CN111814297A - Electric automobile battery cell monomer direct current internal resistance measuring method, electronic equipment and storage medium - Google Patents
Electric automobile battery cell monomer direct current internal resistance measuring method, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method for measuring direct current internal resistance of a single battery cell of an electric automobile, electronic equipment and a storage medium. The method comprises the following steps: acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform; inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model. According to the method, the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile are obtained through the cloud platform, so that the monomer direct current internal resistance of the to-be-detected battery cell is predicted based on the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile, the influence of various parameter changes on the monomer direct current internal resistance of the battery cell in the driving process of the automobile is fully considered, the DCR change of the battery system is monitored on line, and the safety state of the battery system is evaluated.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a method for measuring direct current internal resistance of a single battery cell of an electric automobile, electronic equipment and a storage medium.
Background
The method mainly comprises the steps Of testing the internal resistance Of a single battery cell in a battery system by adopting a static HPPC (Hybrid pulse power Characteristic) which is a Characteristic for embodying the pulse Charge-discharge performance Of a power battery, and testing the direct current internal resistance Of the battery.
However, the offline test of the battery cell can only judge the battery cell condition in a laboratory, cannot consider the multi-factor influence of complex road conditions in the operation of the electric vehicle, and cannot predict the direct current internal resistance of the battery cell by using online operation data.
For the operation platform of the electric vehicle, because the operation platform manages a large number of vehicles, the running time of the vehicles is accumulated quickly, the driving range is long, the capacity of the battery is attenuated continuously along with the increase of the charging and discharging times and the driving mileage, and the potential safety hazard of the battery system is increased, however, the prior art cannot realize online detection of Direct-current internal resistance (DCR), and the potential safety hazard is easily caused. In addition, the lithium battery is a typical dynamic nonlinear electrochemical system, the internal resistance is an important index for measuring the performance of the battery cell, the internal resistance is influenced by a plurality of factors, and the prior art is difficult to establish a multi-parameter influenced DCR model.
Disclosure of Invention
In view of the above, it is necessary to provide a method for measuring a direct current internal resistance of a cell unit of an electric vehicle, an electronic device, and a storage medium, for solving the technical problem that the prior art fails to use online operation data to predict the direct current internal resistance of the cell unit.
The invention provides a method for measuring direct current internal resistance of a battery cell monomer of an electric automobile, which comprises the following steps:
acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
Further, the monomer parameters include: and the cell direct current internal resistance of the adjacent cell in a preset range by taking the cell to be tested as the center, and/or the average cell direct current internal resistance of the adjacent cell.
Still further, the environmental parameters include: driving range, battery system charging current, battery system total voltage, battery system state of charge, and/or battery system temperature.
Further, still include:
acquiring historical environmental parameters, historical monomer parameters and historical direct current internal resistance calculation parameters of the electric core to be tested of the electric automobile from a cloud platform;
calculating a plurality of training monomer direct current internal resistances of the electric core to be tested according to the historical direct current internal resistance calculation parameters of the electric core to be tested, selecting a historical environment parameter corresponding to each training monomer direct current internal resistance as a training environment parameter from a cloud platform, and selecting a historical monomer parameter corresponding to each training monomer direct current internal resistance as a training historical monomer parameter;
and obtaining a single direct current internal resistance prediction model of the battery cell to be tested through machine learning training by taking the training environment parameters and the training single parameters as input and taking the corresponding training single direct current internal resistance as a response.
Further, the calculating a plurality of training monomer direct-current internal resistances of the to-be-tested battery cell according to the historical direct-current internal resistance calculation parameters of the to-be-tested battery cell, selecting, from the cloud platform, a historical environment parameter corresponding to each training monomer direct-current internal resistance as a training environment parameter, and selecting a historical monomer parameter corresponding to each training monomer direct-current internal resistance as a training historical monomer parameter specifically includes:
the direct current internal resistance calculation parameters comprise: the method comprises the following steps of (1) identifying a cell voltage, a battery system charging current and a battery system charging state;
searching the moment when the charging state identifier of the battery system is converted from a preparation state before charging to a charging state as an initial moment from the historical direct current internal resistance calculation parameters;
for each initial moment, selecting the monomer voltage corresponding to the initial moment from the historical direct current internal resistance calculation parameters as an initial voltage, selecting the monomer voltage corresponding to the end moment as an end voltage by taking the moment when a preset measurement time period passes after the initial moment as an end moment, and calculating the training monomer direct current internal resistance corresponding to each end moment as the absolute value of the difference value of the initial voltage and the end voltage divided by the charging current of the battery system in the measurement time period after the initial moment;
and for the direct current internal resistance of the training monomer corresponding to each ending moment, selecting the historical environment parameter corresponding to the ending moment from the historical environment parameters of the electric core to be tested as the training environment parameter corresponding to the direct current internal resistance of the training monomer, and selecting the historical monomer parameter corresponding to the ending moment from the historical monomer parameters of the electric core to be tested as the training monomer parameter corresponding to the direct current internal resistance of the training monomer.
