CN114240260A - New energy group vehicle thermal runaway risk assessment method based on digital twinning - Google Patents
New energy group vehicle thermal runaway risk assessment method based on digital twinning Download PDFInfo
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Abstract
The invention discloses a new energy group vehicle thermal runaway risk assessment method based on digital twinning, which comprises the following steps: preprocessing battery data; establishing a new energy vehicle group; establishing a cloud battery model; extracting thermal runaway probability information gain of the thermal runaway inducers; and (4) early warning of thermal runaway. The method can realize the refined digital twin modeling of the thermal runaway of the single vehicle, the group digital twin modeling of the thermal runaway behavior of the vehicle group, the probability sequence of the thermal runaway occurring in the group vehicles, the thermal runaway early warning information and the battery state information aiming at the single vehicle are provided for new energy automobile manufacturers and battery manufacturers, the thermal runaway risk of the single vehicle which cannot be quantitatively evaluated is converted into the thermal runaway probability of the vehicle group which is evaluated, the quantitative description of the thermal runaway probability of the single vehicle is realized, and the prediction of the thermal runaway event of the single vehicle and the comprehensive management of the group thermal runaway risk are realized.
Description
Technical Field
The invention belongs to the field of intelligent networked automobile cloud collaborative battery management, and particularly relates to a new energy group vehicle thermal runaway risk assessment method based on digital twins.
Background
The long endurance and the low energy consumption are key indexes of the electric automobile, and the lithium ion battery with high specific energy and long service life is an excellent vehicle-mounted energy storage system for realizing the indexes. However, with the increase of the energy density of the battery, the increase of the capacity of the battery pack and the complexity of the use environment of the new energy automobile, due to the influence of factors such as poor production and manufacture, thermal abuse, electrical abuse and mechanical abuse, safety accidents of the lithium ion battery system including thermal runaway often occur, the safety of passengers and property is seriously threatened, and the further popularization and the technical improvement of the new energy automobile are restricted.
The conventional Battery Management System (BMS) calculates a Battery state through an algorithm by detecting data such as Battery pack capacity, voltage, charge-discharge current, temperature and the like and voltage, current and temperature of a single Battery, and performs safety management (voltage management, thermal management and the like) on the Battery to ensure that a power Battery operates within a safety window (voltage, temperature and the like), but the conventional Battery management means cannot perform early warning and risk assessment and evaluation for thermal runaway of the power Battery. The traditional battery management system is deployed on a vehicle-mounted embedded controller and is limited by limited computing power and small storage space of the embedded controller, the battery management system cannot deploy complex algorithms, parameters input by the algorithms are limited to information such as maximum voltage, minimum voltage and temperature of a single body, and monitoring of the whole life cycle of all battery single bodies is abandoned. The traditional battery management system cannot detect small changes of voltage and temperature and changes of battery electrochemical characteristic quantity on a long time scale for a single body which is potentially subjected to thermal runaway, so that a thermal runaway precursor characteristic cannot be captured. Therefore, the conventional battery management system has no excellent solution to the thermal runaway problem of the power battery.
In recent years, with the development of cloud technology and mobile networks, a large amount of vehicle data are accessed to the cloud, the cloud stores the data of the full life cycle of the vehicle, the data include all use data from the purchase of the vehicle to the current moment, and different sensors on the vehicle are used for acquiring data such as a motor, a battery, a power converter, a driver driving behavior and the like. The advent of cloud technology has made it possible to monitor vehicle conditions in real time. Meanwhile, the data interaction and the vehicle cloud communication are established, so that the algorithm which cannot be deployed on the vehicle embedded controller originally can be moved to the cloud deployment. On the other hand, the cloud can synthesize the information of all vehicle groups connected with the cloud for data analysis. By utilizing the characteristics of high computing power and huge data volume of a cloud, various parameters of the managed energy automobiles are detected and evaluated in real time by using a statistical principle, and the technology combining the vehicle-cloud cooperative architecture and the cloud big data provides a solution for the thermal runaway quantitative evaluation of a new energy automobile group. However, the chinese patent application with application number CN202011334011.5 provides a cloud power battery health degree evaluation system and method, which uses cloud computing power to evaluate and predict the health degree of a vehicle power battery, but in the system, a vehicle cloud cooperative object is a vehicle single body, and only the health degree of the single power battery can be judged, and the thermal runaway early warning trigger condition is relatively single and fixed; in addition, the characteristics of high computing power of the cloud are not effectively utilized, the input parameters and data quantity for evaluating the health degree of the power battery are small, the coupling condition among all the parameters is not considered, and the judgment and early warning of thermal runaway are not favorable.
