Patent Description:
An energy storage device is widely used in an uninterruptible power system, a DC power supply or an AC power supply included in a stabilized power supply, and the like. Further, the energy storage device is expansively used in a large-scale system for storing renewable energy or electric power generated in an existing power generating system.

An energy storage module has a configuration where energy storage cells are connected in series. An energy storage cell is known to deteriorate progressively as a result of repeated charge-discharge cycles. Patent Document <NUM> discloses a technique configured, based on a database for storing a predicted value of deterioration rate in accordance with a plurality of usage conditions of a storage battery and based on data for a usage condition and a deterioration rate of a storage battery that is actually in operation, to predict a service life of the storage battery (that is actually in operation).

In the energy storage module where the energy storage cells are connected in series, each of the energy storage cells has a difference from others of the energy storage cells, such as a difference in self discharge during the charge-discharge or a difference in speed of deterioration during the usage. As a result, the energy storage cells exhibit a variation in voltage or a variation in state of charge. Patent Document <NUM> discloses a technique to balance the variation in voltage or the variation in state of charge between the energy storage cells. Patent Document <NUM> discloses a battery performance analysis method for valve-regulated lead-acid batteries. Multiple parameters such as the maximum voltage of each battery in equalized charge period and the voltage of each battery at the end of the short-term discharge are online gathered and given to an artificial network that calculates and outputs the performance prediction results of each battery.

It is desired to grasp at an early stage a degree of deterioration or an abnormal state of an energy storage cell (energy storage module) that is provided in a mobile object or a facility. In view of this, a possibility of employing artificial intelligence (hereinafter, referred to as "AI") techniques is explored.

An aspect of the present invention provides a monitoring device, a monitoring method, and a computer program, each employing AI techniques.

Another aspect of the present invention provides a deterioration determination method, a deterioration determination device, and a deterioration determination system, each configured to detect at the early stage an energy storage device that deteriorates relatively quickly.

A monitoring device is provided as defined in claim <NUM>.

A monitoring method is provided as defined in claim <NUM>.

The acquisition unit acquires the information regarding whether the learning model configured to detect the state of the energy storage device is in the first mode or in the second mode. The first mode may be a mode to create teaching data, a mode to create correct answer data, or a learning mode. The second mode may be the learning mode or a detection mode (a mode to actually detect the state of the energy storage device based on the learning model that has learned). In the case where the learning model is in the first mode, the change unit changes the operation of the balancer circuit configured to balance the voltage of the energy storage device from the predetermined state.

The predetermined state may correspond to a normal operational state of the balancer circuit. For example, when a voltage difference between a plurality of energy storage cells (e.g., a difference between a maximum voltage and a minimum voltage among respective voltages of the plurality of energy storage cells) is equal to or more than a threshold voltage, the balancer circuit may balance the voltages of the plurality of energy storage cells. A change from the predetermined state may correspond to a restriction on the operation of the balancer circuit. The restriction includes, for example: (<NUM>) increasing the threshold voltage that causes the balancer circuit to start the operation, so as to prevent the balancer circuit from balancing the voltages until the voltage difference between the plurality of energy storage cells further increases to be larger than in the normal state; and (<NUM>) stopping the operation of the balancer circuit to prevent the balancer circuit from balancing the voltages.

With the configuration described above, in a case where the learning model (configured to detect the state of the energy storage device) is caused to learn, it is possible to change a degree, to which the voltage or a state of charge of the energy storage device is automatically adjusted, in accordance with the operation of the balancer circuit. Accordingly, it is possible to acquire data reflecting an actual state of an energy storage device that has deteriorated or an energy storage device that is turning into an abnormal state.

In order to cause an AI to learn (particularly, machine learning), it is desirable to collect a lot of data including data regarding an energy storage device in a normal state and data regarding the energy storage device that has deteriorated. However, it is not easy to obtain the data regarding the energy storage device that has deteriorated. Cost and time are required to experimentally create the energy storage device that has deteriorated. When collecting data from an energy storage device that is provided and actually used in a mobile object or a facility, the energy storage device that has deteriorated exhibits a same behavior as the energy storage device in the normal state (e.g., a behavior in voltage or a behavior in temperature, each detected by a sensor) due to the operation of the balancer circuit in an energy storage module. The change unit described above changes the operation of the balancer circuit from the predetermined state. As a result, it is possible to efficiently collect the data regarding the energy storage device that has deteriorated or the energy storage device that is turning into the abnormal state.

In the case where the learning model is in the first mode, the change unit may change a threshold voltage that causes the balancer circuit to balance the voltage to a larger value. With this configuration, it is easier to identify the energy storage device that has deteriorated or the energy storage device that is turning into the abnormal state, each exhibiting any different behavior from the energy storage device in the normal state.

In the case where the learning model is in the first mode, the change unit may change the operation of the balancer circuit to a stopped state. With this configuration, it is easier to identify the energy storage device that has deteriorated or the energy storage device that is turning into the abnormal state, each exhibiting any different behavior from the energy storage device in the normal state.

In the case where the learning model is in the first mode, the change unit causes one of a plurality of energy storage cells to discharge in order to increase a voltage difference between the plurality of energy storage cells. For example, the change unit causes one of the plurality of energy storage cells exhibiting a minimum voltage to discharge. As a result, the voltage of the energy storage cell exhibiting the minimum voltage is decreased, and a state of charge of the corresponding energy storage cell is decreased. Accordingly, it is possible to simulate the energy storage cell that has deteriorated or the energy storage cell that is turning into the abnormal state.

In a case where the learning model is in the second mode, the change unit may cause the balancer circuit to operate in the predetermined state. For example, in the detection mode where the learning model that has learned actually detects the state of the energy storage device, the change unit may cause the balancer circuit to operate in the predetermined state (e.g., the normal operational state).

With this configuration, it is possible to accurately grasp a degree of the deterioration or abnormality of the energy storage device (energy storage cell/energy storage module) that is provided in the mobile object or the facility, based on the data acquired in actual usage conditions of the energy storage device (energy storage cell/energy storage module) that is provided therein. The learning model has learned the data regarding the energy storage device that has deteriorated or the energy storage device that is turning into the abnormal state, each exhibiting any different behavior from the energy storage device in the normal state. Thus, the learning model instantly detects the deteriorated state or the abnormal state of the energy storage device.

In the case where the learning model is in the second mode, the change unit may change the operation of the balancer circuit from the predetermined state. For example, in the detection mode where the learning model that has learned actually detects the state of the energy storage device, the change unit may change the operation of the balancer circuit from the predetermined state (e.g., impose the restriction on the balancer circuit) to check an output of the learning model. With this configuration, it is possible to verify whether or not the learning model is valid.

Under the actual usage conditions of the energy storage device that is provided in the mobile object or the facility, it is possible to detect the state of the energy storage device in a state where the energy storage device that has deteriorated or the energy storage device that is turning into the abnormal state is made apparent.

The acquisition unit may acquire from a server the information regarding whether the learning model is in the first mode or in the second mode. With this configuration, in a large-scale system including a large number of the monitoring devices, operations of these monitoring devices are remotely managed on an individual basis and on a collective basis.

The monitoring device may include the learning model. The learning model may output the state of the energy storage device based on input data including the voltage and the temperature of the energy storage device. The learning model includes, for example, an algorithm for machine learning such as deep learning. With this configuration, the monitoring device monitors the energy storage device (energy storage cell/energy storage module) and instantly detects the deteriorated state or the abnormal state of the energy storage device (energy storage cell/energy storage module).

A deterioration determination method according to another aspect of the present invention includes steps of: stopping energizing an energy storage device unit including a plurality of energy storage devices; stopping balancing voltages between the plurality of energy storage devices; acquiring a temporal change in voltage of each of the plurality of energy storage devices; and determining whether or not any one of the plurality of energy storage devices has deteriorated quickly based on the acquired temporal change in voltage of each of the plurality of energy storage devices. An energy storage cell or an energy storage module may correspond to the energy storage device, and the energy storage module or a bank as will be described later may correspond to the energy storage device unit.

When the energy storage module includes an energy storage cell that has deteriorated relatively quickly (hereinafter, referred to as an energy storage cell that has deteriorated), the energy storage cell that has deteriorated restricts a performance of the energy storage module. Further, the energy storage cell that has deteriorated affects an overall performance of an energy storage system where the energy storage module is installed. Accordingly, in order to maintain the performance of the energy storage system, it is desirable to detect at an early stage the energy storage cell that has deteriorated. However, even an energy storage cell that deteriorates relatively quickly shows slow deterioration at an initial stage. When the system functions to balance the voltages or states of charge between the plurality of energy storage cells, a difference in behavior between the energy storage cell that has deteriorated and an energy storage cell in a normal state is reduced, and thus it is difficult to detect the energy storage cell that has deteriorated. In a case of failing to detect the energy storage cell that has deteriorated at the early stage and resulting in apparent degradation of the energy storage system, the cause investigation may take longer, thereby requiring a prolonged stoppage of the energy storage system.

With the configuration described above, it is possible to determine whether or not each of the energy storage devices has deteriorated more smoothly than in a conventional configuration. It is thus possible to detect at the early stage any one of the energy storage devices that has deteriorated. Accordingly, it is possible to remove any one of the energy storage devices that has deteriorated before the performance of the energy storage system significantly degrades.

Hereinafter, a monitoring device according to this embodiment will be described with reference to the drawings. <FIG> is a diagram illustrating a schematic configuration of a remote monitoring system (deterioration determination system) <NUM> according to this embodiment. As illustrated in <FIG>, a network N includes a public communication network (such as the Internet) N1, and a carrier network N2 configured to provide wireless communication based on mobile communication standard. The network N is connected to a thermal power generating system F, a mega solar power generating system S, and a wind power generating system W, an uninterruptible power system (UPS) U, a rectifier (a DC power supply or an AC power supply) D arranged in a stabilized power supply system for railways, and the like. Further, the network N is connected to a communication device <NUM>, a server device <NUM>, and a client device <NUM>, as will be described later. The server device <NUM> is configured to collect information from the communication device <NUM>, and the client device <NUM> is configured to acquire the information collected.

