Charge amount calculation system, charge amount calculation method, and charge amount calculation program

Abstract

To provide a charge amount calculation system, a charge amount calculation method, and a charge amount calculation program with which it is possible to calculate the optimum charge amount of a secondary battery which an AGV is provided with, in accordance with the predicated operating state of the AGV and the degradation state of the secondary battery.SOLUTION: The charge amount calculation system comprises: a demand power amount prediction unit 134 for predicting a demand power amount representing the power amount consumed by a secondary battery in a default period; and an optimum charge amount calculation unit 136 for determining the optimum charge amount of an unmanned carrier on the basis of the predicted demand power amount. The demand power amount prediction unit 134 calculates the sum total of a predicted power consumption amount during traveling of the unmanned carrier, a predicted power consumption amount during a standby, and a predicated power consumption amount while being stopped, as the demand power amount. When the demand SOC value is determined to be lower than or equal to a target depth of discharge, the optimum charge amount calculation unit 136 calculates a charge amount such that the SOC at completion of charge and the SOC at completion of discharge of the secondary battery are within a range of SOC upper-limit value and SOC lower-limit value, as an optimum charge amount.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a charge amount calculation system, a charge amount calculation method, and a charge amount calculation program for calculating the charge amount of a secondary battery provided in an automatic guided vehicle (AGV).

Background Art

[0002] Conventionally, various techniques for calculating the charge amount of a secondary battery provided in an AGV have been proposed. As an example of such a technique, in the unmanned conveyance system disclosed in Patent Document 1, a vehicle ECU of an unmanned conveyance vehicle acquires, from a first radio device, information capable of recognizing the shortest arrival time, which is the shortest among the predicted arrival times of other unmanned conveyance vehicles, and transmits, to a charger, a command value of a charging current having a smaller value as the shortest arrival time is longer. The secondary battery is charged with a smaller charging current as the shortest arrival time is longer.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the unmanned conveyance system disclosed in Patent Document 1 has a problem in that it is impossible to calculate the optimal charge amount of the secondary battery according to the predicted operation status of the AGV and the degradation state of the secondary battery.

[0005] The present disclosure has been made to solve such problems, and an object thereof is to provide a charge amount calculation system, a charge amount calculation method, and a charge amount calculation program capable of calculating the optimal charge amount of a secondary battery provided in an AGV according to the predicted operation status of the AGV and the degradation state of the secondary battery.

Means for Solving the Problems

[0006] A charge amount calculation system that calculates the charge amount of a secondary battery equipped in an automated guided vehicle according to the present disclosure includes a required power amount prediction unit that predicts a required power amount, which is the power amount consumed by the secondary battery in a predetermined period, and an optimal charge amount calculation unit that determines an optimal charge amount of the automated guided vehicle based on a required SOC value corresponding to the required power amount predicted by the required power amount prediction unit and the SOH (State Of Health) of the secondary battery. The required power amount prediction unit calculates, as the required power amount, the total value of a predicted power consumption amount during travel of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during travel by the predicted travel time of the automated guided vehicle, a predicted power consumption amount during standby of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during standby by the predicted standby time of the automated guided vehicle, a predicted power consumption amount during stop of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during stop by the predicted stop time of the automated guided vehicle, and a degraded power consumption amount, which is the power amount consumed due to an increase in resistance caused by degradation of the secondary battery. The optimal charge amount calculation unit determines a target discharge depth corresponding to the current SOH of the secondary battery based on information defining the correspondence relationship between the SOH of the secondary battery and the target discharge depth, determines whether the required SOC value is less than or equal to the target discharge depth, and when it is determined that the required SOC value is less than or equal to the target discharge depth, calculates, as the optimal charge amount, a charge amount that makes the SOC at the end of charge of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the end of charge fall within the range of the SOC (State Of Charge) upper limit value and the SOC lower limit value corresponding to the SOH or the target discharge depth.

[0007] When it is determined that the required SOC value is less than or equal to the target discharge depth, the optimal charge amount calculation unit may calculate, as the optimal charge amount, a charge amount that makes the intermediate value between the SOC at the end of charge and the SOC at the end of discharge coincide with the intermediate value between the SOC upper limit value and the SOC lower limit value.

[0008] Further, when the optimal charge amount calculation unit determines that the demand SOC value exceeds the target discharge depth, it can calculate the difference between the SOC upper limit value and the current SOC of the secondary battery as the optimal charge amount.

