CN117347078A - New energy vehicle health state annual inspection platform and method - Google Patents

Abstract

The invention relates to the technical field of vehicle detection, in particular to a new energy vehicle health state annual inspection platform and method. The method uses the platform, and comprises charging detection equipment, wherein the charging detection equipment is used for acquiring the parameter information of the battery system of the new energy automobile; the registration module is used for selecting detection items and registering vehicle information to generate a to-be-detected vehicle list; the on-line detection subsystem is used for collecting historical operation data in the vehicle terminal according to the detection project, analyzing the historical operation data through the cloud platform and generating a vehicle annual inspection report, and comprises an accelerator pedal detection module, a motor temperature detection module, an operation energy consumption detection module and a battery consistency detection module; and the off-line detection subsystem is used for analyzing the parameter information acquired by the charging detection equipment and generating a detection result of the battery health state. The technical scheme provides a annual inspection method for the new energy automobile, and realizes comprehensive detection of the new energy automobile including the health state of the battery.

Description

New energy vehicle health state annual inspection platform and method

Technical Field

The invention relates to the technical field of vehicle detection, in particular to a new energy vehicle health state annual inspection platform and method.

Background

Annual inspection is a key link for ensuring the safety performance, the legality and the vehicle performance of motor vehicles, and plays an important role in ensuring the daily use of vehicles and the safety of road traffic. Along with the rapid development and popularization of new energy automobile technology, the number of new energy automobiles is continuously increased, and new safety problems are brought while the travel demands of people are met.

The State of Health (SOH) of the battery has a critical influence on the range and driving safety of the vehicle. The healthy battery can ensure the stability and safety of the vehicle in the running process, and provide longer endurance mileage for the vehicle, so that a driver can use the vehicle more conveniently. If a problem occurs in the battery, such as overcharge, overdischarge, or aging, a serious safety accident may be caused. These problems may lead to sudden loss of power of the vehicle during running or dangerous situations such as fire during charging.

However, the current annual inspection process for new energy automobiles and the annual inspection process for fuel vehicles are not two-dimensional, and the detection method for the core component battery of the new energy automobile is not free, so that the health state of the battery cannot be detected. The safe driving of the new energy automobile cannot be ensured. Therefore, the detection of the battery is particularly important in annual inspection. In order to ensure safe running of the new energy automobile, the annual inspection mechanism needs to formulate a corresponding detection method aiming at the performance and the health condition of the battery, so as to discover and solve the possible problems of the battery in time and ensure safe and stable running of the vehicle.

Disclosure of Invention

The invention aims at: the technical scheme provides a new energy vehicle health state annual inspection platform and a new energy vehicle health state annual inspection method, and the new energy vehicle health state annual inspection platform and the new energy vehicle health state annual inspection method are used for comprehensively inspecting the new energy vehicle including the battery health state.

To achieve the above object, in a first aspect, an embodiment of the present disclosure provides a new energy vehicle health status annual inspection platform, including:

the charging detection equipment is used for collecting battery system parameter information of the new energy automobile;

the registration module is used for selecting detection items and registering vehicle information to generate a to-be-detected vehicle list;

the on-line detection subsystem is used for collecting historical operation data in the vehicle terminal according to the selected detection items, analyzing the historical operation data through the cloud platform and generating a vehicle annual inspection report, and comprises an accelerator pedal detection module, a motor temperature detection module, an operation energy consumption detection module and a battery consistency detection module;

and the off-line detection subsystem is used for analyzing the parameter information acquired by the charging detection equipment and generating a detection result of the battery health state.

The basic scheme has the beneficial effects that: the charging detection equipment realizes that the on-line annual inspection station can acquire the parameter information of the battery system of the vehicle in real time, so that the annual inspection station can acquire more detection data aiming at the new energy automobile, and is convenient for generating a more comprehensive annual inspection report including the battery health state detection result; the registration module can count the vehicles to be detected and detection items corresponding to the vehicles, so that the platform can better schedule detection resources. The on-line detection module is used for analyzing based on the cloud platform, so that the hardware cost of the annual inspection station can be reduced, meanwhile, the computing power resource of the cloud platform data analysis is improved, the detection efficiency is improved, the accumulation of vehicles to be detected is reduced, meanwhile, the cloud platform unifies the algorithm for analyzing, and the maintenance and iteration of the follow-up related detection algorithm are also facilitated. The off-line detection subsystem is mainly used for detecting the health state of the battery, and is mainly responsible for detecting the health state of the battery, so that the calculation power demand is not quite high, the on-line detection subsystem can be matched with the charging detection equipment to be more conveniently transformed on the basis of the existing annual inspection station, the transformation cost is reduced, and the technical scheme is convenient to popularize.

