Lithium ion battery deterioration estimation method and lithium ion battery deterioration notification method
Patent Information
- Application Number
- JP2023113121
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2023-07-10
- Publication Date
- 2025-12-04
AI Technical Summary
Existing methods for diagnosing lithium ion battery deterioration in EVs face significant errors due to varying correlations between resistance and capacity deterioration, which are influenced by individual battery differences and deterioration modes.
A method that estimates resistance and capacity deterioration by analyzing voltage, current, and temperature time series data, correcting resistance data to a standard temperature, and matching it with standard charging resistance data to calculate deterioration rates without relying on the correlation between resistance and capacity.
Enables accurate estimation and notification of battery deterioration, allowing for timely replacement decisions based on resistance and capacity deterioration without the errors associated with previous methods.
Smart Images
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Abstract
Description
[Technical field]
[0001] The present invention relates to a lithium ion battery deterioration estimation method and a lithium ion battery deterioration notification method. [Background technology]
[0002] In EVs (Electric Vehicles), battery capacity generally decreases over time, shortening the driving range and making it necessary to replace the battery. Batteries used in EVs can be broadly divided into NMC ternary lithium-ion batteries, LFP lithium-ion batteries, and LTO lithium-ion batteries. It has been difficult to diagnose battery deterioration with LFP lithium-ion batteries. The reason for this is that while there is a correlation between the charge rate and the electromotive force of a battery, for lithium-ion batteries this correlation only appears when the battery is fully charged or when the battery is nearly empty. For this reason, one method of diagnosing battery deterioration is to utilize the correlation between battery resistance deterioration and capacity deterioration.
[0003] Such a method is described, for example, in Patent Document 1. Specifically, paragraph 0003 of this document states that "the correlation between the internal resistance degradation and the capacity degradation of a battery is obtained in advance, and this correlation is prepared as a table. Then, the capacity degradation of the battery is calculated using the internal resistance degradation obtained from charge / discharge data during driving and the correlation table." [Prior art documents] [Patent documents]
[0004] [Patent Document 1] JP 2002-243813 A Summary of the Invention [Problem to be solved by the invention]
[0005] However, the correlation between the resistance degradation and the capacity degradation of a battery varies depending on the individual differences of the battery and the degradation mode of the battery (whether cycle degradation or storage degradation is dominant), which results in a large error.
[0006] Therefore, an object of the present invention is to provide a lithium ion battery degradation estimation method and a lithium ion battery degradation notification method that estimate resistance degradation and capacity degradation without using the correlation between battery resistance degradation and capacity degradation. [Means for solving the problem]
[0007] In order to solve the above problems, the lithium-ion battery degradation estimating method of the present invention includes, for example, an acquisition step of acquiring voltage time series data, current time series data, and temperature time series data of a battery during charging; a calculation step of calculating resistance time series data based on the voltage time series data and the current time series data; a temperature correction step of correcting the resistance time series data to resistance time series data converted into a predetermined temperature based on the temperature time series data; a change calculation step of calculating time series data of change from an initial value of a charge amount based on the current time series data; a time series change generating step of generating, as charge amount vs. resistance data, a time series change on a two-dimensional plane whose vertical axis is resistance and whose horizontal axis is a charge amount, based on the change time series data and the corrected resistance time series data; and a matching step of expanding or reducing the charge amount vs. resistance data in the vertical or horizontal axis direction or shifting it in the horizontal axis direction so as to match it with standard charging resistance data, estimating a resistance degradation rate based on a magnification in the vertical axis direction, and estimating a capacity degradation rate based on a magnification in the horizontal axis direction.
[0008] Furthermore, the lithium ion battery degradation notifying method of the present invention creates a message based on the deterioration of the capacity of the battery estimated by the lithium ion battery degradation estimating method, and notifies the user of the message. Effect of the Invention
[0009] According to the present invention, it is possible to provide a lithium ion battery degradation estimation method and a lithium ion battery degradation notification method that estimate resistance degradation and capacity degradation without using the correlation between battery resistance degradation and capacity degradation.
