Lithium battery life detection system based on big data analysis

Abstract

The invention provides a lithium battery life detection system based on big data analysis, and belongs to the field of lithium batteries. The problem of low lithium battery life detection efficiency is solved. Specifically, the data acquisition module acquires battery data and working data; the data fitting module carries out data fusion on the battery data and the working data and calculates a weight coefficient; the battery analysis module constructs a relation equation of the real-time electric quantity and the use time of the lithium battery according to the weight coefficient; acquiring sample data, and constructing a decay equation; combining the real-time electric quantity-service time relation equation with the decay equation to obtain a service life equation of the lithium battery; the user interaction module is used for acquiring user data and calculating the service life of the lithium battery; by acquiring, analyzing and processing related data in the delivery and use processes of the lithium battery, the service life of the lithium battery and the decay condition of the service life of the lithium battery along with the service life are analyzed, so that a user can know the use state of the lithium battery in real time.

Description

Technical Field

[0001] The present invention discloses 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 system has the following shortcomings: High data processing complexity: Since lithium battery life prediction 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, and the data processing complexity is relatively high. The existing system is lacking in data fusion, and does not deeply explore the potential connections between lithium battery-related data, resulting in low data utilization.

[0003] The prediction results are poorly interpretable: Because the current, voltage, power and other related data of lithium batteries change in real time during use and have no fixed proportional relationship, the data needs to be updated in real time to improve the prediction accuracy. Most existing systems use machine learning or deep learning models to deal with the life prediction of lithium batteries. This processing method is only applicable to the use of specific batteries in specific environments and has a narrow scope of application. As a result, machine learning or deep learning models cannot give a definite fitting result, affecting users' trust and acceptance of the prediction results.

[0004] Uncertain prediction results: Existing lithium battery life detection systems are capable of life prediction, but this prediction method is mostly based on ideal conditions, assuming that the voltage, current and power of the battery remain unchanged during discharge, ignoring the changes in voltage and current of the battery during use, so that the prediction results are still uncertain. Summary of the invention

[0005] In view of the shortcomings of the prior art, 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.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions: A lithium battery life detection system based on big data analysis includes: Data acquisition module: used to obtain the electric energy capacity, discharge voltage, discharge current, discharge power and initial battery temperature of the lithium battery to be tested when the lithium battery is not working as battery data; Obtain the working time of the lithium battery at different percentages of power; obtain the actual discharge voltage, actual discharge current and actual working temperature of the lithium battery at different percentages of power as working data; Data fitting module: used to fuse battery data and working data according to Kalman filtering, calculate the step-by-step Kalman gain of the lithium battery from 100% to 99%, 99% to 98%, and finally 1% to 0, and obtain the weight coefficient; Battery analysis module: used to construct the relationship equation between the real-time power of lithium batteries and the usage time according to the weight coefficient and the working time of lithium batteries at different percentages of power; 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 based on the sample data; The relationship equation between real-time power and usage time is combined with the decay equation to obtain the life equation of lithium battery. User interaction module: used to obtain the user's current lithium battery power and usage time as user data; substitute the user data into the life equation, calculate the service life of the lithium battery, and feedback to the user.

[0007] Furthermore, the workflow of the data fitting module is as follows: Process A1: record 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; The working time of the lithium battery at 100% power is recorded as t (100) ; Actual discharge voltage, denoted as dU (100) ; Actual discharge current, denoted as dI (100) ; Actual operating temperature, denoted as dT (100) ; The working time of the lithium battery at 99% power is recorded as t (99) ; Actual discharge voltage, denoted as dU (99) ; Actual discharge current, denoted as dI (99) ; Actual operating temperature, denoted as dT (99) ; By analogy, the working time of the lithium battery at 1% power is recorded as t (1) ; Actual discharge voltage, denoted as dU (1) ; Actual discharge current, denoted as dI (1) ; Actual operating temperature, denoted 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% power, recorded as Pq （100） ;Pq （100） The calculation formula is: ; Among them, Pp （100）Indicates the per unit power of a lithium battery at 100% power, Pp （100） The calculation formula is: Pp （100） =(dU (100) ×D (100) ); Calculate the active power of the lithium battery at 99% power, recorded as Pq （99） ;Pq （99） The calculation formula is: ; Among them, Pp （99） Indicates the per unit power of a lithium battery at 99% of its capacity, Pp （99） The calculation formula is: Pp （99） =(dU (99) ×D (99) ); By analogy, calculate the active power of the lithium battery at 1% power, recorded as Pq （1） ;Pq （1） The calculation formula is: ; Among them, Pp （1） Indicates the per unit power of lithium battery at 1% power, Pp （1） The calculation formula is: Pp （1） =(dU (1) ×D (1) ); Process A3: Define calculation formula a1-1: dC （i） =dU (i) ×D (i) ×t (i) ; Among them, dC （i） Indicates 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: 100~1; dU (100) ~dU (1) ,dI (100) ~dI (1) and t (100) ~t (1) Substitute into formula a1-1 to calculate the actual power consumption of the lithium battery from 100% to 1% power, and get dC （100） , dC （99） ~dC （1） ; Among them, dC （100） Indicates the actual power consumption of a lithium battery at 100% power; dC（99） Indicates the actual power consumption of a lithium battery at 99% power; by analogy, dC （1） Indicates the actual power consumption of the lithium battery at 1% power.

