A lithium battery life prediction system and method

Abstract

The present application relates to a lithium battery life prediction system and method, and relates to the field of battery energy technology. The method includes obtaining battery data; determining theoretical remaining time according to battery data, and determining overall influencing parameters according to theoretical remaining time and fixed influencing parameters; establishing a width detection interval on the time axis, and obtaining external environmental parameters at each time point in the detection interval; randomly constructing a comparison interval with a width consistent with the detection interval on the time axis, and determining a unique similar interval from each comparison interval according to the external environmental parameters; constructing a detection interval with the rear end point of the similar interval, and determining external influencing parameters according to the external environmental parameters at each time point in the detection interval; determining actual influencing parameters according to external influencing parameters, and outputting the width of the detection interval as the actual remaining time when the actual influencing parameters are consistent with the overall influencing parameters. The present application has the effect of improving the accuracy of lithium battery life prediction.

Description

Technical Field

[0001] The present application relates to the field of battery energy technology, and in particular to a lithium battery life prediction system and method. Background Art

[0002] Lithium batteries are a type of battery that uses lithium metal or lithium alloy as the negative electrode material and a non-aqueous electrolyte solution. They mainly rely on the movement of lithium ions between the positive and negative electrodes to achieve the charging and discharging process. The life of lithium batteries is affected by many factors, including material quality, usage factors, etc. In order to ensure the use of lithium batteries, the life of lithium batteries can be predicted, thereby reducing the occurrence of lithium batteries that cannot be used due to reaching the end of their life and affecting external operations.

[0003] In the related art, when predicting the life of a lithium battery, the lithium battery's own data is obtained through various sensors, and the obtained data is loaded into a predetermined life prediction model, which can realize the prediction of the remaining life of the lithium battery.

[0004] In the above-mentioned related technologies, during the process of life prediction, the external environment targeted by the life prediction model is a planned specific environment. However, during the actual use of lithium batteries, the external environment will change, and different environments have different effects on the service life of the battery. Therefore, when only using a predetermined model to predict the life, the impact of environmental factors is not considered, resulting in a large deviation in the life prediction, and there is still room for improvement. Summary of the invention

[0005] In order to improve the accuracy of lithium battery life prediction, the present application provides a lithium battery life prediction system and method.

[0006] In a first aspect, the present application provides a lithium battery life prediction method, which adopts the following technical solution:

[0007] A lithium battery life prediction method, comprising:

[0008] Get the battery's own data;

[0009] Input the battery data into a preset life prediction model to determine the theoretical remaining time, and calculate the overall impact parameter based on the theoretical remaining time and the preset fixed impact parameter;

[0010] A detection interval with a width of a preset detection duration is established on a preset time axis with the current time point as the rear end point, and external environment parameters at each time point are obtained in the detection interval;

[0011] Randomly constructing a comparison interval with a width consistent with the detection interval on the time axis, and determining a unique similarity interval from each comparison interval according to external environmental parameters;

[0012] A detection interval of random width is constructed with the rear end point of the similar interval as the front end point, and the external influence parameters corresponding to the external environment parameters at each time point are determined in the detection interval according to the preset parameter matching relationship;

[0013] The actual influencing parameter is determined by summing up the external influencing parameters in the detection interval, and when the actual influencing parameter is consistent with the overall influencing parameter, the width of the corresponding detection interval is output as the actual remaining time.

[0014] Optionally, the step of determining a unique similarity interval from each comparison interval according to the external environment parameter includes:

[0015] According to the external environment parameters and the preset environmental impact level, rules are drawn to determine the external environment type at each time point;

[0016] Counting the same external environment types in the detection interval to determine the number of single environments, and combining the numbers of each single environment according to a preset combination rule to determine the number of environment combinations;

[0017] Determine a comparison quantity combination according to the quantity of each monomer environment in the comparison interval, and perform comparative analysis based on the comparison quantity combination and the environment quantity combination to determine the combination similarity;

[0018] The combination similarity with the largest value is determined according to the preset sorting rule, and the comparison interval corresponding to the combination similarity is determined as the similarity interval.

