Battery analysis system, battery analysis method, battery analysis program, battery pack, and recording medium recording battery analysis program

Abstract

A data acquisition unit (111) acquires battery data including the voltage and current of a secondary battery included in a battery pack. An SOH estimation unit (113) estimates the SOH by using a two-point open circuit voltage (OCV) method. An SOH progress curve generation unit (115) uses a calculated plurality of SOH sample data to perform curve regression and generate an SOH progress curve for the secondary battery. A period confirmation unit (112) confirms the sampling period of the battery data. The SOH progress curve generation unit (115) increases a weighting to the extent of the SOH calculated on the basis of the battery data in a section in which the sampling period is short.

Description

Battery analysis system, battery analysis method, battery analysis program, battery pack, and recording medium having battery analysis program recorded thereon

[0001] The present disclosure relates to a battery analysis system that collects and analyzes battery data, a battery analysis method, a battery analysis program, a battery pack, and a recording medium on which a battery analysis program is recorded.

[0002] To estimate the state of a battery pack installed in a mobility device such as an electric bicycle, an electric motorcycle, or an electric kick scooter, a configuration is conceivable in which the battery pack is connected to a cloud system via a user's smartphone. In this configuration, it is conceivable that time-series battery data such as voltage and current is collected from the battery pack, and the cloud system performs SOH (State of Health) estimation.

[0003] Considering the limited capacity of the non-volatile memory in the battery management unit (BMU) of the battery pack and the frequency of communication with the smartphone (e.g., connection once a month), battery data needs to be saved at a low sampling rate (e.g., every one minute) to preserve the amount of data until the next connection (e.g., one month later). Ideally, it would be desirable to save battery data at a high sampling rate, but this increases the likelihood of the non-volatile memory running out of capacity. When the cloud system estimates the open circuit voltage (OCV) and state of health (SOH) based on the battery data, the sampling rate of the battery data significantly affects the estimation accuracy.

[0004] Patent Literature 1 discloses a method for wirelessly receiving the initial state of a battery and tracked events associated with the battery at a cloud server and updating an estimated value of battery life using the tracked events. This method focuses on the fact that wireless communication accelerates discharge degradation, but does not change the SOH estimation algorithm.

[0005] Special Publication No. 2023-508045

[0006] The present disclosure has been made in view of these circumstances, and its purpose is to provide a technology for estimating the SOH of a battery pack with high accuracy within the range of the hardware constraints of the battery pack.

[0007] In order to solve the above problem, a battery analysis system according to one aspect of the present disclosure includes: a data acquisition unit that acquires battery data including the voltage and current of a secondary battery included in a battery pack; an SOH estimation unit that estimates an SOH based on the voltage of the secondary battery in a first resting state, the voltage of the secondary battery in a second resting state, an SOC difference based on an SOC-OCV curve of the secondary battery, a current full charge capacity of the secondary battery calculated based on an integrated current value for a period between the first resting state and the second resting state, and an initial full charge capacity of the secondary battery; an SOH transition curve generation unit that generates an SOH transition curve of the secondary battery by performing curve regression using a plurality of calculated SOH sample data; and a cycle confirmation unit that confirms a sampling cycle of the battery data. The SOH transition curve generation unit assigns a higher weight to an SOH calculated based on battery data for a section with a shorter sampling cycle.

[0008] Any combination of the above components, and conversion of the expression of the present disclosure into an apparatus, system, method, computer program, etc., are also valid aspects of the present disclosure.

[0009] According to the present disclosure, the SOH of a battery pack can be estimated with high accuracy within the range of the hardware constraints of the battery pack.

[0010] 1 is a diagram for explaining a battery analysis system according to an embodiment; FIG. 2 is a diagram illustrating an example of the configuration of a battery pack according to an embodiment; FIG. 3 is a diagram illustrating an example of the configuration of a battery analysis system according to an embodiment; FIG. 4 is a diagram illustrating a specific image of an FCC estimation method; FIG. 5 is a diagram illustrating a graph of an SOH transition curve of a secondary battery; FIG. 6 is a diagram illustrating an example of the transition of a measured voltage when a secondary battery is being charged; FIG. 7 is a diagram illustrating an example of the transition of a battery data storage period when the frequency of communication connection with a mobile terminal device is low; and FIG. 8 is a diagram illustrating an example of the transition of a battery data storage period when the frequency of communication connection with a mobile terminal device is high. A flowchart illustrating the flow of a battery data storage and transmission process by a battery pack according to an embodiment; and A flowchart illustrating the flow of a SOH estimation process for a battery pack by a battery analysis system according to an embodiment.

[0011] FIG. 1 is a diagram illustrating a battery analysis system 10 according to an embodiment. The battery analysis system 10 according to the embodiment is a system for analyzing a battery pack 30 mounted on a battery-equipped device (assumed to be an electric bicycle 1 in this embodiment). The battery analysis system 10 may be constructed, for example, on an in-house server installed in the in-house facility or data center of a provider that provides analysis services for the battery pack 30. The battery analysis system 10 may also be constructed on a cloud server used based on a cloud service. The battery analysis system 10 may also be constructed on multiple servers distributed across multiple locations (data centers, in-house facilities). The multiple servers (e.g., a control server and an analysis server) may be a combination of multiple in-house servers, a combination of multiple cloud servers, or a combination of an in-house server and a cloud server.

