Charging safety early warning method and system for full life cycle of vehicle

Abstract

The invention relates to the technical field of vehicle intelligent monitoring, and particularly discloses a vehicle full life cycle-oriented charging safety early warning method and system, and the method comprises the steps: collecting real-time data through an electric core level sensor, and calculating voltage, temperature and current fluctuation coefficients, thereby obtaining a safety coefficient in a charging process; comparing the real-time data with historical data, judging the attenuation degree of the health state of the battery cell, and calculating an attenuation coefficient; a comprehensive risk index is calculated by comprehensively analyzing the safety coefficient and the attenuation coefficient, and then the risk level in the charging process is judged and divided into a high-risk mode and a low-risk mode; for a high risk level, early warning measures are triggered in time, such as sound or visual alarm, automatic charging stopping and the like; and under the condition of low risk, the charging strategy is optimized, the charging current and voltage are adjusted, the charging efficiency is improved, and the service life of the battery is prolonged.

Description

Technical Field

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

[0002] With the popularity of electric vehicles, the construction of charging facilities and the continuous development of battery technology, the charging safety of electric vehicles has received more and more attention. As the core component of electric vehicles, the safety, efficiency and battery life of batteries during the charging process have become key concerns. In order to ensure the safety of batteries during the charging process, many existing battery management systems use real-time monitoring of temperature, voltage and current, combined with protection measures such as overcharging and over-discharging 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 battery's health status, environmental changes, and real-time dynamic data during the charging process.

[0003] At present, the existing charging warning system has the following problems: First, the existing system cannot comprehensively consider the multi-dimensional data of the battery, such as the battery's health status, usage history, and environmental changes, and only relies on a single voltage, temperature, or current data for simple warning and protection, which makes the safety assessment of the charging process have blind spots; secondly, the existing system is relatively limited in optimizing the charging efficiency, and fails to dynamically adjust the charging mode according to real-time risk assessment, thus affecting the charging efficiency and battery life. Summary of the invention

[0004] The purpose of the present invention is to provide a charging safety warning method and system for the entire life cycle of a vehicle. By combining cell-level sensors to collect data in real time, using multi-dimensional data comparison, fuzzy logic analysis and risk index calculation, the safety risks in the charging process can be accurately evaluated. Under the premise of ensuring safety, the charging mode can be intelligently optimized, thereby solving the safety hazards and efficiency optimization in traditional charging systems to solve the problems in the above background.

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

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

[0007] S1: Through the cell-level sensors, the physical parameters of a single cell are collected in real time, including voltage, temperature and current, and the real-time physical parameters are analyzed to evaluate the safety level of the vehicle during charging;

[0008] S2: Perform multi-dimensional data comparison and trend analysis on real-time physical parameters and historical records, and determine the degree of attenuation of the health status of the battery cells during the vehicle charging process based on the analysis results;

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

[0010] 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.

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

[0012] The voltage data, temperature data and current data of each battery cell are acquired in real time, and the voltage fluctuation coefficient, temperature fluctuation coefficient and current fluctuation coefficient are calculated based on the voltage data, temperature data and current data of each battery cell. The voltage fluctuation coefficient, temperature fluctuation coefficient and current fluctuation coefficient are comprehensively processed to calculate the safety factor of the vehicle charging process, and whether the vehicle charging process is safe is determined based on the safety factor.

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

[0014] 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);

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

[0016] For a monitoring period, the mean value calculation expression is used to calculate the voltage mean, temperature mean and current mean of each battery cell in the monitoring period, and the standard deviation calculation expression is used to calculate the corresponding voltage standard deviation, temperature standard deviation and current standard deviation;

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

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

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

[0020] Construct a judgment matrix based on 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 importance ratios of voltage, temperature and current;

[0024] Calculating a weight vector, including calculating a normalized judgment matrix, calculating a weight vector according to the normalized judgment matrix, including a voltage weight vector, a temperature weight vector and a temperature weight vector, and calculating a safety factor according to the weight vector;

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

[0026]

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

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

[0029] It is determined whether the safety factor is greater than or equal to a preset threshold value. 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.

