Notice of Pre-AIA or AIA Status
1.The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
2. This office action is in response to the filing with the office dated 12/23/2024.
Information Disclosure Statement
3. The information disclosure statements (IDS) submitted on 12/23/2024, 05/13/2025, and 11/10/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections – 35 U.S.C. 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
4. Claims 1-14 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Munakata et al (US 2022/0404425 A1).
Regarding independent claim 1, Munakata et al (US 2022/0404425 A1) teaches, A server (element 30, figure 1) for diagnosing a defect in a battery (a system for outputting the deterioration state of a secondary battery [0007]) , the server comprising: a server
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communication unit configured to receive battery data including at least one of a battery voltage, which is a voltage at both ends of the battery, a battery current, which is a current flowing through the battery, and a battery temperature, which is a temperature of the battery, from a battery management system (BMS) ([0034] the deterioration state recognition unit should acquire information indicative of voltage, current, and temperature of the secondary battery to recognize the deterioration state of the secondary battery based on the information. [0035] In the state output system of the present invention, the information indicative of the voltage, current, and temperature of the secondary battery is acquired by the deterioration state recognition unit, and the deterioration state of the secondary battery is recognized based on the information. [0036] Thus, since the deterioration state is recognized based on information easy to acquire, such as the voltage, current, and temperature of the secondary battery, the recognition and determination of the deterioration state of the secondary battery can be performed more efficiently); a server storage unit configured to store a plurality of internal resistance values of the battery calculated based on the battery data at each diagnosis time point for diagnosing the defect in the battery ([0105] The server storage unit 330 is comprised, for example, of a ROM, a RAM and a storage device such as an HDD. The server storage unit 330 stores the use state information DB 331 including use state
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information as information indicating how the secondary batters 170 was used by the most recent time point, or in addition thereto, further stores a geographical characteristic information DB 333 including geographical characteristic information as information indicative of geographical characteristics that affect the deterioration state of the secondary battery 170, an initial state information DB 335 including information indicative of an internal resistance or charging capacity value in the initial state of the secondary battery 170, a predictive model DB 337 in which a predictive model used by the event estimation unit 313 to estimate an event, and an event DB 339 in which information related to each event that can occur in the secondary battery is stored.); and a server control unit configured to extract a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on a diagnosis time point, calculate a moving average, that is an average of the plurality of internal resistance values corresponding to the plurality of diagnosis time points, respectively, (paragraphs 0116]-[0120], figures 4 and 5), compare an internal resistance value with an upper band threshold, which is larger than the moving average by a first predetermined value, and a lower band threshold, which is smaller than the moving average by a second predetermined value, and diagnose the defect in the battery ([0116] FIG. 4 illustrates an example when the tendency of the deterioration state of the secondary battery 170 is acquired based on a moving average value of internal resistance increase degrees. In FIG. 4, N deterioration states OVal as deterioration states during the predetermined period are extracted from past M deterioration states RVal recorded in the deterioration information DB 133, and a moving average value of the extracted N deterioration states OVal becomes a latest moving average value LAve of the N deterioration states. [0117] Further, a line TLin of connecting moving average values at respective past time points calculated in the same way represents a tendency of the deterioration state of the secondary battery 170. Note that a value indicating the deterioration state at each time point is a value of the internal resistance at each time point when a value IVal of the internal resistance in the initial state of the secondary battery 170 is set to 100% (that is to say, 130% if the value of the internal resistance has increased by 30%). [0118] FIG. 5 illustrates an example when the tendency of the deterioration state of the secondary battery 170 is acquired based on a moving average value of capacity remaining degrees. In FIG. 5, N deterioration states OVal2 as deterioration states during the predetermined period are extracted from past M deterioration states RVal2 recorded in the deterioration information DB 133, and a moving average value of the extracted N deterioration states OVal2 becomes a latest moving average value LAve2 of the N deterioration states. [0119] Further, a line TLin2 of connecting moving average values at respective past time points calculated in the same way represents a tendency of the deterioration state of the secondary battery 170. Note that a value indicating the deterioration state at each time point is a value of the remaining capacity at each time point when a value IVal2 of the charging capacity in tire initial state of the secondary battery 170 is set to 100% (that is to say, 70% if the value of the charging capacity has decreased by 30%). [0120] Next, the deviation determination unit 115 determines whether or not a most recent deterioration state is deviating from the tendency of the deterioration state of the secondary battery 170 (FIG. 3A/S50).[0121] For example, the deviation determination unit 115 determines that the most recent deterioration suite is deviating from the tendency when a difference Dev between a most recent moving average value LAve of deterioration states of the secondary battery 170 and the most recent deterioration evaluation value LVal is a predetermined threshold value or more. For example, in FIG. 4, a dashed line ULin above the latest moving average value LAve of deterioration states of the secondary battery 170 represents an upper threshold value of the difference Dev and a dashed line LLin below thereof represents a lower threshold value, respectively).