[0012] The electric bicycle 1 is equipped with a detachable, portable, and replaceable battery pack 30. The battery pack 30 is charged while attached to a charging slot of a charger (not shown). The charged battery pack 30 is removed from the charging slot by the user and attached to the attachment slot of the electric bicycle 1.

[0013] The battery pack 30 and the mobile terminal device 20 (hereinafter, assumed to be a smartphone) carried by the user are connected via short-range wireless communication. Bluetooth (registered trademark), Wi-Fi (registered trademark), infrared communication, etc. can be used as short-range wireless communication. In the following, in this embodiment, it is assumed that BLE (Bluetooth Low Energy) is used as short-range wireless communication. BLE is an extended standard of Bluetooth and is a low-power short-range wireless communication standard using the 2.4 GHz band.

[0014] The mobile terminal device 20 can access the network 2 to which the battery analysis system 10 is connected. The mobile terminal device 20 can access the network 2 via a mobile phone network (4G / 5G) or Wi-Fi.

[0015] The network 2 is a general term for communication paths such as the Internet, a dedicated line, and a virtual private network (VPN), and the communication medium and protocol are not important. Examples of communication media that can be used include a wired LAN, a wireless LAN, a mobile phone network, an optical fiber network, an ADSL network, and a CATV network. Examples of communication protocols that can be used include TCP (Transmission Control Protocol) / IP (Internet Protocol), UDP (User Datagram Protocol) / IP, and Ethernet (registered trademark).

[0016] 2 is a diagram showing an example of the configuration of a battery pack 30 according to an embodiment. The battery pack 30 includes a battery pack 31 and a battery management device 32. The battery pack 31 includes multiple cells E1-En connected in series. The number of cells connected in series is determined by the load specifications. The main loads of the electric bicycle 1 are the motor and the inverter.

[0017] The cells can be lithium-ion battery cells, nickel-metal hydride battery cells, lead battery cells, etc. In the following description, we will assume an example in which lithium-ion battery cells (nominal voltage: 3.6-3.7 V) are used. Note that in each series stage of cells, multiple cells may be connected in parallel to increase capacity.

[0018] A switch SW1 for switching between electrical continuity with the load or charger is inserted in the power line connecting the battery pack 31 and the load or charger. A semiconductor switch or a relay can be used as the switch SW1.

[0019] The battery management device 32 includes a measurement unit 33, a control unit 34, a wireless communication unit 35, an antenna 35a, a nonvolatile memory unit 36, and an LED lamp LD. The measurement unit 33 is configured with an AFE (Analog Front End) IC or an ASIC (Application Specific Integrated Circuit). The control unit 34 is configured with a microcontroller. The nonvolatile memory unit 36 ​​can be an EEPROM (Electrically Erasable Programmable Read Only Memory) or a NAND flash memory.

[0020] The measurement unit 33 is connected to each node of the multiple cells E1-En connected in series by multiple voltage measurement lines, and measures the voltage of each cell E1-En by measuring the voltage between each two adjacent voltage measurement lines.

[0021] The measurement unit 33 includes a multiplexer and an A / D converter. The multiplexer outputs the voltages of the multiple cells E1-En to the A / D converter in a predetermined order. The A / D converter converts the analog voltages input from the multiplexer into digital values. The measurement unit 33 transmits the voltage values ​​of the cells E1-En, converted into digital values, to the control unit 34 via a serial communication interface.

[0022] The measurement unit 33 measures the current flowing through the battery pack 31. A shunt resistor Rs is connected to a power line connecting the battery pack 31 to a load or a charger. A differential amplifier (not shown) amplifies the voltage across the shunt resistor Rs and outputs it to an A / D converter in the measurement unit 33. The A / D converter converts the analog voltage indicating the current flowing through the battery pack 31, which is input from the differential amplifier, into a digital value. The measurement unit 33 transmits the current value converted into a digital value to the control unit 34 via a serial communication interface.

[0023] A temperature sensor T1 (e.g., a thermistor) is installed on the surface of the battery pack 31. A divided voltage between the temperature sensor T1 and a voltage dividing resistor (not shown) is input to a measurement unit 33. An A / D converter in the measurement unit 33 converts the input analog voltage indicating the temperature into a digital value. The measurement unit 33 transmits the converted digital temperature value to the control unit 34 via a serial communication interface.

[0024] The control unit 34 manages the states of the cells E1-En based on the voltage values ​​of the cells E1-En, the current values ​​flowing through the battery pack 31, and the temperature values ​​of the battery pack 31 received from the measurement unit 33. When the control unit 34 detects overcharge, overdischarge, overcurrent, abnormally high temperature, or abnormally low temperature, it sends a shutoff signal for the switch SW1 to the measurement unit 33 to turn off the switch SW1.

[0025] The control unit 34 estimates the SOC by combining the OCV method and the current integration method. The OCV method is a method for estimating the SOC based on the OCV, which is based on the measured cell voltage, and the SOC-OCV curve of the cell. The SOC-OCV curve of the cell is created in advance by the battery manufacturer based on characteristic tests and is registered in the control unit 34 at the time of shipment.

