A method and system for operation self-checking of intelligent shared charging piles and a medium

Abstract

The application provides a kind of operation self-checking method, system and medium for intelligent shared charging pile, belongs to the field of charging pile management;Solve the problem of low efficiency of charging pile self-checking;Specific as follows:Step S1: obtain charging pile data;Step S2: obtain the historical charging data of charging pile, construct estimation equation A and estimation equation B;Step S3: obtain the charging request of electric car, combine estimation equation A and estimation equation B, estimate the maximum output voltage and maximum output current of charging pile, and compare with the actual output voltage and actual output current of charging pile, to judge whether the charging pile is normal;If normal, do not handle;If not normal, mark the charging pile that appears fault;Step S4: continuously monitor the charging pile, summarize the charging pile that appears fault, and feedback;The application obtains, analyzes and processes the relevant data of charging pile, analyzes the electrical parameter change of charging pile in the charging process, and improves the efficiency of charging pile operation self-checking.

Description

Technical Field

[0001] This invention relates to a self-inspection method, system, and medium for the operation of smart shared charging piles, and pertains to the field of charging pile management. Background Technology

[0002] Existing self-inspection methods or systems for shared charging stations have the following shortcomings:

[0003] High maintenance costs: Existing self-testing systems integrate multiple technologies such as network communication, data processing, and remote monitoring, resulting in relatively high manufacturing costs; in order to ensure the stability and reliability of the system, high-quality hardware and advanced software algorithms are required, which further increases the cost.

[0004] Network stability is a critical factor: the normal operation of the self-test system depends on a stable network infrastructure. If the network signal is unstable or interrupted, it will cause data transmission delays or failures, affecting the system's real-time monitoring and fault diagnosis functions.

[0005] Accuracy issues: Most existing self-testing algorithms are based on threshold comparisons of the rated working data of charging piles and lack data analysis capabilities. This can lead to situations where "the charging pile is working normally but the electric vehicle is charging abnormally (i.e., the output power of the charging pile does not match the charging power of the electric vehicle)," posing a safety hazard. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method, system and medium for self-inspection of intelligent shared charging piles, which aims to solve the problem of low self-inspection efficiency of charging piles.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a self-testing method for the operation of intelligent shared charging piles, the method comprising:

[0008] Step S1: Obtain the number of charging piles, the maximum rated power, maximum rated current, and maximum rated voltage of the charging piles, and obtain the charging pile data;

[0009] Step S2: Obtain historical charging data of the charging pile; perform a first mathematical modeling on the historical charging data to analyze the changes in charging time of the charging pile when the electric vehicle has battery overheat protection and when battery overheat protection does not occur during the charging process, and construct the prediction equation A; perform a second mathematical modeling on the historical charging data to analyze the changes in output voltage and charging current of the charging pile with charging power and charging time, and construct the prediction equation B.

[0010] Step S3: Obtain the charging request from the electric vehicle, detect whether the electric vehicle experiences battery overheating protection during charging, and estimate the maximum output voltage and maximum output current of the charging pile during charging by combining the estimation equations A and B; obtain the actual output voltage and actual output current of the charging pile, and compare them with the maximum output voltage and maximum output current to determine whether the charging process of the charging pile is normal; if normal, no action is taken; if abnormal, the faulty charging pile is marked.

[0011] Step S4: Continuously monitor the charging process of the charging piles, summarize the charging piles that have malfunctioned, and provide feedback.

[0012] Furthermore, step S2 is detailed as follows:

[0013] Step S21: Obtain the number of charging piles (ch); obtain the total number of charging times for all charging piles (ne). (1) ~ne( ch) ;

[0014] Step S22: Count the number of times the battery overheat protection occurred at the first charging station. (1) Number of times the battery overheat protection did not occur (1) The critical charge level at which the battery overheats and triggers protection is reached. (1) ;

[0015] Step S221: Place ne (1) As nel, obtain the charging time te for the first charging pile to perform the first to nelth charging operations. (1) ~te (nel) ;

[0016] Step S222: Determine whether the battery overheat protection occurs when the first charging station performs its first charging operation;

[0017] te (1) As tel, obtain the charging power pi of the electric vehicle from the 1st to the telth second during the first charging operation. (1) ~pi (tel) ;

[0018] Step S223: Based on pi (1) ~pi (tel) To determine if the battery has overheated protection;

[0019] If it occurs, then ro (1) Add 1; calculate le (1，1) Value:

[0020] If it does not appear, then ur (1) Add 1, le (1，1) The value is 0;

[0021] Step S224: Determine whether battery overheat protection occurs during the 2nd to nelth charging operations, and calculate the critical charge level le for the 2nd to nelth charging operations. (1，2) ~le (1，nel) ;

[0022] Calculate le (1，1) ~le (1，nel) Calculate the critical charge level le for the first charging station using le and ale. (1) .

[0023] Furthermore, step S2 also includes:

[0024] Step S23: Count the number of times the battery overheat protection occurs at the 2nd to the chth charging pile. (2) ~ro (ch) Number of times the battery overheat protection did not occur (2) ~ur (ch) The critical charge level at which the battery overheats and triggers protection is reached. (2) ~le (ch) And calculate the probability P of the charging station triggering battery overheat protection. (ro) The probability P that the battery overheat protection does not occur. (ur) ;

[0025] Calculate le (2) ~le (ch) The average value of ale; extract le (2) ~le (ch) The maximum value of le (max) minimum value le (min) Calculate the quasi-charge quantity Epr:

[0026] Step S24: Construct the prediction equations A for the 1st to chth charging stations. (1) ~A (ch) For equation A (1) ~A (ch) By fitting the equation, the predicted equation A is obtained;

[0027] Step S25: Analyze the changes in output power, output voltage and output current of the first to ch charging stations over time during the charging process, construct the prediction equation B for the charging station, and proceed to step S3.

