CN111812518A - Battery state monitoring method, storage medium and system - Google Patents

Battery state monitoring method, storage medium and system Download PDF

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CN111812518A
CN111812518A CN201911359532.3A CN201911359532A CN111812518A CN 111812518 A CN111812518 A CN 111812518A CN 201911359532 A CN201911359532 A CN 201911359532A CN 111812518 A CN111812518 A CN 111812518A
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battery
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historical
state monitoring
model
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CN111812518B (en
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杨磊
戴锋
林勇刚
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Beijing Didi Infinity Technology and Development Co Ltd
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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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • 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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

The invention provides a battery state monitoring method, a storage medium and a system, wherein the method comprises the following steps: acquiring a corresponding relation between historical operating parameters of the battery and the pole difference of the battery core; acquiring operation parameters of a battery within a preset time period, and determining the estimated battery core range of the battery at the current moment according to the corresponding relation of the operation parameters of the battery and the corresponding relation; the interval between the preset time period and the current time is less than a set time length; determining whether the battery is in a safe state or not according to the estimated battery core pole difference of the battery; and the cell pole difference is the difference value between the maximum cell voltage value and the minimum cell voltage value in the battery. According to the scheme, after the electric vehicle leaves the factory, the battery cell consistency of the battery is continuously monitored, the battery fault caused by the battery cell consistency problem is timely found, and the safety and the stability of the electric vehicle in the running process are ensured.

Description

Battery state monitoring method, storage medium and system
Technical Field
The invention relates to the technical field of battery safety monitoring in electric vehicles, in particular to a battery state monitoring method, a storage medium and a system.
Background
With the popularization of electric vehicles and the application of car networking technology, more and more electric vehicles enter the consumer market. In the current faults of the electric vehicle, the main faults of the electric vehicle are poor in battery core consistency, low in SOC alarm, single battery under-voltage alarm, unmatched in rechargeable energy storage system and under-voltage in type of the vehicle-mounted energy storage device, the faults can be ranked five in all the fault types of the electric vehicle, and the five types of faults account for 78% of the total fault amount.
The battery is used as a power source of the electric vehicle, and with the increase of charging and discharging times and driving mileage, the capacity of the battery is continuously attenuated, the potential safety hazard of the battery is increased, and the possibility of the failure of the battery is higher and higher. At present, in an electric vehicle, the influence of the consistency of a battery cell in the use process of the battery on the safety and the failure rate of the battery is very obvious.
In the prior art, a method for evaluating the consistency of battery cells mainly adopts a static test method to measure voltage, cell capacity, internal resistance and SOC distribution values of a cell monomer in a battery, then adds evaluation weights to each measurement value, and performs weighted average calculation on each measurement value to obtain an evaluation coefficient of the cell consistency of the battery. In addition, lithium batteries are mostly adopted in batteries, and lithium batteries are typical dynamic nonlinear electrochemical systems, and internal parameters (cell capacity, internal resistance of the battery, internal temperature of the cell and the like) are difficult to measure in real time during application, and battery change state identification and state estimation still have great challenges. Moreover, at present, although the electric vehicle needs to upload the vehicle operation data to the cloud server according to the regulations for the manufacturer to monitor the safety performance of the electric vehicle in the operation process, the data uploading is not performed in real time, and serious hysteresis exists, so that the problem of battery failure is difficult to find in the first time.
Therefore, there is a need for a solution that can continuously monitor the safety of a battery in an electric vehicle in real time after the electric vehicle is shipped from the factory.
Disclosure of Invention
The embodiment of the invention aims to provide a battery state monitoring method, a storage medium and a system, so as to solve the technical problem that the battery cannot be safely monitored after an electric vehicle leaves a factory in the prior art.