[0030] As a further solution of the present invention: performing multi-dimensional data comparison and trend analysis on the real-time physical parameter data and historical records, and judging the attenuation degree of the health status of the battery cell during the vehicle charging process according to the analysis results, specifically includes:

[0031] 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 are obtained. Through multi-dimensional data comparison and trend analysis, the attenuation coefficient during vehicle use is calculated. Based on the attenuation coefficient during vehicle use, the attenuation degree of the battery cell health status during vehicle charging is determined.

[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] Wherein, t represents the time point;

[0035] Calculate the prior probability of the health status of the battery cell, the calculation expression is:

[0036]

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

[0038] The conditional probability P(V diff |H), the conditional probability P(T 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] In the formula, P(H,V diff ,T diff ,I diff ) represents the joint probability;

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

[0043]

[0044] In the formula, 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, and the calculation expression is:

[0046]

[0047] 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;

[0048] According to the expected value of the battery cell health status, the attenuation coefficient during vehicle charging is calculated, and the calculation expression is:

[0049]

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

[0051] As a further solution of the present invention: the determination of the attenuation degree of the health status of the battery cell during the vehicle charging process specifically includes:

[0052] It is determined whether the reduction coefficient is greater than or equal to a preset threshold value. If so, it indicates that the health state of the corresponding battery cell is poor and the attenuation degree is high. If not, it indicates that the health state of the corresponding battery 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:

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

[0055] Using triangular membership functions as fuzzy degrees of input variables;

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

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

[0058] 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 fuzzy set corresponding to the input variable;

[0059] The rule output is fuzzified, and the membership function of the output fuzzy set is:

[0060]

[0061] In the formula, 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;

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

[0063] According to the combined membership function, the comprehensive risk index is calculated, and the calculation expression is:

[0064]

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

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

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

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

[0069] A cell physical parameter collection and real-time analysis module, which collects the physical parameters of each cell in real time through cell-level sensors, and performs preliminary analysis and evaluation to derive the safety level during charging;

[0070] A data comparison and trend analysis module, which determines the health status and attenuation degree of the battery cell and predicts the risks of the battery by performing multi-dimensional comparison and trend analysis of real-time physical parameters and historical data;

[0071] A safety factor and attenuation coefficient comprehensive analysis module, which extracts the safety factor and attenuation coefficient, calculates the comprehensive risk index, and divides the risk level of the charging process by comprehensively analyzing the safety level during the charging process and the attenuation degree of the battery cell health state;

[0072] 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 results, and issues warnings for high-risk charging processes, and makes corresponding charging mode adjustments for low-risk charging processes.

[0073] Beneficial effects of the present invention:

[0074] (1) The charging safety early warning method of the present invention relies on advanced real-time monitoring technology and multi-dimensional data analysis models to comprehensively and accurately evaluate the health status of the battery and the potential safety risks in the charging process. The method monitors the operating status of the battery in real time through detailed cell physical parameter collection and fluctuation coefficient calculation, combined with complex fuzzy logic control and risk index evaluation algorithms, and can accurately identify abnormal conditions that may be caused during the charging process based on the system calculation results. Through such refined real-time data analysis and abnormality identification, the present invention can effectively prevent the battery from causing serious safety accidents such as fire and explosion due to factors such as overheating, short circuit, overcharging, etc., thereby improving the safety of the electric vehicle charging process. Through highly intelligent risk index calculation and comprehensive analysis, the present invention can dynamically and accurately adjust the charging mode according to the safety conditions in different charging processes. This adjustment is not only based on traditional battery status 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 dual protection of 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 improves the charging efficiency by increasing the charging power to avoid unnecessary time waste.