[0009] Furthermore, the demand power amount prediction unit calculates the total value of the predicted power consumption amount during running, the predicted power consumption amount during standby, the predicted power consumption amount during stop, the degradation power consumption amount, and the unexpected power consumption amount consumed due to the unexpected operation status of the automated guided vehicle as the demand power amount. The demand power amount prediction unit can calculate the unexpected power consumption amount by time-integrating the multiplication value of the weight w of the conveyed object carried by the automated guided vehicle, the gravitational acceleration g, the difference Δn between the planned production number of the product related to the conveyed object and the number of products actually manufactured, the running speed v of the automated guided vehicle, and the running time t of the automated guided vehicle.

[0010] Furthermore, the demand power amount prediction unit calculates the total value of the predicted power consumption amount during running, the predicted power consumption amount during standby, the predicted power consumption amount during stop, the degradation power consumption amount, the unexpected power consumption amount, and other power consumption amounts other than these power consumption amounts as the demand power amount. The demand power amount prediction unit can calculate other power consumption amounts by subtracting the predicted power consumption amount during running, the predicted power consumption amount during standby, the predicted power consumption amount during stop, the degradation power consumption amount, and the unexpected power consumption amount corresponding to the total power consumption amount from the actual total power consumption amount based on the measured voltage and measured current of the secondary battery.

[0011] The charge amount calculation system includes a running time prediction model that predicts the running time of the automated guided vehicle in a predetermined period, a standby time prediction model that predicts the standby time of the automated guided vehicle in a predetermined period, and a stop time prediction model that predicts the stop time of the automated guided vehicle in a predetermined period. The running time prediction model outputs a predicted running time when production schedule information is input. The standby time prediction model outputs a predicted standby time when production schedule information is input. When production schedule information is input, the stop time prediction model outputs a predicted stop time, The production schedule information may include the planned production quantity of the product related to the conveyed object carried by the automated guided vehicle and the planned number of workers involved in the production of the product.

[0012] In the charge amount calculation method for calculating the charge amount of the secondary battery equipped with the automated guided vehicle according to the present disclosure, a computer predicts the required power consumption, which is the power consumption of the secondary battery during a predetermined period, and determines the optimal charge amount of the automated guided vehicle based on the required SOC value corresponding to the predicted required power consumption and the SOH of the secondary battery. The step of predicting the required power consumption includes calculating, as the required power consumption, the sum of the predicted power consumption during running of the automated guided vehicle obtained by multiplying the power consumption per unit time during running of the automated guided vehicle by the predicted running time of the automated guided vehicle, the predicted power consumption during standby of the automated guided vehicle obtained by multiplying the power consumption per unit time during standby of the automated guided vehicle by the predicted standby time of the automated guided vehicle, the predicted power consumption during stop of the automated guided vehicle obtained by multiplying the power consumption per unit time during stop of the automated guided vehicle by the predicted stop time of the automated guided vehicle, and the power consumption due to deterioration consumed due to an increase in resistance caused by deterioration of the secondary battery. The step of determining the optimal charge amount includes determining the target discharge depth corresponding to the current SOH of the secondary battery based on information defining the correspondence between the SOH and the target discharge depth of the secondary battery, determining whether the required SOC value is less than or equal to the target discharge depth, and when it is determined that the required SOC value is less than or equal to the target discharge depth, calculating, as the optimal charge amount, the charge amount that makes the SOC at the end of charging of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the end of charging within the range of the SOC upper limit value and the SOC lower limit value corresponding to the SOH or the target discharge depth.

[0013] A charge amount calculation program for calculating the charge amount of a secondary battery provided in an automated guided vehicle according to the present disclosure causes a computer to predict a required power amount, which is the power amount consumed by the secondary battery in a predetermined period, and execute steps of determining an optimal charge amount of the automated guided vehicle based on a required SOC value corresponding to the predicted required power amount and the SOH of the secondary battery. The step of predicting the required power amount includes a step of calculating, as the required power amount, a total value of a predicted power consumption amount during travel of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during travel by the predicted travel time of the automated guided vehicle, a predicted power consumption amount during standby of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during standby by the predicted standby time of the automated guided vehicle, a predicted power consumption amount during stop of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during stop by the predicted stop time of the automated guided vehicle, and a power consumption amount due to deterioration consumed due to an increase in resistance caused by deterioration of the secondary battery. The step of determining the optimal charge amount includes a step of determining a target discharge depth corresponding to the current SOH of the secondary battery based on information defining a correspondence relationship between the SOH of the secondary battery and the target discharge depth, a step of determining whether or not the required SOC value is less than or equal to the target discharge depth, and, when it is determined that the required SOC value is less than or equal to the target discharge depth, a step of calculating, as the optimal charge amount, a charge amount that makes the SOC at the end of charging of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the end of charging within a range of an SOC upper limit value and an SOC lower limit value corresponding to the SOH or the target discharge depth.