[0008] Furthermore, the subsequent process of process A3 is as follows: Process A4: Define calculation formula a1-2: Among them, yC （i） Indicates the remaining capacity of the lithium battery at i% capacity, the value range of i is: 100~1; dC （j） Indicates the actual power consumption of the lithium battery at j% power, and the value range of j is: 100~i; DC （100） ~dC （1） Substitute into formula a1-2 to calculate the remaining power of the lithium battery from 100% to 1%, and get yC （100） ,yC （99） ~yC （1） ; Among them, yC （100） Indicates the remaining power of the lithium battery at 100% power; （99） Indicates the remaining power of the lithium battery at 99% power; and so on. （1） Indicates 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 percentages of power; Process A51: Calculate the Kalman gain of the lithium battery from 100% power to 99% power; Process A52: Calculate the Kalman gain of the lithium battery from 99% to 98% power; Process A53: Similarly, calculate the Kalman gain of the lithium battery from 98% to 97%, from 97% to 96%, and finally from 2% to 1%, and get Kdk （98-97） 、Kdk （97-96） ~Kdk （2-1） ; Among them, Kdk （98-97） Kdk represents the Kalman gain of calculating the lithium battery from 98% to 97% power; （97-96） It represents the Kalman gain of calculating the lithium battery from 97% to 96% power; and so on, Kdk （2-1） It represents the Kalman gain of calculating the lithium battery from 2% power to 1% power; Process A54: Taking matrix EN as the final state, calculate the Kalman gain of the lithium battery from 1% power to 0% power; the mathematical expression of matrix EN is: .

[0009] Process A6: Summarize the Kalman gain Kdk in process A5 （100-99） ~Kdk （1-e） , as the weight coefficient, enters the battery analysis module.

[0010] Furthermore, the specific process of the process A51 is as follows: Process A511: Construct the discharge matrix of the lithium battery at 100% power and record it as matrix B （100） , the discharge matrix at 99% power is recorded as matrix B （99） ; Matrix B （100） The mathematical representation of is: ; Matrix B （99） The mathematical representation of is: ; Process A512: Calculate matrix B （100） Becomes matrix B （99） The state transfer matrix of （100-99） ; Matrix B （100-99） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A513: Calculate matrix B （100） The difference matrix of （100） ; Matrix RB （100） The calculation formula is: ; Where - represents matrix subtraction; Calculate the matrix B （99） The difference matrix of （99） ; Matrix RB （99） The calculation formula is: ; Calculated by matrix B （100） Becomes matrix B （99） The error transition matrix is ​​denoted as matrix QB （100-99） ; Matrix QB （100-99） The mathematical formula is: QB （100-99） =RB （100） -RB （99） ; Process A514: Calculate matrix B （100） The error covariance matrix of （100） ; Matrix Pd （100） The calculation formula is: 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 matrix Pd （100） The transition matrix, APd （100） The calculation formula is: AP （100） =B （100） -[(1 / 4)×I×B （100） ]; where - represents matrix subtraction and I represents the 1 matrix; Process A515: Calculate matrix B （99） The pre-error covariance matrix of （99） ;Pk （99） The calculation formula is: P （99） =B （100-99） ×Pd （100） ×(B （100-99） ) T +QB （100-99） ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 100% to 99% （100-99） , the calculation formula is: .

[0011] Furthermore, the specific process of the process A52 is as follows: Process A521: The discharge matrix of a lithium battery at 99% power is matrix B （99） ; Matrix B （99） The mathematical representation of is: ; Construct the discharge matrix of the lithium battery at 98% power and record it as matrix B （98） ; Matrix B （98） The mathematical representation of is: ; Process A522: Calculate matrix B （99） Becomes matrix B （98） The state transfer matrix of （99-98） ; Matrix B （99-98） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A523: Calculate matrix B （98） The difference matrix of（98） ; Matrix RB （98） The calculation formula is: ; Where - represents matrix subtraction; Calculated by matrix B （99） Becomes matrix B （98） The error transition matrix is ​​denoted as matrix QB （99-98） ; Matrix QB （99-98） The mathematical formula is: QB （99-98） =RB （99） -RB （98） ; Among them, the matrix RB （99） Represents the matrix B （99） The difference matrix of Process A524: Calculate matrix B （99） The error covariance matrix of （99） ; Matrix Pd （99） The calculation formula is: Pd （99） = (1 / 4) × (APd （99） T ×APd （99） ); where × represents matrix multiplication, T represents the transpose of the matrix, and APd （99） Represents the matrix Pd （99） The transition matrix, APd （99） The calculation formula is: AP （99） =B （99） -[(1 / 4)×I×B （99） ]; where - represents matrix subtraction and I represents the 1 matrix; Process A525: Calculate matrix B （98） The pre-error covariance matrix of （98） ;Pk （98） The calculation formula is: P （98） =B （99-98） ×Pd （99） ×(B （99-98） ) T +QB （99-98） ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 99% to 98% （99-98） , the calculation formula is: .

