Secondary battery diagnostic apparatus and secondary battery activating apparatus, and diagnostic method

Abstract

Provided is an apparatus comprising a control device (10) which has the function of passing a pulse current through a secondary battery, and a voltage sensor (15) which is for measuring the voltage between the terminals of the battery. The control device (10) performs, for each of a plurality of measurement conditions stored in a measurement condition storage unit (112), a computation for determining an internal resistance using the amount of a change in the measurement value of the voltage caused by applying a pulse current to a secondary battery, and the peak value of the pulse current, while detecting the charging stage and the discharging stage of the battery on the basis of a change in the measurement value of the voltage sensor (15). Further, the control device (10) determines, for each of set measurement conditions, a model value which represents an acceptable value for the internal resistance measured under that condition, using model data which is saved in a model data storage unit (113) so as to be associated with that condition, and calculates an extent to which the measurement value deviates from the model value.

Description

Diagnostic device and activation device for secondary batteries, and diagnostic method.

[0001] The present invention relates to a device (hereinafter referred to as the "diagnostic device") that is held in an electrically connected state to a secondary battery and diagnoses the degree of deterioration of the secondary battery due to repeated charging and discharging or the passage of time, and a device (hereinafter referred to as the "battery activation device") that restores the secondary battery to a good state by applying a pulsed current to the secondary battery that has been determined to be deteriorated by this diagnosis, thereby eliminating the deteriorated state.

[0002] Furthermore, the present invention relates to a diagnostic method for diagnosing the state of a secondary battery using a terminal device electrically connected to the secondary battery and a cloud server where mathematical models generated by machine learning are stored.

[0003] Lithium-ion batteries, a type of rechargeable battery, have many advantages, such as being small and lightweight, capable of large-capacity energy storage, and able to generate high voltage. For these reasons, they have been used as power sources for various devices in recent years. However, their performance cannot be maintained indefinitely; their charging and discharging functions gradually deteriorate due to factors such as self-discharge, repeated charging and discharging, and temperature changes.

[0004] A useful method for estimating the degree and cause of degradation in lithium-ion batteries is known to be a method (impedance spectroscopy) in which alternating currents of various frequencies are passed through the battery to measure its internal impedance and then these measurements are analyzed. The following Patent Documents 1 to 3 disclose a technique for diagnosing the degradation state of lithium-ion batteries by applying this method, and their contents are briefly explained below.

[0005] Patent Document 1 describes an invention that extracts at least two measurement frequencies from a plotted waveform of AC impedance, inserts these measured values ​​into a pre-created correlation formula between the capacity reduction rate of a lithium-ion battery and AC impedance to calculate the capacity reduction rate or capacity retention rate of the lithium-ion battery, and estimates the degradation state of the lithium-ion battery based on the calculation result. Furthermore, Patent Document 1 describes that, each time certain conditions are met under the usage state of a lithium-ion battery, the AC impedance of one point in a group of frequencies indicating electrolyte degradation, one point in a group of frequencies indicating negative electrode degradation, and one point in a group of frequencies indicating positive electrode degradation is measured, and the capacity reduction rate is determined from these measured values, and when the capacity reduction rate exceeds a predetermined value, it is determined that the battery is degraded and the user is notified.

[0006] Patent Document 2 describes how 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 can yield a phase difference that reflects the state of degradation occurring at a specific location. Furthermore, it describes an experiment that supports this theory. In this experiment, it was shown that when an AC voltage of 10 Hz was applied, a phase difference reflecting an increase in the amount of lithium metal deposited 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 the battery's capacity was obtained.

[0007] Patent Document 3 describes a computer (state estimation model learning device) in which, for each of several batteries, frequency-specific impedances measured under controlled internal temperature conditions, combined with temperature and battery charge data are input as learning data. The learning data is increased by generating the impedance characteristics of a virtual battery by internally dividing the impedance characteristics represented by the multiple learning data, and a state estimation model is learned that takes frequency-specific impedances as input and outputs the internal state of the battery based on these learning data. Furthermore, Patent Document 3 describes that by inputting the impedance measured for each frequency of the battery to be estimated into the state estimation model, it is also possible to estimate the temperature and charge state of the battery, and to estimate the degradation state of the battery by a similar method.

[0008] Regarding techniques for restoring the performance of degraded lithium-ion batteries, a method of applying a high-frequency pulsed current to the lithium-ion battery is known, as described in Patent Documents 4 and 5.

[0009] Patent Document 4 discloses a method effective for restoring capacity by melting whisker-like lithium adhering to the surface of the negative electrode or separator, which involves temporarily suspending charging during the charging of a lithium-ion battery and passing a reverse pulse current (discharge current) multiple times. Patent Document 5 discloses a method for repeatedly performing pulse discharge, in which lithium ions that can move to the positive electrode side have decreased due to the accumulation of lithium ions in the portion of the negative electrode active material layer that does not face the positive electrode active material layer (non-facing portion), are discharged until an over-discharge state is reached, and then the voltage is increased to a predetermined voltage level below the over-discharge region by stopping the discharge, and the discharge is performed while oscillating the current.

[0010] Japanese Patent Publication No. 2014-44149, Japanese Patent Publication No. 2009-244088, Japanese Patent Publication No. 2023-174239, Japanese Patent Publication No. 2014-170741, Japanese Patent Publication No. 2019-106333

[0011] As lithium-ion batteries degrade and their internal resistance increases, their charging and discharging capabilities decrease, leading to more frequent need for charging and a higher risk of accidents such as overheating and fire. These problems can also occur in next-generation rechargeable batteries such as all-solid-state batteries. Therefore, it is necessary to establish a technology that allows for repeated degradation diagnosis of rechargeable batteries under their operational conditions.

[0012] To accurately determine the degree of degradation of a secondary battery, it is necessary to measure not only the ohmic resistance caused by the characteristics of the conductors (electrodes and electrolytes) inside the battery, but also the interfacial resistance generated by chemical reactions on the surface of the electrodes and the diffusion resistance generated by the movement of ions inside the electrodes. Since these internal resistance components each have different characteristics such as magnitude, timing of occurrence, and duration, impedance spectroscopy involves repeatedly changing conditions such as the frequency of the AC current flowing through the battery, the current level, and the timing of the measurement to obtain measurement values ​​that reflect the influence of various internal resistance components.

[0013] Therefore, devices that apply impedance spectroscopy tend to have complex circuit configurations and analytical calculation algorithms, making them likely to be expensive. Moreover, these devices are quite large and require connection to an external AC power source, making it difficult to connect them continuously to rechargeable batteries, which are often used in mobile devices.

[0014] In view of the above problems, the first objective of the present invention is to enable the diagnosis of the degree of deterioration of a commonly used secondary battery while monitoring its condition while it is constantly connected to the battery.

[0015] Furthermore, a second objective of the present invention is to enable the rapid execution of a process to restore a secondary battery from degradation when it is estimated that the degree of degradation of the battery being diagnosed is significant.

[0016] To solve the above problems, the diagnostic device of the present invention applies a DC pulse current to the secondary battery to be diagnosed while varying conditions such as the pulse period and pulse width, and measures the internal resistance necessary for diagnosing the battery.

[0017] This diagnostic device comprises a control device that is electrically connected to a secondary battery and has the function of supplying pulsed current to the battery, and a voltage sensor for measuring the voltage between the positive and negative electrodes of the secondary battery. The control device includes the charge / discharge detection means, internal resistance measurement means, measurement condition storage means, model data storage means, analysis means, and estimation means described below.

[0018] The charge / discharge detection means detects the charging and discharging stages of the secondary battery based on changes in the voltage sensor readings.

[0019] The internal resistance measuring means performs a current application process in which a pulse current with a predetermined pulse period and pulse width and a predetermined peak value is applied to the secondary battery, and a calculation to determine the internal resistance value of the secondary battery using the amount of change in the voltage sensor measurement value caused by the application of the pulse current and the peak value of the pulse current, under conditions that define the pulse period, pulse width, the length of the pulse current application time, and the timing for determining the amount of change in the voltage measurement value.

[0020] The measurement condition storage means stores multiple sets of parameter combinations representing the measurement conditions set for the internal resistance measurement means in order to diagnose the secondary battery. The model data storage means stores, for each measurement condition stored in the measurement condition storage means, data that defines a function representing the relationship between the length of time elapsed, the frequency of charging and discharging, and the acceptable value of the internal resistance, as model data representing the acceptable mode of change when the internal resistance value measured under that measurement condition changes in accordance with the length of time elapsed from a specific point in the past and the frequency of charging and discharging that has occurred during that time.

[0021] The analysis means sequentially sets the combination of parameters for each measurement condition stored in the measurement condition storage means to the internal resistance measuring means and causes it to perform current application processing and calculations. For each of these measurement conditions, it applies the length of time from a specific point in time to the time when the processing of the internal resistance measuring means is performed according to that condition, or the vicinity thereof, and the frequency of charging and discharging determined from the detection results performed by the charging and discharging detection means during that time, to the function corresponding to the model data of that condition, and performs processing to determine the degree of deviation of the internal resistance value obtained by the calculation of the internal resistance measuring means from that model value.

[0022] The estimation means estimates the degree of degradation of the secondary battery based on the degree of deviation determined by the analysis means. This estimation can be performed by comprehensively integrating the degree of deviation for each condition after the analysis means has finished processing all the measurement conditions set in the internal resistance measurement means, or it can be performed to estimate the degree of battery degradation at that time based on the degree of deviation derived each time the analysis means has finished processing for each measurement condition.

[0023] With the device configured as described above, it is possible to obtain the power necessary for operation through the connection path to the secondary battery while simultaneously supplying pulsed current to the battery through the same path. Therefore, by keeping the device's driving power and pulsed current at a level that does not significantly affect the secondary battery, it becomes possible to measure the internal resistance as needed while detecting the charging and discharging stages during the battery's daily use.

[0024] Furthermore, by detecting the charging and discharging stages of the connected secondary battery in advance, and appropriately setting various measurement conditions on the internal resistance means to measure the internal resistance value, saving the hourly measurement values, and applying the patterns of change in the internal resistance value obtained for each measurement condition to a mathematical model generated by machine learning using a considerable amount of sample data, it is possible to identify measurement conditions suitable for diagnosing the connected battery and the model data corresponding to those conditions. Therefore, by storing multiple sets of parameter combinations representing the measurement conditions identified by this method and the model data corresponding to each combination in the measurement condition storage means and model data storage means, and running the analysis means and estimation means, the state of the secondary battery can be estimated with high accuracy.

[0025] For example, if we can identify measurement conditions and model data suitable for measuring and diagnosing each resistance component—such as ohmic resistance in the conductors (electrodes, electrolyte) inside the battery, interfacial resistance on the surface of each electrode, and diffusion resistance related to the movement of ions within the electrodes—then by processing the data using analytical means, when a resistance value with a high degree of deviation from the model value appears, we can estimate which resistance component is increasing and further estimate the cause of that increase.

