Core component fault detection system for new energy vehicles

Abstract

The invention relates to the field of new energy vehicles, and in particular to a core component fault detection system suitable for new energy vehicles, which is used to solve the problems that the existing new energy vehicle fault detection method has high cost and is not convenient for real-time detection of the operating status of core components, and the detection is not comprehensive and the accuracy is not high, resulting in the inability to ensure the safety of new energy vehicles. The system comprises the following modules: an accuracy monitoring module, an information analysis module, an abnormality judgment module, a fault alarm module and a fault detection module. The system ensures the accuracy of fault detection and reduces false alarms and missed alarms by real-time monitoring of sensor accuracy, and also adopts multi-faceted data collection and analysis to achieve in-depth fault detection of core components, improve the efficiency and timeliness of fault detection, comprehensively evaluate the vehicle health status and timely warn of potential faults, and effectively enhance the safety and reliability of new energy vehicles.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicles, and in particular to a core component fault detection system suitable for new energy vehicles. Background Art

[0002] With the global energy structure transformation and the popularization of environmental protection concepts, new energy vehicles are developing rapidly and have become the mainstream direction of the future development of the automotive industry. The performance and life of the core components of new energy vehicles, such as batteries, motors, electronic control systems, and brake systems, are directly related to the safety, reliability, and mileage of the vehicle. However, due to the complexity of new energy vehicle technology, the failure of core components is highly hidden and difficult to predict and diagnose in advance. Once a vehicle fails, it will not only bring safety hazards to the driver, but also cause huge economic losses.

[0003] Existing fault detection methods for new energy vehicles mainly rely on regular professional offline tests, which are costly and inconvenient for real-time detection of the operating status of core components. In addition, the detection is not comprehensive and the accuracy is not high. It is impossible to accurately and timely find faulty core components of new energy vehicles, resulting in the inability to ensure the safety of new energy vehicles. In order to meet the challenges of core component failures in new energy vehicles, a more accurate, real-time and efficient core component fault detection system suitable for new energy vehicles is urgently needed. Summary of the invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a core component fault detection system suitable for new energy vehicles, which solves the problems that the existing new energy vehicle fault detection methods are costly and inconvenient for real-time detection of the operating status of core components, and the detection is not comprehensive and the accuracy is not high, resulting in the inability to ensure the safety of new energy vehicles.

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

[0006] The core component fault detection system applicable to new energy vehicles includes:

[0007] A fault detection module, configured to obtain fault detection parameters of the detection component i after receiving the fault detection instruction, and send the fault detection parameters to the information analysis module; wherein the fault detection parameters include deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY;

[0008] An information analysis module is used to obtain a fault detection coefficient GZi according to the fault detection parameters, and send the fault detection coefficient GZi to the abnormality judgment module;

[0009] The specific process of the information analysis module obtaining the fault detection coefficient GZi is as follows:

[0010] The deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY are normalized, and the values ​​of the deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY are quantized according to a preset fault detection model to obtain a fault detection coefficient GZi;

[0011] Among them, the fault detection model is as follows: Where:

[0012] λ is the preset error adjustment factor, which is set to 0.925;

[0013] e is a mathematical constant;

[0014] g1, g2, g3 and g4 are preset weight factors corresponding to the set deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY respectively, g1, g2, g3 and g4 satisfy g1>g2>g3>g4>1.891, g1=3.66, g2=2.93, g3=2.45, g4=2.01;

[0015] An abnormality judgment module is used to generate a component fault instruction according to the fault detection coefficient GZi, and send the component fault instruction to the fault alarm module;

[0016] The fault alarm module is used to sound a component fault alarm bell after receiving a component fault instruction.

