Diagnostic device for secondary battery, and activating device

Abstract

In order to diagnose the degree of deterioration of a secondary battery (2), a device (1) comprising a sensor unit (11) including a voltage sensor (15), and a control device (10) having a mathematical model storage unit, a history data storage unit, and a computation function, is electrically connected to the secondary battery (2). Numerical data reflecting a measurement value of internal resistance is stored in the history data storage unit in association with the time axis. A mathematical model, which is defined so as to derive a parameter having a value that varies according to the numerical data at each of a plurality of time points in a predetermined period on the time axis, and the length of said period, is saved in the mathematical model storage unit. The control device (10) derives the parameter by computation in which numerical data accumulated within a period from a specific time point in the past to the most recent time point is applied to the mathematical model, and estimates the degree of deterioration of the secondary battery (2) on the basis of the parameter.

Description

Diagnostic device and activation device for secondary batteries

[0001] The present invention relates to a device (hereinafter referred to as a "battery diagnostic device") that is electrically connected to a secondary battery and diagnoses the state of deterioration of the secondary battery that occurs with repeated charging and discharging and the passage of time, and to a device (hereinafter referred to as a "battery activation device") that has this diagnostic function and passes a pulse current through a secondary battery that has been diagnosed as having deteriorated performance, thereby eliminating the deteriorated state and restoring the secondary battery to good performance.

[0002] Lithium-ion batteries, a type of secondary battery, have many advantages, such as being small and lightweight, being capable of storing large amounts of electricity, and being able to generate higher voltages than other secondary batteries, and have therefore been used in recent years as power sources for a variety of devices. However, their performance cannot be maintained forever, and it is known that repeated charging and discharging causes various chemical reactions within lithium-ion batteries, leading to deterioration phenomena such as an increase in the number of lithium ions that become inactive and an increase in the thickness of the surface layers of the electrodes, resulting in a decrease in the performance of lithium-ion batteries.

[0003] Lead-acid batteries, which have been widely used since before the rise of lithium-ion batteries, also have the problem of a gradual decrease in the effective electrode volume and a decline in performance due to a phenomenon known as sulfation, in which lead sulfate generated during a chemical reaction during discharge crystallizes and adheres to the electrodes.

[0004] One method known as a useful method for estimating the degree and cause of degradation of lithium-ion batteries is to pass alternating currents of various frequencies through the battery to measure the internal impedance of the battery and analyze these measurements (impedance spectroscopy). Three patent documents that disclose techniques for diagnosing the degradation state of lithium-ion batteries using this method are listed below, and their disclosures are briefly described.

[0005] Patent Literature 1 describes a method of extracting at least two measurement frequencies from a plot waveform of AC impedance, inserting these measurement values ​​into a correlation equation between the capacity loss rate of a lithium-ion battery and AC impedance created in advance to calculate the capacity loss rate or capacity maintenance rate of the lithium-ion battery, and estimating the deterioration state of the lithium-ion battery based on the calculation results. Patent Literature 1 also describes a method of measuring the AC impedance at each frequency, each time certain conditions are met while the lithium-ion battery is in use, for one frequency point in a group of frequencies indicating electrolyte loss, one frequency point in a group of frequencies indicating negative electrode loss, and one frequency point in a group of frequencies indicating positive electrode loss, and determining the capacity loss rate from these measurements. Patent Literature 1 also describes a method of determining that the battery is degraded when the capacity loss rate exceeds a predetermined value and notifying the user.

[0006] Patent Document 2 describes that a phase difference reflecting the state of deterioration occurring at a specific location can be obtained by applying an AC voltage of a specific frequency to a lithium-ion battery, measuring the response current to this application, and determining the phase difference between the applied voltage and the response current, and further describes an experiment that supports this theory. The experiment shows that when an AC voltage of 10 Hz was applied, a phase difference reflecting an increase in the amount of lithium metal precipitation at the negative electrode was obtained, and when an AC voltage of 1 Hz was applied, a phase difference reflecting the degree of decrease in battery capacity was obtained.

[0007] Patent Document 3 describes a method in which a computer (state estimation model learning device) receives as input, as learning data, a combination of impedance for each frequency measured under a controlled internal temperature state for each of a plurality of batteries, temperature, and battery charging data, and increases the learning data by a method of generating impedance characteristics of a virtual battery by internally dividing the impedance characteristics represented by the plurality of learning data, and learns a state estimation model that inputs impedance for each frequency and outputs the internal state of the battery based on this learning data. Patent Document 3 further describes that by inputting the impedance measured for each frequency of the battery to be estimated into the state estimation model, it is possible to estimate the temperature and state of charge of the battery, and that a similar method can be used to estimate the deterioration state of the battery.

[0008] A known method for restoring the performance of a secondary battery to a good state is to pass a high-frequency pulse current through the secondary battery. Three patent documents are presented regarding this technique.

[0009] Patent Document 4 discloses a method for recovering capacity by melting whisker-like lithium particles attached to the surface of the electrodes or the separator of a lithium ion battery, in which charging of the lithium ion battery is temporarily interrupted and a reverse pulse current (discharge current) is passed multiple times.

[0010] Patent Document 5 describes a lithium-ion battery in which lithium ions have accumulated in a portion of the negative electrode active material layer that does not face the positive electrode active material layer (non-facing portion), resulting in a decrease in the amount of lithium ions that can move toward the positive electrode. The battery is then discharged until it reaches an over-discharge state, and the discharge is stopped to increase the voltage to a predetermined voltage level below the over-discharge region, and pulse discharge is repeatedly performed to discharge the battery while oscillating the current.

[0011] Patent Document 6 discloses a battery activation device that is constantly electrically connected to a lead-acid battery and repeatedly applies a minute pulse current to the battery. This battery activation device has a voltage sensor for measuring the voltage between the positive and negative electrodes of the lead-acid battery and a current sensor for measuring the current flowing in the connection path to the battery. These sensors are used to measure the voltage and current during each pulse current flow period to determine the battery's internal resistance, and parameters (such as the duty ratio and pulse period) that determine how the pulse current flows are varied based on the calculated internal resistance value, thereby enabling the pulse current form to be changed in accordance with changes in the degree of battery deterioration.

[0012] JP 2014-44149 A JP 2009-244088 A JP 2023-174239 A JP 2014-170741 A JP 2019-106333 A International Publication No. 2022 / 018876

[0013] As described in the above Patent Documents 1 to 3 and Patent Document 6, the degree of deterioration of a secondary battery can be estimated based on the values ​​of internal impedance and internal resistance. However, conventional diagnostic methods are limited to using numerical data acquired at a single point on a time axis or within a short period of time, and it is difficult to say that the accuracy of the diagnostic results is sufficient.

[0014] Therefore, the first objective of the present invention is to provide a battery diagnostic device that is constantly connected to a secondary battery, continuously acquires numerical data reflecting the degree of deterioration of the secondary battery, and stores the data in memory, and uses the numerical data stored in memory to estimate the degree of deterioration of the secondary battery with high accuracy.

[0015] Furthermore, a second objective of the present invention is to provide a battery activation device that continuously acquires and stores the above-mentioned numerical data while being constantly connected to a secondary battery, and that uses the numerical data stored in memory to determine with high accuracy when the performance of the secondary battery should be restored and performs control to restore the performance at that time.

[0016] The battery diagnostic device for secondary batteries according to the present invention is a device that is electrically connected to a secondary battery in use and diagnoses the degree of deterioration while collecting numerical data representing the state of the secondary battery, and is equipped with a control device that has the function of passing a pulse current through the electrically connected secondary battery, and detection means for detecting a physical quantity that represents the state of the secondary battery.

[0017] The detecting means includes at least a voltage sensor for detecting the voltage between the positive and negative electrodes of the secondary battery, and may further include a current sensor for detecting the current flowing through the connection path to the secondary battery, and sensors for detecting the ambient conditions of the secondary battery (such as a temperature sensor, humidity sensor, or air pressure sensor).

[0018] The control device includes a history data storage unit and a mathematical model storage unit described below, an internal resistance measuring means, and an estimation means for estimating the degree of deterioration of the secondary battery by calculation using the mathematical model stored in the mathematical model storage unit.

[0019] The internal resistance measurement means repeatedly performs the following steps based on predetermined conditions: applying a pulse current having a predetermined pulse period and pulse width and a predetermined peak value to the secondary battery; and calculating the internal resistance of the secondary battery using the amount of change in voltage detected by a voltage sensor due to the application of the pulse current and the peak value of the pulse current. In this calculation, a value determined by controlling the application of the pulse current can be used as the peak value of the pulse current, but if a current sensor is provided in the connection path to the secondary battery, the current value detected by the current sensor during the peak period of the pulse current can be used as the peak value.

[0020] The history data storage unit stores numerical data reflecting the calculation results of the internal resistance measurement unit in association with a time axis. This numerical data may be the calculated internal resistance value itself, or may be a value obtained by correcting the internal resistance value based on environmental data such as temperature. Alternatively, the internal impedance determined by an inference calculation using the calculated internal resistance value may be stored in the history data storage unit as the above-mentioned numerical data.

[0021] It is desirable to link the numerical data accumulated in the history data storage unit with data representing the time when the numerical data or the internal resistance value that is the basis for the numerical data was acquired. However, if the numerical data is saved in the history data storage unit at approximately regular time intervals, each numerical data can also be associated with a time axis by saving a combination of the numerical data for each time and a number representing the order of occurrence.

[0022] The mathematical model storage unit stores a mathematical model defined to derive parameters whose values ​​vary depending on the values ​​of the above-mentioned numerical data at each of multiple points in time during a predetermined period on a time axis and the length of the period. This mathematical model can be constructed by collecting the above-mentioned numerical data and data representing the actual state of deterioration from an unspecified number of secondary batteries and analyzing them in detail. The construction of the mathematical model can also be performed by a computer with machine learning capabilities.