Still further, still include:
and acquiring the actual monomer direct current internal resistance of the battery cell to be tested, comparing the actual monomer direct current resistance with the predicted monomer direct current resistance, and giving an alarm if the comparison result meets the preset alarm condition.
The invention provides an electronic device for measuring direct current internal resistance of a single battery cell of an electric automobile, which comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively linked to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by at least one of the processors to enable the at least one of the processors to:
acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
Further, the monomer parameters include: and the cell direct current internal resistance of the adjacent cell in a preset range by taking the cell to be tested as the center, and/or the average cell direct current internal resistance of the adjacent cell.
Still further, the environmental parameters include: driving range, battery system charging current, battery system total voltage, battery system state of charge, and/or battery system temperature.
Further, the processor is further capable of:
acquiring historical environmental parameters, historical monomer parameters and historical direct current internal resistance calculation parameters of the electric core to be tested of the electric automobile from a cloud platform;
calculating a plurality of training monomer direct current internal resistances of the electric core to be tested according to the historical direct current internal resistance calculation parameters of the electric core to be tested, selecting a historical environment parameter corresponding to each training monomer direct current internal resistance as a training environment parameter from a cloud platform, and selecting a historical monomer parameter corresponding to each training monomer direct current internal resistance as a training historical monomer parameter;
and obtaining a single direct current internal resistance prediction model of the battery cell to be tested through machine learning training by taking the training environment parameters and the training single parameters as input and taking the corresponding training single direct current internal resistance as a response.
Further, the calculating a plurality of training monomer direct-current internal resistances of the to-be-tested battery cell according to the historical direct-current internal resistance calculation parameters of the to-be-tested battery cell, selecting, from the cloud platform, a historical environment parameter corresponding to each training monomer direct-current internal resistance as a training environment parameter, and selecting a historical monomer parameter corresponding to each training monomer direct-current internal resistance as a training historical monomer parameter specifically includes:
the direct current internal resistance calculation parameters comprise: the method comprises the following steps of (1) identifying a cell voltage, a battery system charging current and a battery system charging state;
searching the moment when the charging state identifier of the battery system is converted from a preparation state before charging to a charging state as an initial moment from the historical direct current internal resistance calculation parameters;
for each initial moment, selecting the monomer voltage corresponding to the initial moment from the historical direct current internal resistance calculation parameters as an initial voltage, selecting the monomer voltage corresponding to the end moment as an end voltage by taking the moment when a preset measurement time period passes after the initial moment as an end moment, and calculating the training monomer direct current internal resistance corresponding to each end moment as the absolute value of the difference value of the initial voltage and the end voltage divided by the charging current of the battery system in the measurement time period after the initial moment;
and for the direct current internal resistance of the training monomer corresponding to each ending moment, selecting the historical environment parameter corresponding to the ending moment from the historical environment parameters of the electric core to be tested as the training environment parameter corresponding to the direct current internal resistance of the training monomer, and selecting the historical monomer parameter corresponding to the ending moment from the historical monomer parameters of the electric core to be tested as the training monomer parameter corresponding to the direct current internal resistance of the training monomer.
Still further, the processor is further capable of:
and acquiring the actual monomer direct current internal resistance of the battery cell to be tested, comparing the actual monomer direct current resistance with the predicted monomer direct current resistance, and giving an alarm if the comparison result meets the preset alarm condition.
The invention provides a storage medium, which stores computer instructions, and when a computer executes the computer instructions, the storage medium is used for executing all the steps of the method for measuring the direct current internal resistance of the electric automobile battery cell monomer.
According to the method, the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile are obtained through the cloud platform, so that the monomer direct current internal resistance of the to-be-detected battery cell is predicted based on the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile, the influence of various parameter changes on the monomer direct current internal resistance of the battery cell in the driving process of the automobile is fully considered, the DCR change of the battery system is monitored on line, and the safety state of the battery system is evaluated.