[0017] As a further solution of the present invention: the specific process of the fault detection module obtaining the fault detection parameters is as follows:

[0018] After receiving the fault detection instruction, all core components of the new energy vehicle are marked as detection components i in sequence, where i=1, ..., m, where m is a positive integer, and i represents the number of the detection component, and m represents the total number of the detection components;

[0019] The volume of the detection component i when it is first used and the volume at the current moment are obtained, and the difference between the two is obtained, and it is marked as the volume difference value TC. The total area of ​​all surfaces of the detection component i when it is first used and the total area of ​​all surfaces at the current moment are obtained, and the difference between the two is obtained, and it is marked as the surface difference value MC. The volume difference value TC and the surface difference value MC are quantified, and the values ​​of the volume difference value TC and the surface difference value MC are extracted, and they are substituted into the formula for calculation. According to the formula Obtain deformation information BX; wherein π is a mathematical constant, c1 and c2 are preset proportional coefficients corresponding to the set volume difference value TC and surface difference value MC, respectively, c1 and c2 satisfy c1+c2=1, 0<c2<c1<1, and c1=0.59 and c2=0.41;

[0020] Obtain the number of surface cracks and the total length of cracks of the detection component i at the current moment, and mark them as the grain value WS and the grain length value WC respectively, use the grain value WS and the grain length value WC as the lengths of the two diagonals of the diamond to draw a diamond figure, obtain the perimeter of the diamond figure, and mark it as damage information SS;

[0021] Obtain the number of vibrations and the maximum vibration displacement of the detection component i per unit time, and mark them as vibration value ZS and vibration displacement value ZY respectively, quantify the vibration value ZS and vibration displacement value ZY, extract the values ​​of the vibration value ZS and vibration displacement value ZY, and substitute them into the formula for calculation. Obtain vibration information ZD; wherein z1 and z2 are preset proportional coefficients corresponding to the set vibration value ZS and vibration displacement value ZY, respectively, z1 and z2 satisfy z1+z2=1, 0<z1<z2<1, and z1=0.33 and z2=0.67;

[0022] Get the real-time temperature and real-time sound intensity of the detection component i, and mark them as temperature value WD and sound value SY respectively, quantify the temperature value WD and sound value SY, extract the values ​​of the temperature value WD and sound value SY, and substitute them into the formula for calculation. The temperature and sound information WY is obtained; wherein π and e are mathematical constants, w1 and w2 are preset proportional coefficients corresponding to the set temperature value WD and the sound value SY, respectively, w1 and w2 satisfy w1+w2=1, 0<w1<w2<1, and w1=0.31 and w2=0.69 are taken;

[0023] The deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY are sent to the information analysis module.

[0024] As a further solution of the present invention: the specific process of the abnormality judgment module generating a component fault instruction is as follows:

[0025] The fault detection coefficient GZi is compared with the preset fault detection threshold GZy, and the comparison results are as follows:

[0026] If the fault detection coefficient GZi≥fault detection threshold GZy, a component fault instruction is generated and sent to the fault alarm module.

[0027] As a further solution of the present invention: the core component fault detection system applicable to new energy vehicles also includes:

[0028] The accuracy monitoring module is used to obtain the accuracy monitoring parameters of the monitoring sensor o and send the accuracy monitoring parameters to the information analysis module; wherein the accuracy monitoring parameters include collection information CJ and usage information SY.

[0029] As a further solution of the present invention: the specific process of the accuracy monitoring module obtaining the accuracy monitoring parameters is as follows:

[0030] All sensors for fault detection of core components of new energy vehicles are marked as monitoring sensors o, o=1, ..., v, v is a positive integer, and o represents the number of the monitoring sensor, and v represents the total number of monitoring sensors;

[0031] Get the start time and completion time of each data collection of the monitoring sensor o per unit time, obtain the time difference between the two, and mark it as the sampling time value CS, get the average value of all the sampling time values ​​CS, and mark it as the sampling time mean CSj, get the difference between the maximum sampling time value CS and the minimum sampling time value CS, and mark it as the sampling time difference CSc, quantify the sampling time mean CSj and the sampling time difference CSc, extract the values ​​of the sampling time mean CSj and the sampling time difference CSc, and substitute them into the formula for calculation. The acquisition information CJ is obtained; wherein, e is a mathematical constant, a1 and a2 are respectively the preset proportional coefficients corresponding to the set acquisition time mean value CSj and the acquisition time difference value CSc, a1 and a2 satisfy a1+a2=1, 0<a2<a1<1, and a1=0.76 and a2=0.24;

[0032] Get the production time and current time of monitoring sensor o, obtain the time difference between the two, and mark it as the production value SC, get the total operating time of monitoring sensor o, and mark it as the operating value YX, quantify the production value SC and the operating value YX, extract the values ​​of the production value SC and the operating value YX, and substitute them into the formula for calculation. Obtain usage information SY; wherein s1 and s2 are preset proportional coefficients corresponding to the set production value SC and the operation value YX, respectively, s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and s1=0.28, s2=0.72;

[0033] The collected information CJ and the used information SY are sent to the information analysis module.

