A method, device and apparatus for monitoring loss of solid-state lithium batteries

Abstract

The present invention relates to the field of battery monitoring technology, and specifically to a loss monitoring method, equipment and device for solid-state lithium batteries. The present invention obtains multiple battery status data sets by analyzing the change trend of data within a historical time range; obtains the battery aging degree at real time based on the distribution of power data of different battery status data sets; obtains the overall durability impact of work on the battery at real time based on the difference fluctuation characteristics of power data between adjacent moments of the working data set; and then obtains the battery durability at real time; obtains the possibility of battery virtual power at real time based on the difference changes in power data between different adjacent moments; corrects the power data at real time based on the change trend of data within the neighborhood range to obtain power correction data at real time; and monitors the loss of lithium batteries. The present invention improves the accuracy of loss monitoring by obtaining accurate power data of the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and in particular to a loss monitoring method, equipment and device for a solid-state lithium battery. Background Art

[0002] During battery use, accurate estimation of the remaining power can help users arrange subsequent work and time reasonably, alleviate users' anxiety about battery life, and improve user experience and dependence. However, when using the ampere-hour integration method to measure power data during lithium battery charging, due to problems such as current sensor accuracy, the power may be inflated, and it is necessary to determine whether the battery has a false power error.

[0003] In the prior art, after the degree of power loss is obtained by analyzing historical power data, a comparison is made based on a threshold set by human experience. If the degree of loss is higher than the threshold, it is determined that the battery power is abnormally drained and a virtual power error occurs. However, due to different degrees of internal battery loss of electronic products under different degrees of use, different degrees of battery aging will occur, affecting the analysis accuracy of the power loss rate, resulting in poor results in judging the virtual power situation of the battery using only the degree of loss. Summary of the invention

[0004] In order to solve the technical problem that the effect of judging the battery virtual power situation by only using the degree of loss is poor, the purpose of the present invention is to provide a loss monitoring method, equipment and device for solid-state lithium batteries. The technical scheme adopted is as follows:

[0005] The present invention provides a loss monitoring method for a solid-state lithium battery, the method comprising:

[0006] Acquire data of the lithium battery in time sequence, the data including power data and temperature data;

[0007] According to the change trend of data within the historical time range, multiple battery status data sets are obtained, and the battery status data sets include a charging data set, a static data set, and a working data set; according to the distribution of power data of different battery status data sets, the battery aging degree at the real time is obtained; according to the difference fluctuation characteristics of power data of the working data set between adjacent moments, the overall durability impact of the work at the real time on the battery is obtained;

[0008] According to the battery aging degree and overall durability impact at the real time, the battery durability at the real time is obtained; according to the difference change of the power data between different adjacent moments, the battery durability and the battery aging degree at the real time, the possibility of battery virtual power at the real time is obtained;

[0009] According to the possibility of battery virtual power at the real time and the change trend of data within the neighborhood, the real time power data is corrected to obtain the real time power correction data;

[0010] The lithium battery loss is monitored based on the real-time power correction data.

[0011] Furthermore, the method for acquiring the battery status data set includes:

[0012] Traverse the power data within the historical time range, obtain the continuous time range that satisfies the power data of the next time is greater than or equal to the power data of the previous time, and form the charging data set with the power data within all the continuous time ranges;

[0013] Obtain the mean of the temperature data at other times except the charging data set as the average temperature; obtain the difference of the power data between each moment and the next moment as the power difference; calculate the mean of the power difference between different moments and the corresponding next moment as the average power difference; if there is a moment where the temperature data is less than the average temperature, and the power difference between the corresponding moment and the next moment is less than the average power difference, the power data at the corresponding moment constitutes the static data set;

[0014] The power data at other times within the historical time range except the charging data set and the static data set are obtained to form a working data set.

[0015] Furthermore, the method for obtaining the battery aging degree includes:

[0016] For each continuous time range in the charging data set, if there are a preset number of identical power data, the corresponding power data is used as the full-charge power, and the number of corresponding power data is used as the full-charge overcharge duration; the ratio between the minimum and maximum values ​​of the full-charge power in all continuous time ranges is obtained as the power influence coefficient;

[0017] If there are no preset number of identical electric quantity data within all continuous time ranges, the electric quantity influence coefficient is set to a positive integer of 1;

[0018] For the working data set and the static data set, the number of power data points that are 0 is counted as the battery power-down duration; all full-charge overcharge durations and battery power-down durations are accumulated as the duration impact coefficient;

[0019] The real-time battery aging degree is obtained based on the power impact coefficient and the duration impact coefficient. The power impact coefficient is negatively correlated with the battery aging degree, while the duration impact coefficient is positively correlated with the battery aging degree.

