Charging Safety Early Warning Method and System for the Whole Vehicle Life Cycle

Abstract

The present invention relates to the technical field of vehicle intelligent monitoring, and specifically discloses a charging safety warning method and system for the entire vehicle life cycle. Real-time data is collected through cell-level sensors, and voltage, temperature, and current fluctuation coefficients are calculated to obtain the safety coefficient during the charging process. The real-time data is compared with historical data to judge the attenuation degree of the cell health state and calculate the attenuation coefficient. By comprehensively analyzing the safety coefficient and the attenuation coefficient, a comprehensive risk index is calculated, and then the risk level during the charging process is judged, which is divided into two modes: high risk and low risk. For the high-risk level, warning measures are triggered in a timely manner, such as emitting sound or visual alarms, automatically stopping charging, etc. In the case of low risk, the charging strategy is optimized, the charging current and voltage are adjusted to improve the charging efficiency, and at the same time, the battery life is extended.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle intelligent monitoring, and particularly relates to a charging safety warning method and system for the entire vehicle life cycle. Background Art

[0002] With the popularization of electric vehicles, the construction of charging facilities and the continuous development of battery technology, the charging safety problem of electric vehicles has attracted more and more attention. As the core component of electric vehicles, the safety, efficiency and battery life during the charging process of the battery have become key concerns. In order to ensure the safety of the battery during charging, many existing battery management systems adopt real-time monitoring of temperature, voltage and current, combined with overcharging, over-discharging and other protection measures to reduce the risk of battery failure. However, these existing technologies still mainly rely on static parameters and preset thresholds, and fail to effectively consider the health status of the battery, environmental changes and real-time dynamic data during the charging process.

[0003] Currently, the existing charging warning systems have the following problems: First, the existing systems cannot comprehensively consider multi-dimensional data of the battery, such as factors like the health status of the battery, usage history and environmental changes, and only rely on single voltage, temperature or current data for simple warning and protection, which makes the safety assessment of the charging process have blind spots; Second, the optimization of the existing systems for charging efficiency is relatively limited, and they fail to dynamically adjust the charging mode according to real-time risk assessment, thus affecting the charging efficiency and the service life of the battery. Summary of the Invention

[0004] The purpose of the present invention is to provide a charging safety warning method and system for the entire vehicle life cycle. By combining real-time data collected by cell-level sensors, using multi-dimensional data comparison, fuzzy logic analysis and risk index calculation, accurately evaluate the safety risks during the charging process, and on the premise of ensuring safety, realize the intelligent optimization of the charging mode, so as to solve the safety hazards and efficiency optimization in the traditional charging system to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A charging safety warning method for the entire vehicle life cycle includes the following steps:

[0007] S1: Through cell-level sensors, real-time collect the physical parameters of a single cell, including voltage, temperature and current, analyze the real-time physical parameters, and evaluate the safety level during the vehicle charging process;

[0008] S2: Conduct multi-dimensional data comparison and trend analysis on the real-time physical parameters and historical records, and judge the attenuation degree of the cell health status during the vehicle charging process according to the analysis results;

[0009] S3: Comprehensively analyze the safety level during the vehicle charging process and the attenuation degree of the cell health state during the vehicle charging process, extract the safety factor and attenuation factor during the charging process, calculate the comprehensive risk index based on the safety factor and attenuation factor, and divide the risk level during the charging process according to the comprehensive risk index;

[0010] S4: Divide the charging process into a high-risk level and a low-risk level, give an early warning for the high-risk level charging mode, and adjust the corresponding charging mode for the charging process of the low-risk level.

[0011] As a further solution of the present invention: The physical parameters of a single cell, including voltage, temperature, and current, are collected in real time through the cell-level sensor, and the real-time physical parameters are analyzed to evaluate the safety level during the vehicle charging process, specifically including:

[0012] Obtain the voltage data, temperature data, and current data of each cell in real time. According to the voltage data, temperature data, and current data of each cell obtained in real time, calculate the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient. Perform comprehensive processing on the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient, calculate the safety factor during the vehicle charging process, and judge whether the vehicle charging process is safe according to the safety factor.

