A lithium battery intelligent energy storage monitoring method and device based on data analysis

Abstract

The present invention is applicable to the technical field of system monitoring, and provides a lithium battery intelligent energy storage monitoring method and device based on data analysis, the lithium battery intelligent energy storage monitoring method based on data analysis includes: calculating the battery health score according to the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity and battery cell pressure difference; calculating the use goodness according to the current number of cycles, deep charge and discharge ratio and operating temperature; calculating the battery remaining life according to the battery health score and use goodness. The above technical solution realizes the prediction of battery health status and remaining life, and significantly improves the management and use efficiency of lithium batteries.

Description

Technical Field

[0001] The present invention belongs to the technical field of system monitoring, and in particular relates to a lithium battery intelligent energy storage monitoring method and device based on data analysis. Background Art

[0002] Battery life is usually expressed in charge and discharge cycles, which is the number of charge and discharge cycles that a battery can complete before its standard capacity drops to a certain percentage (usually 80%). Battery life is affected by many factors, including charge and discharge rate, operating environment temperature, storage conditions, frequency of use, etc. Proper use and maintenance can significantly extend the service life of the battery.

[0003] Traditional battery life measurement is often achieved by measuring the capacity of the battery after it is fully discharged and recording the number of charge and discharge cycles the battery completes before the capacity drops to a certain percentage (usually 80%) of the standard capacity. However, it is impossible to measure the battery life of different users during daily use, which is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a lithium battery intelligent energy storage monitoring method and device based on data analysis to solve the technical problem that the battery life of different users during daily use cannot be calculated.

[0005] A first aspect of an embodiment of the present invention provides a lithium battery intelligent energy storage monitoring system based on data analysis, wherein the lithium battery intelligent energy storage monitoring system based on data analysis includes an intelligent control unit, a sensor network, a battery management system, a storage unit, and a user interface unit;

[0006] The sensor network is used to collect the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power level, and the deep charge and discharge ratio refers to the proportion of the time when the battery is at high power and low power in the total time;

[0007] The intelligent control unit is used to obtain the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack;

[0008] The intelligent control unit is used to calculate a battery health score according to the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference;

[0009] The intelligent control unit is used to calculate the goodness of use according to the current number of cycles, the deep charge and discharge ratio and the operating temperature;

[0010] The intelligent control unit is used to calculate the remaining life of the battery according to the battery health score and the good usage.

[0011] A second aspect of an embodiment of the present invention provides a lithium battery intelligent energy storage monitoring method based on data analysis, and the lithium battery intelligent energy storage monitoring method based on data analysis includes:

[0012] Obtain the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power level, and the deep charge and discharge ratio refers to the ratio of the time when the battery is at high power and low power in the total time;

[0013] Calculating a battery health score according to the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity, and the battery cell voltage difference;

[0014] Calculating the usage goodness according to the current number of cycles, the deep charge and discharge ratio, and the operating temperature;

[0015] The remaining battery life is calculated according to the battery health score and the usage goodness.

[0016] Further, the step of calculating the battery health score according to the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference comprises:

[0017] Substituting the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference into the following first mathematical model to obtain the battery health score;

[0018] The first mathematical model is:

[0019] ;

[0020] in, represents the battery health score, represents the current charging efficiency, represents the standard charging efficiency, represents the current discharge efficiency, represents the standard discharge efficiency, represents the current internal resistance, Indicates the standard internal resistance, represents the capacity difference, Indicates standard capacity, represents the battery cell pressure, , , and Represents the adjustment coefficient.

[0021] Furthermore, the step of calculating the goodness of use according to the current number of cycles, the deep charge and discharge ratio and the operating temperature includes:

[0022] Substituting the current number of cycles, the deep charge and discharge ratio, and the operating temperature into the following second mathematical model to obtain the goodness of use;

[0023] The second mathematical model is:

[0024] ;

[0025] in, Indicates the goodness of use, Indicates the current loop number, Indicates the maximum number of cycles of the battery. Indicates the deep charge and discharge ratio, represents the operating temperature, Indicates the optimal operating temperature of the battery. Indicates the maximum operating temperature of the battery. Indicates the minimum operating temperature of the battery. , and Represents the adjustment factor.