Drawings
Fig. 1 is a working flow chart of a method for measuring direct current internal resistance of a single battery cell of an electric vehicle according to the invention;
fig. 2 is a flowchart illustrating a method for determining direct current internal resistance of a cell monomer of an electric vehicle according to a second embodiment of the present invention;
fig. 3 is a schematic diagram illustrating calculation of direct current internal resistance of a training unit of a battery cell to be tested;
FIG. 4 is a schematic diagram illustrating error analysis and comparison of the prediction results of the training model;
fig. 5 is a diagram of comparison and error between a predicted value and an actual measurement value of a certain cell DCR-10s of the battery system;
fig. 6 is a flowchart illustrating a method for measuring direct-current internal resistance of a cell monomer of an electric vehicle according to a third embodiment of the present invention;
fig. 7 is a schematic diagram of a hardware structure of an electric vehicle battery cell monomer direct-current internal resistance measurement electronic device according to a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
Example one
Fig. 1 is a flowchart illustrating a method for determining a direct current internal resistance of a cell unit of an electric vehicle according to the present invention, including:
step S101, acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
step S102, inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
Specifically, step S101 obtains cloud data of the operating vehicle, where the cloud data includes an environmental parameter and a monomer parameter of the electric core to be tested. The cloud data is uploaded to a cloud platform server, such as a server of an operation platform of the electric vehicle, by the electric vehicle during a driving process. And selecting data with high correlation between the environmental parameters and the monomer parameters of the electric core to be tested. The monomer parameter may be Battery Management System (BMS) national standard data of the electric vehicle.
And S102, inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested. The single direct current internal resistance prediction model can be obtained by training through machine learning, and can be a regression model. And establishing an independent single direct current internal resistance prediction model for each battery cell in a battery system of the electric automobile. The obtained predicted monomer direct-current internal resistance can be used for monitoring the battery cell, so that the DCR change of the battery system is monitored on line, and the safety state of the battery system is evaluated.
According to the method, the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile are obtained through the cloud platform, so that the monomer direct current internal resistance of the to-be-detected battery cell is predicted based on the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile, the influence of various parameter changes on the monomer direct current internal resistance of the battery cell in the driving process of the automobile is fully considered, the DCR change of the battery system is monitored on line, and the safety state of the battery system is evaluated.
Example two
Fig. 2 is a flowchart illustrating a method for determining direct-current internal resistance of a battery cell of an electric vehicle according to a second embodiment of the present invention, including:
step S201, obtaining, from a cloud platform, a historical environmental parameter, a historical monomer parameter, and a historical direct current internal resistance calculation parameter of the electric core to be detected of the electric vehicle, where the monomer parameter includes: the method comprises the following steps of presetting the monomer direct current internal resistance of an adjacent battery cell within a range by taking a battery cell to be tested as a center, and/or the average monomer direct current internal resistance of the adjacent battery cell, wherein the environmental parameters comprise: driving range, battery system charging current, battery system total voltage, battery system state of charge, and/or battery system temperature.
Step S202, calculating a plurality of training monomer direct current internal resistances of the electric core to be tested according to the historical direct current internal resistance calculation parameters of the electric core to be tested, selecting a historical environment parameter corresponding to each training monomer direct current internal resistance as a training environment parameter from a cloud platform, and selecting a historical monomer parameter corresponding to each training monomer direct current internal resistance as a training historical monomer parameter.
Specifically, the direct current internal resistance calculation parameters include: the method comprises the following steps of (1) identifying a cell voltage, a battery system charging current and a battery system charging state;
searching the moment when the charging state identifier of the battery system is converted from a preparation state before charging to a charging state as an initial moment from the historical direct current internal resistance calculation parameters;
for each initial moment, selecting the monomer voltage corresponding to the initial moment from the historical direct current internal resistance calculation parameters as an initial voltage, selecting the monomer voltage corresponding to the end moment as an end voltage by taking the moment when a preset measurement time period passes after the initial moment as an end moment, and calculating the training monomer direct current internal resistance corresponding to each end moment as the absolute value of the difference value of the initial voltage and the end voltage divided by the charging current of the battery system in the measurement time period after the initial moment;
and for the direct current internal resistance of the training monomer corresponding to each ending moment, selecting the historical environment parameter corresponding to the ending moment from the historical environment parameters of the electric core to be tested as the training environment parameter corresponding to the direct current internal resistance of the training monomer, and selecting the historical monomer parameter corresponding to the ending moment from the historical monomer parameters of the electric core to be tested as the training monomer parameter corresponding to the direct current internal resistance of the training monomer.