[0013] As a further solution of the present invention: The process of obtaining the safety factor is as follows:

[0014] Collect the voltage data V i (t), temperature data T i (t), and current data I i (t) of each cell in real time;

[0015] where i represents the number of cells, and t represents the time point;

[0016] For a monitoring period, calculate the voltage average value, temperature average value, and current average value of each cell within the monitoring period through the average value calculation expression, and calculate the corresponding voltage standard deviation, temperature standard deviation, and current standard deviation through the standard deviation calculation expression;

[0017] Calculate the ratio of the voltage average value of each cell to the voltage standard deviation to obtain the voltage fluctuation coefficient

[0018] Calculate the ratio of the temperature average value of each cell to the temperature standard deviation to obtain the temperature fluctuation coefficient

[0019] Calculate the ratio of the current average value of each cell to the current standard deviation to obtain the current fluctuation coefficient

[0020] Construct a judgment matrix based on the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient;

[0021] Among them, the calculation expression of the judgment matrix is:

[0022]

[0023] In the formula, a VT , a VT and a TI respectively represent the relative ratio of the importance of voltage, temperature, and current;

[0024] Calculate the weight vector, including calculating the normalized judgment matrix. According to the normalized judgment matrix, calculate the weight vector, including the voltage weight vector, temperature weight vector, and temperature weight vector. According to the weight vector, calculate the safety factor;

[0025] Among them, the calculation expression of the safety factor is:

[0026]

[0027] In the formula, S represents the safety factor during the vehicle charging process, i represents the number of battery cells, n represents the maximum number of battery cells, w V represents the voltage weight vector, w T temperature weight vector, and w I voltage weight vector.

[0028] As a further solution of the present invention: Judging whether the vehicle is safe during the charging process according to the safety factor specifically includes:

[0029] Judge whether the safety factor is greater than or equal to the preset threshold. If so, it means that the safety level of the corresponding vehicle during the charging process is low. If not, it means that the safety level of the corresponding vehicle during the charging process is high.

[0030] As a further solution of the present invention: Comparing and analyzing the real-time physical parameter data and historical records in multiple dimensions, and judging the attenuation degree of the health state of the battery cells during the vehicle charging process according to the analysis results, specifically including:

[0031] Obtain the voltage data, temperature data, and current data of each battery cell in a real-time monitoring cycle and the voltage data, temperature data, and current data of each battery cell in a historical monitoring cycle. Through multi-dimensional data comparison and trend analysis, calculate the attenuation coefficient during the vehicle use process. According to the attenuation coefficient during the vehicle use process, judge the attenuation degree of the health state of the battery cells during the vehicle charging process.

[0032] As a further solution of the present invention: The process of obtaining the attenuation coefficient is:

[0033] Calculate the difference between the real-time voltage data, temperature data and current data and the historical voltage data, temperature data and current data, and record them as voltage difference V diff (t), temperature difference T diff (t) and the current difference I diff (t);

[0034] Where t represents the time point;

[0035] Calculate the prior probability of the cell's health status using the following expression:

[0036]

[0037] Where, N is the total number of cells, N H represents the number of cells with a health state of H, and P(H) represents the prior probability of the health state of the cell;

[0038] The conditional probability P(V diff |H), the conditional probability P(T) of temperature data diff |H) and the conditional probability P(I diff |H);

[0039] Calculate the joint probability, the calculation expression is:

[0040] P(H,V diff ,T diff ,I diff )=P(H)*P(V diff |H)*P(T diff |H)*p(I diff |H);

[0041] Where, P(H,V diff ,T diff ,I diff ) represents the joint probability;

[0042] Calculate the posterior probability, the calculation expression is:

[0043]

[0044] Where, P(H|V diff ,T diff ,I diff ) represents the posterior probability;

[0045] According to the posterior probability, the expected value of the cell health status is calculated using the following expression:

[0046]

[0047] Wherein, a represents the number of discrete values of the health state, and m represents the maximum number of discrete values of the health state. The expected value of the cell health state;

[0048] According to the expected value of the cell health state, calculate the attenuation coefficient during vehicle charging. The calculation expression is:

[0049]

[0050] Wherein, D represents the attenuation coefficient during vehicle charging.

[0051] As a further solution of the present invention: The judging the attenuation degree of the cell health state during vehicle charging specifically includes:

[0052] Judge whether the attenuation coefficient is greater than or equal to the preset threshold. If so, it means that the health state of the corresponding cell is poor and the attenuation degree is high. If not, it means that the health state of the corresponding cell is good and the attenuation degree is low.

