Electric vehicle charging safety monitoring method and device

Abstract

The present invention discloses a method and device for monitoring the charging safety of an electric vehicle. The method comprises collecting historical charging data and real-time charging data of an electric vehicle to be monitored; obtaining fault charging data of a rechargeable battery of an electric vehicle of the same type as the electric vehicle to be monitored through a big data platform; synchronizing the historical charging data with the fault charging data to obtain a fault case; establishing a fault prediction model according to the fault case, and calculating the failure probability of the rechargeable battery of the electric vehicle to be monitored based on the fault prediction model; determining charging parameters of the electric vehicle to be monitored according to the real-time charging data and a pre-established charging MAP table; extracting health impact factors as samples based on the fault charging data, training a convolutional neural network, and obtaining a life prediction model; extracting features from the historical charging data and inputting them into the life prediction model to obtain a predicted life. The method can perform fault prediction and life prediction on the electric vehicle.

Description

Technical Field

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

[0002] Currently, environmental and energy issues are becoming increasingly severe. Traditional fuel-powered vehicles cause severe environmental pollution and consume large amounts of energy, placing enormous pressure on my country's efforts to build an environmentally and resource-based society and drawing increasing attention and concern. Electric vehicles, on the other hand, use electricity instead of fuel to power their vehicles. Compared to traditional fuel-powered vehicles, they offer advantages such as lower emissions, lower noise levels, and higher energy efficiency. Vigorously developing electric vehicles is an effective way to alleviate the pressures of environmental pollution and energy consumption, a key measure to promote the sustainable development of the automotive industry, and a strategic requirement for my country's automotive industry to transform and upgrade, cultivate new economic growth drivers, achieve new leapfrog development, and enhance its international competitiveness.

[0003] The rapid development of electric vehicles has driven the entire industry chain, particularly the rapid development of the charging industry. As the primary means of recharging electric vehicles, the planning and construction of charging facilities must balance industry development progress and economic efficiency. Consequently, the construction and development of intelligent charging service networks for electric vehicles has been rapid. These networks proactively respond to charging demands and enable orderly charging through the active coordination of chargers, battery management systems, and the power grid. This has formed a networked, intelligent, and standardized service network with wide coverage.

[0004] However, in recent years, electric vehicle safety accidents have increased, and the causes of charging failures are very complex. Due to the current low level of vehicle-pile intelligence and information interconnection, charging operation platforms are unable to conduct real-time analysis and processing of charging pile failures. As a result, charging facility operators are unable to carry out targeted inspections and improvements, which seriously restricts the healthy and rapid development of electric vehicles. Patent document CN (111942186A) discloses a method and system for early warning of safety risks in the charging process of electric vehicles, including: obtaining the insulation resistance value of the charger's charging circuit and the battery pack interaction information and single cell interaction information between the electric vehicle and the charging pile monitored by the charging information monitoring terminal; identifying safety risks in the electric vehicle charging process based on the battery pack interaction information and single cell interaction information between the electric vehicle and the charging pile; using the single cell interaction information between the electric vehicle and the charging pile to identify the risk of abnormal single cell consistency differences in the electric vehicle charging process; using the insulation resistance value of the charger's charging circuit to identify the risk of abnormal insulation resistance in the electric vehicle charging process; and issuing early warnings to the charging operation platform and users based on the above risk identification results. However, this method can only provide early warnings for common risks of electric vehicles and cannot predict some potential risks of electric vehicles. Summary of the Invention

[0005] The present invention provides a method and device for monitoring the charging safety of an electric vehicle, which can perform fault prediction and life prediction on the electric vehicle.

[0006] A method for monitoring charging safety of an electric vehicle, comprising:

[0007] Collect historical charging data and real-time charging data of the electric vehicles to be monitored;

[0008] Acquire fault charging data of rechargeable batteries of electric vehicles of the same type as the electric vehicle to be monitored through a big data platform;

[0009] Synchronize the historical charging data with the fault charging data to obtain fault cases;

[0010] Establishing a fault prediction model according to the fault case, and calculating the failure probability of the rechargeable battery of the electric vehicle to be monitored based on the fault prediction model;

[0011] Determining charging parameters of the electric vehicle to be monitored based on the real-time charging data and a pre-established charging MAP table;

[0012] Extracting health impact factors as samples based on the faulty charging data, training a convolutional neural network, and obtaining a life prediction model;

[0013] Features are extracted from historical charging data of the electric vehicle to be monitored, and the features are input into a life prediction model to obtain a predicted life of the electric vehicle to be monitored.

[0014] Furthermore, the historical charging data includes the usage time of the rechargeable battery of the electric vehicle to be monitored, and the fault charging data includes the usage time of the rechargeable battery of the electric vehicle of the same type that has a fault;

[0015] Synchronizing the historical charging data with the fault charging data to obtain a fault case, including:

[0016] The fault charging data corresponding to the rechargeable battery of the same type of electric vehicle with the same service life as the rechargeable battery of the electric vehicle to be monitored is selected as the fault case.

[0017] Furthermore, a fault prediction model is established based on the fault case, including:

[0018] Obtaining fault parameters and safety parameters corresponding to the faulty rechargeable battery in the fault case, and calculating an average failure rate based on the fault parameters and safety parameters;

[0019] Calculate the standard deviation of the differences between different safety parameters of the rechargeable batteries of each fault;

[0020] The fault prediction model is established according to the average failure rate and the standard deviation.