[0075] (2) On the basis of ensuring charging safety, the system optimizes charging efficiency and extends battery life. Through 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 high-power charging mode, thereby greatly increasing 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 dynamically adjust the charging strategy according to the health status of the battery, accurately control various parameters during the charging process, avoid battery degradation or excessive temperature due to over-rapid charging, and effectively delay the battery aging process. This intelligent charging management mechanism not only improves charging efficiency and reduces charging time, but also extends the battery life through meticulous risk control and health management. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] See also Figure 1 As shown, the present invention is a charging safety warning method for the entire life cycle of a vehicle, comprising the following steps:

[0081] S1: Through the cell-level sensors, the physical parameters of a single cell are collected in real time, including voltage, temperature and current, and the real-time physical parameters are analyzed to evaluate the safety level of the vehicle during charging;

[0082] S2: Perform multi-dimensional data comparison and trend analysis on real-time physical parameters and historical records, and determine the degree of attenuation of the health status of the battery cells during the vehicle charging process based on the analysis results;

[0083] S3: Comprehensively analyze the safety level of the vehicle charging process and the attenuation degree of the battery health status during the vehicle charging process, extract the safety factor and attenuation factor of the charging process, calculate the comprehensive risk index based on the safety factor and attenuation factor, and divide the risk level of the charging process according to the comprehensive risk index;

[0084] 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.

[0085] In S1, the physical parameters of a single cell, including voltage, temperature and current, are collected in real time through cell-level sensors. The real-time physical parameters are analyzed to assess the safety level of the vehicle during charging, including:

[0086] The voltage data, temperature data and current data of each battery cell are obtained in real time, and the voltage fluctuation coefficient, temperature fluctuation coefficient and current fluctuation coefficient are calculated based on the voltage data, temperature data and current data of each battery cell. The voltage fluctuation coefficient, temperature fluctuation coefficient and current fluctuation coefficient are processed comprehensively to calculate the safety factor in the vehicle charging process, and whether the vehicle charging process is safe is determined based on the safety factor.

[0087] Wherein, the cell-level sensor includes: a voltage sensor, a temperature sensor and a current sensor;

[0088] The process of acquiring the voltage data, temperature data and current data of each battery cell is as follows:

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

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

[0091] The voltage sensor detects the real-time voltage value at both ends of each battery cell and records the real-time voltage value in time series;

[0092] The surface temperature of each battery cell is detected by a temperature sensor, and the real-time temperature value is recorded in time series;

[0093] The current value flowing through each battery cell is detected by the current sensor, and the real-time current value is recorded in time series;

[0094] The process of obtaining the safety factor is as follows:

[0095] 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);

[0096] Where i represents the number of cells and t represents the time point;

[0097] For a monitoring period, the mean value calculation expression is used to calculate the voltage mean, temperature mean and current mean of each battery cell in the monitoring period, and the standard deviation calculation expression is used to calculate the corresponding voltage standard deviation, temperature standard deviation and current standard deviation;

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

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

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

[0101] Construct a judgment matrix based on 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 importance ratios of voltage, temperature and current;

[0105] Calculating a weight vector, including calculating a normalized judgment matrix, calculating a weight vector according to the normalized judgment matrix, including a voltage weight vector, a temperature weight vector and a temperature weight vector, and calculating a safety factor according to the weight vector;

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

[0107]

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

[0109] comparing the safety factor with a preset threshold;

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

[0111] If the safety factor is less than the preset threshold, it means that the corresponding vehicle charging process is highly safe;

[0112] It should be noted that the safety factor reflects the vehicle charging process. The larger the value of the safety factor of the vehicle charging process, the lower the health status of the battery cell and the lower the corresponding vehicle safety level.