[0034] As a further solution of the present invention: the information analysis module is also used to obtain the accuracy monitoring coefficient JDo according to the accuracy monitoring parameters, and send the accuracy monitoring coefficient JDo to the abnormality judgment module.

[0035] As a further solution of the present invention: the specific process of the information analysis module obtaining the accuracy monitoring coefficient JD0 is as follows:

[0036] The collected information CJ and the used information SY are normalized, and the values ​​of the collected information CJ and the used information SY are quantified according to a preset accuracy monitoring model to obtain an accuracy monitoring coefficient JDo;

[0037] Among them, the accuracy monitoring model is as follows: Where:

[0038] κ is the preset error adjustment factor, taking κ=0.981;

[0039] e and π are both mathematical constants;

[0040] d1 and d2 are respectively the preset weight factors corresponding to the set collection information CJ and the usage information SY, d1 and d2 satisfy d1>d2>2.517, d1=3.53, d2=2.85;

[0041] The accuracy monitoring coefficient JDo is sent to the abnormality judgment module.

[0042] As a further solution of the present invention: the abnormality judgment module is also used to generate a sensor fault instruction or a fault detection instruction according to the accuracy monitoring coefficient JDo, and send the sensor fault instruction to the fault alarm module, and send the fault detection instruction to the fault detection module.

[0043] As a further solution of the present invention: the specific process of the abnormality judgment module generating a sensor fault instruction or a fault detection instruction is as follows:

[0044] The accuracy monitoring coefficient JDo is compared with the preset accuracy monitoring threshold JDy. The comparison results are as follows:

[0045] If the accuracy monitoring coefficient JDo ≥ the accuracy monitoring threshold JDy, the monitoring sensor o corresponding to the accuracy monitoring coefficient JDo is marked as an abnormal sensor;

[0046] If the number of abnormal sensors is ≥1, a sensor fault instruction is generated and sent to the fault alarm module;

[0047] If the number of abnormal sensors = 0, a fault detection instruction is generated and sent to the fault detection module.

[0048] As a further solution of the present invention: the fault alarm module is also used to sound a sensor fault alarm ring after receiving a sensor fault instruction.

[0049] Beneficial effects of the present invention:

[0050] The core component fault detection system applicable to new energy vehicles of the present invention first monitors the accuracy of monitoring sensors for data collection to obtain accuracy monitoring parameters, and then uses data analysis technology to obtain an accuracy monitoring coefficient based on the accuracy monitoring parameters to comprehensively measure the accuracy deviation of the monitoring sensors, and the larger the accuracy monitoring coefficient, the higher the accuracy deviation. When the data collection accuracy of all monitoring sensors meets the requirements, the core components of the new energy vehicle are fault-detected to obtain fault detection parameters, and then uses data analysis technology to obtain a fault detection coefficient based on the fault detection parameters to comprehensively measure the fault degree of the core components, and the larger the fault detection coefficient, the higher the fault degree. Finally, a fault alarm is issued when a fault occurs.

[0051] The core component fault detection system suitable for new energy vehicles of the present invention ensures the accuracy of fault detection and reduces false alarms and missed alarms by real-time monitoring of sensor accuracy. It also adopts multi-faceted data collection and analysis to achieve in-depth fault detection of core components, improve the efficiency and timeliness of fault detection, and can comprehensively evaluate the health status of the vehicle and timely warn of potential faults, effectively enhance the safety and reliability of new energy vehicles, and provide car owners with a safer and more stable driving experience. At the same time, it also provides data support and optimized decision-making basis for vehicle maintenance, effectively promotes the development and progress of the new energy vehicle industry, and achieves the goals of energy conservation, emission reduction and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below in conjunction with the accompanying drawings.