As an implementation preferable scheme, the charging detection device comprises a charging detection parameter setting module and a reverse connection protection module; the charging and detecting parameter setting module is used for setting battery capacity, charging cut-off SOC, highest single temperature, default auxiliary electric parameters and single voltage parameters; and the reverse connection protection module is used for detecting the positive and negative polarities of the battery and cutting off the power supply when the reverse connection of the polarities is found.

As an implementation preferred scheme, the off-line detection subsystem comprises a first battery health detection module and a second battery health detection module; the first battery health detection module is used for rapidly detecting the health state of the battery of the electric automobile and comprises a model construction sub-module and a first health analysis sub-module; the second battery health detection module is used for accurately detecting the health state of the battery of the electric automobile and comprises a parameter pre-identification sub-module and a second health analysis sub-module.

As a practical preferred scheme, the model construction submodule is used for constructing a battery model and an SOH estimation model, and specifically comprises the following steps:

completely discharging a sample car of a target detected car type through drum operation until SOC=0% or the battery management system is automatically powered off; then carrying out multi-stage constant current charging on the sample car, wherein the charging multiplying power is 0.1C, and the power of the car is cut off when 10% of rated capacity is charged, and the car is kept stand for 1 hour and then is charged until the SOC=100% or the charging current is automatically disconnected;

calculating the charge quantity in the whole charging process and the accumulated charge quantity before each standing to obtain the current capacity and the actual SOC at each standing; after each standing, recording the battery terminal voltage as OCV, thereby obtaining the corresponding relation of SOC-OCV, and obtaining an SOC-OCV lookup table through linear interpolation;

discharging the vehicle to 50% SOC, performing constant current pulse excitation for 10 minutes to obtain voltage feedback data, and performing parameter identification of an equivalent circuit model by using a least square method with forgetting factors so as to establish a battery model;

acquiring a characteristic sequence, randomly defining SOH of a battery model through simulation software, and inputting excitation with a duration of 5 minutes and a current multiplying power of 0.3C; normalizing the generated voltage data to obtain a characteristic sequence;

and constructing a deep convolutional neural network model, taking the characteristic sequence as a model input, taking the corresponding randomly defined SOH as a model output, and training the model to obtain an SOH estimation model.

As an implementation preferable scheme, the first health analysis sub-module performs charging excitation for 5 minutes and with current multiplying power of 0.3C on a detected vehicle with unknown SOH, and acquires voltage data in real time to obtain a feature sequence, inputs the feature sequence into an SOH estimation model, and outputs the feature sequence to obtain an SOH estimation value.

As an implementation preferable scheme, the parameter pre-identification sub-module is configured to determine a reasonable range of parameters of electric vehicles of each vehicle type, and includes the following contents:

performing HPPC full charge test on electric vehicles of all vehicle types to obtain test data, wherein the test data comprises total voltage, highest single voltage, current and SOC of a battery system; estimating the serial number of the single cells in the battery system by using the total voltage and the highest single cell voltage of the battery system to obtain average single cell voltage data of the battery system;

setting a parameter range in an electrochemical mechanism model according to a material system used by the battery;

identifying parameters in the electrochemical mechanism model by using an optimization algorithm, wherein a target optimization equation is as follows:

in the method, in the process of the invention,for measuring the voltage +.>For detecting the identification voltage of the vehicle, < >>In order to measure the SOC of the battery,to identify SOC;

and judging whether the voltage root mean square error is smaller than 30mV, otherwise resetting the parameter range.

As an implementation preferable solution, the second health analysis submodule is configured to detect a battery health condition of an electric automobile to be detected, and specifically includes:

controlling the charge detection device to charge the detected vehicle and collect voltage data by using the current working condition,

calling a corresponding parameter set in a model parameter library according to the detected vehicle type, and re-identifying the model parameter as an initial parameter of an optimization algorithm;

substituting the identified parameters into a capacity calculation formula to obtain the available capacity Q of the detected vehicle, wherein the calculation formula is as follows:

wherein F is Faraday constant, A is electrode area,for the porosity of the negative electrode->Maximum lithium ion concentration for negative electrode, +.>The lithium is intercalated for the negative electrode;

the state of health of the battery of the detected vehicle is calculated by the following calculation formula:

in the method, in the process of the invention,taking the average value calculated for a plurality of times as the final SOH for the rated capacity of the battery of the nameplate of the vehicle.

As an implementation preferred scheme, the off-line detection subsystem further comprises a model selection module, and the first battery health detection module or the second battery health detection module is selected and invoked according to the number of vehicles to be detected.

As an implementation preferred scheme, the model selection module is further configured to analyze according to historical data, predict detection requirements and trends in different time periods in the future, where the historical data includes a number of vehicles to be detected in the history, a historical detection time and a historical detection result, and adjust and call the first battery health detection module or the second battery detection module in advance according to the prediction result.