Advantages of the Invention

[0014] According to the present disclosure, it is possible to provide a charge amount calculation system, a charge amount calculation method, and a charge amount calculation program capable of calculating an optimal charge amount of a secondary battery provided in an AGV according to a predicted operation status of the AGV and a deterioration state of the secondary battery.

Brief Description of the Drawings

[0015]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Mode for Carrying Out the Invention

[0016] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of a charge amount calculation device 10 according to the present disclosure. The charge amount calculation device 10 is a device that calculates the optimum charge amount of a secondary battery provided in an AGV. As a specific example of the charge amount calculation device 10, for example, an information processing device such as a server can be mentioned. The charge amount calculation device 10 corresponds to a charge amount calculation system.

[0017] The charge amount calculation device 10 includes a communication interface (I / F) 11, a storage device 12, and an arithmetic device 13. The communication I / F 11 is an interface that transmits and receives signals between the charge amount calculation device 10 and the AGV and other devices.

[0018] The storage device 12 is a storage device that stores the programs executed by the arithmetic unit 13 and various information processed by the arithmetic unit 13.

[0019] The arithmetic unit 13 is an arithmetic unit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). The arithmetic unit 13 executes the programs stored in the storage device 12, thereby executing the methods defined by the programs. The programs executed by the arithmetic unit 13 include an acquisition unit 130, a travel time prediction model 131, a standby time prediction model 132, a stop time prediction model 133, a required power prediction unit 134, an SOC calculation unit 135, and an optimal charge amount calculation unit 136.

[0020] Note that an integrated circuit such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit) may execute the above-described programs. Integrated circuits such as CPUs, MPUs, FPGAs, and ASICs correspond to a computer.

[0021] The acquisition unit 130 is a program that acquires various information related to the calculation of the charge amount of the secondary battery of the AGV. These information include the speed of the AGV, the travel time, standby time, and stop time of the AGV, the SOC value of the secondary battery, the measured current value of the secondary battery, the measured voltage value of the secondary battery, and the temperature of the secondary battery, and production information of products related to the conveyed goods carried by the AGV. The AGV includes a speed sensor, a timer, a temperature sensor for the secondary battery, a current sensor for the secondary battery, and a voltage sensor for the secondary battery. The AGV provides the measured speed of the AGV, the measured time of the AGV (travel time, standby time, and stop time), the measured temperature of the secondary battery, the measured current of the secondary battery, and the measured voltage of the secondary battery to the charge amount calculation device 10. The acquisition unit 130 can acquire this information from the AGV.

[0022] The travel time prediction model 131 is a program that predicts the travel time of the AGV within a predetermined period. The predetermined period can be, for example, one day or the like. The travel time prediction model 131 is a trained model learned by machine learning such as deep learning. The travel time prediction model 131 can be learned using teacher data with the past production information of the product as input information and the actual travel time of the AGV related to the production information as output information. The production information of the product includes the production quantity of the product (for example, the number of production units, etc.) and the number of workers involved in the production of the product (for example, the number of workers, etc.) as shown in FIG. 2. In addition, the production information of the product may include the number of transported items transported by the AGV and the working hours of the worker. When production schedule information is input, the travel time prediction model 131 outputs a predicted travel time. The production schedule information includes the planned production quantity of the product and the planned number of workers involved in the production of the product.

[0023] The waiting time prediction model 132 is a program that predicts the waiting time of the AGV within a predetermined period. The waiting time prediction model 132 is a trained model learned by machine learning such as deep learning. The waiting time is the time when the AGV does not move while the power of the AGV is on. The waiting time prediction model 132 can be learned using teacher data with the past production information of the product as input information and the actual waiting time of the AGV related to the production information as output information. When production schedule information is input, the waiting time prediction model 132 outputs a predicted waiting time.

[0024] The stop time prediction model 133 is a program that predicts the stop time of the AGV within a predetermined period. The stop time prediction model 133 is a trained model learned by machine learning such as deep learning. The stop time is the time when the power of the AGV is off. The stop time prediction model 133 can be learned using teacher data with the past production information of the product as input information and the actual stop time of the AGV related to the production information as output information. When production schedule information is input, the stop time prediction model 133 outputs a predicted stop time.