[0021] Furthermore, the real-time charging data includes the SOC of the charging battery; the charging MAP table includes a mapping relationship between the charging temperature, SOC and charging current;

[0022] Determine the charging parameters of the electric vehicle to be monitored based on the real-time charging data and the pre-established charging MAP table, including:

[0023] Detecting the temperature of each battery cell in the rechargeable battery of the electric vehicle to be monitored;

[0024] Select the minimum temperature among all cells as the initial charging temperature;

[0025] Determining a charging current for a next charging cycle according to the initial charging temperature, the SOC of the rechargeable battery, and the charging MAP table;

[0026] During the charging process, the real-time temperature of each battery cell of the rechargeable battery of the monitored electric vehicle is collected, and the minimum real-time temperature is selected as the current temperature. The charging current of the next charging cycle is determined based on the current temperature, the rechargeable battery SOC and the charging MAP table.

[0027] Furthermore, the fault charging data includes battery capacity and battery operating parameters;

[0028] The health impact factors are extracted from the faulty charging data as samples, and a convolutional neural network is trained to obtain a life prediction model, including:

[0029] constructing an initial correlation model for battery capacity based on the fault charging data;

[0030] Performing matrix transformation on the initial correlation model to obtain a final correlation model;

[0031] Obtaining a first correlation coefficient between each pair of battery operating parameters according to the final correlation model;

[0032] Taking battery operating parameters and battery capacity as indicators, obtaining weights of the indicators based on a hierarchical analysis method, establishing a weight matrix according to the weights of the indicators, and obtaining a second correlation coefficient according to the weight matrix;

[0033] selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient;

[0034] Selecting an initial correlation influencing factor having a first correlation coefficient greater than a first preset threshold as a health influencing factor;

[0035] The health impact factors and the corresponding battery life are labeled to obtain samples, and the samples are input into a convolutional neural network for training to obtain a life prediction model.

[0036] Furthermore, the fault charging data includes the battery capacity corresponding to each operating parameter in different time periods;

[0037] The initial correlation model is as follows:

[0038]

[0039] Where Q i (k) represents the historical battery capacity corresponding to the battery operating parameter i in the kth time period, Q j (k-1) represents the historical battery capacity corresponding to the battery operating parameter j in the k-1th time period, α ji Indicates the degree of influence of the historical battery capacity data corresponding to the battery operating parameter j in the k-1th time period on the historical battery capacity data corresponding to the battery operating parameter i in the kth time period, β i is a constant, γ i (k) represents the error.

[0040] Furthermore, the first correlation coefficient is determined by the following formula:

[0041]

[0042] Where r ij represents the correlation coefficient of battery operating parameter i to battery operating parameter j, λ is an adjustable parameter, and n is the number of time periods; ρ i (t) represents the proportion of the battery capacity corresponding to the battery operating parameter i in the tth time period to the original total battery capacity.

[0043] Furthermore, selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient includes:

[0044] The battery operating parameter corresponding to the second correlation coefficient greater than the second preset threshold is used as the initial correlation influencing factor.

[0045] Furthermore, selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient includes:

[0046] The second correlation coefficients are sorted in descending order, and the battery operating parameters corresponding to a preset number of second correlation coefficients that are ranked higher are selected as initial correlation influencing factors.

[0047] An electric vehicle charging safety monitoring device, comprising:

[0048] The acquisition module is used to collect historical charging data and real-time charging data of the electric vehicle to be monitored;

[0049] A data acquisition module, configured to acquire, through a big data platform, fault charging data of rechargeable batteries of electric vehicles of the same type as the electric vehicle to be monitored;

[0050] A synchronization module, configured to synchronize the historical charging data with the fault charging data to obtain a fault case;

[0051] A fault prediction module, configured to establish a fault prediction model according to the fault case, and calculate the failure probability of the rechargeable battery of the electric vehicle to be monitored based on the fault prediction model;

[0052] A charging control module, configured to determine the charging parameters of the electric vehicle to be monitored based on the real-time charging data and a pre-established charging MAP table;

[0053] A life prediction model establishment module is used to extract health impact factors as samples based on the fault charging data, train a convolutional neural network, and obtain a life prediction model;

[0054] The life prediction module is used to extract features from the historical charging data of the electric vehicle to be monitored, input the features into a life prediction model, and obtain the predicted life of the electric vehicle to be monitored.

[0055] Furthermore, the historical charging data includes the usage time of the rechargeable battery of the electric vehicle to be monitored, and the fault charging data includes the usage time of the rechargeable battery of the electric vehicle of the same type that has a fault;

[0056] The synchronization module synchronizes the historical charging data with the fault charging data to obtain a fault case, including:

[0057] The fault charging data corresponding to the rechargeable battery of the same type of electric vehicle with the same service life as the rechargeable battery of the electric vehicle to be monitored is selected as the fault case.

[0058] Furthermore, the fault prediction module establishes a fault prediction model based on the fault case, including:

[0059] Obtaining fault parameters and safety parameters corresponding to the faulty rechargeable battery in the fault case, and calculating an average failure rate based on the fault parameters and safety parameters;

[0060] Calculate the standard deviation of the differences between different safety parameters of the rechargeable batteries of each fault;

[0061] The fault prediction model is established according to the average failure rate and the standard deviation.