[0026] Furthermore, if the above-mentioned diagnostic device is equipped with a current sensor for measuring the current flowing through the connection path between the secondary battery and the device, the current sensor's measurement value relative to the peak of the pulsed current applied to the secondary battery by the current application process can be used in calculations to determine the internal resistance value of the secondary battery, thereby increasing the reliability of the calculation results and the degradation diagnosis based on those results.

[0027] The control device of the diagnostic device described above may further include: a mathematical model storage means that stores a mathematical model representing the relationship between measurement conditions applicable to measuring the internal resistance of a secondary battery and the characteristics of the change in the internal resistance value measured under those conditions; a data storage means for storing one or more types of event data, including event data that links data representing the measurement conditions applied to the internal resistance measuring means with the internal resistance value obtained by calculations under those conditions; and a preparation means that performs a process multiple times in which multiple measurement conditions are set sequentially on the internal resistance measuring means and the current application process and the calculations are performed for each condition, and then, within a predetermined period including the period in which these processes are performed, data from the group of event data stored in the data storage means that matches the set measurement conditions are applied to the mathematical model in the mathematical model storage means, and by calculations, parameters representing multiple measurement conditions used for diagnosing the secondary battery and model data applied to these measurement conditions are identified, and these are stored in the measurement condition storage means and the model data storage means.

[0028] The mathematical model described above can be generated by measuring and analyzing multiple types of event data, including internal resistance values, for various types of secondary batteries, and then training a computer with machine learning capabilities on the patterns of change in each event data over time and with increasing charge-discharge cycles. The event data can include data linking internal resistance values ​​measured by an internal resistance measurement device with measurement conditions, as well as changes in voltage and current during charging and discharging, charge-discharge speed, number of charge-discharge cycles, and environmental data such as temperature, humidity, and atmospheric pressure.

[0029] According to the control device equipped with the above-described mathematical model storage means, data storage means, and preparation means, event data acquired from the connected secondary battery can be applied to the mathematical model to identify the measurement conditions and model data to be used in the processing of the analysis means.

[0030] The present invention further provides a battery activation device having a similar device configuration to the diagnostic device described above, wherein the control device in the configuration is equipped with a recovery control means that supplies a pulsed current to the secondary battery to restore it from deterioration when it is determined by the analysis means that the degree of deviation of the internal resistance value related to any of the measurement conditions set exceeds a predetermined threshold. This device can also be constantly connected to the secondary battery and perform a diagnosis of the battery, and when it is estimated that the internal resistance value of the secondary battery has reached an abnormal level, it can quickly supply a recovery pulsed current to the battery.

[0031] The above-mentioned battery activation device is also equipped with a current sensor, and the internal resistance of the secondary battery can be determined by calculation using the current sensor's measurement value for the peak of the pulse current applied to the secondary battery by the current application process and the amount of change in the voltage sensor's measurement value caused by the application of the pulse current.

[0032] Furthermore, the battery activation device described above may also include the mathematical model storage means, data storage means, and preparation means mentioned earlier. In this case, the mathematical model storage means may include a mathematical model for determining measurement conditions and model data, as well as a second mathematical model that represents the relationship between changes in event data in a degraded secondary battery and the mode of the recovery pulse current suitable for resolving that degradation, in order to derive parameters representing the mode of the recovery pulse current supplied by the recovery control means.

[0033] However, it is not always necessary to provide mathematical model storage and preparation means in diagnostic devices and battery activation devices. For example, these devices could be configured as terminal devices capable of communicating with a cloud server where various mathematical models are stored. In the terminal device, the current application process and the calculation to determine the internal resistance value could be repeated while sequentially setting multiple measurement conditions. One or more types of event data, including event data linking data representing the set measurement conditions with the internal resistance value obtained by the calculation under those conditions, could be sent to the cloud server. The cloud server could then apply a predetermined capacity of event data to a mathematical model and perform calculations, from which parameters representing the measurement conditions and model data could be received.

[0034] Furthermore, regarding the transmission of event data from the terminal device to the cloud server, the terminal device can accumulate a certain amount of data before sending it to the cloud server all at once. Alternatively, the terminal device can send the data obtained from the current application process and calculations to the cloud server each time it determines the internal resistance value, accumulating event data on the cloud server until it reaches a suitable volume for calculations using mathematical models.

[0035] Furthermore, even after the secondary battery is diagnosed by the terminal device, event data including the internal resistance value obtained from the diagnosis and other event data are accumulated in the terminal device or cloud server. If it is determined that the degree of deviation of the internal resistance from the model value related to any measurement condition exceeds a threshold, the cloud server derives parameters representing the characteristics of the recovery pulse current by applying the event data accumulated up to that point to a mathematical model and feeds them back to the terminal device, and a pulse current to which those parameters are applied can be supplied to the secondary battery.

[0036] According to the present invention, the condition of a secondary battery can be diagnosed as needed while the secondary battery is being used normally, thus preventing the secondary battery from being used until its degree of deterioration increases significantly and it becomes dangerous.

[0037] Furthermore, according to the present invention, a secondary battery that is estimated to be significantly degraded can be quickly restored from degradation by supplying a recovery pulse current.

[0038] Furthermore, by using mathematical models generated through machine learning with event data such as internal resistance values ​​obtained from various types of secondary batteries, it becomes possible to derive the measurement conditions, model data, and recovery pulse current patterns used in the above-mentioned diagnosis, regardless of the type of battery connected, and to perform diagnostic and recovery processing tailored to the characteristics of each battery.

[0039] This is a block diagram showing the electrical configuration of a battery activation device for lithium-ion batteries. This is a functional block diagram of the above battery activation device. This is an explanatory diagram showing the parameters and basic measurement method representing the measurement conditions for measuring the internal resistance of a lithium-ion battery. This is an explanatory diagram showing an example of setting the pulse current pattern and measurement timing used for measuring internal resistance for each internal resistance component to be measured. This is a graph showing the pattern of change in interface resistance that occurs with respect to the time length t from a reference point and the number of charge / discharge cycles N(t) within that time. This is a flowchart showing the main processing flow in the above battery activation device. This is a flowchart showing the detailed procedure of degradation diagnosis (step S8) in Figure 6. This is an explanatory diagram showing the pulse current pattern used for performance recovery control. This is an explanatory diagram showing a network system consisting of multiple battery activation devices and a cloud server.

[0040] Figure 1 is a block diagram showing an example of the circuit configuration of a battery activation device 1 for lithium-ion batteries to which the present invention is applied. The battery activation device 1 of this embodiment has a function to diagnose the degree of degradation of a lithium-ion battery 2 (hereinafter simply referred to as "battery 2"), and a function to control (hereinafter referred to as "performance recovery control") to supply a pulse current in order to eliminate the degradation and restore the performance of battery 2 when the diagnosis determines that degradation has occurred beyond an acceptable level. It consists of a circuit board on which the components shown in Figure 1 are mounted, and a housing (not shown) in which this board is housed.

[0041] The battery activation device 1 is connected to the positive terminal 20A and negative terminal 20B of the battery 2 via connection terminals 110A and 110B provided on the circuit board and cables (not shown), and is placed on the surface of the battery 2 or in its vicinity.

[0042] In Figure 1, the transmission paths for control signals and data are represented by lines with arrows, while the power supply path is represented by lines without arrows. As shown by the dashed lines in the figure, external devices or charging devices that act as loads are connected to terminals 20A and 20B of the battery 2. However, even when these are not connected, the battery activator 1 is connected to the battery 2 as a load. In other words, the battery activator 1 continues to operate using the voltage between terminals 20A and 20B of the battery 2 as its power supply voltage, but it is designed to operate at a power level that does not significantly affect the battery 2.

[0043] The battery activation device 1 includes a control device 10, a sensor unit 11, a power supply circuit 12, a pulse generation circuit 13, a wireless communication circuit 14, and the like. The power supply circuit 12 includes several types of DC-DC converters, which convert the DC voltage applied between terminals 110A and 110B into a voltage suitable for the operation of the various components described above, and these are supplied to each component.

[0044] The control device 10 is a computer with large-capacity memory and advanced computational capabilities. The memory contains programs for assigning each of the functions shown in Figure 2 to the control device 10, as well as folders that serve as various storage units.

[0045] The pulse generation circuit 13 includes an oscillator and a current adjustment circuit. In response to a control signal from the control device 10, it generates a pulsed current with adjusted current level, pulse period, pulse width, and number of pulses, and sends this to the connection circuit to the battery 2.

[0046] 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 the user, or with the cloud server 3 described later.

[0047] The sensor unit 11 includes a voltage sensor 15, a current sensor 16, a temperature sensor 17, a humidity sensor 18, and a pressure sensor 19. Of these, the voltage sensor 15 and the current sensor 16 are incorporated into the connection circuit between terminals 110A and 110B and the pulse generation circuit 13. The voltage sensor 15 detects the voltage applied between terminals 110A and 110B (essentially the voltage between the electrodes 20A and 20B of the battery 2), and the current sensor 16 detects the current flowing between the pulse generation circuit 13 and the battery 2.

[0048] The temperature sensor 17 detects the temperature around the battery 2, the humidity sensor 18 detects the humidity around the battery 2, and the pressure sensor 19 detects the pressure around the battery 2. The detection signals from the various sensors 15 to 19 of the sensor unit 11 are, in principle, taken up by the control device 10 at regular time intervals and stored in the control device 10's memory (history data storage unit 114 shown in Figure 2) as measured values ​​of the corresponding physical quantities (voltage, current, temperature, humidity, and pressure).

[0049] It is not mandatory to place the temperature sensor 17, humidity sensor 18, and pressure sensor 19 inside the housing of the battery activation device 1; these sensors 17, 18, and 19 may be placed outside the housing and electrically connected to the circuit board via connectors or the like. It is also not necessarily required to use all three types of sensors 17, 18, and 19; one or two types may be used. However, since temperature can fluctuate due to the effects of charging and discharging, and internal resistance can fluctuate due to the influence of ambient temperature, it is desirable not to exclude the temperature sensor 17.

[0050] Figure 2 is a functional block diagram showing the functions provided in the control device 10 of the battery activation device 1, along with their relationship to other components of the device 1.

[0051] The control device 10 is equipped with a dedicated program that pre-installs functions for the main control unit 100, voltage measurement unit 101, current measurement unit 102, environmental data measurement unit 103, pulse control unit 104, and communication control unit 105, as well as storage means such as a mathematical model storage unit 111, a measurement condition storage unit 112, a model data storage unit 113, and a history data storage unit 114.