[0012] Furthermore, the specific process of the process A54 is as follows: Process A541: The discharge matrix of a lithium battery at 2% power is matrix B （2） ; Matrix B （2） The mathematical representation of is: ; Construct the discharge matrix of the lithium battery at 1% power and record it as matrix B （1） ; Matrix B （1） The mathematical representation of is: ; Process A542: Calculate matrix B （1） The state transfer matrix of matrix EN is converted into matrix B （1-e） ; Matrix B （1-e） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A543: Calculate matrix B （1） The difference matrix of （1） ; Matrix RB （1） The calculation formula is: ; Where - represents matrix subtraction; yC （2） Indicates the remaining power of the lithium battery at 2% power; Calculate the difference matrix of matrix EN, recorded as matrix REN; the calculation formula of matrix REN is: Among them, yC （1） Indicates the remaining power of the lithium battery at 1% power; Calculated by matrix B （1） The error transition matrix of matrix EN is converted into matrix QB （1-e） ; Matrix QB （1-e） The mathematical formula is: QB （1-e） =RB （1） -REN; Process A544: Calculate the error covariance matrix of the matrix EN, denoted as matrix Pde; the calculation formula of the matrix Pde is: Pde=(1 / 4)×(APde T ×APde); where × represents matrix multiplication, T represents the transpose of the matrix, APde represents the transition matrix of the matrix Pde, and the calculation formula of APde is: APde = EN - [(1 / 4) × I × EN]; where - represents matrix subtraction and I represents the 1 matrix; Process A545: Calculate the pre-error covariance matrix of the matrix EN, denoted as Pke; the calculation formula of Pke is: ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 1% power to 0% power （1-e） , the calculation formula is: .

[0013] Furthermore, the working process of the battery analysis module is as follows: Process B1: Extract Kdk （100-99） ~Kdk （1-e） The maximum value is denoted as KM, and the minimum value is denoted as KL; Take (0,0.001] as the error range and extract Kdk （100-99） ~Kdk （1-e） The majority of is denoted as KZ; Process B2: Take (0,0.001] as the error range and calculate Kdk （100-99） ~Kdk （1-e） The number of modes, denoted by nz; Let the combined weight be KK, determine the difference between (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 to i%, where the value of i ranges from 100 to 1; Assuming the battery life is n, construct the relationship equation between the real-time battery power and the usage time, recorded as equation T(i); the mathematical expression of equation T(i) is: ; Where × represents multiplication; Tt represents the continuous use time for the battery to change 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 lithium battery over time; 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; Process B5: Substitute α(n) into the decay equation to obtain C(n); Substitute C(n) into the equation T(i), calculate the integral of T(i) and record it as in(i); the mathematical expression is: ; Process B6: Summarize the relationship equation between real-time power and usage time, decay equation and decay coefficient equation as the life equation of lithium battery.

[0014] Furthermore, the specific process of process B4 is as follows: Process B41: Count the total number of electric vehicles using the lithium batteries to be tested in the target area, recorded as cn; The service life of the first lithium battery is obtained as cy1, the discharge voltage is recorded as cU1, and the discharge current is recorded as cI1; The service life of the second lithium battery is obtained as cy2, the discharge voltage is recorded as cU2, and the discharge current is recorded as cI2; By analogy, the service life of the cnth lithium battery is recorded as cy cn , discharge voltage is recorded as cU cn , discharge current is denoted as cI cn ; Process B42: Calculate the decay binary of the first lithium battery, recorded as ca(1), ca(1) = [LL(1), cy1]; where LL(1) represents the left term of ca(1), i.e., the decay coefficient of the service life cy1; the calculation formula of LL(1) is: ; Calculate the decay binary of two lithium batteries, denoted as ca(2), ca(2) = [LL(2), cy2]; LL(2) represents the left term of ca(2), i.e. the decay coefficient of cy2 years of use; the calculation formula of LL(2) is: ; Similarly, the decay binary of the cnth lithium battery is calculated, denoted as ca(cn), ca(cn) = [LL(cn), cy cn ]; LL (cn) represents the left term of ca (cn), that is, the service life is cy cn The decay coefficient of LL (cn) is calculated as: ; Process B43: Summarize the decay tuples ca(1)~ca(cn) of the 1st to cnth lithium batteries; Taking the decay binary group as the division interval, the average value of the decay coefficient corresponding to each service life is calculated, and then integrated using MATLAB to obtain the decay coefficient equation α(n).

[0015] Compared with the prior art, the present invention has the following beneficial effects: Real-time prediction capability: 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, and uses the discharge data to perform real-time detection of battery life, making the detection results specific and practical, which can significantly improve the efficiency and accuracy of energy management.

[0016] Simple and easy to implement: Compared with the traditional model-based prediction method, the present invention does not need to start from the complex physical model of the lithium-ion battery. That is, the present invention evaluates the usable life of the battery by analyzing the charging and discharging data of the lithium-ion battery as well as the temperature, power and other data. 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.

[0017] Improve the accuracy of prediction: The present invention can integrate multi-source data, such as discharge data, temperature data, power data, etc., and use Kalman filtering to analyze the weight value between real data and 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 rules and characteristics of the battery during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 It is a schematic diagram of the system of the present invention; Figure 2 This is a schematic diagram of data processing of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] See also 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 connected to the database and the server respectively.