[0026] The current integration method is a method for estimating the SOC based on the OCV at the start of charging and discharging the cell and the integrated value of the measured current. With the current integration method, current measurement errors accumulate as the charging and discharging time increases. Therefore, it is preferable to use a weighted average of the SOC estimated by the current integration method and the SOC estimated by the OCV method.

[0027] The control unit 34 stores battery data including the cell voltage, current, temperature, and SOC of the battery pack 30 in the non-volatile memory unit 36. As the cell voltages, the voltage values ​​of all the cells E1-En may be stored, or only the maximum cell voltage and the minimum cell voltage may be stored.

[0028] The wireless communication unit 35 performs signal processing for short-range wireless communication. The wireless communication unit 35 pairs with the mobile terminal device 20 and transmits battery data stored in the non-volatile memory unit 36 ​​via short-range wireless communication. In this embodiment, the wireless communication unit 35 is configured with a BLE module, and the antenna 35a is configured with a chip antenna or a pattern antenna built into the BLE module.

[0029] The control unit 34 manages the frequency of wireless connections with the mobile terminal device 20, and changes the period for saving the measured battery data in the non-volatile memory unit 36 ​​according to the connection frequency. The control unit 34 shortens the period for saving the battery data as the connection frequency increases.

[0030] Mobile terminal device 20 is a terminal device carried by the user of electric bicycle 1. The user downloads an application program for managing electric bicycle 1 (hereinafter referred to as a bicycle app) from a distribution server to mobile terminal device 20 and installs it on mobile terminal device 20. The bicycle app is uploaded to the distribution server in advance, and the distribution server provides the bicycle app it manages to mobile terminal device 20 that accesses it via network 2.

[0031] The bicycle app works in conjunction with a map app installed on the mobile terminal device 20 and a GPS (Global Positioning System) sensor to display the current location of the electric bicycle 1 on the display unit of the mobile terminal device 20. The bicycle app can also estimate the speed, distance traveled, calories burned, etc. based on the movement trajectory of the position of the electric bicycle 1 and display this on the display unit of the mobile terminal device 20.

[0032] When the user of the electric bicycle 1 launches the bicycle app on the portable terminal device 20 while Bluetooth on the portable terminal device 20 is on, a connection is established between the short-range wireless communication unit of the portable terminal device 20 and the wireless communication unit 35 of the battery pack 30.

[0033] When a connection is established between the battery pack 30 and the mobile terminal device 20, the control unit 34 of the battery pack 30 transmits the battery data stored in the non-volatile memory unit 36 ​​to the mobile terminal device 20 via the wireless communication unit 35. The mobile terminal device 20 transfers the battery data received from the short-range wireless communication unit to the battery analysis system 10 via the network 2. The mobile terminal device 20 can access the network 2 via a mobile phone network (4G / 5G) or Wi-Fi.

[0034] After transmitting the battery data to the battery analysis system 10 via the mobile terminal device 20 , the control unit 34 of the battery pack 30 erases the transmitted battery data from the non-volatile storage unit 36 ​​.

[0035] The control unit 34 of the battery pack 30 can notify the user of an alert when the free space in the non-volatile memory unit 36 ​​falls below a set value. An LED lamp LD is connected to the control unit 34. When the free space in the non-volatile memory unit 36 ​​falls below the set value, the control unit 34 passes current through the LED lamp LD to turn it on. At this time, the control unit 34 may blink the LED lamp LD using PWM control. The LED lamp LD may be a dedicated LED lamp provided for alert notification, or may be an LED lamp that is repurposed to display the remaining charge of the battery pack 31.

[0036] When the free space in non-volatile memory unit 36 ​​falls below a set value, control unit 34 of battery pack 30 may transmit an alert signal to mobile terminal device 20 via wireless communication unit 35 to notify the user of the lack of free space in non-volatile memory unit 36. When the short-range wireless communication unit of mobile terminal device 20 receives the alert signal, mobile terminal device 20 displays on the lock screen or in a pop-up that a notification has been received by the bicycle app, prompting the user to launch the bicycle app.

[0037] A meter display for displaying the remaining charge of the battery pack 30 and the assembled battery 31, which is connected by wire to the battery pack 30, may be attached to the handle or the like of the electric bicycle 1. In this case, when the free space in the non-volatile memory unit 36 ​​falls below a set value, the control unit 34 of the battery pack 30 may send an alert signal to the meter display, causing the meter display to display a notification urging the user to start the bicycle app on the mobile terminal device 20.

[0038] 3 is a diagram showing an example of the configuration of a battery analysis system 10 according to an embodiment. The battery analysis system 10 includes a control unit 11, a storage unit 12, and a communication unit 13. The communication unit 13 is an external communication interface (e.g., a network interface card (NIC)) for connecting to the network 2 via a wired or wireless connection.

[0039] The control unit 11 includes a data acquisition unit 111, a cycle confirmation unit 112, an SOH estimation unit 113, a weight calculation unit 114, an SOH transition curve generation unit 115, and a life prediction unit 116. The functions of the control unit 11 can be realized by a combination of hardware resources and software resources, or by hardware resources alone. Examples of hardware resources that can be used include a CPU, ROM, RAM, GPU (Graphics Processing Unit), NPU (Neural Network Processing Unit), ASIC, FPGA (Field Programmable Gate Array), and other LSIs. Examples of software resources that can be used include programs such as an operating system and applications.

