JP6973099B2 - Monitoring equipment, monitoring methods and computer programs - Google Patents

Description

The present invention relates to a monitoring device, a monitoring method and a computer program.

Energy storage devices are widely used in uninterruptible power supplies, DC or AC power supplies included in regulated power supplies, and the like. In addition, the use of power storage elements in large-scale systems for storing renewable energy or power generated by existing power generation systems is expanding.

The power storage module has a configuration in which storage cells are connected in series. It is known that the storage cell deteriorates by repeating charging and discharging. Patent Document 1 describes the life of a storage battery by using a database that stores predicted deterioration rate values corresponding to a plurality of usage conditions of the storage battery and data on the usage conditions and the deterioration rate obtained from the actually operating storage battery. The technology for making predictions is disclosed.

In a power storage module in which storage cells are connected in series, there is a difference in self-discharge or a difference in deterioration rate between individual storage cells during charging or use, so that there are variations in voltage or charging state between storage cells. Occurs. Patent Document 2 discloses a technique for equalizing such variations in voltage or charge state between storage cells.

Japanese Unexamined Patent Publication No. 2015-121520 Japanese Patent No. 5573075

It is desired to grasp the deterioration progress or abnormality of the power storage cell (power storage module) installed in a mobile body or facility at an early stage, and for that purpose, it is considered to use artificial intelligence (hereinafter referred to as "AI"). ing.

An object of the present invention is to provide a monitoring device, a monitoring method, and a computer program using AI.

The power storage element monitoring device includes an acquisition unit that acquires information on whether the learning model for detecting the state of the power storage element is the first mode or the second mode, and the case where the learning model is the first mode. A change unit for changing the operation of the balance circuit for balancing the voltage of the power storage element from a predetermined state is provided.

In the method of monitoring the power storage element, information on whether the learning model for detecting the state of the power storage element is the first mode or the second mode is acquired, and when the learning model is the first mode, the power storage is performed. The operation of the balance circuit that balances the voltage of the element is changed from the predetermined state.

The acquisition unit acquires information on whether the learning model for detecting the state of the power storage element is the first mode or the second mode. The first mode may be a teacher data creation mode, a correct answer data creation mode, or a learning mode. The second mode may be a learning mode or a detection mode (a mode in which the state of the power storage element is actually detected by using the learned learning model). When the learning model is in the first mode, the changing unit changes the operation of the balance circuit (balancer) that balances the voltage of the power storage element from a predetermined state.

The predetermined state may be the normal operating state of the equilibrium circuit. For example, when the voltage difference between the plurality of storage cells (for example, the difference between the maximum voltage and the minimum voltage of the voltage of each storage cell) becomes equal to or larger than the threshold voltage, the equilibrium can be performed. Changing from a predetermined state may be to limit the operation of the equilibrium circuit. For example, (1) increase the threshold voltage at which the equilibrium circuit starts operation so that equilibrium is not performed unless the voltage difference between a plurality of storage cells becomes larger than the normal state, and (2) equilibrium. It involves stopping the operation of the circuit to prevent balancing.

With the above configuration, when learning a learning model for detecting the state of the power storage element, the degree to which the voltage or charge state of the power storage element is automatically adjusted can be changed by the operation of the equilibrium circuit. This makes it possible to acquire data that reflects the actual state of the power storage element whose deterioration has progressed or the power storage element which is in an abnormal state.

In order for AI to learn (especially machine learning), it is desirable to collect a large amount of data including data of normal power storage elements and data of deteriorated power storage elements. However, it is not easy to obtain the data of the deteriorated power storage element. It takes cost and time to make a deteriorated power storage element on a trial basis. When collecting data from a power storage element installed in a mobile object or facility and actually used, the deteriorated power storage element behaves in the same way as a normal power storage element due to the operation of the balance circuit provided in the power storage module (for example). , Voltage behavior detected by the sensor, temperature behavior) is shown. By changing the operation of the equilibrium circuit from the predetermined state, the above-mentioned changing unit can efficiently collect the data of the power storage element whose deterioration has progressed or the power storage element which is becoming an abnormal state.

