Vehicle storage battery abnormity early warning method and device, storage medium and terminal

Abstract

The invention discloses a vehicle storage battery abnormity early warning method and device, a storage medium and a terminal, and the method comprises the steps: collecting state parameter data of a vehicle storage battery in real time, the state parameter data comprising temperature data, SOC data, voltage data and insulation resistance value data; judging whether the state parameter data meets an early warning requirement or not, if so, sending early warning information to the client, and transmitting the state data to a background server, otherwise, transmitting the state data to the background server; wherein the condition that the state parameter data meets the early warning requirement is that the state parameter data meets at least one of the early warning conditions; the early warning condition comprises that the temperature data meets the temperature early warning condition; the SOC data meets an electric quantity early warning condition; the voltage data meets a voltage early warning condition; the insulation resistance value data meets a resistance early warning condition. According to the invention, the voltage of the storage battery of the vehicle can be monitored in real time, and the escape time is bought for passengers to the greatest extent before a fire occurs, so that the safety coefficient is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery warning, and relates to a method for abnormal warning of a vehicle storage battery, and particularly relates to a method and device for abnormal warning of a vehicle storage battery, a storage medium and a terminal. Background Art

[0002] With the continuous progress of technology and the continuous enhancement of environmental awareness, the popularization speed of electric vehicles in society is gradually accelerating. With the increasing number of electric vehicles, the number of disaster accidents of corresponding electric vehicles is also increasing rapidly. With the popularization of electric vehicles and hybrid vehicles, the safety problem of the vehicle battery system has received more and more attention. Especially, how to ensure the safety of personnel and other property in the case of battery spontaneous combustion has become one of the key research points in the industry. At present, there is a problem of battery fire in electric vehicles. After the lithium battery catches fire, due to reasons such as rapid combustion, too short escape time for the driver, and inconvenient fire fighting, basically the vehicle will be completely burned out, and even threaten personal safety.

[0003] For the existing battery fire warning system of new energy vehicles, it often directly checks the relay, fuse or conductive terminal. However, the reaction time left for the vehicle occupants by this warning method is too short, and it can only give a warning two to three seconds in advance, and cannot achieve the purpose of early warning. Therefore, there is an urgent need for a method for abnormal warning of a new energy vehicle battery that can leave sufficient reaction time for the vehicle occupants. Summary of the Invention

[0004] The purpose of the present application is to provide a method and device for abnormal warning of a vehicle storage battery, a storage medium and a terminal, which are used to solve the problem that the abnormal situation of the existing vehicle storage battery cannot be warned in time, endangering personal and property safety.

[0005] In a first aspect, the present application provides a method for abnormal warning of a vehicle storage battery, including:

[0006] Real-time collecting state parameter data of the vehicle storage battery, where the state parameter data includes temperature data, SOC data, voltage data and insulation resistance value data;

[0007] Judging whether the state parameter data meets the warning requirements. If so, sending a warning message to the client and transmitting the state data to the background server; otherwise, transmitting the state data to the background server;

[0008] Wherein, the state parameter data meeting the warning requirements means that the state parameter data meets at least one of the warning conditions; the warning conditions include: the temperature data meets the temperature warning condition; the SOC data meets the power warning condition; the voltage data meets the voltage warning condition; the insulation resistance value data meets the resistance warning condition.

[0009] In an embodiment of the present application, the temperature data includes the highest temperature inside the vehicle battery and the temperature outside the vehicle battery; determining whether the temperature data meets the temperature warning condition includes:

[0010] Determining whether the difference between the highest temperature inside the vehicle battery and the temperature when the vehicle battery is not in use is greater than a preset temperature threshold. If so, continue to determine whether the growth rate of the temperature outside the vehicle battery is greater than a preset temperature growth rate. If so, it means that the temperature data meets the temperature warning condition, otherwise it means that the temperature data does not meet the temperature warning condition.

[0011] In an embodiment of the present application, the SOC data includes the remaining battery power. Determining whether the SOC data meets the power warning condition includes:

[0012] Determining whether the deceleration rate of the remaining battery power is greater than a preset power deceleration rate. If so, based on a preset trend determination method, determine whether the attenuation trend of the remaining battery power in the target time period is divergent. If so, it means that the SOC data meets the power warning condition, otherwise it means that the SOC data does not meet the power warning condition.