[0040] The storage unit 12 includes a (non-transitory) non-volatile recording medium such as a hard disk drive (HDD) or a solid state drive (SSD), and stores various data. The storage unit 12 includes a battery data holding unit 121.

[0041] The data acquisition unit 111 acquires battery data of the battery pack 30 via the network 2 using the mobile terminal device 20 as a relay. The data acquisition unit 111 stores the acquired battery data in the battery data holding unit 121.

[0042] The SOH estimation unit 113 uses a two-point OCV method to estimate the SOH of each cell, the minimum voltage cell, or the assembled battery 31. When estimating the SOH of the assembled battery 31, the SOH estimation unit 113 estimates the voltage of the assembled battery 31 by adding up the cell voltages of the plurality of series-connected cells E1-En included in the battery data.

[0043] The SOH estimation unit 113 calculates the SOC difference (ΔSOC) between the SOC in the first rest state and the SOC in the second rest state based on the voltage of the cell or battery pack 31 in the first rest state, the voltage of the cell or battery pack 31 in the second rest state, and the SOC-OCV curve of the cell or battery pack 31. The SOH estimation unit 113 calculates the current integrated value Q (= charge / discharge capacity) for the period between the first rest state and the second rest state based on the current value included in the battery data. The SOH estimation unit 113 calculates the current FCC of the cell or battery pack 31 based on the current integrated value Q and ΔSOC. The SOH estimation unit 113 calculates the SOH based on the current FCC and initial FCC of the cell or battery pack 31.

[0044] 4 is a diagram showing a specific image of the FCC estimation method. The SOH estimation unit 113 identifies two voltages, the first rest state and the second rest state, and sets these as the OCVs at two points. The SOH estimation unit 113 references the SOC-OCV curve to identify SOC1 and SOC2 corresponding to OCV1 and OCV2, respectively, and calculates ΔSOC between SOC1 and SOC2 and the current integrated value Q.

[0045] The SOH estimation unit 113 calculates the following (Equation 1) to estimate the FCC.

[0046] FCC=Q / ΔSOC (Equation 1) SOH is defined as the ratio of the current FCC to the initial FCC, and the lower the value (closer to 0%), the more advanced the deterioration. The SOH estimation unit 113 calculates the following (Equation 2) to estimate the SOH.

[0047] SOH=current FCC / initial FCC×100 (Equation 2) The SOH transition curve generator 115 performs curve regression using a plurality of sample data of SOH calculated in time series for the cell or assembled battery 31 to generate an SOH transition curve (deterioration curve) for the cell or assembled battery 31. For example, the least squares method can be used for the curve regression.

[0048] 5 is a graph showing the SOH transition curve of a secondary battery. It is known that the deterioration of a secondary battery progresses in proportion to the square root of time (0.5 power law), as shown in the following (Equation 3).

[0049] SOH = w ０ +w １ √t...(Formula 3) w ０ is the initial value, w １ is the degradation coefficient.

[0050] The SOH transition curve generating unit 115 calculates the deterioration coefficient w in the above (Equation 3) by exponential curve regression of 0.5 with time t as an independent variable and SOH as a dependent variable. １ Ask for. w ０ is common and is usually set in the range of 1.0 to 1.1. If the actual initial capacity matches the nominal value, ０ = 1.0, and the nominal value is set to the minimum guaranteed amount, and if it is set lower than the actual initial capacity, a value greater than 1.0 is set.

[0051] The life prediction unit 116 can predict the period (remaining life) until the SOH at which the battery pack 30 should be terminated is reached by substituting the SOH (e.g., 70%) at which the battery pack 30 should be terminated into the generated SOH transition curve. Note that the SOH value at any future date and time can also be predicted by substituting any future date and time into the SOH transition curve.

[0052] The SOH transition curve generating unit 115 can assign a weight to each SOH sample data in the range of 0 to 1. The cycle checking unit 112 checks the sampling cycle of the battery data for the interval in which each SOH is calculated. The sampling cycle of the battery data can be checked from the timestamp of each piece of data. The sampling cycle of the battery data corresponds to the cycle in which the battery data in the battery pack 30 is stored in the non-volatile memory unit 36. The weight calculating unit 114 calculates a weight coefficient we for weighting each SOH sample data in accordance with the sampling cycle of the battery data checked by the cycle checking unit 112. The weight coefficient we is calculated as shown in the following (Equation 4).

[0053] we=α×sampling period (Equation 4) where α is a correction coefficient. The following (Equation 5) is a normal regression, and the SOH transition curve generating unit 115 calculates the residual ε ｉ The deterioration coefficient w in the above (Equation 3) is calculated so that the sum of squares E is minimized. １ is derived.

[0054] E = Σε ｉ ２　　 (i=1 to n) (Equation 5) The following (Equation 6) is a weighted regression, and the SOH transition curve generating unit 115 calculates the residual ε ｉ The square of the weight coefficient we ｉ The deterioration coefficient w in the above (Equation 3) is calculated so that the sum E of the values ​​multiplied by １ is derived.