[0019] Optionally, after the combination similarity is determined, the lithium battery life prediction method further includes:

[0020] Determine whether there are at least two comparison intervals with the same and maximum combination similarity;

[0021] If there are not at least two comparison intervals with the same and largest combination similarity, the comparison interval corresponding to the largest combination similarity is determined as the similarity interval;

[0022] If there are at least two comparison intervals with the same and largest combination similarity, the comparison interval corresponding to the largest combination similarity is defined as the candidate interval;

[0023] Performing comparative analysis based on the comparison quantity combination of any two candidate intervals to determine the candidate similarity, and outputting a binding signal in the two candidate intervals when the candidate similarity is greater than a preset reference similarity;

[0024] Counting the binding signals corresponding to each candidate interval to determine the binding quantity;

[0025] The binding quantity with the largest value is determined according to the sorting rule, and the candidate interval corresponding to the binding quantity is determined as the similar interval.

[0026] Optionally, after the binding quantity is determined, the lithium battery life prediction method further includes:

[0027] Determine whether there are at least two candidate intervals with the same and largest binding quantity;

[0028] If there are not at least two candidate intervals with the same and largest binding number, the candidate interval corresponding to the largest binding number is determined as the similar interval;

[0029] If there are at least two candidate intervals with the same and largest binding number, the average similarity is determined by calculating the average value according to the candidate similarities corresponding to each candidate interval;

[0030] The mean similarity with the largest value is determined according to the sorting rule, and the candidate interval corresponding to the mean similarity is determined as the similarity interval.

[0031] Optionally, the step of constructing a detection interval of random width with the rear end point of the similar interval as the front end point includes:

[0032] The area behind the rear endpoint of the similar interval on the time axis is defined as the detection area, and a preset fixed number of unit intervals are randomly demarcated in the detection area;

[0033] Determine the single-point influencing parameters according to the external environment parameters at each time point in the unit interval, and determine the influencing parameter proportion of each single-point influencing parameter according to all the single-point influencing parameters;

[0034] In all unit intervals, the influence parameter ratios of the same single-point influence parameter are averaged to determine the influence mean ratio, and the detection influence combination is determined according to each influence mean ratio;

[0035] The theoretical life span is determined by calculation based on the detection impact combination and the overall impact parameters, and the interval width range is constructed based on the preset similar duration and the theoretical life span, and the width value is selected within the interval width range to construct the detection interval.

[0036] Optionally, the step of selecting a width value within the interval width range to construct a detection interval includes:

[0037] Construct the detection interval based on the theoretical life span and determine the actual impact parameters;

[0038] When the actual influencing parameters are inconsistent with the overall influencing parameters, a difference calculation is performed based on the actual influencing parameters and the overall influencing parameters to determine the difference influencing parameters;

[0039] Determine whether the phase difference influence parameter is less than zero;

[0040] If the phase difference influence parameter is less than zero, a new interval width range is constructed based on the theoretical life span and the rear end point of the interval width range, and a detection interval is constructed in the new interval width range;

[0041] If the phase difference influence parameter is not less than zero, a new interval width range is constructed based on the theoretical life span and the front end point of the interval width range, and a detection interval is constructed in the new interval width range.

[0042] Optionally, the step of constructing a detection interval within a new interval width range includes:

[0043] Define the width value selected in the previous build detection interval as the build width duration;

[0044] Determine the deviation duration corresponding to the absolute value of the phase difference influencing parameter according to a preset deviation matching relationship;

[0045] The required construction time is determined by calculation based on the construction width time and the deviation time, and the detection interval is constructed based on the required construction time, and a new interval width range is reconstructed based on the new detection interval until the actual impact parameter is consistent with the overall impact parameter.