[0052] The voltage measurement unit 101 measures the voltage value indicated by the detection signal of the voltage sensor 15, and the current measurement unit 102 measures the current value indicated by the detection signal of the current sensor 16. The environmental data measurement unit 103 measures the temperature, humidity, and atmospheric pressure values ​​indicated by the detection signals of the temperature sensor 17, humidity sensor 18, and atmospheric pressure sensor 19 (hereinafter, these are collectively referred to as "environmental data").

[0053] The pulse control unit 104 controls the operation of the pulse generation circuit 13 to send out pulse currents in a manner determined by the main control unit 100. The communication control unit 105 performs processes such as converting the data received from the main control unit 100 into a transmission signal based on a communication standard and transmitting it from the wireless communication circuit 14, and extracting specific data from signals received by the wireless communication circuit 14 from an external source.

[0054] The history data storage unit 114 stores numerical data obtained from measurements taken by each measurement unit 101, 102, and 103, as well as numerical data obtained from calculations using the measured voltage and current values ​​(such as the value of internal resistance, the amount of voltage and current fluctuation, the length of the charging and discharging periods, the charging and discharging speed, and the number of charge-discharging cycles; hereafter, these numerical data will also be referred to as "measured values"), combined with data representing the date and time when they were acquired (hereinafter referred to as "date and time data").

[0055] Hereinafter, the combination of the various measured values ​​and date / time data described above will be collectively referred to as "event data." Immediately after the battery activation device 1 connected to the battery 2 is started for the first time, the history data storage unit 114 is almost empty, but as the period of time since use begins increases, the number of event data stored in the history data storage unit 114 also increases. As will be described later, event data related to internal resistance includes not only measured values ​​and date / time data, but also an identification code for the measurement conditions.

[0056] The mathematical model storage unit 111 stores multiple types of mathematical models constructed by an external computer equipped with high-order machine learning capabilities. In this embodiment, the main mathematical models stored in the mathematical model storage unit 111 include multiple types of mathematical models used for analytical processing to estimate the characteristics of the battery 2 (hereinafter referred to as "analysis models"), multiple types of mathematical models used for calculations to determine the mode of pulse current used for measuring the internal resistance (hereinafter referred to as "measurement models"), and multiple types of mathematical models used for calculations to determine the mode of pulse current used for performance recovery control (hereinafter referred to as "performance recovery models").

[0057] The analysis model defines how numerical data representing the basic performance of a lithium-ion battery, such as the frequency of charging and discharging, the terminal voltage at full charge, and the level of ohmic resistance immediately after applying a predetermined type of pulsed current, changes over time, with the number of charge / discharge cycles, and temperature. The measurement model defines the relationship between the type of pulsed current flowing through the battery and the characteristics of the change in internal resistance value that occurs in response to that pulsed current (changes in internal resistance value due to the passage of time, the number of charge / discharge cycles, etc.). The performance recovery model defines the relationship between the changes in event data in a degraded lithium-ion battery and the type of recovery pulsed current suitable for resolving the degradation.

[0058] These mathematical models are constructed using a database formed by accumulating various numerical data (internal resistance, voltage, current, temperature, humidity, atmospheric pressure, etc.) derived from experiments conducted on various types of batteries with different characteristics, and associating them with the characteristics, operating history, and degree of degradation of each battery. In this embodiment, the characteristics of a battery are represented by a combination of multiple types of characteristic data (charge capacity, years of use, battery material, etc.), and lithium-ion batteries with various characteristics are classified into multiple types (hereinafter referred to as "characteristic types") with different combinations of the above characteristic data, and an analysis model is generated for each characteristic type. Measurement models and performance recovery models are generated for each characteristic type and each type of internal resistance component, respectively.

[0059] The measurement condition storage unit 112 stores multiple sets of parameter combinations representing the measurement conditions for internal resistance, for each of the above characteristic types (hereinafter, each parameter is referred to as a "measurement parameter"). The specific measurement parameters are five types: the period of the pulse current applied to the battery 2 during measurement, the pulse width, the level of the pulse current peak (hereinafter referred to as "peak current") (hereinafter referred to as "peak value"), the number of pulses (number of times the peak current is applied), and the length of the period between the time when the application of the pulse current ends and the time when the change in voltage caused by the application is determined (hereinafter, this period is referred to as the "buffer period," and the length of this period is referred to as the "buffer time") (see Figure 3(A)). However, instead of the number of pulses, the length of the period during which the pulse current is applied (hereinafter referred to as the "pulse application period") (corresponding to the time length obtained by multiplying the pulse period and the number of pulses) may be used as a measurement parameter.

[0060] The model data storage unit 113 stores model data representing the characteristics of the internal resistance value obtained by the measurement process to which each measurement condition stored in the measurement condition storage unit 112 is applied.

[0061] From the time the control device 10 is first activated after being connected to the battery 2 until a certain period of time has elapsed, the main control unit 100 stores a combination of various measurement values ​​obtained from the measurement units 101, 102, and 103 and date / time data as event data in the history data storage unit 114. It also constantly monitors the voltage measurement value and determines that discharge has started when the voltage drops above a certain value, and that charging has started when the voltage rises above another certain value. Furthermore, the main control unit 100 determines the end time of charging or discharging based on the changes in voltage, temperature, etc., after these determinations, identifies each charging and discharging period, and calculates the length of these periods and the amount of voltage and current fluctuations during these periods. These calculation results are also stored in the history data storage unit 114 as event data. Additionally, the main control unit 100 estimates the voltage range corresponding to the fully charged state of the battery 2 based on the final voltage of each charging period. The estimated range is used to determine the timing of the diagnostic process.

[0062] Furthermore, the main control unit 100 detects a period during which there is almost no voltage fluctuation due to charging and discharging, and during that period, it sequentially sets multiple measurement conditions and measures the internal resistance for each of those conditions. The measurement conditions are represented by combinations of five types of control parameters, similar to those stored in the measurement condition storage unit 112, and the measurements are also performed using the same method as shown in Figures 3 and 4, which will be described later. The internal resistance value obtained in each measurement is compiled into event data, which is linked to date and time data representing the time the measurement was performed and the identification code of the measurement condition applied to that measurement, and stored in the history data storage unit 114.

[0063] When various event data, including the internal resistance value obtained through the above calculations, are stored in the history data storage unit 114 until the predetermined capacity is exceeded, the main control unit 100 applies the length of elapsed time, the number of charge / discharge cycles, temperature history data, etc. up to that point to various characteristic type analysis models and derives numerical data representing the basic performance of the battery as described above for each characteristic type. The main control unit 100 then identifies the characteristic type of the analysis model that yields the calculation result closest to the performance indicated by the event data stored in the history data storage unit 114 as the type suitable for the battery 2, and uses the measurement model corresponding to that characteristic type and the event data of the internal resistance value stored in the history data storage unit 114 to determine measurement conditions suitable for detecting changes in the internal resistance occurring in the connected battery 2.

[0064] As described above, a measurement model is set up for each type of internal resistance component, and calculations to identify the measurement conditions are also performed for each measurement model. In these calculations, based on the identification code of the measurement conditions included in the event data of the internal resistance value, event data linked to the measurement conditions related to the internal resistance component of the object being measured, as assumed by the measurement model being used, are selected, and the internal resistance values ​​included in them are applied to the calculation formula.

[0065] The measurement conditions are represented by a combination of the five types of measurement parameters mentioned above (pulse period, pulse width, peak value, number of pulses, and buffer time). The main control unit 100 identifies multiple combinations of the above measurement parameters by calculation using the measurement model, and stores these combinations in the measurement condition storage unit 112, linking them to individual identification codes.

[0066] Furthermore, the main control unit 100 derives model data representing the acceptable mode of change in the internal resistance value measured under specified measurement conditions. The model data is a combination of numerical values ​​corresponding to the constants in equations (1), (2), and (3) described later, and is stored in the model data storage unit 113, associated with the same identification code as the corresponding measurement conditions.

[0067] Hereafter, we will refer to combinations of constants included in the same model function as a single model data, and the numerical values ​​representing these constants as "model parameters." However, the format of the model data is not limited to this; the entire expression of the model function may also be stored as model data in the model data storage unit 113.

[0068] In this way, the measurement parameters and model parameters are determined, and the control device 10, having completed saving these parameters to the measurement condition storage unit 112 and the model data storage unit 113, becomes ready to perform a degradation diagnosis of the battery 2. Even after reaching this state, the main control unit 100 repeatedly performs the processes of acquiring measurement values ​​from the measurement units 101, 102, and 103, acquiring secondary measurement values ​​through calculations using these values, and saving event data based on various measurement values ​​to the history data storage unit 114, and performs a degradation diagnosis when predetermined conditions are met.

[0069] In this embodiment, degradation diagnosis is performed when the voltage measurement immediately after the end of the charging period falls within the range of the fully charged voltage, and when the voltage measurement immediately after the end of the discharge period falls within a predetermined numerical range lower than the fully charged voltage (for example, a range corresponding to 40% of the fully charged voltage). The conditions for degradation diagnosis are not limited to these and can be changed based on the characteristics of the battery 2, etc.

[0070] In the degradation diagnosis, multiple measurement conditions (combinations of five types of measurement parameters) stored in the measurement condition storage unit 112 are set sequentially. For each condition, a process is performed to measure the internal resistance, and a calculation is performed to determine the difference (degree of deviation from the model value) between the measured value obtained from the measurement and the model value corresponding to the measurement condition. The internal resistance value measured in each hourly degradation diagnosis, as well as the difference between the measured value and the model value, are also stored in the history data storage unit 114, combined with an identification code representing the measurement condition and date / time data.

[0071] If, during the above procedure, a measurement value is found that exceeds a predetermined tolerance value in relation to the model value, the main control unit 100 stops the degradation diagnosis, performs inference calculations using the performance recovery model to derive a combination of control parameters for performance recovery control, and executes performance recovery control using a pulse current to which these control parameters are applied. The parameters for performance recovery control include the period, pulse width, peak value, and number of pulses of the pulse current.

[0072] Below, we will briefly summarize the concept of the internal resistance component that is the target of measurement in this embodiment, and then explain in order the measurement conditions and model data set for degradation diagnosis, as well as the processing of the control device, including degradation diagnosis and performance recovery control.

[0073] <Method for Measuring Internal Resistance> Figure 3 schematically shows five types of parameters representing the measurement conditions for internal resistance and the basic measurement method. The control device 10 in this embodiment applies a pulse current to the battery 2 to which the pulse period, pulse width, and peak value according to the set measurement conditions are applied, and repeats the above pulse period a number of times corresponding to the number of pulses in the same measurement conditions. The control device 10 also determines the timing for calculating the internal resistance based on the time when the application of the pulse current ends and the buffer time. The buffer time can take either a positive or negative value.