[0013] In an embodiment of the present application, the voltage data includes the voltage values of each single cell in the vehicle battery. Determining whether the voltage data meets the voltage warning condition includes:

[0014] Determining whether there is a single cell in the vehicle battery whose voltage value drops by more than a preset amplitude within a preset time interval. If so, continue to determine whether the growth trend of the temperature outside the vehicle battery in the temperature data in the target time period is divergent based on a preset trend determination method. If so, it means that the voltage data meets the voltage warning condition, otherwise it means that the voltage data does not meet the voltage warning condition.

[0015] In an embodiment of the present application, the preset trend determination method is:

[0016] Dividing the data to be determined obtained within the target time period into multiple groups of determination data;

[0017] Obtaining the optimal solution corresponding to each group of determination data through a preset key point optimal solution obtaining method, and determining whether all the optimal solutions meet the divergence condition. If so, it is determined that the trend of the data to be determined in the target time period is divergent, otherwise it is determined that the trend of the data to be determined in the target time period is convergent;

[0018] Among them, obtaining the optimal solution corresponding to the Nth group of determination data through a preset key point optimal solution obtaining method includes:

[0019] Screening the Nth group of determination data based on a preset local model to obtain a key point data set;

[0020] Fit the key point data set to obtain the Nth group of curve expressions;

[0021] Substitute the first group of curve expressions to the Nth group of curve expressions into a preset quadratic function to obtain the Nth total expression function, and find the optimal solution of the Nth total expression function as the optimal solution of the Nth group of determination data;

[0022] Where N is a positive integer, the data to be determined is the external temperature of the vehicle battery in the remaining battery power or temperature data, and the end time of the target time period is the current time.

[0023] In an embodiment of the present application, the preset local model is:

[0024] f(X) = λ T ·x + β

[0025] Where λ and β are conventional parameters. When the data to be determined is the remaining battery power, x represents the remaining battery power, and f(X) represents the voltage corresponding to the remaining battery power; when the data to be determined is the external temperature of the vehicle battery, x represents time, and f(X) represents the external temperature of the vehicle battery;

[0026] The preset quadratic function is:

[0027]

[0028] Where m is the total number of groups of determination data, F(x i , y i ) represents the i-th group of curve expressions.

[0029] In an embodiment of the present application, determining whether the insulation resistance value data meets the resistance warning condition includes:

[0030] Determine whether the insulation resistance value is zero. If so, it means that the insulation resistance value data meets the resistance warning condition; otherwise, it means that the insulation resistance value data does not meet the resistance warning condition.

[0031] In an embodiment of the present application, the vehicle battery abnormal warning method further includes: determining whether the charging times of the battery cells in the vehicle battery exceed the charging threshold. If so, send a maintenance reminder signal to the client.

[0032] In a second aspect, the present application further provides a vehicle battery abnormal warning device, including a data acquisition module and a warning determination module;

[0033] The data acquisition module is used to collect the state parameter data of the vehicle battery in real time, and the state parameter data includes temperature data, SOC data, voltage data and insulation resistance value data;

[0034] The early warning determination module is used to determine whether the status parameter data meets the early warning requirements. If so, it sends an early warning message to the client and transmits the status data to the background server. Otherwise, it transmits the status data to the background server;

[0035] Among them, the status parameter data meeting the early warning requirements means that the status parameter data meets at least one of the early warning conditions; the early warning conditions include: the temperature data meets the temperature early warning condition; the SOC data meets the power early warning condition; the voltage data meets the voltage early warning condition; the insulation resistance value data meets the resistance early warning condition.

[0036] In a third aspect, the present application also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the vehicle battery abnormal early warning method described above.

[0037] In a fourth aspect, the present application also provides a terminal, including: a processor and a memory, and the memory is communicatively connected to the processor;

[0038] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the vehicle battery abnormal early warning method as described above.