[0023] The history data storage unit can also store feature quantities calculated from voltage measurements, such as the amount of voltage fluctuation, charge / discharge time, and number of charge / discharge cycles, as well as feature quantities derived by inference calculations using these feature quantities, such as discharge capacity and discharge rate. In this case, some or all of these feature quantities can be incorporated into the elements of the mathematical model so that the above parameters vary depending on the relationship between changes in these feature quantities and changes in the numerical data reflecting the calculation results of the internal resistance.

[0024] The estimation means applies a period from a specific point in the past to the most recent time to the mathematical model, performs a calculation using the mathematical model by applying numerical data stored in the history data storage unit in association with the application period, and estimates the degree of deterioration of the secondary battery using parameter values ​​derived by the calculation. The parameters calculated by the calculation can also be stored in the history data storage unit in association with a time axis, and the stored parameters can also be used in subsequent calculations.

[0025] In the battery diagnostic device configured as described above, a mathematical model defined so that the parameter value increases (or decreases) as the number of occurrences of numerical data indicating the possibility of secondary battery degradation increases and as the period applied to the mathematical model becomes longer can be stored in the mathematical model storage unit. It is also possible to store a mathematical model defined so that the parameter value varies depending on the length of the period applied and the amount of fluctuation in the numerical data during that period. In any mathematical model, when multiple calculations are performed while maintaining the starting point of the application period, the length of the period applied to each calculation increases over time, so the parameter value also naturally increases (or decreases) over time.

[0026] According to the calculation using the mathematical model, it is possible to calculate parameters that accurately represent the current degree of deterioration by taking into account the elapsed time and the change over time in numerical data that reflects the internal resistance value, which is likely to fluctuate due to deterioration of the secondary battery. Therefore, it is possible to estimate the degree of deterioration of the secondary battery with high accuracy using the parameters calculated by this calculation.

[0027] The mathematical model storage unit of the first embodiment of the battery diagnostic device stores a first mathematical model for determining an index value representing the degree of deterioration of the secondary battery, and a second mathematical model for deriving a feature value representing the current performance of the secondary battery.

[0028] The estimation means in the above embodiment applies numerical data stored in the history data storage unit, associated with a period from a past point to the most recent, to a second mathematical model to obtain an initial value of the feature quantity, and determines a starting point of the period to be applied to the first mathematical model. Thereafter, the estimation means executes one or more cycles of a first calculation to calculate the index value using the first mathematical model to which the numerical data stored in the history data storage unit, associated with the period from the starting point to the most recent, and a second calculation to attenuate the feature quantity by varying the degree of attenuation based on the length of time elapsed from the starting point and the current value of the index value, and estimates the degree of deterioration of the secondary battery based on the result of each second calculation.

[0029] According to the first embodiment, when the control device is initially started up or when a performance recovery pulse current (described later) is applied, a feature quantity representing the current performance of the secondary battery is calculated by a calculation using the second mathematical model, the calculated value is used as an initial value, and a starting point of a period to be applied to the first mathematical model is determined. Then, by repeating the first calculation and the second calculation, the feature quantity can be attenuated to a degree corresponding to the change over time in the numerical data that occurs after calculation of the initial value and the length of time that has elapsed since the starting point. This makes it possible to increase the reliability of the estimation result using the result of the second calculation.

[0030] The battery diagnostic device of the second embodiment is used to diagnose secondary batteries, such as lithium-ion batteries, that generate multiple types of internal resistance components with different frequency characteristics, and is provided with a function for applying multiple pulse currents with different combinations of pulse period, pulse width, and peak value to the internal resistance measuring means and performing the above calculation for each of the pulse currents. This function makes it possible to calculate multiple types of internal resistance components that are generated by different factors.

[0031] In the second embodiment, the mathematical model storage unit stores, in addition to the mathematical model used in the calculation by the estimation means, a second mathematical model representing the relationship between the multiple types of internal resistance components and the internal impedance caused by those internal resistance components. The mathematical model used in the calculation by the estimation means is defined so that the values ​​of the parameters vary depending on the values ​​of the internal impedance at each of multiple time points during a period applied to the mathematical model and the length of the period. Furthermore, the history data storage unit stores, as the numerical data, values ​​of the internal impedance obtained by calculation applying the values ​​of the multiple types of internal resistance components calculated by the internal resistance measurement means to the second mathematical model.

[0032] According to the second embodiment, it is possible to individually obtain values ​​of multiple types of internal resistance components generated in a secondary battery to be diagnosed using multiple pulse currents with different frequencies, current levels, and application times, and to derive parameters for deterioration diagnosis based on the internal impedance generated by the interaction of these internal resistance components. In this way, it is possible to obtain inference results that reflect the levels of multiple types of internal resistance components with different frequency characteristics and changes in the levels of these components over time, and it is possible to estimate the deterioration state of a secondary battery with high accuracy without passing an AC current.

[0033] The present invention also provides a battery activation device having a detection means and a control device similar to those of the battery diagnosis device. The control device of this battery activation device also includes the above-mentioned history data storage unit, mathematical model storage unit, and internal resistance measurement means, and is also provided with parameter calculation means that applies a period from a specific past point to the most recent time to a mathematical model and repeatedly performs calculations using the mathematical model applying numerical data stored in the history data storage unit in association with the application period, and recovery control means that determines, based on the calculation results by the parameter calculation means, when to pass a pulse current to the secondary battery to restore its performance and controls the pass of the pulse current at that time.

[0034] According to the above configuration, by repeating calculations that reflect two elements, namely, the value of the numerical data that reflects the internal resistance value, which is likely to fluctuate due to deterioration of the secondary battery, and the length of time that has passed, it is possible to determine with high accuracy the appropriate time to pass a pulse current for performance recovery, and to pass the pulse current at that time.

[0035] Furthermore, in response to the control by the recovery control means, the parameter calculation means of the battery activation device changes the starting point of the period applied to the mathematical model in subsequent calculations to a point later than the starting point applied before the control (i.e., moves the starting point applied to the mathematical model closer to the present on the time axis).This change makes it possible to perform calculations using numerical data generated after the control to flow a pulse current as a main element and obtain calculation results that reflect the effects of the control, thereby maintaining the reliability of calculations and determinations even after the performance of the secondary battery is restored by the pulse current.

[0036] In the first embodiment of the battery activation device, the mathematical model storage unit includes a second mathematical model defined to vary the value of a control parameter that determines the mode of the performance recovery pulse current in accordance with the value of the parameter calculated by the parameter calculation unit. In response to the arrival of the time to flow the performance recovery pulse current to the secondary battery, the recovery control unit executes a calculation using the second mathematical model to which the parameter calculated by the parameter calculation unit most recently at that time is applied, and controls the flow of a pulse current to the secondary battery using the control parameter derived by the calculation.

[0037] In the second embodiment of the battery activation device, similar to the first embodiment of the battery diagnostic device, a mathematical model storage unit stores a first mathematical model for determining an index value representing the degree of deterioration of a secondary battery and a second mathematical model for determining a feature value representing the current performance of the secondary battery. The parameter calculation unit uses the second mathematical model to perform a calculation similar to that performed by the estimation unit of the first embodiment of the battery diagnostic device to determine an initial value of the feature value and determine a starting point of a period to be applied to the first mathematical model. Then, the first and second calculations are repeated to update the index value and attenuate the feature value based on the current value of the index value and the length of the period to be applied to the calculation. The recovery control unit uses the result of the second calculation each time to determine the timing to apply a performance recovery pulse current to the secondary battery.

[0038] Furthermore, the mathematical model storage unit of the second embodiment includes a third mathematical model defined to vary the value of a control parameter that determines the mode of the performance recovery pulse current in accordance with the index value calculated by the first calculation. Furthermore, in response to the arrival of the time to flow the performance recovery pulse current, the recovery control means executes a calculation using the third mathematical model to which the index value calculated immediately before that time is applied, and executes control to flow the pulse current applying the control parameter derived by the calculation.

[0039] According to the first and second embodiments, when controlling the flow of pulse current, it is possible to vary the pulse current mode depending on the degree of deterioration of the secondary battery at the time of control, and the pulse current mode can be finely adjusted.

[0040] Furthermore, by applying the second embodiment of the battery diagnosis device to a battery activation device, it is possible to individually measure multiple types of internal resistance components that occur in the secondary battery to be diagnosed, and derive parameters to be used for determining the timing to pass a pulse current for performance recovery based on the changes over time in internal parameters that reflect these measured values ​​and the length of time that has passed.

[0041] The control device of the battery diagnostic device or battery activation device of the present invention can be provided with a function of communicating with a cloud server that has the function of generating a mathematical model of the target to be stored in the mathematical model storage unit through machine learning, a function of using this communication function to transmit numerical data accumulated in the history data storage unit to the cloud server, and a function of receiving a mathematical model generated based on the numerical data from the cloud server that has received the numerical data, and updating the contents of the mathematical model storage unit with the received mathematical model.

[0042] These functions allow various numerical data stored in the mathematical model storage unit to be sent to a cloud server and used for machine learning on the server, thereby constructing a mathematical model suited to the characteristics of the secondary battery connected to the battery diagnosis device or battery activation device, and feeding this back to the device that sent the numerical data. Thus, by incorporating a mathematical model suited to the characteristics of the secondary battery connected to each battery diagnosis device or battery activation device, it becomes possible to perform higher-level calculations and control.

[0043] The battery diagnostic device of the present invention repeatedly applies a pulse current to an electrically connected secondary battery, and calculates the internal resistance of the secondary battery using the resulting voltage change and the peak value of the pulse current, and calculates parameters reflecting the degree of deterioration of the secondary battery based on the history of numerical data reflecting the results of each calculation from a specific point in the past and the length of time elapsed since that specific point in time. Thus, the degree of deterioration of the secondary battery can be estimated with high accuracy based on the parameters.

[0044] The battery activation device of the present invention can accurately determine the timing of the control to pass a performance-restoring pulse current through the secondary battery and execute the control at that time, thereby quickly restoring the deteriorated performance of the secondary battery. Furthermore, by updating the starting point of the period applied to the calculation thereafter, the reliability of the calculation and control can be maintained. Therefore, by keeping the battery activation device of the present invention connected to the secondary battery, the life of the secondary battery can be significantly extended.