Step S203, using the training environment parameters and the training monomer parameters as input, using the corresponding training monomer direct current internal resistance as a response, and obtaining a monomer direct current internal resistance prediction model of the electric core to be tested through machine learning training.
Step S204, obtaining the environmental parameters and the monomer parameters of the to-be-tested battery cell of the battery pack of the electric vehicle in the cloud platform.
Step S205, inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric vehicle into the monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
Specifically, step S201 to step S203 train the single direct current internal resistance prediction model. Wherein:
step S201 may download BMS national standard data of the electric vehicle operating within a preset time period from a database of the cloud platform:
1. the data downloading and arranging can be carried out by taking months as a unit. For example: 2018.2.01-2.28;
2. the data storage format is not limited, for example: *. xls, xlsx, csv, etc.
And then cleaning the data to obtain historical direct current internal resistance calculation parameters. Data cleansing is the process of re-examining and verifying data with the aim of deleting duplicate information, correcting existing errors, and providing data availability. The source data uploaded to the cloud end by the BMS system are huge and complex, the source data have the phenomena of dislocation, repetition and null value, and the wrong or conflicting data need to be cleaned.
And S202, calculating the direct current internal resistance of the training monomer according to the historical direct current internal resistance calculation parameters.
Fig. 3 is a schematic diagram illustrating calculation of direct current internal resistance of a training cell of a to-be-measured battery cell, specifically, a preparation state S of a charging state identifier of a battery system before charging is searched from historical direct current internal resistance calculation parameters0Transition to the charging State S1Is an initial time t0. Since the historical direct current internal resistance calculation parameters are multiple, multiple preparation states S before charging can be found from the historical direct current internal resistance calculation parameters0Transition to the charging State S1At an initial time t0. For each initial instant t0At the initial time t0The moment of the subsequent measurement period is the end moment t1The measurement period is preferably 10 seconds, i.e. t1-t010 s. There will therefore be a pair of initial instants t0And an end time t1. For each pair of initial instants t0And an end time t1Searching and initial time t in the historical instruction internal resistance calculation parameters0Corresponding cell voltage as initial voltage U0And end time t1Corresponding cell voltage as the end voltage U1. With each initial time t0Corresponding training monomer direct current internal resistance
Wherein I0At an initial time t0To the end time t1The battery system charging current in between. Finally, the time t is selected and ended again1Corresponding historical environmental parameters and historical monomer parameters.
Wherein the monomer parameters include: and the cell direct current internal resistance of the adjacent cell in a preset range by taking the cell to be tested as the center, and/or the average cell direct current internal resistance of the adjacent cell. The environmental parameters include: driving range, battery system charging current, battery system total voltage, battery system state of charge, and/or battery system temperature.
Based on the training data in step S202, step S203 builds an internal resistance model. The monomer direct current internal resistance prediction model is preferably a battery system battery cell internal resistance regression model established by adopting an MATLAB machine learning function.
The DCR of the single lithium battery is influenced by a plurality of factors, wherein the main factors are current, SOC, temperature, mileage, total voltage, partial cell DCR value and the like. Using a small amount of highly correlated data increases the accuracy and learning efficiency of the learning model. And selecting a part of the electric cores around the electric core to be tested, for example, selecting No. 84, No. 85, No. 86 and No. 88 electric cores for No. 87 electric cores.
In this embodiment, to improve the quality of the parameter framework, the original training 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. And finally, averaging the obtained K experimental results, wherein the experimental results can be the difference value between a predicted value and a check value, so that the smaller the difference value is, the better the difference value is, the best classification is determined, and the training of the model is realized. In this example, Regression Learner APP built in MATLAB is used to train and predict data, and K takes a value of 4. The regression effect is evaluated in this example using the curtailment coefficient Rs. The coefficient Of determinability is the proportion Of the regression Sum Of Squares (ESS) to the Total Sum Of squared differences (TSS), and is calculated as:
wherein:
ωirepresenting a data weight;
yia true observation value;
SSR is regression sum of squares, SST is total deviation sum of squares;
and n is the model data quantity.
