Electric vehicle charging safety assessment method and device and medium

Abstract

The invention relates to the technical field of charging safety, and discloses an electric vehicle charging safety evaluation method and device and a medium, and the method comprises the steps: constructing an evaluation index system of the charging safety of an electric vehicle; obtaining a subjective weight and an objective weight of each index, and determining a comprehensive weight of each index based on the subjective weight and the objective weight; establishing a security level division table, and determining a threshold interval of each index corresponding to each security level by using a membership function to obtain the security level division table; and acquiring real-time data of each index in the charging process of the target electric vehicle, and performing charging safety evaluation on the target electric vehicle. According to the invention, the evaluation index system for the charging safety of the electric vehicle is constructed, the threshold interval of each index is divided into a plurality of safety levels, and the fuzzy evaluation is carried out by using the membership function to realize the charging safety evaluation, so that the evaluation result is more accurate, and the feasibility and practicability of the evaluation method are practically improved; and the possibility of charging safety accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the field of charging safety technology, and in particular to a method, device and medium for evaluating the charging safety of an electric vehicle. Background Art

[0002] In recent years, energy shortages and environmental pollution have become significant challenges to human development. To reduce carbon emissions, mitigate the threat posed by fossil fuel consumption to national energy security, and alleviate environmental crises, accelerating the development and deployment of new energy vehicles has become a global consensus. As a key technology for electric vehicles, power batteries and their safe and efficient charging have become a key technological advantage that countries are vying for. Ensuring safe charging of electric vehicles is paramount. Therefore, establishing a sound safety indicator system for electric vehicle charging processes is crucial. Effectively assessing electric vehicle charging safety risks by combining both subjective and objective factors is an essential step and foundation for establishing an electric vehicle charging safety monitoring and early warning system.

[0003] Currently, research on electric vehicle charging safety assessments, both domestically and internationally, primarily focuses on qualitative analysis and the development of corresponding countermeasures. Qualitative analysis methods make it difficult to intuitively and rationally assess charging safety, and the development of corresponding countermeasures can only improve the safety of individual nodes or paths, making it difficult to systematically and holistically assess charging safety. Without the ability to construct a reasonable characteristic indicator system and conduct quantitative analysis, and without basing charging safety impact indicators solely on subjective factors rather than objective causes of charging accidents, the development of corresponding safety monitoring and early warning strategies will inevitably be somewhat blind. Summary of the Invention

[0004] In view of this, the present invention provides a method, device and medium for evaluating the charging safety of an electric vehicle to solve the problem of inaccurate evaluation of the charging safety of an electric vehicle.

[0005] In a first aspect, the present invention provides a method for evaluating the charging safety of an electric vehicle, the method comprising:

[0006] Construct an evaluation index system for electric vehicle charging safety;

[0007] Obtain the subjective weight of each indicator in the evaluation index system based on the grey correlation degree and the objective weight based on the actual data, and determine the comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight;

[0008] Establish a security level classification table, and use the membership function to determine the threshold interval of each indicator in the evaluation index system corresponding to each security level to obtain a security level classification table;

[0009] Obtain real-time data of various indicators during the charging process of the target electric vehicle, and use the safety level classification table and the comprehensive weight of each indicator to conduct a charging safety assessment of the target electric vehicle based on the real-time data.

[0010] The electric vehicle charging safety assessment method provided by the present invention constructs an evaluation index system for electric vehicle charging safety, divides the threshold range of each index into multiple safety levels, and uses a membership function to perform fuzzy evaluation to achieve charging safety assessment. The assessment results are more accurate, effectively improving the feasibility and practicality of the assessment method and reducing the possibility of charging safety accidents.

[0011] In an optional embodiment, an evaluation index system for electric vehicle charging safety is constructed, including:

[0012] According to the principles of constructing a preset indicator system, an evaluation indicator system for electric vehicle charging safety is constructed from four dimensions: environmental safety, battery safety, charging equipment safety, and distribution network safety.

[0013] The electric vehicle charging safety assessment method provided by the present invention constructs an assessment index system from four dimensions: environmental safety, battery safety, charging equipment safety, and distribution network safety. It is not easily affected by external factors, has high stability, and the assessment indicators can be quantified, thereby improving the accuracy of the assessment results.

[0014] In an optional implementation, obtaining the subjective weight of each indicator in the evaluation indicator system based on the grey relational degree includes:

[0015] Obtain the expert's experience scores for each indicator, and establish a weighted parent sequence based on the experience scores. Select the maximum value of the experience scores for each indicator to establish a weighted reference sequence.

[0016] Compare the weighted parent sequence with the weighted reference sequence and calculate the correlation degree of each indicator;

[0017] The correlation of each indicator is normalized to obtain the subjective weight of each indicator.

[0018] The electric vehicle charging safety assessment method provided by the present invention utilizes grey correlation to process expert experience scoring, while considering the influence of multiple indicators. By calculating the weight reference sequence of each indicator, the subjective weight of each indicator is determined, making the analysis results more consistent with the actual situation and simplifying the complex decision-making process.

[0019] In an optional embodiment, obtaining the objective weight of each indicator in the evaluation indicator system based on actual data includes:

[0020] Obtain historical accident data of electric vehicle charging and the corresponding causes of the historical accident data;

[0021] Determine the corresponding indicators in the evaluation index system based on the inducing causes corresponding to historical accident data, and calculate the accident frequency of each indicator as the inducing cause in combination with historical accident data;

[0022] The objective weight of each indicator is determined based on the frequency of accidents for which each indicator is the inducing cause.