Regarding dependent claim 2, Munakata et al (US 2022/0404425 A1) teaches the server of claim 1.
Munakata et al (US 2022/0404425 A1) further teaches, wherein the server control unit is configured to: calculate an error value by multiplying a standard deviation average value, which is an average of a plurality of standard deviations corresponding to the plurality of diagnosis time points, respectively, by a predetermined multiple, calculate the upper band threshold by adding the error value to the moving average, and calculate the lower band threshold by subtracting the error value from the moving average ([0120] Next, the deviation determination unit 115 determines whether or not a most recent deterioration state is deviating from the tendency of the deterioration state of the secondary battery 170 (FIG. 3A/S50).[0121] For example, the deviation determination unit 115 determines that the most recent deterioration
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suite is deviating from the tendency when a difference Dev between a most recent moving average value LAve of deterioration states of the secondary battery 170 and the most recent deterioration evaluation value LVal is a predetermined threshold value or more. For example, in FIG. 4, a dashed line ULin above the latest moving average value LAve of deterioration states of the secondary battery 170 represents an upper threshold value of the difference Dev and a dashed line LLin below thereof represents a lower threshold value,
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respectively. [0122] In other words, the distance from the line TLin of connecting the moving average values to the dashed line ULin above and the distance therefrom to the dashed line LLin below become allowable deviations ADev in up and down directions, respectively. [0123] In FIG. 4, since the most recent deterioration evaluation value LVal is a value exceeding the upper dashed line ULin, the deviation determination unit 115 determines that the most recent deterioration state is deviating from the tendency of the deterioration state of the secondary battery 170. On the other hand, the deviation determination unit 115 determines that the most recent deterioration state is not deviating from the tendency of the deterioration state of the secondary battery 170 when the most recent deterioration evaluation value LVal is a value on the upper dashed line ULin or less and on the lower dashed line LLin or more. [0126] FIG. 6 illustrates an example when the remaining life estimation unit 117 predicts a value of future internal resistance of the secondary
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battery 170 from the tendency of the internal resistance increase degree of the secondary battery 170. In FIG. 6, for example, the remaining life estimation unit 117 determines an approximate curve of the line TLin that represents the tendency of the deterioration state of the secondary battery 170 to derive a line FLin that represents the future internal resistance of the secondary battery 170. [0127] Then, the remaining life estimation unit 117 determines time point t2 corresponding to an intersection P between the dashed line
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MVal indicative of the predefined upper limit of the internal resistance value (in the present example, the value of the internal resistance is 130%), and the line FLin to estimate that a length Len of a period between the time point t2 and time point t1, at which the most recent deterioration evaluation value LVal was acquired, is the remaining life of the secondary battery 170. [0128] FIG. 7 illustrates an example when the remaining life estimation unit 117 predicts a value of future internal resistance of the secondary battery 170 from the tendency of the internal resistance increase degree of the secondary battery 170. In FIG. 7, for example, the remaining life estimation unit 117 determines an approximate curve of the line TLin2 that represents the tendency of the deterioration state of the secondary battery 170 to derive a line FLin2 that represents a change in future charging capacity of the secondary battery 170. [0129] Then, the remaining life estimation unit 117 determines time point t4 corresponding to an intersection P2 between a dot-dashed line MVal2 indicative of a predefined lower limit of the charging capacity value (in the present example, the charging capacity value is 70%), and the line FLin2 to estimate that a length Len2 of a period between the lime point t4 and time point t3, at which a most recent deterioration evaluation value LVal2 was acquired, is the remaining life of the secondary battery 170. [0130] Then, the user device output unit 150 outputs the most recent deterioration state of the secondary battery 170 recognized by the deterioration state recognition unit 111 and the remaining life estimated by the remaining life estimation unit 117 (FIG. 3B/S110), and the series of processing is ended).