In a second aspect, an embodiment of the present disclosure further provides a new energy vehicle health status annual inspection method, which uses the new energy vehicle health status annual inspection platform, and includes the following steps:

step S100, after the user arrives at the annual inspection station, selecting a new energy automobile detection project required to be performed on the vehicle, and registering;

step S200, after the registration is completed, the registration information is sent to a platform, and the platform judges whether to call the historical running information of the vehicle and whether to use the charging detection equipment according to the registration information of the vehicle;

step S300, the online detection subsystem generates an annual inspection report by calling vehicle history information;

step S400, if the health condition of the battery needs to be detected, a detector starts a user vehicle to a new energy automobile charging detection device, and inserts a charging gun;

step S500, selecting a vehicle to be detected from a registration list, wherein the charging detection device detects that a charging gun is used, namely, starts to detect; the charging detection equipment transmits the parameter information to the platform through a SOCKET interface in the detection process;

step S600, after receiving the message of finishing detection, the platform detects the battery health state of the electric automobile through the registered service information and the data collected by the charging detection equipment, displays the detected result, and updates the information into the annual inspection report of the automobile;

and step S700, after the annual inspection report is generated, auditing the report, and after the audit is passed, transmitting the report to the annual inspection station.

Drawings

FIG. 1 is a schematic structural diagram of a new energy vehicle health status annual inspection platform;

FIG. 2 is a schematic diagram of a single particle model structure;

FIG. 3 is a schematic diagram of current conditions;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the technical scheme and advantages thereof more clear, the technical scheme of the present invention will be described in further detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of some of the embodiments of the present invention and are not limiting of the present application. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be regarded as being isolated, and they may be combined with each other to achieve a better technical effect. The same reference numerals appearing in the drawings of the embodiments described below represent the same features or components and are applicable to the different embodiments.

Furthermore, unless defined otherwise, technical or scientific terms used in the description of the invention should be given the ordinary meaning as understood by one of ordinary skill in the art to which the invention pertains.

Furthermore, it should be noted that in the description of the present invention, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

The invention is described in further detail below with reference to the accompanying drawings:

reference numerals illustrate: an electronic device 500, a processor 501, a communication interface 502, a memory 503, a bus 504.

Referring to fig. 1, a new energy vehicle health status annual inspection platform includes:

and the charging detection equipment is used for collecting the parameter information of the battery system of the new energy automobile. The charging detection equipment can read part of basic parameters of the battery system, and collect environmental parameters and part of vehicle static information through the sensor and the manual input interface.

The charging detection equipment comprises a charging detection parameter setting module, wherein the charging detection parameter setting module is used for setting battery capacity, a charging stop SOC (State of Charge), a highest monomer temperature, default auxiliary electric parameters, monomer voltage parameters and the like. After the equipment is accessed, the charging detection equipment can use a built-in algorithm and installed hardware of the charging detection equipment to acquire the parameter information of the battery system of the new energy automobile in real time.

The charging detection equipment also comprises a reverse connection protection module which is used for detecting the positive and negative polarities of the battery and immediately cutting off the power supply when the polarity reverse connection is found so as to prevent the battery from overheating and damaging and ensure the use safety of the equipment. The charging detection device can be provided with a polarity reversal protection circuit, and can also analyze whether the polarity reversal occurs by analyzing the voltage and the current in the charging process.

The registration module is used for selecting new energy automobile detection items to be performed, registering vehicle information, wherein the registered information comprises vehicle models, license plates, vehicle identification codes and the like, and generating a to-be-detected vehicle list.

And the on-line detection subsystem is used for collecting historical operation data of the vehicle terminal, analyzing the data through the cloud platform and generating a vehicle annual inspection report. The on-line detection subsystem comprises an accelerator pedal detection module, a motor temperature detection module, an operation energy consumption detection module and a battery consistency detection module.

And the accelerator pedal detection module is used for detecting whether the accelerator pedal has a problem of abnormal response. The pedal data processing sub-module, the pedal analysis sub-module and the pedal abnormality judging sub-module are included.

The pedal data processing sub-module refers to table 1 for performing data validity processing on pedal data in the history running information of the vehicle.

TABLE 1

The pedal analysis submodule is mainly used for carrying out calculation analysis on accelerator pedal data and specifically comprises the following contents:

the safety element is extracted, the safety element index is the ratio of the torque of the accelerator pedal or the brake pedal to the torque of the output motor, and the calculation formula is as follows:

in the method, in the process of the invention,accelatatopnpedal represents accelerator pedal opening and closing, brake pedal opening and closing, and motorrequirement represents drive motor torque.

Amplifying the signal and carrying out safe quantization, wherein the calculation formula is as follows:

in the method, in the process of the invention,representing the amplification factor, generally 20,/is taken>The safety element is represented, the signal amplification function is to amplify the difference, and the subsequent quantization processing is convenient; n is the length of the sequence, U is the result of signal amplification, and p is the variance entropy.