Disclosure of Invention
The invention provides a thermal runaway risk assessment method for new energy group vehicles based on digital twinning, aiming at the problem that thermal runaway of a single-vehicle power battery of a new energy automobile cannot be quantitatively characterized. The invention consists of three parts, which are respectively: the method comprises three parts of data preprocessing, automobile group data and battery model based on digital twins, new energy automobile group thermal runaway evaluation and risk sequencing:
the data preprocessing part is mainly used for uploading original data in a database to serve as primary observed quantities by the BMS at the vehicle end according to data in the cloud BMS database, and on the basis, secondary observed quantities independent from the primary observed quantities are obtained through feature extraction.
And constructing a vehicle group database and a single battery database based on the preprocessed data, and establishing a vehicle group digital twin model and a single battery digital twin model. The database of the digital twin model stores all vehicle data in a specified format in a full life cycle, and comprises primary observed quantities such as voltage, temperature, current and the like of a battery monomer (optimized battery data fields recommended by national standards), and secondary observed quantities such as self-discharge rate, internal resistance, relaxation curves during standing and the like of the battery which are independently processed according to the primary observed quantities.
The invention provides a new energy automobile group thermal runaway assessment and risk sequencing method based on a digital twin model and big data. For a new energy automobile, thermal runaway of the new energy automobile is a small-probability incident, so that the traditional method cannot make effective analysis aiming at the thermal runaway prediction of the new energy automobile, and according to a law of large numbers, for a group of new energy automobiles, the probability of the thermal runaway tends to be inevitable. The method comprises the steps of extracting and classifying the characteristics of thermal runaway accidents aiming at a single vehicle by using a statistical method (a new energy vehicle group using the same battery pack in the thermal runaway assessment and evaluation method disclosed by the invention) through all data and digital twin models in a cloud platform, extracting the thermal runaway probability information gain of the thermal runaway inducement, obtaining the weight value and the deviation value of each thermal runaway characteristic through data fitting, and sequencing and thermal runaway risk assessment in the group. And calculating an evaluation value of the thermal runaway possibly occurring in the power battery of each vehicle by taking a logarithmic value of the gain of the thermal runaway probability information. The method comprises the steps of conducting mathematical sequencing on all the vehicle evaluation values in a new energy vehicle group, dynamically adjusting an alarm threshold value by a cloud BMS according to sequencing results, correcting a safety threshold value in real time according to observed group thermal runaway behavior characteristics, the current vehicle group scale and the current cloud idle computing power, selecting a vehicle where a battery higher than the threshold value is located according to the safety threshold value, warning vehicle manufacturers and battery manufacturers that the vehicle manufacturers and the battery manufacturers may have thermal runaway risks, and giving a processing and protecting method aiming at the current battery condition to correct problems and enable the battery thermal runaway evaluation values to be reduced below a safety value.
By combining the method, the method can realize refined digital twin modeling of the thermal runaway of the bicycle, group digital twin modeling of the thermal runaway behavior of the vehicle group, giving out the probability sequence of the thermal runaway occurring in the group vehicles, providing thermal runaway early warning information and battery state information aiming at the bicycle for new energy automobile manufacturers and battery manufacturers, converting the thermal runaway risk of the bicycle which cannot be quantitatively evaluated into the thermal runaway probability of the bicycle group to be evaluated in the vehicle group, realizing the quantitative description of the thermal runaway probability of the bicycle, and realizing the prediction of the thermal runaway event of the bicycle and the comprehensive management of the group thermal runaway risk.
The invention adopts the following technical scheme:
a new energy group vehicle thermal runaway risk assessment method based on digital twinning comprises the following steps:
s1, battery data preprocessing: extracting the data of the BMS battery pack at the vehicle end as primary observation characteristic quantity, and processing the primary observation characteristic quantity to obtain characteristic quantity as secondary observation characteristic quantity; storing the primary observation characteristic quantity and the secondary observation characteristic quantity to a cloud BMS database by taking the battery pack as a unit;
s2, establishing a new energy vehicle group: storing all vehicle information connected to the cloud BMS, and establishing parallel new energy vehicle groups at the same level according to the battery model, specification and batch;
s3, establishing a cloud battery model: establishing a cloud battery digital twin model according to a cloud BMS database;
s4, extracting the gain of the thermal runaway probability information of the thermal runaway inducement: simulating different new energy vehicle groups through a cloud battery digital twin model, and calculating different vehicle monomer thermal runaway evaluation values of the new energy vehicle groups by utilizing the primary observation characteristic quantity, the secondary observation characteristic quantity, the thermal runaway probability information weighted value and the deviation value;
s5, warning of thermal runaway: and the cloud BMS observes the whole new energy vehicle group, calculates and fits and updates the thermal runaway warning threshold of the new energy vehicle group in real time, selects the vehicle monomer with the thermal runaway evaluation value higher than the threshold, and sends the thermal runaway warning.