Regarding dependent claim 3, Munakata et al (US 2022/0404425 A1) teaches the server of claim 2.
Munakata et al (US 2022/0404425 A1) further teaches, wherein when the internal resistance value exceeds the upper band threshold, the server control unit diagnoses that a disconnection defect has occurred in at least one of a plurality of battery cells included in the battery (Paragraph [0133]).
Regarding dependent claim 4, Munakata et al (US 2022/0404425 A1) teaches the server of claim 2.
Munakata et al (US 2022/0404425 A1) further teaches, wherein when the internal resistance value is less than the lower band threshold, the server control unit diagnoses that a short defect has occurred in at least one of a plurality of battery cells included in the battery (Paragraph [0146]).
Regarding dependent claim 5, Munakata et al (US 2022/0404425 A1) teaches the server of claim 2.
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Munakata et al (US 2022/0404425 A1) further teaches, wherein: the server storage unit is further configured to store environmental data including at least one of a State of Charge (SOC) estimated by a predetermined method and the battery temperature, and the server control unit is further configured to extract the plurality of diagnosis time points based on a first condition including the environmental data corresponding to environmental data at the diagnosis time point within a predetermined range, and a second condition, which is a previous diagnosis time point corresponding to the predetermined number of samples based on the diagnosis time point (paragraphs [0095], [0097], [0112],[0113],[0115]).
Regarding dependent claim 6, Munakata et al (US 2022/0404425 A1) teaches the server of claim 5.
Munakata et al (US 2022/0404425 A1) further teaches, wherein the first condition is a condition in which the battery temperature at a predetermined diagnosis time point belongs to a predetermined temperature section to which the battery temperature corresponding to the diagnosis time point belongs among a plurality of temperature sections set at predetermined temperature size intervals (paragraphs [0095], [0097], [0112],[0113],[0115]).
Regarding dependent claim 7, Munakata et al (US 2022/0404425 A1) teaches the server of claim 5.