[0021] Data acquisition module: used to obtain the electric energy capacity, (rated) discharge voltage, (rated) discharge current, discharge power and initial battery temperature of the lithium battery when it is not working, as battery data; (Through the BMS system in the electric vehicle, i.e. the battery management system) obtain the working time of the lithium battery at different percentages of power; obtain the actual discharge voltage, actual discharge current and actual working temperature of the lithium battery at different percentages of power as working data (for a single electric vehicle); It should be noted that the "lithium battery to be tested" in the present invention refers to the battery for which the lithium battery life test is performed using the present invention (a lithium battery life test system based on big data analysis); the "lithium battery" that appears later, unless otherwise specified, refers to the "lithium battery to be tested"; The “actual discharge voltage, actual discharge current and actual operating temperature of the lithium battery at different percentages of power” in the present invention are the average values ​​of the voltage, current and temperature output or heat generated by the lithium battery at a certain percentage of power. For example, if a lithium battery works for 10 minutes at 100% power, 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 operating temperature" is the average value of the heating temperature of the lithium battery within 10 minutes at 100% power. Data fitting module: used to fuse battery data and working data according to Kalman filtering, calculate the step-by-step Kalman gain of the lithium battery from 100% to 99%, 99% to 98%, and finally 1% to 0, and obtain the weight coefficient; Process A: Please refer to Figure 2 ,The workflow of the data fitting module is as follows: Process A1: The electric energy capacity of the lithium battery to be tested is recorded as CO, the (rated) discharge voltage is recorded as oU, the (rated) discharge current is recorded as oI, the discharge power is recorded as PO, and the initial battery temperature is recorded as To; The working time of the lithium battery at 100% power is recorded as t (100) ; Actual discharge voltage, denoted as dU (100) ; Actual discharge current, denoted as dI (100) ; Actual operating temperature, denoted as dT (100) ; The working time of the lithium battery at 99% power is recorded as t (99) ; Actual discharge voltage, denoted as dU (99) ; Actual discharge current, denoted as dI (99) ; Actual operating temperature, denoted as dT (99) ; By analogy, the working time of the lithium battery at 1% power is recorded as t (1) ; Actual discharge voltage, denoted as dU (1) ; Actual discharge current, denoted as dI(1) ; Actual operating temperature, denoted as dT (1) ; Process A2: Obtain the weight of the lithium battery as m, and the specific heat capacity (of the lithium battery) as rc; Calculate the active power of the lithium battery at 100% power, recorded as Pq （100） ;Pq （100） The calculation formula is: ; Among them, Pp （100） Indicates the per unit power of a lithium battery at 100% power, Pp （100） The calculation formula is: Pp （100） =(dU (100) ×D (100) ); Calculate the active power of the lithium battery at 99% power, recorded as Pq （99） ;Pq （99） The calculation formula is: ; Among them, Pp （99） Indicates the per unit power of a lithium battery at 99% of its capacity, Pp （99） The calculation formula is: Pp （99） =(dU (99) ×D (99) ); By analogy, calculate the active power of the lithium battery at 1% power, recorded as Pq （1） ;Pq （1） The calculation formula is: ; Among them, Pp （1） Indicates the per unit power of lithium battery at 1% power, Pp （1） The calculation formula is: Pp （1） =(dU (1) ×D (1) ); Process A3: Define calculation formula a1-1: dC （i） =dU (i) ×D (i) ×t (i) ; Among them, dC （i） Indicates 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: 100~1; dU (100) ~dU(1) ,dI (100) ~dI (1) and t (100) ~t (1) Substitute into formula a1-1 to calculate the actual power consumption of the lithium battery from 100% to 1% power, and get dC （100） , dC （99） ~dC （1） ; Among them, dC （100） Indicates the actual power consumption of a lithium battery at 100% power; dC （99） Indicates the actual power consumption of a lithium battery at 99% power; by analogy, dC （1） Indicates the actual power consumption of the lithium battery at 1% power; Process A4: Define calculation formula a1-2: Among them, yC （i） Indicates the remaining capacity of the lithium battery at i% capacity, the value range of i is: 100~1; dC （j） Indicates the actual power consumption of the lithium battery at j% power, and the value range of j is: 100~i; DC （100） ~dC （1） Substitute into formula a1-2 to calculate the remaining power of the lithium battery from 100% to 1%, and get yC （100） ,yC （99） ~yC （1） ; Among them, yC （100） Indicates the remaining power of the lithium battery at 100% power; （99） Indicates the remaining power of the lithium battery at 99% power; and so on. （1） Indicates 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 percentages of power; Process A51: Calculate the Kalman gain of the lithium battery from 100% power to 99% power; Process A511: Construct the discharge matrix of the lithium battery at 100% power and record it as matrix B （100） , the discharge matrix at 99% power is recorded as matrix B （99） ; Matrix B （100） The mathematical representation of is: ; Matrix B （99） The mathematical representation of is: ; Process A512: Calculate matrix B （100） Becomes matrix B（99） The state transfer matrix of （100-99） ; Matrix B （100-99） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A513: Calculate matrix B （100） The difference matrix of （100） ; Matrix RB （100） The calculation formula is: ; Where - represents matrix subtraction; Calculate the matrix B （99） The difference matrix of （99） ; Matrix RB （99） The calculation formula is: ; Calculated by matrix B （100） Becomes matrix B （99） The error transition matrix is ​​denoted as matrix QB （100-99） ; Matrix QB （100-99） The mathematical formula is: QB （100-99） =RB （100） -RB （99） ; Process A514: Calculate matrix B （100） The error covariance matrix of （100） ; Matrix Pd （100） The calculation formula is: 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 matrix Pd （100） The transition matrix, APd （100） The calculation formula is: AP （100） =B （100） -[(1 / 4)×I×B （100） ]; where - represents matrix subtraction, and I represents a 1 matrix (i.e. a matrix with 4 rows and 4 columns, where each row and column is 1); Process A515: Calculate matrix B （99） The pre-error covariance matrix of （99） ;Pk （99） The calculation formula is: P （99） =B（100-99） ×Pd （100） ×(B （100-99） ) T +QB （100-99） ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 100% to 99% （100-99） , the calculation formula is: ; Process A52: Calculate the Kalman gain of the lithium battery from 99% to 98% power; Process A521: The discharge matrix of a lithium battery at 99% power is matrix B （99） ; Matrix B （99） The mathematical representation of is: ; Construct the discharge matrix of the lithium battery at 98% power and record it as matrix B （98） ; Matrix B （98） The mathematical representation of is: ; Process A522: Calculate matrix B （99） Becomes matrix B （98） The state transfer matrix of （99-98） ; Matrix B （99-98） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A523: Calculate matrix B （98） The difference matrix of （98） ; Matrix RB （98） The calculation formula is: ; Where - represents matrix subtraction; Calculated by matrix B （99） Becomes matrix B （98） The error transition matrix is ​​denoted as matrix QB （99-98） ; Matrix QB （99-98） The mathematical formula is: QB （99-98） =RB （99） -RB （98） ; Among them, the matrix RB （99） Represents the matrix B （99） The difference matrix of Process A524: Calculate matrix B （99） The error covariance matrix of （99） ; Matrix Pd（99） The calculation formula is: Pd （99） = (1 / 4) × (APd （99） T ×APd （99） ); where × represents matrix multiplication, T represents the transpose of the matrix, and APd （99） Represents the matrix Pd （99） The transition matrix, APd （99） The calculation formula is: AP （99） =B （99） -[(1 / 4)×I×B （99） ]; where - represents matrix subtraction, and I represents a 1 matrix (i.e. a matrix with 4 rows and 4 columns, where each row and column is 1); Process A525: Calculate matrix B （98） The pre-error covariance matrix of （98） ;Pk （98） The calculation formula is: P （98） =B （99-98） ×Pd （99） ×(B （99-98） ) T +QB （99-98） ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 99% to 98% （99-98） , the calculation formula is: ; Process A53: Similarly, calculate the Kalman gain of the lithium battery from 98% to 97%, from 97% to 96%, and finally from 2% to 1%, and get Kdk （98-97） 、Kdk （97-96） ~Kdk （2-1） ; Among them, Kdk （98-97） Kdk represents the Kalman gain of calculating the lithium battery from 98% to 97% power; （97-96） It represents the Kalman gain of calculating the lithium battery from 97% to 96% power; and so on, Kdk （2-1） It represents the Kalman gain of calculating the lithium battery from 2% power to 1% power; Process A54: Taking matrix EN as the final state, calculate the Kalman gain of the lithium battery from 1% power to 0% power; the mathematical expression of matrix EN is: ; Process A541: The discharge matrix of a lithium battery at 2% power is matrix B （2）; Matrix B （2） The mathematical representation of is: ; Construct the discharge matrix of the lithium battery at 1% power and record it as matrix B （1） ; Matrix B （1） The mathematical representation of is: ; Process A542: Calculate matrix B （1） The state transfer matrix of matrix EN is converted into matrix B （1-e） ; Matrix B （1-e） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A543: Calculate matrix B （1） The difference matrix of （1） ; Matrix RB （1） The calculation formula is: ; Where - represents matrix subtraction; yC （2） Indicates the remaining power of the lithium battery at 2% power; Calculate the difference matrix of matrix EN, recorded as matrix REN; the calculation formula of matrix REN is: Among them, yC （1） Indicates the remaining power of the lithium battery at 1% power; Calculated by matrix B （1） The error transition matrix of matrix EN is converted into matrix QB （1-e） ; Matrix QB （1-e） The mathematical formula is: QB （1-e） =RB （1） -REN; Process A544: Calculate the error covariance matrix of the matrix EN, denoted as matrix Pde; the calculation formula of the matrix Pde is: Pde=(1 / 4)×(APde T ×APde); where × represents matrix multiplication, T represents the transpose of the matrix, APde represents the transition matrix of the matrix Pde, and the calculation formula of APde is: APde = EN - [(1 / 4) × I × EN]; where - represents matrix subtraction, and I represents a 1 matrix (i.e. a matrix with 4 rows and 4 columns, where each row and column is 1); Process A545: Calculate the pre-error covariance matrix of the matrix EN, denoted as Pke; the calculation formula of Pke is: ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 1% power to 0% power （1-e） , the calculation formula is: ; Process A6: Summarize the Kalman gain Kdk in process A5 （100-99） ~Kdk （1-e） , as the weight coefficient, enters the battery analysis module.