[0026] Furthermore, the step of calculating the remaining battery life according to the battery health score and the good use degree includes:

[0027] Calculate the remaining number of battery cycles according to the battery health score, the goodness of use, the current number of cycles, and the maximum number of cycles;

[0028] The average cycle period is calculated according to the usage time and the current cycle number, and the remaining battery life is calculated according to the remaining battery cycle number and the average cycle period.

[0029] Further, the step of calculating the remaining number of battery cycles according to the battery health score, the goodness of use, the current number of cycles and the maximum number of cycles includes:

[0030] Substituting the battery health score, the usage goodness, the current cycle number and the maximum cycle number into a third mathematical model to obtain the remaining cycle number of the battery;

[0031] The third mathematical model is:

[0032] ;

[0033] in, Indicates the remaining number of cycles of the battery, represents the battery health score, Indicates the goodness of use, Indicates the current loop number, represents the maximum number of cycles, , and Represents the adjustment factor.

[0034] Further, the step of calculating the average cycle period according to the usage time and the current cycle number, and calculating the remaining battery life according to the remaining battery cycle number and the average cycle period includes:

[0035] Divide the usage time by the current number of cycles to obtain the average cycle period;

[0036] The remaining battery life is obtained by multiplying the remaining battery cycle number and the average cycle period.

[0037] A third aspect of an embodiment of the present invention provides a lithium battery intelligent energy storage monitoring device based on data analysis, comprising:

[0038] An acquisition unit is used to acquire the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power, and the deep charge and discharge ratio refers to the ratio of the time when the battery is at high power and low power in the total time;

[0039] a first calculation unit, configured to calculate a battery health score according to the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity, and the battery cell voltage difference;

[0040] A second calculation unit is used to calculate the use goodness according to the current cycle number, the deep charge and discharge ratio and the operating temperature;

[0041] The third calculation unit is used to calculate the remaining battery life according to the battery health score and the usage goodness.

[0042] A third aspect of an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0043] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0044] Compared with the prior art, the embodiment of the present invention has the following beneficial effects: the sensor network can collect multiple key parameters of the lithium battery pack in real time, including the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell pressure difference, current number of cycles, maximum number of cycles, deep charge and discharge ratio and operating temperature. The intelligent control unit uses the collected data and combines complex data analysis algorithms to calculate the battery health score. The score comprehensively considers multiple indicators such as the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity and battery cell pressure difference, and can accurately reflect the current health status of the lithium battery. By analyzing the current number of cycles, deep charge and discharge ratio and operating temperature of the battery, the intelligent control unit can calculate the battery's use wellness. This evaluation is used to analyze the impact of usage habits and environment on battery life. Based on the comprehensive results of the battery health score and the use wellness, the intelligent control unit can accurately predict the remaining life of the battery. Knowing the replacement time of the battery in advance can avoid inconvenience or safety hazards caused by sudden battery failure. In summary, the lithium battery intelligent energy storage monitoring system significantly improves the use management and life prediction capabilities of lithium batteries by integrating intelligent control units, sensor networks, battery management systems, storage units and user interface units. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. 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 creative work.

[0046] Figure 1 A schematic flow chart of a lithium battery intelligent energy storage monitoring method based on data analysis provided by the present invention is shown;

[0047] Figure 2 A schematic diagram of a lithium battery intelligent energy storage monitoring device based on data analysis provided by an embodiment of the present invention is shown;

[0048] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0050] The embodiment of the present invention provides a lithium battery intelligent energy storage monitoring method and device based on data analysis to solve the technical problem that the battery life of different users during daily use cannot be calculated.

[0051] First, the present invention provides a lithium battery intelligent energy storage monitoring system based on data analysis. The lithium battery intelligent energy storage monitoring system includes an intelligent control unit, a sensor network, a battery management system, a storage unit and a user interface unit; the sensor network is used to collect the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average of the voltage difference between each battery cell at a fixed power level, and the deep charge and discharge ratio refers to the proportion of the time when the battery is at high power and low power in the total time; the intelligent control unit is used to obtain the current charging efficiency of the lithium battery pack efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current number of cycles, maximum number of cycles, deep charge and discharge ratio and operating temperature; the intelligent control unit is used to calculate the battery health score according to the current charging efficiency, the standard charging efficiency, the current discharge efficiency, the standard discharge efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference; the intelligent control unit is used to calculate the use goodness according to the current number of cycles, the deep charge and discharge ratio and the operating temperature; the intelligent control unit is used to calculate the remaining life of the battery according to the battery health score and the use goodness.