[0023] The electric vehicle charging safety assessment method provided by the present invention effectively avoids the disadvantage of lack of objectivity by analyzing the objective weights of various indicators using real data, improves the rationality of weight fitting, solves the one-sidedness that is currently prevalent in risk assessment during electric vehicle charging, and improves the accuracy of risk assessment.

[0024] In an optional embodiment, the comprehensive weight of each indicator in the evaluation index system is determined based on the subjective weight and the objective weight, including:

[0025] Calculate the first standard deviation of the subjective weight of each indicator and the second standard deviation of the objective weight of each indicator;

[0026] The comprehensive weight of each indicator is determined using the standard deviation method based on the first standard deviation and the second standard deviation.

[0027] In an optional implementation, the comprehensive weight of each indicator is determined using the standard deviation method according to the first standard deviation and the second standard deviation, including:

[0028] Determine the weight distribution ratio coefficient of each indicator based on the first standard deviation and the second standard deviation;

[0029] The comprehensive weight of each indicator is determined based on the weight distribution ratio coefficient of each indicator and the subjective weight and objective weight of each indicator.

[0030] The electric vehicle charging safety assessment method provided by the present invention determines the safety index system of electric vehicle charging safety by combining subjective weights and objective weights, and finally derives the weights of comprehensive indicators, thereby effectively analyzing the factors that induce charging safety accidents during the charging process of electric vehicles. It not only retains the authority of experts in the subjective weights, but also fully reflects the objective characteristics of the real data itself.

[0031] In an optional implementation, establishing a security level classification table includes:

[0032] Obtain the safety threshold range of each indicator during normal charging of electric vehicles, and classify the safety level of each indicator according to the safety threshold range;

[0033] Obtain the danger threshold interval of each indicator when the electric vehicle charging fails, and classify the danger level of each indicator according to the danger threshold interval;

[0034] According to the safety level and danger level, combined with the preset industry standards, the safety evaluation level thresholds of various indicators of electric vehicle charging are determined, and a safety level classification table is constructed based on the safety evaluation level thresholds.

[0035] The electric vehicle charging safety assessment method provided by the present invention uses a membership function to classify charging safety and set reasonable thresholds, constructs a trapezoidal distribution membership function, and realizes safety warning of electric vehicle charging through a fuzzy comprehensive evaluation method, effectively reducing the possibility of charging safety accidents.

[0036] In an optional embodiment, a charging safety assessment of a target electric vehicle is performed based on real-time data using a safety level classification table and the comprehensive weights of various indicators, including:

[0037] Determine the actual safety level of target electric vehicle charging based on real-time data of various indicators;

[0038] A corresponding scoring table is established according to the safety level classification table, and the charging safety score value of the target electric vehicle is determined based on the actual safety level and the comprehensive weight of each indicator as the charging safety score result. The higher the charging safety score value, the higher the safety.

[0039] The electric vehicle charging safety assessment method provided by the present invention uses a membership function to divide the safety levels, realizing a quantitative assessment of the safety level during the electric vehicle charging process. The overall fuzzy comprehensive evaluation method is used to design an electric vehicle charging safety risk assessment scheme, ensuring the rationality and reliability of the assessment results.

[0040] In a second aspect, the present invention provides an electric vehicle charging safety assessment device, comprising:

[0041] System construction module, used to build an evaluation index system for electric vehicle charging safety;

[0042] A comprehensive weight determination module is used to obtain the subjective weight of each indicator in the evaluation index system based on the grey correlation degree and the objective weight based on the actual data, and determine the comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight;

[0043] The security level classification module is used to establish a security level classification table and use the membership function to determine the threshold interval of each indicator in the evaluation index system corresponding to each security level to obtain the security level classification table;

[0044] The safety assessment module is used to obtain real-time data of various indicators during the charging process of the target electric vehicle, and use the safety level classification table and the comprehensive weight of each indicator to conduct a charging safety assessment of the target electric vehicle based on the real-time data.

[0045] In a third aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 is a flow chart of a method for evaluating the safety of electric vehicle charging according to an embodiment of the present invention;

[0048] Figure 2 A method for evaluating the charging safety of an electric vehicle according to an embodiment of the present invention;

[0049] Figure 3 A method for evaluating the charging safety of an electric vehicle according to an embodiment of the present invention;

[0050] Figure 4 is a flow chart of another electric vehicle charging safety assessment method according to an embodiment of the present invention;

[0051] Figure 5 4 is a structural block diagram of an electric vehicle charging safety assessment device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0053] The charging safety risk assessment of electric vehicles is different from other assessment objects. It requires external analysis and combined with mathematical methods. Among the current major safety risk assessment methods, there are mainly electrochemical models focusing on power batteries, equivalent circuit models, data-driven methods, etc. However, there are still certain deficiencies in realizing the comprehensive assessment of electric vehicle charging safety, and it is easy to have modeling difficulties and poor robustness, which may lead to misjudgments in the assessment process.

[0054] Related technologies also use deep learning network models based on convolutional neural networks to conduct charging safety risk assessments. During each charging process of an electric vehicle, the various charging parameters of the power battery will be transmitted to the monitoring platform, making the charging data present a big data trend. However, due to the complex structure and large number of parameters, when processing large amounts of data, there will be disadvantages such as slow model convergence and long training time.