[0039] Compared with the prior art, one or more of the above embodiments may have the following advantages or beneficial effects:

[0040] Applying the vehicle battery abnormal early warning method provided by the embodiment of the present invention, by collecting and performing early warning analysis on multi-level data of the internal and external temperature of the vehicle battery, remaining power, the voltage of the battery cells inside the vehicle battery, and the insulation resistance value inside the battery in real time, a comprehensive analysis of the internal and external environment of the battery is realized to enhance the safety factor. Compared with early warning through relays or fuses, etc., it has a faster ability to detect battery abnormalities; at the same time, this embodiment adopts a form of early warning as long as any one of multiple conditions is met, improving the sensitivity of the early warning system and providing more escape time for the people in the vehicle. The present invention also adopts a stepped early warning method for the remaining power data and the voltage value of a single battery cell. That is, when the remaining power data or the voltage value of a certain single battery cell meets the first-level early warning condition, to reduce the false alarm rate, this method also judges the remaining power data or temperature data within a period of time through a trend determination method to further determine whether the data is abnormal through the data trend. This determination method further improves the early warning accuracy. The present invention can monitor the voltage of the vehicle battery in real time, and maximize the escape time for passengers before a fire occurs to improve the safety factor.

[0041] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the specification, claims and drawings. Description of the Drawings

[0042] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0043] Figure 1 It shows a schematic flowchart of the vehicle battery abnormal warning method described in the embodiments of the present application.

[0044] Figure 2 It shows a comparison diagram of the normal temperature curve and the abnormal temperature curve in the vehicle battery abnormal warning method described in the embodiments of the present application.

[0045] Figure 3 It shows a schematic structural diagram of the vehicle battery abnormal warning device described in the embodiments of the present application.

[0046] Figure 4 It shows a schematic structural diagram of the terminal described in the embodiments of the present application. Detailed Embodiments

[0047] The following uses specific specific examples to illustrate the embodiments of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0048] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0049] The following embodiments of the present application provide a vehicle battery abnormal warning method, device, storage medium and terminal. The following will elaborate in detail the principle and implementation manner of a complete application name of this embodiment, so that those skilled in the art can understand the vehicle battery abnormal warning method, device, storage medium and terminal of this embodiment without creative labor.

[0050] As Figure 1 shown, this embodiment provides a method for abnormal warning of vehicle batteries, including the following steps.

[0051] Step S101: Collect the state parameter data of the vehicle battery in real time. The state parameter data includes temperature data, SOC data, voltage data, and insulation resistance value data.

[0052] Specifically, the state parameters of the vehicle battery are collected in real time to obtain the temperature data, SOC data, voltage data, and insulation resistance value data of the vehicle battery. The temperature data includes the highest temperature inside the vehicle battery and the external temperature of the vehicle battery. The temperature data is specifically obtained by temperature sensors arranged inside and outside the vehicle battery. The SOC data includes the remaining battery power data, which can be specifically obtained by reading the built-in battery remaining power display device or battery remaining power collection device on the vehicle, or obtained by other reasonable means. The voltage data includes the voltage values of each single cell in the vehicle battery. The specific voltage data can be obtained by arranging voltage sensors inside the vehicle battery. The insulation resistance value data can also be obtained by resistance detection devices arranged inside the battery.

[0053] When collecting the vehicle battery data in this embodiment, the vehicle can be in a normal driving state or a stationary state, that is, it is ensured that the state parameters in any state of the vehicle can be collected.

[0054] Step S102: Determine whether the state parameter data meets the warning requirements. If so, send a warning message to the client and transmit the state data to the background server. Otherwise, transmit the state data to the background server.

[0055] Specifically, the collected state parameter data is analyzed separately to determine whether the state parameters meet the warning requirements. If so, it means that there is an abnormal condition in the current vehicle battery, and a warning message needs to be sent to the client to give a warning prompt to the passengers in the vehicle. At the same time, the corresponding state data is transmitted to the background server, and the background server stores the data in the historical data memory for subsequent further analysis and review. If it is determined that the collected state parameter data does not meet the warning requirements, that is, it means that the current battery is in a normal state and no warning is required. At this time, only the corresponding state data needs to be transmitted to the background server, and the background server stores the data in the historical data memory.

[0056] The method for judging whether the state parameter data meets the warning requirements is that the state parameter data meets at least one of the warning conditions. The warning conditions include four, specifically: the temperature data meets the temperature warning condition; the SOC data meets the power warning condition; the voltage data meets the voltage warning condition; and the insulation resistance value data meets the resistance warning condition.