[0113] In S2, real-time physical parameter data and historical records are compared and trended in multiple dimensions, and the degree of attenuation of the health status of the battery cells during the vehicle charging process is determined based on the analysis results, including:

[0114] 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, calculate the attenuation coefficient during vehicle use through multi-dimensional data comparison and trend analysis, and determine the attenuation degree of the battery cell health status during vehicle charging;

[0115] Wherein, the duration of the monitoring cycles is the same;

[0116] 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 Idiff (t);

[0117] Wherein, t represents the time point;

[0118] Calculate the prior probability of the health status of the battery cell, the calculation expression is:

[0119]

[0120] Where N is the total number of cells, N H represents the number of cells with a health status of H, and P(H) represents the prior probability of the health status 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] In the formula, P(H,V diff ,T idff ,I diff ) represents the joint probability;

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

[0126]

[0127] In the formula, 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, and the calculation expression is:

[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 battery cell health status, the attenuation coefficient during vehicle charging is calculated, and the calculation expression is:

[0132]

[0133] Where D represents the attenuation coefficient during vehicle charging;

[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 means that the health state of the corresponding battery cell is worse and the attenuation degree is higher. If the attenuation coefficient is less than the preset threshold, it means that the health state of the corresponding battery 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 of the health status of the battery cells during the vehicle charging process, and the larger the value of the attenuation coefficient, the higher the degree of attenuation of the health status of the corresponding battery cells.

[0136] In S3, the safety level during vehicle charging and the attenuation degree of the health status of the battery cells during vehicle charging are comprehensively analyzed, the safety factor and attenuation factor during charging are extracted, and the comprehensive risk index is calculated based on the safety factor and attenuation factor. Based on the comprehensive risk index, the risk level during charging is divided, including:

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

[0138] Using the triangular membership function as the fuzzy degree 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) represents the membership function;

[0141] Establishing a fuzzy rule base, the rule base is: high risk and low risk;

[0142] Calculate the activation strength, 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 fuzzy set corresponding to the input variable;

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

[0145]

[0146] In the formula, 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] In the formula, R max Indicates the maximum value of 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 a high-risk level and a 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 during vehicle 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 is carried out 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 stop charging: If the risk is serious, the charging process can be automatically stopped to avoid serious consequences such as battery damage or fire;

[0160] Notify the car owner or operating platform: notify the car owner or operating platform through mobile phone applications, text messages or emails, etc., to remind them of the safety risks during the charging process;

[0161] For low-risk charging processes, the charging mode is adjusted to further optimize charging efficiency and extend battery life. The adjustment measures include:

[0162] If the risk of the charging process is low, the system will increase the charging current and voltage, 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 charging environment to avoid overheating or other potential risks caused by charging too quickly.

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

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

[0166] It should be noted that: based on the comprehensive risk index calculated in real time, the charging system can dynamically adjust the charging mode by adjusting the current and voltage to ensure the safety and efficiency of the charging process. When the risk is high, the safety hazard can be reduced by reducing the charging current and voltage, and when the risk is low, the charging efficiency can be improved by increasing the charging current and voltage.

[0167] See also Figure 2 As shown in the figure, the charging safety warning system for the entire life cycle of the vehicle includes:

[0168] A cell physical parameter collection and real-time analysis module, which collects the physical parameters of each cell in real time through cell-level sensors, and performs preliminary analysis and evaluation to derive the safety level during charging;

[0169] A data comparison and trend analysis module, which determines the health status and attenuation degree of the battery cell and predicts the possible risks of the battery by performing multi-dimensional comparison and trend analysis of real-time physical parameters and historical data;

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

[0171] 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 results, and issues warnings for high-risk charging processes, and makes corresponding charging mode adjustments for low-risk charging processes.

[0172] The working principle of the present invention is as follows: the real-time data of each battery cell is accurately collected through the battery cell-level sensor, and multi-dimensional comparison and trend analysis are performed in combination with historical data to determine the health status and attenuation degree of the battery cell; the safety factor and attenuation factor are constructed by calculating the fluctuation coefficients of voltage, temperature and current, and combining the attenuation coefficient of the battery cell health status, and further using advanced algorithms such as fuzzy logic to calculate the comprehensive risk index to evaluate the risk level in the charging process; for high-risk charging processes, the system will issue an early warning and take measures such as reducing the charging current and voltage; in low-risk situations, the charging mode improves the charging efficiency and extends the battery life by optimizing the charging current and voltage.