[0055] Related technologies also include methods for conducting quantitative risk assessments of electric vehicle charging safety by dividing the electric vehicle charging system into multiple dimensions, such as electric vehicle charging piles, operation and management platforms, user assets, and the communication links and communication data between them. First, expert opinions are obtained and quantified through research. However, obtaining expert opinions and quantification is usually achieved through the Delphi brainstorming method. Then, the value weight and security threat weight of each indicator are calculated based on the fuzzy hierarchical analysis method, and the risk value of each indicator is calculated to identify the vulnerabilities and security risks in the charging process. However, in this method, it is difficult to ensure the rationality of the weight distribution of each indicator based solely on the subjective experience of experts. It is too subjective and lacks the objective support of actual data.

[0056] To solve the above problems, an embodiment of the present invention provides an electric vehicle charging safety assessment method. By constructing an evaluation index system for electric vehicle charging safety, the threshold interval of each index is divided into multiple safety levels, and fuzzy evaluation is performed using a membership function to achieve the effect of accurately assessing charging safety and reducing the possibility of charging safety accidents.

[0057] According to an embodiment of the present invention, an embodiment of a method for assessing the charging safety of an electric vehicle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0058] In this embodiment, a method for evaluating the charging safety of an electric vehicle is provided, which can be used in the above-mentioned computer system. Figure 1 FIG. 1 is a flow chart of a method for evaluating charging safety of an electric vehicle according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0059] Step S101: construct an evaluation index system for electric vehicle charging safety.

[0060] Specifically, if Figure 2As shown in the figure, the construction idea of the evaluation index system for electric vehicle charging safety is as follows. By investigating and analyzing historical electric vehicle charging safety accidents, characteristic indicators that induce charging safety accidents are selected; key indicators are selected from relevant specifications for electric vehicle charging safety, and the characteristic indicators and key indicators are preliminarily constructed into an evaluation index system. However, the preliminarily constructed evaluation index system may contain unreasonable or even erroneous problems, so it is necessary to calculate the expert opinion concentration of each indicator in the preliminarily constructed evaluation index system, and obtain important indicators with high expert opinion concentration to construct the final evaluation index system.

[0061] In an optional embodiment, an evaluation index system for electric vehicle charging safety is constructed, including:

[0062] According to the principles of the preset indicator system construction, an evaluation indicator system for electric vehicle charging safety is constructed from four dimensions: environmental safety, battery safety, charging equipment safety, and distribution network safety. Figure 3 shown.

[0063] Specifically, when evaluating the charging safety status of electric vehicles, it is necessary to construct an electric vehicle charging safety assessment index system. In order to improve the reliability of the charging safety assessment, reasonable characteristic indicators that conform to the principles of constructing the preset index system should be selected. The principles of constructing the preset index system include but are not limited to:

[0064] (1) Scientific principle: When constructing an evaluation index system for charging safety, the selected indicators must have clear concepts and corresponding scientific connotations. At the same time, they must be able to be quantitatively evaluated and reflect the actual safety status of the electric vehicle charging process.

[0065] (2) Comprehensiveness principle: The safety of electric vehicle charging is affected by different factors. When selecting evaluation indicators, comprehensive consideration should be given to multiple angles from the environment to the distribution network. Only an evaluation indicator system established from multiple angles can truly reflect the safe operation of charging.

[0066] (3) Operability principle: The designed charging safety warning model can be put into practice and can ensure the correctness of the evaluation results. The selected indicators should be quantifiable and authentic, and can both reflect the connotation of the evaluation object and ensure the accuracy of the evaluation results.

[0067] (4) Independence principle: The indicators in the evaluation index system should be independent of each other, that is, they should not be affected by other indicators and should be able to describe the situation of a specific aspect independently without being affected by other aspects. If there is a dependency or overlap between indicators, it may lead to repeated counting or calculation deviation.

[0068] (5) Stability principle: When constructing an evaluation index system, one should select indicators with higher stability and avoid selecting indicators with higher volatility. Indicators with higher stability will not be affected by external factors and produce significant fluctuations, while indicators with lower stability may be affected by external factors and produce larger fluctuations.

[0069] (6) Comparability principle: The selected indicators must be common to the charging process and cannot be attributes unique to a certain charging process. Only common evaluation indicators can be comparable in different charging processes.

[0070] According to the above-mentioned preset indicator system construction principles, through the analysis of the safety characteristics of electric vehicle charging, the evaluation indicator system of four dimensions, namely environmental safety, battery safety, charging equipment safety and distribution network safety, is determined. Figure 3 The following is a schematic diagram of the evaluation index system. The indicators in the evaluation index system are only used as examples, but are not limited to them. They include:

[0071] (1) Power battery safety characteristics. Based on the safe charging and discharging principles and production process of automotive power batteries, battery safety indicators include:

[0072] Battery temperature indicators include the temperature change rate and the maximum temperature within the battery pack. During charging, if the temperature change rate or the maximum temperature within the battery pack exceeds a certain value, it indicates that the battery pack is partially overheating or the temperature is rising too quickly. This temperature inconsistency indicates that the internal resistance of a single cell has changed. This may lead to the potential risk of thermal runaway in the battery pack.

[0073] Voltage range index: single cell voltage change rate, indicating the voltage range of voltage inconsistency.

[0074] Battery pack health status: Indicates the health status of the power battery, covering factors such as service life and internal structure aging.

[0075] Internal resistance indicator: The internal resistance of individual cells within a battery pack is inconsistent. When the internal resistance of one or more cells in a battery pack is significantly greater than that of other cells in the pack, this may mean that the battery has degraded more severely than the other cells, resulting in a significant increase in the battery's internal resistance. In this case, a warning message of potential safety hazards should be issued.