[0020] Furthermore, the method for obtaining the overall durability impact includes:

[0021] Obtain the difference in power data between the previous adjacent moment and each moment, and calculate the ratio of the difference result and the moment difference as the decline rate corresponding to each moment;

[0022] For each continuous time range in the working data set, obtain the rate difference between the maximum and minimum values ​​of the corresponding decrease rate in all moments; obtain the variance of the corresponding decrease rate at all moments as the fluctuation frequency; multiply the rate difference and the fluctuation frequency to obtain the local durability impact of the work on the battery in each continuous time range;

[0023] Calculate the average of the local durability impact in all continuous time ranges and normalize it as the overall durability impact of the work on the battery at the real time;

[0024] The formula for overall durability impact is:

[0025] ;

[0026] in, Indicates real time The impact of the following operations on the overall durability of the battery; Represents the number of consecutive time ranges in the working data set; Indicates the working data set The variance of the corresponding drop rate at all moments in a continuous time range is the fluctuation frequency; Indicates The rate difference between the maximum and minimum values ​​of the corresponding descent rate at all moments in a continuous time range; represents the normalization function; Indicates The impact of working in a continuous time range on the local durability of the battery.

[0027] Furthermore, the method for obtaining the battery durability includes:

[0028] The real-time battery aging degree and the overall durability impact are integrated and negatively correlated to obtain the real-time battery durability.

[0029] Furthermore, the method for obtaining the possibility of battery virtual power includes:

[0030] Obtaining a ratio between a maximum value of the decrease rate in the working data set and the battery durability as a corrected maximum value of the decrease rate;

[0031] If the descent rate corresponding to the real time moment is greater than the maximum value of the descent rate correction, the difference between the descent rate corresponding to the real time moment and the maximum value of the descent rate correction is calculated as the degree of excess power reduction at the real time moment;

[0032] The product of the battery aging degree and the power over-reduction degree at the real time is obtained, and normalized and mapped as the battery virtual power possibility at the real time;

[0033] The formula for the possibility of battery virtual power is expressed as:

[0034] ;

[0035] ;

[0036] in, Indicates real time The possibility of battery virtual discharge; Indicates real time The degree of excess power drop; Indicates real time The degree of battery aging; Indicates real time The corresponding descent rate; Indicates the maximum value of the drop rate in the working data set; Indicates real time Battery durability; Indicates the maximum value of the descent rate correction at the real time; represents the logistic function.

[0037] Furthermore, the method for acquiring the power correction data includes:

[0038] Determine whether the real-time power data needs to be corrected according to the possibility of battery virtual power at the real-time moment;

[0039] If it is determined that correction is needed, the power data of consecutive adjacent moments within the neighborhood is greater than or equal to the power data at the real time, and the temperature data is less than or equal to the temperature data at the real time, and the corresponding adjacent time range constitutes the working time interval of the battery;

[0040] Calculate the ratio of the average drop rate at all times between the working data set and the working time interval as the rate ratio; calculate the product of the rate ratio and the power data at the real time as the power correction data at the real time;

[0041] The formula for power correction data is:

[0042] ;

[0043] in, Indicates real time The power correction data; Indicates real time The power data; It represents the mean of the corresponding decline rate at all times in the working data set, that is, the average decline rate in the working data set; It represents the mean of the corresponding decline rate at all times in the working time interval, that is, the average decline rate in the working time interval; It represents the rate ratio; Indicates the number of moments in the working time interval.

[0044] Furthermore, judging whether it is necessary to correct the power data at the real time according to the possibility of battery virtual power at the real time includes:

[0045] If the real-time battery false power possibility is greater than or equal to the preset false power threshold, it is determined that the real-time power data needs to be corrected.

[0046] The present invention also proposes a loss monitoring device for a solid-state lithium battery, including a data acquisition module, a data processing module, a virtual power evaluation module, a power correction module, and a battery monitoring module:

[0047] Data acquisition module: acquires lithium battery data in time sequence, including power data and temperature data;

[0048] Data processing module: according to the change trend of data within the historical time range, multiple battery status data sets are obtained, and the battery status data sets include charging data sets, static data sets and working data sets; according to the distribution of power data of different battery status data sets, the battery aging degree at the real time is obtained; according to the difference fluctuation characteristics of power data of the working data set between adjacent moments, the overall durability impact of the work at the real time on the battery is obtained;

[0049] Virtual power evaluation module: obtains the battery durability at real time according to the battery aging degree and overall durability impact at real time; obtains the possibility of battery virtual power at real time according to the difference change of power data between different adjacent moments, the battery durability and battery aging degree at real time;

[0050] Power correction module: according to the possibility of battery virtual power at real time and the change trend of data within the neighborhood, the real-time power data is corrected to obtain the real-time power correction data;

[0051] Battery monitoring module: monitors the loss of lithium batteries based on real-time power correction data.

[0052] The present invention also proposes a loss monitoring device for a solid-state lithium battery, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of a loss monitoring method for a solid-state lithium battery as described above are implemented.