[0062] Furthermore, the real-time charging data includes the SOC of the charging battery; the charging MAP table includes a mapping relationship between the charging temperature, SOC and charging current;

[0063] The charging control module determines the charging parameters of the electric vehicle to be monitored based on the real-time charging data and the pre-established charging MAP table, including:

[0064] Detecting the temperature of each battery cell in the rechargeable battery of the electric vehicle to be monitored;

[0065] Select the minimum temperature among all cells as the initial charging temperature;

[0066] Determining a charging current for a next charging cycle according to the initial charging temperature, the SOC of the rechargeable battery, and the charging MAP table;

[0067] During the charging process, the real-time temperature of each battery cell of the rechargeable battery of the monitored electric vehicle is collected, and the minimum real-time temperature is selected as the current temperature. The charging current of the next charging cycle is determined based on the current temperature, the rechargeable battery SOC and the charging MAP table.

[0068] Furthermore, the fault charging data includes battery capacity and battery operating parameters;

[0069] The life prediction model establishment module extracts health impact factors as samples based on the fault charging data, trains a convolutional neural network, and obtains a life prediction model, including:

[0070] constructing an initial correlation model for battery capacity based on the fault charging data;

[0071] Performing matrix transformation on the initial correlation model to obtain a final correlation model;

[0072] Obtaining a first correlation coefficient between each pair of battery operating parameters according to the final correlation model;

[0073] Taking battery operating parameters and battery capacity as indicators, obtaining weights of the indicators based on a hierarchical analysis method, establishing a weight matrix according to the weights of the indicators, and obtaining a second correlation coefficient according to the weight matrix;

[0074] selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient;

[0075] Selecting an initial correlation influencing factor having a first correlation coefficient greater than a first preset threshold as a health influencing factor;

[0076] The health impact factors and the corresponding battery life are labeled to obtain samples, and the samples are input into a convolutional neural network for training to obtain a life prediction model.

[0077] Furthermore, the fault charging data includes the battery capacity corresponding to each operating parameter in different time periods;

[0078] The initial correlation model is as follows:

[0079]

[0080] Where Q i (k) represents the historical battery capacity corresponding to the battery operating parameter i in the kth time period, Q j (k-1) represents the historical battery capacity corresponding to the battery operating parameter j in the k-1th time period, α ji Indicates the degree of influence of the historical battery capacity data corresponding to the battery operating parameter j in the k-1th time period on the historical battery capacity data corresponding to the battery operating parameter i in the kth time period, β i is a constant, γ i (k) represents the error.

[0081] Furthermore, the first correlation coefficient is determined by the following formula:

[0082]

[0083] Where r ij represents the correlation coefficient of battery operating parameter i to battery operating parameter j, λ is an adjustable parameter, and n is the number of time periods; ρ i (t) represents the proportion of the battery capacity corresponding to the battery operating parameter i in the tth time period to the original total battery capacity.

[0084] Furthermore, the life prediction model establishment module selects relevant battery operating parameters as initial correlation influencing factors according to the second correlation coefficient, including:

[0085] The battery operating parameter corresponding to the second correlation coefficient greater than the second preset threshold is used as the initial correlation influencing factor.

[0086] Furthermore, the life prediction model establishment module selects relevant battery operating parameters as initial correlation influencing factors according to the second correlation coefficient, including:

[0087] The second correlation coefficients are sorted in descending order, and the battery operating parameters corresponding to a preset number of second correlation coefficients that are ranked higher are selected as initial correlation influencing factors.

[0088] The electric vehicle charging safety monitoring method and device provided by the present invention at least include the following

[0089] Beneficial effects:

[0090] (1) Analyze the failure cases of similar electric vehicle rechargeable batteries obtained through the big data platform to obtain the failure probability of the electric vehicle to be monitored, which can provide reliable early warning for electric vehicle failures and indicate potential risks of electric vehicles;

[0091] (2) During the charging process, the charging current is adjusted according to the real-time charging data to further ensure the safety of charging;

[0092] (3) The fault charging data is analyzed and modeled, and the life prediction model obtained can predict the life of the current electric vehicle to be monitored, prompt the potential risks of the electric vehicle, and ensure the safe operation of the electric vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 This is a flow chart of an embodiment of the electric vehicle charging safety monitoring method provided by the present invention.

[0094] Figure 2 The present invention provides a flowchart of an embodiment of establishing a fault prediction model in the electric vehicle charging safety monitoring method.

[0095] Figure 3 The present invention provides a flow chart of an embodiment of determining the charging parameters of an electric vehicle to be monitored in the electric vehicle charging safety monitoring method provided by the present invention.

[0096] Figure 4 This is a flowchart of an embodiment of obtaining a life prediction model in the electric vehicle charging safety monitoring method provided by the present invention.

[0097] Figure 5 This is a flow chart of an embodiment of the electric vehicle charging safety monitoring device provided by the present invention. DETAILED DESCRIPTION

[0098] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0099] refer to Figure 1 In some embodiments, a method for monitoring charging safety of an electric vehicle is provided, comprising:

[0100] S1. Collect historical charging data and real-time charging data of the electric vehicle to be monitored;

[0101] S2. Obtaining, through a big data platform, fault charging data of rechargeable batteries of electric vehicles of the same type as the electric vehicle to be monitored;

[0102] S3. Synchronize the historical charging data with the fault charging data to obtain a fault case;

[0103] S4. Establishing a fault prediction model according to the fault case, and calculating the failure probability of the rechargeable battery of the electric vehicle to be monitored based on the fault prediction model;

[0104] S5. Determine the charging parameters of the electric vehicle to be monitored based on the real-time charging data and a pre-established charging MAP table;

[0105] S6. Extracting health impact factors as samples based on the faulty charging data, training a convolutional neural network, and obtaining a life prediction model;

[0106] S7. Extracting features from the historical charging data of the electric vehicle to be monitored, and inputting the features into a life prediction model to obtain a predicted life of the electric vehicle to be monitored.

