operation support device 1 and operation support method OSM3 of this embodiment are the same as those of the operation support device 1 and operation support method OSM of Embodiment 1 described above, so the same reference numerals are used for the same parts and their description is omitted.

[0085] In this embodiment, when the operation support device 1 starts the operation support method OSM3 shown in Figure 3, it executes steps S1 to S4 in the same manner as in the previously described embodiment 1. After that, the operation support device 1 executes the sale assessment process S7. This process S7 includes, for example, the following steps S71 to S73. In step S71, the operation planning unit 14 of the operation support device 1 determines whether each of the multiple electric work machines EWM1, EWM2, ..., EWMn satisfies the evaluation index for the used market.

[0086] More specifically, the operation planning unit 14 acquires, for example, the state of health (SOH), service life, and total operating hours of the battery B of each electric power machine EWM stored in the storage device by the information storage unit 11, as shown in Table 3A below. The operation planning unit 14 also acquires the used market evaluation index for each of the multiple electric power machines EWM1, EWM2, ..., EWMn stored in the storage device by the information storage unit 11, as shown in Table 3B below.

[0087] [Table 3A]

[0088] [Table 3B]

[0089] The used market valuation indicators for each electric power machine (EWM) include, for example, the service life, total operating hours, and state of health (SOH) of battery B set for each of the multiple electric power machines EWM1, EWM2, ..., EWMn, as shown in Table 3B. These conditions can be set to any conditions that would allow each electric power machine (EWM) to be sold on the used market at a price above a predetermined price.

[0090] Furthermore, the condition of each electric power tool (EWM), its state of health (SOH), service life, total operating hours, and used market valuation index change over time, and can therefore be updated periodically. Additionally, if the battery manufacturer or capacity changes, for example, due to battery replacement (e.g., battery B), this information will also be updated.

[0091] In step S71, the operation planning unit 14 excludes, for example, the electric power machine EWM3 that was determined to be unsuitable (NA) for the work site WS1 in step S3 from the evaluation target. Furthermore, in step S71, the operation planning unit 14 compares, for example, the degradation state (SOH), usage period, and total operating time of each of the multiple electric power machines EWM1, EWM2, ..., EWMn shown in Table 3A with the conditions of each electric power machine EWM shown in Table 3B.

[0092] In the example shown in Table 3A, with the exception of electric power machine EWM3, the deterioration state of battery B (SOH), service life, and total operating hours of electric power machines EWM1, EWM2, and EWM4 of machines No. 1, 2, and 4 (enclosed in thick borders) satisfy the used market evaluation indicators for each electric power machine EWM1, EWM2, and EWM4, which are enclosed in thick borders in Table 3B. In this case, the operation planning unit 14 determines in process S71 that electric power machines EWM1, EWM2, and EWM4 satisfy the used market evaluation indicators (YES).

[0093] Furthermore, in process S71, the operation planning unit 14 determines that the electric power machines EWM3 and EWMn do not satisfy the evaluation criteria for the used market (NO). Subsequently, the operation planning unit 14 executes process S72 to determine whether the electric power machines EWM1, EWM2, and EWM4 that satisfy the evaluation criteria for the used market can be sold.

[0094] More specifically, in step S72, the operation planning unit 14 sends an inquiry to the user's terminal regarding the saleability of the electric power machines EWM1, EWM2, and EWM4, and receives input from the user's terminal regarding the saleability. In step S72, the operation planning unit 14 determines, for example, whether the electric power machines EWM1, EWM2, and EWM4 are for sale based on the input. If the operation planning unit 14 determines in step S72 that the electric power machines EWM1, EWM2, and EWM4 are not for sale (NO), the first step S5b is executed.

[0095] In this first step S5b, the operation planning unit 14 selects the work site WS1 with the highest workload, similar to the first embodiment described above. Furthermore, as shown in Table 3A, the operation planning unit 14 assigns the electric work machine EWM2 that results in the smallest battery degradation amount ΔSOH of battery B when working at the selected work site WS1 to the selected work site WS1. After that, the operation planning unit 14 executes the second step S71 via step S6.