Taking operation data of an electric vehicle of a certain brand as an example, acquiring data such as total current, total voltage, SOC, cell temperature, driving mileage and the like, randomly selecting N cell data including a cell to be tested and average DCR value data of the cell, using the N values trained by using different types of data as a data training set (marked as a sample A), wherein the accuracy of the model is obviously influenced by N values, and the accuracy of the DCR model is influenced by N which is too large or too small, i.e. the average value of the cell is important training data, and if the average value is missing, the accuracy of the DCR model is obviously influenced. The results are shown in the figure. Using sample a as the data input sample, the Rs value was calculated using K cross-test training and validation models, as shown in fig. 4. Fig. 4 shows the error analysis results of cross-validation of a model obtained using 2-month (2-month and 3-month in 2018) operation data of a certain brand of vehicle as training data, with an Rs value of approximately 1; this shows that the model accuracy is good.
And then step S204, acquiring the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile in the cloud platform, namely acquiring online data. And in step S205, prediction is performed based on the trained single direct current internal resistance prediction model.
Using the running data of the vehicle for 1 month (4 months in 2018) as input data, the model trained in step S203 is used for prediction, actual measurement data is compared, and the average absolute value error ERR is calculated, and as a result, as shown in fig. 5, the model accuracy is good, where ERR is 1.08e-13%, is approximately equal to 0. Wherein ERR is represented by
Wherein, yiIs a true observed value, y'iThe predicted value of the model is n, and the data volume of the model is n;
and (3) respectively using DCR values of other cells (No. 84, No. 86 and No. 88) as model response values, predicting the corresponding cells after training the model and verifying the accuracy of the result, wherein the ERR result is approximate to 1, which indicates that the method has universal applicability.
The embodiment provides a brand-new modeling method for direct current internal resistance of a single battery cell, which comprises the following steps: according to historical data of the electric automobile in the cloud platform, a database of the direct current internal resistance of the battery system battery cell monomers is established, a multi-factor DCR prediction model with high correlation such as current, SOC, temperature, driving mileage, total voltage, a plurality of battery cell DCR values, a battery cell DCR average value and the like is established through a machine learning mechanism to predict the single DCR, the DCR change of the battery system is monitored on line, and then the safety state of the battery system is evaluated.
EXAMPLE III
Fig. 6 is a flowchart illustrating a method for determining direct-current internal resistance of a battery cell of an electric vehicle according to a third embodiment of the present invention, including:
step S601, acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
step S602, inputting environmental parameters and monomer parameters of a to-be-detected electric core of an electric vehicle into a monomer direct current internal resistance prediction model of the to-be-detected electric core to obtain predicted monomer direct current internal resistance of the to-be-detected electric core returned by the monomer direct current internal resistance prediction model;
step S603, obtaining the actual monomer direct current internal resistance of the electric core to be tested, comparing the actual monomer direct current resistance with the predicted monomer direct current resistance, and giving an alarm if the comparison result meets the preset alarm condition.
Specifically, the actual individual direct-current internal resistance can be calculated by the aforementioned method according to the real-time direct-current internal resistance calculation parameter. Namely, obtaining real-time direct current internal resistance calculation parameters, and when the charging state identifier of the battery system is in a preparation state S before charging0Transition to the charging State S1Is marked as the initial time t0. Then obtaining the initial time t0The moment of the subsequent measurement period is the end moment t1The measurement period is preferably 10 seconds, i.e. t1-t010 s. Obtaining and initial time t from real-time direct current internal resistance calculation parameters0Corresponding cell voltage as initial voltage U0And end time t1Corresponding cell voltage as the end voltage U1. Calculating the actual DC internal resistance of the single body
Wherein I0At an initial time t0To the end time t1The battery system charging current in between.
In this embodiment, an alarm is given according to a comparison result between the actual monomer direct-current internal resistance and the predicted monomer direct-current internal resistance. Since the predicted individual direct-current internal resistance is obtained by prediction according to the historical data, it can be considered that the predicted individual direct-current internal resistance is the cell individual direct-current internal resistance in the fault-free state. And if the actual monomer direct current internal resistance obtained by actual measurement is greatly different from the predicted monomer direct current internal resistance, the fault can be considered to occur, and an alarm is given.
Example four
Fig. 7 is a schematic diagram of a hardware structure of an electric vehicle battery cell unit direct-current internal resistance measurement electronic device according to a fourth embodiment of the present invention, including:
at least one processor 701; and the number of the first and second groups,
a memory 702 communicatively linked to at least one of the processors 701; wherein the content of the first and second substances,
the memory 702 stores instructions executable by at least one of the processors 701 to cause at least one of the processors 701 to:
acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
The electronic device is preferably a server of a cloud platform, such as a server of an operation platform of an electric vehicle. The electronic device may further include: an input device 703 and a display device 704.
The processor 701, the memory 702, the input device 703 and the display device 704 may be linked by a bus or other means, and the bus is illustrated as an example.