[0057] Further, determining whether the temperature data meets the temperature warning condition specifically includes: first, determining whether the temperature difference between the highest temperature inside the vehicle battery and the temperature of the vehicle battery when it is not in use is greater than a preset temperature threshold. The vehicle battery not being in use means that the vehicle battery is neither in a discharging state nor in a charging state. Usually, the temperature of the vehicle battery when it is not in use, which is compared with the highest temperature inside the vehicle battery, is also the temperature of the vehicle battery when it is not in use closest to this determination, so as to avoid the influence of other factors such as the environment. If the temperature difference between the highest temperature inside the vehicle battery and the temperature of the vehicle battery when it is not in use is greater than the preset temperature threshold, it means that the temperature inside the vehicle battery may be too high. At this time, it is necessary to further determine the external temperature increase rate of the vehicle battery, that is, to determine whether the external temperature increase rate of the vehicle battery is greater than the preset temperature increase rate. If so, it means that the current battery is abnormal after double determination, and further, the temperature data meets the temperature warning condition. If it is determined that the temperature difference between the highest temperature inside the vehicle battery and the temperature of the vehicle battery when it is not in use is not greater than the preset temperature threshold or the external temperature increase rate of the vehicle battery is not greater than the preset temperature increase rate, it means that the current battery is not in an abnormal state, that is, the temperature data does not meet the temperature warning condition. Preferably, the preset temperature threshold can be set to 5 degrees Celsius, and the preset temperature increase rate is 5°C / min.

[0058] The reason for the excessively high highest temperature inside the vehicle battery may be as follows: Since lithium dendrites are generated after lithium ions are precipitated, the lithium dendrites will continuously increase during the charge and discharge process. When the number of lithium dendrites reaches a certain value, the lithium dendrites will pierce the battery diaphragm, resulting in an internal short circuit of the battery cell, and then causing battery thermal runaway. At this time, the internal temperature of the battery will reach 50 to 70 degrees Celsius within 5 seconds, affecting the battery life and even causing a fire.

[0059] Further, determining whether the SOC data meets the power warning condition specifically includes: first, determining whether the reduction rate of the remaining battery power collected is greater than the preset power reduction rate. If so, it means that the decrease in the remaining battery power is abnormal. At this time, it is also necessary to determine whether the decay trend of the remaining battery power in the target time period is divergent based on the preset trend determination method. If so, it can be determined that the current battery is in an abnormal state, that is, the SOC data meets the power warning condition. If the reduction rate of the remaining battery power is not greater than the preset power reduction rate or the decay trend of the remaining battery power in the target time period is not divergent, it means that the current battery state is normal, that is, the SOC data does not meet the power warning condition. Preferably, the preset power reduction rate can be set to 20% per second of the overall power. The end time of the above target time period is set as the current time, which can ensure that the selected target time period is a period of time before the current time, avoiding inaccurate trend judgment caused by selecting a too far time period. The start time of the target time period can be set based on the time length of the target time period, and no fixed limit is imposed on it here.

[0060] Furthermore, vehicle abnormal conditions can be specifically displayed through the battery usage. Further, since the change form of the collected remaining power data is related to the usage of the vehicle battery, it is sometimes difficult to determine whether there is a real abnormality in the vehicle battery only from the remaining power data within a certain period of time. In this embodiment, usually taking the current time point as the time node, a previous period of time is used as the target time period, and then based on a preset trend determination method, the decay trend of the remaining battery power in this time period is determined as a further determination condition for whether the battery is abnormal.

[0061] Determining whether the decay trend of the remaining battery power in the target time period diverges based on the preset trend determination method specifically includes: dividing the battery remaining power data obtained in the target time period into multiple groups of determination data, and then processing each group of determination data through a preset optimal solution acquisition method for key points to obtain the optimal solution corresponding to each group of judgment data. Finally, based on all the obtained optimal solutions, it is determined whether the decay trend of the remaining battery power in the target time period diverges, that is, it is determined whether all the optimal solutions meet the divergence condition. If so, it is determined that the trend of the data to be determined in the target time period diverges, otherwise it is determined that the trend of the data to be determined in the target time period converges. The divergence condition can be set according to the actual situation and will not be elaborated here too much.