[0010] Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]
[0011] [Figure 1] FIG. 1 is a diagram illustrating an example of a schematic configuration of a system. [Diagram 2] FIG. 2 is a diagram illustrating an example of the configuration of a battery pack. [Diagram 3] FIG. 13 is a diagram showing an example of charge amount versus resistance data based on thinned data. [Figure 4] FIG. 1 is a diagram for explaining matching in the first embodiment. [Diagram 5] FIG. 11 is a diagram showing an example of charge amount versus voltage data. [Figure 6] FIG. 2 is a diagram showing an example of SOC vs. OCV data. [Figure 7] FIG. 11 is a diagram for explaining matching in the second embodiment. [Figure 8] FIG. 8 shows an example of the negative electrode potential function. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the drawings, the same components are designated by the same reference numerals, and detailed description of overlapping parts will be omitted. EXAMPLES
[0013] Fig. 1 is a diagram showing an example of the schematic configuration of the system. As shown in Fig. 1, each EV 1 sends battery data (time series data of the voltage of each battery in the battery pack, time series data of the current flowing through the battery pack, and time series data of the temperatures of several batteries) to a server 10 via a communication device 2.
[0014] Server 10 includes a communication unit 11, a resistance calculation unit 12, a storage unit 13, and a matching unit 14. Server 10 then calculates the resistance deterioration rate and capacity deterioration rate of the battery pack based on the received battery data. If necessary, server 10 may also calculate the battery energy capacity (Wh) from the capacity deterioration rate and resistance deterioration rate. Based on the calculation result, a message is sent to the owner of EV1 notifying the need for battery replacement.
[0015] Next, an example of the configuration of a battery pack in the EV 1 will be described with reference to Fig. 2. L batteries 21 connected in parallel form a battery cell 23, and the battery pack has N battery cells 23 connected in series. The battery pack also has a thermistor (temperature sensor) 22, an ammeter 24, and a voltmeter (not shown). The battery pack also has a BMU (Battery Management Unit) 25 that manages the battery based on the voltage, current, and temperature acquired by the thermistor 22, ammeter 24, and voltmeter. The BMU 25 transmits the battery voltage, current, and temperature to the communication device 2.
[0016] Next, the principle of the lithium-ion battery deterioration estimation method implemented by the server 10 will be described. The communication unit 11 receives battery data, i.e., a time series of the voltage of each battery in the battery pack, a time series of the current flowing through the battery pack, and a time series of the temperatures of several batteries. The battery data may be data extracted from when the EV1 is charged by a charger, or data extracted from when the EV1 is running. The battery data for one charge is composed of the voltage time series data Vj(t), current time series data I(t), and temperature time series data Temp(t) of battery j. Here, t represents time. When there are multiple temperature sensors 22, an ID is assigned to each temperature sensor, and the temperature time series data measured by the temperature sensor 22 with ID k is represented as Tempk(t). When there are multiple temperature sensors 22, the average of the temperatures measured by each temperature sensor 22 may be taken as Temp(t).
[0017] When the battery 21 is left for a long time after charging and discharging is stopped, the battery 21 will have a voltage according to the state of charge (SOC). This voltage is expressed as OCV(SOC) as a function of the state of charge SOC. The OCV(SOC) is stored in advance in the storage unit 13 as SOC vs. OCV data.
[0018] The resistance time series data Rj(t) of the battery j is expressed by the formula (1). Therefore, by using the formula (1), the resistance time series data Rj(t) can be calculated based on the voltage time series data Vj(t) and the current time series data I(t) of the battery j.
[0019]
number
[0020] Here, SOCj(t) in equation (1) is the charging rate of battery j at time t, and is expressed by equation (2) using SOCi(j), which represents the initial SOC of battery j, and the Ah capacity Qmax(j) of battery j as variables.
[0021]
number
[0022] Therefore, when equation (2) is used in equation (1), the resistance time series data Rj(t) in equation (1) becomes a function including two variables, SOCi(j) and Qmax(j). Note that ∫I(t)dt corresponds to the change in the charge level of battery j from its initial value.