[0096] In this second step S71, the operation planning unit 14 determines that the electric power machines EWM1 and EWM4 satisfy the evaluation criteria for the used market (YES), and executes the second step S72. In this second step S72, if the operation planning unit 14 determines that the electric power machines EWM1 and EWM4 cannot be sold (NO), the second step S5b is executed. In this second step S5b, the operation planning unit 14 assigns the electric power machine EWM4, which has the smallest battery degradation amount ΔSOH, from among the remaining electric power machines EWM1, EWM3, EWM4, and EWMn to the work site WS2, which has the second highest workload.

[0097] Subsequently, the operation planning unit 14 executes the third process S71 after going through process S6. In this third process S71, the operation planning unit 14 determines that the electric power machines EWM1 and EWM3 satisfy the evaluation indicators for the used market (YES), and executes the third process S72. In this third process S72, if the operation planning unit 14 determines, for example, that electric power machine EWM1 cannot be sold (NO) and electric power machine EWM3 can be sold (YES), then the next process S73 is executed.

[0098] In step S73, the operation planning unit 14 records information about the electric power machine EWM3 to be sold in a storage device, for example, via the information storage unit 11. Then, the third step S5b is executed. In this third step S5b, the operation planning unit 14 assigns the electric power machine EWM3 to be sold, for example, which was determined to be sellable (YES) in the previous step S72, to the work site WS3 with the third highest workload. The electric power machine EWM3 to be sold is sold on the used market after the work at work site WS3 is completed, for example. Then, the operation planning unit 14 executes the fourth step S71 via step S6.

[0099] In this fourth step S71, the operation planning unit 14 determines that the electric power machine EWM1 satisfies the evaluation criteria for the used market (YES), and executes the fourth step S72. In this fourth step S72, if the operation planning unit 14 determines, for example, that the electric power machine EWM1 cannot be sold (NO), the fourth step S5b is executed. In this fourth step S5b, the operation planning unit 14 assigns, for example, the electric power machine EWMn with the smallest battery degradation amount ΔSOH from among the remaining multiple electric power machines EWM1, EWMn to the work site WS4, which has the fourth highest workload.

[0100] As described above, in the operation support device 1 of this embodiment, the operation planning unit 14 records, among a plurality of electric power machines EWM1, EWM2, ..., EWMn, an electric power machine EWM4 that satisfies predetermined used market evaluation indicators and is determined to be sellable by the user as a target for sale. With this configuration, the operation support device 1 and operation support method OSM3 of this embodiment not only achieve the same effects as the operation support device 1 and operation support method OSM of Embodiment 1 described above, but also enable the selection of electric power machines EWM with high market value and their sale at a high price in the used market.

[0101] [Embodiment 4] Hereinafter, with reference to Figure 1A, Figure 4, and Table 4, Embodiment 4 of the operation support device and operation support method for electric power machinery according to this disclosure will be described. Figure 4 is a flowchart illustrating the operation of the operation support device 1 of this embodiment, and a flowchart illustrating each step of the operation support method OSM4 of this embodiment.

[0102] The operation support device 1 and operation support method OSM4 of this embodiment differ from the operation support device 1 and operation support method OSM of Embodiment 1 in the operation plan creation process S5c. Other aspects of the operation support device 1 and operation support method OSM4 of this embodiment are the same as those of the operation support device 1 and operation support method OSM of Embodiment 1, so the same reference numerals are used for the same parts and their descriptions are omitted.

[0103] In this embodiment, when the operation support device 1 starts the operation support method OSM4 shown in Figure 4, it executes steps S1 to S4, similar to the previous embodiment 1. After that, the operation support device 1 executes the operation plan creation step S5c. In this step S5c, the operation planning unit 14 of the operation support device 1 selects work sites WS in order of highest workload based on the information stored by the information storage unit 11, similar to the previous embodiment 1.

[0104] Furthermore, the operation planning unit 14 assigns a specific electric power machine EWM from among multiple electric power machine EWMs to the selected work site WS, based on the predicted deterioration state ESOH, similar to Embodiment 1 described above. Here, the operation planning unit 14 assigns, for example, the electric power machine EWM with the highest predicted deterioration state ESOH to the selected work site WS, as shown in Table 4 below.