[0062] The specific process of obtaining the optimal solution corresponding to the Nth group of determination data through the preset optimal solution acquisition method for key points is as follows: screening the Nth group of determination data based on the preset local model to obtain the corresponding key point data set; further, it is determined whether the battery remaining power and its corresponding capacitance data in the Nth group of determination data meet the preset local model. If they meet, the corresponding battery remaining power is identified as key point data, otherwise the corresponding battery remaining power is not identified as key point data. By analogy, all key point data in the Nth group of determination data are obtained to form a key point data set. Next, the data in the key point data set are fitted to obtain the curve expression corresponding to the Nth group. Based on the curve expressions obtained in the process of obtaining the optimal solutions of other groups of determination data, the curve expressions from the 1st group to the Nth group are all substituted into the preset quadratic function, and the curve expressions from the 1st group to the Nth group are integrated according to the preset quadratic function method to obtain the Nth total expression function. Finally, the optimal solution of the Nth total expression function is obtained as the optimal solution of the Nth group of determination data. Preferably, in the above process, the battery remaining power in the target time period and its corresponding voltage data can form a corresponding curve as a reference curve.

[0063] The preset local model should select the charge model in the battery charging and discharging process. Specifically, the local model here can be set as:

[0064] f(X) = λ T ·x + β

[0065] Among them, λ and β are conventional parameters. When the data to be determined is the remaining battery power, x represents the remaining battery power, and f(X) represents the voltage corresponding to the remaining battery power. The screening process for a certain remaining battery power in the Nth group of determination data based on the preset local model is as follows: taking this remaining battery power as the actual remaining battery power, taking the voltage corresponding to the actual remaining battery power as x and inputting it into the above local model to obtain the output remaining battery power, and determining whether the output remaining battery power is the same as the actual remaining battery power. If so, it is determined that this remaining battery power is key point data; otherwise, it is determined that this remaining battery power is not key point data.

[0066] The preset quadratic function can be set as:

[0067]

[0068] Among them, m is the total number of groups of determination data, and F(x i ,y i ) represents the curve expression of the ith group.

[0069] Furthermore, determining whether the voltage data meets the voltage warning condition specifically includes: respectively determining the voltage values of all single cells in the vehicle battery, that is, determining whether the voltage value of a single cell drops by more than the preset amplitude value within the preset time interval. If there is a voltage value of a single cell that drops by more than the preset amplitude value within the preset time interval, it indicates that the battery may be abnormal; at this time, further abnormal determination needs to be made based on the temperature data, that is, determining whether the growth trend of the external temperature of the vehicle battery in the temperature data within the target time period is divergent based on the preset trend determination method. If it is determined that the external temperature of the vehicle battery in the temperature data within the target time period is divergent, it indicates that the current vehicle battery is abnormal, that is, the voltage data meets the voltage warning condition; if the voltage values of all single cells in the vehicle battery do not meet the condition that the voltage drops by more than the preset amplitude value within the preset time interval, or it is determined that the external temperature of the vehicle battery in the temperature data within the target time period is not divergent, it indicates that all single cells in the current vehicle battery are normal, that is, the voltage data does not meet the voltage warning condition. Preferably, the preset amplitude value within the preset time interval can be set to 10% or more of the voltage drop within 3s.

[0070] It should be noted that the above process of determining the voltage values of all individual battery cells in the vehicle battery can also be changed to a process of determining the temperature values of all individual battery cells in the vehicle battery, that is, changing the voltage sensor installed inside the battery to a temperature sensor to collect the temperature values of all individual battery cells in the battery, and then determining whether the temperature values of each individual battery cell decrease by a preset temperature value within a preset time. The specific process of obtaining and determining the temperature values of individual battery cells is the same as that of obtaining and determining the voltage values of individual battery cells, and will not be elaborated here. Preferably, the preset temperature value within the preset time interval can be set to 10% or more of the temperature drop within 3s.

[0071] Further, Figure 2 It is shown as a comparison chart of the normal temperature curve and the abnormal temperature curve in the vehicle battery abnormal warning method described in the embodiment of the present application. Since the change form of the collected voltage data is related to the usage of the vehicle battery, it is sometimes difficult to determine whether the vehicle battery is truly abnormal only from the voltage data within a certain period of time. Therefore, in this embodiment, usually taking the current time point as the time node, a previous period of time is used as the target time period, and then based on a preset trend determination method, the trend of the external temperature data of the vehicle battery in this time period is determined as a further determination condition for whether the battery is abnormal.