[0028] Furthermore, the specific steps of step S24 are as follows:

[0029] Step S241: Construct the prediction equation A (1) ; will ur (1) As the URL, retrieve the charging time tl for the first to the urlth charging in the target data. (1) ~tl (url)Battery percentage (ec) (1) ~ec (url) Battery capacity ba (1) ~ba (url) Charging power pii (1) ~pii (url) ;

[0030] Step S242: Calculate the total charge amount re during the first charge. (1) The total charge amount for the urlth charge is re (url) ;

[0031] Construct matrices X and Y;

[0032] Step S243: Let the charging time for the h-th charge be tl. (h) The charging power of the electric vehicle is pii (h) The electric vehicle's battery percentage is ec (h) The battery capacity is ba (h) The total charging capacity is re (h) ;

[0033] Define equation A (1) Relationship coefficient β (0) ~β (5) Construct equation A (1) The initial equation;

[0034] Step S244: Construct the relation coefficient matrix B; denote the regularization parameter as λ, and calculate the residual matrix Zz;

[0035] Step S245: Define the residual sum of squares (RSS) of matrix B, construct the judgment formula for RSS, iterate the judgment formula until the value of RSS is minimized, and obtain matrix Bb;

[0036] Substituting the parameters in matrix Bb into the initial equation, we obtain equation A. (1) ;

[0037] Step S245: Construct the prediction equations A for the 2nd to chth charging stations. (2) ~A (ch) .

[0038] Furthermore, the specific steps of step S25 are as follows:

[0039] Step S251: Construct the voltage and current prediction equation B for the first charging station. (1) ;

[0040] ur (1) As the URL, retrieve the charging time tl for the first to the urlth charging operation in the target data. (1) ~tl (url) ;

[0041] Step S252: tl (1) As tll, the output power Po from the first to the tll second during the first charge is obtained. (1) ~Po (tll) Output voltage Uo (1) ~Uo (tll) Output current Io (1) ~Io(tll);

[0042] Calculate the autoregressive coefficient Pφ of the output power (1,1) and moving average coefficient Pθ (1,1) ;

[0043] Step S253: Calculate the autoregressive coefficient Pφ of the output power of the first charging station during the second to urlth charging operations. (1,2) ~Pθ (1,url) Moving average coefficient Pθ (1,2) ~Pθ (1,url) ;

[0044] Calculate Pφ (1,1) ~Pθ (1,url) The average value is used as the autoregressive coefficient Pφ of the output power of the first charging station. (1) ; Calculate Pθ (1,1) ~Pθ (1,url) The average value is used as the moving average coefficient Pθ of the output power of the first charging station. (1) .

[0045] Furthermore, the specific steps of step S252 are as follows:

[0046] Step S2521: Calculate Po (1) ~Po (tll) The average value aPo;

[0047] Calculate the white noise ψ of the output power from the 1st to the tllth second. (1) ~ψ (tll) ; Calculate ψ (1) ~ψ (tll) The variance va;

[0048] Step S2522: According to ψ (1) ~ψ (tll) The `va` constructor is used to logarithmize the function, resulting in a logarithmized function.

[0049] Step S2523: Define Pφ (1,1) and Pθ (1,1) Iterative relationship:

[0050] Let the deviation value at the k-th second be de. (k)Constructor S(Pφ) (1,1) , Pθ (1,1) ), and calculate the partial derivatives Ss(Pφ) of the autoregressive coefficients. (1,1) The partial derivatives of the moving average coefficients Ss(Pθ) and Pθ (1,1) );

[0051] Furthermore, the subsequent steps of step S2523 are as follows:

[0052] Step S2524: Denote the autoregressive coefficients after the m-th iteration as (Pφ) (m) (1,1) The moving average coefficient is denoted as (Pθ). (m) (1,1) The partial derivatives of the autoregressive coefficients are denoted as Ss. (m) (Pφ (1,1) The partial derivatives of the moving average coefficients are denoted as Ss. (m) (Pθ (1,1) );

[0053] The autoregressive coefficients after the (m+1)th iteration are denoted as (Pφ) (m+1) (1,1) The moving average coefficient is denoted as (Pθ). (m+1) (1,1) Define the iterative relation;

[0054] Step S2525: Set the deviation value de from the 1st to the tllth second. (1) ~de (tll) As white noise, it is substituted into the logarithmic function in reverse; according to the iterative relationship, Pφ is... (1,1) and Pθ (1,1) The iteration continues until the logarithmic function converges, yielding the autoregressive coefficients Pφ. (1,1) and moving average coefficient Pθ (1,1) .

[0055] Furthermore, the specific steps of step S3 are as follows:

[0056] Step S31: Obtain the quasi-charge level Epr and the probability P of battery overheat protection. (ro) The probability P that the battery overheat protection does not occur (ur) ;

[0057] Get the current battery percentage en, battery capacity at, charging power Pe, and charging power Ppe when the battery is under overheat protection.

[0058] Obtain the actual output power nPP, actual output voltage nUU, and actual output current nII of the charging pile;

[0059] Step S32: Substitute en, at, and Pe into the prediction equation A to calculate the ideal charging time gt;

[0060] Substituting Epr and Pe into the prediction equation A, the charging time ti is calculated. (1) ;Will

[0061] Substituting en, at, Epr, and Ppe into the prediction equation A, the charging time ti is calculated. (2) The desired charging time qt is obtained;

[0062] Step S33: Calculate and extract the maximum output power P of the charging pile from the 1st to the qtth second according to the prediction equation B. (max) Maximum output voltage U (max) Maximum output current I (max) ;

[0063] Step S34: Denote the maximum rated power of the charging pile as Pw (max) The maximum rated current is denoted as Uw (max) The maximum rated voltage is denoted as Iw. (max) ; To determine the charging process of the charging station.

[0064] Furthermore, the charging process of the charging station is assessed, specifically as follows:

[0065] Let the real-time output voltage of the charging station during the charging process be Ut, and the real-time output current be It;

[0066] judge Is it valid?