[0046] In a second aspect, the present application provides a lithium battery life prediction system, which adopts the following technical solution:

[0047] A lithium battery life prediction system, comprising:

[0048] Acquisition module, used to obtain battery data;

[0049] A processing module, connected to the acquisition module, for storing and processing information;

[0050] The processing module inputs the battery data into a preset life prediction model to determine the theoretical remaining time, and calculates the overall impact parameter based on the theoretical remaining time and the preset fixed impact parameter;

[0051] The processing module establishes a detection interval with a width of a preset detection time length on a preset time axis with the current time point as the rear end point, and enables the acquisition module to acquire the external environment parameters at each time point in the detection interval;

[0052] The processing module randomly constructs a comparison interval with a width consistent with the detection interval on the time axis, and determines a unique similarity interval from each comparison interval according to external environmental parameters;

[0053] The processing module constructs a detection interval of random width with the rear end point of the similar interval as the front end point, and determines the external influence parameters corresponding to the external environment parameters at each time point in the detection interval according to the preset parameter matching relationship;

[0054] The processing module performs summation calculation according to the external influencing parameters in the detection interval to determine the actual influencing parameter, and outputs the width of the corresponding detection interval as the actual remaining time when the actual influencing parameter is consistent with the overall influencing parameter.

[0055] In summary, the present application includes at least one of the following beneficial technical effects:

[0056] In the process of lithium battery life prediction, the external environment in which the battery may be located is predicted, so that the specific usage of the lithium battery can be known more accurately, thereby improving the accuracy of lithium battery life prediction; in the process of establishing the detection interval, the detection interval can be constructed according to the actual situation, thereby reducing the number of times the detection interval is established and improving the overall efficiency of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the lithium battery life prediction method.

[0058] Figure 2 It is a module flow chart of the lithium battery life prediction method. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-Figure 2 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0061] The present application embodiment discloses a lithium battery life prediction method, referring to Figure 1 The method flow of the lithium battery life prediction method includes the following steps:

[0062] Step S100: Obtaining battery data.

[0063] The battery data is data on the battery parameters obtained by using various sensors on the lithium battery itself.

[0064] Step S101: inputting the battery data into a preset life prediction model to determine a theoretical remaining time, and performing calculations based on the theoretical remaining time and preset fixed influencing parameters to determine an overall influencing parameter.

[0065] The life prediction model is a pre-established model that can predict the life of a lithium battery when operating under a set theoretical environment through the battery's own data. It is established and processed by the staff. The theoretical remaining time is the remaining life of the lithium battery under theoretical circumstances; the fixed influencing parameter is the parameter that will affect the life of the lithium battery per unit time under the theoretical environment when constructing the life prediction model. This value is determined by the staff through multiple tests in advance and will not be repeated here; the overall influencing parameter is the total parameter value of the lithium battery that can be affected by the environment at present, which is determined by multiplying the theoretical remaining time by the fixed influencing parameter. That is, when the sum of the influencing parameters caused by the external environment reaches the overall influencing parameter, the life of the lithium battery reaches the end and becomes scrapped.

[0066] Step S102: establishing a detection interval with a width of a preset detection duration on a preset time axis with the current time point as the rear end point, and obtaining external environment parameters at each time point in the detection interval.

[0067] The time axis is a coordinate axis formed by the combination of various time points, and the coordinate axis points from the time points that have passed to the time points that have not yet arrived, wherein the time points that have passed are on the left side of the time axis, and the time points that have not yet arrived are on the right side of the time axis. The left side of the time axis is defined as the front, and the right side of the time axis is defined as the back. The detection duration is a fixed duration set by the staff. Establishing a detection interval can facilitate the acquisition of data within the detection duration, which is convenient for subsequent analysis; the external environmental parameters, namely the temperature and humidity of the environment in which the lithium battery is located, can be acquired by installing a temperature and humidity sensor on the lithium battery.

[0068] Step S103: randomly constructing comparison intervals on the time axis whose width is consistent with the detection interval, and determining a unique similarity interval from each comparison interval according to external environment parameters.

[0069] Constructing a comparison interval can determine an interval on the time axis that has the same width as the current detection interval, which is convenient for subsequent analysis; a similar interval is a comparison interval that is most similar to the external environment of the detection interval, and the similar interval can be determined by the method in steps S200-S203.

[0070] Step S104: constructing a detection interval of random width with the rear end point of the similar interval as the front end point, and determining the external influence parameters corresponding to the external environment parameters at each time point in the detection interval according to a preset parameter matching relationship.

[0071] By constructing a detection interval, the data at each time point after the endpoint of the similar interval can be obtained, which is convenient for subsequent analysis; the external influence parameters are the influence parameters that the lithium battery will be affected by the external environment under the external influence parameters. Different external environmental parameters have different effects on the lithium battery, and the corresponding external influence parameters are also different. The parameter matching relationship between the two is determined by the staff through multiple tests in advance, which will not be repeated here.