[0022] Battery analysis module: used to construct the relationship equation between the real-time power of lithium batteries and the usage time according to the weight coefficient and the working time of lithium batteries at different percentages of power; Obtain the service life, discharge voltage and discharge current of all lithium batteries of the same model (electric vehicles) in the target area as sample data (multiple electric vehicles); calculate the decay equation of lithium batteries based on the sample data; The relationship equation between real-time power and usage time is combined with the decay equation to obtain the life equation of lithium battery. It should be noted that the "target area" in the present invention means: the city-level area where the present invention is used to perform lithium battery life testing; It should be noted that the "same model lithium battery" in the battery analysis module means: batteries with the same model as the "(to be tested) lithium battery"; Process B: The workflow of the battery analysis module is as follows: Process B1: Extract Kdk （100-99） ~Kdk （1-e） The maximum value is denoted as KM, and the minimum value is denoted as KL; Take (0,0.001] as the error range and extract Kdk （100-99） ~Kdk （1-e） The majority of is denoted as KZ; Process B2: Take (0,0.001] as the error range and calculate Kdk （100-99） ~Kdk （1-e） The number of modes, denoted by nz; Let the combined weight be KK, determine the difference between (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 to i%, where the value of i ranges from 100 to 1; Assuming the battery life is n, construct the relationship equation between the real-time battery power and the usage time, recorded as equation T(i); the mathematical expression of equation T(i) is: ; Where × represents multiplication; Tt represents the continuous use time of the battery (without charging) 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 lithium battery over time; Process B4: Obtain the service life, discharge voltage and discharge current of all lithium batteries of the same model (electric vehicles) in the target area as sample data (multiple electric vehicles); calculate the decay equation C(n) of the lithium battery based on the sample data; Process B41: Count the total number of electric vehicles using the lithium batteries to be tested in the target area, recorded as cn; Get the service life of the first lithium battery as cy1, the discharge voltage as cU1, and the discharge current as cI1; (the first electric car) The service life of the second lithium battery is obtained as cy2, the discharge voltage is recorded as cU2, and the discharge current is recorded as cI2; (the second electric car) By analogy, the service life of the cnth lithium battery is recorded as cy cn , discharge voltage is recorded as cU cn , discharge current is denoted as cI cn ; (cnth electric car) Process B42: Calculate the decay binary of the first lithium battery, recorded as ca(1), ca(1) = [LL(1), cy1]; where LL(1) represents the left term of ca(1), i.e., the decay coefficient of the service life cy1; the calculation formula of LL(1) is: ; Calculate the decay binary of two lithium batteries, denoted as ca(2), ca(2) = [LL(2), cy2]; LL(2) represents the left term of ca(2), i.e. the decay coefficient of cy2 years of use; the calculation formula of LL(2) is: ; Similarly, the decay binary of the cnth lithium battery is calculated, denoted as ca(cn), ca(cn) = [LL(cn), cy cn ]; LL (cn) represents the left term of ca (cn), that is, the service life is cy cn The decay coefficient of LL (cn) is calculated as: ; Process B43: Summarize the decay tuples ca(1)~ca(cn) of the 1st to cnth lithium batteries; Taking decay binary groups as the division interval, the average value of the decay coefficient corresponding to each service life is calculated, and then integrated using MATLAB to obtain the decay coefficient equation α (n); Process B5: Substitute α(n) into the decay equation to obtain C(n); Substitute C(n) into the equation T(i), calculate the integral of T(i) and record it as in(i); the mathematical expression is: ; (MATLAB stands for Matrix Laboratory, which is an application software used for solving mathematical problems such as matrix operations, numerical solutions, function optimization, and solving ordinary differential equations) Process B6: Summarize the relationship equation between real-time power and usage time, decay equation and decay coefficient equation as the life equation of lithium battery.

