A lithium battery life detection system based on big data analysis

Abstract

The present invention provides a lithium battery life detection system based on big data analysis, belonging to the field of lithium batteries; it solves the problem of low efficiency in lithium battery life detection; specifically as follows: a data acquisition module acquires battery data and working data; a data fitting module performs data fusion on the battery data and the working data and calculates a weight coefficient; a battery analysis module constructs a relationship equation between the real-time power and the usage time of the lithium battery according to the weight coefficient; obtains sample data and constructs a decay equation; combines the relationship equation between the real-time power and the usage time with the decay equation to obtain a life equation of the lithium battery; a user interaction module: acquires user data and calculates the service life of the lithium battery; the present invention acquires, analyzes and processes relevant data during the factory production and use process of the lithium battery, analyzes the life of the lithium battery and the decay condition of the lithium battery life with the service life, enabling users to understand the usage status of the lithium battery in real time.

Description

Technical Field

[0001] The present invention relates to a lithium battery life detection system based on big data analysis, which relates to the field of lithium batteries. Background Art

[0002] The existing lithium battery life detection systems have the following deficiencies:

[0003] High data processing complexity: Since the prediction of lithium battery life involves the fusion and analysis of multi-source data, a series of operations such as data fusion, classification, and feature induction are required to extract useful information, resulting in a relatively high complexity of data processing; the existing systems lack in data fusion and do not deeply explore the potential connections of lithium battery-related data, leading to low data utilization.

[0004] Poor interpretability of prediction results: Because the relevant data such as current, voltage, and power of lithium batteries change in real time during use and there is no fixed proportional relationship, it is necessary to update the data in real time to improve the prediction accuracy. Most of the existing systems use machine learning or deep learning models to handle the lithium battery life prediction problem. This processing method is only applicable to the use of specific batteries in specific environments, with a narrow scope of application, resulting in the inability of machine learning or deep learning models to give a definite fitting result, affecting the user's trust and acceptance of the prediction results.

[0005] Uncertain prediction results: The existing lithium battery life detection systems can achieve life prediction, but this prediction method is mostly under ideal conditions, assuming that the voltage, current, and power remain unchanged during the discharge process of the battery, ignoring the changes in voltage and current during the use of the battery, making the prediction results still uncertain. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a lithium battery life detection system based on big data analysis, aiming to solve the problem of low efficiency of lithium battery life detection.

[0007] To achieve the above purpose, the present invention is implemented through the following technical solutions: A lithium battery life detection system based on big data analysis includes:

[0008] Data acquisition module: used to acquire the electric energy capacity, discharge voltage, discharge current, discharge power of the lithium battery to be detected, and the initial battery temperature when the lithium battery is not working, as battery data;

[0009] Obtain the working time of the lithium battery at different percentage power levels; obtain the actual discharge voltage, actual discharge current of the lithium battery at different percentage power levels, and the actual working temperature of the lithium battery, as working data;

[0010] Data fitting module: used to perform data fusion on battery data and working data according to Kalman filtering, calculate the step-by-step Kalman gain when the battery power of the lithium battery changes from 100% to 99%, from 99% to 98%, and so on until from 1% to 0, and obtain the weight coefficient;

[0011] Battery analysis module: used to construct a relationship equation between the real-time power and the usage time of the lithium battery according to the weight coefficient and the working time of the lithium battery at different percentage powers;

[0012] Obtain the service life, discharge voltage, and discharge current of all lithium batteries of the same model in the target area as sample data; calculate the decay equation of the lithium battery according to the sample data;

[0013] Combine the relationship equation between real-time power and usage time with the decay equation to obtain the life equation of the lithium battery;

[0014] User interaction module: used to obtain the power and usage time of the user's current lithium battery as user data; substitute the user data into the life equation to calculate the service life of the lithium battery and feedback it to the user.

[0015] Furthermore, the working process of the data fitting module is as follows:

[0016] Process A1: Denote the electric energy capacity as CO, the discharge voltage as oU, the discharge current as oI, the discharge power as PO, and the initial battery temperature as To;

[0017] Denote the working time of the lithium battery at 100% power as t (100) ; the actual discharge voltage as dU (100) ; the actual discharge current as dI (100) ; the actual working temperature as dT (100) ;

[0018] Denote the working time of the lithium battery at 99% power as t (99) ; the actual discharge voltage as dU (99) ; the actual discharge current as dI (99) ; the actual working temperature as dT (99) ;

[0019] And so on, denote the working time of the lithium battery at 1% power as t (1) ; the actual discharge voltage as dU (1) ; the actual discharge current as dI (1) ; the actual working temperature as dT (1) ;

[0020] Process A2: Obtain the weight of the lithium battery denoted as m and the specific heat capacity denoted as rc;

[0021] Calculate the active power of the lithium battery at 100% battery power, denoted as Pq （100） ; Pq （100） The calculation formula of Pq is as follows: ;

[0022] Among them, Pp （100） represents the per-unit power of the lithium battery at 100% battery power. The calculation formula of Pp （100） is: Pp （100） = (dU (100) × dI (100) );

[0023] Calculate the active power of the lithium battery at 99% battery power, denoted as Pq （99） ; Pq （99） The calculation formula of Pq is as follows: ;

[0024] Among them, Pp （99） represents the per-unit power of the lithium battery at 99% battery power. The calculation formula of Pp （99） is: Pp （99） = (dU (99) × dI (99) );

[0025] And so on, calculate the active power of the lithium battery at 1% battery power, denoted as Pq （1） ; Pq （1） The calculation formula of Pq is as follows: ;

[0026] Among them, Pp （1） represents the per-unit power of the lithium battery at 1% battery power. The calculation formula of Pp （1） is: Pp （1） = (dU (1) × dI (1) );

[0027] Process A3: Define calculation formula a1-1: dC （i） = dU (i) × dI (i) × t (i) ; Among them, dC （i） represents the actual power consumption of the lithium battery at i% battery power; dU (i) , dI (i) and t (i) , respectively represent the actual discharge voltage, actual discharge current and working time of the lithium battery at i% battery power. The value range of i is: 100 to 1;

[0028] Regarding dU (100) ~ dU (1) , dI (100) ~ dI(1) and t (100) ~t (1) Substitute into the calculation formula a1-1 to calculate the actual power consumption of the lithium battery at 100% to 1% power, and obtain dC （100） 、dC （99） ~dC （1） ;

[0029] Among them, dC （100） represents the actual power consumption of the lithium battery at 100% power; dC （99） represents the actual power consumption of the lithium battery at 99% power; and so on, dC （1） represents the actual power consumption of the lithium battery at 1% power.