[0072] Step S105: performing a sum calculation according to the external influence parameters in the detection interval to determine the actual influence parameter, and outputting the width of the corresponding detection interval as the actual remaining time when the actual influence parameter is consistent with the overall influence parameter.

[0073] The actual influencing parameter is the actual influencing parameter of the external environment that the lithium battery will be affected by in the detection interval, and is determined by adding all the external influencing parameters. When the actual influencing parameter is consistent with the overall influencing parameter, it means that the lithium battery will basically reach a scrap state under the current circumstances, that is, the width of the current detection interval is the remaining life of the lithium battery under theoretical circumstances. At this time, the corresponding actual remaining time is output to predict the actual life of the lithium battery.

[0074] The step of determining a unique similarity interval from each comparison interval according to the external environment parameter comprises:

[0075] Step S200: Determine the external environment type at each time point according to external environment parameters and preset environmental impact degree demarcation rules.

[0076] The rules for defining the degree of environmental impact are the rules for classifying the external environment according to external environmental parameters. For example, the following external environment types can be obtained according to different temperature and humidity values, including low temperature and low humidity, low temperature and medium humidity, low temperature and high humidity, medium temperature and low humidity, medium temperature and medium humidity, medium temperature and high humidity, high temperature and low humidity, high temperature and medium humidity, and high temperature and high humidity. Different types have different impacts on lithium batteries.

[0077] Step S201: Counting the same external environment types in the detection interval to determine the number of single environments, and combining the numbers of each single environment according to a preset combination rule to determine an environment number combination.

[0078] The number of monomer environments is the total number of data detected for each external environment type, and the combination rule is a method of sorting and combining the number of monomer environments corresponding to different external environment types in sequence. For example, the above-mentioned low temperature and low humidity, low temperature and medium humidity, low temperature and high humidity, medium temperature and low humidity, medium temperature and medium humidity, medium temperature and high humidity, high temperature and low humidity, high temperature and medium humidity, and high temperature and high humidity are used for combination, where the corresponding number of monomer environments is 9, 8, 7, 6, 5, 4, 3, 2, and 1 in sequence, and the number of environment combinations that can be obtained is 987654321.

[0079] Step S202: determining a comparison quantity combination according to the quantity of each monomer environment in the comparison interval, and performing comparative analysis based on the comparison quantity combination and the environment quantity combination to determine the combination similarity.

[0080] The comparison quantity combination is the combination of the number of monomer environments of each external environment type in the comparison interval according to the combination rule. The combination similarity is a value reflecting the similarity between the comparison quantity combination and the environment quantity combination. The calculation formula is: ,in is the combination similarity, The first combination of the number of environments bit data, is the first combination of comparison quantity bit data, It is the total number of digits in the comparison quantity combination, that is, the total number of external environment types.

[0081] Step S203: determining the combined similarity with the largest value according to a preset sorting rule, and determining the comparison interval corresponding to the combined similarity as the similarity interval.

[0082] The sorting rule is a method set by the staff to sort the size of values, such as the bubble method. The sorting rule can be used to determine the combination similarity with the largest value. That is, the comparison interval corresponding to the combination similarity is most similar to the external environment in the set detection interval at this time, and it is determined as a similar interval for subsequent analysis.

[0083] After the combination similarity is determined, the lithium battery life prediction method also includes:

[0084] Step S300: Determine whether there are at least two comparison intervals with the same and maximum combined similarity.

[0085] The purpose of the judgment is to find out whether there are multiple comparison intervals that meet the combination similarity requirements, so as to facilitate the determination of the only similar interval.

[0086] Step S3001: If there are not at least two comparison intervals with the same and maximum combined similarity, the comparison interval corresponding to the maximum combined similarity is determined as the similarity interval.

[0087] When there are not at least two comparison intervals with the same and maximum combined similarity, it means that there is only one comparison interval that meets the requirements, and it can be determined as a similar interval.

[0088] Step S3002: If there are at least two comparison intervals with the same and largest combined similarity, the comparison interval corresponding to the largest combined similarity is defined as a candidate interval.