[0107] Specifically, in step S1, a communication connection can be established between the portable detection terminal and the onboard BMS, dedicated charging pile, etc. of the electric vehicle to be monitored to capture the historical charging data of the electric vehicle to be monitored, and the real-time charging data can be collected through the charging pile during the charging process.

[0108] Furthermore, in some embodiments, the historical charging data includes the usage time of the rechargeable battery of the electric vehicle to be monitored, and in step S2, the fault charging data includes the usage time of the rechargeable battery of the electric vehicle of the same type that has a fault;

[0109] Specifically, in step S3, the historical charging data and the fault charging data are synchronized to obtain a fault case, including:

[0110] The fault charging data corresponding to the rechargeable battery of the same type of electric vehicle with the same service life as the rechargeable battery of the electric vehicle to be monitored is selected as the fault case.

[0111] Further, refer to Figure 2 In step S4, a fault prediction model is established according to the fault case, including:

[0112] S41. Obtaining fault parameters and safety parameters corresponding to the faulty rechargeable battery in the fault case, and calculating an average failure rate based on the fault parameters and safety parameters;

[0113] S42. Calculate the standard deviation of the differences between different safety parameters of the rechargeable batteries with various faults;

[0114] S43. Establish the fault prediction model according to the average failure rate and standard deviation.

[0115] Specifically, the fault charging data includes the relevant charging data of all faulty rechargeable batteries. The charging data related to the fault of each rechargeable battery may be different. Therefore, the charging data related to each faulty rechargeable battery is used as a fault parameter, and all fault-related data is used as safety parameters. In step S41, the ratio of the number of fault parameters to the number of safety parameters can be used as the average failure rate.

[0116] Furthermore, in step S42, all security parameters are regarded as sample data, and the standard deviation is the difference between multiple samples in the sample data. Therefore, the standard deviation is calculated by the following formula:

[0117]

[0118] Where β represents the standard deviation of the differences between different safety parameters, Xi represents the i-th safety parameter, and m represents the number of safety parameters. Indicates the arithmetic mean of the safety parameter.

[0119] As a statistic, the standard deviation reflects the degree of deviation of each safety parameter value from the average value of these parameters, thereby evaluating the consistency and variability of safety parameters of different rechargeable batteries.

[0120] Furthermore, in step S43, the fault prediction model is as follows:

[0121]

[0122] Where n represents the number of failure cases, γ represents the failure probability of the rechargeable battery of the monitored electric vehicle, T represents the usage frequency of the rechargeable battery within the preset time, which is the ratio of the usage time of the rechargeable battery within the preset time to the preset time; α represents the average failure rate in the failure cases, and β represents the standard deviation of the differences between different safety parameters.

[0123] Specifically, the usage frequency T of the rechargeable battery within a preset time can be regarded as a time scale in the model, which affects the probability of failure occurrence.

[0124] In the method provided in the above embodiment, by analyzing the failures of electric vehicles of the same type, the failure probability of the electric vehicle to be monitored can be predicted based on the usage time of the rechargeable battery of the electric vehicle to be monitored, thereby achieving more comprehensive monitoring of the electric vehicle and ensuring the safe operation of the electric vehicle.

[0125] Furthermore, in step S5, the real-time charging data includes the SOC of the charging battery; the charging MAP table includes a mapping relationship between the charging temperature, SOC, and charging current;

[0126] refer to Figure 3 In step S51, the charging parameters of the electric vehicle to be monitored are determined according to the real-time charging data and the pre-established charging MAP table, including:

[0127] S51, detecting the temperature of each battery cell in the rechargeable battery of the electric vehicle to be monitored;

[0128] S52, selecting the minimum temperature among all battery cells as the initial charging temperature;

[0129] S53, determining a charging current for the next charging cycle according to the initial charging temperature, the rechargeable battery SOC, and the charging MAP table;

[0130] S54. During the charging process, the real-time temperature of each battery cell of the rechargeable battery of the monitored electric vehicle is collected, and the minimum real-time temperature is selected as the current temperature. The charging current of the next charging cycle is determined according to the current temperature, the rechargeable battery SOC and the charging MAP table.

[0131] Specifically, a pre-created charging MAP table can be referenced from a common vehicle battery charging process. This table, typically provided by the battery manufacturer, includes information on charging temperature ranges, battery SOC, and charging current. Initially, the same initial charging temperature can be set for all locations. Based on this initial charging temperature and SOC, and the pre-created MAP table, the corresponding charging current for the next charging cycle is determined. The charging current further influences changes in charging temperature and SOC.

[0132] Furthermore, by selecting the lowest temperature in each battery cell as the characteristic temperature and obtaining the charging current for the next charging cycle, the corresponding charging current can be made more consistent with the safety of the rechargeable battery, thereby ensuring the charging safety of the monitored electric vehicle.

[0133] In some embodiments, the real-time charging data may further include single cell voltage, single cell temperature difference, vehicle current measurement value, vehicle voltage measurement value, vehicle voltage difference, vehicle current difference, etc.