[0067] If this is true, then the charging station is charging normally.

[0068] If not true, then judge Is it valid?

[0069] If this is true, the charging station is malfunctioning.

[0070] If this is not the case, then analyze the real-time output power of the charging pile;

[0071] Step S35: Calculate the real-time output power Pt;

[0072] Determine if Pt > Pw (max) Is it valid?

[0073] If this is true, the charging station is malfunctioning.

[0074] If not true, then determine P. (max) U (max) and I (max) Are the rates of change the same?

[0075] If the results are the same, the charging station is working properly.

[0076] If they are different, the charging station is malfunctioning.

[0077] A self-testing system for the operation of smart shared charging piles, the system comprising:

[0078] Data acquisition module: used to acquire the number of charging piles, the maximum rated power, maximum rated current and maximum rated voltage of the charging piles, and obtain charging pile data;

[0079] Data Analysis Module: Used to acquire historical charging data of charging piles; perform a first mathematical modeling on the historical charging data to analyze the changes in charging time of the charging pile when the electric vehicle has battery overheat protection and when battery overheat protection does not occur during the charging process, and construct the prediction equation A; perform a second mathematical modeling on the historical charging data to analyze the output voltage and charging current of the charging pile as they change with charging power and charging time, and construct the prediction equation B.

[0080] Fault monitoring module: Used to acquire the charging request of the electric vehicle, detect whether the electric vehicle has battery overheat protection during the charging process, and estimate the maximum output voltage and maximum output current of the charging pile during charging by combining the prediction equation A and prediction equation B; acquire the actual output voltage and actual output current of the charging pile, and compare them with the maximum output voltage and maximum output current to determine whether the charging process of the charging pile is normal; if normal, no action is taken; if abnormal, the charging pile with the fault is marked.

[0081] Continuous monitoring module: Used to continuously monitor the charging process of charging piles, summarize the charging piles that have malfunctioned, and provide feedback.

[0082] A storage medium for self-testing the operation of a smart shared charging pile, wherein a computer program is stored thereon, and when the computer program is executed by a processor, it runs any one of the above-described methods for self-testing the operation of a smart shared charging pile.

[0083] Compared with the prior art, the beneficial effects of the present invention are:

[0084] Real-time monitoring and fault diagnosis: This invention can collect various electrical data of the charging pile in real time during the charging process. By using data analysis and mathematical modeling analysis techniques, the collected electrical data can be analyzed in depth. By comparing with preset standard parameters and historical data, the system can quickly and accurately determine whether there is a fault in the charging pile, with high self-test accuracy.

[0085] Improving charging safety: This invention analyzes historical charging data and performs mathematical modeling to analyze the charging power and charging amount of different electric vehicles, as well as the changes in charging time and the output voltage and current of the charging pile. It predicts the charging process of different electric vehicles in advance, avoiding the phenomenon of "mismatch between the output power of the charging pile and the charging power of the electric vehicle", and ensuring the safety and reliability of electric vehicles during the charging process.

[0086] Reduced operation and maintenance costs: This invention can automatically perform regular inspections of charging piles, mark and summarize faulty charging piles; this method not only helps managers to understand the operating status of charging piles in a timely manner, but also provides data support for the maintenance and upgrading of charging stations. Attached Figure Description

[0087] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0088] Figure 1 This is a schematic diagram of the method of the present invention;

[0089] Figure 2 This is a schematic diagram of the charging pile of the present invention;

[0090] Figure 3 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0091] To make the above-mentioned 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.

[0092] Example 1

[0093] Please see Figure 1 and Figure 2 A self-testing method for the operation of smart shared charging piles includes:

[0094] Step S1: Obtain the number of charging piles in the target area, and obtain the maximum rated power, maximum rated current, and maximum rated voltage of the charging piles (during the charging process) to obtain charging pile data;

[0095] It should be noted that the "target area" in this invention refers to the city-level area where the charging piles are self-tested using this invention (a method, system and medium for self-testing the operation of smart shared charging piles).

[0096] Step S2: Obtain historical charging data of the charging pile; perform a first mathematical modeling on the historical charging data to analyze the changes in charging time of the charging pile when the electric vehicle has battery overheat protection and when battery overheat protection does not occur during the charging process, and construct the (charging time) prediction equation A; perform a second mathematical modeling on the historical charging data to analyze the output voltage and charging current of the charging pile, and construct the (voltage and current) prediction equation B as the charging power and charging time change.

[0097] The specific steps of step S2 are as follows:

[0098] Step S21: Obtain the number of charging piles (within the target area), denoted as ch;

[0099] Retrieve the number of times each of the first to the pith charging piles (within the target area) has been used in the past month, denoted as ne. (1) ~ne (ch) ;

[0100] (Acquire the charging time, electric vehicle's battery percentage before charging, battery capacity, and charging power of each charging station during the charging operation, as well as the real-time output power, output voltage, and output current of the charging station during the charging process, as historical charging data.)

[0101] Step S22: Count the number of times the battery overheat protection occurred during the first charging station's charging operation. (1) Number of times the battery overheat protection did not occur (1) The critical charge level at which the battery overheats and triggers protection is reached. (1) ;(ro (1) ,ur (1) with le (1) (The initial values ​​are all 0)

[0102] Step S221: Place ne (1) As nel, the charging time of the first charging station (within the past month) from the first to the nelth charging operation is recorded, denoted as te. (1) ~te (nel) (Unit: seconds)

[0103] Step S222: Determine whether the battery overheat protection occurs during the first charging operation of the first charging station (within the past month);

[0104] te (1) As tel, the charging power of the electric vehicle from the 1st to the telth second (when the first charging operation is carried out at the first charging station within the past month) is obtained and denoted as pi. (1) ~pi (tel) ;

[0105] Define relation A-1:

[0106] Where, pi (1) This represents the charging power of the trolley in the z-th second; the value of z ranges from 2 to tel; ε represents the error value (ε is 0.1; users or relevant technical personnel can adjust the value of ε according to actual needs).