[0053] Figure 1 It is a principle block diagram of the core component fault detection system applicable to new energy vehicles in the present invention;

[0054] Figure 2 It is a flow chart of the working method of the core component fault detection system applicable to new energy vehicles in the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with 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.

[0056] Example 1: Please refer to Figure 1 As shown, this embodiment is a core component fault detection system suitable for new energy vehicles, including the following modules: an accuracy monitoring module, an information analysis module, an abnormality judgment module, a fault alarm module and a fault detection module;

[0057] The precision monitoring module is used to obtain the precision monitoring parameters of the monitoring sensor o and send the precision monitoring parameters to the information analysis module; wherein the precision monitoring parameters include the collection information CJ and the use information SY;

[0058] The information analysis module is used to obtain the accuracy monitoring coefficient JDo according to the accuracy monitoring parameters, and send the accuracy monitoring coefficient JDo to the abnormality judgment module; it is also used to obtain the fault detection coefficient GZi according to the fault detection parameters, and send the fault detection coefficient GZi to the abnormality judgment module;

[0059] The abnormality judgment module is used to generate a sensor fault instruction or a fault detection instruction according to the accuracy monitoring coefficient JDo, and send the sensor fault instruction to the fault alarm module, and send the fault detection instruction to the fault detection module; it is also used to generate a component fault instruction according to the fault detection coefficient GZi, and send the component fault instruction to the fault alarm module;

[0060] The fault alarm module is used to sound a sensor fault alarm bell after receiving a sensor fault instruction; and is also used to sound a component fault alarm bell after receiving a component fault instruction;

[0061] Among them, the fault detection module is used to obtain the fault detection parameters of the detection component i after receiving the fault detection instruction, and send the fault detection parameters to the information analysis module; wherein the fault detection parameters include deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY.

[0062] Example 2: Please refer to Figure 2 As shown, this embodiment is a working method of a core component fault detection system applicable to new energy vehicles, comprising the following steps:

[0063] Step 1: The accuracy monitoring module obtains the accuracy monitoring parameters of the monitoring sensor o, wherein the accuracy monitoring parameters include the acquisition information CJ and the usage information SY, and sends the accuracy monitoring parameters to the information analysis module;

[0064] Step 2: The information analysis module obtains the accuracy monitoring coefficient JDo according to the accuracy monitoring parameters, and sends the accuracy monitoring coefficient JDo to the abnormality judgment module;

[0065] Step 3: The abnormality judgment module generates a sensor fault instruction or a fault detection instruction according to the accuracy monitoring coefficient JDo, and sends the sensor fault instruction to the fault alarm module and sends the fault detection instruction to the fault detection module;

[0066] Step 4: After receiving the sensor fault command, the fault alarm module sounds a sensor fault alarm bell;

[0067] Step 5: After receiving the fault detection instruction, the fault detection module obtains the fault detection parameters of the detection component i, wherein the fault detection parameters include deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY, and sends the fault detection parameters to the information analysis module;

[0068] Step 6: The information analysis module obtains the fault detection coefficient GZi according to the fault detection parameters, and sends the fault detection coefficient GZi to the abnormality judgment module;

[0069] Step 7: The abnormality judgment module generates a component fault instruction according to the fault detection coefficient GZi, and sends the component fault instruction to the fault alarm module;

[0070] Step 8: After receiving the component failure instruction, the fault alarm module sounds a component failure alarm bell.

[0071] Embodiment 3: Based on any of the above embodiments, Embodiment 3 of the present invention is an accuracy monitoring module. The function of the accuracy monitoring module is to obtain accuracy monitoring parameters, wherein the accuracy monitoring parameters include acquisition information CJ and usage information SY. The specific process is as follows:

[0072] The accuracy monitoring module marks all sensors for fault detection of core components of new energy vehicles as monitoring sensors o, o=1, ..., v, v is a positive integer, and o represents the number of the monitoring sensor, and v represents the total number of monitoring sensors;