[0134] During charging, if the single cell voltage exceeds the preset single cell voltage maximum value (4.25V) or the single cell voltage growth rate exceeds the preset growth rate maximum value (0.007575V / min), an early warning will be issued;

[0135] When the temperature difference of a single battery exceeds the preset temperature difference range (2-6°C), an early warning will be issued;

[0136] When the waveform frequency of the vehicle current measurement value in the early stage of charging exceeds the first preset frequency maximum or does not oscillate, the average value in the middle stage of charging exceeds the first preset vehicle current range (much greater than or much less than 77.8A), and the real-time monitored current value in the late stage of charging exceeds the second preset vehicle current range (much greater than or much less than 47.4A) or is unstable, an early warning is issued;

[0137] When the vehicle voltage measurement value exceeds the preset vehicle voltage range (much less than 359V or much greater than 407V) or the growth rate of the vehicle voltage measurement value exceeds the preset growth range (much greater than or much less than 0.7272V / min), an early warning is issued;

[0138] When the rate of decrease of the vehicle voltage difference exceeds a preset decrease range (much greater than or much less than 0.7424 V / min), an early warning is issued;

[0139] When the waveform frequency of the vehicle current difference in the early stage of charging exceeds the second preset frequency maximum value or does not oscillate, the average value in the middle stage of charging exceeds the first preset difference range, and the vehicle current difference monitored in real time in the late stage of charging exceeds the second preset difference range or is unstable, an early warning is issued.

[0140] In some embodiments, the real-time charging data further includes: electric vehicle measured voltage and electric vehicle measured current.

[0141] Assume that the charging voltage required by the electric vehicle is V r , the electric vehicle measures the voltage as V m , the maximum allowable total charging voltage V is obtained during the parameter configuration phase of the electric vehicle charging process b , then the voltage safety factor of the electric vehicle charging process is V f The calculation formula is:

[0142]

[0143] Warning voltage safety factor threshold V fm The calculation formula is:

[0144]

[0145] Assume that the charging current demanded by the electric vehicle is A r , the electric vehicle measures the current as A m , the maximum allowable total charging current A is obtained during the parameter configuration phase of the electric vehicle charging process b , then the current safety factor A during the electric vehicle charging process f The calculation formula is:

[0146]

[0147] Warning current safety factor threshold A fm The calculation formula is:

[0148]

[0149] According to the specific vehicle model, the voltage safety factor V of each SOC stage is obtained soc , current safety factor A soc , then the warning threshold alarm formula for quantitative scoring is:

[0150] V soc ≥V fm ; (7)

[0151] A soc ≥A fm ; (8)

[0152] If the calculation result of a certain SOC stage is V soc ≥V fm or A soc ≥A fm , then a charging safety warning is issued;

[0153] If the calculation result of a certain SOC stage is 0.75*V fm ≤V soc <V fm or 0.75*A fm ≤A soc fm , the battery status is judged as passing, no warning is issued, and the user is reminded to pay attention to battery maintenance;

[0154] If the calculation result of a certain SOC stage is 0.5*V fm ≤V soc <0.75*V fm or 0.5*A fm ≤A soc <0.75*A fm , the battery status is judged to be medium, no warning is issued, and the user is reminded to pay attention to battery maintenance;

[0155] If the calculation result of a certain SOC stage is 0.25*V fm ≤V soc <0.5*V fm or 0.25*A fm ≤A soc <0.5*A fm , the battery status is judged to be good and no warning is issued;

[0156] If the calculation result of a certain SOC stage is 0≤V soc <0.25*V fm ​or 0≤A soc <0.25*A fm , the battery status is judged to be excellent and no warning is issued.

[0157] Further, in step S6, the fault charging data includes battery capacity and battery operating parameters;

[0158] refer to Figure 4 In step S6, health impact factors are extracted as samples based on the faulty charging data, and a convolutional neural network is trained to obtain a life prediction model, including:

[0159] S61. Constructing an initial correlation model for battery capacity based on the fault charging data;

[0160] S62, performing matrix transformation on the initial correlation model to obtain a final correlation model;

[0161] S63. Obtaining a first correlation coefficient between each pair of battery operating parameters according to the final correlation model;

[0162] S64, using the battery operating parameters and battery capacity as indicators, obtaining weights of the indicators based on the hierarchical analysis method, establishing a weight matrix according to the weights of the indicators, and obtaining a second correlation coefficient according to the weight matrix;

[0163] S65. Selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient;

[0164] S66: Selecting an initial correlation influencing factor having a first correlation coefficient greater than a first preset threshold as a health influencing factor;

[0165] S67. Label the health impact factors and the corresponding battery life to obtain samples, input the samples into a convolutional neural network for training, and obtain a life prediction model.

[0166] Furthermore, in step S61, the fault charging data includes the battery capacity corresponding to each battery operating parameter in different time periods; the battery operating parameters may include power battery voltage, current, temperature, temperature rise rate, charging time, etc.

[0167] The initial correlation model is as follows:

[0168]

[0169] Where Q i (k) represents the historical battery capacity corresponding to the battery operating parameter i in the kth time period, Q j (k-1) represents the historical battery capacity corresponding to the battery operating parameter j in the k-1th time period, αji Indicates the degree of influence of the historical battery capacity data corresponding to the battery operating parameter j in the k-1th time period on the historical battery capacity data corresponding to the battery operating parameter i in the kth time period, β i is a constant, γ i (k) represents the error.

[0170] Furthermore, in step S62, the process of performing matrix conversion on the initial correlation model is as follows:

[0171] Q in (k) = Q im (k)·α i +γ

[0172] =[Q i (k-n+1)Q i (k-n+2)…Q i (k-1)] T ; (10)

[0173]

[0174] α i =[α 0i α 1i …α Ni ] T ; (12)

[0175] γ=[γ1γ2…γ n-1 ] T ; (13)

[0176] The final correlation model is shown in Equations (10)-(13).