Therefore, the invention provides a battery state monitoring method, which comprises the following steps:
acquiring a corresponding relation between historical operating parameters of the battery and the pole difference of the battery core;
acquiring operation parameters of a battery within a preset time period, and determining the estimated battery core range of the battery at the current moment according to the corresponding relation of the operation parameters of the battery and the corresponding relation; the interval between the preset time period and the current time is less than a set time length;
determining whether the battery is in a safe state or not according to the estimated battery core pole difference of the battery;
and the cell pole difference is the difference value between the maximum cell voltage value and the minimum cell voltage value in the battery.
Optionally, in the battery state monitoring method described above:
the historical operating parameters of the battery are historical charging parameters when the battery is in a charging process in a historical time period;
and the operation parameters of the battery in the preset time period are the charging parameters of the battery in the charging process in the preset time period.
Optionally, in the battery state monitoring method described above:
the step of obtaining the corresponding relation between the historical charging parameters and the battery core range according to the historical charging data of the battery comprises the following steps: extracting a battery current value, a battery temperature, a battery voltage value and/or a battery SOC value in the historical charging data as the historical charging parameters, and taking the historical charging parameters as input samples; extracting battery cell range differences in the historical charging data as output samples, wherein the acquisition time of the output samples lags behind the acquisition time of the input samples; training a machine learning model according to the input sample and the output sample, and taking the trained machine learning model as a state monitoring model; the state monitoring model is used for representing the corresponding relation;
in the step of determining the estimated battery core pole difference of the battery at the current moment according to the charging parameters of the battery and the corresponding relation: extracting a battery current value, a battery temperature, a battery voltage value and/or a battery SOC value in the charging parameters of the battery as input parameters, inputting the input parameters into the state monitoring model, and taking the output of the state monitoring model as an estimated battery core range of the battery; wherein the time interval between the acquisition time of the output sample and the acquisition time of the input sample is used as the set duration.
Optionally, in the battery state monitoring method, the step of obtaining a corresponding relationship between the historical charging parameters and the battery cell pole difference according to the historical charging data of the battery includes:
a data grouping step: dividing the historical charging data into two groups, wherein the data volume of the first group of historical charging data is larger than that of the second group of historical charging data;
model training: extracting input samples and output samples in a first group of historical charging data to train the machine learning model to obtain an initial state monitoring model;
a model verification step: and extracting input samples and output samples in a second group of historical charging data to verify the initial state monitoring model, taking the initial state monitoring model as the state monitoring model if the initial state monitoring model passes the verification, and returning to the model training step after increasing the data volume of the first group of historical charging data if the initial state monitoring model passes the verification.
Optionally, in the battery state monitoring method, the step of obtaining the corresponding relationship between the historical charging parameters and the cell pole difference according to the historical charging data of the battery includes:
a data grouping step: dividing the historical charging data into K groups, taking one group of historical charging data as test sample data, and taking the other groups of historical charging data as training sample data;
model training: extracting input samples and output samples in training sample data to train the machine learning model to obtain an initial state monitoring model;
a model verification step: extracting an input sample and an output sample in test sample data to verify the initial state monitoring model, and if the initial state monitoring model passes the verification, taking the initial state monitoring model as an alternative state monitoring model;
and (3) cross validation: taking another group of historical charging data as test sample data, taking the other groups of historical charging data as training sample data, and repeating the model training step and the model verification step until all K groups of historical charging data are selected as the test sample data;
a model determining step: and determining the state monitoring model according to all the alternative state monitoring models.
Optionally, in the above battery state monitoring method, in the model determining step:
taking the alternative state monitoring model with the highest verification result accuracy as a state monitoring model; or, taking the average value of the adjusting coefficients in all the alternative state monitoring models as the actual adjusting coefficient to obtain the state monitoring model.
Optionally, in the battery state monitoring method, the step of determining whether the battery is in a safe state according to the estimated battery core pole difference of the battery includes:
acquiring cell consistency parameters of the battery according to the following modes:
Figure BDA0002336811760000041
wherein, Vgap_1To estimate the cell range, Vgap_0Presetting a cell range threshold;
and if R _ V is larger than R _ V _0, judging that the battery is in an unsafe state, and R _ V _0 is a preset electric core consistency parameter threshold value.