The risk accumulation probability is calculated according to the following formula:

in the method, in the process of the invention,is the probability of steady operation in the time window, namely the variance entropy, 1-p is the risk probability thereof, sp is the risk integration sequence in the current time window,>represents the maximum risk in the current observation period, i.e. Sp sequence maximum, < >>Representing the accumulated risk up to the present moment, +.>Representing the accumulated risk up to the last observation time, the current absolute risk QValue determination, ->（0≤/>And +.l) is a measure of the rate of risk change, if there is no significant risk change in the current observation period +.>=0。

The pedal abnormality judging sub-module is used for calculating and judging whether the accelerator pedal is abnormal according to the calculation result, and specifically comprises the following steps:

the risk relative cumulative velocity η is calculated as follows:

wherein MaxRiskSpeedis the velocity filtering of the Sp risk integration sequence, and then the maximum risk velocity is obtained, and normRiskSpeedis the square averaging of the Sp risk integration sequence.

And setting judgment conditions, and carrying out early warning when any condition is met, wherein the vehicle is considered to have higher starting abnormal risk.

The judging conditions include: (1) eta is more than 50; (2) pc > 0.5; (3) MaxRiskSpeed > 0.0025; (4) MaxRiskSpeed < 0.0025; (5) the number of the risk frequency R is more than 3.

The motor temperature detection module is used for detecting whether the temperature of the driving motor of the new energy automobile is abnormally increased, so that the occurrence probability of abnormal temperature rise of the driving motor is effectively reduced, and the life and property safety of a user is ensured. The system comprises a motor data processing sub-module, a temperature analysis sub-module and a motor abnormality judging sub-module.

And the motor data processing sub-module is used for extracting motor temperature detection data from the vehicle-mounted terminal and cleaning invalid data and null data. The motor temperature detection book includes: time, total current of the whole vehicle, total voltage, driving motor information, battery temperature probe information and the like are required to be included.

The temperature analysis sub-module is used for analyzing the data of each section of continuous operation, selecting the minimum temperature of the probe as an environmental temperature reference, and calculating the difference and variance between the motor temperature and the minimum temperature of the probe at each moment, wherein the calculation formula is as follows:

in the method, in the process of the invention,represents the motor temperature at time t, +.>The probe temperature at time t, +.>Indicating minimum temperature of probe, +.>Representing the difference between the motor temperature at time t and the minimum temperature of the probe, < >>Representing the variance at time t.

And carrying out motor temperature rise abnormality identification on the data of each section of operation, carrying out normalization through variance entropy, calculating through an accumulation integration method, filtering an integration sequence through speed filtering, taking the maximum value after filtering as failure accumulation probability, judging whether the risk exists or the failure occurs according to the failure accumulation probability, and adopting the calculation formula as follows:

in the method, in the process of the invention,represents the entropy of the variance at time t +.>Indicating the cumulative probability of failure at time t,

the motor abnormality judging submodule judges the current driving motor state according to the threshold value, outputs an early warning result and sets a risk threshold valueAnd fault threshold->In this embodiment, the number of the first and second electrodes,

there is a high risk of abnormal temperature rise:

there is a risk of abnormal temperature rise:。

the running energy consumption detection module is used for detecting whether the electric automobile has abnormal energy consumption conditions or not, and can timely find out vehicles with aged batteries and abnormal transmission system loss. The system comprises an energy consumption data processing sub-module, an energy consumption analysis sub-module and an energy consumption abnormality judging sub-module.

The energy consumption data processing sub-module is used for collecting energy consumption data of the electric automobile, wherein the energy consumption data comprise accumulated mileage values, total voltage, total current and the like. And removing missing values and data of the energy consumption data, cleaning the data, dividing driving fragments, and in the embodiment, dividing the driving fragments according to the accumulated driving mileage value of 1000 km.

The energy consumption analysis submodule extracts the discharge segment of each driving segment and calculates the characteristic evaluation energy consumption; extracting a discharge segment with total current greater than 0 in each driving segment, extracting data of total current I and total voltage U of the discharge segment, and calculating energy A required to be consumed by an electric vehicle for driving a corresponding accumulated mileage value, wherein the calculation formula is as follows:

；

in the method, in the process of the invention,for the energy consumption value->For consuming voltage +.>To consume current.

And the energy consumption abnormality judging sub-module is used for judging whether the energy consumption data has abnormality or not. The energy consumption of a driving segment is used as statistics to be analyzed, and an abnormal energy consumption threshold X is established, wherein the abnormal energy consumption threshold X is represented by the following formula:

in the method, in the process of the invention,is the average value of energy consumption values +.>Standard deviation of energy consumption value; application 3->The rule establishes an abnormal energy consumption threshold X of the statistic, and can be properly adjusted for different vehicle types and running environment thresholds.

