CN113064939B - New energy vehicle three-electric system safety feature database construction method - Google Patents
New energy vehicle three-electric system safety feature database construction method Download PDFInfo
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Abstract
A new energy vehicle three-electric system safety feature database construction method can carry out high-precision analysis on safety features of a full life cycle of a power battery system from multiple seasons and multiple regional scales, forms a long-time scale, high-precision and multi-working-condition key part mathematical statistics coupling relation model and a characteristic parameter threshold standard, and can be applied to application scenes such as safety standard inquiry, safety state diagnosis, safety performance comparison, vehicle performance evolution rule research and the like. With the establishment and perfection of the national big data platform of the new energy automobile, the database can be updated in real time, and the defects of low comprehensiveness, neglecting of use scene difference, being unfavorable for platform supervision and the like of the traditional three-electric system safety detection mode are effectively overcome, so that the method has wider applicability.
Description
Technical Field
The invention belongs to the technical field of big data of new energy vehicles, and particularly relates to a method for establishing a safety feature database aiming at a three-electric system of a new energy vehicle comprising a battery, a motor and an electric control.
Background
With the rapid development of new energy vehicles, the safety performance of three electric systems including batteries, motors and electric control is one of the most concerned indexes of consumers, manufacturers and traffic management departments. Along with the trend of rapid popularization of new energy vehicles in China in the current period, the requirements for solving the problems of good real-time and accurate assessment of the safety state of the three-electric system of the new energy vehicle, real-vehicle safety fault diagnosis, early warning and the like are also urgent.
The existing evaluation mode of the safety state of the three-electric system of the new energy vehicle is mainly used for carrying out on-line diagnosis on the health state of the three-electric system, is mainly limited to a few small parameters such as voltage, temperature and current, cannot fully consider all factors affecting the safety of the three-electric system, for example, safety standard differences under different conditions are often ignored, real vehicle big data are not effectively utilized as a basis, and the reliability and the universality of a related model are insufficient.
Disclosure of Invention
In view of the above, the invention aims to fully exert the advantages of the real vehicle big data of the new energy vehicle and comprehensively consider factors affecting the safety of the three-electric system, and provides a new energy vehicle three-electric system safety feature database construction method, which specifically comprises the following steps:
(1) Aiming at factors affecting the safety of the three-electric system, respectively selecting a battery safety characteristic parameter set, a motor safety characteristic parameter set and an electric control safety characteristic parameter set based on development and research, expert consultation and experience data;
(2) Collecting original data of the working conditions of the vehicle by a vehicle-mounted terminal, sensor equipment, communication equipment and the like of the new energy vehicle, and uploading the original data to a big data platform of the new energy vehicle;
(3) Preprocessing the uploaded original data at the new energy vehicle big data platform end, wherein the preprocessing comprises the following steps: rejecting outliers and repeated frames that are problematic in terms of time, current, voltage, soC; judging whether the charging state is normal or not and processing abnormal data; expanding the characteristics of the original data, and establishing labels corresponding to months and seasons and intermediate parameter labels for calculating characteristic parameters;
(4) Extracting corresponding parameters in each parameter set from the preprocessed data aiming at the three selected safety feature parameter sets; three labels of region, season and working condition are respectively added to the extracted battery, motor and electric control safety characteristic parameters, and a driving mileage label is also added to the battery safety characteristic parameters; dividing the extracted parameters into segments by using the established various labels to establish a grouping subset;
(5) And carrying out statistical analysis on historical data by using the new energy vehicle big data platform, respectively setting corresponding battery safety characteristic parameter threshold, motor safety characteristic parameter threshold and electric control safety characteristic parameter threshold for each established grouping subset, completing the construction of the database and being used for on-line safety diagnosis and early warning of the target vehicle.
Further, the battery safety feature parameter set in step (1) specifically includes: open-circuit voltage, average differential pressure, monomer voltage difference extreme value, voltage term of standing voltage, capacity and internal resistance term of fixed SoC section (85% -100%), capacity of full charge, ohm internal resistance, pulse internal resistance, temperature term of charging current, average temperature, highest temperature, lowest temperature distribution;
the motor safety characteristic parameter set specifically comprises: torque integral slope, temperature integral slope, motor temperature;
the electric control safety characteristic parameter set specifically comprises: motor controller voltage, motor controller temperature, motor direct current bus current, insulation resistance.