Further, in step S1, the first observation feature amount includes: chemical materials, the number of monomers, the shape of the monomers, the number of use cycles, a series-parallel connection mode, a heat dissipation mode, battery pack voltage, battery pack temperature, charging current, discharging current, monomer voltage and monomer temperature; the secondary observation feature quantity includes: performing filtering operation on all voltages, temperatures and currents in the characteristic quantity through primary observation, and calculating to obtain the differentials of all voltages, temperatures and currents; and (4) processing according to the once observed characteristic quantity to obtain the efficiency, the internal resistance, the battery self-discharge rate and a relaxation curve during standing.
Further, the step S2 further includes: establishing two layers of cloud databases, wherein the first layer of databases are overall databases and comprise an index information database and a vehicle group overall database; the second-layer database comprises a power battery digital twin model database, a vehicle group database facing automobile manufacturers, a vehicle group database facing battery manufacturers and a vehicle group database facing users.
Further, the power battery digital twin model database stores all vehicle data in the whole life cycle of the vehicle group, and the battery models and specifications of the vehicle group are similar; the database for the automobile manufacturer stores processing data for preventing thermal runaway events of a power domain and a chassis domain which are sent to the automobile manufacturer; a database facing a battery manufacturer stores primary observation characteristic quantity and secondary observation characteristic quantity; the user-oriented vehicle group database stores the maximum temperature of the single battery, the maximum voltage, the SOC value of the battery, the SOH value of the battery, the timestamp of the latest abnormal state of the battery and safe use suggestions sent by automobile manufacturers and battery manufacturers, which can be checked by users.
Further, step S3 includes establishing a cloud battery digital twin model according to the power battery digital twin model database, where the cloud battery digital twin model includes physical parameters of the battery, process parameters of the battery, and secondary observation characteristic quantities.
Further, in the step S4, a weight value of the feature amount is givenW x Initial value of (1), given deviation valueBMultiplying the characteristic quantity by the weight value to obtain a probability factor of the thermal runaway, and summing the probability factor and the deviation value to obtain a thermal runaway probability valueSThe formula is as follows:
recording thermal runaway asYThe formula for the thermal runaway probability is as follows:
converting the probability formula into a rating formula through logarithmic transformation, and obtaining the thermal runaway evaluation value of the vehicle monomer through the rating formulaAThe formula is as follows:
wherein the chemical material isX 1The number of the monomers isX 2In the form of a single body ofX 3The number of use cycles isX 4Has an efficiency ofηInternal resistance ofRIn a series-parallel connection mode ofX 5The heat dissipation mode isX 6The voltage of the battery pack isV pck The charging current of the battery pack isI chg The discharge current of the battery pack isI dischg The temperature of each temperature acquisition point of the battery pack isT 1…T N 、SOC OCV Calculated for open circuit voltage methodSOC、SOC APTICalculated for ampere-hour integrationSOC、SOC Kalman Calculated for Kalman filteringSOC、SOH CAP Calculated for volumetric methodSOH、SOH POW Calculated for maximum available powerSOHThe cell voltage isV cell1…V cellN The temperature of the battery cell isT cell1…T cellN Differential value of cell voltage of
Differential value of cell current of
Differential value of the cell temperature of
。
Further, still include: and (3) weight value correction, namely, BMS observation once quantity data is uploaded every time at the vehicle end, the following method is used for fitting the weight values of all input parameters in real time according to the thermal runaway behavior characteristics of the BMS observation once, and a function loss value is calculated through a log-likelihood function, wherein the formula is as follows:
and calculating the derivative of each weight value, and updating each weight value by using a gradient descent method, so that the evaluation value of the power battery with the group thermal runaway behavior characteristic is increased and ranked at the top.