[0052] In this embodiment, the sensor network can collect multiple key parameters of the lithium battery pack in real time, including the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell pressure difference, current number of cycles, maximum number of cycles, deep charge and discharge ratio and operating temperature. The intelligent control unit uses the collected data and combines complex data analysis algorithms to calculate the battery health score. The score comprehensively considers multiple indicators such as the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity and battery cell pressure difference, and can accurately reflect the current health status of the lithium battery. By analyzing the current number of cycles, deep charge and discharge ratio and operating temperature of the battery, the intelligent control unit can calculate the battery's good use. This evaluation is used to analyze the impact of usage habits and environment on battery life. Based on the comprehensive results of battery health score and good use, the intelligent control unit can accurately predict the remaining life of the battery. Knowing the replacement time of the battery in advance can avoid inconvenience or safety hazards caused by sudden battery failure. In summary, the lithium battery intelligent energy storage monitoring system significantly improves the use management and life prediction capabilities of lithium batteries by integrating intelligent control units, sensor networks, battery management systems, storage units and user interface units.

[0053] Secondly, the present invention provides a lithium battery intelligent energy storage monitoring method based on data analysis. Figure 1 , Figure 1 FIG. 1 is a schematic flow chart of a lithium battery intelligent energy storage monitoring method based on data analysis provided by the present invention. Figure 1 As shown, the lithium battery intelligent energy storage monitoring method based on data analysis may include the following steps:

[0054] Step 101: Obtain the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average of the voltage difference between each battery cell at a fixed power level, and the deep charge and discharge ratio refers to the ratio of the time when the battery is at high power and low power in the total time.

[0055] The current charging efficiency refers to the charging efficiency currently collected. The standard charging efficiency refers to the charging efficiency of the battery under ideal conditions. The current discharge efficiency refers to the discharge efficiency currently collected. The standard discharge efficiency refers to the discharge efficiency of the battery under ideal conditions.

[0056] Battery internal resistance refers to the resistance inside the battery, which is an important characteristic of the battery. Battery internal resistance affects the battery's output power, voltage stability and energy efficiency. Current internal resistance refers to the currently measured internal resistance, and standard internal resistance refers to the internal resistance of the battery under ideal conditions.

[0057] Battery capacity is usually measured in milliampere hours (mAh), which is a unit of measure for the amount of charge a battery can supply at a rated voltage. Actual capacity refers to the current capacity of the battery, while standard capacity refers to the capacity under ideal conditions.

[0058] The battery cycle number refers to the number of times the battery has been charged and discharged. The current cycle number refers to the number of times the battery has been charged and discharged. The maximum cycle number refers to the theoretical maximum cycle number of the battery, which is generally based on experimental data and experience.

[0059] The deep charge and discharge ratio refers to the ratio of the time when the battery is at high and low power in the total time. High power means that the battery capacity exceeds the maximum capacity by more than 80%, and low power means that the battery capacity is less than 20% of the maximum capacity.

[0060] The operating temperature refers to the ambient temperature around the battery collected at multiple sampling times and averaged.

[0061] Step 102: Calculate a battery health score based on the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference.

[0062] Battery health score is a method to evaluate battery performance and health. It can be used to understand the remaining life and performance of the battery. The battery health score can be calculated by comprehensively analyzing the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity and battery cell voltage difference. The charging efficiency reflects the energy loss of the battery during the charging process. The comparison between the current charging efficiency and the standard charging efficiency can evaluate the battery charging performance. The discharge efficiency refers to the energy loss of the battery during the discharge process. The comparison between the current discharge efficiency and the standard discharge efficiency can evaluate the battery discharge performance. The internal resistance of the battery affects the charging and discharging efficiency of the battery. The larger the internal resistance, the worse the battery performance. The difference between the current capacity and the standard capacity reflects the battery capacity attenuation. The larger the capacity difference, the shorter the battery life may be. The voltage difference between different battery cells will affect the performance and health of the entire battery pack. Excessive voltage difference may cause battery imbalance and safety hazards. A scoring model is established by comprehensively considering the above factors. The battery health score is calculated by setting the weights and scoring criteria of different factors. This score is used to judge the health of the battery. The specific calculation logic is as follows:

[0063] Specifically, step 102 includes:

[0064] Substituting the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference into the following first mathematical model to obtain the battery health score;

[0065] The first mathematical model is:

[0066] ;

[0067] in, represents the battery health score, represents the current charging efficiency, represents the standard charging efficiency, represents the current discharge efficiency, represents the standard discharge efficiency, represents the current internal resistance, Indicates the standard internal resistance, represents the capacity difference, Indicates standard capacity, represents the battery cell pressure, , , and Represents the adjustment coefficient.