Munakata et al (US 2022/0404425 A1) further teaches, wherein the first condition is a condition in which the SOC at a predetermined diagnosis time point belongs to a predetermined SOC section to which the SOC corresponding to the diagnosis time point belongs among a plurality of SOC sections set at predetermined SOC size intervals. ([0094] The remaining life estimation unit 117 estimates a remaining life as a period until the secondary battery 170 becomes a predetermined deterioration state. [0113] More specifically, for example, the deterioration state recognition unit 111 acquires, from the user device storage unit 130, information indicative of an internal resistance or charging capacity value in the initial state of the secondary battery 170, and performs a predetermined operation based on the information indicative of the voltage, current, and temperature of the secondary battery 170 acquired from the secondary battery 170 to acquire information indicative of a most recent internal resistance or charging capacity value of the secondary battery 170. Then, for example, the deterioration state recognition unit 111 calculates a ratio between the internal resistance or charging capacity in the initial state of the secondary battery 170 and the most recent internal resistance or charging capacity to determine the internal resistance increase rate or capacity remaining rate of the secondary battery 170 in order to recognize the deterioration state of the secondary battery 170 based on the determined internal resistance increase rate or capacity remaining rate. [0115] More specifically, for example, the tendency acquisition unit 113 calculates the moving average value of deterioration states of the secondary battery 170 for the predetermined period by using, as the deterioration evaluation value, the internal resistance increase decree or the capacity remaining degree of the seconders battery 170 determined by the deterioration state recognition unit 111 in S10. The predetermined period is, for example, one month, half a year, or the like, but it may be any period shorter or longer than the predetermined period, or the period mac be changed at any time according to the values of the information indicative of the voltage, current and temperature of the secondary battery 170 and the deterioration evaluation value.[0118] FIG. 5 illustrates an example when the tendency of the deterioration state of the secondary battery 170 is acquired based on a moving average value of capacity remaining degrees. In FIG. 5, N deterioration states OVal2 as deterioration states during the predetermined period are extracted from past M deterioration states RVal2 recorded in the deterioration information DB 133, and a moving average value of the extracted N deterioration states OVal2 becomes a latest moving average value LAve2 of the N deterioration states. [0119] Further, a line TLin2 of connecting moving average values at respective past time points calculated in the same way represents a tendency of the deterioration state of the secondary battery 170. Note that a value indicating the deterioration state at each time point is a value of the remaining capacity at each time point when a value IVal2 of the charging capacity in tire initial state of the secondary battery 170 is set to 100% (that is to say, 70% if the value of the charging capacity has decreased by 30%).
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Regarding independent claim 8, Munakata et al (US 2022/0404425 A1) teaches, A method of diagnosing a battery (paragraph [0004]), the method comprising: a data receiving operation of receiving, by a server (element 30, figure 1), battery data including at least one of a battery voltage, which is a voltage at both ends of the battery, a battery current, which is a current flowing through the battery, and a battery temperature, which is a temperature of the battery, from a battery management system (BMS) ([0034] the deterioration state recognition unit should acquire information indicative of voltage, current, and temperature of the secondary battery to recognize the deterioration state of the secondary battery based on the information. [0035] In the state output system of the present invention, the information indicative of the voltage, current, and temperature of the secondary battery is acquired by the deterioration state recognition unit, and the deterioration state of the secondary battery is recognized based on the information. [0036] Thus, since the deterioration state is recognized based on information easy to acquire, such as the voltage, current, and temperature of the secondary battery, the recognition and determination of the deterioration state of the secondary battery can be performed more efficiently); a sample group determining operation of extracting, at a diagnosis time point for diagnosing a defect of the battery, a plurality of previous diagnosis time points corresponding to a predetermined number of samples based on the diagnosis time point ([0091] The deterioration state recognition unit 111 recognizes the deterioration state of the secondary battery 170 at any time or in a