Optionally, in the above method for monitoring a battery state, before the step of obtaining a corresponding relationship between a historical charging parameter and a cell pole difference according to historical charging data of the battery, the method further includes:
acquiring a historical state data set of a battery in a historical time period, wherein each historical state data in the historical state data set is configured with a state identifier;
and if the state identifier represents the charging state, taking the historical state data as the historical charging data.
Optionally, in the above method for monitoring a battery state, acquiring a historical state data set of a battery in a historical time period, where each historical state data in the historical state data set is configured with a state identifier, further includes:
and screening out repeated historical state data, error historical state data and incomplete historical state data in the historical state data set.
The invention also provides a computer-readable storage medium, wherein the storage medium stores program instructions, and after the program instructions are read by a computer, the computer executes the battery state monitoring method.
The invention also provides a battery charging process state monitoring system, which comprises at least one processor and at least one memory, wherein program instructions are stored in the at least one memory, and the at least one processor reads the program instructions and then executes the battery state monitoring method.
Compared with the prior art, the technical scheme provided by the invention at least has the following beneficial effects:
according to the battery state monitoring method, the storage medium and the system, the influence of the historical battery operation data on the future battery core pole difference can be obtained according to the historical battery operation data, so that the corresponding relation between the battery parameters and the battery core pole difference in the historical battery operation process is determined; therefore, the estimated battery core range of the battery at the current moment can be predicted according to the corresponding relation and the battery operation data in a period of time before the current moment, so that whether the battery is in a safe state or not can be determined according to the estimated battery core range of the battery at the current moment. Therefore, after the electric vehicle leaves the factory, the battery cell consistency of the battery is continuously monitored, battery faults caused by the battery cell consistency problem can be found in time, and the safety and the stability of the electric vehicle in the running process are ensured.
Drawings
FIG. 1 is a flow chart of a battery condition monitoring method according to an embodiment of the present invention;
fig. 2 is a flowchart of a battery status monitoring method according to another embodiment of the present invention;
fig. 3 is a schematic diagram of a data processing flow for predicting cell consistency by using a linear regression model according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a deviation value between a cell consistency result predicted by using the linear regression model according to the embodiment of the present invention and an actual cell consistency result;
fig. 5 is a schematic diagram of a hardware connection relationship of the battery charging process state monitoring system according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description of the present invention, and do not indicate or imply that the device or assembly referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Wherein the terms "first position" and "second position" are two different positions.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, and the two components can be communicated with each other. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
The embodiment provides a battery state monitoring method, which can be applied to a vehicle management platform, and as shown in fig. 1, the method includes the following steps:
s101: and acquiring the corresponding relation between the historical operating parameters of the battery and the pole difference of the battery core. The vehicle management platform can monitor and manage a large number of electric vehicles, and can collect and store all data in the running process of the electric vehicles. Each managed electric vehicle has a unique vehicle identifier, and accordingly, a battery of each electric vehicle and related data of the battery in the operation process are assigned with a specific data identifier and stored, so that the battery can be uniquely determined by identifying the data identifier or the vehicle identifier in the step. In the time period, battery operation parameters obtained by pushing forward the time of one week between the cell pole difference of a certain battery and the corresponding time of the cell pole difference are used as a group of data, so that the corresponding relationship between the operation parameters of the battery and the cell pole difference after one week is obtained. Accordingly, the time period can be adjusted according to actual needs.
S102: acquiring operation parameters of a battery within a preset time period, and determining the estimated battery core range of the battery at the current moment according to the corresponding relation of the operation parameters of the battery and the corresponding relation; the interval between the preset time period and the current time is less than a set time length; the set time duration in this step may be referred to according to a time interval between the historical data and the cell pole difference selected when the correspondence is obtained in step S101.
