Method for predicting remaining battery life

Abstract

The present application discloses a method for predicting the remaining service life of a battery, comprising the steps of: obtaining reference aging data of a reference battery after multiple charge and discharge cycles; detecting the number of charge and discharge cycles that the battery to be tested has executed; calculating historical health status data based on the initial capacity of the battery to be tested and the capacity after each charge and discharge cycle; analyzing the relationship between the historical health status data and the number of charge and discharge cycles to generate historical aging data; based on the reference aging data, estimating the future aging data of the battery to be tested after executing more charge and discharge cycles based on a ratio parameter of the reference aging data and the historical aging data, so as to predict the remaining service life of the battery to be tested.

Description

Technical Field

[0001] The present application relates to the field of batteries, and in particular to a method for predicting the remaining service life of a battery, which can detect the number of charge and discharge cycles currently remaining to be executed by a battery to be tested. Background Art

[0002] Power batteries are widely used in all aspects of people's daily lives. Whether it is for new energy vehicles using power batteries or backup power supplies for wireless communication base stations, the prediction of the service life of power batteries has become a key factor in the development of various industries. In particular, accurate prediction of the life of power batteries is the core of safety technology and also a difficult problem currently faced. Summary of the invention

[0003] The technical problem to be solved by the present application is to provide a method for predicting the remaining service life of a battery in view of the shortcomings of the prior art, comprising the following steps: defining a charge and a discharge performed continuously as completing a charge and discharge cycle, obtaining reference health status data obtained after a reference battery performs charge and discharge cycles multiple times; analyzing the relationship between the number of charge and discharge cycles of the reference battery and the reference health status data to establish reference aging data; detecting the number of charge and discharge cycles performed by the battery to be tested; detecting the initial capacity of the battery to be tested and the capacity after each charge and discharge cycle to calculate the historical health status data of the battery to be tested; analyzing the relationship between the number of charge and discharge cycles performed by the battery to be tested and the historical health status data to generate reference aging data; Generate historical aging data; calculate the ratio parameter of the reference aging data and the historical aging data; based on the reference aging data and according to the ratio parameter, estimate the health status data of the battery to be tested after executing more charge and discharge cycles to generate future aging data, wherein the future aging data includes future health status data corresponding to different charge and discharge cycle numbers; define the end-of-life health status of the battery to be tested; find the number of charge and discharge cycles corresponding to the future health status data that is closest to the end-of-life health status from the future aging data as the predicted total number of cycles; and subtract the predicted total number of cycles from the number of charge and discharge cycles that the battery to be tested has executed to obtain the remaining number of charge and discharge cycles that the battery to be tested can currently execute.

[0004] In one embodiment, the battery remaining service life prediction method further includes the following steps: detecting the initial capacity of a reference battery before any charge and discharge is performed; charging and discharging the reference battery multiple times to reduce the capacity of the reference battery from the initial capacity to a threshold value; recording the number of charge and discharge cycles performed by the reference battery; detecting the capacity of the reference battery after each charge and discharge cycle; calculating reference health status data of the reference battery based on the initial capacity of the reference battery and the capacity of the reference battery detected each time; and analyzing the relationship between the number of charge and discharge cycles performed by the reference battery and the reference health status data to generate reference aging data.

[0005] In one embodiment, the battery remaining service life prediction method further includes the following steps: analyzing the relationship between the number of charge and discharge cycles performed by the reference battery and the reference health status to establish a reference aging curve, which is included in the reference aging data; and analyzing the relationship between the number of charge and discharge cycles performed by the battery to be tested and the historical health status data to establish a historical aging curve, which is included in the historical aging data.

[0006] In one embodiment, the battery remaining service life prediction method further includes the following steps: in the reference aging curve and the historical aging curve, according to the battery health status data corresponding to the number of charge and discharge cycles at each point, respectively, the reference aging data and the historical aging data of the same number of charge and discharge cycles, and according to the relationship between the reference aging data and the historical aging data, calculating the proportional parameter; based on the reference aging curve, according to the proportional parameter, estimating the future aging curve of the battery to be tested; and from the future aging curve, finding the number of charge and discharge cycles corresponding to the future health status data that is closest to the end-of-life health status as the predicted total number of cycles.