[0030] Furthermore, the subsequent process of the process A3 is as follows:

[0031] Process A4: Define the calculation formula a1-2: ; Among them, yC （i） represents the remaining power of the lithium battery at i% power, and the value range of i is: 100~1; dC （j） represents the actual power consumption of the lithium battery at j% power, and the value range of j is: 100~i;

[0032] Substitute dC （100） ~dC （1） into the calculation formula a1-2 to calculate the remaining power of the lithium battery at 100% to 1% power, and obtain yC （100） 、yC （99） ~yC （1） ;

[0033] Among them, yC （100） represents the remaining power of the lithium battery at 100% power; yC （99） represents the remaining power of the lithium battery at 99% power; and so on, yC （1） represents the remaining power of the lithium battery at 1% power;

[0034] Process A5: Summarize the data in Processes A1~A4, and calculate the Kalman gain of the lithium battery at different percentage powers;

[0035] Process A51: Calculate the Kalman gain when the lithium battery changes from 100% power to 99% power;

[0036] Process A52: Calculate the Kalman gain when the lithium battery changes from 99% power to 98% power;

[0037] Process A53: And so on, calculate the Kalman gain when the lithium battery's power changes from 98% to 97%, from 97% to 96%, until from 2% to 1%, to obtain Kdk （98-97） 、Kdk （97-96） ~Kdk （2-1） ;

[0038] Among them, Kdk （98-97） represents the Kalman gain when the lithium battery's power changes from 98% to 97%; Kdk （97-96） represents the Kalman gain when the lithium battery's power changes from 97% to 96%; and so on, Kdk （2-1） represents the Kalman gain when the lithium battery's power changes from 2% to 1%;

[0039] Process A54: Taking matrix EN as the final state, calculate the Kalman gain when the lithium battery's power changes from 1% to 0%; the mathematical expression of matrix EN is: .

[0040] Process A6: Aggregate the Kalman gains Kdk （100-99） ~Kdk （1-e） , and use them as weight coefficients to enter the battery analysis module.

[0041] Furthermore, the specific process of the said Process A51 is as follows:

[0042] Process A511: Construct the discharge matrix of the lithium battery at 100% power, denoted as matrix B （100） , and the discharge matrix at 99% power, denoted as matrix B （99） ;

[0043] Matrix B （100） 's mathematical representation is: ;

[0044] Matrix B （99） 's mathematical representation is: ;

[0045] Process A512: Calculate the state transition matrix when matrix B （100） becomes matrix B （99） , denoted as matrix B （100-99） ; the calculation formula of matrix B （100-99） is:

[0046] ; among them, × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0047] Process A513: Calculate the difference matrix of matrix B （100） , denoted as matrix RB （100） ; matrix RB（100） The calculation formula is:

[0048] ; where, - represents matrix subtraction;

[0049] Calculate matrix B （99） 's difference matrix, denoted as matrix RB （99） Matrix RB （99） The calculation formula is:

[0050] ;

[0051] Calculate the error transition matrix from matrix B （100） to matrix B （99） , denoted as matrix QB （100-99） Matrix QB （100-99） The mathematical calculation formula is:

[0052] QB （100-99） = RB （100） - RB （99） ;

[0053] Process A514: Calculate the error covariance matrix of matrix B （100） , denoted as matrix Pd （100） Matrix Pd （100） The calculation formula is:

[0054] Pd （100） = (1 / 4) × (APd （100） T × APd （100） ); where, × represents matrix multiplication, T represents the transpose of the matrix, and APd （100） represents the transition matrix of matrix Pd （100） , and the calculation formula of APd （100） is:

[0055] APd （100） = B （100） - [(1 / 4) × I × B （100） ; where, - represents matrix subtraction, and I represents the identity matrix;

[0056] Process A515: Calculate the pre-error covariance matrix of matrix B （99） , denoted as Pk （99） Pk （99） The calculation formula is:

[0057] Pk （99） = B （100-99） × Pd （100） × (B （100-99） ) T + QB （100-99）; where, × represents matrix multiplication, T represents the transpose of a matrix, and + represents matrix addition;

[0058] Calculate the Kalman gain Kdk when the lithium battery changes from 100% power to 99% power （100-99） , and the calculation formula is:

[0059] .

[0060] Furthermore, the specific process of the process A52 is as follows:

[0061] Process A521: The discharge matrix of the lithium battery at 99% power is matrix B （99） ;

[0062] Matrix B （99） The mathematical representation of is: ;

[0063] Construct the discharge matrix of the lithium battery at 98% power and denote it as matrix B （98） ;

[0064] Matrix B （98） The mathematical representation of is: ;

[0065] Process A522: Calculate matrix B （99） to matrix B （98） The state transition matrix of, denoted as matrix B （99-98） ; Matrix B （99-98） The calculation formula of is:

[0066] ; where, × represents matrix multiplication, T represents the transpose of a matrix, and -1 represents the inverse of a matrix;

[0067] Process A523: Calculate the difference matrix of matrix B （98） and denote it as matrix RB （98） ; Matrix RB （98） The calculation formula of is:

[0068] ; where, - represents matrix subtraction;

[0069] Calculate the error transition matrix from matrix B （99） to matrix B （98） and denote it as matrix QB （99-98） ; Matrix QB （99-98） The mathematical calculation formula of is:

[0070] QB （99-98） = RB （99） - RB （98） ; where, matrix RB （99） represents matrix B（99） Difference matrix;

[0071] Process A524: Calculate matrix B （99） Error covariance matrix, denoted as matrix Pd （99） ; Matrix Pd （99） The calculation formula is:

[0072] Pd （99） = (1 / 4) × (APd （99） T × APd （99） ); where, × represents matrix multiplication, T represents the transpose of the matrix, APd （99） represents the transition matrix of matrix Pd （99） The calculation formula of APd （99） is:

[0073] APd （99） = B （99） - [(1 / 4) × I × B （99） ; where, - represents matrix subtraction, I represents the identity matrix;

[0074] Process A525: Calculate the pre-error covariance matrix of matrix B （98） denoted as Pk （98） ; Pk （98） The calculation formula is:

[0075] Pk （98） = B （99-98） × Pd （99） × (B （99-98） ) T + QB （99-98） ; where, × represents matrix multiplication, T represents the transpose of the matrix, + represents matrix addition;

[0076] Calculate the Kalman gain Kdk when the lithium battery changes from 99% power to 98% power （99-98） , the calculation formula is:

[0077] .