[0089] When there are at least two comparison intervals with the same and largest combination similarity, it means that there are multiple comparison intervals that meet the combination similarity requirements and need further analysis. Therefore, they are defined as candidate intervals to distinguish different comparison intervals.

[0090] Step S301: performing comparative analysis based on the comparison quantity combination of any two candidate intervals to determine the candidate similarity, and outputting a binding signal in the two candidate intervals when the candidate similarity is greater than a preset reference similarity.

[0091] The alternative similarity is the degree of similarity between two alternative intervals. The calculation method is the same as the above-mentioned combined similarity, which will not be elaborated here. The benchmark similarity is the minimum alternative similarity set by the staff to determine that the degree of change of two alternative intervals is relatively similar. When the alternative similarity is greater than the benchmark similarity, it means that the changes of the two alternative intervals are relatively similar. At this time, both alternative intervals output binding signals to identify the situation for subsequent analysis.

[0092] Step S302: Count the binding signals corresponding to each candidate interval to determine the binding quantity.

[0093] The binding quantity is the total number of binding signals output by a single candidate interval.

[0094] Step S303: determining the binding quantity with the largest value according to the sorting rule, and determining the candidate interval corresponding to the binding quantity as the similar interval.

[0095] The sorting rule can be used to determine the number of bindings with the largest value, which means that the corresponding candidate interval can represent the largest number of other candidate intervals, which means that the candidate interval is most suitable as a similar interval and can be defined as a similar interval.

[0096] After the binding quantity is determined, the lithium battery life prediction method also includes:

[0097] Step S400: Determine whether there are at least two candidate intervals with the same and largest binding quantity.

[0098] The purpose of the judgment is to find out whether there are multiple candidate intervals that meet the binding quantity requirements, which is convenient for subsequent analysis.

[0099] Step S4001: If there are not at least two candidate intervals with the same and largest binding quantity, the candidate interval corresponding to the largest binding quantity is determined as a similar interval.

[0100] When there are not at least two candidate intervals with the same and largest binding quantity, it means that there is only one candidate interval that meets the requirements, and it can be determined as a similar interval.

[0101] Step S4002: If there are at least two candidate intervals with the same and largest binding quantity, average calculation is performed according to the candidate similarities corresponding to each candidate interval to determine the average similarity.

[0102] When there are at least two candidate intervals with the same and largest binding number, it means that there are multiple candidate intervals that meet the requirements and further analysis is needed; the mean similarity is the average value of the candidate similarities determined by the candidate intervals with the largest binding number.

[0103] Step S401: determining the mean similarity with the largest value according to the sorting rule, and determining the candidate interval corresponding to the mean similarity as the similar interval.

[0104] The mean similarity with the largest value can be determined through the sorting rule, that is, the current candidate interval has the highest similarity with the remaining candidate intervals, and it can be determined as a similar interval.

[0105] The steps of constructing a detection interval of random width with the rear end point of the similar interval as the front end point include:

[0106] Step S500: defining the area behind the rear end point of the similar interval on the time axis as a detection area, and randomly defining a preset fixed number of unit intervals in the detection area.

[0107] The detection area is defined to determine the area where the detection interval can be constructed, which is convenient for subsequent analysis; the fixed number is a fixed value number set by the staff, and the construction of the unit interval can analyze the situation in the detection area.

[0108] Step S501: determining a single-point influencing parameter according to the external environment parameters at each time point in a unit interval, and determining an influencing parameter ratio of each single-point influencing parameter according to all the single-point influencing parameters.

[0109] The single-point influencing parameter is the influencing parameter on the lithium battery under the external environmental parameters corresponding to a single time point, and the influencing parameter proportion is the ratio of the same single-point influencing parameter to all single-point influencing parameters.

[0110] Step S502: performing mean calculation according to the influence parameter ratios of the same single-point influence parameter in all unit intervals to determine the influence mean ratio, and determining the detection influence combination according to each influence mean ratio.

[0111] The influence mean ratio is the average of the influence parameter ratios of the same single point influence parameter determined in all unit intervals; the detection influence combination is the combination composed of all influence mean ratios.