When the learning model is in the first mode, the changing unit may change the threshold voltage at which the balancing circuit balances the voltage to a large value. As a result, the power storage element whose deterioration has progressed or the power storage element which is in an abnormal state tends to behave differently from the normal power storage element.

The change unit may change the operation of the equilibrium circuit to the stopped state when the learning model is in the first mode. As a result, the power storage element whose deterioration has progressed or the power storage element which is in an abnormal state tends to behave differently from the normal power storage element.

When the learning model is in the first mode, the changing unit may discharge one of the plurality of storage cells to increase the voltage difference between the plurality of storage cells. For example, by discharging the storage cell showing the minimum voltage among the plurality of storage cells, the voltage of the storage cell is lowered and the charging state is lowered. Thereby, it is possible to simulate a storage cell in which deterioration has progressed or a storage cell in an abnormal state.

The change unit may operate the equilibrium circuit in a predetermined state when the learning model is in the second mode. For example, in the detection mode in which the state of the power storage element is actually detected by the learned learning model, the equilibrium circuit can be operated in a predetermined state (for example, a normal operating state).

As a result, it is possible to accurately grasp the deterioration progress or abnormality of the power storage element (power storage cell, power storage module) based on the data under the actual usage conditions of the power storage element installed in the mobile body or the facility. Since the learning model has already learned the data related to the power storage element that has progressed to deteriorate or is in an abnormal state, which behaves differently from the normal power storage element, the deterioration state or abnormal state of the power storage element can be detected quickly. can do.

The change unit may change the operation of the equilibrium circuit from a predetermined state when the learning model is in the second mode. For example, by changing the equilibrium circuit from a predetermined state (for example, limiting the operation of the equilibrium circuit) and checking the output of the learning model in the detection mode in which the state of the power storage element is actually detected by the learned learning model. , The validity of the learning model can be verified.

Under the actual usage conditions of a power storage element installed in a mobile body or facility, the state of the power storage element is detected in a state where the power storage element that has deteriorated or is in an abnormal state is manifested. Can be done.

The acquisition unit may acquire information on whether the learning model is in the first mode or the second mode from the server. This makes it possible to remotely and collectively manage the operations of individual monitoring devices in a large-scale system or the like equipped with a large number of monitoring devices.

The monitoring device may include a learning model. The learning model may output the state of the power storage element based on the input data including the voltage and temperature of the power storage element. The learning model includes algorithms for machine learning, including, for example, deep learning. As a result, the monitoring device can quickly detect the deteriorated state or the abnormal state of the power storage element (power storage cell, power storage module) that it monitors.

It is a figure which shows the outline of the remote monitoring system of this embodiment. It is a block diagram which shows an example of the configuration of a remote monitoring system. It is a figure which shows an example of the connection form of a communication device. It is a block diagram which shows an example of the structure of a control board and a battery management device. It is a figure which shows an example of the transition of the state of a storage cell in a power storage module. It is a figure which shows an example of the control operation of a battery management apparatus. It is a block diagram which shows an example of another configuration of a battery management device. It is a flowchart which shows an example of the processing procedure performed by a battery management apparatus.

Hereinafter, the monitoring device according to the present embodiment will be described with reference to the drawings. FIG. 1 is a diagram showing an outline of the remote monitoring system 100 of the present embodiment. As shown in FIG. 1, a network N including a public communication network (for example, the Internet) N1 and a carrier network N2 that realizes wireless communication according to a mobile communication standard includes a thermal power generation system F, a mega solar power generation system S, and a wind power supply. A power generation system W, an uninterruptible power supply (UPS) U, and a rectifier (DC power supply or AC power supply) D arranged in a regulated power supply system for railways are connected. Further, a communication device 1 described later, a server device 2 for collecting information from the communication device 1, a client device 3 for acquiring the collected information, and the like are connected to the network N.

More specifically, the carrier network N2 includes the base station BS, and the client device 3 can communicate with the server device 2 from the base station BS via the network N. Further, an access point AP is connected to the public communication network N1, and the client device 3 can send and receive information from the access point AP to and from the server device 2 via the network N.