Further, the new energy vehicle big data platform in the step (2) includes, but is not limited to, a new energy vehicle national big data platform in China, a central server or cloud server constructed based on the new energy vehicle big data, and the like.
Further, the step (3) of judging whether the charging state is normal and processing the abnormal data specifically includes:
different state labels are set for the current frame by combining the current, the vehicle speed and the value of SoC, and are used for respectively representing the vehicles: traveling, temporary parking, parking charging, driving charging, full power standby, flameout, and fault data status; each frame data is finally set with 3 tags on driving, charging and full power stationary states by bias and sliding window filtering for determining a state after the retention process or a corresponding value after the replacement process with the original state, compared with the current state of the vehicle in the raw data.
Further, setting the respective battery safety feature parameter thresholds for each of the established subset of packets in step (5) specifically includes employing a respective statistical method for:
1) For an open circuit voltage safety threshold: calculating upper and lower quartiles of the data in each sub-data set as an open circuit voltage safety threshold;
2) Safety threshold for cell voltage differential: calculating the upper and lower quartiles of each sub-data set as a single voltage differential safety threshold;
3) Safety threshold for highest cell voltage: dividing each sub data set into five groups of 0-20% of SoC, 20-40% of SoC, 40-60% of SoC, 60-80% of SoC and 80-100% of SoC according to SoC data items, counting the highest monomer voltage in each group, and calculating an average value as the highest monomer voltage safety threshold value of different SoC sections;
4) Safety threshold for lowest cell voltage: dividing each sub data set into five groups of 0-20% of SoC, 20-40% of SoC, 40-60% of SoC, 60-80% of SoC and 80-100% of SoC according to SoC data items, counting the lowest monomer voltage in each group, and calculating a mean value as the lowest monomer voltage safety threshold of different SOC segments;
5) For the internal resistance safety threshold: calculating the upper and lower quartiles of each sub-data set as an internal resistance safety threshold;
6) Safety threshold for ohmic internal resistance: calculating the upper and lower quartiles of each sub-data set as an ohmic internal resistance safety threshold;
7) For a charge current safety threshold: calculating and applying K-Means clustering calculation to obtain two charging currents of a slow charging mode and a fast charging mode in each sub-data set as charging current safety thresholds;
8) Safety threshold for maximum temperature: counting the highest temperature items in each sub-data set, and calculating an average value as a highest temperature safety threshold;
9) For the lowest temperature safety threshold: counting the lowest temperature item in each sub-data set, and calculating an average value as a lowest temperature safety threshold;
10 For an average temperature safety threshold): counting the average temperature items in each sub-data set, and calculating an average value as an average temperature safety threshold;
11 For a rest voltage safety threshold: counting the standing voltage items in each sub-data set, and calculating an average value as a standing voltage safety threshold;
12 For a full charge capacity safety threshold): counting full charge capacity items in each sub-data set, and calculating upper and lower quartiles as a safety threshold of the full charge capacity;
13 For a fixed SoC segment (85% -100%) capacity safety threshold: the fixed SoC segments (85% -100%) Rong Liangxiang in each sub-data set are counted, and the upper and lower quartiles are calculated to serve as safety thresholds of the capacities of the fixed SoC segments (85% -100%).
Further, setting the respective motor safety feature parameter thresholds for each of the established subset of packets in step (5) specifically includes employing a respective statistical method for:
1) For a motor torque integral slope safety threshold: extracting data of each bicycle in the sub-data set, linearly regressing the torque integral of the bicycle and the driving mileage of the bicycle to obtain a primary function of the bicycle mileage and the torque integral, and calculating the upper and lower quartiles of the slope of the primary function of the mileage and the torque integral of each bicycle in each sub-data set to be used as a safety threshold value of the motor torque integral slope;
2) For motor temperature safety threshold: extracting data of each bicycle in the sub-data set, linearly regressing the temperature integral of the bicycle and the bicycle mileage to obtain a primary function of the bicycle mileage and the temperature integral, and calculating the upper and lower quartiles of the slope of the primary function of the mileage and the temperature integral of each bicycle in each sub-data set to be used as a safety threshold of the motor temperature integral slope;
3) For motor temperature safety threshold: the upper and lower quartiles of each sub-dataset are calculated as motor temperature safety thresholds.