[0053] The present invention has the following beneficial effects:

[0054] The present invention takes into account that the performance and state of the lithium battery will change with time, use conditions and other factors. According to the change trend of data within the historical time range, multiple battery status data sets are obtained, which is helpful to understand the behavior pattern of the battery and conduct targeted analysis; according to the distribution of power data of different battery status data sets, the battery aging degree at the real time is obtained, which more intuitively reflects the degradation of the battery over time; according to the difference fluctuation characteristics of power data between adjacent moments of the working data set, the overall durability impact of the work on the battery at the real time is obtained, reflecting the impact of the working conditions on the battery durability; according to the battery aging degree and the overall durability impact at the real time, the battery durability at the real time is obtained, which is helpful to evaluate the use of the battery and better understand the actual condition of the battery; combined with the difference change of power data between different adjacent moments, the battery virtual power possibility at the real time is obtained, which can more comprehensively evaluate the battery virtual power possibility at the real time; according to the battery virtual power possibility at the real time and the change trend of data within the neighborhood range, the power data at the real time is corrected to obtain the power correction data at the real time, more accurately understand the remaining power of the battery, and reduce the power estimation error; according to the power correction data at the real time, the lithium battery is monitored for loss. The present invention improves the accuracy of loss monitoring by obtaining accurate battery power data. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flow chart of a loss monitoring method for a solid-state lithium battery provided by one embodiment of the present invention;

[0057] Figure 2 A flow chart of a method for obtaining the degree of battery aging provided by an embodiment of the present invention;

[0058] Figure 3 A flow chart of a method for obtaining overall durability impact provided by an embodiment of the present invention;

[0059] Figure 4 A flow chart of a method for obtaining power correction data provided by an embodiment of the present invention;

[0060] Figure 5A structural block diagram of a solid-state lithium battery loss monitoring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the loss monitoring method, equipment and device of a solid-state lithium battery proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0062] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0063] The specific scheme of a loss monitoring method, equipment and device for a solid-state lithium battery provided by the present invention is described in detail below with reference to the accompanying drawings.

[0064] See also Figure 1 , which shows a flow chart of a loss monitoring method for a solid-state lithium battery provided by an embodiment of the present invention, and the specific method includes:

[0065] Step S1: Acquire data of the lithium battery in time sequence, the data including power data and temperature data.

[0066] In an embodiment of the present invention, in order to avoid the occurrence of false power errors in the battery, which affects the accuracy of the estimation of the remaining battery power, the reliability of the user's reference is poor and the working time cannot be reasonably arranged. Therefore, the present invention monitors the loss of solid-state lithium batteries and needs to analyze the monitoring data during the charging and discharging process; first, by installing a battery management system chip at a reasonable position of the lithium battery of the electronic device, the operating parameters of the battery such as voltage, current, and temperature are monitored, and the remaining power data is estimated by the ampere-hour integration method, so that the percentage power converted according to the ratio of the percentage to the rated power is displayed on the linked display screen, providing the user with intuitive battery remaining power information, and acquiring the lithium battery data in time sequence, the data including power data and temperature data.

[0067] It should be noted that, in one embodiment of the present invention, the timing interval is 1 minute. In other embodiments of the present invention, the size of the timing interval can be set according to specific circumstances, which is not limited or elaborated here.

[0068] It should be noted that in the embodiment of the present invention, in order to facilitate subsequent data processing and avoid differences in units and numerical magnitudes between data, the electrical quantity data and temperature data are standardized to eliminate the influence of dimensions in data calculations.

[0069] Step S2: According to the change trend of data within the historical time range, multiple battery status data sets are obtained, and the battery status data sets include a charging data set, a static data set and a working data set; according to the power data distribution of different battery status data sets, the battery aging degree at the real time is obtained; according to the difference fluctuation characteristics of the power data of the working data set between adjacent moments, the overall durability impact of the work on the battery at the real time is obtained.

[0070] Since the batteries of electronic devices usually have three states: working, charging, and static, the behavior and performance of the batteries in different states are different. Dividing the states by the changing trends of the data helps to determine the key characteristic variables of the batteries in different states. The data can be analyzed locally in a targeted manner. According to the changing trends of the data within the historical time range, multiple battery status data sets can be obtained. The battery status data sets include charging data sets, static data sets, and working data sets.

[0071] Preferably, in one embodiment of the present invention, the method for acquiring the battery status data set includes:

[0072] Considering that the battery power data in the charging state will gradually decrease compared to the working and static states, the battery power data in the charging state will continue to increase. The power data in the historical time range is traversed to obtain the continuous time range that satisfies the power data of the next moment is greater than or equal to the power data of the previous adjacent moment. The power data in all continuous time ranges constitute the charging data set;

[0073] Since the loss of battery power data in the static state is slower than that in the working state, and the temperature remains stable or in a decreasing state, the average of the temperature data at other times except the charging data set is obtained as the average temperature; the difference of the power data between each moment and the next moment is obtained as the power difference; the average of the power differences between different moments and the corresponding next moment is calculated as the average power difference; if there is a moment when the temperature data is less than the average temperature, and the power difference between the corresponding moment and the next moment is less than the average power difference, the power data at the corresponding moment constitutes the static data set;

[0074] The power data at other times within the historical time range except the charging data set and the static data set are obtained to form a working data set.

[0075] It should be noted that, in one embodiment of the present invention, the historical moment range is a range consisting of all historical moments, and the continuous moment range is a range consisting of continuous moments in the data set.