And carrying out energy consumption abnormality assessment on the energy consumption value of each vehicle to be tested in each driving segment according to the abnormal energy consumption threshold value, and generating an abnormality assessment result. Specifically, the energy consumption value of each vehicle to be tested in each driving segment is compared with an abnormal energy consumption threshold value, if the energy consumption value is smaller than the abnormal energy consumption threshold value, the vehicle is a normal vehicle, and otherwise, the vehicle is an abnormal vehicle.

And the battery consistency detection module is used for timely finding out inconsistent problems such as voltage difference, capacity difference and the like in the battery pack, and avoiding faults such as overheating, overcharging, overdischarging and the like of the battery caused by the inconsistency, so that the safety of the battery pack is improved. The system comprises a battery data acquisition sub-module, a consistency analysis sub-module and a consistency judgment sub-module.

The battery data acquisition sub-module is used for acquiring and processing battery signal data, including Time, charge_status, sum_current, V (voltage matrix) and the like. The battery signal data is cleaned, invalid data such as NAN and blank space are deleted, and if there is an abnormal value in the data, the data is cleaned by one time of the sliding average value, and the data with voltage data larger than 6V and smaller than 1V are deleted.

And the consistency analysis sub-module is used for calculating and analyzing the battery signal data. The voltage data of the discharge state is selected for calculation, that is, corresponding Time and V at the Time of extraction (charge_status= 3).

Calculating an average voltage value between each cell; and extracting the characteristics of the voltage data V. The voltage data V has N columns, each column representing one cell, and N cells in total. The number of rows represents time, which is in seconds. Then calculating the average voltage value between the cells in each row, wherein the calculation formula of the m-th row is as follows:

；

calculating the difference between each cell voltage value and the average voltage value, namely: the voltage value of each column of each row is different from the average voltage value of the row, and the calculation formula of the m-th row is as follows:

and taking the absolute value as the voltage difference to obtain a voltage difference matrix D.

And the consistency judging sub-module is used for judging whether the voltage difference value data is abnormal or not. Traversing the row vectors in the voltage difference matrix D, solving the 25 quantile and the 75 quantile of the vectors, namely sequencing the row vectors, calculating the bit j=C×75% (C is the column number), rounding upwards if C×75% is not an integer, and finally 75 quantileIs the average of items j and (j+1), i.e. +.>. Likewise, 25 quantiles may be obtained. Finally obtaining the upper limit of the abnormal thresholdThe abnormal upper threshold value changes in real time according to the change of the cell voltage.

Traversing N voltage difference values of each row in the voltage difference matrix D, judging whether the voltage difference value is larger than the upper limit of an abnormal threshold, and if the condition is met, namely:and i represents time, j represents the jth cell, and whether the voltage difference value of the adjacent time points is larger than the upper limit of the abnormal threshold is judged.

If the voltage difference value of the adjacent time points is larger than the upper limit of the abnormal threshold value, marking the time pointAnd judging the frequency of meeting the abnormality judgment condition in a period of time, namely the frequency of the voltage difference value being greater than the upper limit of the abnormality threshold, if the voltage difference value is fullAfter the marking time, the condition for abnormality judgment is satisfied at least once every 2 hours. The conditions satisfying at least one abnormality judgment every 2 hours are accumulated, and so on. When the number of times of occurrence is more than 4 times and the time condition is met, the consistency abnormality is judged to be actually generated, and the time and the abnormal cell number are marked.

And the off-line detection subsystem is used for analyzing the detection data acquired by the charging detection equipment, analyzing the detection data and generating a annual inspection report of the health state of the battery. The off-line detection subsystem comprises a first battery health detection module, a second battery health detection module and a model selection module.

The first battery health detection module is used for rapidly detecting the health state of the lithium battery of the electric automobile. The system comprises a model construction sub-module and a first health analysis sub-module.

The model construction submodule is used for constructing a battery model and an SOH estimation model and specifically comprises the following contents:

firstly, completely discharging a sample vehicle of a target detected vehicle type through drum operation until SOC=0% or a Battery Management System (BMS) is automatically powered off; and then carrying out multi-stage constant current charging on the sample car, wherein the charging multiplying power is 0.1C, and the power of the car is cut off every 10% of rated capacity, and the car is kept stand for 1 hour and then is charged until the SOC=100% or the charging current is automatically cut off.

And calculating the charge quantity in the whole charging process and the accumulated charge quantity before each standing time by an ampere-hour integration method to obtain the current capacity and the actual SOC at each standing time. After each standing, recording the battery terminal voltage as OCV, thereby obtaining the corresponding relation of SOC-OCV, and obtaining the SOC-OCV lookup table through linear interpolation.

Discharging the vehicle to 50% SOC, performing constant current pulse excitation for 10 minutes to obtain voltage feedback data, and performing parameter identification of an equivalent circuit model by using a least square method with forgetting factors, thereby establishing a battery model (a first-order equivalent digital twin model).