[0053] As a further solution of the present invention: The process of obtaining the comprehensive risk index is as follows:

[0054] Obtain the safety factor S and attenuation coefficient D of the cell during vehicle charging;

[0055] Use the triangular membership function as the fuzziness of the input variable;

[0056] Establish a fuzzy rule base, and the rule base is: high risk and low risk;

[0057] Calculate the activation strength, and the calculation expression is: α d = min(μ S (S), μ D (D))

[0058] Wherein, α d represents the activation strength, d represents the number of charging vehicles, μ S (S) and μ D (D) represent the membership degrees of the input variables corresponding to the fuzzy sets;

[0059] Rule output fuzzification, and the membership function of the output fuzzy set is:

[0060]

[0061] Wherein, μ R (R) is the membership function of the risk level, represents the membership function of the output fuzzy set, R represents the comprehensive risk index;

[0062] Sum up all the output fuzzy sets to obtain the combined membership function μRz (R);

[0063] Calculate the comprehensive risk index according to the combined membership function. The calculation expression is:

[0064]

[0065] In the formula, R max represents the maximum value of the risk level, R min represents the minimum value of the risk level, and R represents the comprehensive risk index.

[0066] As a further solution of the present invention: divide the risk level during the charging process according to the comprehensive risk index, specifically including:

[0067] Judge whether the comprehensive risk index is greater than or equal to the preset threshold. If so, record it as a high risk level; if not, record it as a low risk level.

[0068] The charging safety warning system for the entire vehicle life cycle includes:

[0069] The physical parameter acquisition and real-time analysis module for battery cells. The physical parameter acquisition and real-time analysis module for battery cells collects the physical parameters of each battery cell in real time through cell-level sensors and conducts preliminary analysis and evaluation to obtain the safety level during the charging process;

[0070] The data comparison and trend analysis module. The data comparison and trend analysis module judges the health status and attenuation degree of the battery cells by performing multi-dimensional comparison and trend analysis on the real-time physical parameters and historical data, and predicts the risks existing in the battery;

[0071] The comprehensive analysis module for safety factor and attenuation coefficient. The comprehensive analysis module for safety factor and attenuation coefficient extracts the safety factor and attenuation coefficient by comprehensively analyzing the safety level during the charging process and the attenuation degree of the health status of the battery cells, calculates the comprehensive risk index, and divides the risk level during the charging process;

[0072] The charging mode adjustment and risk warning module. The charging mode adjustment and risk warning module divides the charging process into two categories: high risk and low risk according to the risk assessment result, and issues a warning for the charging process with a high risk level. For the charging process with a low risk level, corresponding charging mode adjustments are made.

[0073] The beneficial effects of the present invention:

[0074] (1) The charging safety warning method of the present invention relies on advanced real-time monitoring technology and multi-dimensional data analysis models, and can comprehensively and accurately evaluate the battery health status and potential safety risks during the charging process. This method collects detailed physical parameters of the battery cells and calculates the fluctuation coefficients, combines complex fuzzy logic control and risk index evaluation algorithms, monitors the operating status of the battery in real time, and can accurately identify abnormal conditions that may occur during the charging process according to the system calculation results. Through this refined real-time data analysis and anomaly identification, the present invention can effectively prevent serious safety accidents such as fires and explosions caused by factors such as overheating, short circuit, and overcharging of the battery, and improve the safety of the electric vehicle charging process. Through highly intelligent risk index calculation and comprehensive analysis, the present invention can dynamically and precisely adjust the charging mode according to the safety conditions in different charging processes. This adjustment is not only based on traditional battery state data, but also integrates multiple dimensions of battery health status and risk index to ensure that the current and voltage can be automatically optimized according to the actual situation during the charging process, thereby achieving double guarantees for charging efficiency and safety. In high-risk situations, the system automatically reduces the charging current and voltage to reduce the risk of battery damage or overheating. In low-risk situations, the system increases the charging power to improve the charging efficiency and avoid unnecessary time waste.

[0075] (2) On the basis of ensuring charging safety, it realizes the optimization of charging efficiency and the extension of battery service life. Through the dynamic evaluation of the comprehensive risk index, the system can automatically adjust the current and voltage according to the real-time risk status during the charging process. In low-risk situations, the system will automatically increase the charging current and voltage and enter the high-power charging mode, thereby greatly improving the charging speed and shortening the charging time. In high-risk situations, the system intelligently reduces the current and voltage to reduce the risk of overheating and battery damage and avoid safety hazards caused by excessive charging power. In addition, the system can also dynamically adjust the charging strategy according to the health status of the battery, accurately control various parameters during the charging process, avoid battery attenuation or excessive temperature caused by too fast charging, and effectively delay the battery aging process. This intelligent charging management mechanism not only improves the charging efficiency and reduces the charging time, but also extends the service life of the battery through detailed risk control and health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The present invention will be further described below with reference to the accompanying drawings.