[0045] Furthermore, according to the battery activation device of the present invention, whenever it is determined that a performance recovery pulse current should be passed through the secondary battery, a pulse current appropriate for the degree of deterioration of the secondary battery at that time can be passed through the secondary battery.

[0046] 7 is a block diagram showing the electrical configuration of a battery activation device for lithium ion batteries. FIG. 8 is a functional block diagram of the battery activation device. FIG. 9 is an explanatory diagram showing the concept of parameters representing measurement conditions for measuring the internal resistance of a lithium ion battery and a basic measurement method. FIG. 10 is an explanatory diagram showing an example of setting the pulse current mode and measurement timing used to measure the internal resistance. FIG. 11 is an explanatory diagram showing the pulse current pattern used for performance recovery control. FIG. 12 is a flowchart showing the process flow related to event data collection. FIG. 13 is a flowchart showing the main process flow in the battery activation device. FIG. 14 is a flowchart showing the detailed procedure of regular maintenance (step S5) in FIG. 7. FIG. 15 is an explanatory diagram showing a network system consisting of multiple battery activation devices and a cloud server.

[0047] 1 is a block diagram showing an example of the circuit configuration of a battery activation device 1 to which the present invention is applied. The battery activation device 1 of this embodiment has the function of diagnosing the degree of degradation of a lithium-ion battery 2 (hereinafter sometimes simply referred to as "battery 2") and the function of restoring the reduced performance of the battery 2 when the diagnosis determines that the degree of degradation has exceeded an acceptable level. The battery activation device 1 is composed of a circuit board on which the components shown in FIG. 1 are mounted and a housing (not shown) that houses this board.

[0048] The battery activation device 1 is connected to the positive terminal 20A and negative terminal 20B of the lithium-ion battery 2 via connection terminals 110A, 110B provided on the circuit board or via cables (not shown), and is placed on the surface of the battery 2 or in the vicinity of the battery 2. The battery 2 to be connected may be a type formed of only one cell, or may be a battery pack formed by combining multiple cells.

[0049] 1, the transmission paths of control signals and data are represented by lines with arrows, and the power supply paths are represented by lines without arrows. As shown by the dashed dotted lines in the figure, external devices or charging devices are connected as loads to the terminals 20A and 20B of the lithium-ion battery 2, but even when these are not connected, the battery activation device 1 is connected as a load to the lithium-ion battery 2. In other words, the battery activation device 1 continues to operate using the voltage between the terminals 20A and 20B of the lithium-ion battery 2 as its power supply voltage.

[0050] The battery activation device 1 includes a control device 10, a sensor unit 11, a power supply circuit 12, a pulse generation circuit 13, and a wireless communication circuit 14. The power supply circuit 12 includes several types of DC-DC converters, which convert the DC voltage applied between terminals 110A and 110B into voltages at levels suitable for the operation of the various components described above, and supply these to the components.

[0051] The control device 10 is a computer with a large capacity memory and high-level calculation functions. The memory contains programs for providing the control device 10 with the functions shown in Fig. 2, as well as folders for the mathematical model storage unit 106, the measurement condition storage unit 107, and the history data storage unit 108.

[0052] The pulse generating circuit 13 includes an oscillator and a current adjusting circuit, and generates a pulse current with adjusted current level, pulse width, frequency, etc. in response to a control signal from the control device 10, and sends this to the connection circuit with the lithium ion battery 2.

[0053] The wireless communication circuit 14 is a circuit that conforms to communication standards such as Wi-Fi, 4G, and 5G, and is used when the control device 10 communicates with information processing terminal devices such as personal computers and smartphones used by users, and with the cloud server 3 described later.

[0054] The sensor unit 11 includes a voltage sensor 15, a current sensor 16, a temperature sensor 17, a humidity sensor 18, and an air pressure sensor 19. Of these, the voltage sensor 15 and the current sensor 16 are incorporated into a connection circuit between the terminals 110A, 110B and the pulse generating circuit 13, with the voltage sensor 15 detecting the voltage applied between the terminals 110A, 110B (substantially the voltage between the electrodes 20A, 20B of the lithium ion battery 2), and the current sensor 16 detecting the current flowing between the pulse generating circuit 13 and the lithium ion battery 2.

[0055] The temperature sensor 17 detects the temperature around the lithium-ion battery 2, the humidity sensor 18 detects the humidity around the battery 2, and the air pressure sensor 19 detects the air pressure around the battery 2. The control device 10 appropriately takes in detection signals from the various sensors 15 to 19 of the sensor unit 11 and stores the respective level values ​​in an internal memory (the history data storage unit 108 shown in FIG. 2) as measured values ​​of the corresponding physical quantities (voltage, current, temperature, humidity, air pressure).

[0056] It is not essential that the temperature sensor 17, humidity sensor 18, and air pressure sensor 19 are housed within the housing of the battery activation device 1; these sensors 17, 18, and 19 may be placed outside the housing and electrically connected to a circuit board via a connector or the like. It is not necessary to use the above three types of sensors 17, 18, and 19 to measure the conditions around the battery 2; only one or two types of sensors may be used. However, considering that temperature is a physical quantity that fluctuates with the seasons and is likely to fluctuate due to the effects of charging and discharging, and therefore likely to affect internal resistance, it is desirable to include at least the temperature sensor 17 in the battery activation device 1.

[0057] FIG. 2 is a functional block diagram showing the functions provided in the control device 10 of the battery activation device 1, together with the relationship with other components of the device 1.

[0058] The control device 10 is provided with the functions of a main control unit 100, a voltage measurement unit 101, a current measurement unit 102, an environmental data measurement unit 103, a pulse control unit 104, and a communication control unit 105, as well as a mathematical model memory unit 106, a measurement condition memory unit 107, and a history data memory unit 108, all of which are implemented by a dedicated program pre-installed in the control device 10.

[0059] The voltage measurement unit 101 measures the voltage value indicated by the detection signal from the voltage sensor 15, and the current measurement unit 102 measures the current value indicated by the detection signal from the current sensor 16. The environmental data measurement unit 103 measures the temperature, humidity, and atmospheric pressure values ​​indicated by the detection signals from the temperature sensor 17, humidity sensor 18, and atmospheric pressure sensor 19 (hereinafter collectively referred to as "environmental data").

[0060] The pulse control unit 104 controls the operation of the pulse generation circuit 13 to send out a pulse current in a manner determined by the main control unit 100. The communication control unit 105 converts data passed from the main control unit 100 into a transmission signal based on a communication standard and transmits it from the wireless communication circuit 14, and also extracts specific data from a signal received by the wireless communication circuit 14 from the outside.

[0061] A plurality of mathematical models constructed by an external computer equipped with a high-level machine learning function are registered in the mathematical model storage unit 106. The main mathematical models in this embodiment are an analytical model for estimating the characteristics of the lithium ion battery 2, a mathematical model for calculation that corrects the values ​​of three types of internal resistance components described below depending on temperature, a mathematical model for calculation that determines the internal impedance of the lithium ion battery, a mathematical model for calculation performed for deterioration diagnosis, and a mathematical model for calculation that determines parameters for performance recovery control described below.

[0062] These mathematical models are constructed using a database in which various numerical data derived from experiments conducted on lithium-ion batteries with various characteristics are accumulated in association with the battery's operating history and degree of deterioration. In this embodiment, the characteristics of a lithium-ion battery are represented by a combination of multiple types of characteristic data (data representing rated capacity, years of use, number of cells, battery materials, etc.; characteristic data represented by numerical values ​​has a certain numerical range), and lithium-ion batteries with various characteristics are classified into multiple types (hereinafter referred to as "characteristic types") with different combinations of the characteristic data. Mathematical models for the various calculations described above are generated for each characteristic type, and these mathematical models are linked to characteristic type identification codes and registered in the mathematical model storage unit 106.

[0063] The measurement condition storage unit 107 stores a plurality of parameter combinations (described in detail below) that represent the measurement conditions for the internal resistance. Hereinafter, each parameter will be referred to as a "measurement parameter." The combinations of measurement parameters are also determined for each characteristic type, and are stored in the measurement condition storage unit 107 in association with the identification code of each characteristic type.

[0064] The history data storage unit 108 stores the measured values ​​obtained by the measurements of the measuring units 101, 102, and 103 and the numerical data derived by calculations using these measured values ​​(such as the internal resistance value, the amount of fluctuation in voltage and current, the length of the charging and discharging periods, the charging and discharging speeds, and the number of charging and discharging cycles; hereinafter, these numerical data will also be considered to correspond to "measured values") in combination with data indicating the date and time when they were obtained (hereinafter, referred to as "date and time data").

[0065] In addition to the measured values, the history data storage unit 108 also stores feature quantities (such as internal impedance, actual current, and discharge capacity, which will be described later) determined by estimation calculations that apply the measured values ​​to a mathematical model, and data that represent the operation history of the battery 2 (such as the start and end dates and times of charging and discharging).The numerical data that represent the feature quantities is also combined with date and time data that represents the time when the numerical values ​​or the measured values ​​applied to the estimation calculations were obtained.

[0066] Hereinafter, the combinations of the measurement values ​​and date and time data, and the combinations of the feature values ​​and date and time data will be collectively referred to as “event data.” Immediately after the battery activation device 1 connected to the lithium ion battery 2 is started up for the first time, the history data storage unit 108 is almost empty, but as the period since the start of use becomes longer, the number of event data stored in the history data storage unit 108 also increases.

[0067] The main control unit 100 combines measurement values ​​obtained from detection signals from various sensors with date and time data and stores them in the history data storage unit 108. It also constantly monitors voltage measurements, determining that discharging is occurring when the voltage drops below a certain threshold, and determining that charging is occurring when the voltage rises above another certain threshold. Furthermore, it determines the end of charging or discharging based on subsequent changes in voltage, temperature, etc., calculates the length of the period from start to finish, and calculates the amount of fluctuation in voltage and current during that period. It also calculates the rate of charging or discharging based on the amount of voltage fluctuation and the duration of the fluctuation. The numerical data obtained by these calculations is stored in the history data storage unit 108 as the event data described above.