S103: determining whether the battery is in a safe state or not according to the estimated battery core pole difference of the battery; a standard battery core range can be preset, and if the estimated battery core range is not within the standard range, the battery can be determined to be in an unsafe state. Wherein the cell pole difference is the difference between the maximum cell voltage value and the minimum cell voltage value in the battery
According to the technical scheme provided by the embodiment, the safety state of the battery system is evaluated by establishing a historical operating parameter (current/SOC/temperature and the like) of the battery and a voltage range prediction algorithm, and as the vehicle management platform can collect battery data of the electric vehicle in real time and in a long historical period, implicit battery state information and an evolution rule thereof are mined from rated information and state monitoring data (voltage, current, temperature or SOC and the like) of the battery, so that the battery cell consistency prediction of the battery in the charging process is realized, and the dynamic monitoring and prediction of the battery fault condition of the vehicle are assisted by the vehicle management platform. Moreover, since the battery historical operation data is adopted to predict the future battery state, the problems of data delay and hysteresis do not exist, and the battery state can be continuously monitored in real time after the electric vehicle leaves the factory without being limited to the test stage.
Preferably, in the above scheme, the historical operating parameter of the battery is a historical charging parameter when the battery is in a charging process in a historical time period; and the operation parameters of the battery in the preset time period are the charging parameters of the battery in the charging process in the preset time period. Referring to fig. 2, the above method may include the steps of:
s201: and obtaining the corresponding relation between the battery core range and the charging data of the battery according to the historical charging data of the battery. According to the national standard, the components of the battery operation data in the electric vehicle need to be executed according to the national standard, the data in the battery charging process needs to be configured with a charging identifier, and the identifier in the charging situations of a fast charging state and a slow charging state is distinguished. Therefore, before executing this step, the method may further include the steps of: acquiring a historical state data set in a historical time period, wherein each historical state data in the historical state data set is configured with a state identifier; and if the state identifier represents the charging state, taking the historical state data as the historical charging data. For the vehicle management platform, historical charging data can be conveniently extracted from battery operation data of the electric vehicle.
S202: obtaining estimated cell range of the battery according to the charging parameters and the corresponding relation when the battery is in the charging process within a preset time period;
s203: and determining whether the battery is in a safe state or not according to the estimated battery core pole difference of the battery.
Namely, the data base of battery cell consistency prediction is carried out according to the state parameters of the battery in the charging process. Because the vehicle keeps a static state in the charging process, the state parameter change of the battery is the most stable and is minimally influenced by the external environment, and the method has higher accuracy in predicting the consistency of the battery cell by adopting data in the charging process.
Preferably, in the above method, the step S201 includes: extracting a battery current value, a battery temperature, a battery voltage value and/or a battery SOC value in the historical charging data as the historical charging parameters, and taking the historical charging parameters as input samples; extracting battery cell range differences in the historical charging data as output samples, wherein the acquisition time of the output samples lags behind the acquisition time of the input samples; training a machine learning model according to the input sample and the output sample, and taking the trained machine learning model as a state monitoring model; the state monitoring model is used for representing the corresponding relation.
The step S202 includes: extracting a battery current value, a battery temperature, a battery voltage value and/or a battery SOC value in the charging parameters of the battery as input parameters, inputting the input parameters into the state monitoring model, and taking the output of the state monitoring model as an estimated battery core range of the battery; wherein the time interval between the acquisition time of the output sample and the acquisition time of the input sample is used as the set duration.
The machine learning model can be realized by adopting the existing machine learning algorithm in the prior art, and the scheme is realized by adopting an MATLAB machine learning linear regression model. The method specifically comprises the following steps:
(1) obtaining historical charging data for a battery
BMS national standard data uploaded by an electric vehicle running in a preset time period are directly downloaded from the existing battery running data of the electric vehicle in a vehicle management platform, wherein:
1. and downloading and arranging the data in a month unit. For example: 2018.2.01-2.28.