[0077] Figure 1 It is a specific step flowchart of the charging safety warning method for the vehicle's entire life cycle of the present invention;

[0078] Figure 2 It is a flowchart of the charging safety warning system for the vehicle's entire life cycle in the present invention. DETAILED IMPLEMENTATION MANNER

[0079] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0080] Please refer to Figure 1 As shown, the present invention is a charging safety warning method for the entire vehicle life cycle, including the following steps:

[0081] S1: Through cell-level sensors, physically collect the physical parameters of a single cell in real time, including voltage, temperature, and current, analyze the real-time physical parameters, and evaluate the safety level during the vehicle charging process;

[0082] S2: Compare and trend analyze the real-time physical parameters and historical records in multiple dimensions, and based on the analysis results, judge the attenuation degree of the cell health state during the vehicle charging process;

[0083] S3: Conduct a comprehensive analysis of the safety level during the vehicle charging process and the attenuation degree of the cell health state during the vehicle charging process, extract the safety coefficient and attenuation coefficient during the charging process, calculate the comprehensive risk index based on the safety coefficient and attenuation coefficient, and divide the risk level during the charging process according to the comprehensive risk index;

[0084] S4: Divide the charging process into high-risk levels and low-risk levels, give early warnings for high-risk charging modes, and adjust the corresponding charging modes for low-risk charging processes.

[0085] In S1, through cell-level sensors, physically collect the physical parameters of a single cell in real time, including voltage, temperature, and current, analyze the real-time physical parameters, and evaluate the safety level during the vehicle charging process, specifically including:

[0086] Obtain the voltage data, temperature data, and current data of each cell in real time. According to the voltage data, temperature data, and current data of each cell obtained in real time, calculate the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient, perform comprehensive processing on the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient, calculate the safety coefficient during the vehicle charging process, and judge whether the vehicle charging process is safe according to the safety coefficient;

[0087] Among them, the cell-level sensors include: voltage sensors, temperature sensors, and current sensors;

[0088] Among them, the process of obtaining the voltage data, temperature data, and current data of each cell is:

[0089] Start the battery cell monitoring system and calibrate the sensors to ensure measurement accuracy, including zero calibration and range adjustment of voltage sensors, temperature sensors, and current sensors;

[0090] Load the monitoring cycle parameters and set the data acquisition frequency (e.g., once per second);

[0091] Detect the real-time voltage value across each battery cell through the voltage sensor and record the real-time voltage value in time series;

[0092] Detect the surface temperature value of each battery cell through the temperature sensor and record the real-time temperature value in time series;

[0093] Detect the current value flowing through each battery cell through the current sensor and record the real-time current value in time series;

[0094] Among them, the process of obtaining the safety factor is as follows:

[0095] Real-time collect the voltage data V i (t), temperature data T i (t), and current data I i (t) of each battery cell at time point t;

[0096] Among them, i represents the number of battery cells, and t represents the time point;

[0097] For a monitoring cycle, calculate the voltage average value, temperature average value, and current average value of each battery cell within the monitoring cycle through the average value calculation expression, and calculate the corresponding voltage standard deviation, temperature standard deviation, and current standard deviation through the standard deviation calculation expression;

[0098] Calculate the ratio of the voltage average value of each battery cell to the voltage standard deviation to obtain the voltage fluctuation coefficient

[0099] Calculate the ratio of the temperature average value of each battery cell to the temperature standard deviation to obtain the temperature fluctuation coefficient

[0100] Calculate the ratio of the current average value of each battery cell to the current standard deviation to obtain the current fluctuation coefficient

[0101] Construct a judgment matrix based on the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient;

[0102] Among them, the calculation expression of the judgment matrix is:

[0103]

[0104] In the formula, aVT , a VI and a TI respectively represent the relative ratio of the importance of voltage, temperature, and current;

[0105] Calculate the weight vector, including calculating the normalized judgment matrix, and based on the normalized judgment matrix, calculate the weight vector, including the voltage weight vector, the temperature weight vector, and the temperature weight vector, and calculate the safety factor according to the weight vector;

[0106] Among them, the calculation expression of the safety factor is:

[0107]