[0105] [Table 4]

[0106] As shown in Table 4, at the work site WS1 with the highest workload, electric power machine EWM1 is assigned from among the multiple electric power machines EWM1, EWM2, ..., EWMn, excluding electric power machine EWM3 which is excluded as unsuitable (NA). The electric power machine EWM1 has the highest predicted degradation state ESOH. Similarly, at work site WS2, electric power machine EWM2 is assigned, at work site WS3, electric power machine EWMn is assigned, and at work site WS4, electric power machine EWM3 is assigned.

[0107] According to the operation support device 1 and operation support method OSM4 of this embodiment, the electric work machine EWM that will have the maximum predicted battery degradation state ESOH after work can be sequentially assigned from work sites WS with high workload to work sites WS with low workload. Therefore, according to this embodiment, similar to the above-described embodiment 1, it is possible to provide an electric work machine operation support device 1 and operation support method OSM4 that can suppress the degradation of the battery B of the electric work machine EWM and extend its lifespan.

[0108] [Embodiment 5] Hereinafter, with reference to Figure 1A, Figure 5, Table 5A, and Table 5B, Embodiment 5 of the operation support device and operation support method for electric power machinery according to this disclosure will be described. Figure 5 is a flowchart illustrating the operation of the operation support device 1 of this embodiment, and a flowchart illustrating each step of the operation support method OSM5 of this embodiment.

[0109] The operation support device 1 and operation support method OSM 5 of this embodiment differ from the operation support device 1 and operation support method OSM of Embodiment 1 in the operation plan creation process S5d. Other aspects of the operation support device 1 and operation support method OSM 5 of this embodiment are the same as those of the operation support device 1 and operation support method OSM of Embodiment 1, so the same reference numerals are used for the same parts and their descriptions are omitted.

[0110] When the operation support device 1 of this embodiment starts the operation support method OSM5 shown in Figure 5, it executes steps S1 to S4, similar to the above-described embodiment 1. After that, the operation support device 1 executes the operation plan creation step S5d. In the operation support method OSM5 of this embodiment, the operation plan creation step S5d includes, for example, the following steps S51d to S55d. The operation plan creation step S5d by the operation support device 1 of this embodiment will be described below with reference to Tables 5A and 5B.

[0111] [Table 5A]

[0112] [Table 5B]

[0113] In process S51d, the operational planning unit 14 selects the work site WS1 with the highest workload and then determines whether there is an electric power machine EWM that meets predetermined evaluation criteria. Here, the evaluation criteria for the electric power machine EWM include, for example, the usage period, total operating hours, and deterioration state (SOH) conditions set for each electric power machine EWM, as shown in Table 5B.

[0114] Here, we refer to the predicted degradation state ESOH of battery B for each electric power tool EWM when working at work site WS1 shown in Table 5A. The predicted degradation state ESOH of electric power tool EWM1 satisfies the conditions for the degradation state SOH shown in Table 5B, as well as the conditions for service life and total operating hours. Furthermore, the predicted degradation state ESOH of battery B for the other electric power tools EWMs does not satisfy the degradation state SOH of the evaluation index for each electric power tool EWM.

[0115] In this case, the operation planning unit 14 determines in process S51d that there is an electric power machine EWM that satisfies the evaluation criteria (YES), and in the next process S52d, it determines that multiple electric power machines EWMs do not satisfy the evaluation criteria (NO). Furthermore, the operation planning unit 14 executes process S54d and assigns an electric power machine EWM1 that satisfies the evaluation criteria to the selected work site WS1. After that, the operation planning unit 14 executes process S6 and then process S51d a second time.

[0116] In this second step S51d, the operation planning unit 14 selects the work site WS2, which has the second highest workload. Furthermore, the operation planning unit 14 determines whether there is an electric power machine EWM that meets the evaluation criteria in terms of usage period, total operating time, and the predicted degradation state ESOH of battery B when working at the selected work site WS2. At work site WS2, only electric power machine EWM4, machine No. 4, meets the evaluation criteria.