[0177] Furthermore, in step S63, the first correlation coefficient is determined by the following formula:

[0178]

[0179] Where r ij represents the correlation coefficient of battery operating parameter i to battery operating parameter j, λ is an adjustable parameter, and n is the number of time periods; ρ i (t) represents the proportion of the battery capacity corresponding to the battery operating parameter i in the tth time period to the original total battery capacity.

[0180] In step S64, the battery operating parameters and battery capacity are used as indicators, the weights of the various indicators are obtained based on the hierarchical analysis method, a weight matrix is ​​established according to the weights of the various indicators, and the second correlation coefficient is obtained according to the weight matrix, which specifically includes: taking the various battery operating parameters and battery capacity as indicators, constructing a judgment matrix according to the importance of the correlation between the various indicators, calculating the weights of the various indicators according to the judgment matrix and performing a consistency check, establishing a weight matrix according to the weights that pass the consistency check, and each weight in the weight matrix is ​​used as the second correlation coefficient.

[0181] Furthermore, in step S65, selecting relevant battery operating parameters as initial correlation influencing factors according to the second correlation coefficient includes:

[0182] The battery operating parameter corresponding to the second correlation coefficient greater than the second preset threshold is used as the initial correlation influencing factor; or,

[0183] The second correlation coefficients are sorted in descending order, and the battery operating parameters corresponding to a preset number of second correlation coefficients that are ranked higher are selected as initial correlation influencing factors.

[0184] Specifically, when the second correlation coefficient is greater than the second preset threshold, or is ranked high, it means that the corresponding battery operating parameter is strongly correlated with the battery capacity, and therefore it is used as the initial correlation influencing factor.

[0185] Furthermore, the initial correlation influencing factors are further screened by using the second correlation coefficient, that is, the initial correlation influencing factors whose first correlation coefficient is greater than the first preset threshold are taken as health influencing factors.

[0186] In step S67, the health impact factors and the corresponding battery life are marked to obtain samples, and the samples are input into a convolutional neural network for training until the convolutional neural network converges to obtain a life prediction model.

[0187] Furthermore, in step S7, features are extracted from the historical charging data of the electric vehicle to be monitored, that is, relevant battery operating parameters in the historical charging data are extracted and input into a life prediction model to obtain the predicted life of the electric vehicle to be monitored.

[0188] The above embodiment provides a method for monitoring the charging safety of an electric vehicle, which has at least the following beneficial effects:

[0189] (1) Analyze the failure cases of similar electric vehicle rechargeable batteries obtained through the big data platform to obtain the failure probability of the electric vehicle to be monitored, which can provide reliable early warning for electric vehicle failures and indicate potential risks of electric vehicles;

[0190] (2) During the charging process, the charging current is adjusted according to the real-time charging data to further ensure the safety of charging;

[0191] (3) The fault charging data is analyzed and modeled, and the life prediction model obtained can predict the life of the current electric vehicle to be monitored, prompt the potential risks of the electric vehicle, and ensure the safe operation of the electric vehicle.

[0192] refer to Figure 5 In some embodiments, a device for monitoring the charging safety of an electric vehicle is further provided, comprising:

[0193] The collection module 201 is used to collect historical charging data and real-time charging data of the electric vehicle to be monitored;

[0194] The data acquisition module 202 is used to acquire, through a big data platform, fault charging data of rechargeable batteries of electric vehicles of the same type as the electric vehicle to be monitored;

[0195] A synchronization module 203 is used to synchronize the historical charging data with the fault charging data to obtain a fault case;

[0196] A fault prediction module 204 is configured to establish a fault prediction model according to the fault case, and calculate the failure probability of the rechargeable battery of the electric vehicle to be monitored based on the fault prediction model;

[0197] The charging control module 205 is used to determine the charging parameters of the electric vehicle to be monitored based on the real-time charging data and a pre-established charging MAP table;

[0198] A life prediction model building module 206 is configured to extract health impact factors as samples based on the faulty charging data, train a convolutional neural network, and obtain a life prediction model;

[0199] The life prediction module 207 is used to extract features from the historical charging data of the electric vehicle to be monitored, input the features into a life prediction model, and obtain the predicted life of the electric vehicle to be monitored.

[0200] Furthermore, the historical charging data includes the usage time of the rechargeable battery of the electric vehicle to be monitored, and the fault charging data includes the usage time of the rechargeable battery of the electric vehicle of the same type that has a fault;

[0201] The synchronization module 203 synchronizes the historical charging data with the fault charging data to obtain a fault case, including:

[0202] The fault charging data corresponding to the rechargeable battery of the same type of electric vehicle with the same service life as the rechargeable battery of the electric vehicle to be monitored is selected as the fault case.

[0203] Furthermore, the fault prediction module 204 establishes a fault prediction model based on the fault case, including:

[0204] Obtaining fault parameters and safety parameters corresponding to the faulty rechargeable battery in the fault case, and calculating an average failure rate based on the fault parameters and safety parameters;

[0205] Calculate the standard deviation of the differences between different safety parameters of the rechargeable batteries of each fault;

[0206] The fault prediction model is established according to the average failure rate and the standard deviation.

[0207] Furthermore, the fault prediction model is as follows:

[0208]

[0209] Where n represents the number of failure cases, γ represents the failure probability of the rechargeable battery of the monitored electric vehicle, T represents the frequency of use of the rechargeable battery within a preset time, α represents the average failure rate in failure cases, and β represents the standard deviation of the differences between different safety parameters.