[0023] Here, the resistance R(t;Temp) of one battery at temperature Temp is expressed by equation (3) using the resistance R25(t) at 25° C. according to Arrhenius' law.
[0024]
number
[0025] In equation (3), B is a constant and is called the resistance temperature sensitivity. The resistance temperature sensitivity B is measured and set in advance and stored in the memory unit 13. The value of the resistance temperature sensitivity B is, for example, 2500 [K]. For the resistance time series data Rj(t) of equation (1), when the temperature time series data Temp(t) and equation (3) are used, the 25°C converted resistance R25_j(t) is expressed by equation (4).
[0026]
number
[0027] The resistance calculation unit 12 corrects the resistance time series data Rj(t) to resistance time series data R25_j(t) converted at 25° C. based on the temperature time series data Temp(t) and equation (4). Here, the values of Qmax(j) and SOCi(j) are not fixed as variables.
[0028] Furthermore, the resistance calculation unit 12 calculates time series data of the amount of change from the initial value of the charge amount based on the current time series data I(t). This calculation is performed by integrating the current time series data I(t). Then, based on the time series data of the amount of change in the charge amount and the resistance time series data R25_j(t) converted at 25°C, the time series change on a two-dimensional plane with the vertical axis being resistance and the horizontal axis being the amount of charge is generated as charge amount vs. resistance data (see FIG. 3 described later).
[0029] Next, the matching unit 108 in Fig. 1 will be described. Here, the standard charging resistance data of a new battery at 25°C is represented as Rt_25(SOC). The 25°C standard charging resistance data Rt_25(SOC) is measured in advance and stored in the storage unit 13 in advance.
[0030] As battery j deteriorates, its resistance converted at 25°C becomes a fixed multiplier to the 25°C standard charging resistance data Rt_25(SOCj(t)) of a new battery, regardless of the SOC. This multiplier is the resistance deterioration rate SOHR. As the battery deteriorates, the Ah capacity Qmax(j) also deteriorates. This multiplier of the Ah capacity Qmax(j) is the capacity deterioration rate SOHQ. The relationship is SOHQ = 100 x Qmax / catalog Qmax. The matching index L(SOCi(j), Qmax(j), SOHR(j)) is shown in equation (5).
[0031]
number
[0032] n can be 1 or 2. L can also be the maximum value of | R25_j(t)-Rt_25(soc(t))×SOHR(j) / 100 |.
[0033] The matching unit 14 finds Qmax(j), SOHR(j), and SOCi(j) that minimize L. To achieve this, a minimum search algorithm such as the quasi-Newton method is used. Then, the matching unit 14 calculates SOHQ(j) from the found Qmax(j), and outputs SOHQ(j), SOHR(j), and SOCi(j).
[0034] Note that the 25°C-converted resistance time series data R25_j(t) may use thinned data of N points instead of using all the data. An example of charge amount versus resistance data based on thinned data is shown in FIG. 3. In FIG. 3, the vertical axis represents resistance and the horizontal axis represents charge amount Q. As shown in FIG. 3, the resistance calculation unit 12 may generate charge amount versus resistance data using multiple points, for example, five points, of the 25°C-converted resistance time series data R25_j(t).
[0035] FIG. 4 is a diagram for explaining matching in the first embodiment. As shown in FIG. 4, the matching unit 14 adjusts the charge amount versus resistance data so that the charge amount versus resistance data matches the 25° C. standard charging resistance data Rt_25(SOCj(t)). This is called matching. Specifically, the matching unit 14 shifts the charge amount versus resistance data in the horizontal axis direction and expands or reduces the charge amount versus resistance data in the vertical axis direction or horizontal axis direction to perform matching. Shifting the charge amount versus resistance data in the horizontal axis direction corresponds to adjusting the initial SOC, SOCi(j). In addition, the magnification ratio when the charge amount versus resistance data is expanded or reduced in the vertical axis direction corresponds to the resistance deterioration rate SOHR. The magnification ratio when the charge amount versus resistance data is expanded or reduced in the horizontal axis direction corresponds to the capacity deterioration rate SOHQ. Therefore, by matching the charge amount versus resistance data to the 25° C. standard charging resistance Rt_25(SOCj(t)), the initial SOC, SOCi(j), the resistance deterioration rate SOHR, and the capacity deterioration rate SOHQ can be estimated.