2. The data storage format is not limited, for example: *. xls, xlsx, csv, etc.
(2) Data cleaning operation is carried out on historical charging data
Data cleansing is a process of rechecking and verifying data, and aims to delete duplicate information, correct existing errors and ensure data consistency. The source data uploaded to the vehicle management platform by the BMS system comprise information such as current, voltage and SOC value of the battery pack, on one hand, the data are huge and complex, and the data which can be used for battery cell consistency evaluation in the scheme are only a part of the data, so that useless data need to be removed. On the other hand, the source data has the phenomena of dislocation, repetition and null value, and the wrong or conflicting data needs to be cleaned up and only useful data needs to be reserved.
(3) Screening useful data according to the obtained data characteristics to form a new data sample
Because the sampling period of the voltage and the current of the battery is 10-30s, the load current of the battery has the characteristic of frequent change due to the dynamic working condition of the electric vehicle, the voltage change is large, and stable and reliable data cannot be obtained, secondly, the safety problems of overcharge, thermal runaway, lithium separation and the like are easily caused in the lithium battery charging process, in addition, the conditions of long running time, high vehicle utilization rate and high vehicle fast charging ratio of the electric vehicle under the vehicle management platform possibly exist in the electric vehicle, and therefore the data in the charging process are screened to generate the corresponding relation. The charging process can be divided into two conditions of quick charging and slow charging, and a cell consistency prediction model can be respectively established for data in two charging states during actual selection. The steps in the scheme are adopted to predict the consistency of the battery electric core in the slow charging process by using the slow charging data. Three data samples are established in the scheme, as shown in fig. 3, sample data a, sample data B and sample data C. The collection of the sample data is successful, namely the sample data A is earlier than the sample data B in turn, and the sample data B is earlier than the sample data C. In this example, sample data a is a sample of two months before march, sample data B is a sample of the latest month before march, and sample data C is a sample of the latest week. Referring to fig. 3, a battery current I, an SOC value, a temperature T, and a voltage value V in sample data are extracted as characteristic parameters for input as independent variables in the machine learning model, and the machine learning model is trained by using the cell range as an output of the machine learning model. The method comprises the steps of firstly training a machine learning model by using sample data A, testing the trained machine learning model by using sample data B, directly estimating the current electrical core range by using sample data C if the test is passed, and otherwise, training and testing the machine learning model by using the sample data A and the sample data B again. In the above scheme, the verification process for the linear regression model may adopt the following manner:
step a1, data grouping step: dividing the historical charging data into two groups, wherein the data volume of the first group of historical charging data is larger than that of the second group of historical charging data; that is, the data as training verification samples are directly divided into a group A and a group B.
Step A2, model training step: extracting input samples and output samples in a first group of historical charging data to train the machine learning model to obtain an initial state monitoring model; namely, the A group data is used for training the machine learning model to obtain an initial state monitoring model.
Step A3, model verification step: and extracting input samples and output samples in a second group of historical charging data to verify the initial state monitoring model, if the initial state monitoring model passes the verification, taking the initial state monitoring model as the state monitoring model, and if the initial state monitoring model passes the verification, increasing the data volume of the first group of historical charging data and returning to the data grouping step. Namely, the initial state model is verified by using the group B data, and the state monitoring model can be obtained after the verification is passed.
As another way, the machine learning model may be trained and validated as follows:
step B1, data grouping step: dividing the historical charging data into K groups, taking one group of historical charging data as test sample data, and taking the other groups of historical charging data as training sample data;
step B2, model training step: extracting input samples and output samples in training sample data to train the machine learning model to obtain an initial state monitoring model;
step B3, model verification step: extracting an input sample and an output sample in test sample data to verify the initial state monitoring model, and if the initial state monitoring model passes the verification, taking the initial state monitoring model as an alternative state monitoring model;
step B4, cross validation step: taking another group of historical charging data as test sample data, taking the other groups of historical charging data as training sample data, and repeating the model training step and the model verification step until all K groups of historical charging data are selected as the test sample data;
step B5, model determination step: and determining the state monitoring model according to all the alternative state monitoring models.