[0025] The required power prediction unit 134 is a program that predicts the required power, which is the power consumed by the secondary battery equipped in the AGV within a predetermined period. The required power includes the operating power consumption, the degradation power consumption, the unexpected power consumption, and other power consumptions. The operating power consumption is the power consumed by the secondary battery when the AGV transports the transported item. The degradation power consumption is the power consumed due to the increase in resistance caused by the degradation of the secondary battery within a predetermined period. The unexpected power consumption is the power consumed due to the unexpected operating status of the AGV within a predetermined period. Other power consumptions are power consumptions other than the operating power consumption, the degradation power consumption, and the unexpected power consumption.

[0026] Based on Equation 1, the required power prediction unit 134 calculates the predicted value of the current required power W(i + 1).

Equation

[0027] Based on Equation 2, the required power prediction unit 134 calculates the operating power consumption W^ work . The operating power consumption is composed of the power consumption when the AGV is running, the power consumption when the AGV is on standby, and the power consumption when the AGV is stopped.

Equation

[0028] Therefore, the power demand prediction unit 134 multiplies the power consumption per unit time consumed by the automated guided vehicle during travel by the predicted travel time of the automated guided vehicle to obtain the predicted power consumption of the automated guided vehicle during travel, and multiplies the power consumption per unit time consumed by the automated guided vehicle during standby by the predicted standby time of the automated guided vehicle to obtain the predicted power consumption of the automated guided vehicle during standby, and multiplies the power consumption per unit time consumed by the automated guided vehicle when stopped by the predicted stop time of the automated guided vehicle to obtain the predicted power consumption of the automated guided vehicle when stopped, and calculates the total value as the operating power consumption \(W\) work as such.

[0029] The power demand prediction unit 134 calculates the degraded power consumption \(W\) bat_det based on Equation 3.

Equation

[0030] The required power prediction unit 134 calculates the unforeseen power consumption W exception based on Equation 4. [Equation] Here, w is the measured value of the weight of the conveyed object carried by the AGV. g is the acceleration due to gravity. Δn is the difference between the planned number of products to be manufactured related to the conveyed object and the actual number of products manufactured. v is the traveling speed of the AGC. t is the traveling time of the AGC. v and t can be obtained from the log information recording the traveling speed and traveling time of the AGC. Therefore, the required power prediction unit 134 time-integrates the product value of the weight w of the conveyed object carried by the AGV, the acceleration due to gravity g, the difference Δn between the planned number of products to be manufactured and the actual number of products manufactured, the traveling speed v of the AGV, and the traveling time t of the AGV to calculate the unforeseen power consumption W exception can be calculated.

[0031] The required power prediction unit 134 calculates other power consumption W other_lost based on Equation 5. [Equation] As shown in Equation 1, to predict the current required power W(i + 1), the previous other power consumption W other_lost (i) is used. The previous other power consumption W other_lost (i), as shown in Equation 5, can be obtained by adding the product value of the previous value of W other_lost (i) and the weight (1 - α), and the product value of the value before the previous value W other_lost_bef (for example, the average value of the value before the previous value, etc.) and the weight α. W other_lost (i) shown in Equation 5 can be calculated based on Equation 6. [Number] Here, W real is the actual total power consumption (kWh) of the AGV and is defined by Equation 7. [Number] Here, V is the measured voltage value (V) of the secondary battery equipped on the AGV. t is the running time, stop time, and standby time of the AGV. I is the measured current value (A) of the secondary battery equipped on the AGV. Therefore, the other power consumption W other_lost is obtained by subtracting the operating power consumption W^ real (calculated value based on Equation 6), the degradation power consumption W work (predicted value based on Equation 2), the predicted power consumption outside the prediction range W bat_det (predicted value based on Equation 3) and the predicted power consumption outside the prediction range W exception (predicted value based on Equation 4) from the actual total power consumption W

[0032] The SOC calculation unit 135 is a program that calculates the required SOC value (%) corresponding to the required power (kWh) calculated by the required power prediction unit 134. The SOC calculation unit 135 calculates the required SOC value (%) by dividing the required power (kWh) by the maximum power storage capacity (kWh) of the secondary battery.

[0033] The optimal charge amount calculation unit 136 is a program that determines the optimal charge amount of the automated guided vehicle based on the required SOC value corresponding to the required power amount predicted by the required power amount prediction unit 134 and the SOH of the secondary battery. Specifically, the optimal charge amount calculation unit 136 determines the target discharge depth corresponding to the current SOH of the secondary battery based on the information defining the correspondence between the SOH of the secondary battery and the target discharge depth. The target discharge depth is a discharge depth for suppressing the deterioration of the secondary battery and is determined in advance. FIG. 7 is a diagram showing an example of the information defining the correspondence between the SOH and the target discharge depth. As shown in FIG. 7, a target discharge depth for suppressing the deterioration of the secondary battery is associated with each SOH. The target discharge depth is preferably less than the corresponding SOH. Note that the values of the SOH and the target discharge depth shown in FIG. 7 are examples, and other values can be adopted as long as the deterioration of the secondary battery can be suppressed.