[0073] The accuracy monitoring module obtains the start time and completion time of each data collection of the monitoring sensor o per unit time, obtains the time difference between the two, and marks it as the sampling time value CS, obtains the average value of all sampling time values ​​CS, and marks it as the sampling time mean CSj, obtains the difference between the maximum sampling time value CS and the minimum sampling time value CS, and marks it as the sampling time difference CSc, quantifies the sampling time mean CSj and the sampling time difference CSc, extracts the values ​​of the sampling time mean CSj and the sampling time difference CSc, and substitutes them into the formula for calculation. According to the formula The acquisition information CJ is obtained; wherein, e is a mathematical constant, a1 and a2 are respectively the preset proportional coefficients corresponding to the set acquisition time mean value CSj and the acquisition time difference value CSc, a1 and a2 satisfy a1+a2=1, 0<a2<a1<1, and a1=0.76 and a2=0.24;

[0074] The accuracy monitoring module obtains the production time and current time of the monitoring sensor o, obtains the time difference between the two, and marks it as the production value SC, obtains the total operating time of the monitoring sensor o, and marks it as the operating value YX, quantifies the production value SC and the operating value YX, extracts the values ​​of the production value SC and the operating value YX, and substitutes them into the formula for calculation. Obtain usage information SY; wherein s1 and s2 are preset proportional coefficients corresponding to the set production value SC and the operation value YX, respectively, s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and s1=0.28, s2=0.72;

[0075] The accuracy monitoring module sends the collected information CJ and the usage information SY to the information analysis module.

[0076] Embodiment 4: Based on any of the above embodiments, Embodiment 4 of the present invention is an information analysis module, which has two functions;

[0077] One of the functions is to obtain the accuracy monitoring coefficient JDo. The specific process is as follows:

[0078] The information analysis module normalizes the collected information CJ and the used information SY, and quantifies the values ​​of the collected information CJ and the used information SY according to the preset accuracy monitoring model to obtain the accuracy monitoring coefficient JDo;

[0079] Among them, the accuracy monitoring model is as follows: Where:

[0080] κ is the preset error adjustment factor, taking κ=0.981;

[0081] e and π are both mathematical constants;

[0082] d1 and d2 are respectively the preset weight factors corresponding to the set collection information CJ and the usage information SY, d1 and d2 satisfy d1>d2>2.517, d1=3.53, d2=2.85;

[0083] The information analysis module sends the accuracy monitoring coefficient JDo to the abnormality judgment module;

[0084] The second function is to obtain the fault detection coefficient GZi. The specific process is as follows:

[0085] The information analysis module normalizes the deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY, and quantifies the values ​​of the deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY according to the preset fault detection model to obtain the fault detection coefficient GZi;

[0086] Among them, the fault detection model is as follows: Where:

[0087] λ is the preset error adjustment factor, which is set to 0.925;

[0088] e is a mathematical constant;

[0089] g1, g2, g3 and g4 are preset weight factors corresponding to the set deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY respectively, g1, g2, g3 and g4 satisfy g1>g2>g3>g4>1.891, g1=3.66, g2=2.93, g3=2.45, g4=2.01;

[0090] The information analysis module sends the fault detection coefficient GZi to the abnormality judgment module.

[0091] Embodiment 5: Based on any of the above embodiments, Embodiment 5 of the present invention is an abnormality judgment module, and the abnormality judgment module has two functions;

[0092] One of the functions is to generate sensor fault instructions or fault detection instructions. The specific process is as follows:

[0093] The abnormal judgment module compares the accuracy monitoring coefficient JDo with the preset accuracy monitoring threshold JDy. The comparison results are as follows:

[0094] If the accuracy monitoring coefficient JDo ≥ the accuracy monitoring threshold JDy, the monitoring sensor o corresponding to the accuracy monitoring coefficient JDo is marked as an abnormal sensor;

[0095] If the number of abnormal sensors is ≥1, a sensor fault instruction is generated and sent to the fault alarm module;

[0096] If the number of abnormal sensors = 0, a fault detection instruction is generated and sent to the fault detection module;

[0097] The second function is to generate component fault instructions. The specific process is as follows:

[0098] The abnormality judgment module compares the fault detection coefficient GZi with the preset fault detection threshold GZy. The comparison results are as follows:

[0099] If the fault detection coefficient GZi≥fault detection threshold GZy, a component fault instruction is generated and sent to the fault alarm module.