And acquiring a characteristic sequence, randomly defining SOH of the battery model through simulation software, and inputting excitation with the duration of 5 minutes and the current multiplying power of 0.3C. Battery models at different SOHs will produce different voltage feedback; normalizing the generated voltage data to obtain a characteristic sequence, wherein the characteristic sequence acquisition method specifically comprises the following steps: the voltage at each sampling point/cut-off voltage at full charge of the battery, thereby obtaining a normalized voltage sequence. The voltage sequence and the excited current sequence are taken as characteristic sequences.

And constructing a deep convolutional neural network model, taking the characteristic sequence as a model input, taking the corresponding randomly defined SOH as a model output, and training the model to obtain an SOH estimation model.

And the first health analysis submodule is used for obtaining the SOH estimated value of the electric automobile. And controlling a charging detection device for charging excitation with the duration of 5 minutes and the current multiplying power of 0.3C for an unknown SOH detection vehicle, collecting voltage data in real time, obtaining a characteristic sequence through the characteristic sequence acquisition method, inputting the characteristic sequence into an SOH estimation model, and outputting the characteristic sequence to obtain an SOH estimation value, so that the SOH can be rapidly detected.

The second battery health detection module is used for detecting the battery health state of the electric automobile more accurately. The lithium battery of the electric automobile is in a healthy state. The system comprises a parameter pre-identification sub-module and a second health analysis sub-module.

Lithium ion batteries are a complex electrochemical system, and electrochemical mechanism models simulate battery operating characteristics by describing the internal lithium ion migration, diffusion and charge transfer behavior of the battery. Referring to fig. 2, a single particle model (Single particle model, SPM) is shown, which has high simulation accuracy and high calculation speed at a current discharge rate of less than 1C.

The parameter pre-identification sub-module is used for determining the reasonable range of the parameters of the electric automobile of each vehicle type, and comprises the following specific contents:

performing HPPC full charge test on electric vehicles of all vehicle types to obtain test data such as total voltage, highest single voltage, current, SOC and the like of a battery system; and estimating the serial number of the single cells in the battery system by using the total voltage of the battery system and the highest single cell voltage to obtain average single cell voltage data of the battery system.

And setting a reasonable parameter range for parameters in the electrochemical mechanism model according to a material system used by the battery.

And identifying 18 parameters in the electrochemical mechanism model by using a particle swarm optimization algorithm, wherein a target optimization equation of the algorithm is as follows:

in the method, in the process of the invention,for measuring the voltage +.>For detecting the identification voltage of the vehicle, < >>In order to measure the SOC of the battery,to identify the SOC.

And judging whether the voltage root mean square error is smaller than 30mV, otherwise resetting the parameter range.

The second health analysis submodule is used for detecting the health state of the battery of the electric automobile to be detected, and the specific content comprises:

referring to fig. 3, the charge detection device is controlled to charge the detected vehicle using the current condition and collect data such as voltage.

And calling a corresponding parameter set in the model parameter library according to the detected vehicle model, and re-identifying the model parameters as initial parameters of the particle swarm optimization algorithm.

Substituting the identified parameters into a capacity calculation formula to obtain the available capacity Q of the detected vehicle, wherein the calculation formula is as follows:

wherein F is Faraday constant, A is electrode area,for the porosity of the negative electrode->Maximum lithium ion concentration for negative electrode, +.>Is the lithium intercalation range of the cathode.

The state of health of the inspected vehicle battery is calculated as follows:

in the method, in the process of the invention,taking the average value calculated for a plurality of times as the final SOH for the rated capacity of the battery of the nameplate of the vehicle.

The model selection module is used for selecting the battery health detection module according to the number of the vehicles to be detected, predicting future vehicles to be detected according to the historical data, and adjusting the detection model call in advance to reduce the accumulation of the number to be detected as much as possible.

The model selection module is used for analyzing according to the historical data and collecting the historical data, wherein the historical data comprises information such as the number of the historical vehicles to be detected, the historical detection time, the historical detection result and the like. By analysis of these data, the detection needs and trends for different time periods are predicted.

The model selection module adjusts and selects a proper battery health detection module in advance according to the predicted detection requirements and trends so as to optimize the overall detection efficiency and resource utilization.

The embodiment of the disclosure also provides a new energy vehicle health status annual inspection method, which uses a new energy vehicle health status annual inspection platform and comprises the following steps:

step S100, after the user arrives at the annual inspection station, the new energy automobile inspection item required to be carried out is selected for the vehicle, and the new energy automobile inspection item is registered.

Step S200, after the user finishes registration, the registration information is sent to the platform, and the platform judges whether to call the historical driving information of the vehicle and use the charging detection device according to the registration information of the vehicle.

Step S300, the online detection subsystem generates an annual inspection report by calling vehicle history information.

Step S400, if the state of health of the battery needs to be detected, a detector starts the user vehicle to the new energy automobile charging detection equipment, and inserts a charging gun.