[0034] Then, the optimal charge amount calculation unit 136 determines whether or not the required SOC value is less than or equal to the target discharge depth. When it is determined that the required SOC value is less than or equal to the target discharge depth, the optimal charge amount calculation unit 136 sets the SOC at the time of completion of charging of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the time of completion of charging within the range of the SOC upper limit value and the SOC lower limit value corresponding to the target discharge depth, and calculates the charge amount as the optimal charge amount.

[0035] On the other hand, when it is determined that the required SOC value exceeds the target discharge depth, the optimal charge amount calculation unit 136 calculates the difference between the SOC upper limit value and the current SOC of the secondary battery as the optimal charge amount.

[0036] The SOC upper limit value and the SOC lower limit value are respectively the upper limit value and the lower limit value of the SOC for suppressing the deterioration of the secondary battery, and are determined in advance. FIG. 8 is a diagram showing an example of information defining the correspondence relationship between the target discharge depth and the SOC upper limit value and the SOC lower limit value. As shown in FIG. 8, for each target discharge depth, the SOC upper limit value and the SOC lower limit value for suppressing the deterioration of the secondary battery are associated. The SOC upper limit value is preferably a value larger than the corresponding target discharge depth and less than 100%. The SOC lower limit value can be determined based on the corresponding SOC upper limit value and the target discharge depth. For example, the difference between the corresponding SOC upper limit value and the target discharge depth can be used as the SOC lower limit value. Note that the values of the target discharge depth, the SOC upper limit value, and the SOC lower limit value shown in FIG. 8 are examples, and other values can be adopted as long as the deterioration of the secondary battery can be suppressed.

[0037] FIG. 5 is a diagram showing an example of the process executed by the charge amount calculation device 10. In step S1, the acquisition unit 130 acquires the production schedule information of the product. In step S2, the travel time prediction model 131 predicts the travel time of the AGV. In step S3, the standby time prediction model 132 predicts the standby time of the AGV. In step S4, the stop time prediction model 133 predicts the stop time of the AGV.

[0038] In step S5, the power demand prediction unit 134 predicts the operating power consumption using the predicted travel time, predicted standby time, and predicted stop time calculated in steps S2 to S4. In step S6, the power demand prediction unit 134 calculates the deterioration power consumption. In step S7, the power demand prediction unit 134 calculates the unexpected power consumption. In step S8, the power demand prediction unit 134 calculates the other power consumption.

[0039] In step S9, the power demand prediction unit 134 calculates the power demand based on the operating power consumption, the unexpected power consumption, and the other power consumption.

[0040] In step S10, the SOC calculation unit 135 calculates a required SOC value corresponding to the required power amount calculated by the required power amount prediction unit 134. In step S11, the acquisition unit 130 acquires the current SOC value from the AGV. In step S12, the optimal charge amount calculation unit 136 executes an optimal charge amount determination process.

[0041] FIG. 6 is a diagram showing an example of the optimal charge amount determination process executed by the optimal charge amount calculation unit 136. In step S20, the optimal charge amount calculation unit 136 calculates the SOH of the target secondary battery using the initial full charge capacity of the secondary battery and the accumulated degraded power consumption amount based on Equation 8. The SOH can be calculated based on Equation 8. [Equation]

[0042] In step S21, the optimal charge amount calculation unit 136 determines the target discharge depth corresponding to the calculated SOH based on the information defining the correspondence between the SOH and the target discharge depth.

[0043] In step S22, the optimal charge amount calculation unit 136 determines the SOC upper limit value and the SOC lower limit value corresponding to the determined target discharge depth based on the information defining the correspondence between the target discharge depth and the SOC upper limit value and the SOC lower limit value. In the present embodiment, the SOC upper limit value and the SOC lower limit value corresponding thereto are determined based on the target discharge depth. However, in other embodiments, the SOC upper limit value and the SOC lower limit value corresponding thereto may be uniquely determined based on the SOH.

[0044] In step S23, the optimal charge amount calculation unit 136 determines whether the required SOC value is less than the target discharge depth determined in step S21. If it is determined that the required SOC value exceeds the target discharge depth (NO), in step S24, the optimal charge amount calculation unit 136 calculates the optimal charge amount using the SOC upper limit value determined in step S22 and the current SOC based on Equation 9. [Equation]

[0045] On the other hand, when it is determined that the required SOC value is equal to or less than the target discharge depth (YES), in step S25, the optimal charge amount calculation unit 136 calculates the optimal charge amount based on Equation 10 and Equation 11. In other words, the optimal charge amount calculation unit 136 can calculate, as the optimal charge amount, the charge amount that makes the intermediate value between the SOC at the end of charging and the SOC at the end of discharging coincide with the intermediate value between the SOC upper limit value and the SOC lower limit value based on Equation 10 and Equation 11.