[0068] It is worth noting that and They respectively represent the ratios of the current charging efficiency and discharging efficiency to their rated values. The closer these ratios are to 1, the closer the battery performance is to the optimal state.

[0069] It represents the inverse ratio of the current internal resistance to the rated internal resistance. An increase in internal resistance usually means battery aging, so the closer this ratio is to 1, the better.

[0070] It represents the inverse ratio of the capacity difference to the rated capacity. The smaller the capacity difference, the better the battery health.

[0071] It represents an exponential decay function, which is used to represent the impact of the voltage difference of the battery cell on the health score. The larger the voltage difference, the faster the exponential decay and the greater the reduction in the health score.

[0072] The ratio of the first three items is weighted averaged to comprehensively consider the effects of charging efficiency, discharging efficiency and internal resistance. The inverse ratio of the capacity difference is used to further adjust the health score to ensure that the score is significantly reduced when the capacity difference is large. An exponential decay function is used to strongly influence the health score to ensure that the battery cell voltage difference has a significant impact on the health score. In this way, multiple key factors can be comprehensively considered to give a more comprehensive battery health score. Depending on the actual application, the weights and adjustment parameters can be adjusted to suit different types of batteries and usage scenarios.

[0073] By considering the ratio of charging and discharging efficiency separately, the performance of the battery under different working conditions can be comprehensively evaluated. and This allows the degree of focus on charge and discharge efficiency to be adjusted based on application requirements. The increase in internal resistance is usually an important sign of battery aging, and by considering the ratio of internal resistance and giving it a weight , which can accurately reflect the aging degree of the battery.

[0074] By calculating the ratio of the capacity difference to the rated capacity and using the inverse ratio , the mathematical model can effectively reflect the battery performance degradation caused by capacity loss. Used to adjust the extent of this effect.

[0075] The consistency between battery cells is critical to the performance of the overall battery pack. The mathematical model can significantly reflect the impact of battery cell voltage difference on health status, ensuring that the health score of battery packs with large voltage difference is significantly reduced. This formula can be used to evaluate the health status of the battery in real time and accurately, helping the BMS system to detect problems in time and take corresponding measures. This is of great significance for extending battery life, improving system safety and optimizing performance.

[0076] Step 103: Calculate the usage goodness according to the current cycle number, the deep charge and discharge ratio and the operating temperature.

[0077] Goodness of use refers to the degree of damage to the battery during its past use. The life of a battery is usually measured by the number of cycles, and a complete charge and discharge process is called a cycle. Each cycle will cause the aging of the battery material and the decay of the capacity. After a large number of cycles, the chemical substances inside the battery will undergo irreversible changes, such as the deterioration of the electrode material and the decomposition of the electrolyte, which will reduce the capacity and efficiency of the battery. Therefore, the number of cycles is an important indicator for evaluating the health of the battery. Generally speaking, the higher the number of cycles, the greater the degree of battery aging and the lower the goodness of use.

[0078] High depth of discharge will cause more intense chemical reactions inside the battery, which may accelerate electrode corrosion, irreversible loss of lithium ions, and other phenomena, thereby accelerating battery aging.

[0079] The operating temperature of the battery has a significant impact on its performance and life. Too high or too low a temperature will accelerate the aging process of the battery.

[0080] By comprehensively considering the number of cycles, deep charge and discharge ratio, and operating temperature, a more comprehensive battery health assessment model can be established. The specific logic is as follows:

[0081] Specifically, step 103 includes:

[0082] Substituting the current number of cycles, the deep charge and discharge ratio, and the operating temperature into the following second mathematical model to obtain the goodness of use;

[0083] The second mathematical model is:

[0084] ;

[0085] in, Indicates the goodness of use, Indicates the current loop number, Indicates the maximum number of cycles of the battery. Indicates the deep charge and discharge ratio, represents the operating temperature, Indicates the optimal operating temperature of the battery. Indicates the maximum operating temperature of the battery. Indicates the minimum operating temperature of the battery. , and Represents the adjustment factor.