A power conditioner (PCS: Power Conditioning System) P and a power storage system 101 are attached to the mega solar power generation system S, the thermal power generation system F, and the wind power generation system W. The power storage system 101 is configured by arranging a plurality of containers C accommodating the power storage module group L side by side. The power storage module group L includes, for example, a power storage module (also referred to as a module) in which a plurality of power storage cells (also referred to as cells) are connected in series, a bank in which a plurality of power storage modules are connected in series, and a domain in which a plurality of banks are connected in parallel. It is composed of the hierarchical structure of. The power storage element is preferably a rechargeable battery such as a lead storage battery and a lithium ion battery, or a capacitor. A part of the power storage element may be a primary battery that cannot be recharged.

FIG. 2 is a block diagram showing an example of the configuration of the remote monitoring system 100. The remote monitoring system 100 includes a communication device 1, a server device 2, a client device 3, and a battery management device 50 (see FIG. 3) as a monitoring device described later.

As shown in FIG. 2, the communication device 1 is connected to the network N and also to the target devices P, U, D, and M. The target devices P, U, D, and M include a power conditioner P, an uninterruptible power supply device U, a rectifier D, and a management device M described later. From the viewpoint of the remote monitoring target, the battery management device 50 can be included in the management device M.

In the remote monitoring system 100, the state (for example, voltage, current, temperature, charge state (for example, voltage, current, temperature, charge state) of the power storage module (storage cell) in the power storage system 101 is used by using the communication device 1 connected to each target device P, U, D, M. The remote monitoring system 100 monitors the SOC). The remote monitoring system 100 presents the detected state of the storage cell (including the deteriorated state, the abnormal state, etc.) so that the user or the operator (maintenance person) can confirm it.

The communication device 1 includes a control unit 10, a storage unit 11, a first communication unit 12, and a second communication unit 13. The control unit 10 is composed of a CPU (Central Processing Unit) or the like, and controls the entire communication device 1 by using a built-in memory such as a ROM (Read Only Memory) and a RAM (Random Access Memory).

As the storage unit 11, for example, a non-volatile memory such as a flash memory can be used. The storage unit 11 stores the device program 1P read and executed by the control unit 10. The storage unit 11 stores information such as information collected by the processing of the control unit 10 and event logs.

The first communication unit 12 is a communication interface that realizes communication with the target devices P, U, D, and M, and for example, a serial communication interface such as RS-232C or RS-485 can be used.

The second communication unit 13 is an interface that realizes communication via the network N, and uses, for example, a communication interface such as Ethernet (registered trademark) or a wireless communication antenna. The control unit 10 can communicate with the server device 2 via the second communication unit 13.

The server device 2 includes a control unit 20, a storage unit 21, a communication unit 22, a learning model 23, and the like. The server device 2 may be a single server computer, but is not limited to this, and may be configured by a plurality of server computers.

The control unit 20 can be configured by, for example, a CPU, and controls the entire server device 2 by using a built-in memory such as a ROM and a RAM. Further, the control unit 20 may be composed of a CPU and a GPU (Graphics Processing Unit), a multi-core CPU, or a TPU (Tensor Processing Unit). The control unit 20 executes information processing based on the server program 2P stored in the storage unit 21. The server program 2P includes a Web server program, and the control unit 20 functions as a Web server that provides a Web page to the client device 3 and accepts a login to a Web service. The control unit 20 can also collect information from the communication device 1 as a server for SNMP (Simple Network Management Protocol) based on the server program 2P.

As the storage unit 21, a non-volatile memory such as a hard disk or a flash memory can be used. The storage unit 21 stores data including the states of the target devices P, U, D, and M to be monitored, which are collected by the processing of the control unit 20.

The communication unit 22 is a communication device that realizes a communication connection and data transmission / reception via the network N. Specifically, the communication unit 22 is a network card corresponding to the network N.

The learning model 23 describes a state including deterioration or abnormality of the storage cell based on the input data including the voltage and temperature of the storage cell collected from the target devices P, U, D, and M via the communication device 1. Can be output. The learning model 23 includes algorithms for machine learning, including, for example, deep learning. The learning model 23 may use a quantum computer.

The client device 3 may be a computer used by an operator such as an administrator of the power storage system 101 of the power generation systems S and F and a maintenance person of the target devices P, U, D, and M. The client device 3 may be a desktop type or laptop type personal computer, or may be a smartphone or tablet type communication terminal. The client device 3 includes a control unit 30, a storage unit 31, a communication unit 32, a display unit 33, and an operation unit 34.