Number

Number

[0046] FIG. 9 is a diagram showing an example of the temporal change of SOH, target discharge depth, SOC lower limit value, SOC upper limit value, current SOC value, required SOC value, and optimal charge amount with respect to SOC. At time t1, since the SOH is 80%, the target discharge depth is 60%, the SOC lower limit value is 20%, and the SOC lower limit value is 80%. The required SOC value at time t1 is 30%, which is equal to or less than the target discharge depth (60%). Therefore, based on Equation 10 and Equation 11, the optimal charge amount (25%) is calculated. In this way, the SOC can be kept within the range of the SOC lower limit value and the upper limit value at the end of charging and at the end of discharging.

[0047] At time t2, since the SOH is 80%, the target discharge depth is 60%, the SOC lower limit value is 20%, and the SOC lower limit value is 80%. The required SOC value at time t2 is 50%, which is equal to or less than the target discharge depth (60%). Therefore, based on Equation 10 and Equation 11, the optimal charge amount (40%) is calculated. In this way, the SOC can be kept within the range of the SOC lower limit value and the upper limit value at the end of charging and at the end of discharging.

[0048] At time t3, since the SOH is 80%, the target discharge depth is 60%, the lower SOC limit is 20%, and the upper SOC limit is 80%. The required SOC value at time t3 is 90%, which exceeds the target discharge depth (60%). Therefore, based on Equation 9, the optimal charge amount (55%) is calculated. In this way, at the end of charging, the SOC can be kept within the range of the lower and upper SOC limits.

[0049] In the above-described embodiment, the required power consumption prediction unit 134 calculates the sum of the predicted power consumption during the travel of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during travel by the predicted travel time of the automated guided vehicle, the predicted power consumption during the standby of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during standby by the predicted standby time of the automated guided vehicle, the predicted power consumption during the stop of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during stop by the predicted stop time of the automated guided vehicle, and the power consumption due to the increase in resistance caused by the deterioration of the secondary battery, as the required power consumption.

[0050] Then, the optimal charge amount calculation unit 136 determines the target discharge depth corresponding to the current SOH of the secondary battery based on the information defining the correspondence between the SOH of the secondary battery and the target discharge depth. Next, the optimal charge amount calculation unit 136 determines whether or not the required SOC value is less than or equal to the target discharge depth. If it is determined that the required SOC value is less than or equal to the target discharge depth, the optimal charge amount calculation unit 136 calculates the charge amount that makes the SOC at the end of charging of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the end of charging within the range of the SOC upper limit and the SOC lower limit corresponding to the target discharge depth, as the optimal charge amount.

[0051] By adopting the above configuration, according to the predicted power consumption during running, the predicted power consumption during standby, the predicted power consumption during stop, and the degraded power consumption of the automated guided vehicle, in other words, according to the predicted operating status of the automated guided vehicle and the degradation status of the secondary battery, an optimal charge amount can be determined. Further, by setting the SOC upper limit value and the SOC lower limit value to values that suppress the degradation of the secondary battery, an optimal charge amount for suppressing the degradation of the secondary battery can be calculated.

[0052] Furthermore, the required power amount prediction unit 134 calculates, as the required power amount, the total value of the predicted power consumption during running, the predicted power consumption during standby, the predicted power consumption during stop, and the power consumption outside the prediction, which is the power consumption consumed due to the operating status outside the prediction of the automated guided vehicle. Thereby, an optimal charge amount considering the power consumption outside the prediction of the secondary battery can be determined.

[0053] Furthermore, the required power amount prediction unit 134 calculates, as the required power amount, the total value of the predicted power consumption during running, the predicted power consumption during standby, the predicted power consumption during stop, the power consumption outside the prediction, and other power consumption other than these power consumptions. Thereby, an optimal charge amount considering the power consumption other than the predicted power consumption during running, the predicted power consumption during standby, the predicted power consumption during stop, and the power consumption outside the prediction can be determined.