[0086] It is worth noting that the life of a battery is usually directly related to the number of charge and discharge cycles. The more cycles there are, the more significant the capacity decay and internal resistance increase of the battery. This relationship can usually be fitted by empirical formulas or experimental data.

[0087] Use the current loop count Divide by the maximum number of cycles of the battery to indicate what proportion of the cycles have been used, and then use To adjust the nonlinear effect of this relationship. The depth of charge and discharge has a significant impact on the battery life. Too high or too low discharge depth will accelerate the aging of the battery.

[0088] Using a parabola Indicates the degree of deep charge and discharge deviation (0.2), multiplied by an adjustment parameter This form reflects the symmetry, that is, the effect of deviating from the 20% charge and discharge depth is similar whether it is upward or downward. The deviation of the battery's operating temperature from the optimal temperature will affect its performance and life. Too high or too low temperature will accelerate the aging and performance degradation of the battery. The optimal temperature is usually given in its design specifications. Indicates the degree of deviation between the current temperature and the optimal temperature, normalized to the operating temperature range Then the square term is used to represent the impact of this deviation, multiplied by the adjustment parameter This form reflects the symmetrical effects of temperature deviations from the optimum value.

[0089] The second mathematical model is a subtraction model as a whole. The initial value is 1, which indicates the good use of the battery in the best state. As the number of cycles increases, the depth of charge and discharge deviates from the optimal value, and the temperature deviates from the optimal value, the good use of each negative impact item gradually decreases. The highest value is 1, the lowest value is 0, and the use () ensures that the result will not be negative. , and These parameters are used to adjust the weight and degree of nonlinearity of each influencing factor.

[0090] The second mathematical model combines three key factors (cycle number, depth of charge and discharge ratio, and operating temperature), all of which have a significant impact on battery life and performance. Through comprehensive evaluation, the health status of the battery can be more accurately reflected. The second mathematical model introduces exponential and square terms to reflect the nonlinear effects of various factors on battery life. These nonlinear relationships are more in line with the actual battery aging and performance degradation laws, improving the accuracy of the evaluation. The parameters in the formula , and It can be adjusted according to the specific battery type and usage scenario, and can be fitted and optimized through experimental data to further improve the accuracy of the evaluation.

[0091] Step 104: Calculate the remaining battery life according to the battery health score and the usage goodness.

[0092] Specifically, step 104 includes steps 1041 to 1042:

[0093] Step 1041: Calculate the remaining number of battery cycles according to the battery health score, the usage goodness, the current number of cycles, and the maximum number of cycles;

[0094] Specifically, step 1041 includes:

[0095] Substituting the battery health score, the usage goodness, the current cycle number and the maximum cycle number into a third mathematical model to obtain the remaining cycle number of the battery;

[0096] The third mathematical model is:

[0097] ;

[0098] in, Indicates the remaining number of cycles of the battery, represents the battery health score, Indicates the goodness of use, Indicates the current loop number, represents the maximum number of cycles, , and Represents the adjustment factor.

[0099] It is worth noting that the battery health score directly affects the remaining cycle count. The higher the health score, the longer the remaining life. The maximum cycle count provides a benchmark value for calculating the current usage ratio and predicting the remaining life. The current cycle count is used together with the maximum cycle count to calculate the battery usage ratio. It is a tuning parameter used to reflect the exponential decay rate of battery performance as the number of uses increases. It is a tuning parameter used to adjust the nonlinear characteristics of the attenuation curve.

[0100] The degree of usage directly affects the life of the battery. The higher the degree of usage, the smaller the negative impact on the battery. is an adjustment parameter used to reflect the impact of usage quality on battery life. The term describes the nonlinear effect of good use on the number of remaining cycles. Large) will significantly reduce the number of remaining cycles.

[0101] The battery health score, current cycle count, and maximum cycle count are combined to estimate the basic remaining life of the battery through exponential decay and nonlinear correction. By considering the impact of usage wellness, the basic remaining life is adjusted to make the formula more in line with actual usage. By combining multiple key parameters (health score, usage wellness, current cycle number, maximum cycle number), it can more accurately reflect the nonlinear characteristics of the battery life curve.