predetermined cycle. Further, the deterioration state recognition unit 111 transmits, to the server 30, the deterioration state of the secondary battery 170 on a regular basis or at any timing to store the deterioration state in a use state information DB 331. [0100] The server 30 is, for example, a computer including a server control unit 310 and a server storage unit 330. [0101] The server control unit 310 is composed of an arithmetic processing unit such as a CPU, a memory, an I/O device, and the like. The server control unit 310 reads and executes a predetermined program to function, for example, as the detailed deterioration state determination unit 311, or in addition thereto, further as an event estimation unit 313 and a risk level recognition unit 315. [0102] When the deviation determination unit 115 determines that the most recent deterioration state of the secondary battery 170 is deviating from the tendency, the detailed deterioration state determination unit 311 acquires, from the server storage unit 330, use state information as information indicating how the secondary battery was used by the most recent time point, and based on the most recent deterioration state and the use state information, determines a detailed deterioration state including information indicative of a deterioration state of the secondary battery 170 more detailed than the most recent deterioration state. [0105] The server storage unit 330 is comprised, for example, of a ROM, a RAM and a storage device such as an HDD. The server storage unit 330 stores the use state information DB 331 including use state information as information indicating how the secondary batters 170 was used by the most recent time point, or in addition thereto, further stores a geographical characteristic information DB 333 including geographical characteristic information as information indicative of geographical characteristics that affect the deterioration state of the secondary battery 170, an initial state information DB 335 including information indicative of an internal resistance or charging capacity value in the initial state of the secondary battery 170, a predictive model DB 337 in which a predictive model used by the event estimation unit 313 to estimate an event, and an event DB 339 in which information related to each event that can occur in the secondary battery is stored); a reference value determining operation of calculating a moving average, which is an average of a plurality of internal resistance values corresponding to a plurality of diagnosis time points, respectively, an upper band threshold larger than the moving average by a first predetermined value, and a lower band threshold smaller than the moving average by a second predetermined value (paragraphs 0116]-[01120], figures 4 and 5); and a defect diagnosis operation of diagnosing the defect in the battery by comparing an internal resistance value corresponding to the diagnosis time point with the upper band threshold and the lower band threshold (paragraphs 0116]-[0121], figures 4 and 5).
Regarding dependent claim 9, Munakata et al (US 2022/0404425 A1) teaches the method of claim 8.
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Munakata et al (US 2022/0404425 A1) further teaches, wherein the reference value determining operation further includes: calculating an error value by multiplying a standard deviation average value, which is an average of a plurality of standard deviations corresponding to the plurality of diagnosis time points, respectively, by a predetermined multiple; calculating the upper band threshold by adding the error value to the moving average; and calculating the lower band threshold by subtracting the error value from the moving average ([0120] Next, the deviation determination unit 115 determines whether or not a most recent deterioration state is deviating from the tendency of the deterioration state of the secondary battery 170 (FIG. 3A/S50).[0121] For example, the deviation determination unit 115 determines that the most recent deterioration suite is deviating from the tendency when a difference Dev between a most recent moving average value LAve of deterioration states of the secondary battery 170 and the most recent deterioration evaluation value LVal is a predetermined threshold value or more. For example, in FIG. 4, a dashed line ULin above the latest moving average value LAve of deterioration states of the secondary battery 170 represents an upper threshold value of the difference Dev and a dashed line LLin below thereof represents a lower
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threshold value, respectively. [0122] In other words, the distance from the line TLin of connecting the moving average values to the dashed line ULin above and the distance therefrom to the dashed line LLin below become allowable deviations ADev in up and down directions, respectively. [0123] In FIG. 4, since the most recent deterioration evaluation value LVal is a value