[0078] Furthermore, the specific process of Process A54 is as follows:

[0079] Process A541: The discharge matrix of the lithium battery at 2% power is matrix B （2） ;

[0080] Matrix B （2） The mathematical representation is: ;

[0081] Construct the discharge matrix of the lithium battery at 1% power, denoted as matrix B （1） ;

[0082] Matrix B （1） The mathematical representation of is: ;

[0083] Process A542: Calculate Matrix B （1） Becomes the state transition matrix of Matrix EN, denoted as Matrix B （1-e） ; Matrix B （1-e） The calculation formula of is:

[0084] ; where, × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0085] Process A543: Calculate the difference matrix of Matrix B （1） Denoted as Matrix RB （1） ; Matrix RB （1） The calculation formula of is:

[0086] ; where, - represents matrix subtraction; yC （2） Represents the remaining power of the lithium battery at 2% power;

[0087] Calculate the difference matrix of Matrix EN, denoted as Matrix REN; The calculation formula of Matrix REN is:

[0088] ; where, yC （1） Represents the remaining power of the lithium battery at 1% power;

[0089] Calculate the error transition matrix from Matrix B （1） To Matrix EN, denoted as Matrix QB （1-e） ; Matrix QB （1-e） The mathematical calculation formula of is:

[0090] QB （1-e） = RB （1） -REN;

[0091] Process A544: Calculate the error covariance matrix of Matrix EN, denoted as Matrix Pde; The calculation formula of Matrix Pde is:

[0092] Pde = (1 / 4) × (APde T × APde); where, × represents matrix multiplication, T represents the transpose of the matrix, APde represents the transition matrix of Matrix Pde, and the calculation formula of APde is:

[0093] APde = EN - [(1 / 4) × I × EN]; where, - represents matrix subtraction, and I represents the identity matrix;

[0094] Process A545: Calculate the pre-error covariance matrix of matrix EN, denoted as Pke; the calculation formula of Pke is:

[0095] ; where, × represents matrix multiplication, T represents the transpose of the matrix, and + represents matrix addition;

[0096] Calculate the Kalman gain Kdk when the lithium battery changes from 1% power to 0% power （1-e） , and the calculation formula is:

[0097] .

[0098] Furthermore, the working process of the battery analysis module is as follows:

[0099] Process B1: Extract the maximum value in Kdk （100-99） ~Kdk （1-e） and denote it as KM, and the minimum value as KL;

[0100] With an error range of (0, 0.001], extract the mode in Kdk （100-99） ~Kdk （1-e） and denote it as KZ;

[0101] Process B2: With an error range of (0, 0.001], count the number of modes in Kdk （100-99） ~Kdk （1-e） and denote it as nz;

[0102] Denote the combined weight as KK, judge the size of (nz / 100) and 0.5, and calculate the value of KK;

[0103] If (nz / 100) > 0.5, then the value of KK is KZ, that is, KK = KZ;

[0104] If (nz / 100) ≤ 0.5, then the calculation formula of KK is:

[0105] ;

[0106] Process B3: Set the current power of the lithium battery to i%, and the value range of i is 100~1;

[0107] Set the service life of the battery as n, and construct the relationship equation between the real-time power of the battery and the usage time, denoted as equation T(i); the mathematical expression of equation T(i) is:

[0108] ; where, × represents multiplication; Tt represents the continuous usage time when the battery changes from 100% power to i% power;

[0109] Among them, C(n) represents the decay equation, and the mathematical expression of C(n) is:

[0110] C(n) = α(n) × CO;

[0111] Where α(n) represents the decay coefficient equation of the lithium battery over time;

[0112] Process B4: Obtain the service life, discharge voltage, and discharge current of all lithium batteries of the same model in the target area as sample data; calculate the decay equation C(n) of the lithium battery based on the sample data;

[0113] Process B5: Substitute α(n) into the decay equation to obtain C(n);

[0114] Substitute C(n) into the equation T(i), and calculate the integral of T(i) denoted as in(i); the mathematical expression is: ;

[0115] Process B6: Aggregate the relationship equation between real-time power and usage time, the decay equation, and the decay coefficient equation as the life equation of the lithium battery.

[0116] Furthermore, the specific process of Process B4 is as follows:

[0117] Process B41: Count the total number of electric vehicles using the lithium battery to be inspected in the target area, denoted as cn;

[0118] Obtain the service life of the first lithium battery denoted as cy1, the discharge voltage denoted as cU1, and the discharge current denoted as cI1;

[0119] Obtain the service life of the second lithium battery denoted as cy2, the discharge voltage denoted as cU2, and the discharge current denoted as cI2;

[0120] And so on, obtain the service life of the cnth lithium battery denoted as cy cn , the discharge voltage denoted as cU cn , the discharge current denoted as cI cn ;

[0121] Process B42: Calculate the decay binary group of the first lithium battery, denoted as ca(1), ca(1) = [LL(1), cy1]; where LL(1) represents the left term of ca(1), that is, the decay coefficient with a service life of cy1; the calculation formula of LL(1) is: ;

[0122] Calculate the decay binary group of the second lithium battery, denoted as ca(2), ca(2) = [LL(2), cy2]; where LL(2) represents the left term of ca(2), that is, the decay coefficient with a service life of cy2; the calculation formula of LL(2) is: ;

[0123] And so on, calculate the decay binary group of the cn-th lithium battery, denoted as ca(cn), ca(cn) = [LL(cn), cy cn ; where LL(cn) represents the left term of ca(cn), that is, the decay coefficient with the service life of cy cn ; The calculation formula of LL(cn) is: ;

[0124] Process B43: Summarize the decay binary groups ca(1) to ca(cn) of the 1st to cn-th lithium batteries;

[0125] Taking the decay binary group as the division interval, calculate the average value of the decay coefficient corresponding to each service life, and integrate it using MATLAB to obtain the decay coefficient equation α(n).

[0126] Compared with the prior art, the beneficial effects of the present invention are:

[0127] Real-time prediction ability: Traditional lithium battery life prediction methods often rely on a large amount of charging test data, while the present invention focuses on the actual working data (discharge data) of lithium batteries, uses the discharge data to detect the battery life in real time, making the detection results practical, and can significantly improve the efficiency and accuracy of energy management.

[0128] Simple and easy to implement: Compared with traditional model-based prediction methods, the present invention does not need to start from the complex physical model of lithium-ion batteries. That is, the present invention evaluates the available life of the battery by analyzing the charge and discharge data of lithium-ion batteries as well as data such as temperature and power; this method reduces the requirement for in-depth understanding of the internal mechanism of the battery, making the prediction system easier to implement and apply.

[0129] Improve the accuracy of prediction: The present invention can fuse multi-source data, such as discharge data, temperature data, power data, etc., and use Kalman filtering to analyze the weight value between the real data and the predicted data, thereby improving the accuracy of prediction. At the same time, the present invention further improves the accuracy of battery life prediction by analyzing the degradation law and characteristics of the battery during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives and advantages of the present invention will become more obvious:

[0131] Figure 1 It is a schematic diagram of the system of the present invention;

[0132] Figure 2 It is a schematic diagram of data processing of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0133] To make the above objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0134] Please refer to Figure 1 , a lithium battery life detection system based on big data analysis includes: a data acquisition module, a data fitting module, a battery analysis module, a user interaction module, a database, and a server; wherein, the data acquisition module, the data fitting module, the battery analysis module, and the user interaction module are respectively connected to the database and the server.