[0076] Batteries will age slowly over time as they run continuously. However, due to unreasonable and irregular use of batteries, such as charging after the battery is exhausted, continuing to charge after it is fully charged, and leaving the battery in a low-power state for a long time after it is exhausted, the battery ages faster, affecting the battery's power consumption and performance. Analyzing the power data distribution of different battery status data sets helps to discover the internal laws and trends of the battery and more accurately analyze the battery's aging degree and performance. According to the power data distribution of different battery status data sets, the real-time battery aging degree is obtained.

[0077] Preferably, in one embodiment of the present invention, the method for obtaining the battery aging degree is as follows: Figure 2 , which shows a flow chart of a method for obtaining the battery aging degree, including:

[0078] Step S201: For each continuous time range in the charging data set, if there are a preset number of identical power data, the corresponding power data is used as the full charge power, and the number of corresponding power data is used as the full charge overcharge time; the ratio between the minimum and maximum values ​​of the full charge power in all continuous time ranges is obtained as the power influence coefficient; if there are no preset number of identical power data in all continuous time ranges, the power influence coefficient is set to a positive integer 1.

[0079] During the charging process, when the battery power reaches a stable and constant level, it is more likely to correspond to the battery being fully charged. The more consistent the battery power is at full charge under different charging processes, the better the battery condition is maintained and the less aged it is. Quantifying the overcharging behavior under a fully charged state helps to assess battery aging.

[0080] It should be noted that, in an embodiment of the present invention, the setting of the preset number can be analyzed by the implementer according to the specific situation, and is not limited or elaborated here. For example, if the preset number is 3, and there is a continuous time range of power data of 2, 3, 4, 4, 4, 4, then the corresponding power data 4 is the full power, and the full power overcharge time is 4.

[0081] In one embodiment of the present invention, the formula of the power impact coefficient is expressed as:

[0082] ;

[0083] in, Indicates the power impact coefficient; represents the number of consecutive time ranges in the charging dataset; Indicates Full charge within a continuous time range; Indicates the minimum value of full power in a continuous time range; Indicates the maximum value of full power in a continuous time range; represents the maximum value function; Represents the minimum value function.

[0084] In the formula of the power influence coefficient, the smaller the minimum value of the full charge, the larger the maximum value of the full charge, the less the full charge of the battery is close to the initial full charge of the battery, the smaller the power influence coefficient, and the greater the degree of battery aging.

[0085] Step S202: For the working data set and the static data set, the number of power data equal to 0 is counted as the battery power-down duration; all the full-charge overcharge durations and battery power-down durations are accumulated as the duration influence coefficient.

[0086] If the battery is at low power for a long time, the chemical reaction inside the battery will be more intense, accelerating the battery aging process. By considering the full charge time and the battery power loss time, the aging of the battery under different usage conditions can be more comprehensively evaluated.

[0087] In one embodiment of the present invention, the formula of the duration influence coefficient is expressed as:

[0088] ;

[0089] in, represents the duration influence coefficient; Indicates the charging data set Full overcharge duration in a continuous time range; Indicates the battery power-down time; Represents the number of consecutive time ranges in the charging dataset.

[0090] In the formula of the duration impact coefficient, the longer the overcharging time is, the longer the battery is out of power, and the more irregular the use is, the greater the duration impact coefficient is.

[0091] Step S203: According to the power influence coefficient and the duration influence coefficient, the real-time battery aging degree is obtained. The power influence coefficient is negatively correlated with the battery aging degree, and the duration influence coefficient is positively correlated with the battery aging degree.

[0092] Among them, the positive correlation indicates that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. That is, the larger the time influence coefficient is, the longer the battery is in a low-power state and an overcharged state, the greater the impact on the battery, and the greater the degree of battery aging; the negative correlation indicates that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. That is, the larger the power influence coefficient is, the larger the minimum value of the battery's full charge power is, and the closer it is to the normal full charge power, which means that the battery is less damaged and the battery is less aged.

[0093] In one embodiment of the present invention, the formula for the battery aging degree is expressed as:

[0094] ;

[0095] in, Indicates real time The degree of battery aging; Indicates the power impact coefficient; represents the duration influence coefficient; Represents an exponential function with a natural constant as its base.

[0096] In the formula for battery aging, the greater the power impact coefficient, the greater the power of the battery after being fully charged, and the smaller the degree of battery aging; the greater the duration impact coefficient, the greater the proportion of overcharge duration and power-loss duration, the more unfavorable it is to the battery, and the greater the degree of battery aging.

[0097] The difference fluctuation of the battery power data between adjacent moments reflects the usage intensity of the battery at the corresponding moment. The greater the fluctuation, the more frequent the charge and discharge cycles or the higher the load of the battery, which may have a negative impact on the durability of the battery, making the impact of real-time work on battery durability greater. Based on the difference fluctuation characteristics of the power data of the working data set between adjacent moments, the overall impact of real-time work on battery durability is obtained.