[0102] Step 1042: Calculate the average cycle period according to the usage time and the current number of cycles, and calculate the remaining battery life according to the remaining number of battery cycles and the average cycle period.

[0103] Specifically, step 1042 specifically includes: dividing the usage time and the current number of cycles to obtain the average cycle period; multiplying the remaining number of battery cycles and the average cycle period to obtain the remaining battery life.

[0104] The usage time refers to the total usage time of the battery from the time it leaves the factory to the time of testing. Dividing the usage time by the current number of cycles can give the usage time of a single battery cycle (i.e. the average cycle period).

[0105] By multiplying the remaining number of battery cycles and the average cycle period, the remaining battery usage time (i.e., the remaining battery life) can be obtained. It is understandable that since it is not necessary to calculate the extremely accurate battery life, a rough estimate of the remaining battery life can be achieved through the above method. In this way, the replacement time of the battery can be known in advance to avoid the inconvenience or safety hazard caused by sudden battery failure.

[0106] In this embodiment, by collecting parameters such as the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity and battery cell pressure difference of the lithium battery pack, the health status of the battery can be comprehensively and accurately evaluated. The calculation of the battery health score comprehensively considers the above parameters to ensure the accuracy and reliability of the evaluation results. Real-time monitoring of the current number of cycles, deep charge and discharge ratio and operating temperature of the battery can effectively reflect the state of the battery in actual use. By calculating the goodness of use, the use of the battery can be dynamically evaluated and targeted maintenance suggestions can be provided. Combining the battery health score and the goodness of use, the remaining life of the battery is calculated, so that the user can understand the battery life in advance, reasonably arrange the replacement and maintenance plan of the battery, and improve the economy and safety of battery use. By real-time monitoring and analysis of the battery status parameters, the battery charging and discharging strategy is optimized, the battery life is extended, and the overall energy efficiency of the system is improved. According to the usage habits and environmental conditions of different users, personalized battery management and maintenance suggestions are provided to ensure the excellent performance of the battery in various application scenarios. In summary, the lithium battery intelligent energy storage monitoring method based on data analysis provided by the present invention not only solves the problem that traditional technology cannot measure battery life, but also realizes the prediction of battery health status and remaining life through comprehensive analysis of multi-dimensional parameters, thereby significantly improving the management and use efficiency of lithium batteries.

[0107] like Figure 2 The present invention provides a lithium battery intelligent energy storage monitoring device based on data analysis 2, see Figure 2 , Figure 2 A schematic diagram of a lithium battery intelligent energy storage monitoring device based on data analysis provided by the present invention is shown, such as Figure 2 A lithium battery intelligent energy storage monitoring device based on data analysis includes:

[0108] The acquisition unit 21 is used to acquire the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power, and the deep charge and discharge ratio refers to the proportion of the time when the battery is at high power and low power in the total time;

[0109] A first calculation unit 22, configured to calculate a battery health score according to the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference;

[0110] A second calculation unit 23 is used to calculate the usage goodness according to the current cycle number, the deep charge and discharge ratio and the operating temperature;

[0111] The third calculation unit 24 is used to calculate the remaining battery life according to the battery health score and the usage goodness.

[0112] The present invention provides a lithium battery intelligent energy storage monitoring device based on data analysis. By collecting parameters such as the current charging efficiency, standard charging efficiency, current discharge efficiency, standard discharge efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity and battery cell pressure difference of the lithium battery pack, the health status of the battery can be comprehensively and accurately evaluated. The calculation of the battery health score comprehensively considers the above parameters to ensure the accuracy and reliability of the evaluation results. Real-time monitoring of the current cycle number, deep charge and discharge ratio and operating temperature of the battery can effectively reflect the state of the battery in actual use. By calculating the goodness of use, the use of the battery can be dynamically evaluated and targeted maintenance suggestions can be provided. Combining the battery health score and the goodness of use, the remaining life of the battery is calculated, so that the user can understand the battery life in advance, reasonably arrange the replacement and maintenance plan of the battery, and improve the economy and safety of the battery use. By real-time monitoring and analysis of the battery status parameters, the battery charging and discharging strategy is optimized, the battery service life is extended, and the overall energy efficiency of the system is improved. According to the usage habits and environmental conditions of different users, personalized battery management and maintenance suggestions are provided to ensure the excellent performance of the battery in various application scenarios. In summary, the lithium battery intelligent energy storage monitoring method based on data analysis provided by the present invention not only solves the problem that traditional technology cannot measure battery life, but also realizes the prediction of battery health status and remaining life through comprehensive analysis of multi-dimensional parameters, thereby significantly improving the management and use efficiency of lithium batteries.