Further, in step (5), setting the corresponding electrically controlled security feature parameter thresholds for the established subset of packets respectively specifically includes adopting a corresponding statistical method for the following thresholds:
1) For motor controller voltage safety threshold: calculating the upper and lower quartiles of each sub-data set as a motor controller voltage term safety threshold;
2) Safety threshold for dc bus current: calculating upper and lower quartiles of the data in each sub-data set as a safety threshold of a direct current bus current item;
3) For insulation resistance safety threshold: except for abnormal conditions, the insulation resistance is 4000mΩ, so the insulation resistance mode in each sub-data set is used as an insulation resistance term safety threshold;
4) For motor controller temperature safety threshold: the upper and lower quartiles of each sub-dataset are calculated as motor controller temperature safety thresholds.
The novel energy automobile three-electric system safety feature database constructed based on the method provided by the invention can carry out high-precision analysis on the safety features of the whole life cycle of the power battery system from multiple seasons and multiple regional scales, forms a long-time scale, high-precision and multi-working-condition key part mathematical statistics coupling relation model and a characteristic parameter threshold standard, and can be applied to application scenes such as safety standard inquiry, safety state diagnosis, safety performance comparison, vehicle performance evolution rule research and the like. With the establishment and perfection of the national big data platform of the new energy automobile, the database can be updated in real time, and the defects of low comprehensiveness, neglecting of use scene difference, being unfavorable for platform supervision and the like of the traditional three-electric system safety detection mode are effectively overcome, so that the method has wider applicability.
Drawings
FIG. 1 is a general flow of the method provided by the present invention;
FIG. 2 is an illustration of extractable fields in raw data;
FIG. 3 is a process of determining whether a state of charge is normal and processing abnormal data;
FIG. 4 is a representation of the data expansion characteristics after preprocessing based on the method of the present invention;
fig. 5 is an equivalent circuit model employed for extracting capacity and internal resistance terms in an example of the present invention;
fig. 6 is a flow of sectioning and thresholding extracted security feature parameters.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The method for constructing the safety feature database of the three-electric system of the new energy vehicle, as shown in fig. 1, specifically comprises the following steps:
(1) Aiming at factors affecting the safety of the three-electric system, respectively selecting a battery safety characteristic parameter set, a motor safety characteristic parameter set and an electric control safety characteristic parameter set based on development and research, expert consultation and experience data;
(2) Collecting original data of the working conditions of the vehicle by a vehicle-mounted terminal, a sensor device, a communication device and the like of the new energy vehicle, for example, adopting the data and field form shown in the figure 2, and uploading the original data to a big data platform of the new energy vehicle;
(3) Preprocessing the uploaded original data at the new energy vehicle big data platform end, wherein the preprocessing comprises the following steps: rejecting outliers and repeated frames that are problematic in terms of time, current, voltage, soC, as shown in table 1;
table 1 outlier problem and processing method
Firstly, processing abnormal data values existing in original data, replacing the abnormal data values with null values when the abnormal data values exist in the individual data in the form of 'r', then performing time sequence arrangement processing, cleaning the abnormal data of the original data, and deleting data frames with abnormal time fields.
Judging whether the charging state is normal or not and processing the abnormal data, as shown in fig. 3, different state tags can be set for the current frame in combination with the current, the vehicle speed and the SoC value, and the vehicles are respectively represented from 10 to 70: traveling, temporary parking, parking charging, driving charging, full power standby, flameout, and fault data status; the method comprises the steps of providing support for subsequent work, including segment division, related parameter calculation and actual conditions of vehicle use, setting 3 labels of 1 driving, 3 charging and 4 full power standing states for each frame of data through offset and sliding window filtering, and using the labels to support the subsequent work, meanwhile comparing a newly judged state with a vehicle (charging) state of the original data, wherein the same rate is higher than a reasonable value (0.95), namely the original state is regarded as no problem, otherwise, the original state is replaced by the judging state;
the feature of the original data is expanded, as shown in fig. 4, including the extracted label items such as month, season, etc. on the basis of preprocessing, and further including intermediate variables such as integral calculation results for each frame of charging condition frame, etc. for each frame of charging condition frame, and further including fragment number label items for supporting subsequent fragment division work, etc.;
(4) Extracting corresponding parameters in each parameter set from the preprocessed data aiming at the three selected safety feature parameter sets; three labels of region, season and working condition are respectively added to the extracted battery, motor and electric control safety characteristic parameters, and a driving mileage label is also added to the battery safety characteristic parameters; dividing the extracted parameters into segments by using the established various labels to establish a grouping subset;
(5) And carrying out statistical analysis on historical data by using the new energy vehicle big data platform, respectively setting corresponding battery safety characteristic parameter threshold, motor safety characteristic parameter threshold and electric control safety characteristic parameter threshold for each established grouping subset, completing the construction of the database and being used for on-line safety diagnosis and early warning of the target vehicle.
