CN117087485A - Charging safety monitoring system and early warning system of high-power SiC electric automobile - Google Patents
Charging safety monitoring system and early warning system of high-power SiC electric automobile Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 67
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- HBMJWWWQQXIZIP-UHFFFAOYSA-N silicon carbide Chemical compound [Si+]#[C-] HBMJWWWQQXIZIP-UHFFFAOYSA-N 0.000 description 13
- 229910010271 silicon carbide Inorganic materials 0.000 description 12
- 238000001514 detection method Methods 0.000 description 6
- 238000007599 discharging Methods 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 2
- 230000035882 stress Effects 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
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- 229910052744 lithium Inorganic materials 0.000 description 1
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/66—Data transfer between charging stations and vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/60—Navigation input
- B60L2240/66—Ambient conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/60—Navigation input
- B60L2240/66—Ambient conditions
- B60L2240/662—Temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
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Abstract
The invention relates to a charging safety monitoring system and an early warning system of a high-power SiC electric vehicle, and aims to solve the technical problems that the existing charging pile or electric vehicle lacks scientific design of the charging safety monitoring system design and the early warning system of the high-power SiC electric vehicle, and particularly lacks data combination of high temperature, high humidity and high salt in a local climate environment. The method is characterized in that interaction information identification is combined with data of high temperature, high humidity and high salt in a local climate environment in a charging information monitoring terminal of the charging safety monitoring system, the remaining service life of a battery is set for an electric vehicle, a vehicle safety threshold is obtained, and when the charging danger degree of the electric vehicle reaches the vehicle safety threshold, the charging pile or the electric vehicle stops charging; and the charging pile of the early warning system is used for constructing a charging infrastructure information management platform through networking, and a charging pile data online monitoring system is established to form an intra-domain early warning monitoring system.
Description
Technical Field
The invention relates to charging safety of a new energy electric automobile, in particular to a charging safety monitoring system and an early warning system of a high-power SiC electric automobile.
Background
At present, silicon carbide (SiC) has the advantages of high-temperature operation, high blocking voltage, low loss, switching speed and the like because of the characteristic of high current density determined by high heat conductivity, so that the number of power devices, the volume of a radiator and the volume of a filter element in equipment can be greatly reduced under the same power grade, and meanwhile, the efficiency is greatly improved. Some existing new energy electric vehicles adopt the silicon carbide material to manufacture partial silicon carbide elements, such as application number 202110242856.X disclosed in Chinese patent literature, application publication date 2021.05.25, and the invention name of the invention is a high-efficiency SiC electric vehicle power converter system with wide input range; the silicon carbide elements of the new energy electric automobile are easy to generate parts with thermal runaway faults, and fault hidden dangers are easy to find. In addition, the existing safety monitoring of the charging and discharging of the high-power electric automobile is as follows, such as application number 202110659399.4 published in Chinese patent literature, and the authorized bulletin date 2022.09.16, and the invention name is "comprehensive evaluation method, equipment, system and storage medium of the distribution network based on the charging and discharging of the large-scale high-power electric automobile"; but the charging safety monitoring system and the early warning of the existing high-power electric automobile are less in specific to the silicon carbide element, and the charging safety monitoring system and the early warning of the existing high-power electric automobile are less in data of high temperature, high humidity and high salt in a local climatic environment, and are further used for carrying out safety monitoring and early warning on the charging of the electric automobile through a networked charging pile.
Disclosure of Invention
In order to overcome the defects, the invention aims to provide a charging safety monitoring system and an early warning system for a high-power SiC electric vehicle in the field, so that the technical problems that the existing charging pile or electric vehicle lacks scientific design of the charging safety monitoring system design and the early warning system for the high-power SiC electric vehicle, particularly the data combination of high temperature, high humidity and high salt in a local climate environment exists, and the charging of the electric vehicle is less subjected to safety monitoring and early warning through a networked charging pile are solved. The aim is achieved by the following technical scheme.