The carrier network N2 includes a base station BS. The client device <NUM> communicates with the server device <NUM> from the base station BS via the network N. The public communication network N1 is connected to an access point AP. The client device <NUM> transmit/receives the information to/from the server device <NUM> via the access point AP and the network N.

Each of the mega solar power generating system S, the thermal power generating system F, and the wind power generating system W is installed along with a power conditioning system P and an energy storage system <NUM>. The energy storage system <NUM> includes a plurality of containers C, in each of which an energy storage module group L is accommodated. The plurality of containers C are arranged and aligned with each other. The energy storage module group L has a hierarchical configuration including, for example, a plurality of energy storage cells, a plurality of energy storage modules, a plurality of banks, and a domain. In the hierarchical configuration, the plurality of energy storage cells are connected in series in each of the plurality of energy storage modules; the plurality of energy storage modules are connected in series in each of the plurality of banks; and the plurality of banks are connected in parallel in the domain. An energy storage device is preferably a secondary battery such as a lead-acid battery and a lithium ion battery, or a rechargeable battery such as a capacitor. Some of the energy storage devices may be a non-rechargeable primary battery.

<FIG> is a block diagram showing an example of a configuration of the remote monitoring system <NUM>. The remote monitoring system <NUM> includes the communication device <NUM>, the server device <NUM>, the client device <NUM>, and a battery management unit <NUM>. The battery management unit <NUM> (see <FIG>) functions as a monitoring device as will be described later.

As shown in <FIG>, the communication device <NUM> is connected to the network N, and is concurrently connected to each of target devices P, U, D, and M. The target devices P, U, D, and M respectively correspond to the power conditioning system P, the uninterruptible power system U, the rectifier D, and a management device M as will be described later. Note that, the battery management unit <NUM> may be included in the management device M as a remote monitoring target.

The remote monitoring system <NUM> uses the communication device <NUM>, to which each of the target devices P, U, D, and M is connected, to monitor a state of each of the energy storage modules (energy storage cells) in the energy storage system <NUM>. The state of each of the energy storage modules (energy storage cells) is, for example, a voltage, a current, a temperature, a state of charge (SOC). The remote monitoring system <NUM> displays the state of each of the energy storage cells detected (including a deteriorated state, an abnormal state, or the like), so as to cause a user or an operator (maintenance staff) to confirm the state.

The communication device <NUM> includes a control unit <NUM>, a storage unit <NUM>, a first communication unit <NUM> and a second communication unit <NUM>. The control unit <NUM> is a central processing unit (CPU) or the like, and uses a read only memory (ROM) built in, a random access memory (RAM) built in, and the like to control overall of the communication device <NUM>.

The storage unit <NUM> is, for example, a non-volatile memory such as a flash memory. The storage unit <NUM> stores a device program 1P that the control unit <NUM> is configured to read and execute. The storage unit <NUM> stores information collected in processes that the control unit <NUM> has executed, such as an event log.

The first communication unit <NUM> is a communication interface to provide communication with the target devices P, U, D, and M. The first communication unit <NUM> is, for example, a serial communication interface such as RS-232C or RS-<NUM>.

The second communication unit <NUM> is a communication interface to provide the communication via the network N. The second communication unit <NUM> is, for example, a communication interface such as Ethernet (registered trademark) or a wireless communication antenna. The control unit <NUM> communicates with the server device <NUM> via the second communication unit <NUM>.

The server device <NUM> includes a control unit <NUM>, a storage unit <NUM>, a communication unit <NUM>, a learning model <NUM>, and the like. The server device <NUM> may be a single server computer, but is not limited thereto. The server device <NUM> may include a plurality of server computers.

The control unit <NUM> may be, for example, a CPU, and uses a memory such as a ROM built in, a RAM built in, and the like to control overall of the server device <NUM>. The control unit <NUM> may be a CPU, a graphics processing unit (GPU), a multi-core CPU, or a tensor processing unit (TPU). The control unit <NUM> executes information processing based on a server program 2P stored in the storage unit <NUM>. The server program 2P includes a web server program, and the control unit <NUM> functions as a web server to provide a web page to the client device <NUM> and to accept a login from the client device <NUM> to a web service. Based on the server program 2P, the control unit <NUM> may also be a simple network management protocol (SNMP) server to collect the information from the communication device <NUM>.

The storage unit <NUM> may be a non-volatile memory such as a hard disk or a flash memory. The storage unit <NUM> stores data collected in processes that the control unit <NUM> has executed, the data including a state of each of the target devices P, U, D, and M to be monitored.

The communication unit <NUM> is a communication device to transmit/receive communication and data to/from systems connected via the network N. More specifically, the communication unit <NUM> is a network card for communications via the network N.

The learning model <NUM> collects input data from each of the target devices P, U, D, and M via the communication device <NUM>, the input data including the voltage and the temperature of each of the energy storage cells. Based on the input data, the learning model <NUM> outputs the state of each of the energy storage cells including the deteriorated state or the abnormal state. The learning model <NUM> includes, for example, an algorithm for machine learning such as deep learning. The learning model <NUM> may be a quantum computer.

The client device <NUM> may be a computer for an administrator of the energy storage system <NUM> of the mega solar power generating system S and the thermal power generating system F, or the operator such as the maintenance staff of the target devices P, U, D, and M. The client device <NUM> may be a desktop or laptop personal computer, or may be a communication terminal such as a smartphone or a tablet. The client device <NUM> includes a control unit <NUM>, a storage unit <NUM>, a communication unit <NUM>, a display unit <NUM>, and an operation unit <NUM>.

The control unit <NUM> is a CPU processor. The control unit <NUM> uses a web browser program stored in the storage unit <NUM> to cause the display unit <NUM> to display the web page provided by the server device <NUM> or the communication device <NUM>.

The storage unit <NUM> is a non-volatile memory such as a hard disk or a flash memory. The storage unit <NUM> stores various programs including the web browser program.

The communication unit <NUM> may be a communication device such as a network card for wired communication, a wireless communication device for the mobile communication connected to the base station BS (see <FIG>), or a wireless communication device used to be connected to the access point AP. The control unit <NUM> causes the communication unit <NUM> to communicate with or transmit/receive the information to/from the server device <NUM> or the communication device <NUM> via the network N.

The display unit <NUM> may be a display such as a liquid crystal display or an organic electro luminescence (EL) display. The control unit <NUM> executes processes based on the web browser program, and as a result, the display unit <NUM> displays an image of the web page that the server device <NUM> provides.

The operation unit <NUM> is a user interface, such as a keyboard, a pointing device, or a voice input unit, for inputting/outputting to/from the control unit <NUM>. The operation unit <NUM> may be a touch panel on the display unit <NUM>, or a physical button provided in a housing. The operation unit <NUM> informs the control unit <NUM> of information regarding an operation by the user.

<FIG> is a diagram showing an example of a connected state of the communication device <NUM>. As shown in <FIG>, the communication device <NUM> is connected to the management device M. The management device M is connected to the battery management unit <NUM> as a monitoring device provided in each of banks #<NUM> to #N. Note that, the communication device <NUM> may be a terminal device (measurement monitor) that communicates with the battery management unit <NUM> to receive information regarding each of the energy storage devices. Alternatively, the communication device <NUM> may be a network card communication device connectable to a power supply device.

Each of the banks #<NUM> to #N includes a plurality of energy storage modules <NUM>, and each of the energy storage modules <NUM> includes a cell monitoring unit <NUM>. The cell monitoring unit <NUM> has communication functions and is internally installed in each of the energy storage modules <NUM>. In each of the banks #<NUM> to #N, the battery management unit <NUM> communicates with the cell monitoring unit <NUM> via the serial communication. Concurrently, the battery management unit <NUM> transmit/receives information to/from the management device M. The management device M integrates the information from the battery management unit <NUM> in each of the banks #<NUM> to #N in the domain, and outputs the information integrated to the communication device <NUM>.

<FIG> is a block diagram showing an example of a configuration of the cell monitoring unit <NUM> and the battery management unit <NUM>. The cell monitoring unit <NUM> includes a balancer circuit <NUM>, a drive unit <NUM>, a voltage acquisition unit <NUM>, a control unit <NUM>, a storage unit <NUM>, a communication unit <NUM>, and the like. Each of the energy storage modules <NUM> has a plurality of energy storage cells 61a, 61b, 61c, 61d, and 61e, each connected in series to the others. In <FIG>, the number of the energy storage cells connected in series is five only for convenience of description. Thus, the number of energy storage cells included in energy storage module <NUM> is not limited to five.

The balancer circuit <NUM> includes a series circuit of a resistor 71a and a switch 72a, a series circuit of a resistor 71b and a switch 72b, a series circuit of a resistor 71c and a switch 72c, a series circuit of a resistor 71d and a switch 72d, and a series circuit of a resistor 71e and a switch 72e. The series circuit of the resistor 71a and the switch 72a is connected in parallel to the energy storage cell 61a; the series circuit of the resistor 71b and the switch 72b is connected in parallel to the energy storage cell 61b; the series circuit of the resistor 71c and the switch 72c is connected in parallel to the energy storage cell 61c; the series circuit of the resistor 71d and the switch 72d is connected in parallel to the energy storage cell 61d; and the series circuit of the resistor 71e and the switch 72e is connected in parallel to the energy storage cell 61e. Each of the switches 72a to 72e is, for example, a field effect transistor (FET), or may be a relay.

The drive unit <NUM> drives each of the switches 72a to 72e to be on or off. When each of the switches 72a to 72e is the FET, the drive unit <NUM> outputs a gate signal to a gate of the FET to turn the FET on and off.

The voltage acquisition unit <NUM> acquires a voltage of each of the energy storage cells 61a to 61e.

The storage unit <NUM> stores a threshold voltage predetermined.

The control unit <NUM> identifies a maximum voltage and a minimum voltage among the respective voltages of the energy storage cells 61a to 61e that the voltage acquisition unit <NUM> has acquired. When a voltage difference between the maximum voltage and the minimum voltage is equal to or more than the threshold voltage, the control unit <NUM> turns on the switch connected in parallel to the energy storage cell exhibiting the maximum voltage, so as to cause the energy storage cell (exhibiting the maximum voltage) to discharge via the resistor. As a result, the voltage (state of charge) of the energy storage cell (exhibiting the maximum voltage) is decreased. With this configuration, the voltages (states of charge) between the energy storage cells 61a to 61e are balanced.