In a preferred embodiment of the invention, the extraction of the battery safety feature parameters is obtained in particular by:
(1) The following parameters are calculated for the capacity and internal resistance terms:
1) The calculation capacity is as follows:
the calculation method of the calculated capacity is that in the segmented data fragments, the data fragments with the charging fragments are screened, the added value dsoc of the increase of the soc in the charging process and the added value dc of the capacity in the charging process are extracted, the added value dc is converted into the capacity value cap with the soc of 100, and the calculation formula is as follows:
cap＝100*dc/dsoc (1)
2) Fixed SOC segment capacity
And integrating and calculating to obtain the capacity of 85-100 segments of soc.
3) Ohmic internal resistance
The increase of the ohmic internal resistance of the battery pack has stronger correlation with the increase of SEI film, the decomposition of electrolyte and the corrosion of a current collector, and the current health state of the battery pack can be reflected by constructing a model and extracting the ohmic internal resistance of the battery pack.
And constructing a first-order rc Equivalent Circuit Model (ECM), and calculating the ohmic internal resistance of the battery pack through a least square algorithm based on forgetting factors.
The ECM construction employs a 1-order ECM model, as shown in FIG. 5, where Uoc represents open circuit voltage, U represents terminal voltage, I represents current, R 1 Represents ohmic internal resistance, can reflect the aging state of the battery, R 2 And C represents the polarized internal resistance and polarized capacitance of the battery and reflects the dynamic characteristics of the battery.
After the time domain and the frequency domain are converted, the following relation can be obtained:
t is the uploading time interval of the data in the platform data, and is 10s or 30s at most, and k represents the moment.
Will be
Set to a->
Set to b->
C, carrying out parameter identification by a least square method combined with forgetting factors to obtain a, b and c, and solving an equation to obtain R 1 ,R 2 And the value of C.
4) Internal resistance of pulse
And taking the data of the current step at the end of charging, and calculating the impedance value of the battery by using the ratio of the voltage difference to the current difference.
(2) For the temperature term, the following parameters were calculated:
1) Including the highest temperature and the lowest temperature of the battery pack, the average temperature
2) Extreme value of monomer temperature difference
(3) For the voltage term, the following parameters are calculated:
1) And (3) full-charge static voltage, extracting voltage data which is static for a long time after full charge, limiting static time by a filtering method, and ensuring that the full-charge static voltage is extracted by slope verification.
2) Extreme value of single voltage
Comprising a highest voltage of a single body, a lowest voltage of the single body, a voltage difference value and the like
3) Open circuit voltage
The first frame voltage data started after standing for a long time is taken as an open circuit voltage value.
In the extraction of the motor safety characteristic parameters, the motor is a key part for driving a vehicle in a three-electric system, and the characteristic parameters reflecting the motor safety include alarm data, driving motor current, driving motor voltage, driving motor temperature, driving motor rotating speed, driving motor torque and the like.
Real-time monitoring to motor security state can be realized through alarm parameter, and driving motor temperature is as safe characteristic parameter, can realize the real-time monitoring to motor temperature, avoids the potential safety hazard that overheated leads to. The rotation speed of the driving motor is combined with the torque of the driving motor and is combined with the input current and voltage, so that the safety state of the motor in efficiency can be reflected, and the normal/abnormal working state of the motor can be reflected. Parameters reflecting the safety of the driving motor controller include the temperature of the driving motor controller, the input voltage of the motor controller, the direct current bus current of the motor controller and the like, and the parameters are combined with the safety characteristic parameters of the rest motors, so that the safety state of the motor can be more comprehensively analyzed, and the state misjudgment caused by the failure of the motor controller is avoided.
The specific extracted motor characteristic parameters comprise motor temperature integration and motor torque integration, namely, the temperature and the torque of the motor are integrated in the time dimension, and in addition, the motor temperature, the current and the like.