[0108] In the formula, S represents the safety factor during the vehicle charging process, i represents the number of battery cells, n represents the maximum number of battery cells, w V represents the voltage weight vector, w T the temperature weight vector, and w I the voltage weight vector;

[0109] Compare the safety factor with the preset threshold;

[0110] If the safety factor is greater than or equal to the preset threshold, it indicates that the safety level during the corresponding vehicle charging process is low;

[0111] If the safety factor is less than the preset threshold, it indicates that the safety level during the corresponding vehicle charging process is high;

[0112] It should be noted that: the safety factor reflects that during the vehicle charging process, if the value of the safety factor during the vehicle charging process is larger, the health state of the battery cells is lower, and the safety level of the corresponding vehicle is lower.

[0113] In S2, perform multi-dimensional data comparison and trend analysis on the real-time physical parameter data and historical records, and based on the analysis results, judge the attenuation degree of the health state of the battery cells during the vehicle charging process, specifically including:

[0114] Obtain the voltage data, temperature data, and current data of each battery cell in a real-time monitoring period and the voltage data, temperature data, and current data of each battery cell in a historical monitoring period. Through multi-dimensional data comparison and trend analysis, calculate the attenuation coefficient during the vehicle use process, and judge the attenuation degree of the health state of the battery cells during the vehicle charging process;

[0115] Among them, the time of the monitoring period is the same;

[0116] Calculate the differences between the real-time voltage data, temperature data, and current data and the historical voltage data, temperature data, and current data, and record them as the voltage difference V diff (t), the temperature difference T diff (t), and the current difference Idiff (t);

[0117] Where t represents the time point;

[0118] Calculate the prior probability of the cell's health status using the following expression:

[0119]

[0120] Where, N is the total number of cells, N H represents the number of cells with a health state of H, and P(H) represents the prior probability of the health state of the cell;

[0121] The conditional probability P(V diff |H), the conditional probability P(T diff |H) and the conditional probability P(I diff |H);

[0122] Calculate the joint probability, the calculation expression is:

[0123] P(H,V diff ,T diff ,I diff )=P(H)*P(V diff |H)*P(T diff |H)*P(I diff |H);

[0124] Where, P(H,V diff ,T idff ,I diff ) represents the joint probability;

[0125] Calculate the posterior probability, the calculation expression is:

[0126]

[0127] Where, P(H|V diff ,T diff ,I diff ) represents the posterior probability;

[0128] According to the posterior probability, the expected value of the cell health status is calculated using the following expression:

[0129]

[0130] In the formula, a represents the number of discrete values of the health state, m represents the maximum number of discrete values of the health state, Expected value of the battery cell health status;

[0131] According to the expected value of the cell health state, calculate the attenuation coefficient during the vehicle charging process. The calculation expression is:

[0132]

[0133] In the formula, D represents the attenuation coefficient during the vehicle charging process;

[0134] Compare the attenuation coefficient during the vehicle charging process with the preset threshold. If the attenuation coefficient is greater than or equal to the preset threshold, it indicates that the health state of the corresponding cell is worse and the attenuation degree is higher. If the attenuation coefficient is less than the preset threshold, it indicates that the health state of the corresponding cell is better and the attenuation degree is lower;

[0135] It should be noted that: the attenuation coefficient during the vehicle charging process reflects the attenuation situation of the cell health state during the vehicle charging process, and when the value of the attenuation coefficient is larger, the attenuation degree of the corresponding cell health state is higher.

[0136] In S3, comprehensively analyze the safety level during the vehicle charging process and the attenuation degree of the cell health state during the vehicle charging process, extract the safety coefficient and attenuation coefficient during the charging process, calculate the comprehensive risk index according to the safety coefficient and attenuation coefficient, and divide the risk level during the charging process according to the comprehensive risk index. Specifically, it includes:

[0137] Obtain the safety coefficient S and attenuation coefficient D of the cell during the vehicle charging process;

[0138] Use the triangular membership function as the fuzziness of the input variable. The calculation expression is:

[0139]

[0140] In the formula, μ LS (S), μ MS (S), μ HS (S), μ LD (D), μ MD (D) and μ HD (D) represent the membership function;

[0141] Establish a fuzzy rule base. The rule base is: high risk and low risk;

[0142] Calculate the activation intensity. The calculation expression is: α d =min(μ S (S), μ D (D))