[0036] Note that the initial SOC SOCi(j), the resistance deterioration rate SOHR, and the capacity deterioration rate SOHQ are for each cell, and when judging deterioration as a battery pack unit, the Ah capacity of the battery pack relative to a new product, the state of imbalance (SOC of each cell when the battery pack is fully charged), and the Wh capacity of the battery pack are required. This method may be the method described in International Publication No. 22 / 540225.
[0037] The communication unit 11 transmits the estimated resistance deterioration rate SOHR and capacitance deterioration rate SOHQ to the communication device.
[0038] The communication device 2 issues a message saying "Battery replacement required" when it is determined that the Wh capacity of the battery has become, for example, 80% or 70% compared to when it was new, based on the capacity deterioration rate SOHQ acquired from the communication unit 11. The parameter used to determine battery deterioration may be the Ah capacity ratio of the battery pack instead of the Wh capacity. Furthermore, the communication device 2 may plot the date on the horizontal axis and the Ah capacity or Wh capacity of the battery pack on the vertical axis to grasp the trend of the Ah capacity or Wh capacity of the battery pack, predict when the Ah capacity or Wh capacity will reach 70% or 80% compared to when it was new, and output "Battery life predicted in XX year XX month." In this way, the lithium ion battery deterioration notification method creates a message based on the deterioration of the battery capacity estimated by the lithium ion battery deterioration estimation method, and notifies the user.
[0039] According to the present invention, it is possible to provide a method for estimating resistance degradation and capacity degradation of a battery without using the correlation between resistance degradation and capacity degradation. EXAMPLES
[0040] Since a battery has a positive electrode and a negative electrode, and the deterioration characteristics of the positive electrode and the negative electrode are different, the shape of the resistance table may be deviated. For this reason, in this embodiment, a method of capturing the deterioration characteristics of the positive and negative electrodes will be described.
[0041] FIG. 5 is a diagram showing an example of charge amount versus voltage data. FIG. 5 is an example of a case where a battery with a positive electrode LFP and a negative electrode graphite is charged at a constant current. The data shown in FIG. 5 is generated by the resistance calculation unit 12 as charge amount versus voltage data, which is a time series change on a two-dimensional plane with the vertical axis being the cell voltage and the horizontal axis being the charge amount Q [Ah], based on the voltage time series data Vj(t) and the time series data of the change from the initial value of the charge amount. After the start of charging, the cell voltage rises for a while due to the polarization of the battery. Then, the voltage becomes almost constant. However, an inflection point IPv appears due to a change in the potential of the negative electrode. After that, the voltage becomes constant again, and then, when the battery is close to full charge, the voltage rises (part C of FIG. 5). Part C is caused by the increase in the charging resistance of the positive electrode, which causes the potential of the positive electrode to rise. For this reason, the matching unit 14 performs matching separately for the positive and negative electrodes when there is an inflection point in the charge amount versus voltage data and there is data showing a sharp rise in the cell voltage at the end of charging, as shown in FIG. 5. In this case, the case where charging is not performed until the battery is fully charged, but ends near the full charge state, will be described.
[0042] FIG. 6 is a diagram showing an example of SOC vs. OCV data. In FIG. 6, the horizontal axis is SOC, the vertical axis is OCV, and the inflection point is represented as IPocv. The value of SOC at the inflection point IPocv is almost fixed. In this embodiment, the SOC at the inflection point IPocv is 54%. The inflection point IPocv corresponds to the inflection point IPv of the charge amount vs. voltage data of FIG. 5. Therefore, if the SOC at the inflection point IPv of FIG. 5 is SOCf, then SOCf is 54%. The charge amount at the inflection point IPv of FIG. 5 is represented as Q1. The value of SOCf is stored in advance in the storage unit 13 based on the SOC vs. OCV data.