[0112] Step S503: Calculate based on the detection impact combination and the overall impact parameter to determine the theoretical life span, and construct an interval width range based on the preset similar duration and the theoretical life span, and select a width value within the interval width range to construct a detection interval.

[0113] The theoretical life duration refers to the remaining life duration of the lithium battery under theoretical circumstances when subsequent environmental influencing factors are distributed according to the numerical values ​​of the detection influence combination; the approximate duration is the fixed duration set by the staff, the upper end point of the interval width range is the theoretical life duration plus the approximate duration, and the lower end point of the interval width range is the theoretical life duration minus the approximate duration. By constructing the detection interval only by selecting the width value within the interval width range, the number of detection intervals that need to be constructed can be reduced, thereby improving the overall data processing efficiency.

[0114] The steps of selecting a width value within the interval width range to construct a detection interval include:

[0115] Step S600: construct a detection interval based on the theoretical life span and determine actual influencing parameters.

[0116] This method can be used to determine the actual influencing parameters corresponding to the detection interval under the theoretical life span, which is convenient for subsequent analysis.

[0117] Step S601: When the actual influencing parameter is inconsistent with the overall influencing parameter, a difference calculation is performed according to the actual influencing parameter and the overall influencing parameter to determine the difference influencing parameter.

[0118] When the actual influencing parameter is inconsistent with the overall influencing parameter, it means that the appropriate detection interval width value has not been determined and further analysis is needed; the difference influencing parameter is the parameter value obtained by subtracting the overall influencing parameter from the actual influencing parameter.

[0119] Step S602: Determine whether the phase difference influence parameter is less than zero.

[0120] The purpose of the judgment is to find out whether the width of the currently determined detection interval is too large or too small.

[0121] Step S6021: If the phase difference influence parameter is less than zero, a new interval width range is constructed based on the theoretical life span and the rear end point of the interval width range, and a detection interval is constructed in the new interval width range.

[0122] When the phase difference influence parameter is less than zero, it means that the currently determined width is too small, so that the actual influence parameter has not yet met the requirements of the overall influence parameter. Therefore, a new interval width range is constructed by the theoretical life span and the rear end point of the interval width range, which can facilitate the construction of the detection interval. The construction process of the detection interval is constantly approaching the final goal, reducing the situation of meaningless construction of the detection interval and improving data analysis efficiency.

[0123] Step S6022: If the phase difference influence parameter is not less than zero, a new interval width range is constructed based on the theoretical life span and the front end point of the interval width range, and a detection interval is constructed in the new interval width range.

[0124] When the phase difference influence parameter is not less than zero, it means that the currently determined width is too large, causing the actual influence parameter to exceed the requirement of the overall influence parameter. Therefore, a new interval width range is constructed by the theoretical life span and the front end point of the interval width range, which makes it easier to construct the detection interval. The construction process of the detection interval is constantly approaching the final goal, reducing the situation of meaningless construction of the detection interval and improving data analysis efficiency.

[0125] The steps to construct a detection interval within a new interval width range include:

[0126] Step S700: defining the width value selected in the previous construction detection interval as the construction width duration.

[0127] Define the build width duration to distinguish different width values ​​for subsequent analysis.

[0128] Step S701: determining the deviation duration corresponding to the absolute value of the phase difference influencing parameter according to a preset deviation matching relationship.

[0129] The deviation duration is the width difference duration on the detection interval corresponding to the absolute value of the phase difference influence parameter under theoretical circumstances. Different absolute values ​​of the phase difference influence parameters indicate that the currently determined detection interval is different from the actually required detection interval. At this time, the corresponding deviation duration is also different. The deviation matching relationship needs to ensure that the absolute value of the phase difference influence parameter is proportional to the deviation duration.

[0130] Step S702: Calculate the required construction duration based on the construction width duration and the deviation duration, construct a detection interval based on the required construction duration, and reconstruct a new interval width range based on the new detection interval until the actual impact parameter is consistent with the overall impact parameter.

[0131] When the phase difference influence parameter is less than zero, the required construction time is the value obtained by adding the construction width time to the deviation time. When the phase difference influence parameter is not less than zero, the required construction time is the value obtained by subtracting the deviation time from the construction width time. By constructing the detection interval according to the required construction time, the width of the detection interval can be quickly approached to the required target width, thereby greatly reducing the number of detection intervals that need to be constructed and improving the overall efficiency of data processing.