[0072] Determining whether the growth trend of the external temperature of the vehicle battery in the target time period is divergent based on the preset trend determination method specifically includes: dividing the external temperature data of the vehicle battery obtained in the target time period into multiple groups of determination data, and then processing each group of determination data through a preset key point optimal solution obtaining method to obtain the optimal solution corresponding to each group of judgment data. Finally, based on all the obtained optimal solutions, it is judged whether the growth trend of the external temperature data of the vehicle battery in the target time period is divergent, that is, it is judged whether all the optimal solutions meet the divergence condition. If so, it is determined that the trend of the data to be determined in the target time period is divergent, otherwise it is determined that the trend of the data to be determined in the target time period is convergent. The divergence condition can be set according to the actual situation and will not be elaborated here too much.

[0073] The specific process of obtaining the optimal solution corresponding to the Nth group of judgment data through the preset optimal solution acquisition method for key points is as follows: Based on the preset local model corresponding to the Nth group of judgment data, screening is performed to obtain the corresponding key point data set. Further, it is determined whether the external temperature data of the vehicle battery and the corresponding time in the Nth group of judgment data satisfy the preset local model. If satisfied, the corresponding external temperature data of the vehicle battery is recognized as key point data; otherwise, the corresponding external temperature data of the vehicle battery is recognized as non-key point data. By analogy, all key point data in the Nth group of judgment data is obtained to form a key point data set. Next, the data in the key point data set is fitted to obtain the curve expression corresponding to the Nth group. Based on the curve expressions obtained in the process of obtaining the optimal solutions of other groups of judgment data, the first group of curve expressions to the Nth group of curve expressions are all substituted into the preset quadratic function, and the first group of curve expressions to the Nth group of curve expressions are integrated according to the preset quadratic function method to obtain the Nth total expression function. Finally, the optimal solution of the Nth total expression function is obtained as the optimal solution of the Nth group of judgment data. Preferably, in the above process, the external temperature data of the vehicle battery within the target time period and its corresponding time can form a corresponding curve as a reference curve.

[0074] Among them, the preset local model should select the temperature model during the battery charging and discharging process. Specifically, the local model here can be set as:

[0075] f(X) = λ T ·x + β

[0076] Among them, λ and β are conventional parameters. When the data to be judged is the external temperature data of the vehicle battery, x represents time, and f(X) represents the external temperature of the vehicle battery. The screening process for a certain external temperature of the vehicle battery in the Nth group of judgment data based on the preset local model is as follows: Taking this external temperature of the vehicle battery as the actual external temperature of the vehicle battery, and taking the time corresponding to the actual external temperature of the vehicle battery as x and inputting it into the above local model to obtain the output external temperature of the vehicle battery. It is judged whether the output external temperature of the vehicle battery is the same as the actual external temperature of the vehicle battery. If so, it is determined that this external temperature of the vehicle battery is key point data; otherwise, it is recognized that this external temperature of the vehicle battery is non-key point data.

[0077] The preset quadratic function can be set as:

[0078]

[0079] Among them, m is the total number of groups of judgment data, F(x i , y i ) represents the curve expression of the i-th group.

[0080] Further, determining whether the insulation resistance value data meets the resistance warning condition specifically includes: determining whether the collected insulation resistance value is zero. If so, it can be directly determined that the insulation resistance value data meets the resistance warning condition; otherwise, it indicates that the insulation resistance value data does not meet the resistance warning condition.

[0081] If there are burrs or impurities in some single cells inside the storage battery and the process is not up to standard, it means that there is a problem of cell inconsistency inside the storage battery. Since there are single cells with different working efficiencies inside the storage battery, it may accelerate the aging of some cells, and then cause a short circuit and fire. To avoid this situation, the present invention also sets step S103.

[0082] Step S103: Determine whether the charging times of the cells in the vehicle storage battery exceed the charging threshold. If so, send a maintenance reminder signal to the client; otherwise, do not send a maintenance reminder signal to the client.

[0083] Since the charging and discharging of the batteries inside the storage battery are carried out simultaneously, it is only necessary to determine whether the cycle times of the cells inside the storage battery are greater than 500 times. If so, a maintenance reminder signal needs to be sent to the client to remind the client to replace or maintain the vehicle storage battery as soon as possible; otherwise, there is no need to send a maintenance reminder signal to the client.