[0135] Data acquisition module: used to acquire the electrical energy capacity, (rated) discharge voltage, (rated) discharge current, discharge power, and initial battery temperature when the lithium battery is not working of the lithium battery to be tested as battery data;

[0136] (Through the BMS system in the electric vehicle, that is, the battery management system) acquire the working time of the lithium battery at different percentage powers; acquire the actual discharge voltage, actual discharge current, and actual working temperature of the lithium battery at different percentage powers as working data (for a single electric vehicle);

[0137] It should be noted that the "lithium battery to be tested" in the present invention refers to the battery for lithium battery life detection using the present invention (a lithium battery life detection system based on big data analysis); the subsequent "lithium battery", without special instructions, all refers to the "lithium battery to be tested";

[0138] The "actual discharge voltage, actual discharge current, and actual working temperature of the lithium battery at different percentage powers" in the present invention are all the average values of the voltage, current, and temperature output or generated by the lithium battery at a certain percentage power;

[0139] For example: if the lithium battery works for 10 minutes at 100% power, then the "actual discharge voltage" is: the average value of the output voltage of the lithium battery within 10 minutes at 100% power, the "actual discharge current" is: the average value of the output current of the lithium battery within 10 minutes at 100% power, and the "actual working temperature" is: the average value of the battery heating temperature of the lithium battery within 10 minutes at 100% power;

[0140] Data fitting module: used to perform data fusion on the battery data and working data according to the Kalman filter, calculate the step-by-step Kalman gain when the power of the lithium battery changes from 100% to 99%, 99% to 98%, until 1% to 0, and obtain the weight coefficient;

[0141] Procedure A: Please refer to Figure 2 , the working process of the data fitting module is as follows:

[0142] Process A1: Denote the electrical energy capacity of the lithium battery to be tested as CO, the (rated) discharge voltage as oU, the (rated) discharge current as oI, the discharge power as PO, and the initial battery temperature as To;

[0143] Denote the working time of the lithium battery at 100% battery level as t (100) ; Denote the actual discharge voltage as dU (100) ; Denote the actual discharge current as dI (100) ; Denote the actual working temperature as dT (100) ;

[0144] Denote the working time of the lithium battery at 99% battery level as t (99) ; Denote the actual discharge voltage as dU (99) ; Denote the actual discharge current as dI (99) ; Denote the actual working temperature as dT (99) ;

[0145] And so on, denote the working time of the lithium battery at 1% battery level as t (1) ; Denote the actual discharge voltage as dU (1) ; Denote the actual discharge current as dI (1) ; Denote the actual working temperature as dT (1) ;

[0146] Process A2: Obtain the weight of the lithium battery, denoted as m, and the specific heat capacity (of the lithium battery) as rc;

[0147] Calculate the active power of the lithium battery at 100% battery level, denoted as Pq （100） ; Pq （100） The calculation formula for Pq is: ;

[0148] Where, Pp （100） represents the per-unit power of the lithium battery at 100% battery level, and the calculation formula for Pp （100） is: Pp （100） = (dU (100) × dI (100) );

[0149] Calculate the active power of the lithium battery at 99% battery level, denoted as Pq （99） ; Pq （99） The calculation formula for Pq is: ;

[0150] Where, Pp （99） represents the per-unit power of the lithium battery at 99% battery level, and the calculation formula for Pp （99） is: Pp （99） = (dU (99) × dI (99) );

[0151] And so on, calculate the active power of the lithium battery at 1% power, denoted as Pq （1） ; Pq （1） The calculation formula of is: ;

[0152] Among them, Pp （1） represents the per-unit power of the lithium battery at 1% power, and the calculation formula of Pp （1） is: Pp （1） = (dU (1) × dI (1) );

[0153] Process A3: Define calculation formula a1-1: dC （i） = dU (i) × dI (i) × t (i) ; Among them, dC （i） represents the actual power consumption of the lithium battery at i% power; dU (i) , dI (i) and t (i) respectively represent the actual discharge voltage, actual discharge current and working time of the lithium battery at i% power, and the value range of i is: 100 to 1;

[0154] Substitute dU (100) ~ dU (1) , dI (100) ~ dI (1) and t (100) ~ t (1) into the calculation formula a1-1 to calculate the actual power consumption of the lithium battery from 100% to 1% power, and obtain dC （100） , dC （99） ~ dC （1） ;

[0155] Among them, dC （100） represents the actual power consumption of the lithium battery at 100% power; dC （99） represents the actual power consumption of the lithium battery at 99% power; and so on, dC （1） represents the actual power consumption of the lithium battery at 1% power;

[0156] Process A4: Define calculation formula a1-2: ; Among them, yC （i） represents the remaining power of the lithium battery at i% power, and the value range of i is: 100 to 1; dC （j） represents the actual power consumption of the lithium battery at j% power, and the value range of j is: 100 to i;

[0157] Substitute dC （100） ~ dC（1） Substitute into calculation formula a1-2 to calculate the remaining power of the lithium battery at 100% to 1% power, and obtain yC （100） 、yC （99） ~yC （1） ;

[0158] Among them, yC （100） represents the remaining power of the lithium battery at 100% power; yC （99） represents the remaining power of the lithium battery at 99% power; and so on, yC （1） represents the remaining power of the lithium battery at 1% power;

[0159] Process A5: Summarize the data in Processes A1 to A4, and calculate the Kalman gain of the lithium battery at different percentage powers;

[0160] Process A51: Calculate the Kalman gain when the lithium battery changes from 100% power to 99% power;

[0161] Process A511: Construct the discharge matrix of the lithium battery at 100% power and denote it as matrix B （100） , and the discharge matrix at 99% power is denoted as matrix B （99） ;

[0162] Matrix B （100） The mathematical representation of is: ;

[0163] Matrix B （99） The mathematical representation of is: ;

[0164] Process A512: Calculate the state transition matrix when matrix B （100） becomes matrix B （99） , and denote it as matrix B （100-99） ; The calculation formula of matrix B （100-99） is:

[0165] ; Among them, × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0166] Process A513: Calculate the difference matrix of matrix B （100） , and denote it as matrix RB （100） ; The calculation formula of matrix RB （100） is:

[0167] ; Among them, - represents matrix subtraction;

[0168] Calculate the difference matrix of matrix B （99） , and denote it as matrix RB （99） ; Matrix RB （99）The calculation formula is:

[0169] ;

[0170] Calculate the error transition matrix for the transformation of matrix B （100） to matrix B （99） , denoted as matrix QB （100-99） ; The mathematical calculation formula for matrix QB （100-99） is:

[0171] QB （100-99） = RB （100） - RB （99） ;

[0172] Process A514: Calculate the error covariance matrix of matrix B （100） , denoted as matrix Pd （100） ; The calculation formula for matrix Pd （100） is:

[0173] Pd （100） = (1 / 4) × (APd （100） T × APd （100） ); where, × represents matrix multiplication, T represents the transpose of the matrix, and APd （100） represents the transition matrix of matrix Pd （100） , and the calculation formula for APd （100） is:

[0174] APd （100） = B （100） - [(1 / 4) × I × B （100） ; where, - represents matrix subtraction, and I represents the 1 matrix (i.e., a 4-row and 4-column matrix with 1 in each row and column);

[0175] Process A515: Calculate the pre-error covariance matrix of matrix B （99） , denoted as Pk （99） ; The calculation formula for Pk （99） is:

[0176] Pk （99） = B （100-99） × Pd （100） × (B （100-99） ) T + QB （100-99） ; where, × represents matrix multiplication, T represents the transpose of the matrix, and + represents matrix addition;

[0177] Calculate the Kalman gain Kdk （100-99） for the lithium battery to change from 100% power to 99% power, and the calculation formula is:

[0178] ;

[0179] Process A52: Calculate the Kalman gain when the lithium battery's power changes from 99% to 98%.