[0074] Note that the pulse current shown in FIG. 3 and the following FIG. 4 is assumed to flow in the direction from the positive electrode to the negative electrode, that is, in the same direction as the current during discharge. On the contrary, there may be a case where the pulse current flows in the direction from the negative electrode to the positive electrode. In any of the pulse currents, the peak current is set to a level (about several tens of milliamperes to several hundreds of milliamperes) that does not cause a large chemical reaction in the battery 2.

[0075] The control device 10 measures the voltage V when the peak current during the pulse application period is not applied, in parallel with the process of applying the pulse current. ０ And the actual value I of the peak current, and the voltage V changed by the application of the peak current. １ And by executing an operation in which these measured values are applied to the following arithmetic expression (A), the value R of the internal resistance with respect to the pulse current is calculated. ａｃｔ is calculated.

[0076] R ａｃｔ = |V ０ −V １ | / I ・・・ (A)

[0077] The voltage V ０ can be measured under a state not affected by the peak current, such as at a time before the first pulse during the pulse application period. The timing of the measurement of the voltage V １ is determined by the buffer time. When the buffer time takes a positive value, as shown in FIG. 3(B), the voltage at the time when the application of the last peak current and the buffer time have elapsed is measured. When the buffer time takes a negative value, the absolute value thereof is set shorter than the pulse width, and as shown in FIG. 3(C), the voltage during the period when the last peak current is applied is measured. Regarding the current I, not limited to the measured value of the last pulse current, the peak current may be measured for each pulse during the pulse application period, and the average value of those measured values may be applied to I. (The same also applies to the example of FIG. 4.)

[0078] The operation of the above formula (A) is performed immediately after the measurement of the voltage V １ . Also, regardless of whether the buffer time takes a positive or negative value, since all variables of the formula (A) are determined by measuring the voltage V １ , the voltage V１ The value of the internal resistance is determined by measuring the voltage V. Therefore, below, the voltage V １ We will explain the timing of the measurement, assuming that the internal resistance is measured when the other value is measured.

[0079] <Internal Resistance Components to be Diagnosed> In the degradation diagnosis of this embodiment, the internal resistance of battery 2 is measured by dividing it into three components: ohmic resistance, interfacial resistance, and diffusion resistance. A discrimination process using model data is then performed for each measured value.

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

[0081] Interfacial resistance is the resistance component that arises 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). When the thickness of the surface layer and the degree of surface irregularity increase due to chemical reactions that occur between the surface layer and the electrolyte during charging and discharging, the interfacial resistance also increases, and the rate of lithium ion passage slows down.

[0082] Diffusion resistance is a resistive component that restricts the movement of lithium ions when they are inserted into or removed from the crystal structure of the electrode material. Diffusion resistance increases due to changes in the surface layer and degradation of the electrode material.

[0083] Ohmic resistance is generated quickly in response to the rising edge of a pulsed current and disappears quickly in response to the falling edge of the pulsed current. In contrast, interfacial resistance and diffusion resistance are generated in conjunction with ion movement and chemical reactions, so they are generated at a later timing than ohmic resistance and persist for a while even after the pulsed current has fallen.

[0084] When a pulsed current is applied to battery 2 in the direction from the positive electrode to the negative electrode (the same direction as the current flow during discharge), resistance components are generated in the order of ohmic resistance, diffusion resistance, and interfacial resistance on the negative electrode side where lithium ions are released, and 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 each insertion, so unless there is a significant abnormality in the electrode material or surface layer, the diffusion resistance and interfacial resistance are considered to be relatively small. On the other hand, at the positive electrode, greater interfacial resistance and diffusion resistance tend to occur than at the negative electrode due to reasons such as the increased chemical reaction between the electrolyte and the surface layer when lithium ions pass through the surface layer, and the time it takes for the insertion locations of individual lithium ions into the crystal structure of the electrode material to be determined. Conversely, when a pulsed current is applied in the direction from the negative electrode to the positive electrode of the battery (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] Based on the above trends, in this embodiment, with respect to interfacial resistance and diffusion resistance, the resistive component generated at the electrode that absorbs lithium ions is dominant over the resistive component generated at the electrode that releases lithium ions, and the resistive component of the latter electrode can be ignored. Based on this assumption, the generation order of three types of resistive components is assumed, and the measurement conditions are determined for each resistive component.

[0086] Figure 4 shows the characteristics of the pulsed current when a pulsed current is applied from the positive electrode to the negative electrode, along with the timing of obtaining the measured value that can be applied to equation (A) above. Pattern A in the figure is an example of the pulsed current when measuring ohmic resistance, pattern B is an example of the pulsed current when measuring interfacial resistance, and pattern C is an example of the pulsed current when measuring diffusion resistance. Each example represents the beginning and end of the pulse application period.

[0087] In measuring the ohmic resistance generated in response to the peak current, the measurement method shown in Figure 3(C) is applied. To obtain a resistance value at a level sufficient to detect the degree of change, the peak value is set to a higher value than the set values ​​for other resistance components. The pulse width and pulse period are set to be short to minimize the generation of interfacial resistance and diffusion resistance. The pulse application period is also set to be short to minimize the effect of the high peak value level on the battery 2.

[0088] In the example of pattern A in Figure 4, the voltage V is applied before the peak current of the first period within the pulse application period. ０ The current I and voltage V were measured while the peak current of the last cycle was applied. １ While this is being measured, it is not limited to this; voltage V ０ The measurement will also be performed in the final cycle, and the voltage V will be measured just before the final peak current is applied. ０ You may measure the voltage V during one cycle of current flow. ０ , V １ The measurement process involves measuring the current I, and this measurement process may be performed multiple times, with the average value of the internal resistance obtained in each process being used as the final measurement value. If the calculation speed of the control device 10 is sufficient, the voltage V may be applied simultaneously with or immediately after the end of the application of the peak current. １ You may also measure it.

[0089] In measuring interfacial resistance and diffusion resistance, the measurement method shown in Figure 3(B) is applied while supplying multiple peak currents with pulse widths suitable for generating the resistive component to the battery 2, and the voltage V before the first period's peak current is applied is measured. ０ And the peak current I of the last cycle and the voltage V at the end of the last cycle and buffer period. １The internal resistance value is calculated using this method. By doing so, even if the peak current is weak, the chemical reactions that cause interfacial resistance and diffusion resistance are gradually activated, and by determining the change in voltage based on the point in time when the chemical reaction has not yet occurred, a resistance value sufficient to detect the degree of change can be obtained. The number of pulses that determine the pulse application period is also set based on this perspective, so that the pulse application period continues until a sufficient chemical reaction occurs.

[0090] In the interfacial resistance measurement shown in Pattern B, a pulse width longer than the peak current used for measuring ohmic resistance is set, provided that the pulse width is long enough to suppress the generation of diffusion resistance. The pulse period is also longer than the set value for ohmic resistance, but considering the effect of the longer pulse width on battery 2, the peak value is lower than the set value for ohmic resistance. Furthermore, at a point in time when it is assumed that ion movement, which is a factor in interfacial resistance, is continuing but significant diffusion of lithium ions has not occurred, the voltage V １ The buffer time length is adjusted so that it can be measured.

[0091] In the diffusion resistance measurement shown in Pattern C, the pulse period and pulse width are set to longer values ​​than those set for other resistance components. To suppress the effect of the long-term application of peak current resulting from this setting on battery 2, the peak value is set to a lower value than that of the other resistance components. Furthermore, the voltage V is set at a point when lithium ion diffusion is expected to continue but ion movement in the surface layer has stopped. １ The length of the buffer period is adjusted so that it can be measured.

[0092] In this embodiment, for degradation diagnosis performed immediately after the end of the charging period, a pulsed current is flowed from the positive electrode to the negative electrode (the same direction as the discharge current; hereafter referred to as the "positive direction"), and for degradation diagnosis performed immediately after the end of the discharge period, a pulsed current is flowed from the negative electrode to the positive electrode (the same direction as the charging current; hereafter referred to as the "negative direction"), and measurement conditions for measuring ohmic resistance, interfacial resistance, and diffusion resistance are defined for each case. Even when a pulsed current is flowed from the negative electrode to the positive electrode, the ohmic resistance of both electrodes and the interfacial resistance and diffusion resistance generated at the negative electrode that absorbs lithium ions are mainly measured, and measurement conditions are set for each resistance component based on the same assumption as explained in the example in Figure 4. Furthermore, regardless of the direction in which the pulsed current is flowed, multiple measurement conditions are set for each resistance component, each with different combinations of values ​​for five types of parameters.

[0093] <Specific examples of model data> In lithium-ion batteries, over time and with repeated charging and discharging, changes occur in the crystal structure of the electrode material, the component ratio of the electrolyte, the ability of the separator to allow ions to pass through, and lithium ion deposition. These effects increase ohmic resistance. Also, over time and with repeated charging and discharging, the thickness of the surface layer of the electrode and the degree of surface irregularity gradually increase, increasing interfacial resistance and diffusion resistance. Interfacial resistance also increases due to deformation or damage to the crystal structure of the electrode material. Changes in the surrounding environment, such as temperature, are also thought to accelerate the chemical reactions in lithium-ion batteries, causing an increase in each resistance component.

[0094] Thus, all internal resistance components increase over time, with repeated charging and discharging cycles, and with changes in the environment. As long as this increase remains within a certain range and the change is gradual, there is no need to perform performance recovery control. However, without constantly observing the behavior of battery 2, it is impossible to predict when a sudden increase in internal resistance will occur.

[0095] Based on the above circumstances, in this embodiment, for each measurement condition stored in the measurement condition storage unit 112, the acceptable mode of change when the internal resistance measured under that condition changes over time or with repeated charging and discharging is set for each type of internal resistance component as a function representing the relationship between the length of elapsed time, the frequency of charging and discharging, and the acceptable value of the internal resistance. Performance recovery control is performed when the degree of deviation of the actual measured value from the model value of the internal resistance derived from these model functions exceeds the acceptable level.

[0096] Specifically in this embodiment, the model function R takes t and N(t) as arguments, where t is the length of time elapsed from a specific point in time (hereinafter referred to as the "reference point"), and N(t) is the number of charge / discharge cycles performed during this time. ｏｈｍ (t, N(t)), R ｉｆ (t, N(t)), R ｄｆ (t, N(t)) is set. ｏｈｍ (t, N(t)) is a model function for ohmic resistance, and R ｉｆ (t, N(t)) is a model function for interfacial resistance, R ｄｆ (t, N(t)) is a model function for diffusion resistance.