[0068] Furthermore, during a period when the measured voltage value is stable (the amount of voltage fluctuation is within a predetermined threshold), the main control unit 100, in cooperation with the pulse control unit 104 and each measurement unit 101, 102, repeatedly executes the process of measuring the internal resistance generated when a pulse current is passed through the battery 2 and the process of calculating the internal impedance of the battery 2 based on the measurement results. Furthermore, the main control unit 100 appropriately diagnoses the deterioration state of the battery 2 (hereinafter referred to as "deterioration diagnosis") using the internal impedance values ​​accumulated in the history data storage unit 108, and if this diagnosis determines that the degree of deterioration of the battery 2 exceeds an allowable level, it executes control to eliminate the deterioration and restore the performance of the battery 2 (hereinafter referred to as "performance restoration control").

[0069] Below, we will explain in order the concept and measurement method of internal resistance, which is the basic data used in deterioration diagnosis, the concept and derivation method of parameters used in deterioration diagnosis, and the contents and processing procedures of the main processing of the control device 10, including deterioration diagnosis and performance recovery control.

[0070] <Method of Measuring Internal Resistance> In this embodiment, in order to determine the mode of the pulse current to be passed through the battery 2 for measuring the internal resistance, measurement conditions are set by combining the following five types of measurement parameters.

[0071] a. The period of the pulse current (hereinafter referred to as the "pulse period") b. The pulse width of the pulse current c. The peak level of the pulse current (hereinafter referred to as the "peak current") (hereinafter referred to as the "peak value") d. The number of pulses (the number of times the peak current is applied) e. The length of the period between the point at which the application of the pulse current ends and the point at which the amount of change in voltage caused by that application is determined (hereinafter this period will be referred to as the "buffer period", and the length of this period will be referred to as the "buffer time")

[0072] Instead of the number of pulses d, the length of the period during which the pulse current is applied, i.e., the time length obtained by multiplying the pulse period by the number of pulses (hereinafter referred to as the "pulse application period"), may be included in the measurement parameters.

[0073] Figure 3 shows a schematic representation of the five parameters and the basic measurement method. The control device 10 of this embodiment applies a pulse current to the battery 2, with the pulse period, pulse width, and peak value set according to the measurement conditions set, until the pulse period is repeated a number of times corresponding to the number of pulses under the same measurement conditions. The control device 10 also determines the timing for calculating the internal resistance based on the end of the application of the pulse current and the buffer time. As shown in Figures 3B and 3C, the buffer time can be either a positive or negative value.

[0074] The pulse current shown in Figure 3 and Figure 4 is assumed to flow from the positive electrode to the negative electrode, i.e., in the same direction as the current during discharge. Conversely, the pulse current may also flow from the negative electrode to the positive electrode. In either case, the peak current is set to a level (several tens to several hundred milliamperes) that does not cause a significant chemical reaction in the battery 2.

[0075] The control device 10 performs a process of applying a pulse current in parallel with the process of applying a pulse current, and calculates a voltage V when a peak current is not being applied during the pulse application period. ０ , the actual value of the peak current I, and the voltage V changed by the application of the peak current. １ Then, by applying these measured values ​​to the following equation (A), the value of the internal resistance R for the pulse current is calculated. ａｃｔ Calculate.

[0076] R ａｃｔ =|V ０ -V １ | / I ... (A)

[0077] Voltage V ０ can be measured under conditions where there is no influence of peak current, such as before the first pulse of the pulse application period. １ The timing of measurement of is determined by the buffer time. When the buffer time is a positive value, the voltage is measured at the point when the buffer time has elapsed since the application of the last peak current, as shown in FIG. 3B. When the buffer time is a negative value, its absolute value is set shorter than the pulse width, and the voltage is measured during the period when the last peak current is applied, as shown in FIG. 3C. Regarding the current I, in this embodiment, the measured value of the last peak current is applied, but this is not limiting. Alternatively, the peak current may be measured for each pulse during the pulse application period, and the average or maximum value of these measured values ​​may be assigned to I (the same applies to the example in FIG. 4).

[0078] The calculation of the above formula (A) is the voltage V １ In addition, regardless of whether the buffer time is positive or negative, the voltage V １ Since all the variables in equation (A) are determined by measuring the voltage V １ The value of the internal resistance is determined by measuring the voltage V １ The timing of the measurement is explained assuming that the internal resistance is measured when the

[0079] <Internal Resistance Components to be Measured> In this example, the internal resistance of the battery 2 is measured by dividing it into three components: ohmic resistance, interface resistance, and diffusion resistance.

[0080] Ohmic resistance is electrical resistance that arises due to the physical configuration of the battery 2, and includes resistance that arises from the electrode material and electrolyte, as well as resistance that arises from the separator's ability to allow lithium ions to pass through.

[0081] Interfacial resistance is the resistance component that occurs when lithium ions pass through the surface layer of the electrode (including the passivation layer and the interface between the passivation layer and the electrolyte). If the thickness of the surface layer or the degree of surface roughness increases due to chemical reactions that occur between the surface layer and the electrolyte during charging and discharging, the interfacial resistance also increases, slowing the passage speed of lithium ions.

[0082] Diffusion resistance is a resistance component that regulates the movement of lithium ions when they are inserted into or extracted from the crystalline structure of the electrode material. Diffusion resistance increases due to changes in the surface layer and deterioration of the electrode material.

[0083] Ohmic resistance appears in rapid response to the rise of the pulse current and disappears in rapid response to the fall of the pulse current. In contrast, interface resistance and diffusion resistance appear later than ohmic resistance because they arise due to ion migration and chemical reactions, and remain for some time after the pulse current falls.

[0084] When a pulse current flows from the positive electrode to the negative electrode of the battery 2 (in the same direction as the current flow during discharge), resistance components occur in the order of ohmic resistance, diffusion resistance, and interfacial resistance on the negative electrode side where lithium ions are desorbed, while resistance components occur in the order of ohmic resistance, interfacial resistance, and diffusion resistance on the positive electrode side where lithium ions are absorbed. At the negative electrode, lithium ions inserted into the electrode material during charging can be released through the paths formed by the insertion. Therefore, unless significant abnormalities occur in the electrode material or surface layer, the diffusion resistance and interfacial resistance are thought to be relatively small. On the other hand, at the positive electrode, interfacial resistance and diffusion resistance tend to be larger than those at the negative electrode due to factors such as active chemical reactions between the electrolyte and the surface layer when lithium ions pass through the surface layer and the time it takes for individual lithium ions to determine their insertion locations in the crystalline structure of the electrode material. Conversely, when a pulse current flows from the negative electrode to the positive electrode of the battery (in the same direction as the current flow during charging), the interfacial resistance and diffusion resistance tend to be larger on the negative electrode side than on the positive electrode side.

[0085] In consideration of the above-mentioned tendency, in this example, it is assumed that, with regard to the interface resistance and the diffusion resistance, the resistance component generated in the electrode on the side that absorbs lithium ions becomes dominant over the resistance component generated in the electrode on the side that releases lithium ions, and that the resistance component of the latter electrode can be ignored. Based on this assumption, it is assumed that three types of resistance components occur in the order of ohmic resistance, interface resistance, and diffusion resistance, and measurement conditions are determined for each resistance component under this assumption.

[0086] <Measurement Conditions for Each Internal Resistance Component> Fig. 4 shows the pulse current pattern when a pulse current is passed from the positive electrode to the negative electrode, along with the timing for acquiring measured values ​​that can be applied to the above-mentioned formula (A). Pattern A in the figure is an example of a pulse current when measuring ohmic resistance, pattern B is an example of a pulse current when measuring interface resistance, and pattern C is an example of a pulse current when measuring diffusion resistance. Each example shows the initial and final portions of the pulse application period.

[0087] The measurement method shown in Figure 3(C) is used to measure the ohmic resistance generated in response to the peak current. In order to obtain a resistance value at a level sufficient to detect the degree of change, the peak value is set to a value higher than the set values ​​for other resistance components. The pulse width and pulse period are set short to make it difficult for interface resistance and diffusion resistance to occur. The pulse application period is also set short to minimize the impact of the high peak current level on the battery 2.

[0088] In the example of pattern A in FIG. 4, the voltage V ０ is measured, and the current I and voltage V are measured while the peak current of the last cycle is applied. １ However, the voltage V ０ The measurement of the voltage V is also performed in the last period, just before the last peak current is applied. ０ Alternatively, the voltage V ０ , V １ The current I may be measured by performing this measurement process multiple times, and the average value of the internal resistance values ​​obtained in each process may be used as the final measurement value. If the calculation speed of the control device 10 can handle this, the voltage V may be measured simultaneously with or immediately after the end of the application of the peak current. １ may be measured.

[0089] In measuring the interface resistance and the diffusion resistance, a peak current with a pulse width suitable for generating the resistance component is applied to the battery 2 multiple times, and the measurement method shown in FIG. 3B is applied to measure the voltage V before the peak current of the first cycle is applied. ０ , the peak current I of the last period, and the voltage V at the end of the last period and buffer period. １The internal resistance is calculated using the above formula. By adjusting the pulse width and pulse period each time so that the next peak current flows before the chemical reaction corresponding to the peak current has ceased, measurements are performed using the method shown in Figure 3(B). This gradually activates the chemical reactions that cause interface resistance and diffusion resistance, even if the peak current is weak. This makes it possible to determine the amount of voltage change based on a point in time when no chemical reaction has yet occurred, thereby obtaining a resistance value at a level sufficient to detect the degree of change. Based on this perspective, the number of pulses that determines the pulse application period is also set so that the pulse application period continues until a sufficient chemical reaction has occurred.

[0090] In the measurement of the interface resistance shown in pattern B, a pulse width longer than the peak current for measuring the ohmic resistance is set, provided that the length is long enough to suppress the occurrence of diffusion resistance. The pulse period is also longer than the set value of the ohmic resistance, but the peak value is set lower than the set value of the ohmic resistance in consideration of the effect that the longer pulse width has on the battery 2. In addition, the voltage V is set at a point where it is assumed that the movement of ions that cause interface resistance continues, but that significant diffusion of lithium ions is not occurring. １ The length of the buffer time is adjusted so that

[0091] In the measurement of the diffusion resistance shown in pattern C, the pulse period and pulse width are set to values ​​longer than the set values ​​of the other resistance components. In order to suppress the influence of the application of a peak current for a long time due to this setting on the battery 2, the peak value is set to a value lower than the set values ​​of the other resistance components. In addition, the voltage V １ The length of the buffer period is adjusted so that .times. ...