[0076] The change in internal resistance of a single cell refers to the difference between the internal resistance during the current charge cycle and the internal resistance during the previous charge cycle. Battery life testing results show that a rapid decrease in battery capacity is often accompanied by a sharp increase in internal resistance. Therefore, the change in internal resistance between two charge cycles is key information indicating potential battery problems.

[0077] (2) Safety characteristics of charging equipment. The current infrastructure of electric vehicle charging technology consists of power supply, charging, monitoring, billing and other components. The relevant charging equipment safety characteristic index parameters mainly include: DC voltage output error; charging voltage regulation accuracy; ripple factor; voltage limiting characteristics; charging equipment failure rate; electric vehicle battery management system response rate; output overvoltage rate; power factor, etc.

[0078] (3) Distribution network safety characteristics. There are many uncertainties in the operation of the distribution network. In order to carry out corresponding control more quickly and accurately and achieve safe operation of the distribution network, multiple factors should be considered comprehensively, including but not limited to: safe power supply level, stable static voltage, weak topology architecture, short-term stability, status of monitoring equipment, risk control, etc. Therefore, the evaluation indicators related to distribution network safety should include: cumulative number of distribution network faults; voltage level fluctuation rate; transformer power over-limit rate (the load power required for electric vehicle charging exceeds the rated power of the distribution network, and then the percentage of the excess part to the rated power is calculated); short-term load rate; distribution transformer capacity load ratio, etc.

[0079] The electric vehicle charging safety assessment method provided in this embodiment constructs an assessment index system from four dimensions: environmental safety, battery safety, charging equipment safety, and distribution network safety. It is not easily affected by external factors, has high stability, and the assessment indicators are quantifiable, thereby improving the accuracy of the assessment results.

[0080] Step S102 , obtaining the subjective weight of each indicator in the evaluation index system based on the grey correlation degree and the objective weight based on the actual data, and determining the comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight.

[0081] Specifically, the weights of each indicator in the electric vehicle charging safety assessment model are affected by multiple factors. In order to ensure the rationality and accuracy of the safety assessment, a method combining subjective weights and objective weights is used to determine the comprehensive weights of each indicator.

[0082] The grey correlation analysis method is developed from the grey theory. The basic idea is to judge whether the connection is close based on the similarity of the geometric shapes of the sequence curves. It does not require a large number of data samples and can be effectively analyzed even when the amount of data is small or the information is incomplete. Therefore, the subjective weight of each indicator can be determined based on the grey correlation. The weighted parent sequence is established through expert experience scoring. The maximum value of the expert experience score is selected and assigned to the reference sequence. The difference between the expert experience weighted parent sequence and the reference sequence is analyzed to determine the correlation. When the correlation is greater, the closer the indicator sequence is to the reference sequence, the higher the importance of the indicator is, and a greater weight should be given. Finally, the correlation of each indicator is normalized to obtain the subjective weight of each indicator.

[0083] The more reasonable the weight distribution is, the more accurate the results of charging safety risk assessment will be. It is difficult to ensure the rationality of weight distribution by relying solely on expert experience. For this reason, objective weights based on actual data are introduced to fit the comprehensive weight results, which can improve the problems of low differentiation between indicator weights and strong subjectivity in grey correlation.

[0084] Step S103: Create a security level classification table, and use the membership function to determine the threshold range of each indicator in the evaluation indicator system corresponding to each security level to obtain the security level classification table.

[0085] Specifically, a safety level classification table V={v1, v2, v3, v4, v5} is established, where v1 represents absolute safety, v2 represents safety, v3 represents general safety, v4 represents danger, and v5 represents extreme danger.

[0086] The membership function is used to determine the threshold ranges for each indicator corresponding to different security levels, resulting in a security level classification table. This table is used to represent the threshold ranges for each indicator corresponding to different security levels. It should be noted that the security assessment level thresholds for each indicator in the security level classification table should comply with relevant international standards and industry standards and specifications.

[0087] Step S104 , obtaining real-time data of various indicators during the charging process of the target electric vehicle, and using the safety level classification table and the comprehensive weight of each indicator to perform a charging safety assessment on the target electric vehicle based on the real-time data.

[0088] Specifically, after completing the construction of the evaluation index system and safety level classification table for electric vehicle charging safety, the real-time data of each indicator of the target electric vehicle during the actual charging process is obtained, and the comprehensive weight of each indicator in the safety level classification table is used to conduct the actual charging safety assessment based on the real-time data.

[0089] The electric vehicle charging safety assessment method provided in this embodiment constructs an electric vehicle charging safety assessment index system, divides the threshold range of each index into multiple safety levels, and uses a membership function to perform fuzzy evaluation to achieve charging safety assessment. The assessment results are more accurate, effectively improving the feasibility and practicality of the assessment method and reducing the possibility of charging safety accidents.

[0090] In this embodiment, a method for evaluating the charging safety of an electric vehicle is provided, which can be used in the above-mentioned computer system. Figure 4 FIG. 1 is a flow chart of a method for evaluating charging safety of an electric vehicle according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0091] Step S201: Construct an evaluation index system for electric vehicle charging safety. Figure 1Step S101 of the illustrated embodiment will not be described in detail here.

[0092] Step S202 , obtaining the subjective weight of each indicator in the evaluation index system based on the grey correlation degree and the objective weight based on the actual data, and determining the comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight.

[0093] Specifically, the subjective weight of each indicator in the evaluation indicator system based on the grey correlation degree is obtained in the above step S202, including:

[0094] Step S2021: Obtain the expert's empirical scores for each indicator, establish a weighted parent sequence based on the empirical scores, and select the maximum value in the empirical scores of each indicator to establish a weighted reference sequence.