[0113] Figure 3 Schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 3 As shown, a terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program for lithium battery intelligent energy storage monitoring based on data analysis. When the processor 30 executes the computer program 32, the steps in each of the above-mentioned embodiments of a lithium battery intelligent energy storage monitoring method based on data analysis are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example, Figure 2 Function of the unit shown.

[0114] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into the following specific functions of each unit:

[0115] An acquisition unit is used to acquire the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power, and the deep charge and discharge ratio refers to the ratio of the time when the battery is at high power and low power in the total time;

[0116] a first calculation unit, configured to calculate a battery health score according to the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity, and the battery cell voltage difference;

[0117] A second calculation unit is used to calculate the use goodness according to the current cycle number, the deep charge and discharge ratio and the operating temperature;

[0118] The third calculation unit is used to calculate the remaining battery life according to the battery health score and the usage goodness.

[0119] The terminal device includes but is not limited to a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of a terminal device 3 and does not constitute a limitation on the terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0120] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0121] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0122] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0123] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0124] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0125] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0126] An embodiment of the present invention provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0128] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0130] In the embodiments provided by the present invention, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed over multiple network units.

[0132] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0133] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0134] As used in the present specification and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to monitoring, depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is monitored" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is monitored" or "in response to monitoring [described condition or event]", depending on the context.

[0135] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0136] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0137] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A lithium battery intelligent energy storage monitoring system based on data analysis, characterized in that: The lithium battery intelligent energy storage monitoring system based on data analysis includes an intelligent control unit, a sensor network, a battery management system, a storage unit and a user interface unit; The sensor network is used to collect the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power level, and the deep charge and discharge ratio refers to the proportion of the time when the battery is at high power and low power in the total time; The intelligent control unit is used to obtain the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; The intelligent control unit is used to substitute the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference into the following first mathematical model to obtain a battery health score; The first mathematical model is: ； in, represents the battery health score, represents the current charging efficiency, represents the standard charging efficiency, represents the current discharge efficiency, represents the standard discharge efficiency, represents the current internal resistance, Indicates the standard internal resistance, represents the capacity difference, Indicates standard capacity, represents the battery cell pressure, , , and represents the adjustment coefficient; The intelligent control unit is used to substitute the current number of cycles, the deep charge and discharge ratio and the operating temperature into the following second mathematical model to obtain the goodness of use; The second mathematical model is: ； in, Indicates the goodness of use, Indicates the current loop number, Indicates the maximum number of cycles of the battery. Indicates the deep charge and discharge ratio, represents the operating temperature, Indicates the optimal operating temperature of the battery. Indicates the maximum operating temperature of the battery. Indicates the minimum operating temperature of the battery. , and represents the adjustment factor; The intelligent control unit is used to substitute the battery health score, the usage goodness, the current cycle number and the maximum cycle number into a third mathematical model to obtain the remaining cycle number of the battery; The third mathematical model is: ； in, Indicates the remaining number of cycles of the battery, represents the battery health score, Indicates the goodness of use, Indicates the current loop number, represents the maximum number of cycles, , and represents the adjustment factor; The average cycle period is calculated according to the usage time and the current cycle number, and the remaining battery life is calculated according to the remaining battery cycle number and the average cycle period.