[0100] Embodiment 6: Based on any of the above embodiments, Embodiment 6 of the present invention is a fault alarm module, and the fault alarm module has two functions;

[0101] One function is to sound a sensor failure alarm bell after receiving a sensor failure command;

[0102] The second function is to sound a component failure alarm bell after receiving a component failure command. Example

[0103] Based on any of the above embodiments, Embodiment 7 of the present invention is a fault detection module. The function of the fault detection module is to obtain fault detection parameters, wherein the fault detection parameters include deformation information BX, damage information SS, vibration information ZD, and temperature and sound information WY. The specific process is as follows:

[0104] After receiving the fault detection instruction, the fault detection module marks all the core components of the new energy vehicle as detection components i in sequence, where i=1, ..., m, where m is a positive integer, and i represents the number of the detection component, and m represents the total number of the detection components;

[0105] The fault detection module obtains the volume of the detection component i when it is first used and the volume at the current moment, obtains the difference between the two, and marks it as the volume difference value TC, obtains the total area of ​​all surfaces of the detection component i when it is first used and the total area of ​​all surfaces at the current moment, obtains the difference between the two, and marks it as the surface difference value MC, quantifies the volume difference value TC and the surface difference value MC, extracts the values ​​of the volume difference value TC and the surface difference value MC, and substitutes them into the formula for calculation. According to the formula Obtain deformation information BX; wherein π is a mathematical constant, c1 and c2 are preset proportional coefficients corresponding to the set volume difference value TC and surface difference value MC, respectively, c1 and c2 satisfy c1+c2=1, 0<c2<c1<1, and c1=0.59 and c2=0.41;

[0106] The fault detection module obtains the number of surface cracks and the total length of cracks of the detection component i at the current moment, and marks them as the grain value WS and the grain length value WC respectively, and uses the grain value WS and the grain length value WC as the lengths of the two diagonal lines of the diamond to draw a diamond figure, obtains the perimeter of the diamond figure, and marks it as damage information SS;

[0107] The fault detection module obtains the vibration times and maximum vibration displacement of the detection component i per unit time, and marks them as vibration value ZS and vibration displacement value ZY respectively, quantifies the vibration value ZS and vibration displacement value ZY, extracts the values ​​of the vibration value ZS and vibration displacement value ZY, and substitutes them into the formula for calculation. Obtain vibration information ZD; wherein z1 and z2 are preset proportional coefficients corresponding to the set vibration value ZS and vibration displacement value ZY, respectively, z1 and z2 satisfy z1+z2=1, 0<z1<z2<1, and z1=0.33 and z2=0.67;

[0108] The fault detection module obtains the real-time temperature and real-time sound intensity of the detection component i, and marks them as temperature value WD and sound value SY respectively, quantifies the temperature value WD and sound value SY, extracts the values ​​of the temperature value WD and sound value SY, and substitutes them into the formula for calculation. The temperature and sound information WY is obtained; wherein π and e are mathematical constants, w1 and w2 are preset proportional coefficients corresponding to the set temperature value WD and the sound value SY, respectively, w1 and w2 satisfy w1+w2=1, 0<w1<w2<1, and w1=0.31 and w2=0.69 are taken;

[0109] The fault detection module sends the deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY to the information analysis module.

[0110] Based on the above embodiments 1-7, the working principle of the present invention is as follows:

[0111] The core component fault detection system applicable to new energy vehicles of the present invention obtains the precision monitoring parameters of the monitoring sensor through the precision monitoring module, wherein the precision monitoring parameters include collection information and usage information, obtains the precision monitoring coefficient according to the precision monitoring parameters through the information analysis module, generates the sensor fault instruction or fault detection instruction according to the precision monitoring coefficient through the abnormal judgment module, sounds the sensor fault alarm bell after receiving the sensor fault instruction through the fault alarm module, obtains the fault detection parameters of the detection component after receiving the fault detection instruction through the fault detection module, wherein the fault detection parameters include deformation information, damage information, vibration information and temperature and sound information, obtains the fault detection coefficient according to the fault detection parameters through the information analysis module, generates the component fault instruction according to the fault detection coefficient through the abnormal judgment module, and sounds the component fault alarm bell after receiving the component fault instruction through the fault alarm module; the system first performs precision monitoring on the monitoring sensor for data collection to obtain the precision monitoring parameters, and then uses the data analysis technology to obtain the precision monitoring parameters according to the precision monitoring parameters The monitoring coefficient can comprehensively measure the degree of accuracy deviation of the monitoring sensor, and the larger the accuracy monitoring coefficient, the higher the degree of accuracy deviation. When the data acquisition accuracy of all monitoring sensors meets the requirements, the core components of the new energy vehicle are fault detected to obtain fault detection parameters. Then, the fault detection coefficient obtained according to the fault detection parameters using data analysis technology can comprehensively measure the degree of fault of the core components, and the larger the fault detection coefficient, the higher the degree of fault. Finally, a fault alarm is issued when a fault occurs. The system ensures the accuracy of fault detection and reduces false alarms and missed alarms by real-time monitoring of sensor accuracy. It also uses multi-faceted data collection and analysis to achieve in-depth fault detection of core components, improve the efficiency and timeliness of fault detection, and can comprehensively evaluate the health status of the vehicle and timely warn of potential faults, effectively enhance the safety and reliability of new energy vehicles, and provide car owners with a safer and more stable driving experience. At the same time, it also provides data support and optimized decision-making basis for vehicle maintenance, effectively promoting the development and progress of the new energy vehicle industry and achieving the goals of energy conservation, emission reduction and sustainable development.

[0112] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0113] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0114] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A core component fault detection system applicable to new energy vehicles, characterized in that: include: A fault detection module, configured to obtain fault detection parameters of the detection component i after receiving the fault detection instruction, and send the fault detection parameters to the information analysis module; wherein the fault detection parameters include deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY; An information analysis module is used to obtain a fault detection coefficient GZi according to the fault detection parameters, and send the fault detection coefficient GZi to the abnormality judgment module; it is also used to obtain an accuracy monitoring coefficient JDo according to the accuracy monitoring parameters, and send the accuracy monitoring coefficient JDo to the abnormality judgment module; The specific process of the information analysis module obtaining the fault detection coefficient GZi is as follows: The values ​​of deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY are quantified according to the preset fault detection model to obtain the fault detection coefficient GZi; Among them, the fault detection model is as follows: Where: λ is the preset error adjustment factor; e is a mathematical constant; g1, g2, g3 and g4 are preset weight factors corresponding to the set deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY respectively; The specific process of the information analysis module obtaining the accuracy monitoring coefficient JDo is as follows: The values ​​of the collected information CJ and the used information SY are quantified according to the preset accuracy monitoring model to obtain the accuracy monitoring coefficient JDo; Among them, the accuracy monitoring model is as follows: Where: κ is the preset error adjustment factor; e and π are both mathematical constants; d1 and d2 are preset weight factors corresponding to the set collection information CJ and usage information SY respectively; An abnormality judgment module is used to generate a component fault instruction according to the fault detection coefficient GZi, and send the component fault instruction to the fault alarm module; The fault alarm module is used to sound a component fault alarm bell after receiving a component fault instruction.

2. The core component fault detection system suitable for new energy vehicles according to claim 1 is characterized in that: The specific process of the fault detection module obtaining the fault detection parameters is as follows: After receiving the fault detection instruction, all core components of the new energy vehicle are marked as detection components i in sequence, where i=1, ..., m, where m is a positive integer, and i represents the number of the detection component, and m represents the total number of the detection components; Get the volume of the detection component i when it is first used and the volume at the current moment, get the difference between the two, and mark it as the volume difference value TC, get the total area of ​​all surfaces of the detection component i when it is first used and the total area of ​​all surfaces at the current moment, get the difference between the two, and mark it as the surface difference value MC, quantify the volume difference value TC and the surface difference value MC, and use the formula Obtain deformation information BX; wherein π is a mathematical constant, c1 and c2 are preset proportional coefficients corresponding to the set volume difference value TC and surface difference value MC respectively; Obtain the number of surface cracks and the total length of cracks of the detection component i at the current moment, and mark them as the grain value WS and the grain length value WC respectively, use the grain value WS and the grain length value WC as the lengths of the two diagonals of the diamond to draw a diamond figure, obtain the perimeter of the diamond figure, and mark it as damage information SS; Obtain the number of vibrations and the maximum vibration displacement of the detection component i per unit time, and mark them as the vibration value ZS and the vibration displacement value ZY respectively. The vibration value ZS and the vibration displacement value ZY are quantified according to the formula Obtain vibration information ZD; wherein z1 and z2 are preset proportional coefficients corresponding to the set vibration value ZS and vibration displacement value ZY respectively; Get the real-time temperature and real-time sound intensity of the detection component i, and mark them as temperature value WD and sound value SY respectively. Quantify the temperature value WD and sound value SY according to the formula Get the temperature and sound information WY; wherein π and e are mathematical constants, and w1 and w2 are preset proportional coefficients corresponding to the set temperature value WD and the sound value SY respectively; The deformation information BX, damage information SS, vibration information ZD and temperature and sound information WY are sent to the information analysis module.