[0107] Step S223: Denote the critical charge level for the first charging operation as le. (1，1) ;(pi) (2) ~pi (tel) Substitute into relation A-1) and determine whether relation A-1 is true;

[0108] If true, then the battery overheat protection occurred during the first charging operation (during the first charging station within the past month). (1) Increment the value by 1; calculate le (1，1) :

[0109] Where, pi (V) This represents the charging power of the tram in the Vth second; the value of v ranges from 1 to z.

[0110] If this is not the case, then the first charging operation (conducted within the past month at the first charging station) did not trigger battery overheat protection. (1) Add 1 to the value of le (1，1) The value is 0;

[0111] Step S224: Repeat the same steps as determining whether battery overheat protection occurred during the first charging operation (i.e., steps S222 to S223), determine whether battery overheat protection occurred during the second to nelth charging operations (performed at the first charging station within the past month), and calculate the critical charge level le for the second to nelth charging operations. (1，2) ~le (1，nel) ;

[0112] Calculate le (1，1) ~le (1，nel) Calculate the critical charge level le for the first charging station using le and ale. (1) :le (1) =ale / ro (1) ;

[0113] Step S23: Repeat ro (1) ,ur (1) and le (1) The statistical and calculation process involves counting the number of times the battery overheat protection occurred during charging operations at the 2nd to the chth charging piles. (2) ~ro (ch) Number of times the battery overheat protection did not occur (2)~ur (ch) The critical power le at which battery overheat protection occurs (2) ~le (ch) ;

[0114] Calculate ne (1) ~ne (ch) The sum ane, ro (1) ~ro (ch) The sum aro, ur (1) ~ur (ch) The sum aur;

[0115] Calculate the probability P that the battery overheat protection occurs when the charging pile is performing the charging operation (ro) , P (ro) =aro / ane; The probability P that the battery overheat protection does not occur (ur) , P (ur) =aur / ane

[0116] Calculate le (2) ~le (ch) The average value ale; Extract the maximum value le (2) ~le (ch) in; The minimum value le (max) , Calculate the quasi-charging power Epr at which the battery overheat protection occurs for the charging pile: (min) Among them, le

[0117] where le (s) represents the critical power at which the battery overheat protection occurs for the s-th charging pile; The value range of s is: 1~ch;

[0118] Step S24: Construct the prediction equation A for the charging time of the 1st to ch-th charging stations (1) ~A (ch) ; (Using MATLAB software) Fit the equation A (1) ~A (ch) to obtain the prediction equation A for the charging time;

[0119] Step S241: Construct the prediction equation A for the charging time of the 1st charging station (1) ;

[0120] Take ur (1) as url, obtain the historical charging data of the 1st charging station without battery overheat protection as the target data;

[0121] Record the charging times of the 1st to url-th charging operations in the target data as tl (1) ~tl (url) ;

[0122] The percentage of battery charge (before the electric vehicle is charged) is denoted as ec. (1) ~ec (url) Battery capacity, denoted as ba (1) ~ba (url) Charging power, denoted as pii (1) ~pii (url) ;

[0123] Step S242: Calculate the total charging amount re for the first charging operation. (1) ,re (1) =(1-ec) (1) )×ba (1) ;

[0124] The total charging capacity of the second charging operation is re (2) ,re (2) =(1-ec) (2) )×ba (2) ;

[0125] And so on, the total charging amount for the urlth charging operation is re (url) ,re (url) =(1-ec) (url) )×ba (url) ;

[0126] With charging power (i.e., pii) (1) ~pii (url) ) and total charge (i.e., re (1) ~re (url) ) are the independent variables (i.e., two independent variables), and charging time is the dependent variable (i.e., tl). (1) ~tl (url) Construct the independent variable matrix X and the dependent variable matrix Y;

[0127] Matrix X:

[0128]

[0129] Matrix Y:

[0130]

[0131] Step S243: Let the charging time of the h-th charging operation be tl. (h) (Dependent variable) The charging power of the trolley is pii (h) The electric vehicle's battery percentage before charging is ec. (h) The battery capacity is ba (h) The total charging capacity is re (h) ;

[0132] Equation A (1) The relationship coefficient, denoted as β(0) ~β (5) ;β (0) ~β (5) The initial value is 1;

[0133] Construct formula A-2-1 as equation A (1) Initial equations:

[0134]

[0135] Step S244: Construct a (6×1) relation coefficient matrix, denoted as matrix B: (Right now )

[0136] Let the regularization parameter be denoted as λ; (λ takes the value of 0.01; users or relevant technical personnel can adjust the value of λ according to actual needs). Define the relation A-2-2 for the iteration matrix B:

[0137] Where Zz represents the residual matrix, * represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; I (6) Represents a (6×6) matrix of all 1s;

[0138] Step S245: Define the judgment A-2-3 for matrix B:

[0139] RSS = (Zz - X * B) T *(Zz-X*B); where RSS represents the sum of squared residuals of matrix B;

[0140] (Based on judgment A-2-3 and relation A-2-2) Iterate through matrix B until the value of RSS is minimized to obtain matrix Bb;

[0141] Substituting the parameters in matrix Bb into formula A-2-1, we obtain equation A. (1) ;

[0142] Step S245: Repeat equation A (1) The construction process involves constructing the (charging time) prediction equations A for the 2nd to chth charging stations. (2) ~A (ch) ;

[0143] Step S25: Analyze the changes in output power, output voltage, and output current of the charging piles during the charging process of the first to the chth charging stations, and construct the voltage and current prediction equation B for the charging stations.

[0144] Step S251: Construct the voltage and current prediction equation B for the first charging station. (1) ;

[0145] ur(1) Use the URL to retrieve historical charging data for the first charging station where battery overheat protection did not occur, and use this as the target data.