[0132] Reference Figure 2 Based on the same inventive concept, an embodiment of the present invention provides a lithium battery life prediction system, comprising:

[0133] Acquisition module, used to obtain battery data;

[0134] A processing module, connected to the acquisition module, for storing and processing information;

[0135] The processing module inputs the battery data into a preset life prediction model to determine the theoretical remaining time, and calculates the overall impact parameter based on the theoretical remaining time and the preset fixed impact parameter;

[0136] The processing module establishes a detection interval with a width of a preset detection time length on a preset time axis with the current time point as the rear end point, and enables the acquisition module to acquire the external environment parameters at each time point in the detection interval;

[0137] The processing module randomly constructs a comparison interval with a width consistent with the detection interval on the time axis, and determines a unique similarity interval from each comparison interval according to external environmental parameters;

[0138] The processing module constructs a detection interval of random width with the rear end point of the similar interval as the front end point, and determines the external influence parameters corresponding to the external environment parameters at each time point in the detection interval according to the preset parameter matching relationship;

[0139] The processing module performs summation calculation according to the external influencing parameters in the detection interval to determine the actual influencing parameter, and outputs the width of the corresponding detection interval as the actual remaining time when the actual influencing parameter is consistent with the overall influencing parameter;

[0140] A similarity interval determination module, used for determining a unique similarity interval;

[0141] A comparison interval screening module is used to screen multiple comparison intervals that meet the requirements;

[0142] The candidate interval screening module is used to screen multiple candidate intervals that meet the requirements;

[0143] The detection interval construction module is used to shorten the width range of the detection interval and improve the efficiency of data analysis;

[0144] Width range precision module, used to accurately process the width range of the detection interval;

[0145] The width value precision module is used to accurately process the width value of the detection interval.

[0146] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

Claims

1. A lithium battery life prediction method, characterized in that: include: Get the battery's own data; Input the battery data into a preset life prediction model to determine the theoretical remaining time, and calculate the overall impact parameter based on the theoretical remaining time and the preset fixed impact parameter; A detection interval with a width of a preset detection duration is established on a preset time axis with the current time point as the rear end point, and external environment parameters at each time point are obtained in the detection interval; Randomly constructing a comparison interval with a width consistent with the detection interval on the time axis, and determining a unique similarity interval from each comparison interval according to external environmental parameters; A detection interval of random width is constructed with the rear end point of the similar interval as the front end point, and the external influence parameters corresponding to the external environment parameters at each time point are determined in the detection interval according to the preset parameter matching relationship; The actual influencing parameter is determined by summing up the external influencing parameters in the detection interval, and when the actual influencing parameter is consistent with the overall influencing parameter, the width of the corresponding detection interval is output as the actual remaining time.

2. The lithium battery life prediction method according to claim 1, characterized in that: The step of determining a unique similarity interval from each comparison interval according to the external environment parameter comprises: According to the external environment parameters and the preset environmental impact level, rules are drawn to determine the external environment type at each time point; Counting the same external environment types in the detection interval to determine the number of single environments, and combining the numbers of each single environment according to a preset combination rule to determine the number of environment combinations; Determine a comparison quantity combination according to the quantity of each monomer environment in the comparison interval, and perform comparative analysis based on the comparison quantity combination and the environment quantity combination to determine the combination similarity; The combination similarity with the largest value is determined according to the preset sorting rule, and the comparison interval corresponding to the combination similarity is determined as the similarity interval.

3. The lithium battery life prediction method according to claim 2, characterized in that: After the combination similarity is determined, the lithium battery life prediction method also includes: Determine whether there are at least two comparison intervals with the same and maximum combination similarity; If there are not at least two comparison intervals with the same and largest combination similarity, the comparison interval corresponding to the largest combination similarity is determined as the similarity interval; If there are at least two comparison intervals with the same and largest combination similarity, the comparison interval corresponding to the largest combination similarity is defined as the candidate interval; Performing comparative analysis based on the comparison quantity combination of any two candidate intervals to determine the candidate similarity, and outputting a binding signal in the two candidate intervals when the candidate similarity is greater than a preset reference similarity; Counting the binding signals corresponding to each candidate interval to determine the binding quantity; The binding quantity with the largest value is determined according to the sorting rule, and the candidate interval corresponding to the binding quantity is determined as the similar interval.