In the scheme, a cross validation mode is preferred, and the parameter frames of the models are optimized in the cross validation method. The reliability of the algorithm depends on the parameter framework, that is, which battery data is most efficient for the results produced. The lithium battery is used as a system for providing electric energy through electrochemical reaction, and the working process of the lithium battery relates to a plurality of physical and chemical processes such as porous solid-phase diffusion, ion migration in liquid, surface layer electrochemical reaction, solid conduction and the like, so that the comprehensive performance of the lithium battery is obviously influenced by factors such as battery design, working temperature, working current, SOC value and the like. For a specific vehicle, the distribution of the cell voltage of the lithium battery is influenced by a plurality of factors, mainly including the factors of total current, total voltage, cell temperature, SOC, and the like. Using a small amount of data associated with the model target value increases the accuracy and learning efficiency of the learning model. In this embodiment, to improve the quality of the parameter framework, the original data is first randomly divided into K parts. Of the K parts, one part is selected as test data, and the remaining K-1 parts are used as training data to obtain corresponding experimental results. Then, another part is selected as test data, the rest K-1 parts are used as training data, and the like, and the cross test is repeated for K times. In each experiment, a different part is selected from the K parts to be used as test data, the K parts of data are ensured to be respectively subjected to test data, and the rest K-1 parts are used as training data to be subjected to experiments.
In the above step B5, the candidate state monitoring model with the highest accuracy of the verification result is used as the state monitoring model; or, taking the average value of the adjusting coefficients in all the alternative state monitoring models as the actual adjusting coefficient to obtain the state monitoring model. That is, preferably, the K obtained experimental results are averaged, and the experimental result may be a difference between a predicted value and a check value, so that the smaller the difference is, the better the difference is, thereby determining the optimal classification and implementing the model training. Referring to fig. 3, a Regression learning APP built in MATLAB is used in the present scheme to train and predict data, and K takes a value of 4. Regression effects were evaluated using the curability coefficients (Rs). The coefficient of determinability is the proportion of the regression sum of squares (ESS-extended sum of squares) in the total variation (TSS-total sum of squares), and the calculation formula is as follows:
Figure BDA0002336811760000111
wherein y isi' cell uniformity junctions for predictionFruit, yiAnd the result is the actual cell consistency.
Referring also to fig. 3, using data sample a as a data input sample, using K times of cross-validation training and verification models to calculate Rs1 value, the training sample may result in poor regression effect due to small data volume, i.e. Rs1 value cannot reach the required value (denoted as Rs _ std), in this example, using Rs _ std equal to 0.99, training samples recorded for a longer time are required to be input to train the model, i.e. the process returns to the beginning of "data input"; if the Rs1 value is verified, proceed down: using the sample B as an input, calculating Rs2, judging whether Rs2 passes the verification (the requirement Rs _ std cannot be met), if the value of Rs2 does not pass the verification, returning to the beginning of the data input again, increasing the data input amount of the data A again, and executing the flow again; if the value of Rs2 is verified, proceed to the next step to obtain the final usable regression model. In the example, the historical data of one month of vehicle operation is used as an A sample to train the sample, the data of one month is used as a sample B to carry out verification, the Rs1 is found to reach the standard, the Rs2 is found not to reach the standard, and the historical data of two months of vehicle operation is used as an A sample to train the model, so that the Rs1 and the Rs2 both reach the standard. Referring to fig. 4, if the relationship between the battery SOC value and the cell pole difference value is used as the criterion for consistency evaluation, the error values between the result predicted by the machine learning model and the actual result in the present scheme are all within ± 0.3%, and the accuracy is very high. In the scheme, as long as the average absolute value Error (ERR) is within 0.90%, the ERR is considered to be smaller than the preset electric core consistency parameter threshold, and the model training result is available.