[0210] Furthermore, the real-time charging data includes the SOC of the charging battery; the charging MAP table includes a mapping relationship between the charging temperature, SOC and charging current;

[0211] The charging control module 205 determines the charging parameters of the electric vehicle to be monitored based on the real-time charging data and the pre-established charging MAP table, including:

[0212] Detecting the temperature of each battery cell in the rechargeable battery of the electric vehicle to be monitored;

[0213] Select the minimum temperature among all cells as the initial charging temperature;

[0214] Determining a charging current for a next charging cycle according to the initial charging temperature, the SOC of the rechargeable battery, and the charging MAP table;

[0215] During the charging process, the real-time temperature of each battery cell of the rechargeable battery of the monitored electric vehicle is collected, and the minimum real-time temperature is selected as the current temperature. The charging current of the next charging cycle is determined based on the current temperature, the rechargeable battery SOC and the charging MAP table.

[0216] Furthermore, the fault charging data includes battery capacity and battery operating parameters;

[0217] The life prediction model building module 206 extracts health impact factors as samples based on the faulty charging data, trains a convolutional neural network, and obtains a life prediction model, including:

[0218] constructing an initial correlation model for battery capacity based on the fault charging data;

[0219] Performing matrix transformation on the initial correlation model to obtain a final correlation model;

[0220] Obtaining a first correlation coefficient between each pair of battery operating parameters according to the final correlation model;

[0221] Taking the battery operating parameters and the battery capacity as indicators, obtaining weights of the indicators based on the hierarchical analysis method, establishing a weight matrix according to the weights of the indicators, and obtaining a second correlation coefficient between the battery operating parameters and the battery capacity according to the weight matrix;

[0222] selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient;

[0223] Selecting an initial correlation influencing factor having a first correlation coefficient greater than a first preset threshold as a health influencing factor;

[0224] The health impact factors and the corresponding battery life are labeled to obtain samples, and the samples are input into a convolutional neural network for training to obtain a life prediction model.

[0225] Furthermore, the fault charging data includes the battery capacity corresponding to each operating parameter in different time periods;

[0226] The initial correlation model is as follows:

[0227]

[0228] Where Q i (k) represents the historical battery capacity corresponding to the battery operating parameter i in the kth time period, Q j (k-1) represents the historical battery capacity corresponding to the battery operating parameter j in the k-1th time period, α ji Indicates the degree of influence of the historical battery capacity data corresponding to the battery operating parameter j in the k-1th time period on the historical battery capacity data corresponding to the battery operating parameter i in the kth time period, β i is a constant, γ i (k) represents the error.

[0229] Furthermore, the first correlation coefficient is determined by the following formula:

[0230]

[0231] Where r ij represents the correlation coefficient of battery operating parameter i to battery operating parameter j, λ is an adjustable parameter, and n is the number of time periods; ρ i (t) represents the proportion of the battery capacity corresponding to the battery operating parameter i in the tth time period to the original total battery capacity.

[0232] Furthermore, the life prediction model building module 206 selects relevant battery operating parameters as initial correlation influencing factors according to the second correlation coefficient, including:

[0233] The battery operating parameter corresponding to the second correlation coefficient greater than the second preset threshold is used as the initial correlation influencing factor; or,

[0234] The second correlation coefficients are sorted in descending order, and the battery operating parameters corresponding to a preset number of second correlation coefficients that are ranked higher are selected as initial correlation influencing factors.

[0235] Please refer to the method embodiment for the specific working principle, which will not be described here in detail.

[0236] The above embodiment provides an electric vehicle charging safety monitoring device, which has at least the following beneficial effects:

[0237] (1) Analyze the failure cases of similar electric vehicle rechargeable batteries obtained through the big data platform to obtain the failure probability of the electric vehicle to be monitored, which can provide reliable early warning for electric vehicle failures and indicate potential risks of electric vehicles;

[0238] (2) During the charging process, the charging current is adjusted according to the real-time charging data to further ensure the safety of charging;

[0239] (3) The fault charging data is analyzed and modeled, and the life prediction model obtained can predict the life of the current electric vehicle to be monitored, prompt the potential risks of the electric vehicle, and ensure the safe operation of the electric vehicle.

[0240] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A method for monitoring the charging safety of an electric vehicle, characterized in that: include: Collecting historical charging data and real-time charging data of the electric vehicle to be monitored, wherein the historical charging data includes the usage time of the rechargeable battery of the electric vehicle to be monitored; Acquire, through a big data platform, fault charging data of rechargeable batteries of electric vehicles of the same type as the electric vehicle to be monitored, wherein the fault charging data includes the usage time, battery capacity, and battery operating parameters of rechargeable batteries of electric vehicles of the same type that have experienced a fault; Synchronize the historical charging data with the fault charging data to obtain a fault case: select the fault charging data corresponding to the same type of electric vehicle rechargeable battery with the same service life as the rechargeable battery of the electric vehicle to be monitored as the fault case; Establishing a fault prediction model based on the fault case: obtaining fault parameters and safety parameters corresponding to the rechargeable battery that failed in the fault case, and calculating an average failure rate based on the fault parameters and safety parameters; calculating the standard deviation of differences between different safety parameters of the rechargeable batteries with various faults; establishing the fault prediction model based on the average failure rate and standard deviation; and calculating the failure probability of the rechargeable battery of the electric vehicle to be monitored based on the fault prediction model; Determining charging parameters of the electric vehicle to be monitored based on the real-time charging data and a pre-established charging MAP table; Extracting health impact factors as samples from the faulty charging data, training a convolutional neural network to obtain a life prediction model; constructing an initial correlation model for battery capacity based on the faulty charging data; performing matrix transformation on the initial correlation model to obtain a final correlation model; and obtaining first correlation coefficients between each pair of battery operating parameters based on the final correlation model; Using battery operating parameters and battery capacity as indicators, obtaining weights of the indicators based on a hierarchical analysis method, establishing a weight matrix based on the weights of the indicators, and obtaining a second correlation coefficient based on the weight matrix; selecting relevant battery operating parameters as initial correlation influencing factors based on the second correlation coefficient; An initial correlation influencing factor having a first correlation coefficient greater than a first preset threshold is selected as a health influencing factor; the health influencing factor is labeled with a corresponding battery life to obtain a sample, and the sample is input into a convolutional neural network for training to obtain a life prediction model; Features are extracted from historical charging data of the electric vehicle to be monitored, and the features are input into a life prediction model to obtain a predicted life of the electric vehicle to be monitored.