[0054] In the above example, when the program is loaded into a computer, it includes a set of instructions (or software code) for causing the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, a computer-readable medium or a tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD), or other memory technologies, CD-ROM, digital versatile disk (DVD), Blu-ray (registered trademark) disk, or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage, or other magnetic storage devices. The program may also be transmitted on a transitory computer-readable medium or a communication medium. By way of example and not limitation, a transitory computer-readable medium or a communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

[0055] The present disclosure is not limited to the above-described embodiments, and can be appropriately modified without departing from the spirit of the present disclosure. For example, in other embodiments, the power demand prediction unit 134 may calculate the operating power consumption, which is the sum of the predicted power consumption of the automated guided vehicle, the predicted power consumption during standby, and the predicted power consumption during stop, as the power demand.

[0056] Also, in other embodiments, the power demand prediction unit 134 may calculate the sum of the operating power consumption and the degradation power consumption, which is the power consumed due to the increase in resistance caused by the degradation of the secondary battery, as the power demand.

[0057] Furthermore, in other embodiments, the power demand prediction unit 134 may calculate the sum of the operating power consumption, the degradation power consumption, and the unexpected power consumption, which is the power consumed due to the unexpected operating conditions of the automated guided vehicle, as the power demand.

[0058] Furthermore, in other embodiments, the power demand prediction unit 134 may calculate the total value of the operating power consumption, the degradation power consumption, the unforecast power consumption, and other power consumptions other than these power consumptions as the power demand.

[0059] Furthermore, in the above-described embodiment, the charge amount calculation device 10, which is a single device, includes the acquisition unit 130, the travel time prediction model 131, the standby time prediction model 132, the stop time prediction model 133, the power demand prediction unit 134, the SOC calculation unit 135, and the optimal charge amount calculation unit 136. However, in other embodiments, these functional means can be implemented by being distributed among a plurality of devices.

Description of Reference Numerals

[0060] 10: Charge amount calculation device 11: Communication I / F 12: Storage device 13: Arithmetic unit 130: Acquisition unit 131: Travel time prediction model 132: Standby time prediction model 133: Stop time prediction model 134: Power demand prediction unit 135: SOC calculation unit 136: Optimal charge amount calculation unit

Claims

1. A charge amount calculation system for calculating the charge amount of a secondary battery provided in an automated guided vehicle, comprising: a required power amount prediction unit that predicts a required power amount, which is the amount of power consumed by the secondary battery during a predetermined period; an optimal charge amount calculation unit that determines an optimal charge amount of the automated guided vehicle based on a required SOC value corresponding to the required power amount predicted by the required power amount prediction unit and the SOH (State Of Health) of the secondary battery; The required power amount prediction unit: calculates, as the required power amount, a total value of a predicted power consumption amount during traveling of the automated guided vehicle obtained by multiplying a power consumption per unit time consumed by the automated guided vehicle during traveling by a predicted traveling time of the automated guided vehicle, a predicted power consumption amount during standby of the automated guided vehicle obtained by multiplying a power consumption per unit time consumed by the automated guided vehicle during standby by a predicted standby time of the automated guided vehicle, a predicted power consumption amount during stop of the automated guided vehicle obtained by multiplying a power consumption per unit time consumed by the automated guided vehicle during stop by a predicted stop time of the automated guided vehicle, and a power consumption amount due to an increase in resistance caused by deterioration of the secondary battery; The optimal charge amount calculation unit: determines a target depth of discharge corresponding to the current SOH of the secondary battery based on information defining a correspondence relationship between the SOH and the target depth of discharge of the secondary battery; determines whether the required SOC value is less than or equal to the target depth of discharge; when it is determined that the required SOC value is less than or equal to the target depth of discharge, calculates, as the optimal charge amount, a charge amount that makes the SOC at the end of charging of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the end of charging fall within a range of an SOC (State Of Charge) upper limit value and an SOC lower limit value corresponding to the SOH or the target depth of discharge; A charge amount calculation system.

2. The charge amount calculation system according to claim 1, wherein when it is determined that the required SOC value is less than or equal to the target depth of discharge, the optimal charge amount calculation unit calculates, as the optimal charge amount, a charge amount that makes the intermediate value between the SOC at the end of charging and the SOC at the end of discharge coincide with the intermediate value between the SOC upper limit value and the SOC lower limit value.

3. When the optimal charge amount calculation unit determines that the required SOC value exceeds the target discharge depth, the optimal charge amount is calculated as the difference between the SOC upper limit value and the current SOC of the secondary battery. The charge amount calculation system according to claim 1 or 2.