[0007] In one embodiment, the battery remaining service life prediction method further comprises the following steps: dividing the current capacity of the reference battery after each charge and discharge cycle by the initial capacity of the reference battery and multiplying by 100% to calculate the reference health status data of the reference battery, which is expressed by the following equation:

[0008] SOH=(C N / C O )×100%,

[0009] Where SOH represents the reference state of health data, N represents the number of charge and discharge cycles performed by the reference battery, and C N Represents the current capacity of the reference battery after N charge and discharge cycles, C O Represents the initial capacity of the reference battery.

[0010] In one embodiment, the battery remaining service life prediction method further comprises the following steps: using a polynomial function, fitting the number of charge and discharge cycles executed with the reference health status data, expressed as the following equation:

[0011] SOHF=a×N 3 +b×N 2 +c×N+d,

[0012] Among them, SOHF represents the reference health status data fitted by a polynomial function, N represents the number of charge and discharge cycles, and a, b, c, and d represent coefficients.

[0013] In one embodiment, the battery remaining service life prediction method further comprises the following steps: dividing the current capacity of the battery to be tested after each charge and discharge cycle by the initial capacity of the battery to be tested and multiplying by 100% to calculate the historical health status data of the battery to be tested, which is expressed by the following equation:

[0014] SOH=(C N / C O )×100%,

[0015] Among them, SOH represents the historical health status data, N represents the number of charge and discharge cycles performed by the battery under test, and C N Represents the current capacity of the battery under test after N charge and discharge cycles, C O Represents the initial capacity of the battery to be tested.

[0016] In one embodiment, the battery remaining service life prediction method further comprises the following steps: using a polynomial function, fitting the number of charge and discharge cycles executed with the historical health status data, expressed as the following equation:

[0017] SOHF=a×N 3 +b×N 2 +c×N+d,

[0018] Among them, SOHF represents the historical health status data fitted by a polynomial function, N represents the number of charge and discharge cycles, and a, b, c, and d represent coefficients.

[0019] As described above, the present application provides a method for predicting the remaining service life of a battery, which can foresee the degree of battery aging and provide a predicted remaining service life of the battery, which can be used to remind users to change their usage habits or limit the usage output to extend the battery life. In addition, this technology can determine the end of battery life in advance and remind users to replace it with a new one, avoiding losses caused by power outages.

[0020] To further understand the features and technical contents of the present application, please refer to the following detailed description and drawings of the present application. However, the drawings provided are only for reference and illustration and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flowchart of the steps of detecting a reference battery in the method for predicting the remaining service life of a battery according to an embodiment of the present application.

[0022] Figure 2 This is a flow chart of the steps of detecting a battery to be tested in a method for predicting the remaining service life of a battery according to an embodiment of the present application.

[0023] Figure 3This is a flow chart of the steps of calculating the remaining number of charge and discharge cycles of a battery to be tested in the method for predicting the remaining service life of a battery according to an embodiment of the present application.

[0024] Figure 4 The battery remaining service life prediction method of the embodiment of the present application detects the charge and discharge cycle numbers of the reference battery and the battery health status data to establish a reference aging curve of the reference battery and a historical aging curve of the battery to be tested.

[0025] Figure 5 A graph of the future aging curve of a battery to be tested established by the battery remaining service life prediction method according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following is an explanation of the implementation methods of the present application through specific specific embodiments. Those skilled in the art can understand the advantages and effects of the present application from the contents disclosed in this specification. The present application can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the concept of the present application. In addition, the drawings of the present application are only simple schematic illustrations and are not depicted according to actual dimensions. It is stated in advance. The following implementation methods will further explain the relevant technical contents of the present application in detail, but the disclosed contents are not intended to limit the scope of protection of the present application. In addition, the term "or" used herein may include any one or more combinations of the associated listed items depending on the actual situation.

[0027] See also Figure 1 and Figure 4 ,in Figure 1 This is a flowchart of the steps of detecting a reference battery in the method for predicting the remaining service life of a battery according to an embodiment of the present application. Figure 4 The battery remaining service life prediction method of the embodiment of the present application detects the charge and discharge cycle numbers of the reference battery and the battery health status data to establish a reference aging curve of the reference battery and a historical aging curve of the battery to be tested.