[0180] Process A521: The discharge matrix of the lithium battery at 99% power is matrix B （99） ;

[0181] Matrix B （99） is mathematically represented as: ;

[0182] Construct the discharge matrix of the lithium battery at 98% power and denote it as matrix B （98） ;

[0183] Matrix B （98） is mathematically represented as: ;

[0184] Process A522: Calculate matrix B （99） changing to matrix B （98） 's state transition matrix, denoted as matrix B （99-98） ; Matrix B （99-98） 's calculation formula is:

[0185] ; where, × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0186] Process A523: Calculate the difference matrix of matrix B （98） and denote it as matrix RB （98） ; Matrix RB （98） 's calculation formula is:

[0187] ; where, - represents matrix subtraction;

[0188] Calculate the error transition matrix from matrix B （99） changing to matrix B （98） and denote it as matrix QB （99-98） ; Matrix QB （99-98） 's mathematical calculation formula is:

[0189] QB （99-98） = RB （99） - RB （98） ; where, matrix RB （99） represents the difference matrix of matrix B （99） ;

[0190] Process A524: Calculate the error covariance matrix of matrix B （99） and denote it as matrix Pd （99） ; Matrix Pd （99） 's calculation formula is:

[0191] Pd （99） = (1 / 4) × (APd （99） T × APd （99） ); where, × represents matrix multiplication, T represents the transpose of a matrix, and APd （99） represents the transition matrix of matrix Pd （99） The calculation formula of APd （99） is:

[0192] APd （99） = B （99） - [(1 / 4) × I × B （99） ; where, - represents matrix subtraction, and I represents the identity matrix (i.e., a 4x4 matrix with 1s in each row and column);

[0193] Process A525: Calculate the prior error covariance matrix of matrix B （98） , denoted as Pk （98） ; The calculation formula of Pk （98） is:

[0194] Pk （98） = B （99-98） × Pd （99） × (B （99-98） ) T + QB （99-98） ; where, × represents matrix multiplication, T represents the transpose of a matrix, and + represents matrix addition;

[0195] Calculate the Kalman gain Kdk when the lithium battery's power changes from 99% to 98% （99-98） , and its calculation formula is:

[0196] ;

[0197] Process A53: And so on, calculate the Kalman gains when the lithium battery's power changes from 98% to 97%, from 97% to 96%, until from 2% to 1%, and obtain Kdk （98-97） , Kdk （97-96） ~ Kdk （2-1） ;

[0198] Among them, Kdk （98-97） represents the Kalman gain when the lithium battery's power changes from 98% to 97%; Kdk （97-96） represents the Kalman gain when the lithium battery's power changes from 97% to 96%; and so on, Kdk （2-1） represents the Kalman gain when the lithium battery's power changes from 2% to 1%;

[0199] Process A54: With matrix EN as the final state, calculate the Kalman gain when the battery level of the lithium battery changes from 1% to 0%; the mathematical expression of matrix EN is: ;

[0200] Process A541: The discharge matrix of the lithium battery at 2% battery level is matrix B （2） ;

[0201] Matrix B （2） The mathematical representation is: ;

[0202] Construct the discharge matrix of the lithium battery at 1% battery level and denote it as matrix B （1） ;

[0203] Matrix B （1） The mathematical representation is: ;

[0204] Process A542: Calculate the state transition matrix from matrix B （1） to matrix EN, denoted as matrix B （1-e） ; Matrix B （1-e） The calculation formula is:

[0205] ; where, × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0206] Process A543: Calculate the difference matrix of matrix B （1） and denote it as matrix RB （1） ; Matrix RB （1） The calculation formula is:

[0207] ; where, - represents matrix subtraction; yC （2） represents the remaining battery level of the lithium battery at 2% battery level;

[0208] Calculate the difference matrix of matrix EN and denote it as matrix REN; the calculation formula of matrix REN is:

[0209] ; where, yC （1） represents the remaining battery level of the lithium battery at 1% battery level;

[0210] Calculate the error transition matrix from matrix B （1） to matrix EN and denote it as matrix QB （1-e） ; Matrix QB （1-e） The mathematical calculation formula is:

[0211] QB （1-e） = RB （1） -REN;

[0212] Process A544: Calculate the error covariance matrix of matrix EN, denoted as matrix Pde; the calculation formula of matrix Pde is:

[0213] Pde = (1 / 4) × (APde T × APde); where, × represents matrix multiplication, T represents the transpose of the matrix, and APde represents the transition matrix of matrix Pde. The calculation formula of APde is:

[0214] APde = EN - [(1 / 4) × I × EN]; where, - represents matrix subtraction, and I represents the identity matrix (i.e., a 4×4 matrix with 1 in each row and column);

[0215] Process A545: Calculate the pre-error covariance matrix of matrix EN, denoted as Pke; the calculation formula of Pke is:

[0216] ; where, × represents matrix multiplication, T represents the transpose of the matrix, and + represents matrix addition;

[0217] Calculate the Kalman gain Kdk when the lithium battery's power changes from 1% to 0% （1-e） , the calculation formula of which is:

[0218] ;

[0219] Process A6: Aggregate the Kalman gains Kdk （100-99） ~Kdk （1-e） , as weight coefficients, enter the battery analysis module.