[0092] A combination of measurement conditions for the above three types of internal resistance components is set for each battery characteristic type and stored in the measurement condition storage unit 107. The main control unit 100 estimates the characteristics of the battery 2 by analyzing event data accumulated through the event data collection process described below, and selects measurement conditions suitable for those characteristics to measure each internal resistance component.

[0093] To obtain the internal impedance, the mathematical model storage unit 106 stores a mathematical model that defines the relationship between the above three types of internal resistance components and the internal impedance that occurs in the battery 2 due to their superposition. This mathematical model is also set for each of multiple characteristic types, and a mathematical model that matches the battery characteristics estimated by analysis is selected from among them.

[0094] The main control unit 100 sequentially sets measurement conditions suitable for each of the three types of internal resistance components and measures each internal resistance component, then corrects these measurements using temperature measurements obtained during the same period, and applies the corrected values ​​to the mathematical model to calculate the internal impedance of the battery 2. The calculated internal impedance is combined with date and time data and stored in the history data storage unit 108, and is used as the main data for deterioration diagnosis.

[0095] <Method of Deterioration Diagnosis> For the purpose of deterioration diagnosis, this embodiment calculates an index value that indicates the degree of deterioration of the lithium ion battery 2 and two types of feature quantities that indicate the current performance of the lithium ion battery 2. The mathematical model storage unit 106 stores dedicated mathematical models for each of these index values ​​and feature quantities and for each battery characteristic type.

[0096] In this embodiment, the index value representing the degree of deterioration of the lithium-ion battery 2 is used as a variable in a calculation to attenuate the characteristic quantity representing the performance of the battery 2 (calculation formulas (1) and (2) described below) and in a calculation to obtain a control parameter that determines the mode of the pulse current for performance recovery, and is therefore hereinafter referred to as a "deterioration index parameter."

[0097] In the mathematical model for determining the deterioration index parameter, the parameter is defined to take any value greater than 1, and the value of the deterioration index parameter is defined to vary depending on the internal impedance value derived from a specific point in the past to the present, the length of that period, etc. More specifically, this mathematical model is based on the premise that the internal impedance values ​​accumulated in the history data storage unit 108 from a specific point in the past as the starting point to the present are used, and is defined so that the value of the deterioration index parameter increases as the number of data used (i.e., the longer the time elapsed since the starting point) and as the numerical value indicating a deterioration in the performance of the battery 2 (internal impedance value exceeding a predetermined threshold) increases.

[0098] The above definition was established taking into account the fact that deterioration of lithium-ion batteries 2 does not progress uniformly, but that the degree of deterioration varies depending on the charging and discharging conditions and changes in the environment around the battery 2, and that the rate of deterioration may increase as time passes. This definition can improve the fit of the value of the deterioration index parameter to the actual degree of deterioration of the battery 2. Note that the data representing the length of elapsed time applied to the above mathematical model is not limited to the number of numerical data representing the internal impedance, and may be the length of elapsed time itself.

[0099] In this embodiment, the feature quantities representing the performance of the lithium-ion battery 2 are calculated as the current flowing through the battery 2 when the battery is discharged at a certain level (hereinafter referred to as the "actual current") and the current discharge capacity of the battery 2. Mathematical models for calculating these feature quantities are also set for each characteristic type of battery and each type of feature quantity, and in each mathematical model, the relationship between the change over time in several types of event data, including the internal impedance, and the value of the feature quantity is defined.

[0100] In this embodiment, the event data accumulated in the history data storage unit 108 is applied to the above mathematical model to find initial values ​​of the actual current and the discharge capacity, and then a calculation to calculate a deterioration index parameter and a calculation using the following equations (1) and (2) to which the calculated deterioration index parameter is applied are repeated, thereby gradually attenuating the two types of feature quantities.

[0101]

[0102] Equation (1) is a formula for calculating the current actual current It (unit: amperes). t is the length of the period (unit: minutes or seconds) from the point in time when the actual current is calculated by calculation using a mathematical model (hereinafter referred to as the "starting point") to the point in time when equation (1) is executed, and t ｐ It is the length of the period (unit: minutes or seconds) from the starting point to a certain point before the present (at which point the calculation is also performed using the calculation formula (1)). ｐ is the distance from the starting point above to ｐ is the actual current value calculated by equation (1) when time has elapsed, and k is the current value of the deterioration index parameter.

[0103] The calculation formula (2) is a formula for calculating the current discharge capacity Ct (unit: ampere-hour) of the lithium ion battery 2. ｐ The definition of is the same as in equation (1). ｐ is the time from the point (starting point) when the discharge capacity is calculated by the mathematical model to t ｐ is the estimated value calculated by equation (2) executed when time has passed, and k is the current value of the deterioration index parameter (k>1).

[0104] It of the first calculation by the calculation formulas (1) and (2) ｐ , Ct ｐ The initial value obtained by the mathematical model described above is applied to . The starting point applied to the arithmetic expressions (1) and (2) may be the same as the starting point applied to the calculation of the deterioration index parameter.

[0105] Assuming that no pulse current for performance recovery is applied from the starting point to the time when the calculation is executed, and that the starting point is not changed, when the calculation of the above formulas (1) and (2) is repeatedly executed, t>t ｐ In addition, since the value of k is greater than 1, the values ​​of It and Ct calculated in each calculation attenuate over time, and the greater the value of k, the greater the amount of attenuation.

[0106] As described above, in this embodiment, highly reliable values ​​for each of the actual current and discharge capacity are calculated using a mathematical model and set as initial values. Then, starting from the time when these calculations were performed or a time nearby, the following processes are repeatedly performed: updating the deterioration index parameter by applying to the mathematical model the internal impedance values ​​stored in the history data storage unit 108 from the time when these calculations were performed until the present; and calculating using equations (1) and (2) that apply the elapsed time from the above-mentioned starting point and the latest deterioration index parameter. This series of processes allows the characteristic quantities of the actual current and discharge capacity to be attenuated in accordance with the length of time elapsed from the starting point and changes in the internal impedance that have occurred in the battery 2 over that time, while maintaining the reliability of the values.

[0107] <Performance Recovery Control> During performance recovery control, the main control unit 100 also performs calculations to determine control parameters that determine the mode of the pulse current used in performance recovery control, using the event data and degradation index parameters stored in the history data storage unit 108. The derived control parameters are transmitted to the pulse control unit 104, and a pulse current to which the control parameters have been applied is caused to flow through the connection circuit to the battery 2 through cooperative processing between the pulse control unit 104 and the pulse generation circuit 13.

[0108] Fig. 5 shows a basic pattern of the pulse current used in performance recovery control, where the current axis (vertical axis) is positive in the direction from the positive electrode to the negative electrode (direction of current during discharge) and negative in the direction from the negative electrode to the positive electrode (direction of current during charge).

[0109] Figure 5(A) shows control in which a pulse current with a constant peak value is passed in the positive direction, while Figure 5(B) shows control in which the peak value of the current is gradually reduced while passing the current in the positive direction. Figure 5(C) shows control in which two types of pulse current with different peak levels are passed alternately in the positive direction. Figures 5(D) and 5(E) show a process in which a current is passed in the positive direction followed immediately by a current in the negative direction, repeated at a fixed cycle. In the control of Figure 5(D), the peak value of the current in each direction is maintained at a constant value, while in the control of Figure 5(E), the peak value of the current in the negative direction is adjusted to gradually decrease. Either set of pulse currents can be converted into a high-frequency signal that can vary in the range from tens of kilohertz to several megahertz.

[0110] The mathematical model storage unit 106 stores mathematical models for determining control parameters (current direction, pulse width, pulse period, peak value, number of pulses, etc.) that represent the specific aspects of each of these five patterns. These mathematical models are also set for each battery characteristic type. A deterioration index parameter is applied to each mathematical model, and it is defined that the larger the deterioration index parameter, the larger the pulse width, the higher the peak value, etc., and the more the control parameters fluctuate in the direction that strengthens the force that the pulse current exerts on the battery 2. This makes it possible to flow a pulse current in a mode appropriate for the deterioration state of the battery 2.

[0111] <Main Processing Flow> When the battery activation device 1 of this embodiment is electrically connected to the lithium ion battery 2 and activated, it enters a standby mode for a while to collect event data necessary to determine the characteristics of the battery 2.

[0112] In the preparation mode, the control device 10 repeatedly collects event data by sequentially applying the combinations of measurement conditions for ohmic resistance, interface resistance, and diffusion resistance stored in the measurement condition memory unit 107 (all combinations stored for each characteristic type), provided that the measured voltage value is above a predetermined level and the fluctuation amount is stable within a predetermined threshold.

[0113] 6 shows the procedure for collecting event data for one cycle. In this process, the internal resistance components of ohmic resistance, interface resistance, and diffused resistance are measured in order based on the measurement conditions applied to the selected characteristic type (steps S101, S102, and S103). In parallel with these measurements, environmental data such as temperature, humidity, and atmospheric pressure are measured (steps S104, S105, and S106).

[0114] Although not explicitly shown in FIG. 6, during the event data collection period after charging is completed, the measurement pulse currents in steps S101, S102, and S103 are made to flow in the direction from the positive electrode to the negative electrode, and during the event data collection period after discharging is completed, these measurement pulse currents are made to flow in the direction from the negative electrode to the positive electrode.

[0115] 6, for convenience of illustration, steps S101 and S104, steps S102 and S105, and steps S103 and S106 are shown to be executed in parallel, but in reality, the processes are not linked in this manner. Also, while one measurement of steps S101, S102, and S103 is being performed, measurements of steps S104, S105, and S106 may be performed multiple times.