Further, in step S5, the sending of the warning of thermal runaway specifically includes: sending thermal runaway warnings to all vehicles in the same group, wherein the vehicles are higher than the thermal runaway warning threshold value, and shortening a vehicle sampling period; extracting data from a vehicle group database facing an automobile manufacturer and a vehicle group database facing a battery manufacturer, warning the vehicle manufacturer and the battery manufacturer that the vehicle manufacturer and the battery manufacturer may have a thermal runaway risk, and providing a processing protection method aiming at the current battery condition so as to correct the problem and reduce a thermal runaway evaluation value of the battery below a safety value; data are extracted from a user-oriented database, a warning report is sent to a user to remind the user of safe vehicle utilization, and meanwhile, the time interval between data acquisition and data uploading of a vehicle-end BMS is shortened, so that thermal runaway of a vehicle power battery is prevented.
Further, in step S5, with the estimated value of the vehicle at 5 times the percentage of the number of thermal runaway already occurring in the current vehicle group to the total number of the group as the current thermal runaway warning threshold, the calculation method is as follows:
in the formula:
indicates the number of vehicles in which thermal runaway occurs,Nthe total number of the group vehicles is represented,R threshold a percentage of the rank representing the selection threshold,A threshold indicating a thermal runaway warning threshold selected according to the ranking percentage.
Further, the real-time fitting updating of the warning threshold for thermal runaway of the new energy vehicle group is specifically that if the evaluation values of the vehicles ranked at the front in the current group are all less than 25% of the threshold, the evaluation values are reduced to 0.99 times of the original evaluation values in each period by taking 10s as a period until the evaluation values of the vehicles are greater than or equal to the threshold; if the number of the vehicles above the threshold is higher than 35% of the total number of the vehicle groups, the number of the vehicles above the threshold is increased to 1.1 times of the original number of the vehicles per cycle by taking 10s as a cycle until the number of the vehicles above the threshold is 15% -20% of the total number of the vehicle groups, so that the computing pressure and the network occupancy rate of the cloud are reduced; if the current cloud computing power utilization rate is less than 5%, the interval of vehicle end uploading data is more than or equal to 20s, the cloud reduces the threshold, computing power and network resources are distributed to vehicle units 1% before the thermal runaway evaluation value rank occurs, and otherwise, if the cloud computing power utilization rate or the network occupancy rate is more than 75%, the threshold is increased.
The invention has the advantages that:
(1) group management: the method has the advantages that the automobile group is established, because the thermal runaway of the power battery is a small-probability event, the occurrence probability of the thermal runaway is difficult to calculate, the small-probability event of the monomer thermal runaway is converted into the group thermal runaway evaluation ranking, the probability of the possible occurrence of the monomer is not limited, and the method is more prone to concern the partial vehicle monomers with the potential possibility of the thermal runaway.
(2) Early warning can be carried out: the invention only calculates the thermal runaway evaluation value of the power battery, but not the probability value, so that the battery states of a plurality of vehicles can be compared with each other to intervene in time.
(3) The real-time performance is high: by using a digital twinning technology, the vehicle-mounted BMS and the cloud BMS are corrected mutually, so that the effect of real-time monitoring is achieved;
(4) fine management: integrating the data of all the new energy vehicles connected with the cloud end by using a big data technology to calculate an evaluation value for each vehicle;
(5) data deep mining: the method comprises the steps of utilizing a cloud database and high calculation power, calculating secondary observed quantity data through vehicle-end observation primary observed quantity data, inputting all primary and secondary observed quantities into a model for operation, simultaneously considering the coupling relation among different observed quantities by the model, and enabling the data to have the characteristics of comprehensiveness, multilevel, progressive and the like.
(6) The following can be evaluated: and converting the unpredictable events in the battery thermal runaway small-probability events into the events which can be evaluated by using a data-driven statistical method.
Drawings
FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a schematic structural view of the present invention;
FIG. 3 is a diagram illustrating a database structure according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The work flow of the invention from the system level is shown in fig. 1. The method needs to be realized by a vehicle-end battery, a vehicle-end BMS, a cloud-end battery model, a battery pack database and a cloud-end computing center. According to the digital twinning technology, the data of the battery pack and the battery monomer are collected through the vehicle-mounted BMS, various data of the battery are calculated and uploaded to the cloud through 5G, and various corresponding indexes are calculated through the received data by the cloud. The cloud BMS obtains the correction data through calculation, and then modifies the cloud model battery data, transmits the modified model data to the vehicle-mounted BMS. The model operation flow is shown in fig. 2.