[0043] Next, the charge amount Q2 (=Qf-Q1) from the charge amount Q1 to the charge amount Qf at full charge is calculated. Specifically, the charge amount Qf at the full charge determination voltage (e.g., 3.75 V) is estimated by extrapolating the cell voltage, and then the charge amount Q2 is calculated.
[0044] The extrapolation method involves first calculating resistance time series data Rj(t) from voltage time series data Vj(t) and current time series data I(t) at the end of charging, and then converting this time series into 25°C converted resistance time series data R25_j(t). Since the OCV of the positive electrode is nearly constant at, for example, 3.4185 V at the end of charging, the 25°C converted resistance time series data R25_j(t) is expressed by equation (6) where 3.4185 V is substituted for the OCV in equation (4).
[0045]
number
[0046] Using the calculated 25°C converted resistance time series data R25_j(t), charge amount vs. resistance data is created in the same manner as in Example 1. Then, the standard positive electrode charging resistance data (vertical axis resistance, horizontal axis SOC) is matched to the charge amount vs. resistance data. FIG. 7 is a diagram for explaining matching in Example 2. Matching is performed by expanding or reducing the standard positive electrode charging resistance data in the vertical or horizontal axis direction and shifting it in the vertical axis direction. The amount of shift in the vertical axis direction is called resistance bias. The charge amount at the maximum resistance value of the standard positive electrode charging data when matching is set as the charge amount Qf of full charge. Then, the Ah capacity Qmax of the battery is calculated as 100×Q2÷(100-SOCf). Here, the charge amount Q2 is Qf-Q1 as described above. Note that, as in Example 1, the 25°C converted resistance time series data R25_j(t) may use only a thinned-out multiple points instead of using all points.
[0047] Note that the resistance R25_j(t) in equation (6) includes the resistance of the negative electrode, but since the resistance of the negative electrode is constant near full charge, the resistance of the negative electrode is interpreted as equivalent to the resistance bias. Also, the data of resistance R25_j(t) to be extracted is the data where the OCV exceeds 3.4185V in this example.
[0048] Let the vertical axis scaling rate be η and the resistance bias be rb. η is the resistance magnification of the positive electrode, and rb indicates the resistance of the negative electrode. If the resistance rn at high SOC when the negative electrode is new is known, SOHR can be set to 100 x rb / rn. This is because if SOHR is used as an indicator of mileage, the resistance of the discharge side will be used, and the resistance of the positive electrode on the discharge side will be almost constant regardless of SOC, so the resistance magnification of the negative electrode becomes important.
[0049] However, this method may have a large error. Therefore, it is possible to use nearby data in the second stage, assume that this data is a feature of only the negative electrode, and perform matching using standard charging resistance data of only the negative electrode. This method is the same as the matching in Example 1, but SOCf is fixed instead of SOCi, and a bias in the vertical direction (which becomes the positive electrode resistance) is added instead. The vertical expansion and contraction rate is then SOHR.
[0050] In addition, the resistance magnification of the positive and negative electrodes may be different. In this case, the magnification of the resistance table at a certain SOC (SOC0, for example, SOC50%) may be used. Specifically, the current resistance may be calculated as standard negative electrode charging resistance (SOC0) x negative electrode magnification + positive electrode charging resistance (SOC0) x positive electrode magnification, and this value may be divided by the resistance at the initial SOC0 to obtain SOHR.
[0051] In addition, since the standard charging resistance table is for positive and negative electrodes, the current charging resistance converted to 25°C can be found from the positive electrode magnification and the negative electrode magnification. Alternatively, if a negative electrode discharge resistance table is prepared, the current discharge resistance for the positive and negative electrodes can be combined to calculate the discharge Wh capacity.
[0052] According to this embodiment, the resistance deterioration can be observed separately for the positive electrode and the negative electrode. EXAMPLES
[0053] The positions of the positive and negative electrodes may become misaligned due to deterioration. This may cause the SOCf value to shift or the shape of the OCV to change. This section describes how to deal with this.