The charging safety monitoring system of the high-power SiC electric automobile establishes interaction information identification according to connection and charging of the electric automobile and a charging pile, the interaction information identification is arranged in a charging information monitoring terminal, the charging information monitoring terminal is arranged in the charging pile or the electric automobile, the interaction information identification of the charging information monitoring terminal comprises travel mode data and charging mode data, and the travel mode data and the charging mode data comprise the number of driving mileage immediately before charging, the charging time, the residual electric quantity immediately before charging, the charging start time and the charging end time; the method is characterized in that the interactive information of the charging information monitoring terminal is used for identifying and combining data of high temperature, high humidity and high salt in a local climate environment, the electric vehicle is provided with the residual service life of a battery to obtain a vehicle safety threshold, when the charging danger degree of the electric vehicle reaches the vehicle safety threshold, the charging pile or the electric vehicle stops charging, the electric vehicle is shot through a camera of the charging pile to obtain a charging picture, a license plate number in the charging picture is extracted and identified, and the electric vehicle sends out a vehicle overhaul alarm; when the electric vehicle charging danger degree is lower than the vehicle safety threshold, the charging pile or the electric vehicle starts to charge, and charging information is recorded, wherein the charging information comprises charging time, charging duration, charging amount, charging power and charging temperature change.
The interactive information identification of the charging information monitoring terminal adopts a CNN-LSTM electric vehicle charging load estimation model;
step s1, a CNN model calculation formula is: c t ＝f(W CNN ×n t +b CNN ) Wherein W is CNN The method comprises the steps of representing a weight coefficient of a filter in the convolution of high-power charging data of the electric automobile, namely a convolution kernel; n is n t The electric vehicle charging data at the time t is represented; b CNN The deviation coefficient of the convolution operation of the charging data of the electric automobile is represented; c t Charging data sequences of the electric automobile extracted after convolution; f represents an activation function of the convolution operation of the charging data of the electric automobile;
the calculation formula of the LSTM model is as follows:
wherein h is t 、h i Respectively representing the states of a forward hidden layer and a backward hidden layer of a single LSTM, wherein the number of hidden layer units is n, after LSTM processing, the number of hidden layer units in the LSTM is n, and finally, the hidden layer outputs an n multiplied by m feature vector;
step s2, calculating the charging mode, including charging start, charging end time and charging place; the formula is:
wherein DeltaT is time precision, which is set to be half an hour, delta (Li (u), lj (v)) is a coincidence formula, when the charging piles of two users coincide, the value is 1, otherwise, the value is 0; if COL is greater than 1/3, then the electric automobile is charged in the same charging pile, and n users are supposed to be charged in a certain delta T as time precision;
step s3, calculating the residual electric quantity, and in a charging mode, calculating the distance between the residual electric quantity and the daily driving distance;
the formula is: q (Q) r,u (t)＝Q 0,u -d u (t)w u ；
Wherein Q is 0,u In fact, the battery power, Q r,u (t) the remaining power at the immediately preceding charging; then, counting each moment of each week by a big data method, wherein a half hour is taken as a counting period;
average residual electric quantity of all electric vehicles of a certain charging pile at the moment before charging;
the formula is:
the method comprises the steps that charging load statistics of a site of a certain charging pile at a certain moment is carried out, and average time length from the beginning of charging of the charging pile to the ending of the moment is obtained; the formula is:
wherein, C represents the average capacity of n user batteries at the time of the time precision end of DeltaT, when DeltaT takes 30 minutes, each DeltaT start time is considered as i to be 1, each DeltaT end time is considered as 30 to obtain the average capacity of n user batteries at the end time; charging efficiency of the charging pile in eta time, and Pc is average power of the charging pile;
step s4, according to the data, obtaining a formula:f is as above map For the vehicle safety threshold, RUL σ Remaining life of battery of electric automobile, I r-j Is the measured value of temperature, humidity and salinity on the resistance current.
The remaining battery life of the electric automobile comprises battery capacity, internal resistance and battery stress: the formula is:
RUL t ＝a×RUL c +b×RUL r +c×RUL σ
s.t.a+b+c＝1
wherein, the weight coefficients of a, b and c are selected according to different occasions, RUL c And RUL (Rul) r The remaining life of the battery, RUL, defined in terms of capacity and internal resistance, respectively σ Defined for battery life.