The invention discloses a new energy group vehicle thermal runaway evaluation method based on digital twinning, which comprises the following specific implementation methods:
s1, battery data preprocessing: according to data in the cloud BMS database, original data uploaded to the database by the BMS at the vehicle end serve as primary observed quantities, and on the basis, secondary observed quantities independent of the primary observed quantities are obtained through feature extraction. According to the method, the primary observation quantity and the secondary observation quantity are used as the input of the digital twin model together, so that the data dimension of the model input is improved.
With car end BMS battery pack data as battery once observation characteristic extraction, once observe the characteristic and include: chemical materials, monomer number, monomer shape, use cycle times, series-parallel connection mode, heat dissipation mode, battery pack voltage, temperature, charge/discharge current, and all monomer voltage and temperature. The invention also respectively utilizes an OCV method, an ampere-hour integration method and a Kalman filtering method to calculate the SOC value, and uses two methods of capacity attenuation based and power attenuation based to calculate the SOH, and all the obtained SOC and SOH values are also used as one-time observed quantity. And simultaneously, independently processing the efficiency, the internal resistance, the battery self-discharge rate and a relaxation curve during standing according to the primary observation characteristic quantity to be used as the secondary observation characteristic quantity to expand and describe the characteristic quantity of the battery. And storing the obtained battery characteristic quantity to a cloud BMS database by taking the battery pack as a unit.
S2, establishing a new energy vehicle group: and storing the information of all vehicles connected to the cloud, and establishing parallel new energy vehicle groups of the same level according to the battery model, specification and batch.
As shown in fig. 3, two layers of database structures are established according to different object-oriented, and the first layer of database structure is an overall database and contains all internal database information. The second layer database needs to establish a power battery digital twin model database, a vehicle group database facing automobile manufacturers, a vehicle group database facing battery manufacturers and a vehicle group database facing users. The power battery digital twin model database needs to store all vehicle data in a specified format in the whole life cycle of a vehicle group, and batteries of the vehicle group in the power battery digital twin model database should meet the requirements of similar models and specifications, so that the group evaluation and sequencing of thermal runaway of the vehicle power batteries are facilitated; the database for the automobile manufacturer stores the processing data for preventing the thermal runaway event of the power domain and the chassis domain which are calculated by the subsequent steps and are sent to the automobile manufacturer; the database for the battery manufacturer stores the data of secondary observation characteristic quantities such as primary observation characteristic quantities of voltage, current, temperature and the like, efficiency, internal resistance, battery self-discharge rate, relaxation curve during standing and the like which are calculated subsequently; the user-oriented vehicle group database stores the highest temperature, the highest voltage, the SOC value and the SOH value of the single battery which can be checked by the user, the timestamp of the latest abnormal state of the battery and safe use suggestions sent by automobile manufacturers and battery manufacturers.
S3, establishing a cloud battery model: according to a power battery digital twin model database in a second layer database in a cloud BMS database, a cloud battery digital twin model is established, the model comprises the physical parameters of the battery, such as the information of battery production batch, model, physical size, chemical materials, monomer number, monomer shape, use times, a series-parallel connection mode, a heat dissipation mode and the like, the process parameters of the battery, such as battery pack voltage, current, temperature, SOC calculated by different methods, SOH calculated by different methods and the like, and secondary observation characteristic quantities, such as battery efficiency, internal resistance, self-discharge rate, internal resistance, relaxation curve during standing and the like, which are independently processed according to the primary observation characteristic quantities, and the cloud power battery digital twin model is established through various types of parameters of the battery. Different vehicle groups are simulated through the cloud battery digital twin model so as to adjust the weight and deviation of the thermal runaway probability information.