2. A lithium battery intelligent energy storage monitoring method based on data analysis, characterized in that: The lithium battery intelligent energy storage monitoring method based on data analysis includes: Obtain the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power level, and the deep charge and discharge ratio refers to the ratio of the time when the battery is at high power and low power in the total time; Substituting the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference into the following first mathematical model to obtain a battery health score; The first mathematical model is: ； in, represents the battery health score, represents the current charging efficiency, represents the standard charging efficiency, represents the current discharge efficiency, represents the standard discharge efficiency, represents the current internal resistance, Indicates the standard internal resistance, represents the capacity difference, Indicates standard capacity, represents the battery cell pressure, , , and represents the adjustment coefficient; Substituting the current number of cycles, the deep charge and discharge ratio, and the operating temperature into the following second mathematical model to obtain the goodness of use; The second mathematical model is: ； in, Indicates the goodness of use, Indicates the current loop number, Indicates the maximum number of cycles of the battery. Indicates the deep charge and discharge ratio, represents the operating temperature, Indicates the optimal operating temperature of the battery. Indicates the maximum operating temperature of the battery. Indicates the minimum operating temperature of the battery. , and represents the adjustment factor; Substituting the battery health score, the usage goodness, the current cycle number and the maximum cycle number into a third mathematical model to obtain the remaining cycle number of the battery; The third mathematical model is: ； in, Indicates the remaining number of cycles of the battery, represents the battery health score, Indicates the goodness of use, Indicates the current loop number, represents the maximum number of cycles, , and represents the adjustment factor; The average cycle period is calculated according to the usage time and the current cycle number, and the remaining battery life is calculated according to the remaining battery cycle number and the average cycle period.

3. The lithium battery intelligent energy storage monitoring method based on data analysis as claimed in claim 2, characterized in that: The step of calculating the average cycle period according to the usage time and the current cycle number, and calculating the remaining battery life according to the remaining battery cycle number and the average cycle period comprises: Divide the usage time by the current number of cycles to obtain the average cycle period; The remaining battery life is obtained by multiplying the remaining battery cycle number and the average cycle period.

4. A lithium battery intelligent energy storage monitoring device based on data analysis, characterized in that: The lithium battery intelligent energy storage monitoring device based on data analysis includes: An acquisition unit is used to acquire the current charging efficiency, standard charging efficiency, current discharging efficiency, standard discharging efficiency, current internal resistance, standard internal resistance, capacity difference, standard capacity, battery cell voltage difference, current cycle number, maximum cycle number, deep charge and discharge ratio and operating temperature of the lithium battery pack; the capacity difference refers to the difference between the standard capacity and the actual capacity, the battery cell voltage difference refers to the average value of the voltage difference between each battery cell at a fixed power, and the deep charge and discharge ratio refers to the ratio of the time when the battery is at high power and low power in the total time; A first calculation unit is used to substitute the current charging efficiency, the standard charging efficiency, the current discharging efficiency, the standard discharging efficiency, the current internal resistance, the standard internal resistance, the capacity difference, the standard capacity and the battery cell voltage difference into the following first mathematical model to obtain a battery health score; The first mathematical model is: ； in, represents the battery health score, represents the current charging efficiency, represents the standard charging efficiency, represents the current discharge efficiency, represents the standard discharge efficiency, represents the current internal resistance, Indicates the standard internal resistance, represents the capacity difference, Indicates standard capacity, represents the battery cell pressure, , , and represents the adjustment coefficient; A second calculation unit is used to substitute the current number of cycles, the deep charge and discharge ratio and the operating temperature into the following second mathematical model to obtain a good use degree; The second mathematical model is: ； in, Indicates the goodness of use, Indicates the current loop number, Indicates the maximum number of cycles of the battery. Indicates the deep charge and discharge ratio, represents the operating temperature, Indicates the optimal operating temperature of the battery. Indicates the maximum operating temperature of the battery. Indicates the minimum operating temperature of the battery. , and represents the adjustment factor; A third calculation unit is used to substitute the battery health score, the usage goodness, the current cycle number and the maximum cycle number into a third mathematical model to obtain a remaining battery cycle number; The third mathematical model is: ； in, Indicates the remaining number of cycles of the battery, represents the battery health score, Indicates the goodness of use, Indicates the current loop number, represents the maximum number of cycles, , and represents the adjustment factor; The average cycle period is calculated according to the usage time and the current cycle number, and the remaining battery life is calculated according to the remaining battery cycle number and the average cycle period.

5. A terminal device, characterized in that: The device includes: a memory, a processor, and a lithium battery intelligent energy storage monitoring program based on data analysis stored in the memory and executable on the processor, wherein the lithium battery intelligent energy storage monitoring program based on data analysis is configured to implement the steps in the lithium battery intelligent energy storage monitoring method based on data analysis as described in any one of claims 2 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 2 to 3 are implemented.

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