Further, the battery state monitoring method in the above solution determines whether the battery is in a safe state during the charging process by:
acquiring real-time cell consistency parameters of the battery according to the following modes:
Figure BDA0002336811760000121
Figure BDA0002336811760000122
wherein, Vgap_1For the real-time cell range, Vgap_0Presetting a cell range threshold; and if R _ V is larger than R _ V _0, judging that the battery is in an unsafe state, and R _ V _0 is a preset electric core consistency parameter threshold value.
In the step, the battery operation data of the vehicle to be predicted is input into a trained state monitoring model, and the state of the battery cell consistency of the battery is predicted by using the model, so that the safety state of the battery is monitored. The sample C (namely data before one week) can be used as an input sample to calculate the voltage range of the current vehicle battery, and then the cell consistency parameter of the battery is calculated, if R _ V is larger than R _ V _0, the vehicle charging behavior is good, and no safety risk exists; and if the R _ V is less than or equal to the R _ V _0, reporting that the vehicle has a safety risk and needing to send a safety warning or later important monitoring. In this example, R _ V _0 takes a value of 0.9, and the calculation result of R _ V is 0.98, so the conclusion is that: the battery of the electric vehicle is in a safe state in this example.
The scheme provided by the embodiment provides a method for training and optimizing a model by using a machine learning linear regression model and using the historical operating data of a battery, and the safety state of the battery can be accurately predicted by using the trained model, so that the aim of monitoring the safety state of the battery is fulfilled. Therefore, the vehicle management platform can realize the supervision of the safety state of the vehicle, reduce the accident rate of the electric vehicle and ensure the safety of drivers and conductors.
Example 2
The present embodiment also provides a computer-readable storage medium, where program instructions are stored in the storage medium, and after reading the program instructions, a computer executes the battery state monitoring method according to any scheme in embodiment 1.
Example 4
Fig. 5 is a schematic diagram of a hardware structure of a battery charging process status monitoring system provided in this embodiment, where the apparatus includes: one or more processors 501 and a memory 502, with one processor 501 being an example in fig. 5. The apparatus for performing the battery state monitoring method may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
Memory 502, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 501 executes various functional applications and data processing of the server by running nonvolatile software programs, instructions and modules stored in the memory 502, that is, implements the battery state monitoring method of the above-described method embodiment.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in the embodiments of the present application.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (11)

1. A battery state monitoring method is characterized by comprising the following steps:
acquiring a corresponding relation between historical operating parameters of the battery and the pole difference of the battery core;
acquiring operation parameters of a battery within a preset time period, and determining the estimated battery core range of the battery at the current moment according to the corresponding relation of the operation parameters of the battery and the corresponding relation; the interval between the preset time period and the current time is less than a set time length;
determining whether the battery is in a safe state or not according to the estimated battery core pole difference of the battery;
and the cell pole difference is the difference value between the maximum cell voltage value and the minimum cell voltage value in the battery.
2. The battery state monitoring method according to claim 1, characterized in that:
the historical operating parameters of the battery are historical charging parameters when the battery is in a charging process in a historical time period;
and the operation parameters of the battery in the preset time period are the charging parameters of the battery in the charging process in the preset time period.
3. The battery state monitoring method according to claim 2, characterized in that:
the step of obtaining the corresponding relation between the historical charging parameters and the battery core range according to the historical charging data of the battery comprises the following steps: extracting a battery current value, a battery temperature, a battery voltage value and/or a battery SOC value in the historical charging data as the historical charging parameters, and taking the historical charging parameters as input samples; extracting battery cell range differences in the historical charging data as output samples, wherein the acquisition time of the output samples lags behind the acquisition time of the input samples; training a machine learning model according to the input sample and the output sample, and taking the trained machine learning model as a state monitoring model; the state monitoring model is used for representing the corresponding relation;
in the step of determining the estimated battery core pole difference of the battery at the current moment according to the charging parameters of the battery and the corresponding relation: extracting a battery current value, a battery temperature, a battery voltage value and/or a battery SOC value in the charging parameters of the battery as input parameters, inputting the input parameters into the state monitoring model, and taking the output of the state monitoring model as an estimated battery core range of the battery; wherein the time interval between the acquisition time of the output sample and the acquisition time of the input sample is used as the set duration.