[0220] Battery analysis module: Used to construct the relationship equation between the real-time power and usage time of the lithium battery according to the weight coefficients and the working time of the lithium battery at different percentage powers;

[0221] Obtain the service life, discharge voltage, and discharge current of all lithium batteries of the same model (for electric vehicles) in the target area as sample data (for multiple electric vehicles); calculate the decay equation of the lithium battery according to the sample data;

[0222] Combine the relationship equation between real-time power and usage time with the decay equation to obtain the life equation of the lithium battery;

[0223] It should be noted that the "target area" in the present invention refers to: the municipal area where the life detection of the lithium battery is carried out by using the present invention;

[0224] It should be noted that the "lithium batteries of the same model" in the battery analysis module refers to: batteries with the same model as the "(to-be-tested) lithium battery";

[0225] Process B: The working process of the battery analysis module is as follows:

[0226] Process B1: Extract Kdk （100-99） ~Kdk （1-e） The maximum value in it is denoted as KM, and the minimum value is denoted as KL;

[0227] With an error range of (0, 0.001], extract Kdk （100-99） ~Kdk （1-e） The mode in it is denoted as KZ;

[0228] Process B2: With an error range of (0, 0.001], count the number of modes of Kdk （100-99） ~Kdk （1-e） in it, denoted as nz;

[0229] Denote the combined weight as KK, judge the size of (nz / 100) and 0.5, and calculate the value of KK;

[0230] If (nz / 100) > 0.5, then the value of KK is KZ, that is, KK = KZ;

[0231] If (nz / 100) ≤ 0.5, then the calculation formula of KK is:

[0232] ;

[0233] Process B3: Set the current battery level of the lithium battery as i%, and the value range of i is 100 to 1;

[0234] Set the service life of the battery as n, and construct the relationship equation between the real-time battery level and the usage time, denoted as equation T(i); the mathematical expression of equation T(i) is:

[0235] ; where, × represents multiplication; Tt represents the continuous usage time of the battery from 100% battery level to i% battery level (without charging);

[0236] Among them, C(n) represents the decay equation, and the mathematical expression of C(n) is:

[0237] C(n) = α(n) × CO;

[0238] Among them, α(n) represents the decay coefficient equation of the lithium battery over time;

[0239] Process B4: Obtain the service life, discharge voltage, and discharge current of all lithium batteries of the same model (for electric vehicles) in the target area as sample data (for multiple electric vehicles); calculate the decay equation C(n) of the lithium battery according to the sample data;

[0240] Process B41: Count the total number of electric vehicles using the lithium battery to be inspected in the target area, denoted as cn;

[0241] Obtain the service life of the first lithium battery as cy1, the discharge voltage as cU1, and the discharge current as cI1; (the first electric vehicle)

[0242] Obtain the service life of the second lithium battery as cy2, the discharge voltage as cU2, and the discharge current as cI2; (the second electric vehicle)

[0243] And so on, obtain the service life of the nth lithium battery as cy cn , the discharge voltage as cU cn , the discharge current as cI cn ; (the nth electric vehicle)

[0244] Process B42: Calculate the decay binary group of the first lithium battery, denoted as ca(1), ca(1) = [LL(1), cy1]; where LL(1) represents the left term of ca(1), that is, the decay coefficient with a service life of cy1; the calculation formula of LL(1) is: ;

[0245] Calculate the decay binary group of the second lithium battery, denoted as ca(2), ca(2) = [LL(2), cy2]; where LL(2) represents the left term of ca(2), that is, the decay coefficient with a service life of cy2; the calculation formula of LL(2) is: ;

[0246] And so on, calculate the decay binary group of the nth lithium battery, denoted as ca(cn), ca(cn) = [LL(cn), cy cn ; where LL(cn) represents the left term of ca(cn), that is, the decay coefficient with a service life of cy cn ; the calculation formula of LL(cn) is: ;

[0247] Process B43: Aggregate the decay binary groups ca(1) to ca(cn) of the first to the nth lithium batteries;

[0248] Taking the decay binary group as the division interval, calculate the average value of the decay coefficients corresponding to each service life, and integrate using MATLAB to obtain the decay coefficient equation α(n);

[0249] Process B5: Substitute α(n) into the decay equation to obtain C(n);

[0250] Substitute C(n) into the equation T(i), and calculate the integral of T(i) denoted as in(i); the mathematical expression is: ; (MATLAB, short for Matrix Laboratory, is an application software for solving mathematical problems such as matrix operations, numerical solutions, function optimization, and solving ordinary differential equations)

[0251] Process B6: Summarize the relationship equation between real-time electricity consumption and usage time, the decay equation, and the decay coefficient equation as the life equation of the lithium battery.

[0252] User interaction module: Used to obtain the current electricity consumption and usage time (unit: year) of the user's lithium battery (for electric vehicles) as user data; substitute the user data into the life equation to calculate the service life of the lithium battery and feedback it to the user.

[0253] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation. For example, if there are weight coefficients and proportionality coefficients, the values set are specific numerical values obtained by quantifying each parameter for subsequent comparison. Regarding the magnitudes of the weight coefficients and proportionality coefficients, as long as they do not affect the proportional relationship between the parameters and the quantified values, it is acceptable.

[0254] Finally, it should be noted that: The above-described embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: Any technician familiar with this technical field can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A lithium battery life detection system based on big data analysis, characterized in that, The system includes: A data acquisition module: used to acquire the electric energy capacity, discharge voltage, discharge current, discharge power of the lithium battery to be tested, and the initial battery temperature when the lithium battery is not working, as battery data; Obtain the working time of the lithium battery at different percentage powers; obtain the actual discharge voltage, actual discharge current of the lithium battery at different percentage powers, and the actual working temperature of the lithium battery, as working data; A data fitting module: used to perform data fusion on the battery data and working data according to Kalman filtering, calculate the step-by-step Kalman gain of the lithium battery's power, and obtain the weight coefficient; A battery analysis module: used to construct a relationship equation between the real-time power and usage time of the lithium battery according to the weight coefficient and the working time of the lithium battery at different percentage powers; Obtain the service life, discharge voltage, and discharge current of all lithium batteries of the same model in the target area as sample data; calculate the decay equation of the lithium battery according to the sample data; Combine the relationship equation between real-time power and usage time with the decay equation to obtain the life equation of the lithium battery; The working process of the battery analysis module is as follows: Process B1: Extract the Kalman gain Kdk at different percentage of battery power （100-99） ~Kdk （1-e） The maximum value is denoted as KM, and the minimum value is denoted as KL; Extract Kdk with the error range of (0, 0.001] （100-99） ~Kdk （1-e） The mode in it is denoted as KZ; Process B2: With an error range of (0, 0.001], count the number of Kdk （100-99） ~Kdk （1-e） The number of modes in is denoted as nz; Denote the combined weight as KK, judge the size of (nz / 100) and 0.5, and calculate the value of KK; If (nz / 100) > 0.5, the value of KK is KZ, that is, KK = KZ; If (nz / 100) ≤ 0.5, the calculation formula of KK is: ； Process B3: Set the current power of the lithium battery as i%, and the value range of i is 100 to 1; Set the service life of the battery as n, and construct a relationship equation between the real-time power and usage time of the battery, denoted as equation T(i); the mathematical expression of equation T(i) is: ; wherein, × represents multiplication; Tt represents the continuous usage time when the battery changes from 100% power to i% power; Among them, C(n) represents the decay equation, and the mathematical expression of C(n) is: C(n) = α(n) × CO; Among them, α(n) represents the decay coefficient equation of the lithium battery over time, PO represents the discharge power, and CO represents the electric energy capacity; Process B4: Obtain the service life, discharge voltage, and discharge current of all lithium batteries of the same model in the target area as sample data; calculate the decay equation C(n) of the lithium battery according to the sample data; Process B5: Substitute α(n) into the decay equation to obtain C(n); Substitute C(n) into the equation T(i), and the integral of T(i) is denoted as in(i); the mathematical expression of in(i) is: ; Process B6: Summarize the relationship equation between real-time power and usage time, the decay equation, and the decay coefficient equation as the life equation of the lithium battery; A user interaction module: used to obtain the power and usage time of the user's current lithium battery as user data; substitute the user data into the life equation to calculate the service life of the lithium battery and feedback it to the user.