[0084] In this embodiment, if a warning message is received when the vehicle is in a driving state, the passengers inside the vehicle can turn on the hazard lights and sound the fire alarm, cut off the power supply, and at the same time open the seat belts and doors. If a warning message is received when the vehicle is in a stationary state, the passengers inside the vehicle can cut off the power supply and at the same time open the seat belts and doors to escape.

[0085] The vehicle storage battery abnormal warning method of the present invention has a low price cost and is easy to judge. The judgment method is fast, sensitive and accurate, provides a longer escape time for the passengers inside the vehicle when a fire occurs, can monitor the voltage of the vehicle storage battery in real time, ensure that the storage battery voltage is within the normal range, and at the same time avoid the problems of reduced battery function and shortened service life caused by long-term discharge of the storage battery.

[0086] As Figure 3 shown, this embodiment provides a vehicle storage battery abnormal warning device, including a data acquisition module and a warning judgment module.

[0087] The data acquisition module is used to collect the state parameter data of the vehicle storage battery in real time. The state parameter data includes temperature data, SOC data, voltage data and insulation resistance value data.

[0088] The warning judgment module is used to judge whether the state parameter data meets the warning requirements. If so, send a warning message to the client and transmit the state data to the background server; otherwise, transmit the state data to the background server.

[0089] Among them, the status parameter data conforming to the warning requirements means that the status parameter data meets at least one of the warning conditions; the warning conditions include: the temperature data meets the temperature warning condition; the SOC data meets the power warning condition; the voltage data meets the voltage warning condition; the insulation resistance value data meets the resistance warning condition.

[0090] The vehicle battery abnormal warning device of the present invention has a low price and is easy to judge. The judgment method is fast, sensitive and accurate. When a fire occurs, it provides a longer escape time for the passengers in the vehicle. It can monitor the vehicle battery voltage in real time, ensure that the battery voltage is within the normal range, and at the same time avoid the problems of battery function decline and shortened life caused by long-term battery discharge.

[0091] The embodiment of the present application also provides a computer-readable storage medium. Those of ordinary skill in the art can understand that all or part of the steps in implementing the above embodiment method can be completed by instructing a processor through a program. The program can be stored in the computer-readable storage medium. The storage medium is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof. The above storage medium can be any available medium that the computer can access or a data storage device such as a server or a data center that integrates one or more available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid-state disk (SSD)).

[0092] As Figure 4 shown, the embodiment of the present application provides a terminal.

[0093] The terminal of this embodiment includes a processor and a memory connected to each other; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the terminal can implement all or part of the steps in the above embodiment method when executed.

[0094] The beneficial effects of all or part of the steps of the above embodiment method are the same as those obtained by applying the terminal provided by the embodiment of the present invention, and will not be elaborated here.

[0095] It should be noted that the memory may include a Random Access Memory (RAM), and may also include non-volatile memory, such as at least one disk memory. Similarly, the processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0096] Although the embodiments disclosed in the present invention are as described above, the content described is only an embodiment for facilitating the understanding of the present invention and is not intended to limit the present invention. Any person skilled in the art within the technical field to which the present invention pertains may make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed by the present invention. However, the protection scope of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A method for abnormal warning of vehicle storage batteries, comprising: Collecting in real time the state parameter data of the vehicle storage battery, where the state parameter data includes temperature data, SOC data, voltage data, and insulation resistance value data; Judging whether the state parameter data meets the warning requirements. If so, sending a warning message to the client and transmitting the state data to the background server; otherwise, transmitting the state data to the background server; Among them, the state parameter data meeting the warning requirements means that the state parameter data meets at least one of the warning conditions; the warning conditions include: the temperature data meets the temperature warning condition; the SOC data meets the power warning condition; the voltage data meets the voltage warning condition; the insulation resistance value data meets the resistance warning condition.

2. The method according to claim 1, wherein The temperature data includes the highest temperature inside the vehicle storage battery and the temperature outside the vehicle storage battery; judging whether the temperature data meets the temperature warning condition includes: Judging whether the difference between the highest temperature inside the vehicle storage battery and the temperature when the vehicle storage battery is not in use is greater than a preset temperature threshold. If so, continue to judge whether the growth rate of the temperature outside the vehicle storage battery is greater than a preset temperature growth rate. If so, it means that the temperature data meets the temperature warning condition; otherwise, it means that the temperature data does not meet the temperature warning condition.