[0146] Obtain the charging time tl of the first to the urlth charging operations from the target data. (1) ~tl (url) ;

[0147] Step S252: tl (1) As tll, acquire the (charging station) output power Po from second 1 to second tll during the first charging operation (target data). (1) ~Po (tll) Output voltage Uo (1) ~Uo (tll) Output current Io (1) ~Io (tll) ;

[0148] Calculate the autoregressive coefficient Pφ of the output power of the charging station during the first charging operation. (1,1) and moving average coefficient Pθ (1,1) ;

[0149] Step S2521: Calculate Po (1) ~Po (tll) The average value aPo;

[0150] Calculate the output power and white noise ψ in the first second. (1) , ψ (1) =Po (1) -aPo;

[0151] Output power white noise ψ at 2 seconds (2) , ψ (2) =Po (2) -aPo;Po (2) This indicates the output power of the charging station in the second second of the first charging operation.

[0152] And so on, the output power white noise ψ at the tll second (tll) , ψ (tll) =Po (tll) -aPo;

[0153] Calculate ψ (1) ~ψ (tll) The variance is denoted as va;

[0154] Step S2522: Construct the likelihood function L(φ,θ,va) of white noise as function A-3-1:

[0155] Where, ψ (k)This represents the white noise representing the output power of the charging station in the kth second of the first charging operation, where the value of k ranges from 1 to t11.

[0156] Logarithmize function A-3-1 to obtain function A-3-2:

[0157]

[0158] Step S2523: Define Pφ (1,1) and Pθ (1,1) Iterative relationship:

[0159] Let the deviation value at the k-th second be de. (k) :

[0160]

[0161] Define Pφ (1,1) and Pθ (1,1) The function S(Pφ) (1,1) , Pθ (1,1) ):

[0162]

[0163] According to the function S(Pφ) (1,1) , Pθ (1,1) ), calculate the autoregressive coefficient (i.e., the function S(Pφ) (1,1) , Pθ (1,1) Regarding Pφ (1,1) The partial derivative of )Ss(Pφ) (1,1) );

[0164] Calculate the moving average coefficient (i.e., the function S(Pφ)). (1,1) , Pθ (1,1) Regarding Pθ (1,1) The partial derivative of )Ss(Pθ) (1,1) );

[0165] Step S2524: Let the autoregressive coefficient Pφ (1,1) and moving average coefficient Pθ (1,1) The initial value is 1; the autoregressive coefficient after the m-th iteration is denoted as (Pφ). (m) (1,1) The moving average coefficient is denoted as (Pθ). (m) (1,1) The partial derivatives of the autoregressive coefficients are denoted as Ss. (m) (Pφ (1,1) The partial derivatives of the moving average coefficients are denoted as Ss. (m) (Pθ (1,1) );

[0166] The autoregressive coefficients after the (m+1)th iteration are denoted as (Pφ)(m+1) (1,1) The moving average coefficient is denoted as (Pθ). (m+1) (1,1) );

[0167] Defined iterative relation A-3-3:

[0168] Where η represents the learning rate; (η is 0.01; users or relevant technical personnel can adjust the value of η according to actual needs)

[0169] Step S2525: Set the deviation value de from the 1st to the tllth second. (1) ~de (tll) As white noise, substitute it into function A-3-2 in reverse; according to relation A-3-3 (using the Newton-Raphson method), Pφ (1,1) and Pθ (1,1) Perform iterations until the function A-3-2 converges, and obtain the autoregressive coefficient Pφ. (1,1) and moving average coefficient Pθ (1,1) ;

[0170] Step S253: Repeat the calculation of Pφ (1,1) and Pθ (1,1) Using the same steps, calculate the autoregressive coefficient Pφ of the output power of the first charging station in the second to urlth charging operations. (1,2) ~Pθ (1,url) Moving average coefficient Pθ (1,2) ~Pθ (1,url) ;

[0171] Calculate Pφ (1,1) ~Pθ (1,url) The average value is used as the autoregressive coefficient Pφ of the output power of the first charging station. (1) ;

[0172] Calculate Pθ (1,1) ~Pθ (1,url) The average value is used as the moving average coefficient Pθ of the output power of the first charging station. (1) ;

[0173] Step S254: Repeat the calculation of Pφ (1) and Pφ (1) Using the same steps, calculate the autoregressive coefficients Uφ of the output voltage and output current of the first charging station. (1) and Iφ (1) Moving average coefficient Uθ (1) and Iθ (1) ;

[0174] Let time be t, and let the real-time output power of the charging station at second t be Pn.(t) The real-time output voltage is Un. (t) The real-time output current is In (t) The real-time output power at (t-1) seconds is Pn. (t-1) The real-time output voltage is Un. (t-1) The real-time output current is In (t-1) ;

[0175] The expected output power at second (t+1) is Pq (t+1) The desired output voltage is Uq (t+1) The desired output current is Iq (t+1) ;

[0176] Constructing equations BP (1) :

[0177] Wherein dP (t) Pq represents the deviation of the charging station's output power in seconds t. (t) This represents the expected output power of the charging pile in second t.

[0178] Construction equation BU (1) :

[0179] Among them, dU (t) Uq represents the deviation of the charging station's output voltage over t seconds. (t) This represents the expected output voltage of the charging pile at second t.

[0180] Constructing equation BI (1) :

[0181] Where, dI (t) Iq represents the deviation of the output current of the charging station in seconds t. (t) This represents the expected output current of the charging pile in second t.

[0182] Equation BP (1) Equation BU (1) And equation BI (1) As equation B (1) ;

[0183] Step S255: Repeatedly construct equation B (1) Using the same steps, construct the voltage and current prediction equations B for the 2nd to chth charging stations. (2) ~B (ch) (Using MATLAB software) For equation B (1) ~B (ch) By fitting the equations, we obtain the (voltage and current) prediction equation B.