[0116] Considering that internal resistance components are easily affected by temperature, the main control unit 100 of the control device 10 applies the measured values ​​of each internal resistance component and the measured temperature obtained in steps S101, S102, and S103 to a mathematical model for correction to correct the value of each internal resistance component (step S107), and then applies each corrected value to a mathematical model for calculating the internal impedance (one that matches the selected characteristic type) to derive the value of the internal impedance (step S108).

[0117] Furthermore, the main control unit 100 combines the corrected value of the internal resistance component, the measured value of the environmental data, and the value of the internal impedance obtained by the above series of processes with date and time data, and stores them in the history data storage unit 108 (step S109). Although the times at which these values ​​are obtained are strictly different, in this embodiment, each time the routine of steps S101 to S109 is executed, the same identification code is assigned to the date and time data of the event data saved in that routine, or date and time data unified by the start date and time of the routine, etc. is combined with each value, thereby linking related event data together.

[0118] In the preparation mode, measurement parameters corresponding to all characteristic types are selected in order and the collection process shown in Fig. 6 is repeated, and in response to the detection of a change in voltage indicating charging or discharging, calculations are also performed to calculate the aforementioned feature quantities such as the length of the charging period and discharging period, and the charging rate and discharging rate. The values ​​obtained by these processes are also stored in the history data storage unit 108 as event data combined with date and time data.

[0119] When a certain amount of event data is accumulated in the history data storage unit 108 during the preparation mode, the main control unit 100 of the control device 10 applies this event data to an analytical model of the battery 2 characteristics registered in the mathematical model storage unit 106 to derive estimated values ​​of the characteristics (rated capacity, years of use, number of cells, etc.) of the connected battery 2. Furthermore, the main control unit 100 calculates initial values ​​of the actual current and discharge capacity using a mathematical model of a characteristic type that matches the derived characteristics, and determines initial values ​​of the starting point to be used in subsequent calculations to determine the deterioration index parameters and in calculations using equations (1) and (2). For example, the initial starting point may be the start of the preparation mode, the end of the preparation mode, or a predetermined time after the end of the preparation mode. Furthermore, based on the results of the characteristic estimation, measurement parameters corresponding to a specific characteristic type are set to be applied to subsequent measurements of the internal resistance component.

[0120] Even after the preparation mode ends, the control device 10 continues to collect event data while appropriately diagnosing the deterioration of the battery 2, and if this diagnosis determines that performance recovery control is necessary, it executes that control.

[0121] Fig. 7 is a flowchart showing the flow of the main processing executed by the control device 10 of the battery activation device 1 after the above preparation mode has been completed. Fig. 8 is a flowchart showing the detailed processing flow of the periodic maintenance in step S5 of the flowchart.

[0122] The control device 10 repeatedly measures the detection signals of the various sensors 15-19, checking whether charging or discharging is occurring based on the degree of voltage fluctuation, and whether any changes indicating an abnormal event have occurred in the measurements obtained from the various sensors (steps S1 and S2 in FIG. 5). If no such changes have occurred, steps S1, S2, and S3 result in "NO," and the process proceeds to step S4, returning to step S1, and the process is repeated. In step S4, the routine of FIG. 6 is called, applying measurement conditions that apply to the characteristics of the battery 2, and event data is collected.

[0123] In the above loop, the process proceeds from step S3 to step S5 every time a predetermined time period (for example, 30 minutes) has elapsed, and regular maintenance including deterioration diagnosis is carried out (steps S3 and S5).

[0124] 8, the procedure for the scheduled maintenance in step S5 will now be described. During scheduled maintenance, the main control unit 100 of the control device 10 reads the internal impedances stored in the history data storage unit 108 from the starting point applied to the calculation of the current deterioration index parameter up to the present, based on the date and time data, performs a calculation applying these to a mathematical model for deriving the deterioration index parameter, and updates the deterioration index parameter with the value obtained from this calculation (step S501). Even after this update, the starting point applied to the calculation for deriving the deterioration index parameter is, in principle, maintained.

[0125] Furthermore, the main control unit 100 defines the time length from the starting point applied to the deterioration index parameter to the present as t, and the time length from the same starting point to the execution point of the calculation a predetermined time before the present as t ｐ The deterioration index parameters are applied to the calculations of the formulas (1) and (2) to calculate the current actual current It and discharge capacity Ct (step S502). The values ​​of It and Ct are also combined with the date and time data and stored in the history data storage unit 108. ｐ The time point of the past calculation corresponding to may be the time point of the calculation one step before, or may be the time point of the calculation several steps before.

[0126] When the above calculations are completed, the main control unit 100 calculates the amount of fluctuation for each of the above actual current, discharge capacity, and internal impedance using the calculation results for a predetermined period, including the most recent calculation result, and compares these amounts of fluctuation with predetermined thresholds (steps S503, S504, S505).

[0127] If the amount of fluctuation in the actual current or discharge capacity exceeds the threshold value (step S503 or step S504 is "YES"), the main control unit 100 calculates control parameters that determine the mode of the pulse current for performance recovery control by applying various event data and deterioration index parameters accumulated in the history data storage unit 108 from the start of the calculation period applied to the current deterioration index parameter to a mathematical model for performance recovery control (step S507).

[0128] If the amount of change in the internal impedance exceeds the threshold (if "YES" in step S505), the main control unit 100 calculates the rate of change in the discharge capacity based on the amount of change in the discharge capacity targeted in step S504 and the length of the period used for that calculation. If this rate of change also exceeds the threshold (if "YES" in step S506), the process proceeds to step S507, where a calculation is performed to determine parameters for performance recovery control.

[0129] After the calculation in step S507 is completed, the control device 10 proceeds to step S508 and executes performance recovery control by sending a pulse current to which the parameters determined by the calculation have been applied to the battery 2. When the sending of the predetermined number of pulse currents has been completed, the control device 10 returns to the main routine shown in FIG.

[0130] On the other hand, if the fluctuations of the actual current, discharge capacity, and internal impedance are all below the thresholds, the determinations in steps S503, S504, and S505 are all "NO," and the process returns to the main routine without executing steps S507 and S508.

[0131] Returning to Figure 7, in this embodiment, it is determined whether or not performance recovery control is necessary immediately after a high discharge occurs in the battery 2. Specifically, when the voltage drops as a result of the start of discharge of the battery 2 and the amount of voltage fluctuation exceeds a predetermined threshold (if steps S1 and S6 are "YES"), the system waits until the fluctuation subsides (if step S7 is "YES"), and then enters the loop of steps S8 to S12.

[0132] In the first step S8 of this loop, a constant level I lower than the rated current is ｏ By measuring the detection signal of the current sensor 16 while passing a pulse current with a peak current of I, the value of the peak current actually flowing in the circuit can be calculated. ｒ In the next step S9, these currents I ｏ , I ｒ and the rated capacity C of battery 2 Ｏ (value estimated by analysis in the preparation mode) and the remaining capacity C of the battery 2 at present are calculated by the following calculation formula (3). ｒ (Estimated value) is calculated.

[0133]

[0134] When the above calculation is completed, the main control unit 100 calculates the amount of change in the remaining capacity using the calculation result and the calculation result of the previous step S9, and checks whether the value exceeds a predetermined threshold value (step S10).

[0135] In the first processing of the loop of steps S8 to S12, steps S8 and S9 are executed twice with a predetermined time interval between them, and the result of each calculation is used to calculate the amount of fluctuation to be used for the judgment in step S10.

[0136] If the amount of change in the remaining capacity exceeds the threshold value (if step S10 is "YES"), the main control unit 100 applies the event data and degradation index parameters stored in the history data storage unit 108 from the starting point to the present that are applied to the current degradation index parameters to a mathematical model, calculates parameters that determine the form of the pulse current for performance recovery control (step S11), and executes performance recovery control by flowing a pulse current to which the parameters are applied (step S12).

[0137] The remaining capacity C calculated by the above formula (3) ｒ Unlike It and Ct calculated by the formulas (1) and (2), these are estimated values ​​calculated from instantaneous events without considering the time factor. However, by applying the degradation index parameters and previously acquired event data to the calculation for determining the parameters for performance recovery control in step S11, it is possible to vary the pulse current state depending on the extent to which events related to the deterioration of battery 2 performance occurred before the most recent voltage drop and the length of time elapsed since the start of the calculation.

[0138] Even after the performance recovery control in step S12 is completed, the control device 10 executes steps S8 to S10 again, and if the amount of fluctuation in the remaining capacity calculated from the new measurement value exceeds the threshold, executes steps S11 and S12 again. Immediately after discharge, the behavior of lithium ions may become unstable, causing large fluctuations in the remaining capacity, but by repeating steps S8 to S12, the amount of fluctuation attenuates, and the intensity of the pulse current can also be weakened.

[0139] When a change in the measured value indicating an abnormal event occurs (when step S2 is "YES"), such as when any of the environmental data (temperature, humidity, or atmospheric pressure) fluctuates beyond a predetermined threshold, or when the voltage during charging and discharging fluctuates beyond a threshold, the control device 10 calculates parameters that determine the mode of the pulse current for performance recovery control by applying the event data accumulated in the history data storage unit 108 and the current values ​​of the degradation index parameters (step S13), and executes performance recovery control applying the parameters (step S14). In this case, too, the mode of the pulse current can be varied based not only on the most recent abnormal event, but also on the extent to which events related to the deterioration of battery 2 performance have occurred between the start of calculation applied to the degradation index parameters and the occurrence of the abnormality, and the length of time elapsed since the start of calculation.

[0140] As mentioned above, the initial value of the starting point of the target period for the calculation to determine the degradation index parameter and the calculation using equations (1) and (2) is determined based on the battery 2 characteristics and the performance of the battery 2 at that time estimated in the analysis process in the preparation mode immediately after starting the device. The same starting point is also applied to the target period for the calculation to determine the parameters for performance recovery control.

[0141] After the performance recovery control using pulse current is performed in step S14 described above or step S508 of the periodic maintenance (FIG. 8), or when the amount of change in discharge capacity falls below the threshold value due to the performance recovery control in step S12, step S15 becomes "YES" and the following procedure is executed.