The communication unit <NUM> has a function of proceeding with, for example, the serial communication between the battery management unit <NUM> and a first communication unit <NUM>.

The battery management unit <NUM> includes a control unit <NUM>, the first communication unit <NUM>, a second communication unit <NUM>, and the like.

The first communication unit <NUM> has a function of proceeding with, for example, the serial communication with the communication unit <NUM> of the cell monitoring unit <NUM>.

The second communication unit <NUM> has a function of transmitting/receiving the information to/from the communication device <NUM>. More specifically, the second communication unit <NUM> has a function as an acquisition unit. The second communication unit <NUM> acquires from the server device <NUM> information regarding whether the learning model <NUM> (for detecting the state of each of the energy storage cells) shifts to a learning mode or a detection mode. With this configuration, in a large-scale system including a large number of the battery management units <NUM>, operations of the battery management units <NUM> are remotely managed on an individual basis and on a collective basis.

The detection mode (also referred to as an operational mode) is a mode where the learning model <NUM> that has learned is used to actually detect the state of each of the energy storage cells. In this embodiment, the server device <NUM> includes the learning model <NUM>, but the present invention is not limited thereto, and other devices may include the learning model <NUM>.

The control unit <NUM> may be a CPU or the like. The control unit <NUM> has a function as a change unit. In a case where the learning model <NUM> of the server device <NUM> shifts to the learning mode, the control unit <NUM> controls to change an operation of the balancer circuit <NUM> (configured to balance the voltages of the plurality of energy storage cells) from a predetermined state.

The predetermined state may correspond to a normal operational state of the balancer circuit <NUM>. For example, when the voltage difference between the plurality of energy storage cells 61a to 61e (e.g., the difference between the maximum voltage and the minimum voltage among the respective voltages of the energy storage cells 61a to 61e) is equal to or more than the threshold voltage, the balancer circuit <NUM> may be in a state to balance the voltages. A change from the predetermined state above may correspond to a restriction on the operation of the balancer circuit <NUM>. The restriction includes, for example: (<NUM>) increasing the threshold voltage that causes the balancer circuit <NUM> to start the operation, so as to prevent the balancer circuit <NUM> from balancing the voltages until the voltage difference between the plurality of energy storage cells further increases to be larger than in the normal state; and (<NUM>) stopping the operation of the balancer circuit <NUM> to prevent the balancer circuit <NUM> from balancing the voltages. The operation of the control unit <NUM> to control the balancer circuit <NUM> will be described in detail later.

With the configuration described above, in a case where the learning model <NUM> (configured to detect the state of each of the energy storage cells) is caused to learn, it is possible to change a degree, to which the voltage or the state of charge of each of the energy storage cells is automatically adjusted, in accordance with the operation of the balancer circuit <NUM>. Accordingly, it is possible to acquire data reflecting an actual state of each of the energy storage cell that has deteriorated and the energy storage cell that is turning into the abnormal state.

<FIG> is a diagram showing an example of a transition in the state of each of the energy storage cells in each of the energy storage modules. Here, the energy storage cells are respectively denoted with reference signs a, b, c, d, and e. In <FIG>, the voltage (state of charge) of each of the energy storage cells is indicated by hatch lines. The voltage difference between the energy storage cells is exaggerated in <FIG>, and the actual voltage difference is smaller (e.g., several tens of mV approximately). Each of states A to C in an upper stage shows the transition in the state of each of the energy storage cells when the balancer circuit <NUM> is in the normal operational state. In the state A, the voltages of the energy storage cells a, c, d, and e are approximately identical, but the voltage of the energy storage cell b is smaller than the voltages of the other energy storage cells. In this case, the energy storage cell b has deteriorated or shows a potential sign of abnormality compared with the other energy storage cells.

On an assumption that the energy storage cells in the state A are continuously used, and in the state B, the voltage difference between the energy storage cells (i.e., a difference in voltage between the energy storage cell b and the other energy storage cells in <FIG>) exceeds a threshold voltage Vth. Then, the operation of the balancer circuit <NUM> starts, causing the energy storage cells other than the energy storage cell b to discharge. As a result, the voltages of these energy storage cells are balanced.

Subsequently, for example, when each of the energy storage cells is charged, the voltages of the energy storage cells increase in a balanced state and reach the state C.

Next, the operation of the battery management unit <NUM> to control the balancer circuit <NUM> in this embodiment will be described. The battery management unit <NUM> is capable of changing the threshold voltage that causes the balancer circuit <NUM> to start the operation (balancing), from the threshold voltage Vth in the normal state to a threshold voltage Vth2 of larger value. Each of states D and E in a lower stage shows the transition in the state of each of the energy storage cells when the operation of the balancer circuit <NUM> is changed from the normal state. The state D is the same as the state A.

On an assumption that the energy storage cells in the state D are continuously used, and in the state E, a voltage difference ΔV between the energy storage cells (i.e., the difference in voltage between the energy storage cell b and the other energy storage cells in <FIG>) exceeds the threshold voltage Vth (the voltage difference ΔV is smaller than the threshold voltage Vth2). In this state, the balancer circuit <NUM> does not start balancing as in the normal operational state. For example, in the state E, the battery management unit <NUM> collects data such as the voltage, the current, the temperature, and the SOC of each of the energy storage cells (energy storage modules), and provides the data to the server device <NUM> via the communication device <NUM>, as learning data for the learning model <NUM>. Here, the second communication unit <NUM> transmits the data collected, i.e., various data indicating the state of the energy storage cells (energy storage modules), to the server device <NUM>.

Accordingly, it is possible to acquire data reflecting an actual state of each of the energy storage cell that has deteriorated and the energy storage cell that is turning into the abnormal state.

Next, a relationship between each of the modes of the learning model <NUM> and the operation of the battery management unit <NUM> to control the balancer circuit <NUM> will be described.

<FIG> is a diagram showing an example of the operation of the battery management unit <NUM> to control the balancer circuit <NUM>. Here, cases <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> will be described.

The case <NUM> shows a case where the learning model <NUM> shifts to the learning mode. The control unit <NUM> changes the threshold voltage (that causes the balancer circuit <NUM> to balance the voltages) to a larger value. The control unit <NUM> acquires the data including the voltage and the temperature of each of the energy storage cells, and provides the data to the learning model <NUM>. Accordingly, it is easier to identify the energy storage cell that has deteriorated or the energy storage cell that is turning into the abnormal state, each exhibiting any different behavior from an energy storage cell in a normal state.

The case <NUM> shows the case where the learning model <NUM> shifts to the learning mode. The control unit <NUM> changes the operation of the balancer circuit <NUM> to a stopped state. The control unit <NUM> acquires the data including the voltage and the temperature of each of the energy storage cells, and provides the data to the learning model <NUM>. Accordingly, it is easier to identify the energy storage cell that has deteriorated or the energy storage cell that is turning into the abnormal state, each exhibiting any different behavior from an energy storage cell in a normal state.

The case <NUM> shows the case where the learning model <NUM> shifts to the learning mode. The control unit <NUM> causes one of the plurality of energy storage cells to discharge in order to increase the voltage difference between the plurality of energy storage cells. For example, the control unit <NUM> causes one of the plurality of energy storage cells exhibiting the minimum voltage to discharge. As a result, the voltage and the SOC of the energy storage cell exhibiting the minimum voltage are decreased, thereby increasing the voltage difference between the plurality of energy storage cells (e.g., the difference between the maximum voltage and the minimum voltage). The control unit <NUM> acquires the data including the voltage and the temperature of each of the energy storage cells, and provides the data to the learning model <NUM>. Accordingly, it is possible to simulate the energy storage cell that has deteriorated or the energy storage cell that is turning into the abnormal state.

The case <NUM> shows a case where the learning model <NUM> shifts to the detection mode. The control unit <NUM> causes the balancer circuit <NUM> to operate in the normal operational state (predetermined state). In other words, in the detection mode where the learning model <NUM> (that has learned) actually detects the state of each of the energy storage cells, the control unit <NUM> may cause the balancer circuit <NUM> to operate in the normal operational state. The control unit <NUM> acquires the data including the voltage and the temperature of each of the energy storage cells, and provides the data to the learning model <NUM>.

With this configuration, it is possible to accurately grasp a degree of deterioration or abnormality of an energy storage cell (energy storage module) that is provided in a mobile object or a facility, based on data acquired in actual usage conditions of the energy storage cell (energy storage module). The learning model <NUM> has learned the data regarding the energy storage cell that has deteriorated or the energy storage cell that is turning into the abnormal state, each exhibiting any different behavior from the energy storage cells in the normal state. Thus, the learning model <NUM> instantly detects the deteriorated state or the abnormal state of each of the energy storage cells.

The case <NUM> shows the case where the learning model <NUM> shifts to the detection mode. The control unit <NUM> causes the balancer circuit <NUM> to be in any one of the states in the cases <NUM>, <NUM>, and <NUM> previously described. In other words, in the detection mode where the learning model <NUM> (that has learned) actually detects the state of each of the energy storage cells, the control unit <NUM> changes the state of the balancer circuit <NUM> from the normal operational state. The control unit <NUM> acquires the data including the voltage and the temperature of each of the energy storage cells, and provides the data to the learning model <NUM>.

Accordingly, it is possible to verify whether or not the learning model <NUM> is valid. When the learning model <NUM> (that has learned) instantly detects an energy storage module where the balancer circuit <NUM> is changed from the normal operational state, or detects a specific energy storage cell included in the energy storage module, the learning model <NUM> may be determined as highly valid. With the learning model <NUM> that has learned, it is possible not only to detect any one of the energy storage cells that has deteriorated; but it is also possible to detect the energy storage module including the balancer circuit <NUM> that is not in the normal operational state or the cell monitoring unit <NUM> that is not in a normal operational state, and to detect a bank including the battery management unit <NUM> that is not in a normal operational state.