[0097] The reference point for determining time t is, by default, set to the point when the battery activation device 1 is connected to the battery 2 and detects a change in voltage for the first time (generally the point when the battery 2 is put into use). The number of charge-discharge cycles N(t) is calculated starting from the beginning of the charging period, transitioning to the discharge period after the charging period ends, and then considering the period until the discharge period ends as one cycle. Based on this policy, the main control unit 100 counts the number of charge-discharge cycles while detecting the charging and discharging periods based on the voltage values ​​measured by the voltage sensor 15 and the voltage measurement unit 101 under normal operation monitoring conditions, and sets the count value when the value of time t is determined as N(t).

[0098] The model data storage unit 113 of the control device 10 contains the model function R ｏｈｍ (t, N(t)), R ｉｆ (t, N(t)), R ｒｆMultiple combinations of model parameters that can be applied to (t, N(t)) are stored. These model parameters are derived by assuming that the change in internal resistance represented by the event data used to derive the measurement condition stored in the measurement condition storage unit 112 corresponds to one of the above model functions, and then calculating the constants in that function.

[0099] Below, we will explain specific examples of model data and their characteristics that are applicable when measuring each of the three types of internal resistance components using pulsed current under conditions suitable for measuring that internal resistance component.

[0100] <<Model function R of ohmic resistance ｏｈｍ (t, N(t)) >> This model function is expressed by equation (1) below. R ｏｈｍ (t, N(t))=R ｏｈｍ０ +p・t+q・N(t) ...(1) R ｏｈｍ０ : Value of ohmic resistance at t = 0 (initial value) p: Rate of increase of ohmic resistance over time q: Rate of increase of ohmic resistance with increasing charge / discharge cycles

[0101] R ｏｈｍ０ , p, and q are all positive constants. According to equation (1), the ohmic resistance R ｏｈｍ (t, N(t)) includes a component proportional to the length of time t from the reference point and a component proportional to the number of charge / discharge cycles N(t). As the value of at least one of t and N(t) increases, the ohmic resistance R ｏｈｍ The value of (t, N(t)) also increases. However, as long as p and q take small values, R ｏｈｍ The change in the value of (t, N(t)) becomes gradual.

[0102] <<Model function for interfacial resistance>> R ｉｆ (t, N(t)) = R ｉｆ０ +r ｉｆ × (1-e -α1・ｔ ) + δ ｉｆ × (1-e －β1・Ｎ（ｔ） ) ...(2) R ｉｆ０: The value of the interfacial resistance at t = 0 (initial value) r ｉｆ δ: Maximum increase in interfacial resistance over time ｉｆ : Maximum increase in interfacial resistance with respect to the number of charge / discharge cycles α1: Rate of increase in interfacial resistance over time (increase in resistance per unit time) β1: Rate of increase in interfacial resistance with respect to the increase in the number of charge / discharge cycles (increase in resistance per cycle) The above R ｉｆ０ ,r ｉｆ , δ ｉｆ α1 and β1 are also positive constants.

[0103] Figure 5(A) shows the r in equation (2). ｉｆ × (1-e -α1・ｔ Figure 5(B) is a graph schematically showing the relationship between the value of the term and time t, and is the δ of equation (2). ｉｆ × (1-e －β1・Ｎ（ｔ） This graph schematically shows the relationship between the value of the term and the number of charge / discharge cycles N(t). The value of each term increases as the values ​​of t and N(t) increase, but r ｉｆ × (1-e -α1・ｔ The value of ) increases significantly only for a certain period of time after the baseline, and the rate of increase thereafter becomes small.

[0104] δ shown in Figure 5(B) ｉｆ × (1-e －β1・Ｎ（ｔ） The value of ) also increases significantly until the value of N(t) reaches a certain level, but the rate of increase thereafter becomes small.

[0105] Therefore, the interfacial resistance R is expressed by equation (2). ｉｆ The interfacial resistance represented by the model data (t, N(t)) increases significantly over time and with repeated charging and discharging until a certain amount of time has passed. However, the rate of increase gradually converges and is expected to eventually settle at a nearly constant level.

[0106] However, equation (2) assumes that no major abnormalities have occurred in the surface layer of the electrode. If an abnormality occurs, such as an unexpected increase in the thickness of the surface layer for some reason, or an extreme increase in the degree of unevenness in the thickness of the surface layer, then, as shown by the dashed line in Figures 5(A) and (B), r ｉｆ × (1-e -α1・ｔ ) and δ ｉｆ × (1-e －β1・Ｎ（ｔ） The value of the resistance component corresponding to this may deviate significantly from the value calculated by the calculation.

[0107] <<Model function for diffusion resistance>> R ｄｆ (t, N(t)) = R ｄｆ０ +r ｄｆ × (1-e -α２・ｔ ) + δ ｄｆ × (1-e －β２・Ｎ（ｔ） ) ...(3) R ｄｆ０ : The value of the diffusion resistance at t = 0 (initial value) r ｄｆ δ: Maximum increase in interfacial resistance over time ｄｆ α2: Maximum increase in interfacial resistance with respect to the number of charge / discharge cycles α2: Rate of increase in diffusion resistance over time β2: Rate of increase in diffusion resistance with respect to the increase in the number of charge / discharge cycles

[0108] (3) R in equation ｄｆ０ ,r ｄｆ , δ ｄｆ α² and β² are also positive constants. Therefore, r ｄｆ × (1-e -α２・ｔ ) part and δ ｄｆ × (1-e －β２・Ｎ（ｔ） The change in the ) portion is understood to follow a pattern similar to that shown in the graphs of Figure 5(A) and (B). In that case, the diffusion resistance model function R ｄｆ (t, N(t)) is also the model function R of the interfacial resistance. ｉｆ Similar to (t, N(t)), the value increases significantly over time and with repeated charging and discharging until a certain amount of time has passed, but the rate of increase gradually converges and eventually settles at a nearly constant level.

[0109] However, equation (3) also assumes that no major abnormalities occur. If abnormalities occur in the surface layer as described in the section on interfacial resistance, or if there are significant changes in the crystal structure of the electrode material or damage to the electrode material, the actual r ｄｆ × (1-e -α２・ｔ ) and δ ｄｆ × (1-e －β２・Ｎ（ｔ） The value of the resistance component corresponding to this can deviate significantly from the value calculated by the calculation.

[0110] <Overview of Preparation Phase Processing> As mentioned earlier, in this embodiment, the control device 10 monitors the operation of the battery 2 for a while after being connected to the battery 2 and started up, and performs various measurements. The acquired measurement values ​​and event data resulting from calculations using them are stored in the history data storage unit 114. The characteristic type of the battery 2 is determined using the stored event data and mathematical models, and measurement parameters and model parameters used for diagnosis are identified and stored in the measurement condition storage unit 112 and model data storage unit 113. Hereinafter, the period until the storage of these parameters is completed will be referred to as the "preparation period".

[0111] During the preparation period, the main control unit 100 sequentially applies various measurement conditions defined in the program that constitutes the main control unit 100, and measures the internal resistance in the manner shown in Figures 3 and 4 while supplying pulse currents to the battery 2 to which each condition is applied. The event data obtained from the internal resistance value through this measurement includes date and time data, as well as an identification code for the measurement conditions applied to the measurement.

[0112] When event data exceeding a predetermined capacity is accumulated in the history data storage unit 114, the main control unit 100 determines the characteristic type of the battery 2 through inference calculations using that event data and an analysis model. Furthermore, the main control unit 100 derives multiple combinations of measurement parameters representing the measurement conditions for each of the three types of internal resistance components through inference calculations using event data based on internal resistance values ​​and a measurement model, and derives model parameters that constitute the model data for each of these combinations.

[0113] Specifically, for the measurement conditions for ohmic resistance, R in Equation (1), p, and q, and for the measurement conditions for interface resistance, R in Equation (2), r, δ, α1, and β1, and for the measurement conditions for diffusion resistance, R in Equation (3), r, δ, α2, and β2 are each derived in a plurality of sets. Also, depending on whether the pulse current flows in the direction from the positive electrode to the negative electrode or in the direction from the negative electrode to the positive electrode, the values of the measurement parameters and the model parameters are different, and for each combination of each case and the type of internal resistance component, a plurality of combinations of the measurement parameters and the model parameters are derived. ｏｈｍ０ For the measurement conditions for interface resistance, R in Equation (2), r, δ, α1, and β1 are derived in a plurality of sets. Also, depending on whether the pulse current flows in the direction from the positive electrode to the negative electrode or in the direction from the negative electrode to the positive electrode, the values of the measurement parameters and the model parameters are different, and for each combination of each case and the type of internal resistance component, a plurality of combinations of the measurement parameters and the model parameters are derived. ｉｆ０ r ｉｆ δ ｉｆ For the measurement conditions for diffusion resistance, R in Equation (3), r, δ, α2, and β2 are derived in a plurality of sets. Also, depending on whether the pulse current flows in the direction from the positive electrode to the negative electrode or in the direction from the negative electrode to the positive electrode, the values of the measurement parameters and the model parameters are different, and for each combination of each case and the type of internal resistance component, a plurality of combinations of the measurement parameters and the model parameters are derived. ｄｆ０ r ｄｆ δ ｄｆ α2, and β2 are derived in a plurality of sets. Also, depending on whether the pulse current flows in the direction from the positive electrode to the negative electrode or in the direction from the negative electrode to the positive electrode, the values of the measurement parameters and the model parameters are different, and for each combination of each case and the type of internal resistance component, a plurality of combinations of the measurement parameters and the model parameters are derived.

[0114] In the measurement process of the internal resistance in the deterioration diagnosis, only the pulse current to which the measurement conditions stored in the measurement condition storage unit 112 are applied is used, and in the discrimination process for the measurement values obtained by the measurement, only the model parameters corresponding to the measurement conditions are used. Even in the performance recovery control implemented when it is determined that there is an abnormality in the measurement value by the deterioration diagnosis, the parameters derived by the calculation using the performance recovery model of the resistance component corresponding to the measurement conditions when the measurement value was obtained are used. Therefore, by performing sufficient measurements during the preparation period, determining the characteristic type with a high degree of fitness to the characteristics of the battery 2 based on the accumulated event data, and setting the measurement model, the model parameters for performance recovery, and the parameters for performance recovery control corresponding to the characteristic type, the deterioration diagnosis and the performance recovery control can be performed appropriately and efficiently.

[0115] <Processing procedure after the end of the preparation period> FIG. 6 is a flowchart showing the main processing flow executed in the control device 10 of the battery activation device 1 after the end of the preparation period. FIG. 8 is a flowchart showing the processing flow of the deterioration diagnosis in step S8 in the flowchart.

[0116] First, the basic processing flow will be explained with reference to Figure 6. The control device 10 repeatedly measures the detection signals from various sensors and checks whether charging or discharging is occurring based on the degree of voltage fluctuation, and whether any changes indicating abnormal events have occurred in the measured values ​​(environmental data) obtained from the temperature sensor 17, humidity sensor 18, and atmospheric pressure sensor 19 (steps S1, S2, S3 in Figure 6). As long as no such changes occur, the loop of steps S1, S2, and S3 is repeated every predetermined period of time (for example, 3 minutes). All measured values ​​obtained from measurements taken during this loop are compiled into event data and stored in the history data storage unit 114.