[0095] Specifically, m experts are invited to evaluate and score n indicators with weights, and an m×n weighted mother sequence is obtained. The maximum weight value is selected from the weighted mother sequence and assigned to the weighted reference sequence X0, X0=(x0(1),x0(2),...,x0(m)), where x0(1) represents the weight value corresponding to the indicator with the highest score from the first expert, and x0(2) represents the weight value corresponding to the indicator with the highest score from the second expert. The experience scores of each expert are sorted to obtain the remaining weight sequences in the weighted mother sequence except the weighted reference sequence, including: X1=(x1(1),x1(2),...,x1(m)), X2=(x2(1),x2(2),...,x2(m)),...,X n =(x n (1),x n (2),...,x n (m)).

[0096] Step S2022: compare the weighted mother sequence with the weighted reference sequence and calculate the correlation degree of each indicator.

[0097] Specifically, the correlation coefficients corresponding to each indicator are obtained by comparing the weight reference sequence X0 with the weight mother sequence:

[0098]

[0099] Where i represents multiple indicators, i = 1, 2, ..., n; k represents multiple experts, k = 1, 2, ..., m; ρ represents the resolution coefficient, ρ∈(0, 1), which is generally selected as 0.5 when applied; the correlation calculation formula is:

[0100]

[0101] The degree of correlation of each sequence represents the closeness of each evaluation indicator relative to the reference sequence. The greater the correlation, the higher the concentration of expert evaluation weights, and the greater the weight that should be given.

[0102] Step S2023: normalize the correlation of each indicator to obtain the subjective weight of each indicator.

[0103] Specifically, put γ 0i After the correlation degree is normalized and used as the corresponding weight, the weight sequence of each indicator is obtained: ω=(ω1,ω2,…,ω n ),in:

[0104]

[0105] However, there are some problems in using the above grey correlation method to obtain the indicator weights: the establishment of indicator weights is not objective. Even if a set of the same weight parent sequence is used, different weight results will be obtained if different weight reference sequences X0 or different resolution coefficients are selected. At the same time, the expert evaluation scores are greatly affected by subjective factors. In addition, when the resolution coefficient ρ = 0.5, there is usually γ i >0.333, which makes it difficult to distinguish the importance of indicators.

[0106] The electric vehicle charging safety assessment method provided in this embodiment uses grey correlation to process expert experience scoring, while considering the influence of multiple indicators. By calculating the weight reference sequence of each indicator, the subjective weight of each indicator is determined, making the analysis results more consistent with the actual situation and simplifying the complex decision-making process.

[0107] Specifically, the objective weight of each indicator in the evaluation indicator system based on actual data is obtained in the above step S202, including:

[0108] Step S2024: Obtain historical accident data of electric vehicle charging and the causes corresponding to the historical accident data.

[0109] Specifically, the main accidents that occurred during the charging of electric vehicles in recent years were analyzed to determine the inducing causes of the accidents during the charging process, and the historical data and their corresponding inducing causes were used as actual data.

[0110] Step S2025 , determining the corresponding indicators in the evaluation index system according to the inducing causes corresponding to the historical accident data, and calculating the accident frequency of each indicator as the inducing cause in combination with the historical accident data.

[0111] Specifically, based on the inducing causes corresponding to historical accident data, the indicators corresponding to the inducing causes in the evaluation index system are determined, and the accident frequency of each indicator as the inducing cause is counted.

[0112] Step S2026: Determine the objective weight of each indicator based on the frequency of accidents in which each indicator is a triggering cause.

[0113] Specifically, the higher the frequency of the inducing cause, the greater the weight given to the corresponding indicator, that is:

[0114]

[0115] Among them, v i represents the objective weight of the indicator corresponding to the i-th cause, N represents the total number of accidents caused by charging in recent years, Represents the total number of accidents caused by the i-th cause.

[0116] The electric vehicle charging safety assessment method provided in this embodiment effectively avoids the disadvantage of lack of objectivity by analyzing the objective weights of various indicators using real data, improves the rationality of weight fitting, solves the current one-sidedness that is prevalent in risk assessment during electric vehicle charging, and improves the accuracy of risk assessment.

[0117] Specifically, the comprehensive weight of each indicator in the evaluation indicator system is determined based on the subjective weight and the objective weight in the above step S202, including:

[0118] Step S2027: Calculate the first standard deviation of the subjective weight of each indicator and the second standard deviation of the objective weight of each indicator.

[0119] Specifically, the first standard deviation σ1 of the subjective weight of each indicator and the second standard deviation σ2 of the objective weight of each indicator are calculated. The standard deviation calculation process is a mature existing technology and will not be described in detail here.

[0120] Step S2028: Determine the comprehensive weight of each indicator using the standard deviation method based on the first standard deviation and the second standard deviation.

[0121] Specifically, the standard deviation method is an objective weight allocation method that combines subjective weights and objective weights to derive a comprehensive weight:

[0122] The weight distribution coefficient of each indicator is determined based on the first standard deviation and the second standard deviation, and the item with a larger standard deviation is given a larger proportion μ, and

[0123]

[0124] Where: μ i Assign scaling factors to weights.