The formula of temperature, humidity and salinity in the data of the high temperature, high humidity and high salt existing in the local climate environment for resistive current is as follows:
I r (a,t,h,p)＝a 0 +a 1 t+a 2 h+a 3 th+a 4 thp+a 5 ea1 t+a 6 ea2h +a 7 e a3th +a 8 ea4thp
wherein a= [ a ] 0 ,a 1 ,...,a 7 ]Is a constant coefficient matrix, t, h, p and I r Actual measured values of temperature, humidity, salinity and resistive current respectively; will resistive current actual measurement I r-j And fitting the value toMaking a difference; and the resulting resistive current deviation DeltaI r-j The method comprises the following steps: />Wherein I is r-j Is the j-th measured value of resistive current, ΔI r-j Is the j-th bias value of the resistive current.
And the interaction information of the charging information monitoring terminal identifies the charging risk through the SOC precision correction of the iteration method, the upper limit of the SOC precision correction of the iteration method is 98%, the charging risk is exceeded in the vehicle charging process, and otherwise, the charging risk is not exceeded in the charging process. The calculation of the iteration method SOC precision correction is used for judging the charging current, so that the lithium precipitation can be prevented, the problem that the battery is aged due to the large charging current is reduced, the problem that the discharging current is reduced, the problem of over-discharging phenomenon of the battery is prevented, the service life of the battery is prolonged, and the potential safety hazard of the electric automobile is reduced.
And the interaction information identification of the charging information monitoring terminal comprises charging voltage data and charging current data of the charging pile, and when the charging voltage data and the charging current data are in an oversized safety interval, the charging pile or the electric automobile starts emergency braking and stops a charging process. The emergency braking treatment measures for ensuring the charging safety can be used for restarting the charging safety monitoring system after the problem is checked.
The charging pile comprises communication equipment, charging equipment, a data receiver, a processor and a display; the processor is electrically connected with the communication device, the charging device, the data receiver and the display respectively; the communication equipment is used for receiving charging information from the power grid server, wherein the charging information comprises a license plate number and a vehicle model number of an electric vehicle to be charged; the charging equipment is used for transmitting electric energy to the electric vehicle to be charged according to the standard charging parameters of the vehicle; the data receiver is used for acquiring real-time charging parameters of the electric vehicle to be charged; the processor is used for acquiring the standard charging parameters of the vehicle of the electric vehicle to be charged according to the vehicle model; the real-time charging parameters of the vehicle and the standard charging parameters of the vehicle are input into a pre-trained analysis model of the vehicle safety threshold based on the artificial neural network; the method is characterized in that the charging pile is used for constructing a charging infrastructure information management platform through networking, establishing a charging pile data on-line monitoring system, carrying out mode analysis on a typical state through real-time high-precision data of the charging pile in the process of collecting electric automobile charging, extracting a feature vector, judging the health degree and the safety state in the process of battery charging through a clustering intelligent analysis algorithm, and establishing an early warning monitoring system to provide safety guarantee for electric automobile charging.
The charging equipment of the charging pile is provided with a voltage sensor, a current sensor and an emergency brake switch. Therefore, the safety state analysis is carried out on the battery of the electric automobile based on a data analysis algorithm by collecting and monitoring the voltage and current data of the charging pile in real time, and an early warning system is established.
The modeling method is scientific, the model precision is high, and the method is in line with the charge safety monitoring and early warning of a user by combining the data of high temperature, high humidity and high salt existing in the local climate environment and the residual service life of the battery; the method is suitable for being used as a charging safety monitoring system and an early warning system of a high-power SiC electric automobile and the technical improvement of the same model and method.
Detailed Description
The invention will now be further described in detail by means of specific implementation steps.