[0028] The method for predicting the remaining service life of a battery in the embodiment of the present application may include: Figure 1 In the steps S101 to S117, the data of a battery to be tested first (hereinafter collectively referred to as a reference battery) is analyzed and evaluated as data for subsequent evaluation of unknown batteries (hereinafter collectively referred to as test batteries). In practice, steps S101 to S117 can be omitted and the reference aging data of the reference battery can be directly obtained from the database instead.

[0029] In step S101, a reference battery is obtained.

[0030] In step S105, the reference battery is charged and discharged multiple times.

[0031] In step S107, it is defined that the continuous execution of one charge and one discharge completes one charge and discharge cycle, and the number of charge and discharge cycles is counted.

[0032] In step S109, the capacity of the reference battery after each charge and discharge cycle is detected. It should be understood that as the number of charge and discharge cycles increases, the capacity of the reference battery will decrease.

[0033] In step S111, it is determined whether the current capacity of the reference battery has dropped from the initial capacity to the threshold value. If not, that is, the current capacity of the reference battery is still greater than the threshold value, then the process returns to step S105 and the reference battery is subjected to more charge and discharge cycles. On the contrary, if yes, that is, the current capacity of the reference battery has dropped to the threshold value, then the next step S113 is executed.

[0034] In step S113, the reference state of health (SOH) data of the reference battery is calculated according to the initial capacity of the reference battery and the capacity detected after each charge and discharge cycle, so as to obtain the reference state of health obtained by the cumulative number of charge and discharge cycles.

[0035] For example, the current capacity of the reference battery after N charge and discharge cycles is divided by the initial capacity of the reference battery and multiplied by 100% to calculate the reference health status data of the reference battery, which is expressed by the following equation:

[0036] SOH=(C N / C O )×100%,

[0037] Wherein, SOH represents the health status data of the reference battery (i.e., the reference health status data), N represents the number of charge and discharge cycles performed by the reference battery, which can be any appropriate integer value, and C N Represents the current capacity of the reference battery after N charge and discharge cycles, C O Represents the initial capacity of the reference battery.

[0038] In step S115, the relationship between the number of charge and discharge cycles performed by the reference battery and the reference health status data is analyzed to establish reference aging data in step S117. For example, the battery remaining service life prediction method further includes the following steps: using a polynomial function, the number of charge and discharge cycles performed is fitted with the reference health status data, which is expressed by the following equation:

[0039] SOHF=a×N 3 +b×N 2 +c×N+d,

[0040] Among them, SOHF represents the reference aging data fitted by the polynomial function, N represents the number of charge and discharge cycles, and a, b, c, and d represent coefficients.

[0041] If necessary, the relationship between the number of charge and discharge cycles performed by the reference battery and the reference health status data can be analyzed to establish a reference aging curve in the curve diagram, such as Figure 4 The reference aging curve CU1 shown represents the complete reference aging data of the reference health status data changing with the number of charge and discharge cycles during the process of the reference battery being discharged from the initial capacity of a brand new state (fully charged battery) to a capacity of zero, that is, during the process of the reference health status data dropping from 100% to 0%.

[0042] like Figure 4 As shown in the reference aging curve CU1, after the reference battery performs 1893 charge and discharge cycles, the reference health state of the reference battery will drop to 30%, and after performing 2146 charge and discharge cycles, the reference health state drops to 0%.

[0043] In the embodiment of this invention, after executing steps S101 to S117 as described above, complete reference aging data of the reference battery from a brand new product to when the battery capacity reaches the threshold value is obtained.

[0044] See also Figure 2 and Figure 4 ,in Figure 2 This is a flowchart of the steps of detecting a battery to be tested in the method for predicting the remaining service life of a battery according to an embodiment of the present application. Figure 4 The battery remaining service life prediction method of the embodiment of the present application detects the charge and discharge cycle numbers of the reference battery and the battery health status data to establish a reference aging curve of the reference battery and a historical aging curve of the battery to be tested.