[0054] In the example of the OCV curve shape shown in Figure 6, when the SOC is low, the SOC can be estimated from the OCV. Also, full charge is easily determined because it is determined when a cell reaches the full charge determination voltage (e.g. 3.75V). For this reason, SOCf and OCV are updated based on the data from the day when the cell was fully charged from a low SOC. At this time, the shape of the OCV changes, but in many cases, the SOC is designed with the OCV value at 0% SOC as fixed. For this reason, based on the value of OCV(0), the OCV function is given by equation (7).
[0055]
number
[0056] Vp is the positive electrode potential, which in this case can be fixed at 3.4185 V. Vn(x) is the resistance function of graphite. x is [Ah / g], and is a potential function that has been measured in advance. An example of the negative electrode potential function Vn is shown in Figure 8. Since OCV(0) is fixed, OCV(0) = Vp - Vn(bn). The value of bn can be calculated from this equation, and it is an invariant value. Therefore, the parameter that changes due to deterioration is an. If x at the position of the inflection point IPnv is x2 (fixed value), the inflection point IPnv corresponds to the inflection point IPocv in Figure 6, and the SOC at the inflection point IPnv is SOCf, so equation (8) is established.
[0057]
number
[0058] Next, we will explain how to calculate SOCf. Let Vs be the voltage just before charging starts. In this case, Vs = Vp - Vn (bn - SOCi x an) (SOCi is the initial SOC). This results in equation (9).
[0059]
number
[0060] Vn -1 (x) is the inverse function of the function using the data at high Ah / g in Figure 8. This is because at low Ah / g, the value is almost constant and the inverse function cannot be determined. Next, the condition for Qmax is given by equation (10).
[0061]
number
[0062] Qt is the charge amount Ah from the initial SOC to full charge. If the battery is not fully charged, Qt to reach full charge is calculated using the method described in the second embodiment. Q2 is the charge Ah from the inflection point to full charge, as described in the second embodiment. The relationship between SOCf and SOCi can be determined from equation (10), which is (100-SOCi) / Qt=(100-SOCf) / Q2. From this, SOCi=100-(100-SOCf)Qt / Q2. Substituting this equation into equation (9), we obtain {bn- Vn -1 (Vp-Vs)}=SOCi×an ={100-(100-SOCf)Qt / Q2}×an. Equation (8) is transformed into equation (11) by substituting (bn-x2) / an=SOCf. An can be found by solving equation (11).
[0063]
number
[0064] Specifically, from equation (11), an = [{bn - Vn -1 (Vp-Vs)}-(bn-x2)Qt / Q2]÷(100-100Qt / Q2). Then, by using an in equation (8), SOCf can be calculated. This SOCf is updated. And since the OCV function is determined by an, the OCV function (SOC vs. OCV data) is updated. EXAMPLES
[0065] In the first embodiment, a standard charging resistance table and OCV at 25°C are required. This setting method will be described. As described in the third embodiment, when the battery is fully charged from a low SOC, the OCV can be set. For this reason, when the battery is new, the EV1 battery is once drained and then charged until it is fully charged. In this case, not only the SOCf but also the cell voltage can be obtained. Qmax can also be determined. Therefore, the resistance at 25°C can be obtained from equation (4). Here, data from the start of charging until the polarization settles (for example, 500 seconds) can be ignored. Also, if the current changes during the process, data for a while after the current changes (for example, 500 seconds) can be ignored.