The control unit 30 is a processor using a CPU. The control unit 30 causes the display unit 33 to display the Web page provided by the server device 2 or the communication device 1 based on the Web browser program stored in the storage unit 31.

The storage unit 31 uses a non-volatile memory such as a hard disk or a flash memory. Various programs including the Web browser program are stored in the storage unit 31.

The communication unit 32 uses a communication device such as a network card for wired communication, a wireless communication device for mobile communication connected to a base station BS (see FIG. 1), or a wireless communication device corresponding to connection to an access point AP. be able to. The control unit 30 can make a communication connection or send / receive information to / from the server device 2 or the communication device 1 via the network N by the communication unit 32.

As the display unit 33, a display such as a liquid crystal display or an organic EL (Electro Luminescence) display can be used. The display unit 33 can display an image of a Web page provided by the server device 2 by a process based on the Web browser program of the control unit 30.

The operation unit 34 is a user interface such as a keyboard and a pointing device capable of input / output to / from the control unit 30, or a voice input unit. The operation unit 34 may use the touch panel of the display unit 33 or the physical buttons provided on the housing. The operation unit 34 notifies the control unit 20 of the operation information by the user.

FIG. 3 is a diagram showing an example of a connection form of the communication device 1. As shown in FIG. 3, the communication device 1 is connected to the management device M. A battery management device (BMU: Battery Management Unit) 50 as a monitoring device provided in each of the banks # 1 to #N is connected to the management device M. The communication device 1 may be a terminal device (measurement monitor) that communicates with the battery management device 50 and receives information on the power storage element, or is a network card type communication device that can be connected to a power supply-related device. You may.

Each bank # 1 to #N includes a plurality of power storage modules 60, and each power storage module 60 includes a control board (CMU: Cell Monitoring Unit) 70. The battery management device 50 provided for each bank can communicate with the control board 70 having a communication function built in each of the power storage modules 60 by serial communication, and also can communicate with the management device M. You can send and receive. The management device M aggregates information from the battery management device 50 of the bank belonging to the domain and outputs the information to the communication device 1.

FIG. 4 is a block diagram showing an example of the configuration of the control board 70 and the battery management device 50. The control board 70 includes a balance circuit 71, a drive unit 73, a voltage acquisition unit 74, a control unit 75, a storage unit 76, a communication unit 77, and the like. In the power storage module 60, a plurality of power storage cells 61a to 61e are connected in series. In FIG. 4, for convenience, five energy storage cells connected in series are shown, but the number of energy storage cells constituting the energy storage module 60 is not limited to five.

The balance circuit 71 is a series circuit of the resistor 71a and the switch 72a connected in parallel to the storage cell 61a, a series circuit of the resistor 71b and the switch 72b connected in parallel to the storage cell 61b, and a parallel to the storage cell 61c. A series circuit of the resistor 71c and the switch 72c connected to, a series circuit of the resistor 71d and the switch 72d connected in parallel to the storage cell 61d, and a resistor 71e and the switch connected in parallel to the storage cell 61e. A series circuit with 72e is provided. As the switches 72a to 72e, for example, a FET (Field Effect Transistor) or the like can be used, but a relay or the like may be used.

The drive unit 73 drives the switches 72a to 72e to turn on or off. When the switches 72a to 72e are FETs, the drive unit 73 can output a gate signal to the gate of the FET to turn the FET on and off.

The voltage acquisition unit 74 acquires the voltage of each of the storage cells 61a to 61e.

The storage unit 76 stores a predetermined threshold voltage.

The control unit 75 identifies the maximum voltage and the minimum voltage from the respective voltages of the storage cells 61a to 61e acquired by the voltage acquisition unit 74. When the voltage difference between the maximum voltage and the minimum voltage becomes equal to or greater than the threshold voltage, the control unit 75 turns on a switch connected in parallel to the storage cell from which the maximum voltage has been acquired, thereby performing the control unit 75 via a resistor. Discharge the storage cell. As a result, the voltage (charge state) of the storage cell is lowered to balance the voltage (charge state) between the storage cells 61a to 61e.