4. The required power consumption prediction unit calculates the total value of the predicted power consumption during traveling, the predicted power consumption during standby, the predicted power consumption during stop, the degradation power consumption, and the power consumption due to an unexpected operation status of the automated guided vehicle as the required power consumption. The required power consumption prediction unit calculates the unexpected power consumption by time integrating the multiplication value of the weight w of the conveyed object carried by the automated guided vehicle, the gravitational acceleration g, the difference Δn between the planned production quantity and the actually produced quantity of the product related to the conveyed object, the traveling speed v of the automated guided vehicle, and the traveling time t of the automated guided vehicle. The charge amount calculation system according to claim 1 or 2.

5. The required power consumption prediction unit calculates the total value of the predicted power consumption during traveling, the predicted power consumption during standby, the predicted power consumption during stop, the degradation power consumption, the unexpected power consumption, and other power consumptions other than these power consumptions as the required power consumption. The required power consumption prediction unit calculates the other power consumption by subtracting the predicted power consumption during traveling, the predicted power consumption during standby, the predicted power consumption during stop, the degradation power consumption, and the unexpected power consumption corresponding to the total power consumption from the actual total power consumption based on the measured voltage and measured current of the secondary battery. The charge amount calculation system according to claim 4.

6. A traveling time prediction model for predicting the traveling time of the automated guided vehicle in the predetermined period; A standby time prediction model for predicting the standby time of the automated guided vehicle in the predetermined period; A stop time prediction model for predicting the stop time of the automated guided vehicle in the predetermined period, and The traveling time prediction model outputs the predicted traveling time when production schedule information is input; The standby time prediction model outputs the predicted standby time when the production schedule information is input; The stop time prediction model outputs the predicted stop time when the production schedule information is input. The charging amount calculation system according to claim 1 or 2, wherein the production schedule information includes the scheduled production quantity of a product related to the conveyed object conveyed by the automated guided vehicle and the scheduled number of workers involved in the production of the product.

7. A charging amount calculation method for calculating the charging amount of a secondary battery provided in an automated guided vehicle, wherein a computer predicts the required power amount, which is the power amount consumed by the secondary battery during a predetermined period; executes steps of determining an optimal charging amount of the automated guided vehicle based on a required SOC value corresponding to the predicted required power amount and the SOH of the secondary battery; The step of predicting the required power amount includes a step of calculating, as the required power amount, a total value of a predicted power consumption amount during traveling of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during traveling by the predicted traveling time of the automated guided vehicle, a predicted power consumption amount during standby of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during standby by the predicted standby time of the automated guided vehicle, a predicted power consumption amount during stop of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during stop by the predicted stop time of the automated guided vehicle, and a power consumption amount due to deterioration consumption consumed due to an increase in resistance caused by deterioration of the secondary battery; The step of determining the optimal charging amount includes a step of determining a target discharge depth corresponding to the current SOH of the secondary battery based on information defining a correspondence relationship between the SOH of the secondary battery and the target discharge depth; a step of determining whether the required SOC value is less than or equal to the target discharge depth; when it is determined that the required SOC value is less than or equal to the target discharge depth, a step of calculating, as the optimal charging amount, a charging amount that makes the SOC at the end of charging of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the end of charging within a range of an SOC upper limit value and an SOC lower limit value corresponding to the SOH or the target discharge depth; Charging amount calculation method.

8. A charging amount calculation program for calculating the charging amount of a secondary battery provided in an automated guided vehicle, which causes a computer to predict the required power amount, which is the power amount consumed by the secondary battery during a predetermined period; Execute the step of determining the optimal charge amount of the automated guided vehicle based on the required SOC value corresponding to the predicted required power amount and the SOH of the secondary battery. The step of predicting the required power amount includes: Calculating the total value of the predicted power consumption amount during running of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during running by the predicted running time of the automated guided vehicle, the predicted power consumption amount during standby of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during standby by the predicted standby time of the automated guided vehicle, the predicted power consumption amount during stop of the automated guided vehicle obtained by multiplying the power consumption per unit time consumed by the automated guided vehicle during stop by the predicted stop time of the automated guided vehicle, and the power consumption amount due to deterioration consumed due to the increase in resistance caused by the deterioration of the secondary battery as the required power amount. The step of determining the optimal charge amount includes: Determining the target depth of discharge corresponding to the current SOH of the secondary battery based on the information defining the correspondence between the SOH of the secondary battery and the target depth of discharge; Determining whether the required SOC value is less than or equal to the target depth of discharge; When it is determined that the required SOC value is less than or equal to the target depth of discharge, calculating, as the optimal charge amount, the charge amount that makes the SOC at the end of charging of the secondary battery and the SOC at the end of discharge obtained by subtracting the required SOC value from the SOC at the end of charging within the range of the SOC upper limit value and the SOC lower limit value corresponding to the SOH or the target depth of discharge. Charge amount calculation program.

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