[0173] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0174] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0175] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0176] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0177] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention are still within the scope of the patent coverage of the present invention.

Claims

1. A charging safety warning method for the entire life cycle of a vehicle, characterized in that: The following steps are involved: S1: Through the cell-level sensors, the physical parameters of a single cell are collected in real time, including voltage, temperature and current, and the real-time physical parameters are analyzed to evaluate the safety level of the vehicle during charging; S2: Perform multi-dimensional data comparison and trend analysis on real-time physical parameters and historical records, and determine the degree of attenuation of the health status of the battery cells during the vehicle charging process based on the analysis results; S3: Comprehensively analyze the safety level of the vehicle charging process and the attenuation degree of the battery health status during the vehicle charging process, extract the safety factor and attenuation factor of the charging process, calculate the comprehensive risk index based on the safety factor and attenuation factor, and divide the risk level of the charging process according to the comprehensive risk index; 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.

2. The charging safety early warning method for the entire life cycle of a vehicle according to claim 1 is characterized in that: The cell-level sensor collects the physical parameters of a single cell in real time, including voltage, temperature and current, analyzes the real-time physical parameters, and evaluates the safety level of the vehicle charging process, including: The voltage data, temperature data and current data of each battery cell are acquired in real time, and the voltage fluctuation coefficient, temperature fluctuation coefficient and current fluctuation coefficient are calculated based on the voltage data, temperature data and current data of each battery cell. The voltage fluctuation coefficient, temperature fluctuation coefficient and current fluctuation coefficient are comprehensively processed to calculate the safety factor of the vehicle charging process, and whether the vehicle charging process is safe is determined based on the safety factor.

3. The charging safety early warning method for the entire life cycle of a vehicle according to claim 2 is characterized in that: The process of obtaining the safety factor is: 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, the mean value calculation expression is used to calculate the voltage mean, temperature mean and current mean of each battery cell in the monitoring period, and the standard deviation calculation expression is used to calculate the corresponding voltage standard deviation, temperature standard deviation and current standard deviation; Calculate the ratio of the voltage mean value of each battery cell to the voltage standard deviation to obtain the voltage fluctuation coefficient Calculate the ratio of the mean temperature of each cell to the temperature standard deviation to obtain the temperature fluctuation coefficient Calculate the ratio of the current mean value of each cell to the current standard deviation to obtain the current fluctuation coefficient Construct a judgment matrix based on voltage fluctuation coefficient, temperature fluctuation coefficient and current fluctuation coefficient; Among them, the calculation expression of the judgment matrix is: In the formula, a VT , a VI and a TI Respectively represent the relative importance ratios of voltage, temperature and current; Calculating a weight vector, including calculating a normalized judgment matrix, calculating a weight vector according to the normalized judgment matrix, including a voltage weight vector, a temperature weight vector and a temperature weight vector, and calculating a safety factor according to the weight vector; Wherein, the calculation expression of the safety factor is: In the formula, S represents the safety factor during vehicle charging, i represents the number of cells, n represents the maximum number of cells, and w represents the maximum number of cells. V represents the voltage weight vector, w T The temperature weight vector and w I Voltage weight vector.

4. The charging safety warning method for the entire life cycle of a vehicle according to claim 2 is characterized in that: The determination of whether the vehicle charging process is safe according to the safety factor specifically includes: It is determined whether the safety factor is greater than or equal to a preset threshold value. 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.

5. The charging safety early warning method for the entire life cycle of a vehicle according to claim 1 is characterized in that: The real-time physical parameter data and historical records are compared and analyzed in multiple dimensions, and the degree of attenuation of the health status of the battery cells during the vehicle charging process is determined based on the analysis results, specifically including: 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 are obtained. Through multi-dimensional data comparison and trend analysis, the attenuation coefficient during vehicle use is calculated. Based on the attenuation coefficient during vehicle use, the attenuation degree of the battery cell health status during vehicle charging is determined.