4. The lithium battery life prediction method according to claim 3, characterized in that: After the binding quantity is determined, the lithium battery life prediction method also includes: Determine whether there are at least two candidate intervals with the same and largest binding quantity; If there are not at least two candidate intervals with the same and largest binding number, the candidate interval corresponding to the largest binding number is determined as the similar interval; If there are at least two candidate intervals with the same and largest binding number, the average similarity is determined by calculating the average value according to the candidate similarities corresponding to each candidate interval; The mean similarity with the largest value is determined according to the sorting rule, and the candidate interval corresponding to the mean similarity is determined as the similarity interval.

5. The lithium battery life prediction method according to claim 2, characterized in that: The steps of constructing a detection interval of random width with the rear end point of the similar interval as the front end point include: The area behind the rear endpoint of the similar interval on the time axis is defined as the detection area, and a preset fixed number of unit intervals are randomly demarcated in the detection area; Determine the single-point influencing parameters according to the external environment parameters at each time point in the unit interval, and determine the influencing parameter proportion of each single-point influencing parameter according to all the single-point influencing parameters; In all unit intervals, the influence parameter ratios of the same single-point influence parameter are averaged to determine the influence mean ratio, and the detection influence combination is determined according to each influence mean ratio; The theoretical life span is determined by calculation based on the detection impact combination and the overall impact parameters, and the interval width range is constructed based on the preset similar duration and the theoretical life span, and the width value is selected within the interval width range to construct the detection interval.

6. The lithium battery life prediction method according to claim 5, characterized in that: The steps of selecting a width value within the interval width range to construct a detection interval include: Construct the detection interval based on the theoretical life span and determine the actual impact parameters; When the actual influencing parameters are inconsistent with the overall influencing parameters, a difference calculation is performed based on the actual influencing parameters and the overall influencing parameters to determine the difference influencing parameters; Determine whether the phase difference influence parameter is less than zero; If the phase difference influence parameter is less than zero, a new interval width range is constructed based on the theoretical life span and the rear end point of the interval width range, and a detection interval is constructed in the new interval width range; If the phase difference influence parameter is not less than zero, a new interval width range is constructed based on the theoretical life span and the front end point of the interval width range, and a detection interval is constructed in the new interval width range.

7. The lithium battery life prediction method according to claim 6, characterized in that: The steps to construct a detection interval within a new interval width range include: Define the width value selected in the previous build detection interval as the build width duration; Determine the deviation duration corresponding to the absolute value of the phase difference influencing parameter according to a preset deviation matching relationship; The required construction time is determined by calculation based on the construction width time and the deviation time, and the detection interval is constructed based on the required construction time, and a new interval width range is reconstructed based on the new detection interval until the actual impact parameter is consistent with the overall impact parameter.

8. A lithium battery life prediction system, characterized in that: include: Acquisition module, used to obtain battery data; A processing module, connected to the acquisition module, for storing and processing information; The processing module inputs the battery data into a preset life prediction model to determine the theoretical remaining time, and calculates the overall impact parameter based on the theoretical remaining time and the preset fixed impact parameter; The processing module establishes a detection interval with a width of a preset detection time length on a preset time axis with the current time point as the rear end point, and enables the acquisition module to acquire the external environment parameters at each time point in the detection interval; The processing module randomly constructs a comparison interval with a width consistent with the detection interval on the time axis, and determines a unique similarity interval from each comparison interval according to external environmental parameters; The processing module constructs a detection interval of random width with the rear end point of the similar interval as the front end point, and determines the external influence parameters corresponding to the external environment parameters at each time point in the detection interval according to the preset parameter matching relationship; The processing module performs summation calculation according to the external influencing parameters in the detection interval to determine the actual influencing parameter, and outputs the width of the corresponding detection interval as the actual remaining time when the actual influencing parameter is consistent with the overall influencing parameter.

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