<FIG> is a block diagram showing another example of the configuration of the battery management unit <NUM>. As shown in <FIG>, the battery management unit <NUM> may include a learning model <NUM>. The learning model <NUM> may be provided with the same configuration and function as the learning model <NUM> previously described. The battery management unit <NUM> may be provided with the same function as the server device <NUM>, and thus identifies which mode (the learning mode or the detection mode) the learning model <NUM> is in. With this configuration, the battery management unit <NUM> monitors each of the energy storage modules (energy storage cells) and instantly detects the deteriorated state or the abnormal state of the corresponding energy storage module (energy storage cell).

<FIG> is a flowchart showing an example of a process step sequence performed by the battery management unit <NUM>. For convenience of description, the process step sequence will be described on an assumption that the control unit <NUM> is the subject of the process step sequence. The control unit <NUM> acquires the mode of the learning model (S11), and determines whether or not the learning model is in the learning mode (S12). In a case where the learning model is in the learning mode (S12: YES), the control unit <NUM> changes the state of the balancer circuit from the normal state (S13). The change from the normal state may correspond, for example, to any one of the cases <NUM>, <NUM>, and <NUM> in <FIG>.

The control unit <NUM> acquires the learning data including the voltage and the temperature of each of the energy storage cells (S14). The control unit <NUM> transmits the learning data acquired to the server device <NUM> so that the learning model is provided with the learning data acquired (S15). The control unit <NUM> acquires the learning data by collecting the data detected at a predetermined sampling cycle over a required period of time.

The control unit <NUM> determines whether or not to stop acquisition of the learning data (S16). On a determination not to stop the acquisition of the learning data (S16: NO), the control unit <NUM> continues the process steps from step S14. On a determination to stop the acquisition of the learning data (S16: YES), the control unit <NUM> causes the balancer circuit to be back in the normal state (S17), and proceeds to step S23 as will be described later.

In a case where the learning model is not in the learning mode (S12: NO), in other words, in a case where the learning model is in the detection mode, the control unit <NUM> determines whether or not to change the state of the balancer circuit from the normal state (S18). The change from the normal state may correspond, for example, to any one of the cases <NUM>, <NUM>, and <NUM> in <FIG>. In other words, in the detection mode, the control unit <NUM> may select any one of the states of the balancer circuit as follows: the normal operational state and the restricted form of the normal operational state.

On a determination to change the state of the balancer circuit from the normal state (S18: YES), the control unit <NUM> changes the state of the balancer circuit from the normal state (S19) and proceeds to step S20 as will be described later. On a determination not to change the state of the balancer circuit from the normal state (S18: NO), the control unit <NUM> performs step S20 as will be described later without performing step S19.

The control unit <NUM> acquires the input data including the voltage and the temperature of each of the energy storage cells (S20). The control unit <NUM> transmits the input data acquired to the server device <NUM>, so that the learning model is provided with the input data acquired (S21). Note that, the control unit <NUM> acquires the input data in the detection mode by collecting the data detected at the predetermined sampling cycle over the required period of time.

The control unit <NUM> determines whether or not to end the detection mode (S22). On a determination not to end the detection mode (S22: NO), the control unit <NUM> continues the process steps from the step S20. On a determination to end the detection mode (S22: YES), the control unit <NUM> determines whether or not to end the process step sequence (S23). On a determination not to end the process step sequence (S23: NO), the control unit <NUM> continues the process steps from step S11. On a determination to end the process step sequence (S23: YES), the control unit <NUM> ends the process step sequence.

The control unit <NUM> of this embodiment may be a computer of a general-purpose type, the computer including a CPU (processor), a RAM (memory), and the like. In other words, each process step sequence, such as the process step sequence in <FIG>, may be predetermined in a computer program, and the computer program may be loaded into the RAM (memory) in the computer. Then, the CPU (processor) executes the computer program on the computer to function as the control unit <NUM>. The computer program may be recorded in a recording medium and distributed. The learning model <NUM> that has learned in the server device <NUM> and a computer program based on the learning model <NUM> may be distributed via the network N and the communication device <NUM> to the target devices, P, U, D, and M, the battery management unit <NUM>, and a terminal device, each as the remote monitoring target, so as to be installed therein.

In order to cause the computer to cause the learning model regarding the energy storage devices to learn, the computer program causes the computer to execute three steps as follows: acquiring information regarding whether the learning model is in a first mode or in a second mode; changing the operation of the balancer circuit (configured to balance the voltages between the energy storage devices) from the predetermined state in the case where the learning model is in the first mode; and acquiring input data including at least any one of a voltage, a current, a temperature, and an SOC of each of the energy storage devices to provide the input data to the learning model.

In the case where the learning model is in the first mode, the computer program may cause the computer to further execute a step of acquiring the input data to provide the input data to the learning model, while leaving the operation of the balancer circuit in the predetermined state.

In order to cause the computer to detect a state of each of the energy storage devices, the computer program causes the computer to execute two steps as follows: inputting the input data (including at least any one of the voltage, the current, the temperature, and the SOC of each of the energy storage devices) to the learning model that has learned based on the computer program previously described; and detecting the state of each of the energy storage devices.

As has been described above, with the battery management unit of this embodiment, in the case where the learning model (configured to detect the state of each of the energy storage cells) is caused to learn, it is possible to change the degree, to which the voltage or the SOC of each of the energy storage cells is automatically adjusted, in accordance with the operation of the balancer circuit. Accordingly, it is possible to acquire the data reflecting the actual state of the energy storage cell that has deteriorated or the energy storage cell that is turning into the abnormal state; and it is possible to instantly detect the deteriorated state or the abnormal state of each of the energy storage cells.

In an embodiment, a technical concept may be as follows. A method is a learning method for a learning model. The method includes the steps as follows: changing an operation of a balancer circuit that balances the voltages between energy storage devices from a predetermined state; and acquiring input data including at least any one of a voltage, a current, a temperature, and an SOC of each of the energy storage devices to provide the input data to the learning model. In a single energy storage module or a plurality of energy storage modules, the operation of the balancer circuit may be changed from the predetermined state in various ways, such as increasing or decreasing a threshold voltage that causes the balancer circuit to balance the voltages, or changing the operation of the balancer circuit to a stopped state. Then, the input data is acquired and provided to the learning model. In the single energy storage module or the plurality of energy storage modules, the state of the balancer circuit may not be changed (in other words, the balancer circuit may be left in a normal operational state). Then, the input data is acquired and provided to the learning model. The input data may be provided to a learning model in a server. With these approaches, it is possible to provide, from a limited number of the energy storage modules, the input data in multiple ways to the learning model. In other words, it is possible to efficiently prepare big data for learning.

<FIG> is a block diagram showing a configuration example of an energy storage system <NUM>. The energy storage system <NUM> has a hierarchical configuration including a plurality of energy storage cells, a plurality of energy storage modules <NUM>, a plurality of banks <NUM>, and a domain. In the hierarchical configuration, the plurality of energy storage cells are connected in series in each of the plurality of energy storage modules <NUM>; the plurality of energy storage modules <NUM> are connected in series in each of the plurality of banks <NUM>; and the plurality of banks <NUM> are connected in parallel in the domain. The energy storage system <NUM> in <FIG> includes a single domain.

The energy storage system <NUM> is connected to a power conditioning system P. Each of the plurality of banks <NUM> is connected to the power conditioning system P via a power line <NUM>. Each of the banks <NUM> is charged with electric power supplied via the power conditioning system P. Then, each of the banks <NUM> discharges the electric power that is to be outputted externally via the power conditioning system P. The power conditioning system P is connected, for example, to a power generating system and/or a power transmission system.

Each of the banks <NUM> includes a switch <NUM>. The switch <NUM> connects and disconnects each of the plurality of energy storage modules <NUM> (that are connected in series) to and from the power line <NUM>. When the switch <NUM> is closed, each of the plurality of energy storage modules <NUM> is connected to the power line <NUM>. When the switch <NUM> is open, each of the plurality of energy storage modules <NUM> is disconnected from the power line <NUM>. When each of the plurality of energy storage modules <NUM> is connected to the power line <NUM>, the corresponding energy storage module <NUM> is charged or discharges (i.e., is energized) via the power conditioning system P, the power line <NUM>, and the switch <NUM>.

Each of the banks <NUM> includes the plurality of energy storage modules <NUM> and a battery management unit (BMU) <NUM>. Each of the energy storage modules <NUM> includes a cell monitoring unit (CMU) <NUM>. The cell monitoring unit <NUM> in each of the energy storage modules <NUM> is connected to the battery management unit <NUM>. The battery management unit <NUM> communicates with each of the cell monitoring units <NUM>. The battery management unit <NUM> is supplied with electric power via a power path (not shown) other than the power line <NUM>, and thus operates regardless of a state of the switch <NUM>.

The energy storage system <NUM> includes a management device M. The management device M is a BMU that manages each of energy storage devices in the domain. The battery management unit <NUM> in each of the banks <NUM> is connected to the management device M via a communication line <NUM>. A communication device <NUM> is connected to the management device M and/or the power conditioning system P. The communication device <NUM> may include a communication device connected to the management device M and a communication device connected to the power conditioning system P. The battery management unit <NUM> transmit/receives information to/from the management device M. The management device M integrates the information from a plurality of the battery management units <NUM> and outputs the information to the communication device <NUM>.

<FIG> is a block diagram showing a configuration example of each of the energy storage modules <NUM>. In each of the energy storage modules <NUM>, the cell monitoring unit <NUM> includes a balancer circuit <NUM>, a drive unit <NUM>, a voltage acquisition unit <NUM>, a control unit <NUM>, a storage unit <NUM>, and a communication unit <NUM>. In each of the energy storage modules <NUM>, a plurality of energy storage cells 61a, 61b, 61c, 61d, and 61e are connected in series. In <FIG>, the number of the energy storage cells connected in series is five only for convenience of description. Thus, the number of the energy storage cells included in the energy storage module <NUM> is not limited to five. Further, each of the energy storage modules <NUM> may include an energy storage cell that is connected in parallel to the other energy storage cells. The plurality of energy storage cells 61a to 61e are electrically connected in series to energy storage cells that are included in the others of the plurality of energy storage modules <NUM>. Concurrently, the plurality of energy storage cells 61a to 61e are electrically connected to the switch <NUM>.