The communication unit 77 has, for example, a function of performing serial communication with the first communication unit 52 of the battery management device 50.

The battery management device 50 includes a control unit 51, a first communication unit 52, a second communication unit 53, and the like.

The first communication unit 52 has a function of, for example, performing serial communication with the communication unit 77 of the control board 70.

The second communication unit 53 has a function of transmitting / receiving information to / from the communication device 1. More specifically, the second communication unit 53 has a function as an acquisition unit, and the server device 2 provides information on whether the learning model 23 that detects the state of the storage cell shifts to the learning mode or the detection mode. Get from. As a result, in a large-scale system or the like provided with a large number of battery management devices 50, the operations of the individual battery management devices 50 can be remotely managed and collectively managed.

The detection mode (also referred to as an operation mode) is a mode in which the state of the storage cell is actually detected by using the learned learning model 23. In the present embodiment, the server device 2 is configured to include the learning model 23, but the present invention is not limited to this, and another device may be configured to include the learning model 23.

The control unit 51 can be configured by a CPU or the like. The control unit 51 has a function as a change unit, and when the learning model 23 of the server device 2 shifts to the learning mode, the control unit 51 changes the operation of the balance circuit 71 that balances the voltages of the plurality of storage cells from a predetermined state. The control operation is performed.

The predetermined state may mean a normal operating state of the equilibrium circuit 71, for example, a voltage difference between a plurality of storage cells 61a to 61e (for example, a difference between the maximum voltage and the minimum voltage of the voltage of each storage cell). ) Is equal to or higher than the threshold voltage, the equilibrium can be set. Changing from a predetermined state may mean limiting the operation of the equilibrium circuit 71. For example, (1) the threshold voltage at which the equilibrium circuit 71 starts operating is increased to increase the voltage between a plurality of storage cells. It includes not performing equilibrium unless the difference becomes larger than the normal state, and (2) stopping the operation of the equilibrium circuit 71 to prevent equilibrium. The details of the control operation for the equilibrium circuit 71 performed by the control unit 51 will be described later.

With the above configuration, when the learning model 23 for detecting the state of the storage cell is trained, the degree to which the voltage or the charging state of the storage cell is automatically adjusted can be changed by the operation of the equilibrium circuit 71. This makes it possible to acquire data that reflects the actual state of the storage cell whose deterioration has progressed or the storage cell which is in an abnormal state.

FIG. 5 is a diagram showing an example of the transition of the state of the storage cell in the power storage module. The storage cells are represented by reference numerals a to e. In the figure, the voltage (charge state) of the storage cell is shown with diagonal lines. The voltage difference of the storage cell is exaggerated in FIG. 5, and the actual voltage difference is smaller (for example, about several tens of mV). From the state A to the state C shown in the upper row, the transition of the state of the storage cell in the normal operating state of the equilibrium circuit 71 is shown. In the state A, the voltages of the storage cells a, c, d, and e are almost the same, but the voltage of the storage cell b is smaller than the voltage of the other storage cells. In this case, the storage cell b is deteriorated or has a potential sign of abnormality as compared with other storage cells.

Further, it is assumed that the voltage difference between the storage cells (the difference between the voltage of the storage cell b and the voltage of another storage cell in the figure) becomes equal to or higher than the threshold voltage Vth when the usage state continues and the state B is reached. Then, the operation of the equilibrium circuit 71 is started, and the storage cells other than the storage cell b are discharged. As a result, the voltage of the storage cell is balanced.

After that, for example, when each storage cell is charged, the voltage of each storage cell rises in an equilibrium state, and the state C is obtained.

Next, the control operation for the equilibrium circuit 71 by the battery management device 50 of the present embodiment will be described. The battery management device 50 can change the threshold voltage at which the balancing circuit 71 starts operating (balancing) to a value Vth2 larger than the threshold voltage Vth in the normal state. From the state D to the state E shown in the lower row, the transition of the state of the storage cell when the operation of the equilibrium circuit 71 is changed from the normal state is shown. The state D is the same as the state A.