[0184] Step S3: Obtain the charging request from the electric vehicle (connected to the charging pile), detect whether the electric vehicle experiences battery overheating protection during charging, and estimate the maximum output voltage and maximum output current of the charging pile during charging by combining the (charging time) prediction equation A and the (voltage and current) prediction equation B; obtain the actual output voltage and actual output current of the charging pile, and compare them with the maximum output voltage and maximum output current to determine whether the charging process of the charging pile is normal; if normal, no action is taken; if abnormal, mark the faulty charging pile (and disconnect the charging pile from the electric vehicle's charging connection);

[0185] The specific steps of step S3 are as follows:

[0186] Step S31: Obtain the quasi-charge amount Epr at which the charging station will trigger battery overheat protection, and the probability P of triggering battery overheat protection. (ro) The probability P that the battery overheat protection does not occur (ur) ;

[0187] Get the charging request from the electric vehicle (connected to the charging station);

[0188] The charging request includes: the current battery percentage of the vehicle (en), the battery capacity (at), the charging power (Pe), and the charging power (Ppe) when the battery is under overheat protection.

[0189] Obtain the actual output power nPP, actual output voltage nUU, and actual output current nII of the charging pile (during the charging process);

[0190] Step S32: Denote the expected charging time of the trolley as qt;

[0191] Substitute en, at, and Pe into the (charging time) prediction equation A to calculate the ideal charging time gt for the electric vehicle;

[0192] Determine whether [(1-en)×at]≥Epr is true, and then determine the value of qt;

[0193] If this is not true, then the value of qt is gt;

[0194] If true, then substitute Epr and Pe (once) into the (charging time) prediction equation A to calculate the (normal) charging time ti of the trolley. (1) Substituting [(1-en)×at)-Epr] and Ppe (quadratic) into the (charging time) prediction equation A, the charging time ti of the electric vehicle (battery overheat protection) is calculated. (2) ;

[0195] Calculate the value of qt:

[0196] Wherein, “[((1-en)×at)-Epr]” represents the extra charge amount of the trolley beyond the “standard charge amount Epr”;

[0197] Step S33: Using nPP, nUU, and nII as the initial values ​​of the real-time output power, real-time output voltage, and real-time output current of the charging station in the (voltage and current) prediction equation B, calculate and extract the maximum output power P of the charging pile from the 1st to the qtth second. (max) Maximum output voltage U (max) Maximum output current I (max) ;

[0198] Step S34: Denote the maximum rated power of the charging pile as Pw (max) The maximum rated current is denoted as Uw (max) The maximum rated voltage is denoted as Iw. (max) ;

[0199] Let the real-time output voltage of the charging station during the charging process (at a certain second) be Ut, and the real-time output current be It;

[0200] Judgment Ut≤U (max) With It≤I (max) Whether they are both true;

[0201] If both conditions are met, the charging station is charging normally.

[0202] If they do not both hold true, then determine [(Ut>Uw)]. (max) )∪(It>Iw (max) (i.e., Ut > Uw) (max) Or It > Iw (max) (If one of them is true) is it true?

[0203] If this is true, then (immediately disconnect the power and mark the charging station) the charging station is charging abnormally;

[0204] If this is not the case, then analyze the real-time output power of the charging pile;

[0205] Step S35: Calculate the real-time output power Pt of the charging pile, Pt = Ut × It;

[0206] Determine if Pt > Pw (max) Is it valid?

[0207] If this is true, then (immediately disconnect the power and mark the charging station) the charging station is charging abnormally;

[0208] If this is not true, then define relation B:

[0209]

[0210] Determine whether relation B is true;

[0211] If this is confirmed, the charging station is charging normally.

[0212] If this is not the case, then (immediately disconnect the power and mark the charging station) the charging station is charging abnormally;

[0213] Step S4: Continuously monitor the charging process of the charging piles, summarize the charging piles that have malfunctioned, and provide feedback.

[0214] Example 2

[0215] Please see Figure 3 A self-testing system for the operation of smart shared charging piles includes:

[0216] Data acquisition module: used to acquire the number of charging piles in the target area, the maximum rated power, maximum rated current and maximum rated voltage of the charging piles (during charging), and obtain charging pile data;

[0217] Data Analysis Module: Used to acquire historical charging data of charging piles; perform a first mathematical modeling on the historical charging data to analyze the changes in charging time of the charging pile when the electric vehicle has battery overheat protection and when it does not have battery overheat protection during the charging process, and construct (charging time) prediction equation A; perform a second mathematical modeling on the historical charging data to analyze the output voltage and charging current of the charging pile as they change with charging power and charging time, and construct (voltage and current) prediction equation B.

[0218] Fault monitoring module: Used to acquire charging requests from electric vehicles (connected to charging piles), detect whether the electric vehicle experiences battery overheating protection during charging, and estimate the maximum output voltage and maximum output current of the charging pile during charging by combining the (charging time) prediction equation A and the (voltage and current) prediction equation B; acquire the actual output voltage and actual output current of the charging pile, and compare them with the maximum output voltage and maximum output current to determine whether the charging process of the charging pile is normal; if normal, no action is taken; if abnormal, the faulty charging pile is marked (and the charging connection between the charging pile and the electric vehicle is disconnected);

[0219] Continuous monitoring module: Used to continuously monitor the charging process of charging piles, summarize the charging piles that have malfunctioned, and provide feedback.

[0220] Example 3

[0221] A storage medium for self-testing the operation of intelligent shared charging piles stores a computer program. When the computer program is executed by a processor, it performs the steps of the self-testing method for intelligent shared charging piles as described in any of the above embodiments. Through the above technical solution, when the computer program is executed by a processor, it performs the method in any optional implementation of the above embodiments to achieve the following functions:

[0222] The system acquires, extracts, and processes historical charging data from charging piles, analyzes the impact of different electric vehicles' charging power and charging capacity on charging time, and examines the changes in the charging station's output power, output voltage, and output current with charging duration. It also calculates the electric vehicle's maximum output power, maximum output voltage, and maximum output current during the process by combining the electric vehicle's actual charging power and charging capacity. Finally, it performs a self-check on the charging process of the charging pile by combining the charging pile data.