The charging safety monitoring system establishes interactive information identification according to the connection and charging of the electric automobile and the charging pile, the interactive information identification is arranged in a charging information monitoring terminal, the charging information monitoring terminal is arranged in the charging pile or the electric automobile, the interactive information identification of the charging information monitoring terminal comprises travel mode data and charging mode data, and the travel mode data and the charging mode data comprise the number of travel mileage before charging, the charging duration, the residual electric quantity before charging, the charging start time and the charging end time; the method is characterized in that the interactive information of the charging information monitoring terminal is used for identifying and combining data of high temperature, high humidity and high salt in a local climate environment, the remaining service life of a battery is set, a vehicle safety threshold is obtained, when the charging danger degree of the electric vehicle reaches the vehicle safety threshold, the charging pile or the electric vehicle stops charging, the electric vehicle is shot through a camera of the charging pile, a charging picture is obtained, a license plate number in the charging picture is extracted and identified, and the electric vehicle sends out a vehicle overhaul alarm; when the electric vehicle charging danger degree is lower than the vehicle safety threshold, the charging pile or the electric vehicle starts to charge, and charging information is recorded, wherein the charging information comprises charging time, charging duration, charging amount, charging power and charging temperature change.
The method comprises the specific steps of analyzing data of high temperature, high humidity and high salt existing in a local climatic environment through big data before modeling, wherein the formula of the temperature, humidity and salinity in the data of the high temperature, high humidity and high salt existing in the local climatic environment for resistive current is as follows:
I r (a,t,h,p)＝a 0 +a 1 t+a 2 h+a 3 th+a 4 thp+a 5 ea1 t+a 6 ea2h +a 7 e a3th +a 8 ea4thp
wherein a= [ a ] 0 ,a 1 ,...,a 7 ]Is a constant coefficient matrix, t, h, p and I r Actual measured values of temperature, humidity, salinity and resistive current respectively; actual measurement value I of resistive current r-j And fitting the value toMaking a difference; and the resulting resistive current deviation DeltaI r-j The method comprises the following steps: />Wherein I is r-j Is the j-th measured value of resistive current, ΔI r-j Is the j-th bias value of the resistive current. Obtaining the residual life of the battery of the electric automobile, and
the remaining battery life of the electric automobile comprises battery capacity, internal resistance and battery stress: the formula is:
RUL t ＝a×RUL c +b×RUL r +c×RUL σ
s.t.a+b+c＝1
wherein, the weight coefficients of a, b and c are selected according to different occasions, RUL c And RUL (Rul) r The remaining life of the battery, RUL, defined in terms of capacity and internal resistance, respectively σ Defined for battery life.
Then, the interaction information of the charging information monitoring terminal is identified by adopting a CNN-LSTM electric vehicle charging load estimation model;
step s1, a CNN model calculation formula is: c t ＝f(W CNN ×n t +b CNN ) Wherein W is CNN The method comprises the steps of representing a weight coefficient of a filter in the convolution of high-power charging data of the electric automobile, namely a convolution kernel; n is n t The electric vehicle charging data at the time t is represented; b CNN The deviation coefficient of the convolution operation of the charging data of the electric automobile is represented; c t Charging data sequences of the electric automobile extracted after convolution; f represents an activation function of the convolution operation of the charging data of the electric automobile;
the calculation formula of the LSTM model is as follows:
wherein h is t 、h i Respectively representing the states of a forward hidden layer and a backward hidden layer of a single LSTM, wherein the number of hidden layer units is n, after LSTM processing, the number of hidden layer units in the LSTM is n, and finally, the hidden layer outputs an n multiplied by m feature vector;
step s2, calculating the charging mode, including charging start, charging end time and charging place; the formula is:
wherein DeltaT is time precision, which is set to be half an hour, delta (Li (u), lj (v)) is a coincidence formula, when the charging piles of two users coincide, the value is 1, otherwise, the value is 0; if COL is greater than 1/3, then the electric automobile is charged in the same charging pile, and n users are supposed to be charged in a certain delta T as time precision;
step s3, calculating the residual electric quantity, and in a charging mode, calculating the distance between the residual electric quantity and the daily driving distance;
the formula is: q (Q) r,u (t)＝Q 0,u -d u (t)w u ；
Wherein Q is 0,u In fact, the battery power, Q r,u (t) the remaining power at the immediately preceding charging; then, counting each moment of each week by a big data method, wherein a half hour is taken as a counting period;
average residual electric quantity of all electric vehicles of a certain charging pile at the moment before charging;
the formula is:
the method comprises the steps that charging load statistics of a site of a certain charging pile at a certain moment is carried out, and average time length from the beginning of charging of the charging pile to the ending of the moment is obtained; the formula is:
wherein, C represents the average capacity of n user batteries at the time of the time precision end of DeltaT, when DeltaT takes 30 minutes, each DeltaT start time is considered as i to be 1, each DeltaT end time is considered as 30 to obtain the average capacity of n user batteries at the end time; charging efficiency of the charging pile in eta time, and Pc is average power of the charging pile;
step s4, according to the data, obtaining a formula:f is as above map For the vehicle safety threshold, RUL σ Remaining life of battery of electric automobile, I r-j Is the measured value of temperature, humidity and salinity on the resistance current.