[0117] In this case, the operation planning unit 14 determines in the second step S51d that there is an electric power machine EWM that satisfies the evaluation criteria (YES), and in the second step S52d that multiple electric power machines EWMs do not satisfy the evaluation criteria (NO). Furthermore, the operation planning unit 14 executes the second step S54d and assigns an electric power machine EWM4 that satisfies the evaluation criteria to the selected work site WS2. After that, the operation planning unit 14 executes the third step S51d via step S6.

[0118] In this third step S51d, the operation planning unit 14 selects the work site WS3, which has the third highest workload. Furthermore, the operation planning unit 14 determines whether there is an electric power machine EWM that meets the evaluation criteria in terms of usage period, total operating time, and the predicted degradation state ESOH of battery B when working at the selected work site WS3. At work site WS3, only electric power machine EWM3, machine No. 3, meets the evaluation criteria.

[0119] In this case, the operation planning unit 14 determines in the third step S51d that there is an electric power machine EWM that satisfies the evaluation criteria (YES), and in the third step S52d that multiple electric power machines EWMs do not satisfy the evaluation criteria (NO). Furthermore, the operation planning unit 14 executes the third step S54d and assigns an electric power machine EWM3 that satisfies the evaluation criteria to the selected work site WS3. After that, the operation planning unit 14 executes the fourth step S51d via step S6.

[0120] In this fourth step S51d, the operation planning unit 14 selects the work site WS4, which has the fourth highest workload. Furthermore, the operation planning unit 14 determines whether there are any electric power machines EWMs that meet the evaluation criteria based on their usage period, total operating hours, and the predicted degradation state ESOH of battery B when working at the selected work site WS4. At work site WS4, two electric power machines EWM2 and EWMn, machine No. 2 and n, meet the evaluation criteria.

[0121] In this case, the operation planning unit 14 determines in the fourth step S51d that there is an electric power machine EWM that satisfies the evaluation criteria (YES), and in the fourth step S52d, it determines that multiple electric power machines EWMs satisfy the evaluation criteria (YES). Furthermore, the operation planning unit 14 executes the next step S53d and assigns to the selected work site WS4 the electric power machine EWM2 with the largest predicted deterioration state ESOH among the multiple electric power machines EWM2, EWMn that satisfy the evaluation criteria.

[0122] Meanwhile, if the operation planning unit 14 determines in S51d that there are no work machines that meet the evaluation criteria (NO), it executes process S55d to assign the electric work machine EWM with the minimum battery degradation amount ΔSOH to the selected work site WS. Subsequently, if the operation planning unit 14 determines in process S6 that the assignment of electric work machines EWM to all work sites WS has been completed, the operation support device 1 terminates the operation support method OSM5 shown in Figure 5.

[0123] As described above, in the operation support device 1 of this embodiment, if there is an electric work machine EWM among the multiple electric work machines EWMs that satisfies a predetermined evaluation index, the operation planning unit 14 selects from among the multiple electric work machine EWMs as a specific electric work machine EWM that satisfies the evaluation index and has the maximum predicted degradation state ESOH of battery B.

[0124] In other words, in the operation support device 1 of this embodiment, if there are multiple electric power machines (EWMs) that satisfy predetermined evaluation indicators among the multiple operation support devices 1, the operation planning unit 14 assigns to the selected work site WS the electric power machine EWM that satisfies the evaluation indicators and has the highest predicted degradation state ESOH. With this configuration, the operation support device 1 and operation support method OSM5 of this embodiment can not only achieve the same effects as the above-described embodiment 1, but also appropriately manage the specifications of the electric power machines EWMs assigned to each work site WS.