[0098] Preferably, in one embodiment of the present invention, the method for obtaining the overall durability impact degree can be found in Figure 3 , which shows a flow chart of a method for obtaining the overall durability impact, including:

[0099] Step S301: Obtain the difference in power data between the previous adjacent moment and each moment, and calculate the ratio of the difference result and the moment difference as the drop rate corresponding to each moment.

[0100] The decline rate reflects the speed at which the battery charge decreases between adjacent moments, and reflects the changes in the battery usage intensity and durability. The greater the decline rate, the greater the battery usage intensity and the smaller the durability.

[0101] In one embodiment of the present invention, the formula for the drop rate is expressed as: ,in, Indicates The descent rate corresponding to the moment; Indicates The power data at the moment; Indicates The power data at the moment; Represents the difference between moments, .

[0102] Step S302: For each continuous time range in the working data set, obtain the rate difference between the maximum and minimum values ​​of the corresponding decrease rate in all moments; obtain the variance of the corresponding decrease rate in all moments as the fluctuation frequency; multiply the rate difference and the fluctuation frequency as the local durability impact of the work on the battery in each continuous time range.

[0103] The rate difference measures the range of variation of the decline rate between adjacent moments and reflects the instability of battery power consumption. The larger the rate difference, the greater the fluctuation in battery power consumption, which has a negative impact on battery durability.

[0104] It should be noted that, in one embodiment of the present invention, the fluctuation frequency can be expressed by calculating the variance of the corresponding drop rate of all moments in each continuous time range. The larger the variance, the greater the fluctuation frequency, indicating that there is a large difference in battery power consumption at different moments, which has a negative impact on the durability of the battery. In other embodiments of the present invention, the standard deviation and other methods can also be used to express the fluctuation frequency. The specific means are well known to those skilled in the art and will not be described in detail here.

[0105] Step S303: Calculate the average of the local durability impacts in all continuous time ranges and normalize them to serve as the overall durability impact of the work on the battery at the real time.

[0106] In one embodiment of the present invention, the formula for the overall durability impact is expressed as:

[0107] ;

[0108] in, Indicates real time The impact of the following operations on the overall durability of the battery; Represents the number of consecutive time ranges in the working data set; Indicates the working data set The variance of the corresponding drop rate at all moments in a continuous time range is the fluctuation frequency; Indicates The rate difference between the maximum and minimum values ​​of the corresponding descent rate at all moments in a continuous time range; represents the normalization function; Indicates The impact of working in a continuous time range on the local durability of the battery.

[0109] In the formula for overall durability impact, It means calculating the average of the impact of work on the local durability of the battery in all continuous time ranges. The greater the fluctuation frequency and the greater the rate difference, the more likely it is to affect the durability of the battery, and the greater the impact on the overall durability.

[0110] Step S3: According to the battery aging degree and the overall durability impact at the real time, the battery durability at the real time is obtained; according to the difference in power data between different adjacent moments, the battery durability and the battery aging degree at the real time, the possibility of battery virtual power at the real time is obtained.

[0111] Battery aging is a factor that affects durability. As the battery is used for a longer time, its internal structure and chemical composition will change, resulting in a decrease in battery capacity and reduced battery durability. The overall durability impact combines the impact of battery usage in different working time periods on battery durability, providing a comprehensive evaluation perspective. By combining the battery aging and overall durability impact, the durability of the battery at the real time can be more accurately evaluated, providing a basis for subsequent battery management and maintenance. The battery durability at the real time is obtained based on the battery aging and overall durability impact at the real time.

[0112] Preferably, in one embodiment of the present invention, the method for obtaining battery durability includes:

[0113] The real-time battery aging degree and the overall durability impact are integrated and negatively correlated to obtain the real-time battery durability.

[0114] It should be noted that, in some embodiments of the present invention, the real-time battery aging degree and the overall durability impact can be integrated by addition or multiplication. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0115] In one embodiment of the present invention, the formula for battery durability is expressed as:

[0116] ;

[0117] in, Indicates real time Battery durability; Indicates real time The degree of battery aging; Indicates real time The impact of the following operations on the overall battery life.

[0118] In the battery durability formula, Indicates the calculation real time The product of the battery aging degree and the impact of the work on the overall durability of the battery is to integrate the real-time battery aging degree and the overall durability impact. The greater the battery aging degree, the greater the overall durability impact, the greater the product, and the less durable the battery; It means calculating the difference between the positive integer 1 and the product, that is, performing a negative correlation mapping on the product. That is, the larger the product, the smaller the difference, and the lower the battery durability.

[0119] The difference in power data between adjacent moments reflects the actual consumption of battery power. Abnormal differences may indicate that the battery has virtual power. The battery durability and battery aging at real time provide an evaluation of the battery's performance in the current state. By comprehensively considering the difference in power data, battery durability, and battery aging, the possibility of battery virtual power at real time can be more comprehensively evaluated to avoid problems such as unexpected shutdown or data loss caused by virtual power. The possibility of battery virtual power at real time is obtained based on the difference in power data between different adjacent moments, the battery durability at real time, and the battery aging.