[0143] In the formula, α d represents the activation intensity, d represents the number of charging vehicles, μ S (S) and μ D(D) represents the membership degree of the input variable corresponding to the fuzzy set;

[0144] The rule output is fuzzified, and the membership function of the output fuzzy set is:

[0145]

[0146] Where μ R (R) is the membership function of the risk level, represents the membership function of the output fuzzy set, and R represents the comprehensive risk index;

[0147] Sum all the output fuzzy sets to get the combined membership function μ Rz (R);

[0148] According to the combined membership function, the comprehensive risk index is calculated, and the calculation expression is:

[0149]

[0150] Where R max Indicates the maximum risk level, R min It represents the minimum value of risk level, and R represents the comprehensive risk index;

[0151] It should be noted that the comprehensive risk index reflects the risk level of failure during vehicle charging, and the larger the value of the comprehensive risk index, the higher the risk level of the corresponding charging process.

[0152] In S4, the charging process is divided into high-risk level and low-risk level, and a warning is issued for the high-risk level charging mode. For the low-risk level charging process, the corresponding charging mode is adjusted, including:

[0153] Comparing the comprehensive risk index of the vehicle during charging with a preset threshold;

[0154] If the comprehensive risk index is greater than or equal to the preset threshold, it means that the charging risk level of the vehicle during charging is high and is recorded as a high risk level;

[0155] If the comprehensive risk index is less than the preset threshold, it means that the charging risk level of the vehicle during charging is low and is recorded as a low risk level;

[0156] Early warning measures will be taken for high-risk charging modes. Early warning measures may include:

[0157] Sound alarm: emits an alarm sound to alert the driver or user;

[0158] Visual warning: Displays warning messages on the display, such as "High Risk Charging";

[0159] Automatic charging stop: If the risk is severe, the charging process can be automatically stopped to avoid serious consequences such as battery damage or fire;

[0160] Notify the vehicle owner or the operation platform: Notify the vehicle owner or the operation platform by means of mobile applications, text messages, emails, etc., to prompt the safety risks during the charging process;

[0161] For charging processes with a low risk level, adjust the charging mode to further optimize the charging efficiency and extend the battery life. The adjustment measures include:

[0162] If the risk during the charging process is low, the system selects to increase the charging current and voltage to enter the high-power charging mode and improve the charging efficiency;

[0163] Change the charging strategy, optimize the charging time, and extend the charging time according to the battery health status and the charging environment to avoid overheating or other potential risks caused by too fast charging.

[0164] During each charging process, the system monitors the voltage, temperature, current, and health status of the battery in real time, and adjusts the charging current and voltage according to the risk index calculated in real time;

[0165] The charging mode adjustment will continue until the charging is completed or the risk index returns to a safe level.

[0166] It should be noted that: According to the comprehensive risk index calculated in real time, the charging system can dynamically adjust the charging mode by adjusting the current and voltage, so as to ensure the safety and efficiency of the charging process. When the risk is high, reduce the charging current and voltage to reduce potential safety hazards; when the risk is low, increase the charging current and voltage to improve the charging efficiency.

[0167] Please refer to Figure 2 As shown, the charging safety warning system for the entire vehicle life cycle includes:

[0168] The physical parameter acquisition and real-time analysis module for battery cells. The physical parameter acquisition and real-time analysis module for battery cells collects the physical parameters of each battery cell in real time through cell-level sensors and conducts preliminary analysis and evaluation to obtain the safety level during the charging process;

[0169] The data comparison and trend analysis module. The data comparison and trend analysis module conducts multi-dimensional comparison and trend analysis of the real-time physical parameters and historical data to judge the health status and attenuation degree of the battery cells and predict the possible risks of the battery;

[0170] Safety factor and attenuation factor comprehensive analysis module. The safety factor and attenuation factor comprehensive analysis module extracts the safety factor and attenuation factor, calculates the comprehensive risk index, and divides the risk level during the charging process by comprehensively analyzing the safety level and the attenuation degree of the cell health state during the charging process;

[0171] Charging mode adjustment and risk warning module. The charging mode adjustment and risk warning module divides the charging process into two categories: high risk and low risk according to the risk assessment result, and issues a warning for the charging process with a high risk level. For the low-risk charging process, corresponding charging mode adjustments are made.