[0055] E = Σwe ｉ ε ｉ ２　　 (i=1 to n) (Equation 6) As shown in Equation 4 above, the weighting coefficient we increases as the sampling period of the battery data increases. Therefore, when the SOH transition curve generator 115 performs curve regression, the weight increases for SOH calculated based on battery data in a section with a higher sampling period.

[0056] The higher the sampling period of the battery data, the higher the reliability of the estimated SOH, because the reliability of the OCV used in the two-point OCV method is higher.

[0057] Secondary batteries are electrochemical products, and when a charging current flows through a secondary battery, the measured voltage rises nonlinearly, and when a discharging current flows through a secondary battery, the measured voltage drops nonlinearly. The voltage measured when a current is flowing through a secondary battery is called the closed circuit voltage (CCV) or operating voltage. After charging and discharging, a secondary battery takes time to converge to its OCV, which does not contain overvoltage components. The convergence time to the OCV depends on the cell type, temperature, SOH, etc. In recent years, lithium-ion battery cells that use anode materials containing silicon have been developed. These lithium-ion battery cells take longer to resolve polarization than conventional cells, and it takes 1 to 10 hours or more to converge to the OCV after charging and discharging.

[0058] Fig. 6 is a diagram showing an example of the transition of the measured voltage during charging of a secondary battery. Hereinafter, we consider a case where the voltage at the start of charging and the voltage at the end of charging are used as the two voltage points in the two-point OCV method. As shown in Fig. 6, the measured voltage of the secondary battery rises from the start of charging, and after charging ends, it converges to the OCV over time.

[0059] The voltage measured after a sufficiently long rest period after charging is complete will match the OCV, but the voltage measured after an insufficient rest period will deviate from the OCV. In many cases, the battery pack 30 is attached to the electric bicycle 1 immediately after charging is complete, and the electric bicycle 1 is then used for riding. In such cases, the time-series battery data will not include voltage data corresponding to the OCV after charging is complete.

[0060] In this case, an OCV estimation unit (not shown) generates a voltage convergence curve by fitting multiple voltage data after the end of charging and predicts the OCV at the convergence point. The higher the sampling period of the battery data, the more voltage data points there are, and the more accurate the fitting. This also improves the accuracy of identifying the start point of the voltage convergence curve (the end of charging).

[0061] Similarly, after discharge is complete, the voltage measured after a sufficiently long rest period has elapsed will match the OCV, but the voltage measured after an insufficient rest period will deviate from the OCV. If the battery pack 30 is removed from the electric bicycle 1 immediately after the electric bicycle 1 has finished riding and charging is started, the voltage measured at the start of charging will deviate from the OCV.

[0062] In this case, an OCV estimation unit (not shown) generates a voltage convergence curve by fitting multiple voltage data after the end of discharge, and predicts the OCV at the convergence point as the OCV at the start of charging. As described above, the higher the sampling period of the battery data, the higher the reliability of the OCV and the higher the reliability of the SOH calculated by the two-point OCV method.

[0063] However, due to cost and other considerations, the battery pack 30 often does not include a large-capacity non-volatile memory unit 36. In such cases, shortening the battery data storage period increases the risk of battery data overflow. However, if the frequency of communication connection with the mobile terminal device 20 is high, it is possible to prevent the battery data in the non-volatile memory unit 36 ​​from overflowing. Therefore, in this embodiment, the control unit 34 of the battery pack 30 adaptively switches the storage period of battery data in the non-volatile memory unit 36 ​​depending on the frequency of communication connection with the mobile terminal device 20.

[0064] Fig. 7 is a diagram showing an example of transition of the battery data storage period when the frequency of communication connection with the mobile terminal device 20 is low. Fig. 8 is a diagram showing an example of transition of the battery data storage period when the frequency of communication connection with the mobile terminal device 20 is high.

[0065] The control unit 34 of the battery pack 30 shortens the storage period when the communication connection frequency with the mobile terminal device 20 is equal to or greater than a threshold (one-second period in the examples of FIGS. 7 and 8 ), and lengthens the storage period when the communication connection frequency is less than the threshold (one-minute period in the example of FIG. 7 ). When the communication connection frequency is equal to or greater than the threshold, the battery pack 30 can connect to the mobile terminal device 20 and transmit battery data to the outside before the non-volatile memory unit 36 ​​overflows, allowing the battery data in the non-volatile memory unit 36 ​​to be periodically cleared. Therefore, even if the battery data is saved at a high sampling rate, the data does not overflow. On the other hand, when the communication connection frequency is less than the threshold, the non-volatile memory unit 36 ​​needs to store battery data for a long period of time, so the storage period needs to be lengthened.

[0066] The control unit 34 of the battery pack 30 calculates the communication connection frequency with the mobile terminal device 20, for example, as shown in the following (Equation 7).

[0067] Communication connection frequency=number of communication connections / elapsed time (Equation 7) Furthermore, the control unit 34 may calculate the communication connection frequency with the mobile terminal device 20 as shown in the following (Equation 8).

[0068] Communication connection frequency = communication connection time / elapsed time (Equation 8) In the above (Equation 7) and (Equation 8), the elapsed time may be set to, for example, the past one month, the past three months, or the past six months. In the above (Equation 8), the communication connection time is the time during which a connection is established between the short-range wireless communication unit of the mobile terminal device 20 and the wireless communication unit 35 of the battery pack 30 when Bluetooth of the mobile terminal device 20 is on. Note that when calculating the communication connection frequency, the number of communication connections or the communication connection time in the most recent month may be weighted more heavily. This allows recent user behavior to be more strongly reflected in determining the storage period.