Meanwhile, the interaction information of the charging information monitoring terminal identifies the charging risk corrected by the SOC precision of the iteration method, the upper limit of the SOC precision correction of the iteration method is 98%, the charging risk is exceeded in the vehicle charging process, and otherwise, the charging risk is not exceeded in the charging process. And the interaction information identification of the charging information monitoring terminal comprises charging voltage data and charging current data of the charging pile, and when the charging voltage data and the charging current data are in an oversized safety interval, the charging pile or the electric automobile starts emergency braking and stops a charging process.
According to the charging safety monitoring system, a safety early warning system is further established, and charging piles in the region are networked, specifically as follows: the charging pile comprises communication equipment, charging equipment, a data receiver, a processor and a display; the processor is electrically connected with the communication device, the charging device, the data receiver and the display respectively; the communication equipment is used for receiving charging information from the power grid server, wherein the charging information comprises a license plate number and a vehicle model number of an electric vehicle to be charged; the charging equipment is used for transmitting electric energy to the electric vehicle to be charged according to the standard charging parameters of the vehicle; the data receiver is used for acquiring real-time charging parameters of the electric vehicle to be charged; the processor is used for acquiring the standard charging parameters of the vehicle of the electric vehicle to be charged according to the vehicle model; and inputting the real-time charging parameters of the vehicle and the standard charging parameters of the vehicle into a pre-trained analysis model of the vehicle safety threshold based on the artificial neural network. The charging pile is used for constructing a charging infrastructure information management platform through networking, establishing a charging pile data online monitoring system, analyzing a typical state through real-time high-precision data of the charging pile in the process of collecting electric vehicle charging, extracting a characteristic vector, judging the health degree and the safety state in the process of charging a battery through a clustering intelligent analysis algorithm, and establishing an early warning monitoring system to provide safety guarantee for electric vehicle charging. The charging equipment of the charging pile is provided with a voltage sensor, a current sensor and an emergency brake switch.
The charging safety monitoring system and the early warning system are designed aiming at the technical problems that the charging infrastructure information management platform of the existing new energy electric vehicle of the SiC electric drive system and the built charging pile data on-line monitoring system are difficult to realize real-time high-precision data acquisition, mode analysis and health degree and safety state judgment of the charging pile in the electric vehicle charging process, and the early warning monitoring system is built to provide safety guarantee for the electric vehicle charging. The charging safety monitoring system and the early warning system are based on a charging infrastructure information management platform and an established charging pile data on-line monitoring system, and further collect real-time high-precision data of a charging pile in the charging process of an electric automobile, wherein the real-time collection and monitoring of voltage and current data comprises the following steps: the parallel mode of the data forwarding method and the data direct connection method, and the high-precision data acquisition protocol, formula and model; 2) Carrying out mode analysis on the typical state, and analyzing the battery charging safety state based on a data analysis algorithm; 3) Extracting a feature vector; 4) The health degree and the safety state of the battery in the charging process are judged through a clustering intelligent analysis algorithm, an early warning monitoring system is established, safety monitoring and early warning are continued in the charging process of the electric automobile, and safety guarantee is provided for charging of the electric automobile.