[0223] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. For example, there are weighting coefficients and proportional coefficients. The values ​​set are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. The values ​​of the weighting coefficients and proportional coefficients are only required to not affect the proportional relationship between the parameters and the quantified values.

[0224] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A self-testing method for the operation of intelligent shared charging piles, characterized in that, The method includes: Step S1: Obtain the number of charging piles, the maximum rated power, maximum rated current, and maximum rated voltage of the charging piles, and obtain the charging pile data; Step S2: Obtain historical charging data of the charging pile; perform a first mathematical modeling on the historical charging data to analyze the changes in charging time of the charging pile when the electric vehicle has battery overheat protection and when battery overheat protection does not occur during the charging process, and construct the prediction equation A; perform a second mathematical modeling on the historical charging data to analyze the changes in output voltage and charging current of the charging pile with charging power and charging time, and construct the prediction equation B. The specific details of step S2 are as follows: Step S21: Obtain the number of charging piles (ch); obtain the total number of charging times for all charging piles (ne). （1） ~ne （ch） ; Step S22: Count the number of times the battery overheat protection occurred at the first charging station. （1） Number of times the battery overheat protection did not occur （1） The critical charge level at which the battery overheats and triggers protection is reached. （1） ; Step S221: Place ne （1） As nel, obtain the charging time te for the first charging pile to perform the first to nelth charging operations. （1） ~te （nel） ; Step S222: Determine whether the battery overheat protection occurs when the first charging station performs its first charging operation; te （1） As tel, obtain the charging power pi of the electric vehicle from the 1st to the telth second during the first charging operation. （1） ~pi （tel） ; Step S223: Based on pi （1） ~pi （tel） To determine if the battery has overheated protection; If it occurs, then ro （1） Add 1; calculate le （1，1） Value: If it does not appear, then ur （1） Add 1, le （1，1） The value is 0; Step S224: Determine whether battery overheat protection occurs during the 2nd to nelth charging operations, and calculate the critical charge level le for the 2nd to nelth charging operations. （1，2） ~le （1，nel） ; Calculate le （1，1） ~le （1，nel） Calculate the critical charge level le for the first charging station using le and ale. （1） ; Step S23: Count the number of times the battery overheat protection occurs at the 2nd to the chth charging pile. （2） ~ro （ch） Number of times the battery overheat protection did not occur （2） ~ur （ch） The critical charge level at which the battery overheats and triggers protection is reached. （2） ~le （ch） And calculate the probability P of the charging station triggering battery overheat protection. （ro） The probability P that the battery overheat protection does not occur. （ur） ; Calculate le （2） ~le （ch） The average value of ale; extract le （2） ~le （ch） The maximum value of le （max） minimum value le （min） Calculate the quasi-charge capacity Epr: Step S24: Construct the prediction equations A for the 1st to chth charging stations. （1） ~A （ch） For equation A （1） ~A （ch） By fitting the equation, the predicted equation A is obtained; Step S25: Analyze the changes in output power, output voltage and output current of the first to ch-th charging stations over time during the charging process, and construct the prediction equation B for the charging station. Step S3: Obtain the charging request from the electric vehicle, detect whether the electric vehicle experiences battery overheating protection during charging, and estimate the maximum output voltage and maximum output current of the charging pile during charging by combining the estimation equations A and B; obtain the actual output voltage and actual output current of the charging pile, and compare them with the maximum output voltage and maximum output current to determine whether the charging process of the charging pile is normal; if normal, no action is taken; if abnormal, the faulty charging pile is marked. Step S4: Continuously monitor the charging process of the charging piles, summarize the charging piles that have malfunctioned, and provide feedback.

2. The self-testing method for operation of intelligent shared charging piles according to claim 1, characterized in that, The specific steps of step S24 are as follows: Step S241: Construct the prediction equation A （1） ; will ur （1） As the URL, retrieve the charging time tl for the first to the urlth charging in the target data. （1） ~tl （url） Battery percentage (ec) （1） ~ec （url） Battery capacity ba （1） ~ba （url） Charging power pii （1） ~pii （url） ; Step S242: Calculate the total charge amount re during the first charge. （1） The total charge amount for the urlth charge is re （url） ; Construct matrices X and Y; Step S243: Let the charging time for the h-th charge be tl. （h） The charging power of the electric vehicle is pii （h） The electric vehicle's battery percentage is ec （h） The battery capacity is ba （h） The total charging capacity is re （h） ; Define equation A （1） Relationship coefficient β （0） ~β （5） Construct equation A （1） The initial equation; Step S244: Construct the relation coefficient matrix B; denote the regularization parameter as λ, and calculate the residual matrix Zz; Step S245: Define the residual sum of squares (RSS) of matrix B, construct the judgment formula for RSS, iterate the judgment formula until the value of RSS is minimized, and obtain matrix Bb; Substituting the parameters in matrix Bb into the initial equation, we obtain equation A. （1） ; Step S245: Construct the prediction equations A for the 2nd to chth charging stations. （2） ~A （ch） .

3. The self-testing method for operation of intelligent shared charging piles according to claim 1, characterized in that, The specific steps of step S25 are as follows: Step S251: Construct the voltage and current prediction equation B for the first charging station. （1） ; ur （1） As the URL, retrieve the charging time tl for the first to the urlth charging operation in the target data. （1） ~tl （url） ; Step S252: tl （1） As tll, the output power Po from the first to the tll second during the first charge is obtained. （1） ~Po （tll） Output voltage Uo （1） ~Uo （tll） Output current Io （1） ~Io （tll） ; Calculate the autoregressive coefficient Pφ of the output power （1,1） and moving average coefficient Pθ （1,1） ; Step S253: Calculate the autoregressive coefficient Pφ of the output power of the first charging station during the second to urlth charging operations. （1,2） ~Pθ （1,url） Moving average coefficient Pθ （1,2） ~Pθ （1,url） ; Calculate Pφ （1,1） ~Pθ （1,url） The average value is used as the autoregressive coefficient Pφ of the output power of the first charging station. （1） ; Calculate Pθ （1,1） ~Pθ （1,url） The average value is used as the moving average coefficient Pθ of the output power of the first charging station. （1） .