[0069] In principle, the control unit 34 of the battery pack 30 stores battery data in the non-volatile memory unit 36 ​​at a relatively short first storage period (e.g., 1-second period), and when the free space in the non-volatile memory unit 36 ​​falls below a set value, the control unit 34 may perform a thinning process on the stored battery data so that the stored battery data is converted to battery data at a relatively long second storage period (e.g., 1-minute period).

[0070] 9 is a flowchart showing the flow of the battery data storage and transmission process performed by the battery pack 30 according to the embodiment. When the battery management device 32 of the battery pack 30 is powered on (Y in S30), the measurement unit 33 measures the cell voltage, current, and temperature of the assembled battery 31 (S31). The control unit 34 detects the frequency of wireless connection with the mobile terminal device 20 based on the connection history of the wireless communication unit 35 (S32). The control unit 34 sets a storage period for the battery data, including the cell voltage, current, and temperature of the assembled battery 31 measured by the measurement unit 33, in the non-volatile storage unit 36 ​​according to the frequency of wireless connection with the mobile terminal device 20 (S33). The control unit 34 stores the battery data in the non-volatile storage unit 36 ​​in accordance with the set storage period (S34).

[0071] When a connection is established between the wireless communication unit 35 of the battery pack 30 and the wireless communication unit of the portable terminal device 20 (Y in S35), the control unit 34 of the battery pack 30 transmits the battery data stored in the non-volatile memory unit 36 ​​to the portable terminal device 20 via the wireless communication unit 35 (S36). The control unit 34 erases the transmitted battery data from the non-volatile memory unit 36 ​​(S37). The process proceeds to step S30.

[0072] If a connection is not established between the wireless communication unit 35 of the battery pack 30 and the wireless communication unit of the mobile terminal device 20 (N in S35), the processes of steps S36 and S37 are skipped. If the power supply of the battery management device 32 is turned off (N in S30), the battery data storage and transmission process ends.

[0073] 10 is a flowchart showing the flow of the SOH estimation process for the battery pack 30 performed by the battery analysis system 10 according to the embodiment. The data acquisition unit 111 receives battery data for the battery pack 30 via the mobile terminal device 20 (S10). The SOH estimation unit 113 estimates the SOH of the cell or battery pack 31 using the two-point OCV method (S11).

[0074] The period checker 112 checks the sampling period of the battery data for the interval in which each SOH was calculated (S12). The weight calculator 114 calculates a weighting coefficient for weighting each SOH sample data according to the sampling period for each interval of the battery data (S13). The SOH transition curve generator 115 performs weighted regression using the multiple SOH sample data calculated in time series for the cell or assembled battery 31 and the weighting coefficient for each SOH to generate an SOH transition curve for the cell or assembled battery 31 (S14). The life predictor 116 substitutes the SOH at which the battery pack 30 should be terminated into the generated SOH transition curve to predict the remaining life of the battery pack 30 (S15).

[0075] A notification unit (not shown) of the battery analysis system 10 can notify the mobile terminal device 20 of the current SOH and remaining life of the battery pack 30 via the network 2. The bicycle app of the mobile terminal device 20 can display the current SOH and remaining life of the battery pack 30 received from the battery analysis system 10 on the app screen.

[0076] As described above, according to this embodiment, the SOH of the battery pack 30 can be estimated with high accuracy within the capacity constraints of the non-volatile storage unit 36 ​​of the battery pack 30. When the frequency of communication connection between the battery pack 30 and the mobile terminal device 20 is high, it is not necessary to consider the capacity constraints of the non-volatile storage unit 36, and therefore, storing battery data at a high sampling rate can lead to highly accurate OCV and SOH estimation. When the frequency of communication connection is low, storing battery data at a low sampling rate compresses the amount of stored data.

[0077] Furthermore, by predicting the SOH transition while changing the weighting coefficient (reliability) of the SOH estimated value according to the sampling rate of the battery data, the accuracy of the prediction of the SOH transition can be improved. Specifically, by increasing the contribution of the SOH estimated based on the battery data in the high sampling rate section and decreasing the contribution of the SOH estimated based on the battery data in the low sampling rate section, a highly accurate SOH transition curve can be generated.

[0078] The present disclosure has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of the respective components and processing steps, and that such modifications are also within the scope of the present disclosure.

[0079] In the above-described embodiment, an example was described in which battery data stored in the nonvolatile memory unit 36 ​​in the battery pack 30 is transmitted to the battery analysis system 10 using the portable terminal device 20 as a relay. In this regard, a charger connected to the network 2 via a wire (e.g., a LAN cable) or wirelessly (e.g., Wi-Fi) may be used as the terminal device that relays the battery data. The battery pack 30 is detached from the electric bicycle 1 and charged while attached to the charger's charging slot. In this state, the signal terminals of the battery pack 30 and the charger contact each other, enabling signals to be exchanged between the battery pack 30 and the charger. When the battery pack 30 is attached to the charger's charging slot, the control unit 34 of the battery pack 30 accesses the network 2 via the charger to retrieve the battery data stored in the nonvolatile memory unit 36 ​​and transmits the battery data to the battery analysis system 10. In this example, the wireless communication unit 35 and antenna 35a of the battery pack 30 can be omitted.