[0023] User interaction module: used to obtain the user's current (electric vehicle) lithium battery power and usage time (unit: year) as user data; substitute the user data into the life equation, calculate the service life of the lithium battery, and feedback to the user.

[0024] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technical personnel in this field according to actual conditions. For example, if there are weight coefficients and proportional coefficients, their set sizes are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. Regarding the size of the weight coefficient and the proportional coefficient, it is sufficient as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0025] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; 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 be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A lithium battery life detection system based on big data analysis, characterized in that: The system comprises: Data acquisition module: used to obtain the electric energy capacity, discharge voltage, discharge current, discharge power and initial battery temperature of the lithium battery to be tested when the lithium battery is not working as battery data; Obtain the working time of the lithium battery at different percentages of power; obtain the actual discharge voltage, actual discharge current and actual working temperature of the lithium battery at different percentages of power as working data; Data fitting module: used to fuse battery data and working data according to Kalman filtering, calculate the step-by-step Kalman gain of the lithium battery power, and obtain the weight coefficient; Battery analysis module: used to construct the relationship equation between the real-time power of lithium batteries and the usage time according to the weight coefficient and the working time of lithium batteries at different percentages of power; 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 based on the sample data; The relationship equation between real-time power and usage time is combined with the decay equation to obtain the life equation of lithium battery. User interaction module: used to obtain the user's current lithium battery power and usage time as user data; substitute the user data into the life equation, calculate the service life of the lithium battery, and feed it back to the user.