The balancer circuit <NUM> includes a series circuit of a resistor 71a and a switch 72a, a series circuit of a resistor 71b and a switch 72b, a series circuit of a resistor 71c and a switch 72c, a series circuit of a resistor 71d and a switch 72d, and a series circuit of a resistor 71e and a switch 72e. The series circuit of the resistor 71a and the switch 72a is connected in parallel to the energy storage cell 61a; the series circuit of the resistor 71b and the switch 72b is connected in parallel to the energy storage cell 61b; the series circuit of the resistor 71c and the switch 72c is connected in parallel to the energy storage cell 61c; the series circuit of the resistor 71d and the switch 72d is connected in parallel to the energy storage cell 61d; and the series circuit of the resistor 71e and the switch 72e is connected in parallel to the energy storage cell 61e. Each of the switches 72a to 72e may be a switching element such as a field effect transistor (FET), or a switching circuit such as a relay.

The drive unit <NUM> drives each of the switches 72a to 72e to be on or off. When each of the switches 72a to 72e is the FET, the drive unit <NUM> outputs a gate signal to a gate of the FET to turn the FET on and off. The voltage acquisition unit <NUM> acquires a voltage of each of the energy storage cells 61a to 61e. The storage unit <NUM> stores a threshold voltage predetermined.

The control unit <NUM> identifies a maximum voltage and a minimum voltage among the respective voltages of the energy storage cells 61a to 61e that the voltage acquisition unit <NUM> has acquired. When a voltage difference between the maximum voltage and the minimum voltage is equal to or more than the threshold voltage, the control unit <NUM> turns on a switch connected in parallel to the energy storage cell having the maximum voltage, so as to cause the energy storage cell (having the maximum voltage) to discharge via the resistor. As a result, the voltage of the energy storage cell (having the maximum voltage) is decreased. With this configuration, the voltages are balanced between the energy storage cells 61a to 61e.

When each of the energy storage cells 61a to 61e has a lower SOC than the others of the energy storage cells 61a to 61e, the corresponding energy storage cell 61a, 61b, 61c, 61d, or 61e exhibits a lower voltage than the others of the energy storage cells 61a to 61e. In this state, the balancer circuit <NUM> operates to cause the others of the energy storage cells 61a to 61e to discharge, so that the others of the energy storage cells 61a to 61e exhibit lower SOC and lower voltages too. With this configuration, the voltages and the states of charge of the plurality of energy storage cells in each of the energy storage modules <NUM> are balanced. The balancer circuit <NUM>, the drive unit <NUM>, the voltage acquisition unit <NUM>, the control unit <NUM>, and the storage unit <NUM> correspond to a balancing unit.

The communication unit <NUM> has a function of proceeding with, for example, a serial communication with the battery management unit <NUM>. The control unit <NUM> causes the communication unit <NUM> to transmit, to the battery management unit <NUM>, information indicating the voltage of each of the energy storage cells 61a to 61e that the voltage acquisition unit <NUM> has acquired.

<FIG> is a graph schematically showing a temporal change in voltage of each of the energy storage cells when the balancer circuit <NUM> is in the operational state. In the graph, a horizontal axis represents a period of time during which the energy storage cells are left in a non-energized state; and a vertical axis represents the voltages of the energy storage cells. The voltage may be an open circuit voltage (OCV). Here, a triangle indicates a voltage of an energy storage cell that has deteriorated relatively quickly; and a circle indicates a voltage of an energy storage cell that is in a normal state and exhibits a deterioration rate within a tolerable range. The voltage of the energy storage cell that has deteriorated decreases more quickly, thereby generating a difference from the voltage of the energy storage cell in the normal state. As time elapses, the difference in voltage increases and reaches the threshold voltage. In <FIG>, a broken line shows a point of time when the difference in voltage between the energy storage cell that has deteriorated and the energy storage cell in the normal state reaches the threshold voltage. In <FIG>, a circle drawn with a broken line shows the temporal change in voltage of the energy storage cell in the normal state when the balancer circuit <NUM> is not in the operational state.

As shown in <FIG>, the voltages of the plurality of energy storage cells are balanced. In this state, the voltage of the energy storage cell in the normal state decreases more than when the balancer circuit <NUM> is not in the operational state; and thus, the difference in voltage between the energy storage cell that has deteriorated and the energy storage cell in the normal state is smaller than when the balancer circuit <NUM> is not in the operational state. The difference in voltage between the energy storage cell that has deteriorated and the energy storage cell in the normal state is less prone to increase regardless of time elapsed. Accordingly, when the balancer circuit <NUM> is in the operational state, it is presumably difficult to detect the energy storage cell that has deteriorated based on the temporal change in voltage. On the other hand, when the balancer circuit <NUM> is not in the operational state, the difference in voltage between the energy storage cell that has deteriorated and the energy storage cell in the normal state increases as time elapses. In this state, it is presumably easy to detect the energy storage cell that has deteriorated based on the temporal change in voltage.

<FIG> is a block diagram showing a functional configuration example of the battery management unit <NUM> and the management device M. The battery management unit <NUM> includes a control unit <NUM>, a first communication unit <NUM>, and a second communication unit <NUM>. The control unit <NUM> is a CPU processor. The first communication unit <NUM> is connected to the plurality of cell monitoring units <NUM> in each of the banks <NUM>. The first communication unit <NUM> receives information transmitted from each of the cell monitoring units <NUM>. The second communication unit <NUM> is connected to the management device M via the communication line <NUM>. The control unit <NUM> causes the second communication unit <NUM> to transmit the information received from each of the plurality of cell monitoring units <NUM> to the management device M.

The management device M employs a computer. The management device M includes a control unit <NUM>, a first communication unit <NUM>, and a second communication unit <NUM>. The control unit <NUM> is a CPU processor. The first communication unit <NUM> is connected to the plurality of the battery management units <NUM>. The first communication unit <NUM> receives information transmitted from each of the battery management units <NUM>. The second communication unit <NUM> is connected to the communication device <NUM>. The control unit <NUM> causes the second communication unit <NUM> to transmit the information received from each of the plurality of battery management units <NUM> to the communication device <NUM>. The communication device <NUM> transmits the information received from the management device M to a server device <NUM>. In other words the management device M transmits the information to the server device <NUM> via the communication device <NUM>; and each of the battery management units <NUM> transmits the information to the server device <NUM> via the management device M and the communication device <NUM>.

Next, a deterioration determination method according to this embodiment will be described. The server device <NUM> functions as a deterioration determination device. In order to determine whether or not any one of the energy storage cells has deteriorated based on the temporal change in voltage of each of the energy storage cells, a learning model <NUM> undergoes machine learning. The machine learning is executed, for example, in the server device <NUM>.

When the plurality of energy storage cells are connected in series and are in a substantially identical state of charge, the voltage of each of the plurality of energy storage cells is acquired. In each of the banks <NUM>, when the switch <NUM> is open and the balancer circuit <NUM> is not in the operational state, the voltage of each of the energy storage cells is acquired by the voltage acquisition unit <NUM>. Then, the voltage acquired is transmitted to the server device <NUM> via each of the battery management units <NUM>, the management device M and the communication device <NUM>. The battery management unit <NUM> in each of the banks <NUM> is connected to the management device M via a communication line <NUM>. The temporal change in voltage of each of the plurality of energy storage cells is stored, for example, in a storage unit <NUM> of the server device <NUM>.

<FIG> is a graph schematically showing the temporal change in voltage of each of the energy storage cells when the voltages are not balanced between the energy storage cells. In the graph, a horizontal axis represents a period of time during which the energy storage cells are left in a non-energized state; and a vertical axis represents the voltages of the energy storage cells. The voltage may be the OCV. Here, a triangle indicates the voltage of the energy storage cell that has deteriorated, and a circle indicates the voltage of the energy storage cell that is in the normal state and exhibits the deterioration rate within the tolerable range. The energy storage cell that has deteriorated deteriorates more quickly than the energy storage cell in the normal state. The voltage of the energy storage cell that has deteriorated decreases more quickly, thereby generating a difference from the voltage of the energy storage cell in the normal state. Here, the difference in voltage between the energy storage cell in the normal state and the energy storage cell that has deteriorated continues to increase as time elapses, unlike in <FIG> that shows the temporal change in voltage of each of these two when the voltages are balanced.

When a certain period of time has elapsed, the difference in voltage between the energy storage cell in the normal state and the energy storage cell that has deteriorated is significant. Accordingly, it is possible to determine whether or not each of the energy storage cells has deteriorated based on the voltage of the corresponding energy storage cell. For example, when the energy storage cells have been in the non-energized state for a predetermined period of time, and when any one of the energy storage cells exhibits a voltage less than a threshold value, the corresponding energy storage cell may be determined as deteriorated. Alternatively, when the energy storage cells have been in the non-energized state for the predetermined period of time, and when any one of the energy storage cells exhibits a ratio of the voltage to an initial voltage being less than the threshold value, the corresponding energy storage cell may be determined as deteriorated. Still alternatively, the temporal change in voltage may be approximated to the linear function, and when any one of the energy storage cells shows an absolute value of a ratio of the temporal change in voltage exceeding a threshold value, the corresponding energy storage cell may be determined as deteriorated. Further alternatively, when the plurality of energy storage cells have been in the non-energized state for the predetermined period of time, and when a predetermined number of the plurality of energy storage cells exhibit(s) a further lower value of voltage, the corresponding energy storage cell(s) may be determined as deteriorated.

Based on the temporal change in voltage of each of the plurality of energy storage cells that has been acquired, a human may determine whether or not the corresponding energy storage cell has deteriorated, or a computer may determine whether or not the corresponding energy storage cell has deteriorated. For example, in the server device <NUM>, a control unit <NUM> follows a server program 2P; and based on the temporal change in voltage of each of the energy storage cells stored in the storage unit <NUM>, the control unit <NUM> determines whether or not the corresponding energy storage cell has deteriorated. Based on the determination, each of the energy storage cells is specified as deteriorated or not.