[0125] Finally, the comprehensive weight of each indicator is determined based on the weight distribution ratio coefficient of each indicator and the subjective weight and objective weight of each indicator:

[0126] W i =μ1ω i +μ2v i (i=1,2,…,N) (6)

[0127] The electric vehicle charging safety assessment method provided in this embodiment determines the safety index system of electric vehicle charging safety by combining subjective weights and objective weights, and finally derives the weights of comprehensive indicators. This effectively realizes the analysis of factors that induce charging safety accidents during the charging process of electric vehicles, retains the authority of experts in the subjective weights, and fully reflects the objective characteristics of the real data itself.

[0128] Step S203: Create a security level classification table, and use the membership function to determine the threshold range of each indicator in the evaluation indicator system corresponding to each security level to obtain the security level classification table.

[0129] Specifically, the security level classification table established in step S203 includes:

[0130] Step S2031: Obtain the safety threshold interval of each indicator when the electric vehicle is normally charged, and divide the safety level of each indicator according to the safety threshold interval.

[0131] Specifically, the membership function is usually used to obtain the degree of membership of each indicator to different evaluation levels. The determination process is usually subjective and essentially reflects the gradual change of things. The membership function usually has the following characteristics: 1) a single-peak convex fuzzy set; 2) the dependent variable values of the membership function cover all z-axis values; 3) when two membership functions overlap, the maximum membership cannot appear at the same time. As long as it can reflect the same fuzzy concept, even if the membership functions established are different, they will reach the same goal in practical applications. The trapezoidal distribution membership function has a wide range of applications, is simple to understand, and has strong operability. Therefore, the trapezoidal distribution membership function is used in this embodiment, including small, large, and medium types, and its specific form is:

[0132] Small size:

[0133]

[0134] Large:

[0135]

[0136] Centered type:

[0137]

[0138] The quantitative indicators in the constructed charging safety evaluation system include "extremely large" indicators (the larger the value, the better), "extremely small" indicators (the smaller the value, the better), and "medium" indicators (the closer the value is to a certain range, the better).

[0139] Based on the membership function of the trapezoidal distribution, a rating level V = {v1, v2, v3, v4, v5} is established, where v1 represents absolute safety, v2 represents safety, v3 represents general safety, v4 represents danger, and v5 represents extreme danger. Based on the charging status of electric vehicles, the actual data of each indicator during normal charging is collected and the corresponding safety threshold interval is generated through the cloud model to reasonably divide the safety level. For example, if the ambient temperature is (5, 15) it is absolutely safe, then the safety threshold interval of the ambient temperature indicator under the absolute safety level is (5, 15). This is only an example, but not limited to this. As shown in Table 1, it is an example of safety level division, where U 11 Indicates the ambient temperature, U 12 Indicates ambient humidity, U 13 Indicates the lighting index, U 21 Indicates that the battery is extremely bad, U 22 Indicates battery temperature, U 23 Indicates the health status of the battery pack, U 31 Indicates the output overvoltage rate, U 32 Indicates the response rate of the battery management system, U 33 represents the failure rate, U 41 Indicates the voltage level fluctuation rate, U 42 Indicates the transformer power out-of-bounds rate, U 43 Indicates the cumulative number of distribution network faults.

[0140] Step S2032: Obtain the danger threshold interval of each indicator when the electric vehicle has a charging failure, and classify the danger level of each indicator according to the danger threshold interval.

[0141] Specifically, historical data is used to analyze characteristic data related to various indicators during charging failures to generate threshold ranges for each indicator at the danger level. For example, as shown in Table 1, an ambient temperature of not less than 32°C is considered very dangerous. Therefore, the safety threshold range for the ambient temperature indicator at the very dangerous level is ≥32. This is for example only and is not intended to be limiting.

[0142] Table 1

[0143]

[0144] Step S2033: Determine the safety evaluation level thresholds for each indicator of electric vehicle charging based on the safety level and the danger level in combination with preset industry specifications, and construct a safety level classification table based on the safety evaluation level thresholds.

[0145] Specifically, by combining relevant international standards and industry norms, we sorted out the safety level thresholds of different indicators of electric vehicle charging, and finally constructed a safety level classification table.

[0146] The electric vehicle charging safety assessment method provided in this embodiment uses a membership function to classify charging safety into levels and sets reasonable thresholds, constructs a trapezoidal distribution membership function, and implements safety warnings for electric vehicle charging through a fuzzy comprehensive evaluation method, effectively reducing the possibility of charging safety accidents.

[0147] Step S204 , obtaining real-time data of various indicators during the charging process of the target electric vehicle, and using the safety level classification table and the comprehensive weight of each indicator to perform a charging safety assessment on the target electric vehicle based on the real-time data.

[0148] Specifically, the above step S204 includes:

[0149] Step S2041: determining the actual safety level of charging of the target electric vehicle based on the real-time data of each indicator.

[0150] Specifically, during the actual charging process of the target electric vehicle, the actual safety level of charging is determined based on the real-time data of each indicator. According to the classification of safety levels in Table 1, if the real-time data is: ambient temperature U 11 =10, ambient humidity U 12 =55, lighting index U 13 =98, battery extreme poor U 21 =7, battery temperature U 22 =55, battery health status U 23 =100, output overvoltage rate U 31 =1.0, battery management system response rate U 32 =100, failure rate U 33 =1, voltage level fluctuation rate U 41 =0.1, the transformer power out-of-bounds rate U 42 =0.5, the cumulative number of faults in the distribution network U 43 =0, the actual charging safety level of the target electric vehicle is absolutely safe.

[0151] In step S2042, a corresponding scoring table is established according to the safety level classification table, and a charging safety score value of the target electric vehicle is determined based on the actual safety level and the comprehensive weight of each indicator as a charging safety score result. A higher charging safety score value indicates higher safety.