[0045] The method for predicting the remaining service life of a battery in the embodiment of the present application may include: Figure 2 Steps S201 to S213 are shown to detect the battery to be tested.

[0046] In step S201, a battery to be tested is obtained.

[0047] In step S203, the initial capacity of the battery to be tested is directly obtained or detected.

[0048] In step S205, the number of charge and discharge cycles executed by the battery to be tested is detected.

[0049] In step S207, the capacity of the battery to be tested after each charge and discharge cycle is detected.

[0050] In step S209, historical health status data of the battery to be tested is calculated based on the initial capacity of the battery to be tested and the capacity after each charge and discharge cycle.

[0051] For example, the current capacity of the battery under test after N charge and discharge cycles is divided by the initial capacity of the battery under test and multiplied by 100% to calculate the historical health status data of the battery under test, which is expressed by the following equation:

[0052] SOH=(C N / C O )×100%,

[0053] Among them, SOH represents the historical health status data of the battery under test, N represents the number of charge and discharge cycles performed by the battery under test, and C N Represents the current capacity of the battery under test after N charge and discharge cycles, C O Represents the initial capacity of the battery to be tested.

[0054] In step S211, the relationship between the number of charge and discharge cycles executed by the battery under test and the historical health status data is analyzed to generate the historical aging data of the battery under test in step S213.

[0055] For example, the battery remaining service life prediction method further includes the following steps: using a polynomial function, the number of executed charge and discharge cycles is fitted with the historical health status data, and expressed by the following equation:

[0056] SOHF=a×N 3 +b×N 2 +c×N+d,

[0057] Among them, SOHF represents the historical health status data fitted by a polynomial function, N represents the number of charge and discharge cycles, and a, b, c, and d represent coefficients.

[0058] If necessary, the relationship between the number of charge and discharge cycles performed by the battery under test and the historical health status data can be analyzed to establish a historical aging curve. Figure 4 The historical aging curve CU2 shown represents the change of the historical health status data of the battery under test with the number of charge and discharge cycles executed. Since the battery under test has only undergone a few charge and discharge cycles, it is not possible to fit and depict the future aging curve well. For example, the cumulative number of charge and discharge cycles executed by the battery under test is only about 428 times. Therefore, compared with the reference aging curve CU1 of the reference battery from the beginning of the test to the end of its life, which is a complete curve as a reference, the historical aging curve CU2 of the battery under test is only a small section.

[0059] See also Figures 3 to 5 ,in Figure 3The present invention is a flowchart of the steps of calculating the remaining number of charge and discharge cycles of a battery to be tested in the method for predicting the remaining service life of a battery according to an embodiment of the present invention. Figure 4 A graph of a reference aging curve of a reference battery and a historical aging curve of a battery to be tested, which are established by detecting the number of charge and discharge cycles of a reference battery and a battery to be tested and battery health status data according to the battery remaining service life prediction method of an embodiment of the present application; Figure 5 A graph of the future aging curve of a battery to be tested established by the battery remaining service life prediction method according to an embodiment of the present application.

[0060] The method for predicting the remaining battery life of this embodiment may include the following steps: Figure 3 Steps S301 to S313 shown are executed after step S117 and step S213.

[0061] In step S301, a ratio parameter of the reference aging data and the historical aging data is calculated. For example, in the reference aging curve and the historical aging curve, the battery health status data corresponding to the number of charge and discharge cycles at each point are the reference aging data and the historical aging data of the same number of charge and discharge cycles, and the ratio parameter is calculated according to the relationship between the reference aging data and the historical aging data.

[0062] For example, the ratio parameter of the data of the historical aging curve CU2 included in the historical aging data of the battery to be tested and the data of the reference aging curve CU1 of the reference aging data of the reference battery is calculated by the following equation:

[0063] f(N) H =α(N) H ×g(N),

[0064] Among them, f(N) H Data representing the historical aging curve, α(N) H represents the proportional parameter, and g(N) represents the data of the reference aging curve.