[0172] The working principle of the present invention: Accurately collect the real-time data of each cell through cell-level sensors, and conduct multi-dimensional comparison and trend analysis in combination with historical data to judge the cell health state and attenuation degree; Calculate the fluctuation coefficients of voltage, temperature, and current, and comprehensively combine the cell health state attenuation coefficient to construct the safety factor and attenuation factor, and further use advanced algorithms such as fuzzy logic to calculate the comprehensive risk index to evaluate the risk level during the charging process; For the high-risk charging process, the system will issue a warning and take measures such as reducing the charging current and voltage; In the case of low risk, the charging mode optimizes the charging current and voltage to improve the charging efficiency and extend the battery life.

[0173] The above formulas are all calculated by taking the numerical values without dimensions. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0174] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0175] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context.

[0176] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not indicate the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0177] The above has described in detail one embodiment of the present invention, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention still fall within the scope covered by the patent of the present invention.

Claims

1. A charging safety warning method for the entire vehicle life cycle, characterized in that, Including the following steps: S1: Through cell-level sensors, collect the physical parameters of individual cells in real time, including voltage, temperature, and current. Analyze the real-time physical parameters to evaluate the safety level during vehicle charging, specifically including: Obtain the voltage data, temperature data, and current data of each cell in real time. According to the voltage data, temperature data, and current data of each cell obtained in real time, calculate the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient. Perform comprehensive processing on the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient, calculate the safety coefficient during vehicle charging, and determine whether the vehicle charging process is safe based on the safety coefficient; The process of obtaining the safety coefficient is as follows: Real-time collection of voltage data V of each battery cell at time point t i (t), temperature data T i (t) and current data I i (t); where i represents the number of cells and t represents the time point; For a monitoring period, calculate the voltage mean, temperature mean, and current mean of each cell during the monitoring period through the mean calculation expression, and calculate the corresponding voltage standard deviation, temperature standard deviation, and current standard deviation through the standard deviation calculation expression; Calculate the ratio of the average voltage to the standard deviation of the voltage of each battery cell to obtain the voltage fluctuation coefficient Calculate the ratio of the average temperature of each battery cell to the standard deviation of the temperature to obtain the temperature fluctuation coefficient Calculate the ratio of the average current of each battery cell to the standard deviation of the current to obtain the current fluctuation coefficient Construct a judgment matrix based on the voltage fluctuation coefficient, temperature fluctuation coefficient, and current fluctuation coefficient as criteria; where the calculation expression of the judgment matrix is: where a VT , a VI and a TI respectively represent the relative importance ratios of voltage, temperature, and current; Calculate the weight vector, including calculating the normalized judgment matrix. According to the normalized judgment matrix, calculate the weight vector, including the voltage weight vector, temperature weight vector, and current weight vector, and calculate the safety coefficient according to the weight vector; where the calculation expression of the safety coefficient is: Wherein, S represents the safety factor during the vehicle charging process, i represents the number of battery cells, n represents the maximum number of battery cells, w V represents the voltage weight vector, w T the temperature weight vector, and w I the current weight vector; S2: Perform multi-dimensional data comparison and trend analysis on the real-time physical parameters and historical records, and judge the attenuation degree of the cell health state during vehicle charging according to the analysis results; S3: Perform comprehensive analysis on the safety level during vehicle charging and the attenuation degree of the cell health state during vehicle charging, extract the safety coefficient and attenuation coefficient during the charging process, calculate the comprehensive risk index according to the safety coefficient and attenuation coefficient, and divide the risk level during the charging process according to the comprehensive risk index; S4: Divide the charging process into a high-risk level and a low-risk level, give an alarm for the high-risk level charging mode, and adjust the corresponding charging mode for the low-risk level charging process.

2. The charging safety warning method for the whole vehicle life cycle according to claim 1, characterized in that The judgment of whether the vehicle charging process is safe according to the safety coefficient specifically includes: Judge whether the safety coefficient is greater than or equal to the preset threshold. If so, it means that the safety level of the corresponding vehicle charging process is low. If not, it means that the safety level of the corresponding vehicle charging process is high.

3. The charging safety warning method for the vehicle's entire life cycle according to claim 1, characterized in that The multi-dimensional data comparison and trend analysis of the real-time physical parameter data and historical records, and the judgment of the attenuation degree of the cell health state during vehicle charging according to the analysis results specifically include: Obtain the voltage data, temperature data, and current data of each cell in a real-time monitoring period and the voltage data, temperature data, and current data of each cell in a historical monitoring period. Through multi-dimensional data comparison and trend analysis, calculate the attenuation coefficient during vehicle use, and judge the attenuation degree of the cell health state during vehicle charging according to the attenuation coefficient during vehicle use.