[0152] Specifically, a corresponding scoring set T = (t1, t2, ..., t n ) 3, t1 represents the score of the first indicator, t2 represents the score of the second indicator, t3 represents the score of the third indicator, t n The score for the nth indicator is shown in Table 2. The specific scoring criteria for the scoring set T are shown in Table 2, where "T" represents the score range corresponding to different safety levels. For example, the score range for "absolute safety" is 90 < T ≤ 100. The charging safety score of the target electric vehicle is calculated based on the actual safety level and the combined weights of each indicator. A higher charging safety score indicates higher safety.

[0153] Table 2

[0154]

[0155]

[0156] The electric vehicle charging safety assessment method provided in this embodiment uses a membership function to divide the safety levels, thereby achieving a quantitative assessment of the safety level during the electric vehicle charging process. The overall fuzzy comprehensive evaluation method is used to design an electric vehicle charging safety risk assessment scheme, ensuring the rationality and reliability of the assessment results.

[0157] In a specific embodiment, the values of the indicators actually obtained when the electric vehicle is charging are: ambient temperature U 11 =20, ambient humidity U 12 =75, lighting index U 13 =70, battery extreme value U 21 =100, battery temperature U 22 =60, battery pack health status U 23 =100, output overvoltage rate U 31 =2, battery management system response rate U 32 =90, failure rate U 33 =1.5, voltage level fluctuation rate U 41 =1, the transformer power out-of-bounds rate U 42 =0.5, the cumulative number of faults in the distribution network U 43 =0.

[0158] According to Table 1 and Table 2, calculate the corresponding index scores of each index respectively: the ambient temperature is 20°. According to Table 1, the safety level corresponding to the ambient temperature is "Safety Level V2". The ambient temperature range corresponding to "Safety Level V2" is 15-25. Combining Table 2, we can know that the score corresponding to the ambient temperature of 20° is The scores of other indicators are determined according to the scoring process of ambient temperature, which are: t 12 =90, t 13 =60, t 21 =70, t 22=90, t 23 =100, t 31 =85, t 32 =60, t 33 =95, t 41 =80, t 42 =95, t 43 =100.

[0159] Assume that the comprehensive weight of each scoring indicator is W 11 =0.12, W 12 =0.09, W 13 =0.11, W 21 =0.08, W 22 =0.08, W 23 =0.07, W 31 =0.05, W 32 =0.06, W 33 =0.04, W 41 =0.05, W 42 =0.06, W 43 =0.07, then the one-way score of each indicator is multiplied and added with the corresponding comprehensive weight respectively, and the total score of the current electric vehicle charging is obtained as follows:

[0160] t 11 ×W 11 +t 12 ×W 12 +t 13 ×W 13 +t 21 ×W 21 +t 22 ×W 22 +t 23 ×W 23 +t 31 ×W 31 +t 32 ×W 32 +t 33 ×W 33 +t 41 ×W 41 +t 42 ×W 42 +t 43 ×W 43 =85×0.12+90×0.09+60×0.11+70×0.08+90×0.08+100×0.07+85×0.05+60×0.06+95×0.04+80×0.05+95×0.06+100×0.07=73.05.

[0161] Combining the total score of current electric vehicle charging and Table 2, it can be seen that the current safety level of electric vehicle charging is generally safe.

[0162] In this embodiment, an electric vehicle charging safety assessment device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0163] This embodiment provides an electric vehicle charging safety assessment device, such as Figure 5 As shown, including:

[0164] The system construction module 501 is used to construct an evaluation index system for electric vehicle charging safety.

[0165] The comprehensive weight determination module 502 is used to obtain the subjective weight of each indicator in the evaluation index system based on the grey correlation degree and the objective weight based on actual data, and determine the comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight.

[0166] The security level classification module 503 is used to establish a security level classification table and use a membership function to determine the threshold range of each indicator in the evaluation indicator system corresponding to each security level to obtain the security level classification table.

[0167] The safety assessment module 504 is used to obtain real-time data of various indicators during the charging process of the target electric vehicle, and use the safety level classification table and the comprehensive weight of each indicator to perform charging safety assessment on the target electric vehicle based on the real-time data.

[0168] In some optional implementations, the comprehensive weight determination module 502 includes:

[0169] The reference sequence establishment unit is used to obtain the expert's experience scores for each indicator, establish a weighted parent sequence based on the experience scores, and select the maximum value of the experience scores of each indicator to establish a weighted reference sequence.

[0170] The correlation calculation unit is used to compare the weighted mother sequence with the weighted reference sequence and calculate the correlation of each indicator.

[0171] The normalization processing unit is used to normalize the correlation degree of each indicator to obtain the subjective weight of each indicator.

[0172] The historical data acquisition unit is used to acquire historical accident data of electric vehicle charging and the inducing causes corresponding to the historical accident data.

[0173] The frequency statistics unit is used to determine the corresponding indicators in the evaluation index system according to the inducing causes corresponding to the historical accident data, and calculate the accident frequency of each indicator as the inducing cause in combination with the historical accident data.

[0174] The objective weight determination unit is used to determine the objective weight of each indicator according to the accident frequency of each indicator as an inducing cause.

[0175] The standard deviation calculation unit is used to calculate the first standard deviation of the subjective weight of each indicator and the second standard deviation of the objective weight of each indicator.

[0176] The comprehensive weight calculation unit is used to determine the comprehensive weight of each indicator using the standard deviation method based on the first standard deviation and the second standard deviation.