4. The battery state monitoring method according to claim 3, wherein the step of obtaining the correspondence between the historical charging parameters and the cell range according to the historical charging data of the battery includes:
a data grouping step: dividing the historical charging data into two groups, wherein the data volume of the first group of historical charging data is larger than that of the second group of historical charging data;
model training: extracting input samples and output samples in a first group of historical charging data to train the machine learning model to obtain an initial state monitoring model;
a model verification step: and extracting input samples and output samples in a second group of historical charging data to verify the initial state monitoring model, taking the initial state monitoring model as the state monitoring model if the initial state monitoring model passes the verification, and returning to the model training step after increasing the data volume of the first group of historical charging data if the initial state monitoring model passes the verification.
5. The battery state monitoring method according to claim 3, wherein the step of obtaining the corresponding relationship between the historical charging parameters and the cell range according to the historical charging data of the battery comprises:
a data grouping step: dividing the historical charging data into K groups, taking one group of historical charging data as test sample data, and taking the other groups of historical charging data as training sample data;
model training: extracting input samples and output samples in training sample data to train the machine learning model to obtain an initial state monitoring model;
a model verification step: extracting an input sample and an output sample in test sample data to verify the initial state monitoring model, and if the initial state monitoring model passes the verification, taking the initial state monitoring model as an alternative state monitoring model;
and (3) cross validation: taking another group of historical charging data as test sample data, taking the other groups of historical charging data as training sample data, and repeating the model training step and the model verification step until all K groups of historical charging data are selected as the test sample data;
a model determining step: and determining the state monitoring model according to all the alternative state monitoring models.
6. The battery state monitoring method according to claim 5, wherein in the model determining step:
taking the alternative state monitoring model with the highest verification result accuracy as a state monitoring model; or, taking the average value of the adjusting coefficients in all the alternative state monitoring models as the actual adjusting coefficient to obtain the state monitoring model.
7. The battery state monitoring method according to any one of claims 1 to 6, wherein the step of determining whether the battery is in a safe state according to the estimated cell pole difference of the battery comprises:
acquiring cell consistency parameters of the battery according to the following modes:
Figure FDA0002336811750000031
wherein, Vgap_1To estimate the cell range, Vgap_0Presetting a cell range threshold;
and if R _ V is larger than R _ V _0, judging that the battery is in an unsafe state, and R _ V _0 is a preset electric core consistency parameter threshold value.
8. The battery state monitoring method according to any one of claims 2 to 6, wherein the step of obtaining the correspondence between the historical charging parameters and the cell pole differences according to the historical charging data of the battery further comprises:
acquiring a historical state data set of a battery in a historical time period, wherein each historical state data in the historical state data set is configured with a state identifier;
and if the state identifier represents the charging state, taking the historical state data as the historical charging data.
9. The battery condition monitoring method of claim 8, wherein obtaining a set of historical condition data for the battery over a historical period of time, each of the historical condition data in the set of historical condition data configured with a condition identifier further comprises:
and screening out repeated historical state data, error historical state data and incomplete historical state data in the historical state data set.
10. A computer-readable storage medium, wherein program instructions are stored in the storage medium, and a computer reads the program instructions and executes the battery state monitoring method according to any one of claims 1 to 9.
11. A battery charging process condition monitoring system, comprising at least one processor and at least one memory, at least one of said memories storing program instructions, at least one of said processors reading said program instructions and performing the battery condition monitoring method according to any one of claims 1-9.
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