[0065] In step S303, based on the reference aging data (reference aging curve CU1), according to the ratio parameter obtained by the above calculation, the health status data of the battery to be tested after performing more charge and discharge cycles is predicted to estimate the future aging data (future aging curve CU3), which can be expressed by the following equation:

[0066] f(N) F =α(N) F ×g(N),

[0067] Among them, f(N) F Data representing the future aging curve, α(N) F represents the proportional parameter, g(N) represents the data of the reference aging curve, assuming that α(N)F =α(N) H , α(N) F It will be continuously updated with the accumulated number of charge and discharge cycles performed by the battery under test, and can be calculated from the data of the historical aging curve of the battery under test and the data of the reference aging curve.

[0068] Based on Figure 4 The reference aging curve CU1 shown in FIG. 1 is used to predict the battery under test after performing more charge and discharge cycles based on the proportional parameters. Figure 5 The future aging curve CU3 shown is connected to Figure 4 After the historical aging curve CU2 shown, a complete curve of the battery under test from the time of testing to the end of its life is formed.

[0069] In step S305, the end-of-life health state of the battery to be tested is defined, for example but not limited to 30%, expressed as SOH%=30%, that is, when the capacity of the battery to be tested after multiple charge and discharge cycles accounts for less than 30% of the initial capacity of the new battery to be tested, the battery to be tested is judged to be at the end of life (EOL). It should be understood that the proportion of the end-of-life health state may depend on factors such as the type, type, application product field, and user usage habits of the battery to be tested.

[0070] In step S307, the future health state data closest to the end-of-life health state is searched from the future health state data of the future aging data (future aging curve), such as but not limited to 30%, so as to obtain the number of charge and discharge cycles corresponding to the found future health state data in step S309 as the predicted total number of cycles. That is, when the number of charge and discharge cycles performed by the battery to be tested reaches the predicted total number of cycles, the service life of the battery to be tested will be reduced to the defined end-of-life state.

[0071] In step S311, the predicted total number of cycles is subtracted from the number of charge and discharge cycles that have been executed by the battery under test, so as to obtain the number of charge and discharge cycles that can be executed by the battery under test in step S313, that is, to estimate the remaining useful life (RUL) of the battery under test, as represented by the following equation:

[0072] RUL=Ne-Nu,

[0073] Among them, RUL represents the remaining number of charge and discharge cycles that can be executed by the battery under test, Ne represents the predicted total number of cycles that can be executed by the battery under test from a brand new product to the end of life, that is, the total number of cycles that can be executed by the battery under test from the initial capacity to the end-of-life threshold, such as but not limited to SOH=30%, and Nu represents the number of charge and discharge cycles that have been executed by the battery under test.

[0074] In this way, users or managers of products equipped with the battery to be tested can learn about the remaining service life of the battery, and accordingly can change usage habits or make preparations in advance for replacing the battery with a new one.

[0075] like Figure 5 As shown in the future aging curve CU3, after the battery to be tested performs 1912 charge and discharge cycles, the future health state of the battery to be tested will drop to 30%, which is the end-of-life health state defined in the above example. At this time, the battery to be tested is judged to be end-of-life, but the present application is not limited to this.

[0076] In summary, the present application provides a method for predicting the remaining service life of a battery, which can foresee the degree of battery aging and provide a predicted remaining service life of the battery, which can be used to remind users to change their usage habits or limit the usage output to extend the battery life. In addition, this technology can determine the end of battery life in advance and remind users to replace it with a new one, avoiding losses caused by power outages.

[0077] The contents disclosed above are only preferred feasible embodiments of the present application, and are not intended to limit the claims of the present application. Therefore, all equivalent technical changes made using the description and drawings of the present application are included in the claims of the present application.