S4, extracting the gain of the thermal runaway probability information of the thermal runaway inducement: in the automobile group, vehicle units in both running and charging states are screened. The historical characteristic quantity and the current characteristic quantity value of battery in the last minute in the high in the clouds BMS database are taken out, and current characteristic quantity directly is used for the model input, and historical characteristic quantity is used for the derivation of characteristic quantity to as one of secondary observation characteristic quantity input model, wherein the characteristic quantity includes: chemical materials (asX 1) Number of monomers (notation)X 2) Monomer shape (described asX 3) And the number of cycles of use (noted asX 4) Efficiency (note asη) Internal resistance (note asR) In series-parallel connection (notation)X 5) And the heat dissipation manner (mark asX 6) Voltage of battery pack (note asV pck ) And battery pack charging (note asI chg ) Discharge current (asI dischg ) Temperature of each temperature acquisition point of the battery pack (recorded asT 1…T N ) And three kinds of SOC (SOC calculated by open circuit voltage method, recorded asSOC OCV (ii) a The SOC is calculated by the ampere-hour integration method and is recorded asSOC APTI(ii) a Calculation by Kalman filtering methodSOCIs marked asSOC Kalman ) Two kinds of SOH (volumetric SOH calculation, recorded asSOH CAP Maximum available Power method for calculating SOH, noteSOH POW ) And the cell voltage (described asV cell1…V cellN ) And the cell temperature (described asT cell1…T cellN ) And differential values of all voltages (noted as
) Differential value of current (described as
) Differential value of temperature (described as
). All the characteristic quantities are given their weight values (noted asW x ) Initial value of (d), given deviation value (note as)B) Multiplying the characteristic quantity by the weight value to obtain a probability factor of the thermal runaway, and summing all the factors and the deviation values to obtain a thermal runaway probability valueSThe formula is as follows:
recording thermal runaway asYThen probability of thermal runaway
The formula of (1) is as follows:
converting the probability formula into a rating formula through logarithmic transformation, and obtaining the evaluation value of the vehicle monomer through the rating formulaAThe formula is as follows:
particularly, every time the BMS observes the data once, the BMS real-timely fits the weighted value of each input parameter according to the thermal runaway behavior characteristic, and calculates the function loss value through the log-likelihood function, wherein the formula is as follows:
and calculating the derivative of each weight value, and updating each weight value of the formula by using a gradient descent method, so that the evaluation value of the power battery with the group thermal runaway behavior characteristic is increased, the ranking is advanced, and the reliability of the invention is further improved.
S5, warning of thermal runaway: and sequencing the thermal runaway evaluation values of different automobile units of the same new energy automobile group, and sequencing the evaluation values in all the automobile groups. And taking the evaluation value of the vehicle which is 5 times of the percentage of the number of the thermal runaway generated in the current vehicle group to the total number of the group as the current thermal runaway warning threshold, wherein the thermal runaway warning threshold calculation method comprises the following steps:
in the formula:
indicates the number of vehicles in which thermal runaway occurs,Nthe total number of the group vehicles is represented,R threshold a percentage of the rank representing the selection threshold,A threshold indicating a thermal runaway warning threshold selected according to the ranking percentage.
And sending thermal runaway warning to all vehicles in the same group, wherein the vehicles are higher than the thermal runaway warning threshold, and shortening the vehicle sampling period. And regulating the BMS control parameters of the vehicle end by using a digital twinning technology so as to ensure that the power battery operates again in a safety window. And the cloud end modifies the early warning threshold value in real time according to the current computing power condition and the condition of the vehicle group.
If the vehicle evaluation values ranked at the front in the current group are all smaller than 25% of the threshold value, 10s can be properly taken as a period, and each period is reduced to 0.99 times of the original period until the vehicle evaluation values are larger than or equal to the threshold value; if the number of vehicles above the threshold is higher than 35% of the total number of the vehicle groups, the number of the vehicles above the threshold is increased to 1.1 times of the original number of the vehicles per cycle by taking 10s as a cycle until the number of the vehicles above the threshold is 15% to 20% of the total number of the vehicle groups, so that the computing pressure and the network occupancy rate of the cloud are reduced. If the current cloud computing power utilization rate is less than 5%, the interval of vehicle-side uploaded data is more than or equal to 20s, the cloud can reduce the threshold value according to the strategy so as to distribute more computing power and network resources to the vehicle units which are 1% of the rank of the thermal runaway evaluation value, and otherwise, if the cloud computing power utilization rate or the network occupancy rate is more than 75%, the threshold value can be increased according to the strategy so as to pay close attention to the vehicle units with the high evaluation values.