[0117] In the loop of steps S1 to S3, if charging is detected (the determination in step S1 is "YES"), the main control unit 100 waits until the charging is completed (step S4), and measures the voltage upon completion of charging to determine whether the measured value satisfies the conditions for post-charging degradation diagnosis (step S5). Here, the condition is that the measured value falls within the range of the fully charged voltage estimated during the preparation period. If it is determined that this condition is met, the degradation diagnosis in step S8 is performed; if it is determined that the condition is not met, the parameter management in step S9 is performed.

[0118] Even when discharge is detected in the loop of steps S1 to S3 (the determination in step S2 is "YES"), the main control unit 100 waits until the discharge is completed (step S6), and measures the voltage in response to the completion of the discharge to determine whether the measured value satisfies the conditions for deterioration diagnosis after the discharge is completed (step S7). Here, the condition is that the measured value falls within the range from a value corresponding to 40% of the lower limit of the full charge voltage range to a value corresponding to 40% of the upper limit of the same range. If it is determined that this condition is met, the deterioration diagnosis in step S8 is performed, and if it is determined that the condition is not met, the parameter management in step S9 is performed.

[0119] In the loop of steps S1 to S3, when an abnormality is detected in any of the measured values of temperature, humidity, and atmospheric pressure (the determination in step S3 is "YES"), the control for abnormal times in step S10 is executed.

[0120] Here, switching the reference to FIG. 7, the procedure of the deterioration diagnosis in step S8 will be described. When proceeding to the deterioration diagnosis from step S5 after the end of charging, the main control unit 100 proceeds from step S101 to step S102 and validates the measurement conditions for flowing a positive-direction pulse current among the measurement conditions stored in the measurement condition storage unit 112. When proceeding to the deterioration diagnosis from step S7 in response to the end of discharging, the main control unit 100 proceeds from step S101 to step S103 and validates the measurement conditions for flowing a negative-direction pulse current.

[0121] In accordance with the processing of step S102 or S103 above, only the parameters corresponding to the validated measurement conditions are made valid for the model parameters.

[0122] The main control unit 100 sets one of the validated measurement conditions (step S104) and executes the following processing for that measurement condition.

[0123] First, the main control unit 100 flows a pulse current to which the measurement parameters of the set measurement conditions are applied to the battery 2 to measure the internal resistance (measurement of voltage V １ , V ０ , current I and calculation by formula (A)) (step S105). Subsequently, the length of the time from the reference time point to the time point when the internal resistance is measured and the number of charge / discharge cycles performed during the elapse of that time are obtained, the former value is applied to the variable t, and the latter value is applied to the variable N(t) (step S106).

[0124] Note that it is desirable that the time point when the internal resistance is measured be the time point when the voltage V １ is measured, but for the convenience of program design, it may be the time point immediately before the measurement of the voltage V １ , or the time point immediately before or after the calculation by formula (A) is executed. Also, in the measurement of ohmic resistance, etc., the peak value I and the voltages V ０ , V １If the measurement and calculation by applying them to equation (A) are performed for each period during the pulse application period, and the average value of the calculation results in each period is taken as the internal resistance, then the voltage V in the last period is １ It is preferable to set the measurement time or the time immediately before or after the calculation as the time when the internal resistance was measured. However, the method of calculating t is not limited to the above; t may also be the length of time from the reference time to the time immediately before the start of pulse current application or immediately after the end of application.

[0125] Next, the main control unit 100 applies the values ​​obtained in step S106 to t and N(t) in the model function (one of the above equations (1), (2), or (3)) to which the model parameters corresponding to the selected measurement conditions are applied, and performs a calculation (step S107). This calculation calculates the value (model value) that is measured when the resistance component measured by the selected measurement conditions changes as indicated by the model function.

[0126] The main control unit 100 calculates the absolute difference ΔR(t, N(t)) between the above model value and the measured value obtained in step S105 (step S108), combines the calculation result and the measured value with the measurement conditions and date and time data and stores it in the history data storage unit 114 (step S109), and further compares the value of ΔR(t, N(t)) with a predetermined threshold (step S110). If ΔR(t, N(t)) does not exceed the threshold, the measurement conditions are changed (the determination in step S112 becomes "NO" and the process proceeds to step S113), and the procedures of steps S105 to S110 are executed for the new conditions in the same manner as above.

[0127] Steps S105 to S110 above are performed for all activated measurement conditions. If the value of ΔR(t, N(t)) falls within the threshold for any of the measurement conditions, the processing for the last measurement condition is completed (the judgment in step S112 is "YES"), and the degradation diagnosis is terminated.

[0128] On the other hand, if the value of ΔR(t, N(t)) for any of the selected measurement conditions exceeds a threshold, the main control unit 100 proceeds to the performance recovery control routine (step S111).

[0129] In performance recovery control, the main control unit 100 retrieves a performance recovery model for the resistance component corresponding to the selected measurement conditions (a model that matches the characteristic type of the battery 2) from the mathematical model storage unit 111, and reads event data from the history data storage unit 114, including measured values ​​measured under the selected measurement conditions from a predetermined point in the past to the present, and the difference ΔR(t, N(t)) between the measured values ​​and the model values. Then, by applying these to the performance recovery model, it derives parameters (pulse period, pulse width, peak value, number of pulses) that represent the characteristics of the pulse current for performance recovery.

[0130] Figure 8 shows the basic patterns of pulse current used in performance recovery control. In Figure 8, the current axis (vertical axis) is defined as positive for the direction from the positive electrode to the negative electrode (direction of current during discharge) and negative for the direction from the negative electrode to the positive electrode (direction of current during charging).

[0131] Figure 8(A) shows control that flows a pulsed current in the positive direction while maintaining a constant peak value, Figure 8(B) shows control that flows in the positive direction while gradually decreasing the peak value, and Figure 8(C) shows control that alternately flows two types of pulsed currents with different peak value levels in the positive direction. Figures 8(D) and (E) show control that repeats the process of flowing a current in the negative direction immediately after flowing a current in the positive direction at a constant period. In the control shown in Figure 8(D), the peak values ​​in each direction are maintained at a constant value, and in the control shown in Figure 8(E), the peak value in the negative direction is adjusted to gradually decrease.

[0132] The performance recovery control method is not limited to the examples shown above. Other control methods are also possible, such as a pattern in which pulse current flows only in the negative direction, or a pattern in which the peak value is gradually increased while flowing current in the positive or negative direction. Furthermore, two or more methods can be combined.

[0133] Refer back to Figure 7. The main control unit 100 returns to the degradation diagnosis process after performing performance recovery control using pulse current to which the set parameters are applied for a certain period of time. At this time, the measurement of internal resistance (step S105), identification of the values ​​of t and N(t) (step S106), calculation of model values ​​(step S107), calculation of the absolute value ΔR(t, N(t)) of the difference between the measured value and the model value, and saving of the calculation result and measured value (steps S108, S109) are performed again, targeting the measurement conditions that were set before transitioning to performance recovery control. The value of ΔR(t, N(t)) is compared with a threshold (step S110).

[0134] If the ΔR(t, N(t)) obtained from the second diagnosis exceeds the threshold again, the main control unit 100 proceeds to performance recovery control (step S111) again. In step S111, using the event data set which includes numerical data such as voltage and current measured after the most recent pulse current application, inference calculations are performed again using the performance recovery model to calculate new parameters for performance recovery control, and a pulse current to which these parameters are applied is applied to the battery 2.

[0135] In this way, for measurement conditions in which abnormal measurement values ​​are detected, the system repeatedly performs degradation diagnosis and resets parameters based on the results of each hour's diagnosis to repeat performance recovery control. This allows the measured values ​​to gradually approach the pattern of change in the internal resistance value represented by the model function corresponding to the measurement conditions in question. As a result, when the difference between the measured value and the model value falls below a threshold (the judgment in step S110 becomes "NO"), the main control unit 100 terminates the processing related to the selected measurement conditions.

[0136] Referring back to Figure 6, we will briefly explain the parameter management in step S9 and the abnormality control in step S10.

[0137] In step S9, parameter management, the main control unit 100 determines whether it is necessary to update the measurement conditions or model data based on the results of degradation diagnoses performed over a predetermined period in the past. If ΔR(t, N(t)) does not exceed the threshold for any of the measurement conditions in any of the degradation diagnoses, and there are no measurement conditions in which ΔR(t, N(t)) shows an increasing trend, the main control unit 100 maintains the current measurement parameters and model parameters and returns to the loop of steps S1 to S3.

[0138] On the other hand, if measurement conditions are found in which ΔR(t, N(t)) shows an increasing trend even though the threshold has not yet been exceeded, or if performance recovery control was performed in the most recent degradation diagnosis, the main control unit 100 performs calculations by applying the event data accumulated in the history data storage unit 114 during the period between each degradation diagnosis used as the basis for judgment to the analysis model, and updates the measurement parameters and model parameters based on the calculation results. If a degradation diagnosis is to be performed after these updates, the updated measurement parameters and model parameters will be applied.

[0139] The parameter update process described above can also be performed immediately after the degradation diagnosis, during which performance recovery control has been implemented, is completed. Furthermore, if performance recovery control was performed, the reference point for calculating time t may also be updated. For example, the reference point may be changed to the time when performance recovery control was executed or to a predetermined time earlier.

[0140] In the abnormality control in step S10, the main control unit 100 retrieves a performance recovery model corresponding to the environmental data where the abnormality occurred from the mathematical model storage unit 111. Then, it reads out multiple types of event data, including those related to the environmental data, from the data stored in the history data storage unit 114, and derives a combination of parameters for performance recovery control suitable for resolving the abnormality by applying these to the performance recovery model. After executing a process to apply a pulse current to which this combination is applied for a certain period of time, it performs a process to acquire the latest measurement values ​​for the sensor related to the measurement value where the abnormality occurred for a certain period of time, and determines whether the acquired set of measurement values ​​has returned to the normal range.

[0141] If the above set of measured values ​​has not returned to the normal range, the main control unit 100 includes those measured values ​​in its calculations again using the performance recovery model to derive new parameters, and then executes a process to apply a pulse current to which that combination is applied. By repeating this series of processes, once the measured values ​​return to the normal range, the main control unit 100 terminates the abnormal control and returns to the loop of steps S1 to S3. Even when abnormal control is performed, the measurement parameters and model parameters can be updated in the parameter management performed afterward.