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exceeding the upper dashed line ULin, the deviation determination unit 115 determines that the most recent deterioration state is deviating from the tendency of the deterioration state of the secondary battery 170. On the other hand, the deviation determination unit 115 determines that the most recent deterioration state is not deviating from the tendency of the deterioration state of the secondary battery 170 when the most recent deterioration evaluation value LVal is a value on the upper dashed line ULin or less and on the lower dashed line LLin or more. [0126] FIG. 6 illustrates an example when the remaining life estimation unit 117 predicts a value of future internal resistance of the secondary battery 170 from the tendency of the internal resistance increase degree of the secondary battery 170. In FIG. 6, for example, the remaining life estimation unit 117 determines an approximate curve of the line TLin that represents the tendency of the deterioration state of the secondary battery 170 to derive a line FLin that represents the future internal resistance of the secondary battery 170. [0127] Then, the remaining life estimation unit 117 determines time point t2 corresponding to an intersection P between the dashed line MVal indicative of the predefined upper limit of the internal resistance value (in the present example, the value of the internal resistance is 130%), and the line FLin to estimate that a length Len of a period between the time point t2 and time point t1, at which the most recent deterioration evaluation value LVal was acquired, is the remaining life of the secondary battery 170. [0128] FIG. 7 illustrates an example when the remaining life estimation unit 117 predicts a value of future internal resistance of the secondary battery 170 from the tendency of the internal resistance increase degree of the secondary battery 170. In FIG. 7, for example, the remaining life estimation unit 117 determines an approximate curve of the line TLin2 that represents the tendency of the deterioration state of the secondary battery 170 to derive a line FLin2 that represents a change in future charging capacity of the secondary battery 170. [0129] Then, the remaining life estimation unit 117 determines time point t4 corresponding to an intersection P2 between a dot-dashed line MVal2 indicative of a predefined lower limit of the charging capacity value (in the present example, the charging capacity value is 70%), and the line FLin2 to estimate that a length Len2 of a period between the lime point t4 and time point t3, at which a most recent deterioration evaluation value LVal2 was acquired, is the remaining life of the secondary battery 170. [0130] Then, the user device output unit 150 outputs the most recent deterioration state of the secondary battery 170 recognized by the deterioration state recognition unit 111 and the remaining life estimated by the remaining life estimation unit 117 (FIG. 3B/S110), and the series of processing is ended).
Regarding dependent claim 10, Munakata et al (US 2022/0404425 A1) teaches the method of claim 8.
Munakata et al (US 2022/0404425 A1) further teaches, wherein in the defect diagnosis operation, when the internal resistance value corresponding to the diagnosis time point exceeds the upper band threshold, it is diagnosed that a disconnection defect has occurred in at least one of a plurality of battery cells included in the battery (Paragraph [0133]).
Regarding dependent claim 11, Munakata et al (US 2022/0404425 A1) teaches the method of claim 8.
Munakata et al (US 2022/0404425 A1) further teaches, wherein in the defect diagnosis operation, when the internal resistance value corresponding to the diagnosis time point is less than the lower band threshold, it is diagnosed that a short defect has occurred in at least one of a plurality of battery cells included in the battery (Paragraph [0146]).
Regarding dependent claim 12, Munakata et al (US 2022/0404425 A1) teaches the method of claim 8.
Munakata et al (US 2022/0404425 A1) further teaches, wherein the sample group determining operation further includes extracting the plurality of diagnosis time points based on a first condition including environmental data corresponding to environmental data at the diagnosis time point within a predetermined range, and a second condition, which is a previous diagnosis time point corresponding to the predetermined number of samples based on the diagnosis time point, and the environmental data includes at least one of a State of Charge (SOC) estimated by a predetermined method and the battery temperature (paragraphs [0095], [0097], [0112],[0113],[0115]).
Regarding dependent claim 13, Munakata et al (US 2022/0404425 A1) teaches the method of claim 12.
Munakata et al (US 2022/0404425 A1) further teaches, wherein the first condition is a condition in which the battery temperature at a predetermined diagnosis time point belongs to a predetermined temperature section to which the battery temperature corresponding to the diagnosis time point belongs among a plurality of temperature sections set at predetermined temperature size intervals (paragraphs [0095], [0097], [0112], [0113],[0115])
Regarding dependent claim 14, Munakata et al (US 2022/0404425 A1) teaches the method of claim 12.