And selecting the vehicle where the battery is positioned higher than the threshold value according to the threshold value, extracting data from a vehicle group database facing an automobile manufacturer and a vehicle group database facing a battery manufacturer respectively, warning the automobile manufacturer and the battery manufacturer that the thermal runaway risk possibly exists, and providing a processing and protecting method aiming at the current battery condition so as to correct the problem and reduce the thermal runaway evaluation value of the battery below a safety value. The method comprises the steps of extracting data from a user-oriented database, sending a warning report to a user, reminding the user of safe vehicle utilization, shortening the time interval of data acquisition and data uploading of a vehicle-end BMS (battery management system), wherein the time interval can be 20s, 10s and 5s, preferably, the vehicle battery is monitored in a period of 10s or less, and the thermal runaway of the vehicle power battery is prevented.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (10)
1. A new energy group vehicle thermal runaway risk assessment method based on digital twinning is characterized by comprising the following steps:
s1, battery data preprocessing: extracting the data of the BMS battery pack at the vehicle end as primary observation characteristic quantity, and processing the primary observation characteristic quantity to obtain characteristic quantity as secondary observation characteristic quantity; storing the primary observation characteristic quantity and the secondary observation characteristic quantity to a cloud BMS database by taking the battery pack as a unit;
s2, establishing a new energy vehicle group: storing all vehicle information connected to the cloud BMS, and establishing parallel new energy vehicle groups at the same level according to the battery model, specification and batch;
s3, establishing a cloud battery model: establishing a cloud battery digital twin model according to a cloud BMS database;
s4, extracting the gain of the thermal runaway probability information of the thermal runaway inducement: simulating different new energy vehicle groups through a cloud battery digital twin model, and calculating different vehicle monomer thermal runaway evaluation values of the new energy vehicle groups by utilizing the primary observation characteristic quantity, the secondary observation characteristic quantity, the thermal runaway probability information weighted value and the deviation value;
s5, warning of thermal runaway: and the cloud BMS observes the whole new energy vehicle group, calculates and fits and updates the thermal runaway warning threshold of the new energy vehicle group in real time, selects the vehicle monomer with the thermal runaway evaluation value higher than the threshold, and sends the thermal runaway warning.
2. The method for assessing risk of thermal runaway of a digitally twin-based new energy group vehicle as claimed in claim 1, wherein in step S1, one observation feature quantity comprises: chemical materials, the number of monomers, the shape of the monomers, the number of use cycles, a series-parallel connection mode, a heat dissipation mode, battery pack voltage, battery pack temperature, charging current, discharging current, monomer voltage and monomer temperature; the secondary observation feature quantity includes: performing filtering operation on all voltages, temperatures and currents in the characteristic quantity through primary observation, and calculating to obtain the differentials of all voltages, temperatures and currents; and (4) processing according to the once observed characteristic quantity to obtain the efficiency, the internal resistance, the battery self-discharge rate and a relaxation curve during standing.
3. The method for assessing risk of thermal runaway of a digitally twin-based new energy group vehicle as claimed in claim 1, wherein the step S2 further includes: establishing two layers of cloud databases, wherein the first layer of databases are overall databases and comprise an index information database and a vehicle group overall database; the second-layer database comprises a power battery digital twin model database, a vehicle group database facing automobile manufacturers, a vehicle group database facing battery manufacturers and a vehicle group database facing users.
4. The method for evaluating the risk of thermal runaway of a vehicle in a new energy group based on digital twins as claimed in claim 3, wherein the power battery digital twins model database stores all vehicle data in the whole life cycle of the vehicle group, and the battery models and specifications of the vehicle group are similar; the database for the automobile manufacturer stores processing data for preventing thermal runaway events of a power domain and a chassis domain which are sent to the automobile manufacturer; a database facing a battery manufacturer stores primary observation characteristic quantity and secondary observation characteristic quantity; the user-oriented vehicle group database stores the maximum temperature of the single battery, the maximum voltage, the SOC value of the battery, the SOH value of the battery, the timestamp of the latest abnormal state of the battery and safe use suggestions sent by automobile manufacturers and battery manufacturers, which can be checked by users.
5. The method for assessing risk of vehicle thermal runaway in a new energy group based on digital twinning as claimed in claim 3 or 4, wherein the step S3 further includes establishing a cloud battery digital twinning model according to a power battery digital twinning model database, wherein the cloud battery digital twinning model includes physical parameters of the battery, process parameters of the battery, and secondary observation characteristic quantities.