[0142] First, the control device 10 repeats the event data collection routine (steps S101 to S109 in FIG. 6 ) multiple times using the characteristic type measurement parameters and mathematical model estimated in the previous analysis process, provided that the state is close to the preparation mode and the amount of voltage fluctuation is below the threshold, and stores various event data including the internal impedance in the history data storage unit 108 (step S16). If a voltage change indicating charging or discharging is detected during this period, the control device 10 also performs calculations to calculate feature quantities such as the length of the charging period and discharging period, and the charging rate and discharging rate, and stores the calculation results in the history data storage unit 108.

[0143] Once new event data for the capacity required for analysis has been accumulated, the main control unit 100 of the control device 10 applies the event data to a mathematical model to calculate the degradation index parameter, actual current, and discharge capacity, and updates the current values ​​of the degradation index parameter, actual current, and discharge capacity using these calculated values ​​(step S17). Furthermore, the main control unit 100 updates the starting point of the period applied to subsequent calculations to determine the degradation index parameter, calculations using equations (1) and (2), and calculations to determine the performance recovery control parameters to the time at or near the time of the calculation in step S17 (step S18). After updating, the processing steps described above are repeated.

[0144] In addition, in the calculation of step S17, in addition to the event data accumulated after the performance recovery control, it is also possible to use event data accumulated during the period from a point a predetermined time before the performance recovery control until the performance recovery control.

[0145] By the processing of steps S16 to S18, the degradation index parameter, actual current, and discharge capacity after the performance recovery control are updated to values ​​that reflect the effects of the performance recovery control. The subsequent value of the degradation index parameter and the actual current and discharge capacity calculated by the arithmetic expressions (1) and (2) can also be changed depending on the length of time that has elapsed since the performance recovery control was performed and the degree of change in events that occurred during that time.

[0146] As described above, the control device 10 of this embodiment calculates deterioration index parameters whose values ​​vary depending on the change in the internal impedance of the lithium-ion battery 2 over time and the length of elapsed time through calculations using a high-order mathematical model registered in the mathematical model storage unit 106, and also varies the calculation results in accordance with the change in the internal impedance of the lithium-ion battery 2 over time and the length of elapsed time in calculations to estimate feature quantities (actual current, discharge capacity) that represent the performance of the battery 2 and calculations to calculate parameters for performance recovery control. Therefore, it is possible to determine with high accuracy the time to execute performance recovery control and to supply a pulse current in a mode appropriate for the degree of deterioration at that time, which is far more effective in recovering the performance of the battery 2 than a method of supplying a pulse current in a uniform mode depending on the establishment of uniform conditions.

[0147] <Modifications> The following describes processes that were not included in the above flowchart to avoid complication, and items that can be changed or added.

[0148] 7 and 8 are satisfied, or when a predetermined event occurs, such as when the number of charge / discharge cycles exceeds a predetermined threshold, or when the rate of natural discharge exceeds a predetermined threshold. Natural discharge is determined when a voltage drop below the threshold used in the determination of step S6 continues for a certain period of time or longer.

[0149] The process of recalculating the deterioration index parameters, actual current, and discharge capacity, and the update of the starting point of the calculation period are not limited to after performance recovery control. When it is determined that these current values ​​are inappropriate, the same process as steps S16 to S18 is also executed as appropriate.

[0150] Specifically, the control device 10 predicts a future change in the internal impedance based on the calculated value each time the deterioration index parameter is calculated, and if a difference of a predetermined threshold or more occurs between the change in the internal impedance during the subsequent calculation to calculate the deterioration index parameter and the previously predicted value, the control device 10 changes the start point of the calculation period to a point earlier or later than the current start point, and recalculates the deterioration index parameter using event data from the new start point to the present. When this change is made, the actual current and discharge capacity are also updated by applying several types of event data, including the internal impedance from the new start point, to a mathematical model, and in subsequent scheduled maintenance, estimation calculations are performed using the calculation formulas (1) and (2) to which the new start point is applied.

[0151] 7 and 8, performance recovery control is immediately performed in response to a determination that performance recovery control should be performed, but instead, performance recovery control may be performed when a predetermined time has elapsed since the determination was made. Alternatively, performance recovery control may be performed when a predetermined time has elapsed, such as when the next charge (or discharge) is completed or when the voltage value measured in the event data collection process has dropped to a predetermined level.

[0152] In the previous explanation, the starting point was changed after performance recovery control was executed to the point at which the performance recovery control was completed or to a point close to that point. However, if the degree of deterioration indicated by the deterioration index parameter at the time of control is large, the new starting point may be set to a point midway between the starting point applied before the performance recovery control and the time of control, so that a considerable amount of event data that occurred before the control is also used in the calculations after the control.

[0153] The mathematical model for calculation of the internal impedance can include the discharge capacitance as an element in addition to the internal resistance components of ohmic resistance, interface resistance, and diffusion resistance. In this case, the internal impedance is calculated by applying the measured values ​​(temperature-corrected values) of the three types of internal resistance components and the value of the discharge capacitance Ct calculated by the most recent calculation using the calculation formula (2) to the mathematical model.

[0154] In addition to the internal impedance, the calculation to obtain the deterioration index parameter can also use various feature quantities related to environmental data and charging and discharging (such as the charge / discharge rate, discharge speed, and discharge level). Similar feature quantities can also be used in the calculations to obtain the actual current, discharge capacity, and performance recovery control parameters, and the type of event data used can also be changed depending on the type of calculation.

[0155] The mathematical model for determining the degradation index parameter is not limited to the one used in the above embodiment, and a mathematical model defined so that the value of the degradation index parameter varies depending on the length of the calculation period and the degree of variation of the internal impedance during that period (amount of variation per unit time) may also be used.Furthermore, a mathematical model defined so that the value of the degradation index parameter varies depending on the values ​​or changes in the values ​​of three components, ohmic resistance, interface resistance, and diffusion resistance, instead of the internal impedance, may also be used.

[0156] In the deterioration diagnosis of the above embodiment, the two types of feature quantities, namely the actual current and the discharge capacity, are attenuated in accordance with changes in the value of the deterioration index parameter, and the degree of deterioration is determined based on the values ​​of these feature quantities. However, the degree of deterioration may also be determined by comparing the value of the deterioration index parameter or the change in that value with a threshold value without using the feature quantities.

[0157] In a lithium-ion battery configured with multiple cells packed together, the combined internal resistance of each cell represents the overall internal resistance of the battery. Therefore, even degradation of only a portion of the cells can result in an increase in internal resistance or internal impedance. Taking this into consideration, the mathematical model for performance recovery control in a battery pack must be defined to provide a pulse current that is adjusted to effectively restore degraded cells while avoiding adverse effects on healthy cells. For example, by suppressing the peak current level to a weak level while adjusting the performance recovery control parameters so that the pulse width and pulse number are greater than those for a single cell, it is possible to provide the degraded cells with the energy necessary for their recovery while preventing adverse effects on healthy cells. On the other hand, in a calculation to determine the performance recovery control parameters for a battery with a simple configuration, such as a small battery consisting of only one cell, a constant value may be varied solely based on the value of the degradation index parameter.

[0158] In the battery activation device 1 configured as described above, in order to increase the reliability of the internal resistance measurement value, a current sensor 16 is provided in the connection path between the battery 2 and the device 1 itself, and the internal resistance of the battery 2 is calculated by a calculation (equation (A)) using the measurement value obtained from the current sensor 16 when a peak current flows during the pulse application period and the amount of change in the measurement value of the voltage sensor 15 due to the application of the pulse current. However, if the difference between the level of the current flowing through the circuit and the control level can be kept within an acceptable range, the current sensor 16 may not be provided and the peak value set as the measurement condition may be applied to I in equation (A). Furthermore, it is not necessarily necessary to be able to set the peak value as the measurement condition in fine increments; for example, the height to be applied to the peak value of the measurement condition may be selected from a plurality of predetermined heights.

[0159] Regarding the number of pulses, which is one of the measurement conditions for the internal resistance, it is not necessarily necessary to set different values ​​for each of the internal resistance components, ohmic resistance, interface resistance, and diffusion resistance, and the number of pulses applied to each internal resistance component may be a constant value.

[0160] The battery activation device 1 can use its wireless communication function to accept access from a communication terminal such as a personal computer or smartphone owned by the user, and can also transmit data that constitutes a viewing screen for event data stored in the history data storage unit 108 (such as the operation history of the battery 2, deterioration index parameters, and trends in actual current and discharge capacity) to the communication terminal.

[0161] Furthermore, by having the battery activation device 1 communicate with a cloud server having higher-level functions, it is possible to update various mathematical models registered in the mathematical model storage unit 106 with new models sent from the cloud server, or to send event data accumulated in the history data storage unit 108 to the cloud server, determine deterioration index parameters and various feature quantities through higher-level calculations in the server, and feed back the calculated values ​​to the battery activation device 1. Furthermore, as described below, it is also possible to use the event data sent from the battery activation device 1 for machine learning in the cloud server to improve the level of various mathematical models.

[0162] 9 shows a network system including a cloud server 3 equipped with a high-level machine learning function and multiple battery activation devices 1A, 1B, 1C, etc. Each battery activation device 1A, 1B, 1C, etc. (hereinafter referred to as "battery activation device 1" as before) has the configuration shown in FIGS. 1 and 2 and is electrically connected to an individual lithium ion battery 2A, 2B, 2C, etc. (hereinafter referred to as "lithium ion battery 2" as before).

[0163] In this embodiment, only programs for basic measurement and control and a mathematical model for deriving internal impedance are registered in the battery activation device 1. Meanwhile, in the cloud server 3, a plurality of mathematical models for each characteristic type are registered by machine learning using numerical data on a large number of lithium-ion batteries 2, including a mathematical model for determining a deterioration index parameter, a mathematical model for determining an estimated value of actual current, a mathematical model for determining an estimated value of discharge capacity, and a mathematical model for determining parameters for performance recovery control, and a mathematical model for analysis is also registered to estimate a battery characteristic type that matches a set of event data accumulated in association with a time axis.