[0120] Preferably, in one embodiment of the present invention, the method for obtaining the possibility of battery virtual power includes:

[0121] The ratio between the maximum value of the decline rate in the working data set and the battery durability is obtained as the maximum value of the decline rate correction;

[0122] If the descent rate corresponding to the real time moment is greater than the maximum value of the descent rate correction, the difference between the descent rate corresponding to the real time moment and the maximum value of the descent rate correction is calculated as the degree of excess power reduction at the real time moment;

[0123] The product of the battery aging degree and the power over-reduction degree at the real time is obtained, and normalized mapping is performed as the battery virtual power possibility at the real time.

[0124] In one embodiment of the present invention, the formula for the possibility of battery virtual power is expressed as:

[0125] ;

[0126] ;

[0127] in, Indicates real time The possibility of battery virtual discharge; Indicates real time The degree of excess power drop; Indicates real time The degree of battery aging; Indicates real time The corresponding descent rate; Indicates the maximum value of the drop rate in the working data set; Indicates real time Battery durability; Indicates the maximum value of the descent rate correction at the real time; represents the logistic function.

[0128] In the formula for the possibility of battery virtual charge, It indicates the ratio between the maximum value of the drop rate in the calculated working data set and the battery durability, that is, the maximum value of the drop rate correction. The greater the battery durability, the greater the impact of a large drop in the battery, the more likely it is to be abnormal, and the smaller the maximum value of the drop rate correction; It indicates the difference between the descent rate corresponding to the real time moment and the maximum value of the descent rate correction, that is, the degree of excess power drop. The greater the degree of battery excess drop, the greater the difference, the greater the descent rate corresponding to the real time moment, the more likely the battery will experience excessive power drop at real time, the greater the degree of battery aging, the more serious the decline in battery performance and efficiency, and the greater the possibility of false power.

[0129] Step S4: According to the possibility of battery virtual power at the real time and the change trend of data within the neighborhood, the power data at the real time is corrected to obtain the power correction data at the real time.

[0130] As lithium batteries gradually age during use, the battery capacity decreases and more virtual electricity is generated. By combining the changing trends of data within the neighborhood, we can have a more comprehensive understanding of the actual working status and performance of the battery, and make real-time corrections to the power data to adapt to the changes caused by battery aging, thereby more accurately reflecting the actual status of the battery.

[0131] Preferably, in one embodiment of the present invention, the method for obtaining the power correction data is as follows: Figure 4 , which shows a flow chart of a method for obtaining power correction data, including:

[0132] Step S401: judging whether it is necessary to correct the power data at the real time according to the possibility of battery virtual power at the real time.

[0133] Preferably, in one embodiment of the present invention, judging whether it is necessary to correct the power data at the real time according to the possibility of battery virtual power at the real time includes:

[0134] If the real-time battery false power possibility is greater than or equal to the preset false power threshold, it is determined that the real-time power data needs to be corrected.

[0135] It should be noted that, in one embodiment of the present invention, the preset virtual power threshold is 0.75; in other embodiments of the present invention, the value of the preset virtual power threshold can be set according to specific circumstances, which is not limited or elaborated herein.

[0136] Step S402: If it is determined that correction is needed, the power data of consecutive adjacent moments within the neighborhood range is greater than or equal to the power data at the real time, and the temperature data is less than or equal to the temperature data at the real time, and the corresponding adjacent time range constitutes the working time interval of the battery.

[0137] In order to more accurately evaluate the battery power consumption, the continuous time range in which the power data increases or remains unchanged and the temperature data is small or unchanged before the real time is screened out as a benchmark for the battery to be in a relatively stable working state, reflecting the battery power consumption characteristics.

[0138] It should be noted that, in one embodiment of the present invention, the method for obtaining the neighborhood range is based on the real-time moment and the range constituted by all historical moments; in other embodiments of the present invention, the size of the neighborhood range can be set according to the specific circumstances, which is not limited or elaborated here.

[0139] Step S403: Calculate the ratio of the average decrease rate at all times between the working data set and the working time interval as the rate ratio; calculate the product of the rate ratio and the power data at the real time as the power correction data at the real time.

[0140] The average decrease rate at all corresponding moments between the working data set and the working time interval is obtained by the averaging method, which reflects the average rate of battery power consumption within the working time interval and the average power consumption speed of the battery in all historical working states.

[0141] In one embodiment of the present invention, the formula of the power correction data is expressed as:

[0142] ;

[0143] in, Indicates real time The power correction data; Indicates real time The power data; It represents the mean of the corresponding decline rate at all times in the working data set, that is, the average decline rate in the working data set; It represents the mean of the corresponding decline rate at all times in the working time interval, that is, the average decline rate in the working time interval; It represents the rate ratio; Indicates the number of moments in the working time interval.

[0144] In the formula of the power correction data, since the power data in the working time interval decreases with time, the greater the average decline rate, the greater the decline rate between adjacent moments; the smaller the mean of the decline rate at all moments in the working data set is relative to the decline rate at the real time moment, that is, the smaller the average decline rate in the working data set, the slower the decline rate when working within the historical moment range, the greater the decline rate in the real-time working time interval, the greater the occurrence of virtual electricity, the more it is necessary to reduce the power correction data, and the smaller the power correction data.