6. The method for assessing risk of thermal runaway of a digitally twin-based new energy group vehicle as claimed in claim 1, wherein the step S4 is performed by giving a weight value of the characteristic quantityW x Initial value of (1), given deviation valueBMultiplying the characteristic quantity by the weight value to obtain a probability factor of the thermal runaway, and summing the probability factor and the deviation value to obtain a thermal runaway probability valueSThe formula is as follows:
recording thermal runaway asYThe formula for the thermal runaway probability is as follows:
converting the probability formula into a rating formula through logarithmic transformation, and obtaining the thermal runaway evaluation value of the vehicle monomer through the rating formulaAThe formula is as follows:
wherein the chemical material isX 1The number of the monomers isX 2In the form of a single body ofX 3The number of use cycles isX 4Has an efficiency ofηInternal resistance ofRIn a series-parallel connection mode ofX 5The heat dissipation mode isX 6The voltage of the battery pack isV pck The charging current of the battery pack isI chg The discharge current of the battery pack isI dischg The temperature of each temperature acquisition point of the battery pack isT 1…T N 、SOC OCV Calculated for open circuit voltage methodSOC、SOC APTICalculated for ampere-hour integrationSOC、SOC Kalman Calculated for Kalman filteringSOC、SOH CAP Calculated for volumetric methodSOH、SOH POW Calculated for maximum available powerSOHThe cell voltage isV cell1…V cellN The temperature of the battery cell isT cell1…T cellN Differential value of cell voltage of
Differential value of cell current of
Differential value of the cell temperature of
。
7. The digital twin-based new energy group vehicle thermal runaway risk assessment method of claim 6, further comprising: and (3) weight value correction, namely, BMS observation once quantity data is uploaded every time at the vehicle end, the following method is used for fitting the weight values of all input parameters in real time according to the thermal runaway behavior characteristics of the BMS observation once, and a function loss value is calculated through a log-likelihood function, wherein the formula is as follows:
and calculating the derivative of each weight value, and updating each weight value by using a gradient descent method, so that the evaluation value of the power battery with the group thermal runaway behavior characteristic is increased and ranked at the top.
8. The method for assessing risk of thermal runaway of a vehicle in a new energy group based on digital twins as claimed in claim 1, wherein the step S5 of sending a warning of thermal runaway is specifically as follows: sending thermal runaway warnings to all vehicles in the same group, wherein the vehicles are higher than the thermal runaway warning threshold value, and shortening a vehicle sampling period; extracting data from a vehicle group database facing an automobile manufacturer and a vehicle group database facing a battery manufacturer, warning the vehicle manufacturer and the battery manufacturer that the vehicle manufacturer and the battery manufacturer may have a thermal runaway risk, and providing a processing protection method aiming at the current battery condition so as to correct the problem and reduce a thermal runaway evaluation value of the battery below a safety value; data are extracted from a user-oriented database, a warning report is sent to a user to remind the user of safe vehicle utilization, and meanwhile, the time interval between data acquisition and data uploading of a vehicle-end BMS is shortened, so that thermal runaway of a vehicle power battery is prevented.
9. The method for evaluating the risk of thermal runaway of a vehicle in a new energy group based on digital twinning as claimed in claim 1, wherein the step S5 is implemented by taking the evaluation value of the vehicle at 5 times the percentage of the number of thermal runaway already occurring in the current vehicle group to the total number of the group as the current warning threshold value of thermal runaway, and the calculation method is as follows:
in the formula:
indicates the number of vehicles in which thermal runaway occurs,Nthe total number of the group vehicles is represented,R threshold a percentage of the rank representing the selection threshold,A threshold indicating a thermal runaway warning threshold selected according to the ranking percentage.
10. The method for evaluating the risk of thermal runaway of vehicles in a new energy group based on digital twins as claimed in claim 9, wherein the real-time fitting updating of the warning threshold of thermal runaway of the new energy vehicle group is implemented by reducing the evaluation value of the vehicle in the current group to 0.99 times of the original evaluation value in a cycle of 10s if the evaluation values of the vehicles in the current group are all less than 25% of the threshold value; if the number of the vehicles above the threshold is higher than 35% of the total number of the vehicle groups, the number of the vehicles above the threshold is increased to 1.1 times of the original number of the vehicles per cycle by taking 10s as a cycle until the number of the vehicles above the threshold is 15% -20% of the total number of the vehicle groups, so that the computing pressure and the network occupancy rate of the cloud are reduced; if the current cloud computing power utilization rate is less than 5%, the interval of vehicle end uploading data is more than or equal to 20s, the cloud reduces the threshold, computing power and network resources are distributed to vehicle units 1% before the thermal runaway evaluation value rank occurs, and otherwise, if the cloud computing power utilization rate or the network occupancy rate is more than 75%, the threshold is increased.
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