[0142] According to the series of procedures shown in Figures 6 and 7, while constantly monitoring the state of battery 2, if any internal resistance component increases beyond the acceptable range, or if an abnormality in environmental data occurs that could lead to an accident, such as a sudden rise in temperature, a pulse current suitable for eliminating the malfunction or abnormality is quickly supplied to battery 2. Performance recovery control and abnormality control are repeated until the abnormality in measured values ​​such as internal resistance and temperature is resolved, thereby restoring battery 2 to a good state.

[0143] However, even after repeated performance recovery control and abnormality control, there is a possibility that the abnormally measured values ​​may not return to the normal range. Therefore, it is desirable to set an upper limit on the number of times performance recovery control and abnormality control can be repeated. Even if the abnormality in the measured values ​​cannot be resolved after reaching the upper limit, the main control unit 100 can continue processing according to the procedure shown in Figures 6 and 7 by changing the measurement parameters and model parameters to those suitable for the current state of the battery 2 during the subsequent parameter management.

[0144] Even if the abnormality in the measured value is resolved by performance recovery control or abnormality control, the battery 2 will gradually deteriorate and its performance will decline. However, the changes in various resistance components due to the passage of time and the increase in the frequency of charging and discharging can be made to occur within a range close to the set of model values ​​expressed by equations (1), (2), and (3). Therefore, it is possible to prevent the deterioration of the battery 2 from progressing significantly and to extend the life of the battery 2 considerably.

[0145] Below, I will briefly add some information about matters that were omitted to avoid unnecessary complexity, as well as matters that may be changed or added.

[0146] In the above embodiment, effective measurement conditions were selected in sequence, the internal resistance was measured, and the appropriateness of the obtained measurement values ​​was determined. Performance recovery control was performed in response to the degree of deviation of the measurement values ​​from the model values ​​(the absolute difference ΔR(t, N(t))) exceeding a threshold. However, the timing of performance recovery control is not limited to this. For example, after performing processes such as measuring the internal resistance, calculating the model values, and comparing the difference ΔR(t, N(t)) between the measurement values ​​and the model values ​​with a threshold for all effective measurement conditions, performance recovery control may be performed only for the measurement conditions related to the measurement values ​​where ΔR(t, N(t)) exceeded the threshold.

[0147] Even if no measured values ​​exceeding a threshold deviation from the model value are observed, the trend of internal resistance changes for each measurement condition can be analyzed based on the measured values ​​for each condition and their temporal changes. Performance recovery control can then be performed earlier for measurement conditions where a tendency for large changes is observed. Furthermore, instead of analyzing each measurement condition individually, it is possible to estimate whether the measured values ​​exceed acceptable levels through multidimensional analysis using the processing results of multiple measurement conditions set for the same type of internal resistance component.

[0148] Ohmic resistance can be measured not only during the preparation period or degradation diagnosis, but also using the pulsed current supplied to battery 2 during performance recovery control or abnormal situation control. In this case, the method shown in Figure 3(C) can be applied to measure the internal resistance at each cycle during the pulse application period, and the pulsed current can be continued until these measured values ​​settle at a predetermined level. Similarly, when measuring ohmic resistance during degradation diagnosis, the internal resistance can be measured at each cycle, and the degree of deviation from the model value can be determined for each measured value.

[0149] In the above embodiment, the measurement conditions for interfacial resistance and diffusion resistance were set assuming that the resistance component generated at the electrode where lithium ions are absorbed was measured. However, actual measured values ​​may include the resistance component generated at the electrode where lithium ions are desorbed. Taking this possibility into account, for at least some of the measurement conditions set for interfacial resistance and diffusion resistance, it may be possible to estimate the pattern of change when the combined resistance value of the resistance components generated at both electrodes changes over time or with an increase in charge-discharge cycles, and to set a model function that represents that pattern.

[0150] The internal resistance of battery 2 can fluctuate due to temperature changes inside and outside the battery, even when degradation is not progressing. Taking this possibility into account, the control device 10 can be given a function to correct the internal resistance value calculated in step S105 of the degradation diagnosis based on temperature.

[0151] For example, if the mathematical model storage unit 111 stores a mathematical model that defines the relationship between temperature changes and changes in internal resistance, and if, after a certain period of time has elapsed, the main control unit 100 is equipped with a function to determine a correction coefficient for correcting the internal resistance by applying the temperature and internal resistance measurements accumulated in the history data storage unit 114 during that period to the mathematical model, and a function to correct the internal resistance measured in the degradation diagnosis using the said correction coefficient, then in each degradation diagnosis, the internal resistance value corrected with the latest correction coefficient will be compared with the model value. By expanding the functions of the control device 10 in this way, it becomes possible to estimate the degree of degradation of the battery 2 with greater accuracy.

[0152] The timing of the degradation diagnosis is not limited to when charging or discharging is complete and the voltage meets certain conditions. The program of the main control unit 100 can also be modified to continuously repeat the degradation diagnosis, except during periods when large voltage fluctuations occur.

[0153] In the battery activation device 1 with the above configuration, in order to improve the reliability of the measured value of the internal resistance, a current sensor 16 is provided in the connection path between the battery 2 and the device 1. The internal resistance of the battery 2 is determined by calculation (equation (A)) using the measured value obtained from the current sensor 16 when a peak current flows during the pulse application period and the amount of change in the measured value of the voltage sensor 15 due to the application of the pulse current. However, if the difference between the level of current flowing through the circuit and the control level can be kept within an acceptable range, the current sensor 16 may be omitted, and the peak value set as the measurement condition may be applied to I in equation (A). Furthermore, it is not necessarily required to be able to set the peak value to be used as the measurement condition in fine units. For example, the height to be applied to the peak value of the measurement condition may be selected from several predetermined heights, or the height of the peak value may always be kept constant.

[0154] The battery activation device 1 with the above configuration can use wireless communication functionality to accept access from communication terminals such as personal computers and smartphones owned by the user, and can also transmit data that constitutes a viewing screen of numerical data stored in the history data storage unit 114 (such as the operation history of the battery 2, changes in measured internal resistance, and changes in discharge capacity) to the communication terminal. Furthermore, if various event data and degradation diagnosis results are periodically transmitted from the battery activation device 1 to a cloud server, and this data is managed for each battery 2 on the cloud server, the user can access the cloud server as needed to understand in detail the history of events occurring in the battery 2 and the degree of degradation.

[0155] Furthermore, by having the battery activation device 1 communicate with a cloud server equipped with advanced machine learning capabilities, it is possible to update the various mathematical models registered in the mathematical model storage unit 111 with new models transmitted from the cloud server, and to use the event data accumulated in the history data storage unit 114 for machine learning on the cloud server to improve the level of the various mathematical models. In addition, as described below, it is also possible to use the set of event data transmitted from the battery activation device 1 to perform higher-level calculations on the cloud server to determine measurement conditions, model parameters, and control parameters for performance recovery, and to feed these back to the battery activation device 1.

[0156] Figure 9 shows a network system consisting of a cloud server 3 equipped with advanced machine learning capabilities 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") has the configuration shown in Figures 1 and 2 above and is electrically connected to individual batteries 2A, 2B, 2C, etc. (hereinafter referred to as "battery 2"), respectively. The electrical configuration of each battery activation device 1 and its connection status to battery 2 are the same as those shown in Figure 1.

[0157] In this embodiment, the battery activation device 1 has registered programs for basic measurement and control, as well as measurement parameters representing measurement conditions for measuring internal resistance during the preparation period. However, the mathematical model storage unit 111 only stores a certain number of basic mathematical models. On the other hand, the cloud server 3 has registered a considerable number of analysis models, measurement models, and performance recovery models that are suited to the characteristics of various batteries 2, obtained through machine learning using a collection of event data obtained for a large number of batteries 2.

[0158] After initial startup, the battery activation device 1 enters preparation mode and performs tasks such as detecting the charging and discharging stages, measuring the voltage change associated with charging and discharging, calculating the charge-discharge cycle, measuring internal resistance, and measuring environmental data using sensors 17, 18, and 19. Event data, including numerical values ​​obtained through these processes, is stored in the history data storage unit 114.

[0159] After a certain amount of time has elapsed since the initial startup, the battery activation device 1 transmits various event data stored in the history data storage unit 114 to the cloud server 3. Upon receiving this transmission, the cloud server 3 applies the received event data to an analysis model owned by the device to identify the characteristic type of the battery 2 connected to the battery activation device 1. Then, using the measurement model corresponding to that characteristic type and the group of event data transmitted from the battery activation device 1, it derives measurement parameters and model parameters suitable for diagnosing the battery 2, and transmits them to the battery activation device 1.

[0160] Upon receiving the above transmission, the battery activation device 1 stores the received parameters in the measurement condition storage unit 112 and the model data storage unit 113, and then starts the processing shown in Figures 6 and 7. Subsequently, the battery activation device 1 periodically transmits the event data accumulated in the history data storage unit 114 to the cloud server 3. The cloud server 3 uses the event data transmitted from each battery activation device 1 for machine learning, evolving various mathematical models to a higher level.

[0161] Regarding performance recovery control, the battery activation device 1 transmits event data necessary for deriving the parameters for the control to the cloud server 3. The parameters generated by inference calculations applying this data to the performance recovery model of the cloud server 3 are then fed back from the cloud server 3 to the battery activation device 1 and can be used for performance recovery control.

[0162] According to the system described above, regardless of the characteristics of the lithium-ion battery connected to the battery activation device 1, degradation diagnosis and performance recovery control can be performed using a mathematical model suited to the characteristics of that lithium-ion battery, thereby extending the lifespan of the lithium-ion battery.

[0163] Furthermore, if the circuit configuration shown in Figure 1 is incorporated as an ASIC (Application-Specific Integrated Circuit) into a power supply circuit including a lithium-ion battery, the battery activation device 1 can also be housed inside a device powered by a lithium-ion battery, making it possible to perform performance recovery control on the lithium-ion battery as needed. This would allow the battery activation device 1 of the above embodiment to be introduced into small devices such as smartphones.

[0164] Therefore, it becomes possible to use the same battery activation device 1 configuration for all types of lithium-ion batteries, and by continuously operating these battery activation devices 1 while receiving mathematical models from the cloud server 3, the lifespan of lithium-ion batteries can be significantly extended.

[0165] On the other hand, it is also possible to provide a device with the same hardware configuration as shown in Figure 1, but without the performance recovery control function, i.e., a device that only performs degradation diagnosis. Even with this device, if the difference ΔR(t, N(t)) between the measured value and the model value of the internal resistance exceeds a threshold, or if the threshold is not exceeded but ΔR(t, N(t)) shows an increasing trend, it is possible to prevent lithium-ion batteries that have deteriorated from being used for a long time by providing a function to send a warning to the user via a wireless communication circuit. Furthermore, this device can also be configured to calculate the internal resistance by using the peak value applied to the measurement conditions of the internal resistance and the amount of change in the measured voltage caused by the application of pulse current according to those measurement conditions, without providing a current sensor, and to estimate the degree of degradation of the lithium-ion battery based on that internal resistance value and the model value.