2. The method according to claim 1, characterized in that The real-time charging data includes the SOC of the charging battery; the charging MAP table includes the mapping relationship between the charging temperature, SOC and charging current; Determine the charging parameters of the electric vehicle to be monitored based on the real-time charging data and the pre-established charging MAP table, including: Detecting the temperature of each battery cell in the rechargeable battery of the electric vehicle to be monitored; Select the minimum temperature among all cells as the initial charging temperature; Determining a charging current for a next charging cycle according to the initial charging temperature, the SOC of the rechargeable battery, and the charging MAP table; During the charging process, the real-time temperature of each battery cell of the rechargeable battery of the monitored electric vehicle is collected, and the minimum real-time temperature is selected as the current temperature. The charging current of the next charging cycle is determined based on the current temperature, the rechargeable battery SOC and the charging MAP table.

3. The method according to claim 1, characterized in that The fault charging data includes the battery capacity corresponding to each operating parameter in different time periods; The initial correlation model is as follows: ； Where, Indicates battery operating parameters i In the k The historical battery capacity corresponding to each time period, Indicates battery operating parameters j In the k - Historical battery capacity corresponding to a time period, Indicates battery operating parameters j In the k - Historical battery capacity data corresponding to a time period and battery operating parameters i In the k The impact of the historical battery capacity data corresponding to each time period, is a constant, Indicates error.

4. The method according to claim 3, characterized in that The first correlation coefficient is determined by the following formula: ； Where, Indicates battery operating parameters i Battery operating parameters j The correlation coefficient, is an adjustable parameter, n is the number of time periods; Indicates battery operating parameters i In the t The battery capacity corresponding to each time period accounts for the proportion of the original total battery capacity.

5. The method according to claim 3, characterized in that Selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient includes: The battery operating parameter corresponding to the second correlation coefficient greater than the second preset threshold is used as the initial correlation influencing factor.

6. The method according to claim 3, characterized in that Selecting a relevant battery operating parameter as an initial correlation influencing factor according to the second correlation coefficient includes: The second correlation coefficients are sorted in descending order, and the battery operating parameters corresponding to a preset number of second correlation coefficients that are ranked higher are selected as initial correlation influencing factors.

7. An electric vehicle charging safety monitoring device, characterized in that: include: A collection module is used to collect historical charging data and real-time charging data of the electric vehicle to be monitored, wherein the historical charging data includes the usage time of the rechargeable battery of the electric vehicle to be monitored; a data acquisition module, configured to acquire, through a big data platform, fault charging data of rechargeable batteries of electric vehicles of the same type as the electric vehicle to be monitored, wherein the fault charging data includes the service life, battery capacity, and battery operating parameters of the rechargeable batteries of the electric vehicles of the same type that have experienced a fault; a synchronization module, configured to synchronize the historical charging data with the fault charging data to obtain a fault case: selecting the fault charging data corresponding to a rechargeable battery of the same type of electric vehicle as the rechargeable battery of the electric vehicle to be monitored with the same service life as the fault case; A fault prediction module is configured to establish a fault prediction model based on the fault case: obtain fault parameters and safety parameters corresponding to the rechargeable battery that fails in the fault case, calculate an average failure rate based on the fault parameters and safety parameters; calculate the standard deviation of differences between different safety parameters of the rechargeable batteries with various faults; establish the fault prediction model based on the average failure rate and standard deviation; and calculate the failure probability of the rechargeable battery of the monitored electric vehicle based on the fault prediction model; A charging control module, configured to determine the charging parameters of the electric vehicle to be monitored based on the real-time charging data and a pre-established charging MAP table; A life prediction model establishment module is configured to extract health impact factors as samples based on the faulty charging data, train a convolutional neural network, and obtain a life prediction model: construct an initial correlation model for battery capacity based on the faulty charging data; perform matrix conversion on the initial correlation model to obtain a final correlation model; and obtain a first correlation coefficient between each pair of battery operating parameters based on the final correlation model; Using battery operating parameters and battery capacity as indicators, obtaining weights of the indicators based on a hierarchical analysis method, establishing a weight matrix based on the weights of the indicators, and obtaining a second correlation coefficient based on the weight matrix; selecting relevant battery operating parameters as initial correlation influencing factors based on the second correlation coefficient; An initial correlation influencing factor having a first correlation coefficient greater than a first preset threshold is selected as a health influencing factor; the health influencing factor is labeled with a corresponding battery life to obtain a sample, and the sample is input into a convolutional neural network for training to obtain a life prediction model; The life prediction module is used to extract features from the historical charging data of the electric vehicle to be monitored, input the features into a life prediction model, and obtain the predicted life of the electric vehicle to be monitored.

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