3. The core component fault detection system suitable for new energy vehicles according to claim 1 is characterized in that: The specific process of the abnormal judgment module generating a component fault instruction is as follows: The fault detection coefficient GZi is compared with the preset fault detection threshold GZy, and the comparison results are as follows: If the fault detection coefficient GZi≥fault detection threshold GZy, a component fault instruction is generated and sent to the fault alarm module.

4. The core component fault detection system suitable for new energy vehicles according to claim 1 is characterized in that: Also includes: The accuracy monitoring module is used to obtain the accuracy monitoring parameters of the monitoring sensor o and send the accuracy monitoring parameters to the information analysis module; wherein the accuracy monitoring parameters include collection information CJ and usage information SY.

5. The core component fault detection system suitable for new energy vehicles according to claim 4 is characterized in that: The specific process of the accuracy monitoring module obtaining the accuracy monitoring parameters is as follows: All sensors for fault detection of core components of new energy vehicles are marked as monitoring sensors o, o=1, ..., v, v is a positive integer, and o represents the number of the monitoring sensor, and v represents the total number of monitoring sensors; Get the start time and completion time of each data collection of the monitoring sensor o per unit time, obtain the time difference between the two, and mark it as the sampling time value CS, get the average value of all the sampling time values ​​CS, and mark it as the sampling time mean CSj, get the difference between the maximum sampling time value CS and the minimum sampling time value CS, and mark it as the sampling time difference CSc, quantify the sampling time mean CSj and the sampling time difference CSc, according to the formula The collected information CJ is obtained; wherein, e is a mathematical constant, and a1 and a2 are respectively the preset proportional coefficients corresponding to the set sampling time mean value CSj and the sampling time difference value CSc; Get the production time and current time of monitoring sensor o, obtain the time difference between the two, and mark it as the production value SC, get the total operating time of monitoring sensor o, and mark it as the operating value YX, quantify the production value SC and the operating value YX, according to the formula The usage information SY is obtained; wherein s1 and s2 are respectively the preset ratio systems corresponding to the set production value SC and the operation value YX; The collected information CJ and the used information SY are sent to the information analysis module.

6. The core component fault detection system suitable for new energy vehicles according to claim 1 is characterized in that: The abnormality judgment module is also used to generate a sensor fault instruction or a fault detection instruction according to the accuracy monitoring coefficient JD0, and send the sensor fault instruction to the fault alarm module and send the fault detection instruction to the fault detection module.

7. The core component fault detection system applicable to new energy vehicles according to claim 6 is characterized in that: The specific process of the abnormality judgment module generating a sensor fault instruction or a fault detection instruction is as follows: The accuracy monitoring coefficient JDo is compared with the preset accuracy monitoring threshold JDy. The comparison results are as follows: If the accuracy monitoring coefficient JDo ≥ the accuracy monitoring threshold JDy, the monitoring sensor o corresponding to the accuracy monitoring coefficient JDo is marked as an abnormal sensor; If the number of abnormal sensors is ≥1, a sensor fault instruction is generated and sent to the fault alarm module; If the number of abnormal sensors = 0, a fault detection instruction is generated and sent to the fault detection module.

8. The core component fault detection system applicable to new energy vehicles according to claim 1 is characterized in that: The fault alarm module is also used to sound a sensor fault alarm ring after receiving a sensor fault instruction.

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