[0145] Step S5: Monitor the loss of the lithium battery according to the real-time power correction data.

[0146] Real-time power correction can make corresponding adjustments based on the degree of battery aging, more accurately reflect the actual remaining capacity of the battery, reduce errors, improve monitoring accuracy, and help users better plan usage time and charging plans; through power correction data to monitor the loss of lithium batteries, you can timely understand the health of the battery, discover the battery aging trend, and thus improve battery management efficiency.

[0147] In summary, by analyzing the changing trend of data within the historical time range, multiple battery status data sets are obtained; according to the distribution of power data of different battery status data sets, the battery aging degree at the real time is obtained; according to the difference fluctuation characteristics of power data between adjacent moments in the working data set in the battery status data set, the overall durability impact of work on the battery at the real time is obtained; and then the battery durability at the real time is obtained; combined with the difference changes in power data between different adjacent moments, the possibility of battery virtual power at the real time is obtained; combined with the changing trend of data within the neighborhood range, the power data at the real time is corrected to obtain power correction data at the real time; and the lithium battery is monitored for loss. The present invention improves the accuracy of loss monitoring by obtaining accurate power data of the battery.

[0148] This embodiment further proposes a loss monitoring device for a solid-state lithium battery, such as Figure 5 As shown, the monitoring device includes: a data acquisition module 501, a data processing module 502, a virtual power evaluation module 503, a power correction module 504 and a battery monitoring module 505:

[0149] Data acquisition module 501: acquires lithium battery data in time sequence, the data including power data and temperature data;

[0150] Data processing module 502: according to the change trend of data within the historical time range, multiple battery status data sets are obtained, wherein the battery status data sets include a charging data set, a static data set and a working data set; according to the distribution of power data of different battery status data sets, the battery aging degree at the real time is obtained; according to the difference fluctuation characteristics of power data of the working data set between adjacent moments, the overall durability impact of the work at the real time on the battery is obtained;

[0151] Virtual power evaluation module 503: obtains battery durability at real time according to battery aging degree and overall durability impact at real time; obtains possibility of battery virtual power at real time according to difference change of power data between different adjacent moments, battery durability and battery aging degree at real time;

[0152] The power correction module 504 corrects the power data at the real time according to the possibility of battery virtual power at the real time and the change trend of data within the neighborhood range to obtain the power correction data at the real time;

[0153] Battery monitoring module 505: monitors the loss of lithium batteries according to the real-time power correction data.

[0154] It should be understood that the loss monitoring device for a solid-state lithium battery provided in this embodiment is used to execute the above-mentioned loss monitoring method for a solid-state lithium battery, and therefore has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0155] The present invention also provides a loss monitoring device for a solid-state lithium battery, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of a loss monitoring method for a solid-state lithium battery as described above are implemented.