[0125] While embodiments of the operation support device and operation support method for electric power machinery relating to this disclosure have been described in detail above using drawings, the specific configuration is not limited to these embodiments, and any design changes, etc., that do not depart from the gist of this disclosure are also included in this disclosure. [Explanation of Symbols]

[0126] 1: Server (operation support device) 11: Information Storage Unit 12: State acquisition unit 13: State prediction unit 14: Operations Planning Department B: Battery ESOH: Predicted Degradation State EWM,EWM1-EWMn:Electric working machine OSM,OSM2-OSM5: Operational support method S1: Information storage process S2: Status acquisition process S4: State prediction process S5, S5a-S5d: Operational plan creation process SOH: Deterioration state WS,WS1-WSn:Work site ΔSOH: Battery degradation amount

Claims

1. An operational support device equipped with a server that assigns multiple electric power machines to multiple work sites, The aforementioned server, A storage device that stores information on the workload of each of the multiple work sites and the specifications of the batteries installed in each of the multiple electric work machines, A status acquisition unit that acquires the degradation status of batteries mounted on each of the multiple electric work machines, A state prediction unit calculates the battery degradation rate when each of the multiple electric work machines operates at any of the multiple work sites, based on the information of the workload stored in the storage device and the specifications of the battery, and calculates the predicted degradation state of the battery when each of the multiple electric work machines operates at any of the multiple work sites, based on the calculated degradation rate. An operation planning unit selects work sites in descending order of workload based on the workload information stored in the storage device, and assigns a specific electric work machine from among a plurality of electric work machines in the order of the selected work sites, based on the battery degradation status acquired by the status acquisition unit and the predicted battery degradation status calculated by the status prediction unit. An operational support device for electric power machinery, characterized by being equipped with the following features.

2. The operation planning unit calculates the amount of battery degradation when each of the multiple electric work machines operates at multiple work sites based on the battery degradation state and the predicted battery degradation state, and selects the electric work machine with the smallest amount of battery degradation from among the multiple electric work machines as the specific electric work machine, characterized in that it is an operation support device for electric work machines according to claim 1.

3. The operation planning unit is characterized in that, if there is an electric work machine among the plurality of electric work machines whose predicted battery degradation state is above a predetermined value, it selects from among the plurality of electric work machines as the specific electric work machine whose predicted battery degradation state is above a predetermined value and whose battery degradation amount is the smallest. This is an operation support device for electric work machines according to claim 2.

4. The operation planning unit is characterized in that, if there is no electric work machine among the plurality of electric work machines whose predicted battery degradation state is greater than or equal to a predetermined value, it selects from the plurality of electric work machines the electric work machine with the greatest predicted battery degradation state as the specific electric work machine, as described in claim 2.

5. The operation planning unit is characterized in that, if there is no electric work machine among the plurality of electric work machines whose predicted battery degradation state is greater than or equal to a predetermined value, it selects from the plurality of electric work machines the electric work machine with the greatest predicted battery degradation state as the specific electric work machine, as described in claim 3.

6. The operation planning unit is characterized in that, if there is an electric work machine among the multiple electric work machines that satisfies a predetermined evaluation index, it selects from among the multiple electric work machines as the specific electric work machine that satisfies the evaluation index and has the maximum predicted battery degradation state. This is an operation support device for electric work machines according to claim 1.

7. The operation planning unit is characterized in that it records, among a plurality of electric power machines, electric power machines that meet predetermined used market evaluation indicators and that have been determined by the user to be sellable, as targets for sale.

8. An operational support method for assigning multiple electric work machines to multiple work sites using a computer, The computer performs a state acquisition step to acquire the degradation status of batteries installed in each of the multiple electric work machines, A state prediction step in which, based on information on the workload of each of the multiple work sites and the specifications of the batteries installed in each of the multiple electric work machines, the computer calculates the rate of battery degradation when each of the multiple electric work machines works at any of the multiple work sites, and calculates the predicted degradation state of the battery when each of the multiple electric work machines works at any of the multiple work sites, based on the calculated rate of degradation; The computer selects work sites in descending order of workload based on the workload information, and then, in the order of the selected work sites, assigns a specific electric work machine from among a plurality of electric work machines based on the battery degradation status acquired in the status acquisition step and the predicted battery degradation status calculated in the status prediction step, in an operation plan creation step, A method for supporting the operation of an electric power machine, characterized by having [a specific feature].

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