Teaching data is created with regard to each of the plurality of energy storage cells. In the teaching data, the temporal change in voltage of each of the energy storage cells is correlated with a result of identifying whether or not the corresponding energy storage device has deteriorated. The teaching data is stored, for example, in the storage unit <NUM> of the server device <NUM>. Based on the teaching data created, the learning model <NUM> undergoes the machine learning. For example, in the server device <NUM>, the control unit <NUM> follows the server program 2P to cause the learning model <NUM> to undergo the machine learning. In the machine learning, the learning model <NUM> learns to adjust a parameter. As a result, even when it is unknown whether or not each of the energy storage cells has deteriorated, based on the temporal change in voltage of each of the energy storage cells, it is possible to determine whether or not the corresponding energy storage cell has deteriorated.

The server device <NUM> executes the machine learning processing to obtain the learning model <NUM> that has learned. The machine learning may be executed on a computer other than the server device <NUM>. In this case, learning data as the learning model <NUM> (that has learned based on the machine learning) is created, and the learning data that has been created is inputted to the server device <NUM>. Then, the server device <NUM> stores the learning data in the storage unit <NUM> to obtain the learning model <NUM> that has learned.

<FIG> is a flowchart showing a process step sequence to determine whether or not any one of the energy storage devices has deteriorated. In the server device <NUM> as the deterioration determination device, the control unit <NUM> executes process steps below based on the server program 2P. The control unit <NUM> is configured to determine whether or not each of the energy storage devices has deteriorated. Here, each of the energy storage devices corresponds to each of the energy storage cells included in the energy storage system <NUM>. The control unit <NUM> disconnects the energy storage module <NUM> (that includes the energy storage cells to be determined) from the power line <NUM> (that is used to energize the energy storage module <NUM>). Then, the energy storage module <NUM> is in the non-energized state (S1). For example, the control unit <NUM> causes a communication unit <NUM> to transmit a control signal to the switch <NUM>, which is in the energy storage module <NUM> including the energy storage cells to be determined, to be open. The control signal is transmitted to the switch <NUM> via a communication network N, the communication device <NUM>, and the management device M. When the switch <NUM> is open, the plurality of energy storage modules <NUM> are disconnected from the power line <NUM>, and each of the plurality of energy storage modules <NUM> is left in the non-energized state. In this state, the bank <NUM>, in which the switch <NUM> is open, is disconnected from the power line <NUM> (main circuit). In process step S1, the control signal may be transmitted to the battery management unit <NUM> such that the battery management unit <NUM> follows the control signal to open the switch <NUM>. Instead of transmitting the control signal from the server device <NUM>, the switch <NUM> may be opened independently in the energy storage system <NUM>. For example, the switch <NUM> may be opened manually. Processing in the process step S1 corresponds to an energization stop unit.

Next, the control unit <NUM> stops the balancing of the voltages between the plurality of energy storage cells in each of the energy storage modules <NUM> that is in the non-energized state (S2). For example, the control unit <NUM> causes the communication unit <NUM> to transmit a control signal to the cell monitoring unit <NUM> to stop the operation of the balancer circuit <NUM>, via the communication network N, the communication device <NUM>, the management device M, and the battery management unit <NUM>. Then, in the cell monitoring unit <NUM>, the control unit <NUM> stops the operation of the balancer circuit <NUM>, so that the voltages of the plurality of energy storage cells in each of the energy storage modules <NUM> are not balanced. For example, the operation of the balancer circuit <NUM> stops in each of the energy storage modules <NUM> in the bank <NUM> where the corresponding switch <NUM> is open. Processing in process step S2 corresponds to the balancing stop unit. Alternatively, the process step S2 may be performed simultaneously with the process step S1 or may be performed before the process step S1.

Next, the control unit <NUM> acquires the temporal change in voltage of each of the energy storage cells in each of the energy storage modules <NUM> that is in the non-energized state. In this state, the balancer circuit <NUM> in the corresponding energy storage module <NUM> is not in the operational state (S3). For example, the control unit <NUM> causes the communication unit <NUM> to transmit a control signal to the cell monitoring unit <NUM> in the energy storage module <NUM> that includes the energy storage cells to be determined. The control signal is configured to acquire the voltage of each of the energy storage cells to be determined. The control signal is transmitted to the cell monitoring unit <NUM> via the communication network N, the communication device <NUM>, the management device M, and the battery management unit <NUM>. First, in the cell monitoring unit <NUM>, the voltage of each of the plurality of energy storage cells 61a to 61e in the corresponding energy storage module <NUM> is acquired when the energy storage cells 61a to 61e are in the substantially identical state of charge. Next, the voltage acquisition unit <NUM> repeatedly acquires the voltage of each of the energy storage cells 61a to 61e. The voltage that the voltage acquisition unit <NUM> acquires may be the OCV. The control unit <NUM> causes the communication unit <NUM> to sequentially transmit the information indicating the voltage of each of the energy storage cells 61a to 61e that the voltage acquisition unit <NUM> has acquired. The information indicating the voltage of each of the energy storage cells is sequentially transmitted to the server device <NUM> via the battery management unit <NUM>, the management device M, the communication device <NUM>, and the communication network N. In the server device <NUM>, the communication unit <NUM> receives the information indicating the voltage of each of the energy storage cells. The control unit <NUM> stores the information that the communication unit <NUM> has received into the storage unit <NUM>. As time elapses, the information indicating the voltage of each of the energy storage cells is sequentially received and stored. Alternatively, the information indicating the voltage of each of the energy storage cells over the time elapsed may be collectively transmitted and collectively received. With this configuration, the temporal change in voltage of each of the energy storage cells is acquired. For example, the temporal change in voltage of each individual of the energy storage cells in the bank <NUM> (where the switch <NUM> is open) is acquired. Processing in process step S3 corresponds to an acquisition unit.

Next, the control unit <NUM> reads the information indicating the temporal change in voltage of each of the energy storage cells from the storage unit <NUM> and provides the information to the learning model <NUM>. Based on the temporal change in voltage of each of the energy storage cells, the learning model <NUM> determines whether or not the corresponding energy storage cell has deteriorated (S4). The learning model <NUM> has learned the difference in temporal change in voltage between the energy storage cell in the normal state and the energy storage cell that has deteriorated. Accordingly, based on the temporal change in voltage of each of the energy storage cells, it is possible to determine whether or not the corresponding energy storage cell has deteriorated. Based on the temporal change in voltage of each of the energy storage cells over a different period of time, the learning model <NUM> may determine whether or not the corresponding energy storage cell has deteriorated; and the different period of time is shorter than the period of time during which the temporal change in voltage of each of the energy storage cells was acquired to create the teaching data. Processing in process step S4 corresponds to a determination unit.

Next, the control unit <NUM> outputs a result of the determination whether or not each of the energy storage cells has deteriorated (S5). For example, the control unit <NUM> causes the communication unit <NUM> to transmit information indicating the result of the determination to a client device <NUM> via the communication network N. In the client device <NUM>, a communication unit <NUM> receives the information indicating the result of the determination, and a control unit <NUM> causes a display unit <NUM> to display the result of the determination result based on the information that the communication unit <NUM> has received. The display unit <NUM> displays, for example, identification information provided to each of the energy storage cells, along with the information regarding whether or not the corresponding energy storage cell (that has been identified based on the identification information) has deteriorated. This configuration causes an administrator of the energy storage system to confirm the result of the determination outputted and to thus know which of the energy storage cells has deteriorated. The process step sequence determines whether or not any one of the energy storage cells (any one of the energy storage devices) has deteriorated, and ends here.

When the process steps sequence (to determine whether or not any one of the energy storage cells has deteriorated) has ended, the energy storage module <NUM>, which includes the energy storage cell(s) determined as deteriorated, is removed from the bank <NUM>. The energy storage module <NUM> that has been removed may be, for example, replaced with an energy storage module <NUM> that is newly prepared. In the bank <NUM> from which the energy storage module <NUM> has been removed, the plurality of energy storage modules <NUM> (excluding the energy storage module that has been removed) may be connected to each other. When the determination has been made, the switch <NUM> is closed in the bank <NUM> that has been determined, and each of the energy storage modules <NUM> causes the balancer circuit <NUM> therein to be back in the operational state. Then, the bank <NUM> that has been determined is prepared to resume the operation.

Alternatively, in the energy storage system <NUM>, the determination may be made whether or not any one of the energy storage cells has deteriorated in one of the plurality of banks <NUM>. Then, when the determination has been made in the one of the banks <NUM>, the determination may be made whether or not any one of the energy storage cells has deteriorated in the others of the plurality of banks <NUM> in the energy storage system <NUM>. The determination may be made in one of the banks <NUM> at a time or more than two of the banks <NUM> at a time. With this configuration, the determination whether or not any one of the energy storage cells has deteriorated is sequentially made in each of the banks <NUM>. Accordingly, it is possible to carry out protective maintenance on the energy storage system <NUM> without stopping the overall operation of the energy storage system <NUM>, in other words, while continuing the operation of the energy storage system <NUM>.

As has been described in detail above, in this embodiment, when each of the energy storage modules <NUM> is in the non-energized state and the voltages of the plurality of energy storage cells are thus not balanced, the temporal change in voltage of each of the energy storage cells is acquired. Then, based on the temporal change in voltage of each of the energy storage cells, the corresponding energy storage cell is determined as deteriorated or not. Each of the operations to stop energizing each of the energy storage modules <NUM> and to stop balancing the voltages between the energy storage cells is remotely carried out (carried out via the communication network). When the voltages of the plurality of energy storage cells are not balanced, the difference in temporal change in voltage increases between the energy storage cell in the normal state and the energy storage cell that has deteriorated. Accordingly, based on the temporal change in voltage of each of the energy storage cells, it is possible to determine whether or not the corresponding energy storage cell has deteriorated and to detect any one of the energy storage cells that has deteriorated, more smoothly than in the conventional method. Further, each of the energy storage cells is smoothly determined as deteriorated or not, and thus, it is even possible to identify an energy storage cell that has deteriorated to a smaller degree than an energy storage cell that was determined as deteriorated by the conventional method. Accordingly, it is possible to detect any one of the energy storage cells that has deteriorated at an earlier stage than in the conventional method. Any one of the energy storage cells that has deteriorated is detected at the earlier stage, and thus, it is possible to remove the corresponding energy storage cell that has deteriorated before a performance of the energy storage system <NUM> significantly degrades. Consequently, it is possible to decrease the period of time to stop the operation of each of the banks <NUM> or the energy storage system <NUM>.