It is assumed that the usage state continues, the state E is reached, and the voltage difference between the storage cells (the difference between the voltage of the storage cell b and the voltage of another storage cell in the figure) ΔV becomes the threshold voltage Vth or more (voltage difference ΔV). Is smaller than the threshold voltage Vth2). The equilibrium circuit 71 does not start equilibration as in a normal operating state. For example, in the state E, the battery management device 50 collects data such as voltage, current, temperature, and SOC of the storage cell (storage module), and uses the communication device 1 as learning data of the learning model 23. It can be provided to the server device 2. The second communication unit 53 can transmit various data indicating the state of the collected storage cell (storage module) to the server device 2.

This makes it possible to acquire data that reflects the actual state of the storage cell whose deterioration has progressed or the storage cell which is in an abnormal state.

Next, the relationship between the mode of the learning model 23 and the control operation of the battery management device 50 with respect to the equilibrium circuit 71 will be described.

FIG. 6 is a diagram showing an example of the control operation of the battery management device 50. Here, cases 1 to 5 will be described.

Case 1 shows a case where the learning model 23 shifts to the learning mode. The control unit 51 changes the threshold voltage when the equilibrium circuit 71 balances the voltage to a large value. The control unit 51 acquires data including the voltage and temperature of the storage cell and provides the data to the learning model 23. As a result, the storage cell whose deterioration has progressed or the storage cell which is in an abnormal state tends to behave differently from the normal storage cell.

Case 2 shows a case where the learning model 23 shifts to the learning mode. The control unit 51 changes the operation of the equilibrium circuit 71 to a stopped state. The control unit 51 acquires data including the voltage and temperature of the storage cell and provides the data to the learning model 23. As a result, the storage cell whose deterioration has progressed or the storage cell which is in an abnormal state tends to behave differently from the normal storage cell.

Case 3 shows a case where the learning model 23 shifts to the learning mode. The control unit 51 discharges one of the plurality of storage cells to increase the voltage difference between the plurality of storage cells. For example, by discharging the storage cell showing the minimum voltage among the plurality of storage cells, the voltage of the storage cell is lowered and the SOC is lowered, so that the voltage difference between the plurality of storage cells (for example, the maximum voltage and the minimum voltage) is reduced. The difference from the voltage) can be increased. The control unit 51 acquires data including the voltage and temperature of the storage cell and provides the data to the learning model 23. Thereby, it is possible to simulate a storage cell in which deterioration has progressed or a storage cell in an abnormal state.

Case 4 shows a case where the learning model 23 shifts to the detection mode. The control unit 51 operates the equilibrium circuit 71 in a normal operating state (predetermined state). That is, in the detection mode in which the state of the storage cell is actually detected by the learned learning model 23, the equilibrium circuit 71 can be operated in the normal operating state. The control unit 51 acquires data including the voltage and temperature of the storage cell and provides the data to the learning model 23.

As a result, it is possible to accurately grasp the deterioration progress or abnormality of the power storage cell (power storage module) based on the data under the actual usage conditions of the power storage cell installed in the mobile body or the facility. Since the learning model 23 has already learned the data related to the storage cell that has progressed to deteriorate or is in an abnormal state, which behaves differently from the normal storage cell, the deterioration state or abnormal state of the storage cell can be quickly detected. Can be detected.

Case 5 shows a case where the learning model 23 shifts to the detection mode. The control unit 51 sets the state of the equilibrium circuit 71 to any of the above-mentioned cases 1, 2, and 3. That is, the equilibrium circuit 71 is changed from the normal operating state in the detection mode in which the state of the storage cell is actually detected by the learned learning model 23. The control unit 51 acquires data including the voltage and temperature of the storage cell and provides the data to the learning model 23.

Thereby, the validity of the learning model 23 can be verified. If the learned learning model 23 can quickly detect the power storage module whose equilibrium circuit 71 is changed from the normal operating state or a specific power storage cell included in the power storage module, it can be determined that the learning model 23 is highly valid. By such a learned learning model 23, not only the deteriorated storage cell is detected, but also the storage module having the equilibrium circuit 71 or the control board 70 that is not operating normally, and the battery management device 50 that is not operating normally. It is also possible to detect a bank having a.