In summary, the charging safety detection system develops the new energy automobile battery safety detection service in a nationwide range, performs periodic and forced vehicle-mounted power system safety detection on the new energy automobile, is combined with a charging supervision platform, monitors the battery health of the new energy automobile in the charging process, provides battery safety information for the automobile user, and perfects an essential important link of a new energy automobile safety early warning mechanism. The charging safety detection system can also provide further services in the aspects of vehicle maintenance, vehicle repair, second-hand vehicle pricing, vehicle insurance damage assessment and the like, and creates more social values and market values. Meanwhile, due to the characteristics of high temperature, high humidity and high salt in a certain climate-saving environment, the aging of a new energy automobile system can be accelerated, and safety problems and hidden dangers are more easily caused. In addition, the charging safety detection system is popularized and applied in the full-province charging pile range through the rapid detection of the battery capacity and the safety state analysis in the charging process of the electric automobile, serves a government administration of the charging pile in a certain province, and can provide relevant monitoring services for owners of the full-province new energy automobile at the same time so as to improve the service level of the charging supervision platform in the province and have important significance for the charging safety guarantee of the electric automobile.
Claims (8)
1. The charging safety monitoring system of the high-power SiC electric automobile establishes interaction information identification according to connection and charging of the electric automobile and a charging pile, the interaction information identification is arranged in a charging information monitoring terminal, the charging information monitoring terminal is arranged in the charging pile or the electric automobile, the interaction information identification of the charging information monitoring terminal comprises travel mode data and charging mode data, and the travel mode data and the charging mode data comprise the number of driving mileage immediately before charging, the charging time, the residual electric quantity immediately before charging, the charging start time and the charging end time; the method is characterized in that the interactive information of the charging information monitoring terminal is used for identifying and combining data of high temperature, high humidity and high salt in a local climate environment, the electric vehicle is provided with the residual service life of a battery to obtain a vehicle safety threshold, when the charging danger degree of the electric vehicle reaches the vehicle safety threshold, the charging pile or the electric vehicle stops charging, the electric vehicle is shot through a camera of the charging pile to obtain a charging picture, a license plate number in the charging picture is extracted and identified, and the electric vehicle sends out a vehicle overhaul alarm; when the electric vehicle charging danger degree is lower than the vehicle safety threshold, the charging pile or the electric vehicle starts to charge, and charging information is recorded, wherein the charging information comprises charging time, charging duration, charging amount, charging power and charging temperature change.
2. The charging safety monitoring system of the high-power SiC electric vehicle according to claim 1, wherein the interaction information identification of the charging information monitoring terminal adopts a CNN-LSTM electric vehicle charging load estimation model;
step s1, a CNN model calculation formula is: c t ＝f(W CNN ×n t +b CNN ) Wherein W is CNN The method comprises the steps of representing a weight coefficient of a filter in the convolution of high-power charging data of the electric automobile, namely a convolution kernel; n is n t The electric vehicle charging data at the time t is represented; b CNN The deviation coefficient of the convolution operation of the charging data of the electric automobile is represented; c t Charging data sequences of the electric automobile extracted after convolution; f represents an activation function of the convolution operation of the charging data of the electric automobile;
the calculation formula of the LSTM model is as follows:
wherein h is t 、h i Respectively representing the states of the forward and backward hidden layers of the single LSTM, wherein the number of hidden layer units is n, the number of hidden layer units in the LSTM is n after LSTM processing, and finallyThe hidden layer outputs an n×m feature vector;
step s2, calculating the charging mode, including charging start, charging end time and charging place; the formula is:
wherein DeltaT is time precision, which is set to be half an hour, delta (Li (u), lj (v)) is a coincidence formula, when the charging piles of two users coincide, the value is 1, otherwise, the value is 0; if COL is greater than 1/3, then the electric automobile is charged in the same charging pile, and n users are supposed to be charged in a certain delta T as time precision;
step s3, calculating the residual electric quantity, and in a charging mode, calculating the distance between the residual electric quantity and the daily driving distance;
the formula is: q (Q) r,u (t)＝Q 0,u -d u (t)w u ；
Wherein Q is 0,u In fact, the battery power, Q r,u (t) the remaining power at the immediately preceding charging; then, counting each moment of each week by a big data method, wherein a half hour is taken as a counting period;
average residual electric quantity of all electric vehicles of a certain charging pile at the moment before charging;
the formula is:
the method comprises the steps that charging load statistics of a site of a certain charging pile at a certain moment is carried out, and average time length from the beginning of charging of the charging pile to the ending of the moment is obtained; the formula is:
wherein, C represents the average capacity of n user batteries at the time of the time precision end of DeltaT, when DeltaT takes 30 minutes, each DeltaT start time is considered as i to be 1, each DeltaT end time is considered as 30 to obtain the average capacity of n user batteries at the end time; charging efficiency of the charging pile in eta time, and Pc is average power of the charging pile;
step s4, according to the data, obtaining a formula:f is as above map For the vehicle safety threshold, RUL σ Remaining life of battery of electric automobile, I r-j Is the measured value of temperature, humidity and salinity on the resistance current.