Step S500, a detector needs to select a vehicle to be detected from a registration list on the equipment, and the new energy automobile charging detection equipment detects that a charging gun is used, namely, starts to detect; the new energy automobile charging detection equipment transmits the parameter information to the platform through the SOCKET interface in the detection process.

Step S600, after receiving the message of finishing detection, the platform detects the battery health state of the electric automobile through the service information registered by the user and the data collected by the charging detection equipment, feeds back the detected result to the equipment for display, and updates the information into the annual inspection report of the automobile.

And step S700, after the annual inspection report is generated, the report is audited by a detector, and the report is sent to an annual inspection station after the audit is passed.

The embodiment of the disclosure also provides a storage medium, in which a computer program is stored, and when the computer program is executed by a processor, all contents of the new energy vehicle health status annual inspection platform in the above embodiment can be realized.

Those skilled in the art will appreciate that implementing all or part of the contents of a new energy vehicle health status annual inspection platform may be accomplished by computer programs that instruct the associated hardware, where the programs may be stored in a non-volatile computer readable storage medium, and where the programs, when executed, may include the contents of an embodiment of a new energy vehicle health status annual inspection platform. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

The embodiment of the application also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the content of the new energy vehicle health status annual inspection platform in the embodiment when executing the program. In this embodiment of the present application, the processor is a control center of the computer system, and may be a processor of a physical machine or a processor of a virtual machine.

Referring to fig. 4, the electronic apparatus 500 includes: at least one processor 501, at least one communication interface 502, at least one memory 503, and at least one bus 504. Where bus 504 is used to enable connectivity communications between these components, communication interface 502 is used to communicate signaling or data with other node devices, and memory 503 stores machine readable instructions executable by processor 501. When the electronic device 500 is in operation, the processor 501 communicates with the memory 503 via the bus 504, and machine readable instructions, when invoked by the processor 501, perform the contents of a new energy vehicle health status annual inspection platform as in the above embodiments.

The foregoing is merely exemplary of the present invention, and the specific structures and features that are well known in the art are not described in any way herein, so that those skilled in the art will be aware of all the prior art to which the present invention pertains, and will be able to ascertain all of the prior art in this field, and with the ability to apply the conventional experimental means prior to this date, without the ability of those skilled in the art to perfect and practice this invention with their own skills, without the ability to develop certain typical known structures or methods that would otherwise be the obstacle to practicing this invention by those of ordinary skill in the art. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.

Claims (10)

1. A new energy vehicle health status annual inspection platform is characterized in that: comprising the following steps:

the charging detection equipment is used for collecting battery system parameter information of the new energy automobile;

the registration module is used for selecting detection items and registering vehicle information to generate a to-be-detected vehicle list;

the on-line detection subsystem is used for collecting historical operation data in the vehicle terminal according to the selected detection items, analyzing the historical operation data through the cloud platform and generating a vehicle annual inspection report, and comprises an accelerator pedal detection module, a motor temperature detection module, an operation energy consumption detection module and a battery consistency detection module;

and the off-line detection subsystem is used for analyzing the parameter information acquired by the charging detection equipment and generating a detection result of the battery health state.

2. The new energy vehicle health status annual inspection platform according to claim 1, wherein:

the charging detection equipment comprises a charging detection parameter setting module and a reverse connection protection module;

the charging and detecting parameter setting module is used for setting battery capacity, charging cut-off SOC, highest single temperature, default auxiliary electric parameters and single voltage parameters;

and the reverse connection protection module is used for detecting the positive and negative polarities of the battery and cutting off the power supply when the reverse connection of the polarities is found.

3. The new energy vehicle health status annual inspection platform according to claim 1, wherein:

the off-line detection subsystem comprises a first battery health detection module and a second battery health detection module;

the first battery health detection module is used for rapidly detecting the health state of the battery of the electric automobile and comprises a model construction sub-module and a first health analysis sub-module;

the second battery health detection module is used for accurately detecting the health state of the battery of the electric automobile and comprises a parameter pre-identification sub-module and a second health analysis sub-module.

4. A new energy vehicle health status annual inspection platform according to claim 3, wherein: the model construction submodule is used for constructing a battery model and an SOH estimation model and specifically comprises the following contents:

completely discharging a sample car of a target detected car type through drum operation until SOC=0% or the battery management system is automatically powered off; then carrying out multi-stage constant current charging on the sample car, wherein the charging multiplying power is 0.1C, and the power of the car is cut off when 10% of rated capacity is charged, and the car is kept stand for 1 hour and then is charged until the SOC=100% or the charging current is automatically disconnected;

calculating the charge quantity in the whole charging process and the accumulated charge quantity before each standing to obtain the current capacity and the actual SOC at each standing; after each standing, recording the battery terminal voltage as OCV, thereby obtaining the corresponding relation of SOC-OCV, and obtaining an SOC-OCV lookup table through linear interpolation;

discharging the vehicle to 50% SOC, performing constant current pulse excitation for 10 minutes to obtain voltage feedback data, and performing parameter identification of an equivalent circuit model by using a least square method with forgetting factors so as to establish a battery model;

acquiring a characteristic sequence, randomly defining SOH of a battery model through simulation software, and inputting excitation with a duration of 5 minutes and a current multiplying power of 0.3C; normalizing the generated voltage data to obtain a characteristic sequence;

and constructing a deep convolutional neural network model, taking the characteristic sequence as a model input, taking the corresponding randomly defined SOH as a model output, and training the model to obtain an SOH estimation model.