[0080] In the above-described embodiment, the electric bicycle 1 is assumed as the device to which the battery pack 30 is mounted. In this regard, the device to which the battery pack 30 is mounted may also be an electric motorcycle, an electric kick scooter, a micro EV, a multicopter (drone), an electric ship, an electric boat, a robot vacuum cleaner, or the like.

[0081] The embodiment may be specified by the following items.

[0082] [Item 1] A battery pack (30) includes a data acquisition unit (111) that acquires battery data including voltages and currents of secondary batteries (E1-En) included in the battery pack (30); a SOH estimation unit (113) that estimates an SOH based on the voltages of the secondary batteries (E1-En) in a first resting state, the voltages of the secondary batteries (E1-En) in a second resting state, an SOC difference based on an SOC-OCV curve of the secondary batteries (E1-En), a current full charge capacity of the secondary batteries (E1-En) calculated based on an integrated current value for a period between the first resting state and the second resting state, and an initial full charge capacity of the secondary batteries (E1-En); a SOH transition curve generation unit (115) that generates an SOH transition curve of the secondary batteries (E1-En) by performing curve regression using a plurality of calculated SOH sample data; and a cycle confirmation unit (112) that confirms a sampling cycle of the battery data, The SOH transition curve generating unit (115) assigns a larger weight to an SOH calculated based on battery data in a section with a shorter sampling period.

[0083] According to this, by increasing the weight of the SOH estimation value of the battery data with high reliability, the SOH of the battery pack (30) can be estimated with high accuracy.

[0084] [Item 2] The battery analysis system (10) according to Item 1, wherein the data acquisition unit (111) acquires battery data of the battery pack (30) via a network (2) from a terminal device (20) that has received the battery data from the battery pack (30), and the battery pack (30) includes: secondary batteries (E1-En); a measurement unit (33) that measures voltages and currents of the secondary batteries (E1-En); a non-volatile memory unit (36) for storing battery data including the measured voltages and currents; and a control unit (34) that manages a frequency of connection with the terminal device (20) and shortens a storage period for storing battery data including the measured voltages and currents in the non-volatile memory unit (36) as the connection frequency increases.

[0085] This allows battery data to be stored at as high a sampling rate as possible within the capacity constraints of the nonvolatile storage unit (36).

[0086] [Item 3] A battery analysis method comprising: acquiring battery data including voltages and currents of secondary batteries (E1-En) included in a battery pack (30); estimating an SOH based on the voltages of the secondary batteries (E1-En) in a first resting state included in the battery data, the voltages of the secondary batteries (E1-En) in a second resting state, an SOC difference based on an SOC-OCV curve of the secondary batteries (E1-En), a current full charge capacity of the secondary batteries (E1-En) calculated based on an integrated value of current for a period between the first resting state and the second resting state, and an initial full charge capacity of the secondary batteries (E1-En); checking a sampling period of the battery data; assigning a higher weight to an SOH calculated based on battery data in a section with a shorter sampling period; and generating an SOH transition curve of the secondary batteries (E1-En) by performing curve regression using the calculated sample data of the plurality of SOHs.

[0087] According to this, by increasing the weight of the SOH estimation value of the battery data with high reliability, the SOH of the battery pack (30) can be estimated with high accuracy.

[0088] [Item 4] A battery analysis program that causes a computer to execute the following processes: a process of acquiring battery data including voltages and currents of secondary batteries (E1-En) included in a battery pack (30); a process of estimating an SOH based on the voltages of the secondary batteries (E1-En) in a first resting state, the voltages of the secondary batteries (E1-En) in a second resting state, an SOC difference based on an SOC-OCV curve of the secondary batteries (E1-En), a current full charge capacity of the secondary batteries (E1-En) calculated based on an integrated value of current for a period between the first resting state and the second resting state, and an initial full charge capacity of the secondary batteries (E1-En); a process of checking a sampling period of the battery data; a process of assigning a higher weight to an SOH calculated based on battery data in a section with a shorter sampling period; and a process of generating an SOH transition curve of the secondary batteries (E1-En) by performing curve regression using the calculated sample data of the plurality of SOHs.

[0089] According to this, by increasing the weight of the SOH estimation value of the battery data with high reliability, the SOH of the battery pack (30) can be estimated with high accuracy.

[0090] [Item 5] A battery pack (30) comprising: secondary batteries (E1-En); a measurement unit (33) that measures the voltage and current of the secondary batteries (E1-En); a non-volatile memory unit (36) that stores battery data including the measured voltage and current; and a control unit (34) that manages a connection frequency with a terminal device (20) that can connect to a battery analysis system (10) via a network (2), and that shortens a storage period for storing battery data including the measured voltage and current in the non-volatile memory unit (36) as the connection frequency increases.

[0091] This allows battery data to be stored at as high a sampling rate as possible within the capacity constraints of the nonvolatile storage unit (36).

[0092] [Item 6] The battery pack (30) according to Item 5, further comprising a wireless communication unit (35) for connecting with the terminal device (20) via short-range wireless communication.