2. A lithium battery life detection system based on big data analysis according to claim 1, characterized in that: The workflow of the data fitting module is as follows: Process A1: record 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; The working time of the lithium battery at 100% power is recorded as t (100) ; Actual discharge voltage, recorded as dU (100) ; Actual discharge current, denoted as dI (100) ; Actual operating temperature, denoted as dT (100) ; The working time of the lithium battery at 99% power is recorded as t (99) ; Actual discharge voltage, recorded as dU (99) ; Actual discharge current, denoted as dI (99) ; Actual operating temperature, denoted as dT (99) ; By analogy, the working time of the lithium battery at 1% power is recorded as t (1) ; Actual discharge voltage, recorded as dU (1) ; Actual discharge current, denoted as dI (1) ; Actual operating temperature, denoted 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% power, recorded as Pq （100） ;Pq （100） The calculation formula is: ； Among them, Pp （100） Indicates the per unit power of a lithium battery at 100% power, Pp （100） The calculation formula is: Pp （100） =(dU (100) ×D (100) ); Calculate the active power of the lithium battery at 99% power, recorded as Pq （99） ;Pq （99） The calculation formula is: ； Among them, Pp （99） Indicates the per unit power of a lithium battery at 99% of its capacity, Pp （99） The calculation formula is: Pp （99） =(dU (99) ×D (99) ); By analogy, calculate the active power of the lithium battery at 1% power, recorded as Pq （1） ;Pq （1） The calculation formula is: ； Among them, Pp （1） Indicates the per unit power of lithium battery at 1% power, Pp （1） The calculation formula is: Pp （1） =(dU (1) ×D (1) ); Process A3: Define calculation formula a1-1: dC （i） =dU (i) ×D (i) ×t (i) ; Among them, dC （i） Indicates 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: 100~1; dU (100) ~dU (1) ,dI (100) ~dI (1) and t (100) ~t (1) Substitute into formula a1-1 to calculate the actual power consumption of the lithium battery from 100% to 1% power, and get dC （100） , dC （99） ~dC （1） ; Among them, dC （100） Indicates the actual power consumption of a lithium battery at 100% power; dC （99） Indicates the actual power consumption of a lithium battery at 99% power; by analogy, dC （1） Indicates the actual power consumption of the lithium battery at 1% power.

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: Among them, yC （i） Indicates the remaining capacity of the lithium battery at i% capacity, the value range of i is: 100~1; dC （j） Indicates the actual power consumption of the lithium battery at j% power, and the value range of j is: 100~i; DC （100） ~dC （1） Substitute into formula a1-2 to calculate the remaining power of the lithium battery from 100% to 1%, and get yC （100） ,yC （99） ~yC （1） ; Among them, yC （100） Indicates the remaining power of the lithium battery at 100% power; （99） Indicates the remaining power of the lithium battery at 99% power; and so on. （1） Indicates 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 percentages of power; Process A51: Calculate the Kalman gain of the lithium battery from 100% power to 99% power; Process A52: Calculate the Kalman gain of the lithium battery from 99% to 98% power; Process A53: Similarly, calculate the Kalman gain of the lithium battery from 98% to 97%, from 97% to 96%, and finally from 2% to 1%, and get Kdk （98-97） 、Kdk （97-96） ~Kdk （2-1） ; Among them, Kdk （98-97） Kdk represents the Kalman gain of calculating the lithium battery from 98% to 97% power; （97-96） It represents the Kalman gain of calculating the lithium battery from 97% to 96% power; and so on, Kdk （2-1） It represents the Kalman gain of calculating the lithium battery from 2% power to 1% power; Process A54: Taking matrix EN as the final state, calculate the Kalman gain of the lithium battery from 1% power to 0% power; the mathematical expression of matrix EN is: ; Process A6: Summarize the Kalman gain Kdk in process A5 （100-99） ~Kdk （1-e） , as the weight coefficient, enters 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 process A51 is as follows: Process A511: Construct the discharge matrix of the lithium battery at 100% power and record it as matrix B （100） , the discharge matrix at 99% power is recorded as matrix B （99） ; Matrix B （100） The mathematical representation of is: ; Matrix B （99） The mathematical representation of is: ; Process A512: Calculate matrix B （100） Becomes matrix B （99） The state transfer matrix of （100-99） ; Matrix B （100-99） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A513: Calculate matrix B （100） The difference matrix of （100） ; Matrix RB （100） The calculation formula is: ; Where - represents matrix subtraction; Calculate the matrix B （99） The difference matrix of （99） ; Matrix RB （99） The calculation formula is: ； Calculated by matrix B （100） Becomes matrix B （99） The error transition matrix is ​​denoted as matrix QB （100-99） ; Matrix QB （100-99） The mathematical formula is: QB （100-99） =RB （100） -RB （99） ; Process A514: Calculate matrix B （100） The error covariance matrix of （100） ; Matrix Pd （100） The calculation formula is: 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 matrix Pd （100） The transition matrix, APd （100） The calculation formula is: AP （100） =B （100） -[(1 / 4)×I×B （100） ]; where - represents matrix subtraction and I represents the 1 matrix; Process A515: Calculate matrix B （99） The pre-error covariance matrix of （99） ;Pk （99） The calculation formula is: P （99） =B （100-99） ×Pd （100） ×(B （100-99） ) T +QB （100-99） ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 100% to 99% （100-99） , the calculation formula is: 。 5. The lithium battery life detection system based on big data analysis according to claim 3 is characterized in that: The specific process of process A52 is as follows: Process A521: The discharge matrix of the lithium battery at 99% power is matrix B （99） ; Matrix B （99） The mathematical representation of is: ; Construct the discharge matrix of the lithium battery at 98% power and record it as matrix B （98） ; Matrix B （98） The mathematical representation of is: ; Process A522: Calculate matrix B （99） Becomes matrix B （98） The state transfer matrix of （99-98） ; Matrix B （99-98） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A523: Calculate matrix B （98） The difference matrix of （98） ; Matrix RB （98） The calculation formula is: ; Where - represents matrix subtraction; Calculated by matrix B （99） Becomes matrix B （98） The error transition matrix is ​​denoted as matrix QB （99-98） ; Matrix QB （99-98） The mathematical formula is: QB （99-98） =RB （99） -RB （98） ; Among them, the matrix RB （99） Represents the matrix B （99） The difference matrix of Process A524: Calculate matrix B （99） The error covariance matrix of （99） ; Matrix Pd （99） The calculation formula is: Pd （99） = (1 / 4) × (APd （99） T ×APd （99） ); where × represents matrix multiplication, T represents the transpose of the matrix, and APd （99） Represents the matrix Pd （99） The transition matrix, APd （99） The calculation formula is: AP （99） =B （99） -[(1 / 4)×I×B （99） ]; where - represents matrix subtraction and I represents the 1 matrix; Process A525: Calculate matrix B （98） The pre-error covariance matrix of （98） ;Pk （98） The calculation formula is: P （98） =B （99-98） ×Pd （99） ×(B （99-98） ) T +QB （99-98） ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 99% to 98% （99-98） , the calculation formula is: 。 6. The lithium battery life detection system based on big data analysis according to claim 3 is characterized in that: The specific process of process A54 is as follows: Process A541: The discharge matrix of a lithium battery at 2% power is matrix B （2） ; Matrix B （2） The mathematical representation of is: ; Construct the discharge matrix of the lithium battery at 1% power and record it as matrix B （1） ; Matrix B （1） The mathematical representation of is: ; Process A542: Calculate matrix B （1） The state transfer matrix of matrix EN is converted into matrix B （1-e） ; Matrix B （1-e） The calculation formula is: ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A543: Calculate matrix B （1） The difference matrix of （1） ; Matrix RB （1） The calculation formula is: ; Where - represents matrix subtraction; yC （2） Indicates the remaining power of the lithium battery at 2% power; Calculate the difference matrix of matrix EN, recorded as matrix REN; the calculation formula of matrix REN is: Among them, yC （1） Indicates the remaining power of the lithium battery at 1% power; Calculated by matrix B （1） The error transition matrix of matrix EN is converted into matrix QB （1-e） ; Matrix QB （1-e） The mathematical formula is: QB （1-e） =RB （1） -REN; Process A544: Calculate the error covariance matrix of the matrix EN, denoted as matrix Pde; the calculation formula of the matrix Pde is: Pde=(1 / 4)×(APde T ×APde); where × represents matrix multiplication, T represents the transpose of the matrix, APde represents the transition matrix of the matrix Pde, and the calculation formula of APde is: APde = EN - [(1 / 4) × I × EN]; where - represents matrix subtraction and I represents the 1 matrix; Process A545: Calculate the pre-error covariance matrix of the matrix EN, denoted as Pke; the calculation formula of Pke is: ; Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the lithium battery from 1% power to 0% power （1-e） , the calculation formula is: 。 7. The lithium battery life detection system based on big data analysis according to claim 3 is characterized in that: The working process of the battery analysis module is as follows: Process B1: Extract Kdk （100-99） ~Kdk （1-e） The maximum value is denoted as KM, and the minimum value is denoted as KL; Take (0,0.001] as the error range and extract Kdk （100-99） ~Kdk （1-e） The majority of is denoted as KZ; Process B2: Take (0,0.001] as the error range and calculate Kdk （100-99） ~Kdk （1-e） The number of modes, denoted by nz; Let the combined weight be KK, determine the difference between (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 to i%, where the value of i ranges from 100 to 1; Assuming the battery life is n, construct the relationship equation between the real-time battery power and the usage time, recorded as equation T(i); the mathematical expression of equation T(i) is: ; Where × represents multiplication; Tt represents the continuous use time for the battery to change 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 lithium battery over time; 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; Process B5: Substitute α(n) into the decay equation to obtain C(n); Substitute C(n) into the equation T(i), calculate the integral of T(i) and record it as in(i); the mathematical expression is: ; Process B6: Summarize the relationship equation between real-time power and usage time, decay equation and decay coefficient equation as the life equation of lithium battery.