6. The charging safety warning method for the entire life cycle of a vehicle according to claim 5 is characterized in that: The process of obtaining the attenuation coefficient is: 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); Wherein, t represents the time point; Calculate the prior probability of the health status of the battery cell, the calculation expression is: Where N is the total number of cells, N H represents the number of cells with a health status of H, and P(H) represents the prior probability of the health status of the cell; The conditional probability P(V diff |H), the conditional probability P(T diff |H) and the conditional probability P(I diff |H); Calculate the joint probability, the calculation expression is: P(H,V diff ,Y diff ,I diff )=P(H)*P(V diff |H)*P(T diff |H)*P(I diff |H); In the formula, P(H,V diff ,V diff ,I diff ) represents the joint probability; Calculate the posterior probability, the calculation expression is: In the formula, P(H|V diff ,T diff ,I diff ) represents the posterior probability; According to the posterior probability, the expected value of the cell health status is calculated, and the calculation expression is: 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; According to the expected value of the battery cell health status, the attenuation coefficient during vehicle charging is calculated, and the calculation expression is: Where D represents the attenuation coefficient during vehicle charging.

7. The charging safety warning method for the entire life cycle of a vehicle according to claim 5 is characterized in that: The determination of the attenuation degree of the health status of the battery cell during the vehicle charging process specifically includes: It is determined whether the reduction coefficient is greater than or equal to a preset threshold value. If so, it indicates that the health state of the corresponding battery cell is poor and the attenuation degree is high. If not, it indicates that the health state of the corresponding battery cell is good and the attenuation degree is low.

8. The charging safety early warning method for the entire life cycle of a vehicle according to claim 1 is characterized in that: The process of obtaining the comprehensive risk index is as follows: Obtain the safety factor S and attenuation factor D of the battery cell during vehicle charging; Using triangular membership functions as fuzzy degrees of input variables; Establishing a fuzzy rule base, the rule base is: high risk and low risk; Calculate the activation strength, the calculation expression is: α d =min(μ S (S),μ D (D) 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 fuzzy set corresponding to the input variable; The rule output is fuzzified, and the membership function of the output fuzzy set is: In the formula, 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; Sum all the output fuzzy sets to get the combined membership function μ Rz (R); According to the combined membership function, the comprehensive risk index is calculated, and the calculation expression is: In the formula, R max Indicates the maximum value of risk level, R min It represents the minimum value of risk level, and R represents the comprehensive risk index.

9. The charging safety early warning method for the entire life cycle of a vehicle according to claim 1 is characterized in that: According to the comprehensive risk index, the risk level of the charging process is divided into: Determine whether the comprehensive risk index is greater than or equal to the preset threshold. If so, it is recorded as a high risk level; if not, it is recorded as a low risk level.

10. The charging safety warning system for the entire life cycle of the vehicle is characterized by: A charging safety warning method for the entire life cycle of a vehicle as claimed in any one of claims 1 to 9, comprising: A cell physical parameter collection and real-time analysis module, which collects the physical parameters of each cell in real time through cell-level sensors, and performs preliminary analysis and evaluation to derive the safety level during charging; A data comparison and trend analysis module, which determines the health status and attenuation degree of the battery cell and predicts the risks of the battery by performing multi-dimensional comparison and trend analysis of real-time physical parameters and historical data; A safety factor and attenuation coefficient comprehensive analysis module, which extracts the safety factor and attenuation coefficient, calculates the comprehensive risk index, and divides the risk level of the charging process by comprehensively analyzing the safety level during the charging process and the attenuation degree of the battery cell health state; 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 results, and issues warnings for high-risk charging processes, and makes corresponding charging mode adjustments for low-risk charging processes.

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