FIG. 7 is a block diagram showing an example of another configuration of the battery management device 50. As shown in FIG. 7, the battery management device 50 can include a learning model 54. The learning model 54 can have the same configuration and function as the learning model 23 described above. The battery management device 50 may have the same functions as the server device 2, and can determine the mode (detection mode or learning mode) of the learning model 54. As a result, the battery management device 50 can quickly detect the deteriorated state or the abnormal state of the power storage module (storage cell) that it monitors.

FIG. 8 is a flowchart showing an example of a processing procedure performed by the battery management device 50. Hereinafter, for convenience, the main body of the process will be described as the control unit 51. The control unit 51 acquires the mode of the learning model (S11) and determines whether or not it is the learning mode (S12). In the learning mode (YES in S12), the control unit 51 changes the state of the equilibrium circuit from the normal state (S13). The change from the normal state can be, for example, any of the cases 1, 2, and 3 illustrated in FIG.

The control unit 51 acquires learning data including the voltage and temperature of the storage cell (S14), and transmits the acquired learning data to the server device 2 to provide the learning data to the learning model (S15). The learning data can be acquired by collecting the data detected in a predetermined sampling cycle over a required period.

The control unit 51 determines whether or not to end the acquisition of the learning data (S16), and if the acquisition of the learning data is not completed (NO in S16), the processing after step S14 is continued. When the acquisition of the learning data is completed (YES in S16), the control unit 51 returns the equilibrium circuit to the normal state (S17), and performs the process of step S23 described later.

When not in the learning mode (NO in S12), that is, in the detection mode, the control unit 51 determines whether or not to change the state of the equilibrium circuit from the normal state (S18). The change from the normal state can be, for example, any of the cases 1, 2, and 3 illustrated in FIG. That is, in the detection mode, the state of the equilibrium circuit can be selected from a normal operating state and a state in which the normal operating state is restricted.

When the state of the balanced circuit is changed from the normal state (YES in S18), the control unit 51 changes the state of the balanced circuit from the normal state (S19), and performs the process of step S20 described later. When the state of the equilibrium circuit is not changed from the normal state (NO in S18), the control unit 51 performs the process of step S20 described later without performing the process of step S19.

The control unit 51 acquires input data including the voltage and temperature of the storage cell (S20), and transmits the acquired input data to the server device 2 to provide the learning model (S21). The input data in the detection mode can be acquired by collecting the data detected in a predetermined sampling cycle over a required period.

The control unit 51 determines whether or not to terminate the detection mode (S22), and if the detection mode is not terminated (NO in S22), the processing after step S20 is continued. When the detection mode is terminated (YES in S22), the control unit 51 determines whether or not to terminate the process (S23). If the process is not terminated (NO in S23), the control unit 51 continues the process after step S11, and if the process is terminated (YES in S23), the process is terminated.

The control unit 51 of the present embodiment can also be realized by using a general-purpose computer including a CPU (processor), a RAM (memory), and the like. That is, as shown in FIG. 8, a computer program that defines the procedure for each process is loaded into a RAM (memory) provided in the computer, and the computer program is executed by the CPU (processor) to control the control unit on the computer. 51 can be realized. The computer program may be recorded and distributed on a recording medium. The learning model 23 learned by the server device 2 and the computer program based on the learning model 23 are distributed and installed to the target devices P, U, D, M for remote monitoring, the battery management device 50, and the terminal device via the network N and the communication device 1. You may.

In the computer program, in order to make the computer learn the learning model for the power storage element, the step of causing the computer to acquire information on whether the learning model is the first mode or the second mode, and the learning model is the first mode. If this is the case, the step of changing the operation of the balancing circuit that balances the voltage of the power storage element from a predetermined state and the input data including at least one of the voltage, current, temperature, and SOC of the power storage element are acquired and provided to the learning model. To execute the steps to be performed.

When the learning model is in the first mode, the computer program may further cause the computer to perform a step of acquiring input data and providing the learning model with the operation of the equilibrium circuit in a predetermined state.

In order to make the computer detect the state of the power storage element, the computer program causes the computer to input input data including at least one of the voltage, current, temperature, and SOC of the power storage element to the learning model trained by the above-mentioned computer program. And the step of detecting the state of the power storage element are executed.