2. The lithium battery life detection system based on big data analysis according to claim 1, wherein, The working process of the data fitting module is as follows: Process A1: Denote the electric energy capacity as CO, the discharge voltage as oU, the discharge current as oI, the discharge power as PO, and the initial battery temperature as To; Record the working time of the lithium battery at 100% battery level as t (100) ; Record the actual discharge voltage as dU (100) ; Record the actual discharge current as dI (100) ; Record the actual working temperature as dT (100) ; Record the working time of the lithium battery at 99% battery level as t (99) ; Record the actual discharge voltage as dU (99) ; Record the actual discharge current as dI (99) ; Record the actual working temperature as dT (99) ; And so on, record the working time of the lithium battery at 1% power as t (1) ; Record the actual discharge voltage as dU (1) ; Record the actual discharge current as dI (1) ; Record the actual working temperature as dT (1) ; Process A2: Obtain the weight of the lithium battery as m and the specific heat capacity as rc; Calculate the active power of the lithium battery at 100% battery level, denoted as Pq （100） ; Pq （100） The calculation formula for Pq is as follows: ; Among them, Pp （100） represents the per-unit power of the lithium battery at 100% battery charge. The calculation formula for Pp （100） is: Pp （100） = (dU (100) × dI (100) ); Calculate the active power of the lithium battery at 99% battery charge, denoted as Pq （99） ; Pq （99） The calculation formula for Pq is as follows: ; Among them, Pp （99） represents the per-unit power of the lithium battery at 99% state of charge of the lithium battery, Pp （99） The calculation formula of is: Pp （99） = (dU (99) × dI (99) ); And so on, calculate the active power of the lithium battery at 1% battery level, denoted as Pq （1） ; Pq （1） The calculation formula of Pq is as follows: ; Among them, Pp （1） represents the per-unit power of the lithium battery at 1% battery power, and the calculation formula of Pp （1） is: Pp （1） = (dU (1) × dI (1) ); Process A3: Define calculation formula a1-1: dC （i） = dU (i) × dI (i) × t (i) ; where, dC （i） represents the actual power consumption of the lithium battery at i% battery level; dU (i) , dI (i) and t (i) respectively represent the actual discharge voltage, actual discharge current and working time of the lithium battery at i% battery level, and the value range of i is: 100 to 1; Substitute dU (100) ~dU (1) , dI (100) ~dI (1) and t (100) ~t (1) into the calculation formula a1-1 to calculate the actual power consumption of the lithium battery at 100% to 1% battery power, and obtain dC （100） , dC （99） ~dC （1） ; Among them, dC （100） represents the actual power consumption of the lithium battery at 100% battery level; dC （99） represents the actual power consumption of the lithium battery at 99% battery level; and so on, dC （1） represents the actual power consumption of the lithium battery at 1% battery level.

3. A lithium battery life detection system based on big data analysis according to claim 2, characterized in that, The subsequent process of Process A3 is as follows: Process A4: Define calculation formula a1 - 2: ; where, yC （i） represents the remaining power of the lithium battery at i% power, and the value range of i is: 100 to 1; dC （j） represents the actual power consumption of the lithium battery at j% power, and the value range of j is: 100 to i; Substitute dC （100） ~dC （1） into the calculation formula a1 - 2 to calculate the remaining power of the lithium battery at 100% to 1% power, and obtain yC （100） 、yC （99） ~yC （1） ; Among them, yC （100） represents the remaining power of the lithium battery at 100% power; yC （99） represents the remaining power of the lithium battery at 99% power; and so on, yC （1） represents the remaining power of the lithium battery at 1% power; Process A5: Summarize the data in Processes A1 to A4, and calculate the Kalman gain of the lithium battery at different percentage powers; Process A51: Calculate the Kalman gain when the lithium battery changes from 100% power to 99% power; Process A52: Calculate the Kalman gain when the battery level of a lithium battery changes from 99% to 98%. Process A53: By analogy, calculate the Kalman gains when the lithium battery's power changes from 98% to 97%, from 97% to 96%, and so on until it changes from 2% to 1% to obtain Kdk （98-97） , Kdk （97-96） ~Kdk （2-1） ; Among them, Kdk （98-97） represents the Kalman gain when the lithium battery's power changes from 98% to 97%; Kdk （97-96） represents the Kalman gain when the lithium battery's power changes from 97% to 96%; and so on, Kdk （2-1） represents the Kalman gain when the lithium battery's power changes from 2% to 1%; Process A54: Taking matrix EN as the final state, calculate the Kalman gain when the battery level of the lithium battery changes from 1% to 0%; the mathematical expression of matrix EN is: ; Process A6: Aggregate the Kalman gain Kdk in Process A5 （100-99） ~Kdk （1-e） , as the weight coefficient, enter the battery analysis module.