In this embodiment, the learning model <NUM> using supervised learning is used to determine whether or not each of the energy storage cells has deteriorated. The teaching data includes the temporal change in voltage of each of the energy storage cells, and a result of identifying whether or not the corresponding energy storage cell has deteriorated. By using the teaching data, the learning model <NUM> is caused to learn, based on the temporal change in voltage of each of the energy storage cells, to determine whether or not the corresponding energy storage cell has deteriorated. By using the learning model <NUM>, based on the temporal change in voltage of each of the energy storage cells over a rather short period of time, it is possible to determine whether or not the corresponding energy storage cell has deteriorated. Accordingly, it is possible to decrease the period of time to stop the operation of each of the banks <NUM> or the energy storage system <NUM> in determining whether or not each of the energy storage cells has deteriorated.

In this embodiment, based on a history of an operation of each of the energy storage cells, the corresponding energy storage cell is determined as deteriorated or not. <FIG> is a block diagram showing a functional configuration example of a battery management unit <NUM> and a management device M. A cell monitoring unit <NUM> further includes a current acquisition unit <NUM> and a temperature acquisition unit <NUM>. The current acquisition unit <NUM> sequentially acquires a current flowing through a plurality of the energy storage cells that are connected in series in each of energy storage modules <NUM>. The temperature acquisition unit <NUM> uses a temperature sensor to sequentially acquire a temperature at a single or a plurality of sections in each of the energy storage modules <NUM>. Similarly, a voltage acquisition unit <NUM> sequentially acquires a voltage of each of the energy storage cells.

When each of the energy storage modules <NUM> is in operation, a control unit <NUM> causes a communication unit <NUM> to sequentially transmit information to the battery management unit <NUM>. The information indicates the voltage that the voltage acquisition unit <NUM> has acquired, the current that the current acquisition unit <NUM> has acquired, and the temperature that the temperature acquisition unit <NUM> has acquired. The information indicating the voltage, the current, and the temperature is transmitted to a server device <NUM> via the battery management unit <NUM>, the management device M, a communication device <NUM>, and a communication network N. In the server device <NUM>, a communication unit <NUM> receives the information indicating the voltage, the current, and the temperature. A control unit <NUM> stores the information indicating the voltage, the current, and the temperature into a storage unit <NUM>. Apart from the configuration described above, each of an energy storage system <NUM> and a deterioration determination system <NUM> has the same configuration as described in the first or the second embodiment.

In the server device <NUM>, as time elapses, the information indicating the voltage, the current, and the temperature of the energy storage cells is sequentially received and stored. The information indicating the voltage, the current, and the temperature is stored with regard to each of the energy storage cells. With this configuration, the history of the operation of each of the energy storage cells is acquired.

Teaching data is created with regard to each of the plurality of energy storage cells. In the teaching data, the temporal change in voltage of each of the energy storage cells and the history of the operation of each of the energy storage cells are correlated with a result of identifying whether or not the corresponding energy storage device has deteriorated. Based on the teaching data created, the learning model <NUM> undergoes the machine learning. Each of the energy storage cells exhibits a different behavior in accordance with the history of the operation of the corresponding energy storage cell. For example, in a case where each of the energy storage cells is repeatedly charged and discharges at high frequency, the corresponding energy storage cell deteriorates significantly. As a result, a difference increases between an energy storage cell in a normal state and an energy storage cell that has deteriorated. In the machine learning, the learning model <NUM> learns to adjust a parameter. As a result, even when it is unknown whether or not each of the energy storage cells has deteriorated, based on the temporal change in voltage of each of the energy storage cells and the history of the operation of each of the energy storage cells, it is possible to determine whether or not the corresponding energy storage cell has deteriorated.

Similarly to the second embodiment, the deterioration determination system <NUM> according to this embodiment executes process steps to determine whether or not any one of the energy storage cells has deteriorated as shown in the flowchart of <FIG>. In process step S4, the control unit <NUM> reads from the storage unit <NUM> the information indicating the temporal change in voltage of each of the energy storage cells and information indicating the history of the operation of each of the energy storage cells. Then, the control unit <NUM> provides the information read from the storage unit <NUM> to the learning model <NUM>. Based on the temporal change in voltage of each of the energy storage cells and the history of the operation of each of the energy storage cells, the learning model <NUM> determines whether or not the corresponding energy storage cell has deteriorated. The learning model <NUM> has learned a difference in the temporal change in voltage and a difference in the history of the operation between the energy storage cell in the normal state and the energy storage cell that has deteriorated. Accordingly, based on the temporal change in voltage of each of the energy storage cells and the history of the operation of each of the energy storage cells, it is possible to determine whether or not the corresponding energy storage cell has deteriorated.

As has been described above, in this embodiment, when each of the energy storage modules <NUM> is in the non-energized state and the voltages of the plurality of energy storage cells are thus not balanced, the temporal change in voltage of each of the energy storage cells is acquired. Based on the temporal change in voltage of each of the energy storage cells and the history of the operation of each of the energy storage cells, the learning model <NUM> determines whether or not the corresponding energy storage cell has deteriorated. Each of the energy storage cells exhibits the different behavior in accordance with the history of the operation of the corresponding energy storage cell. However, the teaching data includes the temporal change in voltage of each of the energy storage cells, the history of the operation of each of the energy storage cells, and the result of identifying whether or not the corresponding energy storage cell has deteriorated. Accordingly, by using the teaching data, the learning model <NUM> is caused to learn, based on the temporal change in voltage of each of the energy storage cells and the history of the operation of each of the energy storage cells, to determine whether or not the corresponding energy storage cell has deteriorated. By using the learning model <NUM>, based on the temporal change in voltage of each of the energy storage cells and the history of the operation of each of the energy storage cells, it is possible to determine whether or not the corresponding energy storage cell has deteriorated. Regardless of the history of the operation in each of the energy storage cells, it is possible to smoothly determine whether or not the corresponding energy storage cell has deteriorated. Accordingly, even among the plurality of energy storage cells, each having a different history of operation from the others, it is possible to smoothly detect any one of the energy storage cells that has deteriorated. Further, similarly to the first embodiment, it is possible to detect any one of the energy storage cells that has deteriorated at an earlier stage than in the conventional method.

In this embodiment, a battery management unit <NUM> functions as a deterioration determination device. <FIG> is a block diagram showing a functional configuration example of the battery management unit <NUM>. The battery management unit <NUM> further includes a learning model <NUM> and a storage unit <NUM>. The learning model <NUM> proceeds with the same operation as the learning model <NUM> described in the first or the second embodiment. The storage unit <NUM> is a hard disk or a non-volatile memory. In this embodiment, a server device <NUM> may not include a learning model <NUM>. Apart from the configuration described above, each of an energy storage system <NUM> and a deterioration determination system <NUM> has the same configuration as described in the first or the second embodiment.

Similarly to the first or the second embodiment where the learning model <NUM> undergoes machine learning, the learning model <NUM> undergoes the machine learning. The machine learning may take place in the battery management unit <NUM>. Alternatively, the machine learning may be executed on other computers. In this case, learning data as the learning model <NUM> (that has learned based on the machine learning) is created, and the learning data created is inputted to the battery management unit <NUM>. Then, the battery management unit <NUM> stores the learning data created in the storage unit <NUM> to obtain the learning model <NUM> that has learned.

Similarly to the second or the third embodiment, the deterioration determination system <NUM> according to this embodiment executes a process step sequence to determine whether or not any one of energy storage cells has deteriorated as shown in the flowchart of <FIG>. The battery management unit <NUM> executes an operation as the deterioration determination device. The battery management unit <NUM> opens a switch <NUM> such that each of energy storage modules <NUM> turns into a non-energized state (S1). Then, the battery management unit <NUM> causes a cell monitoring unit <NUM> to stop an operation of a balancer circuit <NUM>, and thus, voltages of the plurality of energy storage cells are not balanced (S2). In the battery management unit <NUM>, a first communication unit <NUM> receives information transmitted from the cell monitoring unit <NUM>, and the storage unit <NUM> stores the information. Then, a temporal change in voltage of each of the energy storage cells is acquired (S3). The learning model <NUM> determines whether or not each of the energy storage cells has deteriorated (S4). Further, the battery management unit <NUM> outputs a result of the determination whether or not each of the energy storage cells has deteriorated (S5). For example, the battery management unit <NUM> transmits information indicating the result of the determination to a client device <NUM> via a management device M, a communication device <NUM>, and a communication network N. The process step sequence to determine whether or not any one of the energy storage cells has deteriorated ends here.

In this embodiment, similarly to the second or the third embodiment, based on a temporal change in voltage of each of the energy storage cells, it is possible to smoothly determine whether or not the corresponding energy storage cell has deteriorated. Further, it is possible to detect any one of the energy storage cells that has deteriorated at an earlier stage than in the conventional method. In the deterioration determination system <NUM>, instead of the battery management unit <NUM>, the management device M may function as the deterioration determination device.

In each of the second, the third, and the fourth embodiments, the determination is made whether or not any one of the energy storage cells has deteriorated based on the learning model. Alternatively, the deterioration determination system <NUM> may determine whether or not any one of the energy storage cells has deteriorated without using the learning model. In this case, the deterioration determination device stops energizing each of the energy storage modules <NUM> and stops balancing the voltages between the plurality of energy storage cells. In this state, the deterioration determination device acquires the temporal change in voltage of each of the energy storage cells. Then, based on the temporal change in voltage of each of the energy storage cells, the deterioration determination device determines whether or not the corresponding energy storage cell has deteriorated. Each of the operations to stop energizing each of the energy storage modules <NUM> and to stop balancing the voltages between the energy storage cells is preferably remotely carried out (carried out via the communication network). When the voltages of the plurality of energy storage cells are not balanced, the difference in temporal change in voltage increases between the energy storage cell in the normal state and the energy storage cell that has deteriorated. Accordingly, it is possible to determine whether or not any one of the plurality of energy storage cells has deteriorated without using the learning model.