As described above, according to the battery management device of the present embodiment, when the learning model for detecting the state of the storage cell is trained, the voltage or SOC of the storage cell is automatically adjusted by the operation of the equilibrium circuit. The degree can be changed. As a result, it becomes possible to acquire data reflecting the actual state of the storage cell whose deterioration has progressed or the storage cell which is in an abnormal state, and it is possible to quickly detect the deterioration state or the abnormal state of the storage cell.

The technical idea may be realized in the following forms. It is a learning method of the learning model, and after changing the operation of the balance circuit that balances the voltage of the power storage element from a predetermined state, the input data including at least one of the voltage, current, temperature, and SOC of the power storage element is acquired. And how to provide it to the learning model. For one or more power storage modules, the operation of the equilibrium circuit is variously changed from the predetermined state (the threshold voltage for voltage balancing is changed to a large value or a small value, or the operation of the equilibrium circuit is changed to the stopped state. Then, the input data may be acquired and provided to the learning model. For one or more power storage modules, input data may be provided to the learning model without changing the state of the balanced circuit from a predetermined state (ie, keeping the balanced circuit in its normal operating state). Input data may be provided for the training model of the server. With such an approach, it is possible to provide input data to the learning model in multiples from a limited number of power storage modules. That is, big data for learning can be efficiently prepared.

The embodiments are exemplary in all respects and are not restrictive. The scope of the present invention is indicated by the scope of claims and includes all modifications within the meaning and scope equivalent to the scope of claims.

50 Battery management device 51 Control unit 52 1st communication unit 53 2nd communication unit 23, 54 Learning model 60 Energy storage module 61a, 61b, 61c, 61d, 61e Energy storage cell 70 Control board 71 Balance circuit

Claims (12)

It is a monitoring device for power storage elements.
An acquisition unit that acquires information on whether the learning model that detects the state of the power storage element is in the learning mode or the detection mode.
When the learning model is in the learning mode, a monitoring device including a changing unit that changes the operation of a balancing circuit that balances the voltage of the power storage element from a predetermined state. The changed part is
The monitoring device according to claim 1, wherein when the learning model is in the learning mode, the balancing circuit changes the threshold voltage at the time of voltage balancing to a large value. The changed part is
The monitoring device according to claim 1, wherein when the learning model is in the learning mode, the operation of the equilibrium circuit is changed to a stopped state. The changed part is
The monitoring device according to claim 1, wherein when the learning model is in the learning mode, one of the plurality of storage cells is discharged to increase the voltage difference between the plurality of storage cells. The changed part is
The monitoring device according to any one of claims 1 to 4, wherein when the learning model is in the detection mode, the equilibrium circuit is operated in the predetermined state. The changed part is
The monitoring device according to any one of claims 1 to 4, wherein when the learning model is in the detection mode, the operation of the equilibrium circuit is changed from the predetermined state. The acquisition unit
The monitoring device according to any one of claims 1 to 6, wherein the learning model acquires information on whether the learning mode or the detection mode is obtained from the server. The monitoring device according to any one of claims 1 to 7, further comprising a learning model for outputting the state of the power storage element based on input data including the voltage and temperature of the power storage element. It is a monitoring method for power storage elements.
Information on whether the learning model for detecting the state of the power storage element is in the learning mode or the detection mode is acquired, and the information is obtained.
A monitoring method for changing the operation of a balancing circuit that balances the voltage of the power storage element from a predetermined state when the learning model is in the learning mode. A computer program that lets a computer learn a learning model for a power storage element.
On the computer
A step of acquiring information on whether the learning model is a learning mode or a detection mode,
When the learning model is in the learning mode, a step of changing the operation of the equilibrium circuit for balancing the voltage of the power storage element from a predetermined state and
A computer program that executes a step of acquiring input data including at least one of the voltage, current, temperature, and SOC of the power storage element and providing it to the learning model. The computer according to claim 10, wherein when the learning model is in the learning mode, the computer further performs a step of acquiring the input data and providing the learning model with the operation of the equilibrium circuit in a predetermined state. program. A computer program that causes a computer to detect the state of a power storage element.
On the computer
A step of causing input data including at least one of the voltage, current, temperature, and SOC of the power storage element to be input to a learning model trained by the computer program according to claim 10 or 11.
A computer program that executes a step of detecting the state of the power storage element.

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