3. The charge safety monitoring system of a high power SiC electric vehicle of claim 2, wherein the battery remaining life comprises battery capacity, internal resistance, and battery stress: the formula is:
RUL t ＝a×RUL c +b×RUL r +c×RUL σ ；
s.t.a+b+c＝1；
wherein, the weight coefficients of a, b and c are selected according to different occasions, RUL c And RUL (Rul) r The remaining life of the battery, RUL, defined in terms of capacity and internal resistance, respectively σ Defined for battery life.
4. The charge safety monitoring system of the high-power SiC electric vehicle according to claim 2, wherein the formula of the temperature, humidity, salinity versus resistive current in the data of the presence of high temperature, high humidity and high salt in the local climate environment is:
I r (a,t,h,p)＝a 0 +a 1 t+a 2 h+a 3 th+a 4 thp+a 5 ea1 t+a 6 ea2h +a 7 e a3th +a 8 ea4thp ；
wherein a= [ a ] 0 ,a 1 ,...,a 7 ]Is a constant coefficient matrix, t, h, p and I r Actual measured values of temperature, humidity, salinity and resistive current respectively; will resistive current actual measurement I r-j And fitting the value toMaking a difference; and the resulting resistive current deviation DeltaI r-j The method comprises the following steps:wherein I is r-j Is the j-th measured value of resistive current, ΔI r-j Is the j-th bias value of the resistive current.
5. The charging safety monitoring system of the high-power SiC electric vehicle according to claim 1, wherein the interaction information of the charging information monitoring terminal identifies the charging risk corrected by the iteration method SOC precision, the upper limit of the iteration method SOC precision correction is 98%, the charging risk is exceeded in the vehicle charging process, and otherwise, the charging risk is not exceeded in the charging process.
6. The charging safety monitoring system of the high-power SiC electric vehicle according to claim 1, wherein the interactive information identification of the charging information monitoring terminal comprises charging voltage data and charging current data of a charging pile, and when the charging voltage data and the charging current data are in an oversized safety interval, the charging pile or the electric vehicle starts emergency braking and stops a charging process.
7. A safety precaution system utilizing the charge safety monitoring system of the high-power SiC electric vehicle of claim 1, the charging pile comprising a communication device, a charging device, a data receiver, a processor and a display; the processor is electrically connected with the communication device, the charging device, the data receiver and the display respectively; the communication equipment is used for receiving charging information from the power grid server, wherein the charging information comprises a license plate number and a vehicle model number of an electric vehicle to be charged; the charging equipment is used for transmitting electric energy to the electric vehicle to be charged according to the standard charging parameters of the vehicle; the data receiver is used for acquiring real-time charging parameters of the electric vehicle to be charged; the processor is used for acquiring the standard charging parameters of the vehicle of the electric vehicle to be charged according to the vehicle model; the real-time charging parameters of the vehicle and the standard charging parameters of the vehicle are input into a pre-trained analysis model of the vehicle safety threshold based on the artificial neural network; the method is characterized in that the charging pile is used for constructing a charging infrastructure information management platform through networking, establishing a charging pile data on-line monitoring system, carrying out mode analysis on a typical state through real-time high-precision data of the charging pile in the process of collecting electric automobile charging, extracting a feature vector, judging the health degree and the safety state in the process of battery charging through a clustering intelligent analysis algorithm, and establishing an early warning monitoring system to provide safety guarantee for electric automobile charging.
8. The safety precaution system of the charging safety monitoring system of the high-power SiC electric automobile according to claim 7, wherein the charging equipment of the charging pile is provided with a voltage sensor, a current sensor and an emergency brake switch.
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