In the extraction of the electric control safety characteristic parameters, the motor and the electric control system are used as substitutes for the functions of a traditional engine (gearbox), and the performances of the motor and the electric control system directly determine main performance indexes such as climbing, accelerating and highest speed of the electric automobile. Based on the collected data of each electric control component, the power voltage of the electric control component (such as ECU), the current of each electric control component, the temperature of part of the electric control component (such as DC-DC), the temperature of the motor controller, the insulation resistance and the like are extracted as electric control safety characteristic parameters.
It should be understood that, the sequence number of each step in the embodiment of the present invention does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present invention.
After the extraction of the parameters is completed, an index tag item is established for facilitating the subsequent application of a database to the safety state diagnosis of the vehicle. The security status of the three-electric system is related to the use environment, the use time and the use condition, and index tag items of security feature data of the three-electric system are selected according to the security status. The motor and the electric control safety feature database select three label items of seasons, working conditions and regions, and the battery safety feature database selects four items of mileage, regions, seasons and working conditions in consideration of the fact that the time factors have great influence on the battery safety state. The season label items are: spring, summer, autumn and winter. The regional tag items are divided according to the city where the vehicle runs, the working condition tag items are divided into a driving working condition, a slow charging working condition and a fast charging working condition, and the mileage tag items are divided into a dimension according to the driving mileage of the vehicle every fifty thousand kilometers.
The mileage label item can reflect the service time of the power battery to a certain extent, and the capacity decline and the internal resistance increase of the power battery inevitably occur in the use process of the power battery, so that the mileage label item has a strong coupling relation with the fixed SOC capacity, the full charge capacity, the internal resistance and the pulse resistance in the characteristic parameters. In addition, the capacity fade can also lead to the drop of the single voltage, so the single voltage item, the open-circuit voltage item and the mileage label have strong coupling relation.
The seasonal tag item can show the change of the ambient temperature, the temperature has obvious influence on the calendar life and the cycle life of the lithium power battery, the high temperature can accelerate the decay of the power battery, increase the internal resistance and influence the charge and discharge performance of the power battery, so the seasonal tag item, the temperature characteristic item, the single voltage characteristic item and the capacity characteristic item. In addition, the seasonal tag item has strong coupling relation with the temperature integral slope, the motor temperature and the motor controller temperature in the aspect of the motor electric control safety characteristic parameters.
The working condition label item represents the state of the vehicle, and is divided according to the working conditions of the vehicle, and the extracted characteristic parameter items under different working conditions are different. The internal resistance, the pulse internal resistance, the open-circuit voltage and the temperature are extracted under the driving working condition, and the charging working condition is divided into a fast charging working condition and a slow charging working condition because the charging current multiplying power also has an influence on the service life and the charging performance of the battery. The corresponding extracted characteristic parameters are as follows: fixed SOC segment charge capacity, slow charge capacity, charge initiation cell voltage and temperature profile. The motor torque integral slope, motor controller voltage, motor bus current, insulation resistance and working condition label term have strong coupling relation in the aspect of motor electric control safety characteristic parameters.
After the extraction of the characteristic parameters and the label setting are completed, the collection of the database is completed, and then the final establishment of the database shown in fig. 6 can be completed by setting corresponding parameter thresholds according to the consideration of different aspects of seasons, working conditions, mileage, regions and the like, and the database is used for subsequent supervision and early warning. Wherein the battery safety profile database data items include open circuit voltage, cell voltage differential, highest and lowest cell voltages for different SOC segments, internal resistance, ohmic internal resistance, charge current, maximum temperature, minimum temperature average temperature, resting voltage, full charge capacity, and fixed SOC segment (85-100) capacity. Firstly, sub-data sets are divided according to seasons (spring, summer, autumn and winter), working conditions (driving, charging and full electricity standing), mileage (0-5 kilometers, 5-10 kilometers, 10-15 kilometers, 15-20 kilometers) and regions (Beijing, chongqing, shanghai, shenyang and Shenzhen), safety characteristic parameter threshold values are calculated in each sub-data set, and finally the safety characteristic threshold values calculated in each sub-data set are summarized to obtain an integral battery safety characteristic database.
The motor safety characteristic database data items comprise three items of motor torque integral slope, temperature integral slope and motor temperature. Firstly, sub-data sets are divided according to seasons (spring, summer, autumn and winter), working conditions (running, charging and full-electricity standing) and regions (Beijing, chongqing, shanghai, shenyang and Shenzhen), calculation of safety feature parameter thresholds is carried out in each sub-data set, and finally the safety feature thresholds obtained by calculation in each sub-data set are summarized to obtain an integral motor safety feature database.