[0166] <Regarding the batteries to be diagnosed> The diagnostic and performance recovery control described in the basic embodiments and modifications above are not limited to lithium-ion batteries. They can also be used for next-generation secondary batteries such as lead-acid batteries and solid-state batteries, which have been widely used for some time. If a model function can be derived that represents the relationship between the pulse current suitable for measuring internal resistance, the length of time elapsed since a specific point in the past, the frequency of charge / discharge operations performed during that time, and the acceptable value of internal resistance measured under the measurement conditions, then the same degradation diagnosis and performance recovery control as in each example can be performed.

[0167] 1 Battery activation device 2 Battery 3 Cloud server 10 Control device 13 Pulse generation circuit 14 Wireless communication circuit 15 Voltage sensor 16 Current sensor 100 Main control unit 101 Voltage measurement unit 102 Current measurement unit 103 Environmental data measurement unit 104 Pulse control unit 105 Communication control unit 111 Mathematical model storage unit 112 Measurement condition storage unit 113 Model data storage unit 114 History data storage unit

Claims

1. A device that operates by being electrically connected to a secondary battery, comprising a control device having a function to supply pulse current to the secondary battery, and a voltage sensor for measuring the voltage between the positive and negative electrodes of the secondary battery, wherein the control device includes: charge / discharge detection means for detecting the charging and discharging stages of the secondary battery based on changes in the measured value of the voltage sensor; internal resistance measuring means for performing a current application process that applies a pulse current to the secondary battery to which a pulse period of a predetermined length, pulse width, and peak value of a predetermined height are applied, and a calculation to determine the internal resistance value of the secondary battery using the amount of change in the measured value of the voltage sensor due to the application of the pulse current and the peak value of the pulse current, under conditions in which measurement conditions are set that define the pulse period, pulse width, length of pulse current application time, and timing for determining the amount of change in the measured value of the voltage; and measurement condition storage means for storing multiple sets of parameter combinations representing the measurement conditions set in the internal resistance measuring means for diagnosing the secondary battery. A model data storage means that stores model data representing the acceptable mode of change when the internal resistance value measured under the measurement condition changes in accordance with the length of time elapsed from a specific point in the past and the frequency of charging and discharging performed during that time, as model data representing the relationship between the length of time elapsed, the frequency of charging and discharging, and the acceptable value of the internal resistance; an analysis means that sequentially sets the combination of parameters for each measurement condition stored in the measurement condition storage means to the internal resistance measuring means and performs the current application process and the calculation, and for each set measurement condition, applies the length of time from the specific point in time to the time when the processing of the internal resistance measuring means is performed according to that condition or near the time, and the frequency of charging and discharging determined from the detection results performed by the charging and discharging detection means during that time to the function of the model data corresponding to that condition to obtain a model value of the internal resistance measured under that condition, and performs a process to obtain the degree of deviation of the internal resistance value obtained by the calculation of the internal resistance measuring means with respect to the model value; A diagnostic device for a secondary battery, comprising: an estimation means for estimating the degree of degradation of the secondary battery based on the degree of deviation determined by the analysis means.

2. A diagnostic device for a secondary battery according to claim 1, further comprising a current sensor for measuring the current flowing through the connection path between the secondary battery and the device, wherein the internal resistance measuring means of the control device derives the internal resistance value of the secondary battery by calculation using the measurement value of the current sensor for the peak of the pulse current applied to the secondary battery by the current application process and the amount of change in the measurement value of the voltage sensor caused by the application of the pulse current.

3. The diagnostic device for a secondary battery according to claim 1, further comprising: a mathematical model storage means storing a mathematical model representing the relationship between measurement conditions applicable to measuring the internal resistance occurring in a secondary battery and the characteristics of the change in the internal resistance value measured under those conditions; a data storage means for storing one or more types of event data, including event data that links data representing the measurement conditions applied to the internal resistance measuring means and the internal resistance value obtained by calculation under those conditions; and a preparation means for sequentially setting multiple measurement conditions on the internal resistance measuring means and executing the current application process and the calculation for each condition multiple times, and within a predetermined period including the period during which these processes are performed, identifying parameters representing multiple measurement conditions used for diagnosing the secondary battery and model data applicable to these measurement conditions by applying data from the group of event data stored in the data storage means that fit the set measurement conditions to the mathematical model in the mathematical model storage means, and storing them in the measurement condition storage means and model data storage means.

4. A device that operates by being electrically connected to a secondary battery, comprising a control device having a function to supply pulse current to the secondary battery, and a voltage sensor for measuring the voltage between the positive and negative electrodes of the secondary battery, wherein the control device includes: charge / discharge detection means for detecting the charging and discharging stages of the secondary battery based on changes in the measured value of the voltage sensor; internal resistance measurement means for performing a current application process that applies a pulse current to the secondary battery to which a pulse period of a predetermined length, pulse width, and peak value of a predetermined height are applied, and a calculation to determine the internal resistance value of the secondary battery using the amount of change in the measured value of the voltage sensor due to the application of the pulse current and the peak value of the pulse current, under conditions in which measurement conditions are set that define the pulse period, pulse width, length of pulse current application time, and timing for determining the amount of change in the measured value of the voltage; and measurement condition storage means for storing multiple sets of parameter combinations representing the measurement conditions set in the internal resistance measurement means for diagnosing the secondary battery. A model data storage means that stores model data representing the acceptable mode of change when the internal resistance value measured under the measurement condition changes in accordance with the length of time elapsed from a specific point in the past and the frequency of charging and discharging performed during that time, as model data representing the relationship between the length of time elapsed, the frequency of charging and discharging, and the acceptable value of the internal resistance; an analysis means that sequentially sets the combination of parameters for each measurement condition stored in the measurement condition storage means to the internal resistance measuring means and performs the current application process and the calculation, and for each set measurement condition, applies the length of time from the specific point in time to the time when the processing of the internal resistance measuring means is performed according to that condition or near the time, and the frequency of charging and discharging determined from the detection results performed by the charging and discharging detection means during that time to the function of the model data corresponding to that condition to obtain a model value of the internal resistance measured under that condition, and performs a process to obtain the degree of deviation of the internal resistance value obtained by the calculation of the internal resistance measuring means with respect to the model value;A battery activation device for a secondary battery, comprising: a recovery control means that supplies a recovery pulse current to the secondary battery in response to the analysis means determining that the degree of deviation of the internal resistance value related to any of the set measurement conditions exceeds a predetermined threshold; and a recovery control means.

5. A battery activation device for a secondary battery according to claim 4, further comprising a current sensor for measuring the current flowing in the connection path between the secondary battery and the device, wherein the internal resistance measuring means of the control device derives the internal resistance value of the secondary battery by calculation using the measurement value of the current sensor for the peak of the pulse current applied to the secondary battery by the current application process and the amount of change that occurs in the measurement value of the voltage sensor due to the application of the pulse current.

6. The control device includes: a mathematical model storage means that stores a first mathematical model representing the relationship between measurement conditions applicable to measuring the internal resistance occurring in a secondary battery and the characteristics of the change in the internal resistance value measured under those conditions; a second mathematical model representing the relationship between the change in event data in a degraded secondary battery and the manner of recovery pulse current suitable for resolving the degradation; and a data storage means for storing one or more types of event data, including event data that links data representing the measurement conditions applied to the internal resistance measuring means and the internal resistance value obtained by calculation under those conditions. The battery activation device for a secondary battery according to claim 4, further comprising: a preparation means for specifying parameters representing multiple measurement conditions used for diagnosing the secondary battery and model data applied to these measurement conditions by performing calculations by sequentially setting multiple measurement conditions in the internal resistance measuring means and executing the current application process and the calculation for each condition; applying data from the group of event data accumulated in the data storage means that conform to the set measurement conditions within a predetermined period including the period during which these processes are performed to a first mathematical model in the mathematical model storage means; and storing them in the measurement condition storage means and model data storage means, respectively; and the recovery control means further comprising means for deriving parameters representing the mode of the recovery pulse current by performing calculations by applying event data accumulated in the data storage means within a predetermined period prior to the time when it is determined that the degree of deviation of the internal resistance exceeds the threshold to a second mathematical model in the mathematical model storage means.

7. A method to be implemented using a terminal device which includes a circuit including a voltage sensor for measuring the voltage between the positive and negative electrodes of a secondary battery, a terminal device which includes a computer electrically connected to the secondary battery via the circuit and having the function of sending pulse current to be applied to the secondary battery from the circuit and the function of detecting the charging and discharging stages of the secondary battery based on changes in the measured value of the voltage sensor, and a cloud server which stores a mathematical model that represents the relationship between measurement conditions applicable to measuring the internal resistance occurring in the secondary battery and the characteristics of the change in the internal resistance value measured by those conditions, wherein in the terminal device, The process involves applying a pulsed current to the secondary battery to which a pulse period and pulse width of a predetermined length and a peak value of a predetermined height are applied; performing a calculation to determine the internal resistance of the secondary battery using the amount of change in the measured value of the voltage sensor due to the application of the pulsed current and the peak value of the pulsed current, while changing the measurement conditions that define the pulse period, pulse width, the length of the pulsed current application time and the timing for determining the amount of change in the measured value of the voltage; transmitting one or more event data, including event data that links data representing the set measurement conditions and the internal resistance value obtained by the calculation under those conditions, to the cloud server; and in the cloud server, when the event data received from the terminal device reaches a predetermined capacity, applying the event data to the mathematical model to identify multiple sets of parameter combinations that represent the measurement conditions used to diagnose the secondary battery connected to the terminal device, and for each of these combinations, identifying a function that represents the relationship between the length of time elapsed from a specific point in the past, the frequency of charging and discharging performed until that time has elapsed, and the acceptable value of the internal resistance measured under the measurement conditions; The process involves transmitting the combination of parameters for each specified measurement condition and model data defining the function to the terminal device, and the terminal device that receives the above transmission then performs the following steps:A method for diagnosing a secondary battery, characterized by sequentially setting combinations of parameters for each measurement condition received from the cloud server and executing the current application process and the calculation for each measurement condition, and for each of those measurement conditions, applying the length of time from a specific point in time to the time when the process of determining the internal resistance value using a pulse current according to that condition is performed or near that point in time, and the frequency of charging and discharging determined from the detection results of charging and discharging performed during that time, to a function based on model data corresponding to that condition, to execute a process to determine a model value of the internal resistance measured by that condition, and a process to determine the degree of deviation of the internal resistance value obtained by the calculation for that model value, and estimating the degree of degradation of the secondary battery based on the degree of deviation.

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