5. The new energy vehicle health status annual inspection platform according to claim 4, wherein: the first health analysis submodule carries out charging excitation with the duration of 5 minutes and the current multiplying power of 0.3C on a detection vehicle with unknown SOH, acquires voltage data in real time to acquire a characteristic sequence, inputs the characteristic sequence into an SOH estimation model, and outputs the characteristic sequence to obtain an SOH estimation value.

6. A new energy vehicle health status annual inspection platform according to claim 3, wherein: the parameter pre-identification sub-module is used for determining the reasonable range of parameters of the electric automobile of each vehicle type, and comprises the following contents:

performing HPPC full charge test on electric vehicles of all vehicle types to obtain test data, wherein the test data comprises total voltage, highest single voltage, current and SOC of a battery system; estimating the serial number of the single cells in the battery system by using the total voltage and the highest single cell voltage of the battery system to obtain average single cell voltage data of the battery system;

setting a parameter range in an electrochemical mechanism model according to a material system used by the battery;

identifying parameters in the electrochemical mechanism model by using an optimization algorithm, wherein a target optimization equation is as follows:

in the method, in the process of the invention,for measuring the voltage +.>For detecting the identification voltage of the vehicle, < >>For measuring SOC, ++>To identify SOC;

and judging whether the voltage root mean square error is smaller than 30mV, otherwise resetting the parameter range.

7. The new energy vehicle health status annual inspection platform according to claim 6, wherein: the second health analysis submodule is used for detecting the health condition of the battery of the electric automobile to be detected, and the specific content comprises:

controlling the charge detection device to charge the detected vehicle and collect voltage data by using the current working condition,

calling a corresponding parameter set in a model parameter library according to the detected vehicle type, and re-identifying the model parameter as an initial parameter of an optimization algorithm;

substituting the identified parameters into a capacity calculation formula to obtain the available capacity Q of the detected vehicle, wherein the calculation formula is as follows:

wherein F is Faraday constant, A is electrode area,for the porosity of the negative electrode->Maximum lithium ion concentration for negative electrode, +.>The lithium is intercalated for the negative electrode;

the state of health of the battery of the detected vehicle is calculated by the following calculation formula:

in the method, in the process of the invention,taking the average value calculated for a plurality of times as the final SOH for the rated capacity of the battery of the nameplate of the vehicle.

8. The new energy vehicle health status annual inspection platform according to any one of claims 1-7, wherein: the off-line detection subsystem further comprises a model selection module, and the first battery health detection module or the second battery health detection module is selected and invoked according to the number of vehicles to be detected.

9. The new energy vehicle health status annual inspection platform of claim 8, wherein: the model selection module is also used for analyzing according to historical data, predicting detection requirements and trends of different time periods in the future, wherein the historical data comprise the number of vehicles to be detected in the history, the historical detection time and the historical detection result, and adjusting and calling the first battery health detection module or the second battery detection module in advance according to the prediction result.

10. A new energy vehicle health status annual inspection method, which uses the new energy vehicle health status annual inspection platform as claimed in any one of claims 1-9, comprising the following steps:

step S100, after the user arrives at the annual inspection station, selecting a new energy automobile detection project required to be performed on the vehicle, and registering;

step S200, after the registration is completed, the registration information is sent to a platform, and the platform judges whether to call the historical running information of the vehicle and whether to use the charging detection equipment according to the registration information of the vehicle;

step S300, the online detection subsystem generates an annual inspection report by calling vehicle history information;

step S400, if the health condition of the battery needs to be detected, a detector starts a user vehicle to a new energy automobile charging detection device, and inserts a charging gun;

step S500, selecting a vehicle to be detected from a registration list, wherein the charging detection device detects that a charging gun is used, namely, starts to detect; the charging detection equipment transmits the parameter information to the platform through a SOCKET interface in the detection process;

step S600, after receiving the message of finishing detection, the platform detects the battery health state of the electric automobile through the registered service information and the data collected by the charging detection equipment, displays the detected result, and updates the information into the annual inspection report of the automobile;

and step S700, after the annual inspection report is generated, auditing the report, and after the audit is passed, transmitting the report to the annual inspection station.