8. The lithium battery life detection system based on big data analysis according to claim 7, characterized in that: The specific process of process B4 is as follows: Process B41: Count the total number of electric vehicles using the lithium batteries to be tested in the target area, recorded as cn; The service life of the first lithium battery is obtained as cy1, the discharge voltage is recorded as cU1, and the discharge current is recorded as cI1; The service life of the second lithium battery is obtained as cy2, the discharge voltage is recorded as cU2, and the discharge current is recorded as cI2; By analogy, the service life of the cnth lithium battery is recorded as cy cn , discharge voltage is recorded as cU cn , discharge current is denoted as cI cn ; Process B42: Calculate the decay binary of the first lithium battery, recorded as ca(1), ca(1) = [LL(1), cy1]; where LL(1) represents the left term of ca(1), i.e., the decay coefficient of the service life cy1; the calculation formula of LL(1) is: ; oU is recorded as discharge voltage, oI is recorded as discharge current; Calculate the decay binary of two lithium batteries, denoted as ca(2), ca(2) = [LL(2), cy2]; LL(2) represents the left term of ca(2), i.e. the decay coefficient of cy2 years of use; the calculation formula of LL(2) is: ; Similarly, the decay binary of the cnth lithium battery is calculated, denoted as ca(cn), ca(cn) = [LL(cn), cy cn ]; LL (cn) represents the left term of ca (cn), that is, the service life is cy cn The decay coefficient of LL (cn) is calculated as: ; Process B43: Summarize the decay tuples ca(1)~ca(cn) of the 1st to cnth lithium batteries; Taking the decay binary group as the division interval, the average value of the decay coefficient corresponding to each service life is calculated, and then integrated using MATLAB to obtain the decay coefficient equation α(n).

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