[0177] In some optional implementations, the security level classification module 503 includes:

[0178] The safety level division unit is used to obtain the safety threshold interval of each indicator when the electric vehicle is normally charged, and to divide the safety level of each indicator according to the safety threshold interval.

[0179] The danger level classification unit is used to obtain the danger threshold interval of each indicator when the electric vehicle charging fails, and classify the danger level of each indicator according to the danger threshold interval.

[0180] The safety level classification table construction unit is used to determine the safety evaluation level thresholds of various indicators of electric vehicle charging based on the safety level and danger level in combination with preset industry specifications, and to construct a safety level classification table based on the safety evaluation level thresholds.

[0181] In some optional implementations, the security assessment module 504 includes:

[0182] The actual safety level determination unit is used to determine the actual safety level of charging of the target electric vehicle based on the real-time data of each indicator.

[0183] The actual safety assessment unit is used to establish a corresponding scoring table according to the safety level classification table, and determine the charging safety score value of the target electric vehicle based on the actual safety level and the comprehensive weight of each indicator as the charging safety score result. The higher the charging safety score value, the higher the safety.

[0184] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0185] The electric vehicle charging safety assessment device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0186] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0187] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for evaluating the charging safety of an electric vehicle, characterized in that: The method comprises: Construct an evaluation index system for electric vehicle charging safety; Obtaining a subjective weight of each indicator in the evaluation index system based on the grey correlation degree and an objective weight based on actual data, and determining a comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight; Establishing a security level classification table, and using a membership function to determine the threshold interval of each indicator in the evaluation index system corresponding to each security level, to obtain a security level classification table; Real-time data of various indicators during the charging process of the target electric vehicle is obtained, and the safety level classification table and the comprehensive weight of each indicator are used to perform a charging safety assessment on the target electric vehicle based on the real-time data.

2. The method according to claim 1, characterized in that Construct an evaluation index system for electric vehicle charging safety, including: According to the principles of constructing a preset indicator system, an evaluation indicator system for electric vehicle charging safety is constructed from four dimensions: environmental safety, battery safety, charging equipment safety, and distribution network safety.

3. The method according to claim 1, characterized in that Obtaining the subjective weight of each indicator in the evaluation index system based on the grey correlation degree, including: Obtain the expert's empirical scores for each indicator, and establish a weighted parent sequence based on the empirical scores. Select the maximum value of the empirical scores for each indicator to establish a weighted reference sequence. Comparing the weighted mother sequence with the weighted reference sequence to calculate the correlation degree of each indicator; The correlation of each indicator is normalized to obtain the subjective weight of each indicator.

4. The method according to claim 1, wherein Obtaining objective weights of each indicator in the evaluation indicator system based on actual data, including: Obtain historical accident data of electric vehicle charging and the corresponding causes of the historical accident data; Determine the corresponding indicators in the evaluation index system according to the inducing causes corresponding to the historical accident data, and calculate the accident frequency of each indicator as the inducing cause in combination with the historical accident data; The objective weight of each indicator is determined based on the frequency of accidents for which each indicator is the inducing cause.

5. The method according to claim 1, wherein Determining the comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight includes: Calculate the first standard deviation of the subjective weight of each indicator and the second standard deviation of the objective weight of each indicator; The comprehensive weight of each indicator is determined using the standard deviation method based on the first standard deviation and the second standard deviation.

6. The method according to claim 5, characterized in that The comprehensive weight of each indicator is determined using the standard deviation method based on the first standard deviation and the second standard deviation, including: Determine the weight distribution ratio coefficient of each indicator according to the first standard deviation and the second standard deviation; The comprehensive weight of each indicator is determined based on the weight distribution ratio coefficient of each indicator and the subjective weight and objective weight of each indicator.

7. The method according to claim 1, characterized in that Establish a security level classification table, including: Obtaining a safety threshold range for each indicator during normal charging of the electric vehicle, and classifying the safety level of each indicator according to the safety threshold range; Obtaining a danger threshold interval of each indicator when an electric vehicle charging failure occurs, and classifying the danger level of each indicator according to the danger threshold interval; According to the safety level and danger level, combined with preset industry standards, the safety evaluation level thresholds of various indicators of electric vehicle charging are determined, and a safety level classification table is constructed based on the safety evaluation level thresholds.

8. The method according to claim 1, characterized in that Using the safety level classification table and the comprehensive weight of each indicator, a charging safety assessment is performed on the target electric vehicle based on the real-time data, including: Determining the actual safety level of charging of the target electric vehicle based on the real-time data of each of the indicators; A corresponding scoring table is established according to the safety level classification table, and a charging safety score value of the target electric vehicle is determined and calculated based on the actual safety level and the comprehensive weight of each indicator as a charging safety score result. A higher charging safety score value indicates higher safety.

9. An electric vehicle charging safety assessment device, characterized in that: The device comprises: System construction module, used to build an evaluation index system for electric vehicle charging safety; A comprehensive weight determination module is used to obtain the subjective weight of each indicator in the evaluation index system based on the grey correlation degree and the objective weight based on actual data, and determine the comprehensive weight of each indicator in the evaluation index system based on the subjective weight and the objective weight; A security level classification module is used to establish a security level classification table and use a membership function to determine the threshold interval of each indicator in the evaluation index system corresponding to each security level to obtain a security level classification table; The safety assessment module is used to obtain real-time data of various indicators during the charging process of the target electric vehicle, and use the safety level classification table and the comprehensive weight of each indicator to perform a charging safety assessment on the target electric vehicle based on the real-time data.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 8.

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