4. The self-testing method for operation of intelligent shared charging piles according to claim 3, characterized in that, The specific steps of step S252 are as follows: Step S2521: Calculate Po （1） ~Po （tll） The average value aPo; Calculate the white noise ψ of the output power from the 1st to the tllth second. （1） ~ψ （tll） ; Calculate ψ （1） ~ψ （tll） The variance va; Step S2522: According to ψ （1） ~ψ （tll） The `va` constructor is used to logarithmize the function, resulting in a logarithmized function. Step S2523: Define Pφ （1,1） and Pθ （1,1） Iterative relationship: Let the deviation value at the k-th second be de. （k） Constructor S(Pφ) （1,1） , Pθ （1,1） ), and calculate the partial derivatives Ss(Pφ) of the autoregressive coefficients. （1,1） The partial derivatives of the moving average coefficients Ss(Pθ) and Pθ （1,1） ).

5. The self-testing method for operation of intelligent shared charging piles according to claim 4, characterized in that, The subsequent steps of step S2523 are as follows: Step S2524: Denote the autoregressive coefficients after the m-th iteration as (Pφ) （m） （1,1） The moving average coefficient is denoted as (Pθ). （m） （1,1） The partial derivatives of the autoregressive coefficients are denoted as Ss. （m） (Pφ) （1,1） The partial derivative of the moving average coefficient is denoted as Ss. （m） (Pθ) （1,1） ); The autoregression coefficients after the (m+1)th iteration are denoted as (Pφ). （m+1） （1,1） The moving average coefficient is denoted as (Pθ). （m+1） （1,1） Define the iterative relation; Step S2525: Set the deviation value de from the 1st to the tllth second. （1） ~de （tll） As white noise, it is substituted into the logarithmic function in reverse; according to the iterative relationship, Pφ is... （1,1） and Pθ （1,1） The iteration continues until the logarithmic function converges, yielding the autoregressive coefficients Pφ. （1,1） and moving average coefficient Pθ （1,1） .

6. The self-testing method for operation of intelligent shared charging piles according to claim 1, characterized in that, The specific steps of step S3 are as follows: Step S31: Obtain the quasi-charge level Epr and the probability P of battery overheat protection. （ro） The probability P that the battery overheat protection does not occur （ur） ; Get the current battery percentage en, battery capacity at, charging power Pe, and charging power Ppe when the battery is under overheat protection. Obtain the actual output power nPP, actual output voltage nUU, and actual output current nII of the charging pile; Step S32: Substitute en, at, and Pe into the prediction equation A to calculate the ideal charging time gt; Substituting Epr and Pe into the prediction equation A, the charging time ti is calculated. （1） ;Will Substituting en, at, Epr, and Ppe into the prediction equation A, the charging time ti is calculated. （2） The desired charging time qt is obtained; Step S33: Calculate and extract the maximum output power P of the charging pile from the 1st to the qtth second according to the prediction equation B. （max） Maximum output voltage U （max） Maximum output current I （max） ; Step S34: Denote the maximum rated power of the charging pile as Pw （max） The maximum rated current is denoted as Uw （max） The maximum rated voltage is denoted as Iw. （max） ; To determine the charging process of the charging station.

7. A self-testing method for the operation of intelligent shared charging piles according to claim 6, characterized in that, The charging process of the charging station is judged as follows: Let the real-time output voltage of the charging station during the charging process be Ut, and the real-time output current be It; judge Is it true? If this is true, then the charging station is charging normally. If not true, then judge Is it valid? If this is true, the charging station is malfunctioning. If this is not the case, then analyze the real-time output power of the charging pile; Step S35: Calculate the real-time output power Pt; Determine if Pt > Pw （max） Is it valid? If this is true, the charging station is malfunctioning. If not true, then determine P. （max） U （max） and I （max） Are the rates of change the same? If the results are the same, the charging station is working properly. If they are different, the charging station is malfunctioning.

8. A self-testing system for the operation of intelligent shared charging piles, applicable to the self-testing method for the operation of intelligent shared charging piles as described in any one of claims 1-7, characterized in that, The system includes: Data acquisition module: used to acquire the number of charging piles, the maximum rated power, maximum rated current and maximum rated voltage of the charging piles, and obtain charging pile data; Data Analysis Module: Used to acquire historical charging data of charging piles; perform a first mathematical modeling on the historical charging data to analyze the changes in charging time of the charging pile when the electric vehicle has battery overheat protection and when battery overheat protection does not occur during the charging process, and construct the prediction equation A; perform a second mathematical modeling on the historical charging data to analyze the output voltage and charging current of the charging pile as they change with charging power and charging time, and construct the prediction equation B. Fault monitoring module: Used to acquire the charging request of the electric vehicle, detect whether the electric vehicle has battery overheat protection during the charging process, and estimate the maximum output voltage and maximum output current of the charging pile during charging by combining the prediction equation A and prediction equation B; acquire the actual output voltage and actual output current of the charging pile, and compare them with the maximum output voltage and maximum output current to determine whether the charging process of the charging pile is normal; if normal, no action is taken; if abnormal, the charging pile with the fault is marked. Continuous monitoring module: Used to continuously monitor the charging process of charging piles, summarize the charging piles that have malfunctioned, and provide feedback.

9. A storage medium for self-testing the operation of intelligent shared charging piles, wherein a computer program is stored thereon, characterized in that, When the computer program is executed by a processor, it performs the steps of the method as described in any one of claims 1-7.

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