[0066] The present invention is not limited to the above-mentioned embodiment, and various modified examples are included. For example, the above-mentioned embodiment has been described in detail to easily explain the present invention, and is not necessarily limited to those having all the described configurations. Here, LFP and graphite are used as an example, but other materials may be used. Although an EV is assumed, a railway, construction machine, forklift, or agricultural machine driven by an EV may also be used. In addition, it is possible to replace a part of the configuration of a certain embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of a certain embodiment. In addition, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration. [Explanation of symbols]
[0067] 1...EV 2. Communication devices 10. Server 11…Communications Department 12...Resistance calculation section 13...Storage section 14…Matching section 21...Battery 22...Temperature sensor 23…Battery cell 24…Ammeter 25…BMU
Claims
1. an acquisition step of acquiring voltage time series data, current time series data, and temperature time series data of a battery being charged; a calculation step of calculating resistance time series data based on the voltage time series data and the current time series data; a temperature correction step of correcting the resistance time series data into resistance time series data converted into a predetermined temperature based on the temperature time series data; a change amount calculation step of calculating time series data of a change amount from an initial value of a charge amount based on the current time series data; a time series change generating step of generating charge amount vs. resistance data on a two-dimensional plane in which the vertical axis represents resistance and the horizontal axis represents charge amount based on the change amount time series data and the corrected resistance time series data; a matching step of enlarging or reducing the charge amount vs. resistance data in a vertical or horizontal direction or shifting the charge amount vs. resistance data in a horizontal direction so as to match the standard charging resistance data, estimating a resistance deterioration rate based on a magnification in the vertical direction, and estimating a capacity deterioration rate based on a magnification in the horizontal direction; A lithium ion battery deterioration estimation method comprising:
2. an acquisition step of acquiring voltage time series data, current time series data, and temperature time series data of a battery being charged; a resistance calculation step of calculating resistance time series data based on the voltage time series data and the current time series data; a temperature correction step of correcting the resistance time series data into resistance time series data converted into a predetermined temperature based on the temperature time series data; a change amount calculation step of calculating time series data of a change amount of a charge amount based on the current time series data; a first data generating step of generating charge amount vs. resistance data representing time series changes on a two-dimensional plane in which the vertical axis represents resistance and the horizontal axis represents charge amount, based on the charge amount change time series data and the corrected resistance time series data; a second data generating step of generating charge amount vs. voltage data representing time series changes on a two-dimensional plane whose vertical axis represents voltage and whose horizontal axis represents charge amount, based on the voltage time series data and the charge amount change time series data; a charge amount estimation step of adjusting the standard positive electrode charging resistance data by enlarging, reducing, or shifting it in the vertical or horizontal direction so as to match the charge amount vs. resistance data, and estimating the charge amount at the maximum resistance value of the adjusted standard positive electrode charging resistance data as the charge amount of full charge; and a capacity estimating step of estimating a capacity of the battery using a charge amount at an inflection point in the charge amount vs. voltage data, the charge amount at the full charge, and an SOC at an inflection point in previously prepared SOC vs. OCV data. A method for estimating deterioration of a lithium ion battery, comprising:
3. The lithium ion battery deterioration estimation method according to claim 2, a positive electrode resistance deterioration estimation step of estimating a resistance deterioration rate of the positive electrode based on a magnification of the adjusted standard positive electrode charging resistance data to the charge amount vs. resistance data in the vertical axis direction; a negative electrode resistance degradation estimation step of adjusting standard negative electrode charging resistance data by enlarging, reducing, or shifting it in the vertical or horizontal direction so as to match the charge amount vs. resistance data, and estimating a resistance degradation rate of the negative electrode based on a magnification of the adjusted standard negative electrode charging resistance data to the charge amount vs. resistance data in the vertical direction; A lithium-ion battery deterioration estimation method comprising:
4. The lithium ion battery deterioration estimation method according to claim 2, and an updating step of: when the battery is charged from an empty state to a fully charged state, calculating a magnification parameter of the negative electrode potential function based on the voltage immediately before the start of charging, a previously prepared negative electrode potential function, the charge amount at the inflection point, and the charge amount at the fully charged state; calculating an SOC at the inflection point based on the magnification parameter; and updating the SOC at the inflection point and the SOC vs. OCV data. A method for estimating deterioration of a lithium ion battery, comprising:
5. 5. A lithium ion battery degradation notification method, comprising: creating a message based on the degradation of the capacity of the battery estimated by the lithium ion battery degradation estimation method according to claim 1, and notifying a user of the message.