Claims

1. A method for predicting remaining battery life, characterized in that: The remaining battery life The method of fortune prediction includes the following steps: A charge-discharge cycle is defined as one charge and one discharge performed continuously, and reference health status data is obtained after the reference battery performs multiple charge-discharge cycles; Analyzing the relationship between the number of charge and discharge cycles of the reference battery and the reference health status data to establish reference aging data; Detect the number of charge and discharge cycles that the battery under test has performed; Detecting the initial capacity of the battery to be tested and the capacity after each charge and discharge cycle to calculate the historical health status data of the battery to be tested; Analyzing the relationship between the number of charge and discharge cycles performed by the battery under test and the historical health status data to generate historical aging data; Calculating a ratio parameter of the reference aging data to the historical aging data; Based on the reference aging data, according to the ratio parameter, the health status data of the battery under test after performing more charge and discharge cycles is estimated to generate future aging data, wherein the future aging data includes the future health status data corresponding to the different charge and discharge cycles performed by the battery under test; Defining the end-of-life health status of the battery under test; From the future aging data, searching for the number of charge and discharge cycles corresponding to the future health state data closest to the end-of-life health state as the predicted total number of cycles; as well as Subtracting the predicted total number of cycles from the number of charge and discharge cycles that the battery under test has executed, to obtain the number of charge and discharge cycles that the battery under test can currently execute; The method for predicting the remaining service life of a battery further comprises the following steps: Detecting the initial capacity of the reference battery without any charge or discharge; The reference battery is charged and discharged multiple times to reduce the capacity of the reference battery from the initial capacity to a threshold value; Recording the number of charge and discharge cycles performed by the reference battery; After each charge and discharge cycle is performed, detecting the capacity of the reference battery; Calculating the reference health status data of the reference battery according to the initial capacity of the reference battery and the capacity of the reference battery detected each time; Analyzing the relationship between the number of charge and discharge cycles performed by the reference battery and the reference health status data to establish a reference aging curve included in the reference aging data; Analyzing the relationship between the number of charge and discharge cycles performed by the battery under test and the historical health status data to establish a historical aging curve included in the historical aging data; In the reference aging curve and the historical aging curve, according to the battery health status data corresponding to the number of charge and discharge cycles at each point, the reference aging data and the historical aging data of the same number of charge and discharge cycles are respectively used, and according to the relationship between the reference aging data and the historical aging data, the proportional parameter is calculated; as well as Based on the reference aging curve and according to the ratio parameter, the future aging curve of the battery to be tested is estimated.

2. The method for predicting the remaining service life of a battery according to claim 1, characterized in that: Place The battery remaining service life prediction method further comprises the following steps: The current capacity of the reference battery after each charge and discharge cycle is divided by the initial capacity of the reference battery and multiplied by 100% to calculate the reference health status data of the reference battery, which is expressed by the following equation: SOH=(C N / C O )×100%, Wherein, SOH represents the reference health status data, N represents the number of charge and discharge cycles performed by the reference battery, C N Represents the current capacity of the reference battery after N charge and discharge cycles, C O represents the initial capacity of the reference battery.

3. The method for predicting the remaining service life of a battery according to claim 1, characterized in that: Place The battery remaining service life prediction method further comprises the following steps: The number of charge and discharge cycles performed is fitted with the reference health status data using a polynomial function, expressed as the following equation: <h2 style=";text-align:left;direction:ltr">SOHF=a×N<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> +b×N<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +c×N+d, Wherein, SOHF represents the reference health status data obtained by fitting a polynomial function, N represents the number of charge and discharge cycles, and a, b, c, and d represent coefficients.

4. The method for predicting the remaining service life of a battery according to claim 1, characterized in that: The method for predicting the remaining battery life also includes the following steps: The current capacity of the battery under test after each charge and discharge cycle is divided by the initial capacity of the battery under test and multiplied by 100% to calculate the historical health status data of the battery under test, which is expressed by the following equation: SOH=(C N / C O )×100%, Wherein, SOH represents the historical health status data, N represents the number of charge and discharge cycles performed by the battery under test, and C N Represents the current capacity of the battery under test after N charge and discharge cycles, C O Represents the initial capacity of the battery to be tested.

5. The method for predicting remaining battery life according to claim 1, characterized in that: The method for predicting the remaining battery life also includes the following steps: The number of charge and discharge cycles performed is fitted with the historical health status data using a polynomial function, expressed as the following equation: <h2 style=";text-align:left;direction:ltr">SOHF=a×N<h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> +b×N<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +c×N+d, Wherein, SOHF represents the historical health status data obtained by fitting a polynomial function, N represents the number of charge and discharge cycles, and a, b, c, and d represent coefficients.

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