4. The charging safety warning method for the whole vehicle life cycle according to claim 3, characterized in that The process of obtaining the attenuation coefficient is as follows: Calculate the differences between the real-time voltage data, temperature data, and current data and the historical voltage data, temperature data, and current data, and denote them as the voltage difference V diff (t), the temperature difference T diff (t), and the current difference I diff (t); where t represents the time point; Calculate the prior probability of the cell health state, and the calculation expression is: Where N is the total number of battery cells, N H represents the number of battery cells with a health state of H, and P(H) represents the prior probability of the battery cell health state; The conditional probabilities P(V diff |H) of the separately calculated voltage data, P(T diff |H) of the temperature data, and P(I diff |H) of the current data; Calculate the joint probability, and the calculation expression is: P(H,V diff ,T diff ,I diff ) = P(H) * P(V diff |H) * P(T diff |H) * P(I diff |H); where P(H, V diff , T diff , I diff ) represents the joint probability; Calculate the posterior probability, and the calculation expression is: Where P(H|V diff ,T diff ,I diff ) represents the posterior probability; According to the posterior probability, calculate the expected value of the cell health state, and the calculation expression is: Where a represents the number of discrete values of the health state, and m represents the maximum number of discrete values of the health state. The expected value of the cell health state; According to the expected value of the cell health state, calculate the attenuation coefficient during the vehicle charging process, and the calculation expression is: In the formula, D represents the attenuation coefficient during the vehicle charging process.

5. The charging safety warning method for the full vehicle life cycle according to claim 3, characterized in that The determination of the attenuation degree of the cell health state during the vehicle charging process specifically includes: Judge whether the attenuation coefficient is greater than or equal to the preset threshold. If so, it means that the health state of the corresponding cell is poor and the attenuation degree is high. If not, it means that the health state of the corresponding cell is good and the attenuation degree is low.

6. The charging safety warning method for the entire vehicle life cycle according to claim 1, wherein The process of obtaining the comprehensive risk index is as follows: Obtain the safety factor S and attenuation coefficient D of the cell during the vehicle charging process; Use the triangular membership function as the fuzziness of the input variable; Establish a fuzzy rule base, and the rule base is: high risk and low risk; Calculate the activation intensity, and the calculation expression is: α d = min(μ S (S), μ D (D)) where α d represents the activation intensity, and μ D (D) represents the membership degree of the input variable corresponding to the fuzzy set; Fuzzify the rule output, and the membership function of the output fuzzy set is: where μ R (R) is the membership function of the risk level, which represents the membership function of the output fuzzy set, and R represents the comprehensive risk index; Sum all the output fuzzy sets to obtain the combined membership function μ Rz (R); According to the combined membership function, calculate the comprehensive risk index, and the calculation expression: Wherein, R max represents the maximum value of the risk level, R min represents the minimum value of the risk level, and R represents the comprehensive risk index.

7. The charging safety warning method for the full vehicle life cycle according to claim 1, characterized in that The classification of the risk level during the charging process according to the comprehensive risk index specifically includes: Judge whether the comprehensive risk index is greater than or equal to the preset threshold. If so, record it as the high risk level. If not, record it as the low risk level.

8. A charging safety warning system for the entire vehicle life cycle, characterized in that, For the charging safety warning method for the entire vehicle life cycle described in any one of claims 1-7, it includes: Cell physical parameter acquisition and real-time analysis module. The cell physical parameter acquisition and real-time analysis module collects the physical parameters of each cell in real time through cell-level sensors and conducts preliminary analysis and evaluation to obtain the safety level during the charging process; Data comparison and trend analysis module. The data comparison and trend analysis module judges the health state and attenuation degree of the cell and predicts the risks existing in the battery by performing multi-dimensional comparison and trend analysis on the real-time physical parameters and historical data; Safety factor and attenuation coefficient comprehensive analysis module. The safety factor and attenuation coefficient comprehensive analysis module extracts the safety factor and attenuation coefficient, calculates the comprehensive risk index, and classifies the risk level during the charging process by comprehensively analyzing the safety level and the attenuation degree of the cell health state during the charging process; Charging mode adjustment and risk warning module. The charging mode adjustment and risk warning module divides the charging process into two categories: high risk and low risk according to the risk assessment result, and issues a warning for the charging process with a high risk level. For the charging process with a low risk, corresponding charging mode adjustments are made.

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