[0093] This allows the battery pack (30) to access the network (2) even if it does not have a communication means for directly accessing the network (2).

[0094] [Item 7] The battery pack (30) according to Item 5, wherein the control unit (34) notifies a user of an alert when the free space in the non-volatile storage unit (36) falls below a set value.

[0095] This can prompt the user to connect the terminal device (20) and the battery pack (30).

[0096] [Item 8] The battery pack (30) according to Item 5, wherein the battery pack (30) is mounted on a mobility (1), and the terminal device (20) is a mobile terminal device (20) carried by a user of the mobility (1).

[0097] This allows battery data of a battery pack (30) installed in a mobility (1) to be transmitted to a battery analysis system (10) using a mobile terminal device (20) carried by a user.

[0098] 1 Electric bicycle, 2 Network, 20 Portable terminal device, 30 Battery pack, 31 Assembled battery, 32 Battery management device, 33 Measurement unit, 34 Control unit, 35 Wireless communication unit, 35a Antenna, 36 Non-volatile memory unit, E1-En Cell, Rs Shunt resistor, T1 Temperature sensor, SW1 Switch, LD LED lamp, 10 Battery analysis system, 11 Control unit, 111 Data acquisition unit, 112 Cycle confirmation unit, 113 SOH estimation unit, 114 Weight calculation unit, 115 SOH transition curve generation unit, 116 Life prediction unit, 12 Memory unit, 121 Battery data retention unit, 13 Communication unit.

Claims

1. A battery analysis system comprising: a data acquisition unit that acquires battery data including the voltage and current of a secondary battery included in a battery pack; a SOH estimation unit that estimates a plurality of SOH (State of Health) sample data based on the voltage of the secondary battery in a first resting state, the voltage of the secondary battery in a second resting state, an SOC difference based on an SOC (State of Charge)-OCV (Open Circuit Voltage) curve of the secondary battery, a current full charge capacity of the secondary battery calculated based on an integrated current value for a period between the first resting state and the second resting state, and an initial full charge capacity of the secondary battery; a SOH transition curve generation unit that generates an SOH transition curve of the secondary battery by performing curve regression using the estimated plurality of SOH sample data; and a cycle confirmation unit that confirms the sampling cycle of the battery data, wherein the SOH transition curve generation unit assigns a higher weight to an SOH estimated based on battery data from a section with a shorter sampling cycle.

2. The battery analysis system of claim 1, wherein the data acquisition unit acquires the battery data of the battery pack via a network from a terminal device that has received the battery data from the battery pack, and the battery pack includes: a secondary battery; a measurement unit that measures the voltage and current of the secondary battery; a non-volatile memory unit that stores battery data including the measured voltage and current; and a control unit that manages the frequency of connection with the terminal device and shortens the storage period for storing battery data including the measured voltage and current in the non-volatile memory unit the higher the connection frequency.

3. A battery analysis method comprising the steps of: acquiring battery data including a voltage and a current of a secondary battery included in a battery pack; estimating sample data of SOH (State of Health) based on the voltage of the secondary battery in a first resting state included in the battery data, the voltage of the secondary battery in a second resting state, an SOC difference based on an SOC (State of Charge)-OCV (Open Circuit Voltage) curve of the secondary battery, a current full charge capacity of the secondary battery calculated based on an integrated current value for a period between the first resting state and the second resting state, and an initial full charge capacity of the secondary battery; checking a sampling period of the battery data; assigning a higher weight to an SOH estimated based on battery data in a section with a shorter sampling period; and generating an SOH transition curve of the secondary battery by performing curve regression using the estimated sample data of SOH.

4. A battery analysis program that causes a computer to execute the following processes: a process of acquiring battery data including the voltage and current of a secondary battery included in a battery pack; a process of estimating sample data of SOH (State of Health) based on the voltage of the secondary battery in a first resting state included in the battery data, the voltage of the secondary battery in a second resting state, an SOC difference based on an SOC (State of Charge)-OCV (Open Circuit Voltage) curve of the secondary battery, a current full charge capacity of the secondary battery calculated based on an integrated current value for the period between the first resting state and the second resting state, and an initial full charge capacity of the secondary battery; a process of checking the sampling period of the battery data; a process of assigning a higher weight to an SOH estimated based on battery data in a section with a shorter sampling period; and a process of generating an SOH transition curve of the secondary battery by performing curve regression using the estimated sample data of the plurality of SOH.

5. A battery pack comprising: a secondary battery; a measuring unit that measures the voltage and current of the secondary battery; a non-volatile memory unit that stores battery data including the measured voltage and current; and a control unit that manages the frequency of connection with a terminal device that can be connected to a battery analysis system via a network, and that shortens the storage period for storing battery data including the measured voltage and current in the non-volatile memory unit as the connection frequency increases.

6. The battery pack according to claim 5, further comprising a wireless communication unit for connecting with the terminal device via short-range wireless communication.

7. The battery pack according to claim 5, wherein the control unit issues an alert to a user when the free space in the nonvolatile memory unit falls below a set value.

8. The battery pack according to claim 5, wherein the battery pack is mounted on a mobility vehicle, and the terminal device is a portable terminal device carried by a user of the mobility vehicle.

9. A non-transitory recording medium on which the program to be executed by the computer according to claim 4 is recorded.

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