The data items of the electric control safety database comprise four items of motor controller voltage, direct current bus current, insulation resistance and motor controller temperature. Firstly, sub-data sets are divided according to seasons (spring, summer, autumn and winter), working conditions (running, charging and full electricity standing) and regions (Beijing, chongqing, shanghai, shenyang and Shenzhen), calculation of safety feature parameter thresholds is carried out in each sub-data set, and finally the safety feature thresholds obtained by calculation in each sub-data set are summarized to obtain an integral electronic control safety feature database.
The invention researches a full life cycle safety feature analysis model of a multi-season and multi-region-scale high-precision power battery system by utilizing real vehicle operation data, establishes a long-time-scale, high-precision and multi-working-condition key part mathematical statistics coupling relation and characteristic parameter threshold value standard database, and can be applied to application scenes such as safety standard inquiry, safety state diagnosis, safety performance comparison, vehicle performance evolution rule research and the like.
The database can be used for importing real vehicle operation data meeting data standards in real time by means of a data interface of the new energy vehicle data platform, and updating safety characteristic parameters of a certain vehicle type under different working conditions in the whole life cycle in real time. By means of the database, corresponding safety state parameters under different working conditions can be inquired through inputting the vehicle type, the running region and the season information of the vehicle. And diagnosing the safety state of the current vehicle by comparing the characteristic parameters of the current vehicle with the safety characteristic parameter standard values in the database, so as to further realize the vehicle risk and fault information pushing. In addition, based on the database vehicle enterprise, researches such as safety performance comparison, accident fault cause analysis, safety performance evolution law and the like among different vehicle types can be carried out, and reference comments are provided for vehicle performance improvement.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. The method for constructing the safety feature database of the three-electric system of the new energy vehicle is characterized by comprising the following steps of: the method specifically comprises the following steps:
(1) Aiming at factors affecting the safety of the three-electric system, respectively selecting a battery safety characteristic parameter set, a motor safety characteristic parameter set and an electric control safety characteristic parameter set based on development and research, expert consultation and experience data; wherein the battery safety feature parameter set specifically includes: open-circuit voltage, average differential pressure, monomer voltage difference extreme value and standing voltage, fixing capacity and internal resistance items composed of SoC section capacity, full charge capacity, ohmic internal resistance and pulse internal resistance, charging current, and temperature items composed of average temperature, highest temperature and lowest temperature distribution; the motor safety characteristic parameter set specifically comprises: torque integral slope, temperature integral slope, motor temperature; the electric control safety characteristic parameter set specifically comprises: motor controller voltage, motor controller temperature, motor direct current bus current, insulation resistance;
(2) Collecting original data of the working conditions of the vehicle by a vehicle-mounted terminal, a sensor device and a communication device of the new energy vehicle, and uploading the original data to a big data platform of the new energy vehicle;
(3) Preprocessing the uploaded original data at the new energy vehicle big data platform end, wherein the preprocessing comprises the following steps: rejecting outliers and repeated frames that are problematic in terms of time, current, voltage, soC; judging whether the charging state is normal or not, processing abnormal data, and setting different state labels for the current frame by combining the current, the vehicle speed and the value of the SoC, wherein the state labels are used for respectively representing vehicles: traveling, temporary parking, parking charging, driving charging, full power standby, flameout, and fault data status; setting each frame data to be 3 labels related to running, charging and full-power static state through bias and sliding window filtering, and comparing the 3 labels with the current state of the vehicle in raw data to determine the state after the retention processing or the corresponding value after the replacement processing with the original state; expanding the characteristics of the original data, and establishing labels corresponding to months and seasons and intermediate parameter labels for calculating characteristic parameters;
(4) Extracting corresponding parameters in each parameter set from the preprocessed data aiming at the three selected safety feature parameter sets; three labels of region, season and working condition are respectively added to the extracted battery, motor and electric control safety characteristic parameters, and a driving mileage label is also added to the battery safety characteristic parameters; dividing the extracted parameters into segments by using the established various labels to establish a grouping subset;
(5) The new energy vehicle big data platform is utilized to carry out statistical analysis on historical data, corresponding battery safety characteristic parameter threshold values, motor safety characteristic parameter threshold values and electric control safety characteristic parameter threshold values are respectively set for each established grouping subset, wherein the motor safety characteristic parameter threshold values specifically comprise the following threshold values by adopting a corresponding statistical method:
1) For a motor torque integral slope safety threshold: extracting data of each bicycle in the sub-data set, linearly regressing the torque integral of the bicycle and the driving mileage of the bicycle to obtain a primary function of the bicycle mileage and the torque integral, and calculating the upper and lower quartiles of the slope of the primary function of the mileage and the torque integral of each bicycle in each sub-data set to be used as a safety threshold value of the motor torque integral slope;
2) For the motor temperature integral slope safety threshold: extracting data of each bicycle in the sub-data set, linearly regressing the temperature integral of the bicycle and the bicycle mileage to obtain a primary function of the bicycle mileage and the temperature integral, and calculating the upper and lower quartiles of the slope of the primary function of the mileage and the temperature integral of each bicycle in each sub-data set to be used as a safety threshold of the motor temperature integral slope;
3) For motor temperature safety threshold: calculating the upper and lower quartiles of each sub-data set as a motor temperature safety threshold;
and completing the construction of the database and being used for on-line safety diagnosis and early warning of the target vehicle.