[0156] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0157] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for monitoring the loss of a solid-state lithium battery, characterized in that: The method comprises: Acquire data of the lithium battery in time sequence, the data including power data and temperature data; According to the change trend of data within the historical time range, multiple battery status data sets are obtained, and the battery status data sets include a charging data set, a static data set, and a working data set; according to the distribution of power data of different battery status data sets, the battery aging degree at the real time is obtained; according to the difference fluctuation characteristics of power data of the working data set between adjacent moments, the overall durability impact of the work at the real time on the battery is obtained; According to the battery aging degree and overall durability impact at the real time, the battery durability at the real time is obtained; according to the difference change of the power data between different adjacent moments, the battery durability and the battery aging degree at the real time, the possibility of battery virtual power at the real time is obtained; According to the possibility of battery virtual power at the real time and the change trend of data within the neighborhood, the real time power data is corrected to obtain the real time power correction data; Monitor the loss of lithium batteries based on real-time power correction data; The method for acquiring the battery status data set includes: Traverse the power data within the historical time range, obtain the continuous time range that satisfies the power data of the next time is greater than or equal to the power data of the previous time, and form the charging data set with the power data within all the continuous time ranges; Obtain the mean of the temperature data at other times except the charging data set as the average temperature; obtain the difference of the power data between each moment and the next moment as the power difference; calculate the mean of the power difference between different moments and the corresponding next moment as the average power difference; if there is a moment where the temperature data is less than the average temperature, and the power difference between the corresponding moment and the next moment is less than the average power difference, the power data at the corresponding moment constitutes the static data set; Obtain the power data of other times except the charging data set and the static data set within the historical time range to form a working data set; The method for obtaining the battery aging degree includes: For each continuous time range in the charging data set, if there are a preset number of identical power data, the corresponding power data is used as the full-charge power, and the number of corresponding power data is used as the full-charge overcharge duration; the ratio between the minimum and maximum values ​​of the full-charge power in all continuous time ranges is obtained as the power influence coefficient; If there are no preset number of identical electric quantity data within all continuous time ranges, the electric quantity influence coefficient is set to a positive integer of 1; For the working data set and the static data set, the number of power data points that are 0 is counted as the battery power-down duration; all full-charge overcharge durations and battery power-down durations are accumulated as the duration impact coefficient; The real-time battery aging degree is obtained based on the power impact coefficient and the duration impact coefficient. The power impact coefficient is negatively correlated with the battery aging degree, while the duration impact coefficient is positively correlated with the battery aging degree. The method for obtaining the overall durability impact includes: Obtain the difference in power data between the previous adjacent moment and each moment, and calculate the ratio of the difference result and the moment difference as the decline rate corresponding to each moment; For each continuous time range in the working data set, obtain the rate difference between the maximum and minimum values ​​of the corresponding decrease rate in all moments; obtain the variance of the corresponding decrease rate at all moments as the fluctuation frequency; multiply the rate difference and the fluctuation frequency as the local durability impact of the work on the battery in each continuous time range; Calculate the average of the local durability impact in all continuous time ranges and normalize it as the overall durability impact of the work on the battery at the real time; The formula for overall durability impact is: ； in, Indicates real time The impact of the following operations on the overall durability of the battery; Represents the number of consecutive time ranges in the working data set; Indicates the working data set The variance of the corresponding drop rate at all moments in a continuous time range is the fluctuation frequency; Indicates The rate difference between the maximum and minimum values ​​of the corresponding descent rate at all moments in a continuous time range; represents the normalization function; Indicates The impact of working in a continuous time range on the local durability of the battery; The method for obtaining battery durability includes: The real-time battery aging degree and the overall durability impact are integrated and negatively correlated to obtain the real-time battery durability. The method for obtaining the possibility of battery virtual power includes: Obtaining a ratio between a maximum value of the decrease rate in the working data set and the battery durability as a corrected maximum value of the decrease rate; If the descent rate corresponding to the real time moment is greater than the maximum value of the descent rate correction, the difference between the descent rate corresponding to the real time moment and the maximum value of the descent rate correction is calculated as the degree of excess power reduction at the real time moment; The product of the battery aging degree and the power over-reduction degree at the real time is obtained, and normalized and mapped as the battery virtual power possibility at the real time; The formula for the possibility of battery virtual power is expressed as: ； ； in, Indicates real time The possibility of battery virtual discharge; Indicates real time The degree of excess power drop; Indicates real time The degree of battery aging; Indicates real time The corresponding descent rate; Indicates the maximum value of the drop rate in the working data set; Indicates real time Battery durability; Indicates the maximum value of the descent rate correction at the real time; represents the logistic function; The method for obtaining the power correction data includes: Determine whether the real-time power data needs to be corrected according to the possibility of battery virtual power at the real-time moment; If it is determined that correction is needed, the power data of consecutive adjacent moments within the neighborhood is greater than or equal to the power data at the real time, and the temperature data is less than or equal to the temperature data at the real time, and the corresponding adjacent time range constitutes the working time interval of the battery; Calculate the ratio of the average drop rate at all times between the working data set and the working time interval as the rate ratio; calculate the product of the rate ratio and the power data at the real time as the power correction data at the real time; The formula for power correction data is: ； in, Indicates real time The power correction data; Indicates real time The power data; It represents the mean of the corresponding decline rate at all times in the working data set, that is, the average decline rate in the working data set; It represents the mean of the corresponding decline rate at all times in the working time interval, that is, the average decline rate in the working time interval; Indicates the rate ratio.

2. The method for monitoring the loss of a solid-state lithium battery according to claim 1, characterized in that: The determining whether it is necessary to correct the power data at the real time according to the possibility of battery virtual power at the real time includes: If the real-time battery false power possibility is greater than or equal to the preset false power threshold, it is determined that the real-time power data needs to be corrected.

3. A solid-state lithium battery loss monitoring device using the method of claim 1, characterized in that: Including data acquisition module, data processing module, virtual power assessment module, power correction module, battery monitoring module: Data acquisition module: acquires lithium battery data in time sequence, including power data and temperature data; Data processing module: according to the change trend of data within the historical time range, multiple battery status data sets are obtained, wherein the battery status data sets include a charging data set, a static data set and a working data set; according to the power data distribution of different battery status data sets, the battery aging degree at the real time is obtained; According to the difference fluctuation characteristics of the power data of the working data set between adjacent moments, the overall impact of the work on the battery life at the real time moment is obtained; Virtual power evaluation module: obtains the battery durability at real time according to the battery aging degree and overall durability impact at real time; obtains the possibility of battery virtual power at real time according to the difference change of power data between different adjacent moments, the battery durability and battery aging degree at real time; Power correction module: according to the possibility of battery virtual power at real time and the change trend of data within the neighborhood, the real-time power data is corrected to obtain the real-time power correction data; Battery monitoring module: monitors the loss of lithium batteries based on real-time power correction data.

4. A loss monitoring device for a solid-state lithium battery, characterized in that: The device stores a program or instruction, and when the program or instruction is executed by the processor, the steps of a loss monitoring method for a solid-state lithium battery as described in any one of claims 1 to 2 are implemented.

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