Munakata et al (US 2022/0404425 A1) further teaches, wherein the first condition is a condition in which the SOC at a predetermined diagnosis time point belongs to a predetermined SOC section to which the SOC corresponding to the diagnosis time point belongs among a plurality of SOC sections set at predetermined SOC size intervals ([0094] The remaining life estimation unit 117 estimates a remaining life as a period until the secondary battery 170 becomes a predetermined deterioration state. [0113] More specifically, for example, the deterioration state recognition unit 111 acquires, from the user device storage unit 130, information indicative of an internal resistance or charging capacity value in the initial state of the secondary battery 170, and performs a predetermined operation based on the information indicative of the voltage, current, and temperature of the secondary battery 170 acquired from the secondary battery 170 to acquire information indicative of a most recent internal resistance or charging capacity value of the secondary battery 170. Then, for example, the deterioration state recognition unit 111 calculates a ratio between the internal resistance or charging capacity in the initial state of the secondary battery 170 and the most recent internal resistance or charging capacity to determine the internal resistance increase rate or capacity remaining rate of the secondary battery 170 in order to recognize the deterioration state of the secondary battery 170 based on the determined internal resistance increase rate or capacity remaining rate. [0115] More specifically, for example, the tendency acquisition unit 113 calculates the moving average value of deterioration states of the secondary battery 170 for the predetermined period by using, as the deterioration evaluation value, the internal resistance increase decree or the capacity remaining degree of the seconders battery 170 determined by the deterioration state recognition unit 111 in S10. The predetermined period is, for example, one month, half a year, or the like, but it may be any period shorter or longer than the predetermined period, or the period mac be changed at any time according to the values of the information indicative of the voltage, current and temperature of the secondary battery 170 and the deterioration evaluation value.[0118] FIG. 5 illustrates an example when the tendency of the deterioration state of the secondary battery 170 is acquired based on a moving average value of capacity remaining degrees. In FIG. 5, N deterioration states OVal2 as deterioration states during the predetermined period are extracted from past M deterioration states RVal2 recorded in the deterioration information DB 133, and a moving average value of the extracted N deterioration states OVal2 becomes a latest moving average value LAve2 of the N deterioration states. [0119] Further, a line TLin2 of connecting moving average values at respective past time points calculated in the same way represents a tendency of the deterioration state of the secondary battery 170. Note that a value indicating the deterioration state at each time point is a value of the remaining capacity at each time point when a value IVal2 of the charging capacity in tire initial state of the secondary battery 170 is set to 100% (that is to say, 70% if the value of the charging capacity has decreased by 30%).
Closest Prior art
5. The following relevant prior art of record is not cited in the office action.
Oono et al (US 2020/0326378 A1) teaches, A chargeable battery abnormality detection apparatus configured to detect an abnormality of a chargeable battery includes a processor; and a memory storing instructions executable by the processor, wherein the processor performs the following when executing instructions: calculating a value of an internal resistance of the chargeable battery; determining whether the chargeable battery is being charged or being discharged; determining that an abnormality has occurred in the chargeable battery if either one of the following occurs: the calculated value of the internal resistance decreases while the chargeable battery is being discharged; and the calculated value of the internal resistance increases while the chargeable battery is being charged; and outputting a determination result of the abnormality.
Kanada et al (US 2018/0208062 A1) A battery system is configured to be mounted on a vehicle. The battery system includes a secondary battery, a detection device, and an electronic control unit. The electronic control unit is configured to perform communication with an external device that accumulates temperature history data of other vehicle, and is configured to acquire, when the battery temperature of the temperature history data of the vehicle contains an abnormal value, the temperature history data of the other vehicle within a period in which the battery temperature of the temperature history data of the vehicle contains the abnormal value, from the external device, and execute correction of the abnormal value of the battery temperature based on the temperature history data acquired from the external device.
Naha et al (US 2021/0088591 A1) teaches, a method for battery fault diagnosis and prevention of hazardous conditions. The method comprises determining a plurality of parameters defined as one or more of current, voltage, or state of charge during operation of a battery-powered device. Further, one or more likelihood ratios related to malfunctioning of the battery are evaluated based on determined parameters. At least one of: a current battery-state or a type of current battery state are determined based on the one or more likelihood ratios as evaluated.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SURESH RAJAPUTRA whose telephone number is (571) 270-0477. The examiner can normally be reached between 8:00 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMAN ALKAFAWI can be reached on 571-272-4448. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SURESH K RAJAPUTRA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 9/8/2026
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