4. A lithium battery life detection system based on big data analysis according to claim 3, characterized in that, The specific process of the said Process A51 is as follows: Process A511: Construct the discharge matrix of the lithium battery at 100% battery charge and denote it as matrix B （100） , and the discharge matrix at 99% battery charge and denote it as matrix B （99） ; Matrix B （100） is mathematically represented as: ; Matrix B （99） is mathematically represented as: ; Process A512: Calculate matrix B （100） Change it to matrix B （99） The state transition matrix of, denoted as matrix B （100-99） ; Matrix B （100-99） The calculation formula of is: ; where, × represents matrix multiplication, T represents the transpose of a matrix, and -1 represents the inverse of a matrix; Process A513: Calculate matrix B （100） The difference matrix is denoted as matrix RB （100） ; For matrix RB （100） The calculation formula is as follows: ; where, - represents matrix subtraction; Calculate matrix B （99） The difference matrix, denoted as matrix RB （99） ; Matrix RB （99） The calculation formula is: ； Calculate the error transition matrix from matrix B （100） to matrix B （99） , denoted as matrix QB （100-99） ; The mathematical formula for matrix QB （100-99） is as follows: QB （100-99） = RB （100） - RB （99） ; Process A514: Calculate matrix B （100） of the error covariance matrix, denoted as matrix Pd （100） ; The calculation formula for matrix Pd （100） is as follows: Pd （100） = (1 / 4) × (APd （100） T × APd （100） ); where, × represents matrix multiplication, T represents the transpose of a matrix, and APd （100） represents the transition matrix of matrix Pd （100） , and the calculation formula of APd （100） is: APd （100） = B （100） - [(1 / 4) × I × B （100） ; where, - represents matrix subtraction, and I represents the identity matrix; Process A515: Calculate matrix B （99） The prior error covariance matrix, denoted as Pk （99） ; Pk （99） The calculation formula for Pk is: Pk （99） = B （100-99） × Pd （100） × (B （100-99） ) T + QB （100-99） ; where × represents matrix multiplication, T represents the transpose of a matrix, and + represents matrix addition; Calculate the Kalman gain Kdk when the lithium battery changes from 100% charge to 99% charge （100-99） , and the calculation formula is as follows: 。 5. A lithium battery life detection system based on big data analysis according to claim 3, characterized in that, The specific process of the said Process A52 is as follows: Process A521: The discharge matrix of the lithium battery at 99% battery level is Matrix B （99） ; Matrix B （99） The mathematical representation of ; Construct the discharge matrix of the lithium battery at 98% battery charge and denote it as matrix B （98） ; Matrix B （98） The mathematical representation of ; Process A522: Calculate matrix B （99） become the state transition matrix of matrix B （98） , denoted as matrix B （99-98） ; The calculation formula of matrix B （99-98） is as follows: ; where, × represents matrix multiplication, T represents the transpose of a matrix, and -1 represents the inverse of a matrix; Process A523: Calculate matrix B （98） The difference matrix is denoted as matrix RB （98） ; Matrix RB （98） The calculation formula is as follows: ; wherein, - represents matrix subtraction; Calculate the error transition matrix from matrix B （99） to matrix B （98） , denoted as matrix QB （99-98） ; The mathematical formula for matrix QB （99-98） is as follows: QB （99-98） = RB （99） - RB （98） ; where the matrix RB （99） represents the difference matrix of matrix B （99） ; Process A524: Calculate matrix B （99） for the error covariance matrix, denoted as matrix Pd （99） ; The calculation formula for matrix Pd （99） is as follows: Pd （99） = (1 / 4) × (APd （99） T × APd （99） ); where, × represents matrix multiplication, T represents the transpose of a matrix, and APd （99） represents the transition matrix of matrix Pd （99） , and the calculation formula of APd （99） is as follows: APd （99） = B （99） - [(1 / 4) × I × B （99） ; where, - represents matrix subtraction, and I represents the identity matrix; Process A525: Calculate matrix B （98） The prior error covariance matrix, denoted as Pk （98） ; Pk （98） The calculation formula for Pk is: Pk （98） = B （99-98） × Pd （99） × (B （99-98） ) T + QB （99-98） ; where × represents matrix multiplication, T represents the transpose of a matrix, and + represents matrix addition; Calculate the Kalman gain Kdk when the lithium battery changes from 99% power to 98% power （99-98） , and the calculation formula is as follows: 。 6. The lithium battery life detection system based on big data analysis according to claim 3, characterized in that, The specific process of the said Process A54 is as follows: Process A541: The discharge matrix of the lithium battery at 2% battery power is matrix B （2） ; Matrix B （2） The mathematical representation of which is: ; Construct the discharge matrix of the lithium battery at 1% battery charge and denote it as matrix B （1） ; Matrix B （1） is mathematically represented as: ; Process A542: Calculate matrix B （1） Becomes the state transition matrix of matrix EN, denoted as matrix B （1-e） ; Matrix B （1-e） The calculation formula of is: ; where, × represents matrix multiplication, T represents the transpose of a matrix, and -1 represents the inverse of a matrix; Process A543: Calculate matrix B （1） The difference matrix, denoted as matrix RB （1） ; Matrix RB （1） The calculation formula is: ; where, - represents matrix subtraction; yC （2） represents the remaining power of the lithium battery at 2% power; Calculate the difference matrix of matrix EN, denoted as matrix REN; the calculation formula of matrix REN is: ; wherein, yC （1） represents the remaining power of the lithium battery at 1% power; Calculate the error transition matrix from matrix B （1） to matrix EN, denoted as matrix QB （1-e） ; The mathematical formula for matrix QB （1-e） is as follows: QB （1-e） = RB （1） - REN; Process A544: Calculate the error covariance matrix of matrix EN, denoted as matrix Pde; the calculation formula of matrix Pde is: Pde = (1 / 4)×(APde T × APde); where, × represents matrix multiplication, T represents the transpose of a matrix, APde represents the transition matrix of matrix Pde, and the calculation formula of APde is: APde = EN - [(1 / 4) × I × EN]; where, - represents matrix subtraction, and I represents the identity matrix; Process A545: Calculate the prior error covariance matrix of matrix EN, denoted as Pke; the calculation formula of Pke is: ; where, × represents matrix multiplication, T represents the transpose of a matrix, and + represents matrix addition; Calculate the Kalman gain Kdk when the lithium battery's power changes from 1% to 0% （1-e） , and the calculation formula is as follows: 。 7. The lithium battery life detection system based on big data analysis according to claim 1, wherein The specific process of the said Process B4 is as follows: Process B41: Count the total number of electric vehicles using the lithium battery to be inspected in the target area, denoted as cn; Obtain the service life of the first lithium battery, denoted as cy1, the discharge voltage, denoted as cU1, and the discharge current, denoted as cI1; Obtain the service life of the second lithium battery, denoted as cy2, the discharge voltage, denoted as cU2, and the discharge current, denoted as cI2; By analogy, the service life of the $n$th lithium battery is denoted as $cy$ cn , the discharge voltage is denoted as $cU$ cn , the discharge current is denoted as $cI$ cn ; Process B42: Calculate the decay binary group of the first lithium battery, denoted as ca(1), ca(1) = [LL(1), cy1]; where LL(1) represents the left term of ca(1), that is, the decay coefficient with a service life of cy1; the calculation formula of LL(1) is: ; oU is denoted as the discharge voltage, and oI is denoted as the discharge current; Calculate the decay binary group of two lithium batteries, denoted as ca(2), ca(2) = [LL(2), cy2]; where LL(2) represents the left term of ca(2), that is, the decay coefficient with a service life of cy2; the calculation formula of LL(2) is: ; And so on, calculate the decay binary group of the cn-th lithium battery, denoted as ca(cn), ca(cn) = [LL(cn), cy cn ; where LL(cn) represents the left term of ca(cn), that is, the decay coefficient with a service life of cy cn ; The calculation formula of LL(cn) is: ; Process B43: Aggregate the decay binary groups ca(1) to ca(cn) of the first to the cnth lithium batteries; Taking the decay binary group as the division interval, calculate the average value of the decay coefficient corresponding to each service life, and use MATLAB to integrate to obtain the decay coefficient equation α(n).

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