In each of the second, the third, and the fourth embodiments, the balancer circuit <NUM> is caused to balance the voltages between the plurality of energy storage cells. Alternatively, instead of using the balancer circuit <NUM>, the cell monitoring unit <NUM> may use other methods to balance the voltages. For example, the cell monitoring unit <NUM> may cause an energy storage cell exhibiting a higher voltage to discharge such that an energy storage cell exhibiting a lower voltage is charged with electricity. As a result, the voltages are balanced between the energy storage cells. In each of the first, the second, and the third embodiments, each of the energy storage devices is determined as deteriorated or not. Here, each of the energy storage devices corresponds to each of the energy storage cells. Alternatively, in the deterioration determination system <NUM>, each of the energy storage modules <NUM> may correspond to each of the energy storage devices to be determined; and each of the banks <NUM> may correspond to an energy storage device unit. In each of the second, the third, and the fourth embodiments, the energy storage system <NUM> includes the plurality of banks <NUM>. Alternatively, the energy storage system <NUM> may include a single number of the bank <NUM>.

As has been described above, a deterioration determination method includes steps of: stopping energizing an energy storage device unit including a plurality of energy storage devices; stopping balancing voltages between the plurality of energy storage devices; acquiring a temporal change in voltage of each of the plurality of energy storage devices; and determining whether or not any one of the plurality of energy storage devices has deteriorated quickly based on the temporal change in voltage of each of the plurality of energy storage devices. The deterioration determination method may be executed by a computer program. The computer program may cause a computer to execute the deterioration determination method that includes process steps of: stopping energizing the energy storage device unit including the plurality of energy storage devices; stopping balancing the voltages between the plurality of energy storage devices; acquiring the temporal change in voltage of each of the plurality of energy storage devices; and determining whether or not any one of the plurality of energy storage devices has deteriorated quickly based on the acquired temporal change in voltage of each of the plurality of energy storage devices.

In the deterioration determination method, based on a temporal change in voltage of each of the plurality of energy storage devices in a state where the energy storage device unit including the plurality of energy storage devices is not energized and the voltages of the plurality of energy storage devices are not balanced, a learning model using supervised learning is used to determine whether or not the corresponding energy storage device has deteriorated. The deterioration determination method may be executed by the computer program.

In the deterioration determination method, the learning model undergoes machine learning based on teaching data. The teaching data includes: the temporal change in voltage of each of the energy storage devices in the state where the energy storage device unit including the plurality of energy storage devices is not energized and the voltages of the plurality of energy storage devices are not balanced; and a result of identifying whether or not the corresponding energy storage device has deteriorated. The deterioration determination method may be executed by the computer program.

The deterioration determination method further includes a step of acquiring a history of an operation of each of the plurality of energy storage devices. In the deterioration determination method, based on the temporal change in voltage of each of the plurality of energy storage devices in the state where the energy storage device unit including the plurality of energy storage devices is not energized and the voltages of the plurality of energy storage devices are not balanced, and based on the history of the operation of each of the plurality of energy storage devices, the learning model using the supervised learning is used to determine whether or not the corresponding energy storage device has deteriorated. The deterioration determination method may be executed by the computer program.

In the deterioration determination method, the learning model undergoes the machine learning based on teaching data. The teaching data includes: the temporal change in voltage of each of the energy storage devices in the state where the energy storage device unit including the plurality of energy storage devices is not energized and the voltages of the plurality of energy storage devices are not balanced; the history of the operation of each of the energy storage devices; and a result of identifying whether or not the corresponding energy storage device has deteriorated. The deterioration determination method may be executed by the computer program.

In the deterioration determination method, a determination is made whether or not any one of the energy storage devices has deteriorated in one of a plurality of the energy storage device units that are connected in parallel, and when the determination has been made in the one of the plurality of the energy storage device units, a determination is made whether or not any one of the energy storage devices has deteriorated in the other or the others of the plurality of the energy storage device units. The deterioration determination method may be executed by the computer program.

A deterioration determination device is configured to determine whether or not any one of energy storage devices has deteriorated quickly. The deterioration determination device includes: an energization stop unit configured to stop energizing an energy storage device unit including a plurality of the energy storage devices; a balancing stop unit configured to stop balancing voltages between the plurality of energy storage devices; an acquisition unit configured to acquire a temporal change in voltage of each of the plurality of energy storage devices, in a state where the energization stop unit has stopped energizing the energy storage device unit including the plurality of energy storage devices and where the balancing stop unit has stopped balancing the voltages between the plurality of energy storage devices; and a determination unit configured to determine whether or not any one of the plurality of energy storage devices has deteriorated based on the temporal change in voltage of each of the plurality of energy storage devices that the acquisition unit has acquired. Any processes that are executed by the deterioration determination device may be executed by a battery management unit <NUM> or a cell monitoring unit <NUM>, each provided in a vicinity of the energy storage device unit as an energy storage system <NUM> or the like. Alternatively, any processes that are executed by the deterioration determination device may be executed by the battery management unit <NUM> and the cell monitoring unit <NUM>. Still alternatively, any processes that are executed by the deterioration determination device may be executed by the server device <NUM> that is connected to the energy storage device unit via a communication network.

In the deterioration determination device, the determination unit causes a learning model using supervised learning to determine, based on the temporal change in voltage of each of the energy storage devices in the state where the energy storage device unit including the plurality of energy storage devices is not energized and the voltages of the plurality of energy storage devices are not balanced, whether or not the corresponding energy storage device has deteriorated.

A deterioration determination system includes: an energy storage device unit including a plurality of energy storage devices; a switch configured to connect and disconnect the energy storage device unit to and from a power line used to energize the energy storage device unit; a balancing unit configured to balance voltages between the plurality of energy storage devices; and a deterioration determination device configured to determine whether or not any one of the plurality of energy storage devices has deteriorated quickly. The deterioration determination device includes: a balancing stop unit configured to stop an operation of the balancing unit; an acquisition unit configured to acquire a temporal change in voltage of each of the plurality of energy storage devices, in a state where the switch has disconnected the energy storage device unit from the power line and where the balancing stop unit has stopped an operation of the balancing unit; and a determination unit configured to determine whether or not any one of the plurality of energy storage devices has deteriorated based on the acquired temporal change in voltage of each of the plurality of energy storage devices.

With the configuration described above, in the state where the energy storage device unit including the plurality of energy storage devices is not energized and the voltages of the plurality of energy storage devices are not balanced, the temporal change in voltage of each of the energy storage devices is acquired, and based on the temporal change in voltage of each of the energy storage devices, the corresponding energy storage device is determined as deteriorated or not. When the voltages of the energy storage devices are not balanced, the difference in temporal change in voltage increases between an energy storage device in a normal state and an energy storage device that has deteriorated. Accordingly, based on the temporal change in voltage of each of the energy storage devices, it is possible to smoothly determine whether or not the corresponding energy storage device is the energy storage cell that has deteriorated.

With the configuration described above, the learning model using the supervised learning is used to determine whether or not each of the energy storage devices has deteriorated. Here, the teaching data includes: the temporal change in voltage of each of the energy storage devices; and the result of identifying whether or not the corresponding energy storage device has deteriorated. By using the teaching data, the learning model is caused to learn, based on the temporal change in voltage of each of the energy storage devices, to determine whether or not the corresponding energy storage device has deteriorated. By using the learning model, based on the temporal change in voltage of each of the energy storage devices over a rather short period of time, it is possible to determine whether or not the corresponding energy storage device has deteriorated.

With the configuration described above, based on the temporal change in voltage of each of the energy storage devices and a history of an operation of each of the energy storage devices, the learning model is used to determine whether or not the corresponding energy storage cell has deteriorated. Each of the energy storage devices exhibits a different behavior in accordance with the history of the operation of the corresponding energy storage device. However, the teaching data includes: the temporal change in voltage of each of the energy storage devices; the history of the operation of each of the energy storage devices; and the result of identifying whether or not the corresponding energy storage device has deteriorated. Accordingly, by using the teaching data, the learning model is caused to learn to determine, based on the temporal change in voltage of each of the energy storage devices and the history of the operation of each of the energy storage devices, whether or not the corresponding energy storage device has deteriorated. By using the learning model, regardless of the history of the operation in each of the energy storage devices, it is possible to smoothly determine whether or not the corresponding energy storage device has deteriorated.

With the configuration described above, in a case of the plurality of energy storage device units, a determination is made whether or not any one of the energy storage devices has deteriorated in one of the plurality of energy storage device units. Then, when the determination has been made in the one of the plurality of the energy storage device units, a determination is made whether or not any one of the energy storage devices has deteriorated in the other or the others of the plurality of the energy storage device units. In a system including the plurality of energy storage device units, it is possible to carry out protective maintenance on the system while continuing an operation of the system.

Claim 1:
A monitoring device configured to monitor an energy storage device that comprises a plurality of energy storage cells (61a, 61b, 61c, 61d, 61e) connected in series, the monitoring device comprising:
a balancer circuit (<NUM>) comprising a series circuit of a resistor (71a, 71b, 71c, 71d, 71e) and a switch (72a, 72b, 72c, 72d, 72e) connected in parallel to each of the energy storage cells (61a, 61b, 61c, 61d, 61e),
the balancer circuit (<NUM>) configured to balance voltages of the energy storage cells (61a, 61b, 61c, 61d, 61e);
an acquisition unit (<NUM>) configured to acquire information regarding whether a learning model (<NUM>) is in a learning mode or in a detection mode, the learning model (<NUM>) configured to detect a state of the energy storage device; and
a change unit (<NUM>) configured to change an operation of the balancer circuit (<NUM>) from a predetermined state in a case where the learning model (<NUM>) is in the learning mode.