[0164] After the initial startup, the battery activation device 1 enters a preparation mode and repeats the collection of event data according to the procedure shown in Fig. 6. When discharging or charging is performed, the event data relating to the discharge or charging is also acquired, and the event data, which is a combination of the values ​​and date and time data, is stored in the history data storage unit 108.

[0165] After a certain amount of time has passed since the initial startup, the battery activation device 1 transmits various event data accumulated in the history data storage unit 108 to the cloud server 3. The cloud server 3, which receives this transmission, applies the received event data to its own analytical model to estimate the characteristics (rated capacity, years of use, number of cells, etc.) of the battery 2 connected to the battery activation device 1, and based on the estimation results, selects the mathematical model that is most suitable for the battery 2 for each type of calculation, and transmits these mathematical models to the battery activation device 1.

[0166] Upon receiving the mathematical models, the battery activation device 1 registers them in the mathematical model storage unit 106 and starts the processing shown in Figures 7 and 8. After this, the battery activation device 1 appropriately transmits the event data accumulated in the history data storage unit 108 to the cloud server 3, and the cloud server 3 uses the event data received from each battery activation device 1 for machine learning to evolve various mathematical models to higher levels. These mathematical models are also transmitted to the battery activation device 1, and the registered information in the mathematical model storage unit 106 is updated accordingly.

[0167] According to the above system, regardless of the characteristics of the lithium-ion battery 2 connected to the battery activation device 1, the degree of deterioration and performance of the battery 2 can be determined with high accuracy by calculations using a mathematical model suitable for the characteristics of the battery 2, thereby improving the quality of performance recovery control.

[0168] 1 is incorporated as an ASIC (application specific integrated circuit chip) into a power supply circuit including a lithium ion battery 2, the battery activation device 1 can be housed inside a device powered by the lithium ion battery 2, and performance recovery control can be appropriately performed on the lithium ion battery 2. This makes it possible to introduce the battery activation device 1 of the above embodiment into small devices such as smartphones.

[0169] Therefore, it becomes possible to use a battery activation device 1 of the same configuration for all types of lithium ion batteries 2, and by continuing to operate these battery activation devices 1 while receiving mathematical models from the cloud server 3, the lifespan of the lithium ion batteries 2 can be significantly extended.

[0170] So far, the battery activation device 1 used for the lithium-ion battery 2 has been described in detail, but the configuration and calculation / control method of the battery activation device 1 of the above embodiment can also be applied to other secondary batteries such as lead-acid batteries. When applied to a lead-acid battery, it is sufficient to measure only the ohmic resistance for the internal resistance, and the deterioration index parameter can also be calculated using accumulated data of measured ohmic resistance.

[0171] For any secondary battery, by performing calculations and performance recovery control using a mathematical model that matches the characteristics of the battery, it is possible to recover reduced performance and significantly extend the life of the secondary battery.

[0172] For secondary batteries that are intended to be replaced when they deteriorate, a battery diagnostic device without a performance recovery control function can be connected. If this battery diagnostic device is equipped with a function for performing a deterioration diagnosis similar to that of the above embodiment and a function for notifying the user of the diagnosis results by displaying or transmitting data, the user can easily know when it is time to replace the secondary battery in use.

[0173] REFERENCE SIGNS LIST 1 Battery activation device 2 Lithium ion battery 3 Cloud server 10 Control device 13 Pulse generation circuit 14 Wireless communication circuit 15 Voltage sensor 16 Current sensor 17 Temperature sensor 18 Humidity sensor 19 Barometric pressure sensor 100 Main control unit 101 Voltage measurement unit 102 Current measurement unit 104 Pulse control unit 105 Communication control unit 106 Mathematical model storage unit 108 History data storage unit

Claims

1. A device electrically connected to a secondary battery for diagnosing the degree of deterioration of the secondary battery, comprising: a control device having a function of passing a pulse current through the secondary battery; and detection means for detecting a physical quantity representing the state of the secondary battery, wherein the detection means includes a voltage sensor for detecting the voltage between the positive and negative electrodes of the secondary battery, and the control device comprises: internal resistance measurement means for repeatedly performing, based on predetermined conditions, a process of applying to the secondary battery a pulse current having a pulse period and pulse width of a predetermined length and a peak value of a predetermined height; and a calculation for determining the internal resistance of the secondary battery using the amount of change in voltage detected by the voltage sensor due to the application of the pulse current and the peak value of the pulse current; a history data storage unit for storing numerical data reflecting the calculation results of the internal resistance measurement means in correspondence with a time axis; and a mathematical model storage unit for storing mathematical models defined to derive parameters whose values vary depending on the values of the numerical data at each of a plurality of points in time during a predetermined period on the time axis and the length of the period. a diagnostic device for a secondary battery, the diagnostic device comprising: an estimation means for applying a period from a specific point in the past to the most recent period to the mathematical model, performing a calculation using the mathematical model by applying numerical data stored in the history data storage unit in association with the application period, and estimating the degree of deterioration of the secondary battery using parameter values derived by the calculation.

2. The secondary battery diagnostic device of claim 1, wherein the mathematical model storage unit stores a first mathematical model for determining an index value representing the degree of deterioration of the secondary battery, and a second mathematical model for determining a feature quantity representing the current performance of the secondary battery, and the estimation means performs a preparatory step of applying numerical data stored in the history data storage unit, associated with a period from a past point to the most recent, to the second mathematical model to determine an initial value of the feature quantity and determining a starting point of the period to be applied to the first mathematical model, and then performs one or more cycles of a first calculation to calculate the index value by an operation using the first mathematical model to which numerical data stored in the history data storage unit, associated with the period from the starting point to the most recent, and a second calculation to attenuate the feature quantity by varying the degree of attenuation based on the length of time elapsed from the starting point and the current value of the index value, and estimates the degree of deterioration of the secondary battery based on the result of each second calculation.

3. The diagnostic device for a secondary battery as described in claim 1, wherein the internal resistance measuring means calculates values of multiple types of internal resistance components generated in the secondary battery by applying multiple pulse currents with different combinations of pulse periods, pulse widths, and peak values and performing the calculation for each of those pulse currents; the mathematical model storage unit stores, in addition to the mathematical model used for the calculation in the estimation means, a second mathematical model that represents the relationship between the multiple types of internal resistance components and the internal impedance caused by those internal resistance components; the mathematical model used for the calculation in the estimation means is defined so that the value of the parameter varies depending on the value of the internal impedance at each of multiple time points during a period applied to the mathematical model and the length of the period; and the history data storage unit stores, as the numerical data, values of internal impedance determined by calculation applying values of the multiple types of internal resistance components calculated by the internal resistance measuring means to the second mathematical model.

4. The secondary battery diagnostic device described in claim 1, wherein the control device is further provided with the following functions: a function for communicating with a cloud server having a function for generating a mathematical model of the target to be stored in the mathematical model storage unit by machine learning; a function for using this communication function to transmit numerical data accumulated in the history data storage unit to the cloud server; and a function for receiving a mathematical model generated based on the numerical data from the cloud server that has received the numerical data, and updating the contents of the mathematical model storage unit with the received mathematical model.

5. A device electrically connected to a secondary battery to diagnose the degree of deterioration of the secondary battery and, if the diagnosis determines that the secondary battery has deteriorated, control the flow of a pulse current to the secondary battery to restore its performance, comprising: a control device having the function of flowing a pulse current to the secondary battery; and detection means for detecting a physical quantity representing the state of the secondary battery, wherein the detection means includes a voltage sensor for detecting the voltage between the positive and negative electrodes of the secondary battery, and the control device comprises: internal resistance measurement means for repeatedly performing, based on predetermined conditions, a process of applying to the secondary battery a pulse current having a pulse period and pulse width of a predetermined length and a peak value of a predetermined height; and a calculation for determining the internal resistance of the secondary battery using the amount of change in voltage detected by the voltage sensor due to the application of the pulse current and the peak value of the pulse current; and a history data storage unit for storing numerical data reflecting the calculation results of the internal resistance measurement means in correspondence with a time axis. a parameter calculation means for applying a period from a specific point in the past to the most recent to the mathematical model, and repeatedly performing calculations using the mathematical model to which numerical data stored in the history data storage means is associated in association with the application period; and a recovery control means for determining a time to flow a performance recovery pulse current to the secondary battery based on the calculation results by the parameter calculation means, and performing control to flow the performance recovery pulse current at that time, wherein, in response to control by the recovery control means, the parameter calculation means changes the starting point of the period to be applied to the mathematical model in subsequent calculations to a time later than the starting point applied before the control.

6. The secondary battery activation device described in claim 5, wherein the mathematical model storage unit includes a second mathematical model defined to vary the value of a control parameter that determines the mode of the performance recovery pulse current in accordance with the value of the parameter calculated by the parameter calculation means, and the recovery control means, in response to the arrival of the time to pass a performance recovery pulse current through the secondary battery, executes a calculation using the second mathematical model to which parameters calculated by the parameter calculation means immediately prior to that time are applied, and controls the flow of a pulse current to the secondary battery to which the control parameters derived by that calculation are applied.

7. The mathematical model storage unit includes a first mathematical model for determining an index value representing the degree of deterioration of the secondary battery, a second mathematical model for determining a feature quantity representing the current performance of the secondary battery, and a third mathematical model defined to vary the value of a control parameter that determines the mode of the performance recovery pulse current according to the index value, and the parameter calculation means performs one or more cycles of a first calculation to calculate the index value by calculation using the first mathematical model to which the numerical data stored in the history data storage unit, corresponding to a period from a past point to the most recent, is applied to the second mathematical model to determine an initial value of the feature quantity and to determine a starting point of the period to be applied to the first mathematical model, and a second calculation to attenuate the feature quantity by varying the degree of attenuation based on the length of time elapsed from the starting point and the current value of the index value, 6. The secondary battery activation device of claim 5, wherein the recovery control means uses the result of the second calculation each time to determine the time to pass a pulse current for performance recovery to the secondary battery, and when that time arrives, performs a calculation using the third mathematical model to which the index value calculated immediately before that time is applied, and controls the passing of a pulse current applying the control parameters derived by the calculation.

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