2. The method of claim 1, wherein: the new energy vehicle big data platform in the step (2) comprises a central server or a cloud server constructed based on the new energy vehicle big data.
3. The method of claim 1, wherein: in the step (5), setting the corresponding battery safety feature parameter threshold for each set of the established subset includes adopting a corresponding statistical method for the following thresholds:
1) For an open circuit voltage safety threshold: calculating upper and lower quartiles of the data in each sub-data set as an open circuit voltage safety threshold;
2) Safety threshold for cell voltage differential: calculating the upper and lower quartiles of each sub-data set as a single voltage differential safety threshold;
3) Safety threshold for highest cell voltage: dividing each sub data set into five groups of 0-20% of SoC, 20-40% of SoC, 40-60% of SoC, 60-80% of SoC and 80-100% of SoC according to SoC data items, counting the highest monomer voltage in each group, and calculating an average value as the highest monomer voltage safety threshold value of different SoC sections;
4) Safety threshold for lowest cell voltage: dividing each sub data set into five groups of 0-20% of SoC, 20-40% of SoC, 40-60% of SoC, 60-80% of SoC and 80-100% of SoC according to SoC data items, counting the lowest monomer voltage in each group, and calculating a mean value as the lowest monomer voltage safety threshold of different SOC segments;
5) For the internal resistance safety threshold: calculating the upper and lower quartiles of each sub-data set as an internal resistance safety threshold;
6) Safety threshold for ohmic internal resistance: calculating the upper and lower quartiles of each sub-data set as an ohmic internal resistance safety threshold;
7) For a charge current safety threshold: calculating and applying K-Means clustering calculation to obtain two charging currents of a slow charging mode and a fast charging mode in each sub-data set as charging current safety thresholds;
8) Safety threshold for maximum temperature: counting the highest temperature items in each sub-data set, and calculating an average value as a highest temperature safety threshold;
9) For the lowest temperature safety threshold: counting the lowest temperature item in each sub-data set, and calculating an average value as a lowest temperature safety threshold;
10 For an average temperature safety threshold): counting the average temperature items in each sub-data set, and calculating an average value as an average temperature safety threshold;
11 For a rest voltage safety threshold: counting the standing voltage items in each sub-data set, and calculating an average value as a standing voltage safety threshold;
12 For a full charge capacity safety threshold): counting full charge capacity items in each sub-data set, and calculating upper and lower quartiles as a safety threshold of the full charge capacity;
13 For a fixed SoC segment capacity security threshold: the fixed SoC segment Rong Liangxiang in each sub-data set is counted, and the upper and lower quartiles are calculated as the safety threshold of the fixed SoC segment capacity.
4. The method of claim 1, wherein: in the step (5), setting the corresponding electric control safety characteristic parameter threshold for each established group subset respectively specifically comprises adopting a corresponding statistical method aiming at the following threshold:
1) For motor controller voltage safety threshold: calculating the upper and lower quartiles of each sub-data set as a motor controller voltage term safety threshold;
2) Safety threshold for dc bus current: calculating upper and lower quartiles of the data in each sub-data set as a safety threshold of a direct current bus current item;
3) For insulation resistance safety threshold: taking the insulation resistance mode in each sub-data set as an insulation resistance term safety threshold;
4) For motor controller temperature safety threshold: the upper and lower quartiles of each sub-dataset are calculated as motor controller temperature safety thresholds.
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