3. The method according to claim 1, wherein The SOC data includes the remaining battery power. Judging whether the SOC data meets the power warning condition includes: Judging whether the deceleration rate of the remaining battery power is greater than a preset power deceleration rate. If so, judging whether the decay trend of the remaining battery power in the target time period is divergent based on a preset trend determination method. If so, it means that the SOC data meets the power warning condition; otherwise, it means that the SOC data does not meet the power warning condition.

4. The method according to claim 1, characterized in that The voltage data includes the voltage value of each single cell in the vehicle storage battery. Judging whether the voltage data meets the voltage warning condition includes: Judging whether there is a single cell voltage value in the vehicle storage battery whose decrease amplitude within a preset time interval is greater than a preset amplitude value. If so, continue to judge whether the growth trend of the temperature outside the vehicle storage battery in the temperature data in the target time period is divergent based on a preset trend determination method. If so, it means that the voltage data meets the voltage warning condition; otherwise, it means that the voltage data does not meet the voltage warning condition.

5. The method according to claim 3 or 4, characterized in that, The preset trend determination method is: Dividing the data to be determined obtained within the target time period into multiple groups of determination data; Obtaining the optimal solution corresponding to each group of determination data through a preset key point optimal solution obtaining method, and judging whether all the optimal solutions meet the divergence condition. If so, determining that the trend of the data to be determined within the target time period is divergent; otherwise, determining that the trend of the data to be determined within the target time period is convergent; Among them, obtaining the optimal solution corresponding to the Nth group of determination data through a preset key point optimal solution obtaining method includes: Screening the Nth group of determination data based on a preset local model to obtain a key point data set; Fitting the key point data set to obtain the Nth group of curve expressions; Substitute the curve expressions from the first group to the Nth group into the preset quadratic function to obtain the Nth overall expression function, and find the optimal solution of the Nth overall expression function as the optimal solution of the Nth group of determination data; where N is a positive integer, the data to be determined is the external temperature of the vehicle battery in the remaining battery power or temperature data, and the end time of the target time period is the current time.

6. The method according to claim 5, wherein The preset local model is: f(X) = λ T ·x + β where λ and β are conventional parameters. When the data to be determined is the remaining battery power, x represents the remaining battery power, and f(X) represents the voltage corresponding to the remaining battery power; when the data to be determined is the external temperature of the vehicle battery, x represents time, and f(X) represents the external temperature of the vehicle battery; The preset quadratic function is: where m is the total number of groups of determination data, F(x i , y i ) represents the curve expression of the i-th group.

7. The method according to claim 1, wherein Judging whether the insulation resistance value data meets the resistance warning condition includes: Judging whether the insulation resistance value is zero. If so, it means that the insulation resistance value data meets the resistance warning condition; otherwise, it means that the insulation resistance value data does not meet the resistance warning condition.

8. The method according to claim 1, wherein It further includes: Judging whether the charging times of the battery cells in the vehicle battery exceed the charging threshold. If so, send a maintenance reminder signal to the client; otherwise, do not send a maintenance reminder signal to the client.

9. An abnormal warning device for a vehicle battery, characterized in that, It includes a data acquisition module and a warning determination module; The data acquisition module is used to collect the status parameter data of the vehicle battery in real time. The status parameter data includes temperature data, SOC data, voltage data, and insulation resistance value data; The warning determination module is used to judge whether the status parameter data meets the warning requirements. If so, send a warning message to the client and transmit the status data to the background server; otherwise, transmit the status data to the background server; where the status parameter data meeting the warning requirements means that the status parameter data meets at least one of the warning conditions; the warning conditions include: the temperature data meets the temperature warning condition; the SOC data meets the power warning condition; the voltage data meets the voltage warning condition; the insulation resistance value data meets the resistance warning condition.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the vehicle battery abnormal warning method described in any one of claims 1 to 8.

11. A terminal, characterized in that, It includes: A processor and a memory, and the memory is communicatively connected to the processor; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the terminal executes the vehicle battery abnormal warning method described in any one of claims 1 to 8.

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