The memory 702 is used as a non-volatile computer-readable storage medium, and can be used to store a non-volatile software program, a non-volatile computer-executable program, and modules, such as program instructions/modules corresponding to the method for determining the direct current internal resistance of the electric vehicle battery cell in the embodiment of the present application, for example, the method flow shown in fig. 1. The processor 701 executes various functional applications and data processing by running the nonvolatile software program, instructions and modules stored in the memory 702, that is, implements the method for measuring the direct current internal resistance of the electric vehicle battery cell in the foregoing embodiment.
The memory 702 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area can store data and the like created according to the use of the direct current internal resistance measuring method of the electric automobile battery cell monomer. Further, the memory 702 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 702 may optionally include a memory remotely located from the processor 701, and these remote memories may be linked via a network to a device that performs the method for determining the dc internal resistance of the electric vehicle cell units. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 703 may receive an input of a user click and generate signal inputs related to user settings and function control of the method for determining the dc internal resistance of the electric vehicle battery cell. Display device 704 may include a display screen or the like.
When the one or more modules are stored in the memory 702 and executed by the one or more processors 701, the method for determining the direct current internal resistance of the electric vehicle battery cell in any of the above-described method embodiments is executed.
According to the method, the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile are obtained through the cloud platform, so that the monomer direct current internal resistance of the to-be-detected battery cell is predicted based on the environmental parameters and the monomer parameters of the to-be-detected battery cell of the electric automobile, the influence of various parameter changes on the monomer direct current internal resistance of the battery cell in the driving process of the automobile is fully considered, the DCR change of the battery system is monitored on line, and the safety state of the battery system is evaluated.
EXAMPLE five
A fifth embodiment of the present invention provides an electronic device for measuring direct current internal resistance of electric core monomers of electric vehicles, including:
at least one processor;
a memory communicatively linked to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by at least one of the processors to enable the at least one of the processors to:
acquiring historical environmental parameters, historical monomer parameters and historical direct current internal resistance calculation parameters of the electric core to be detected of the electric automobile from a cloud platform, wherein the monomer parameters comprise: the method comprises the following steps of presetting the monomer direct current internal resistance of an adjacent battery cell within a range by taking a battery cell to be tested as a center, and/or the average monomer direct current internal resistance of the adjacent battery cell, wherein the environmental parameters comprise: driving range, battery system charging current, battery system total voltage, battery system state of charge, and/or battery system temperature.
Calculating a plurality of training monomer direct current internal resistances of the electric core to be tested according to the historical direct current internal resistance calculation parameters of the electric core to be tested, selecting a historical environment parameter corresponding to each training monomer direct current internal resistance as a training environment parameter from the cloud platform, and selecting a historical monomer parameter corresponding to each training monomer direct current internal resistance as a training historical monomer parameter.
Specifically, the direct current internal resistance calculation parameters include: the method comprises the following steps of (1) identifying a cell voltage, a battery system charging current and a battery system charging state;
searching the moment when the charging state identifier of the battery system is converted from a preparation state before charging to a charging state as an initial moment from the historical direct current internal resistance calculation parameters;
for each initial moment, selecting the monomer voltage corresponding to the initial moment from the historical direct current internal resistance calculation parameters as an initial voltage, selecting the monomer voltage corresponding to the end moment as an end voltage by taking the moment when a preset measurement time period passes after the initial moment as an end moment, and calculating the training monomer direct current internal resistance corresponding to each end moment as the absolute value of the difference value of the initial voltage and the end voltage divided by the charging current of the battery system in the measurement time period after the initial moment;
and for the direct current internal resistance of the training monomer corresponding to each ending moment, selecting the historical environment parameter corresponding to the ending moment from the historical environment parameters of the electric core to be tested as the training environment parameter corresponding to the direct current internal resistance of the training monomer, and selecting the historical monomer parameter corresponding to the ending moment from the historical monomer parameters of the electric core to be tested as the training monomer parameter corresponding to the direct current internal resistance of the training monomer.
And obtaining a single direct current internal resistance prediction model of the battery cell to be tested through machine learning training by taking the training environment parameters and the training single parameters as input and taking the corresponding training single direct current internal resistance as a response.
The method comprises the steps of obtaining environmental parameters and monomer parameters of a to-be-detected battery core of a battery pack of the electric vehicle in the cloud platform.
Inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
The embodiment provides a brand-new modeling method for direct current internal resistance of a single battery cell, which comprises the following steps: according to historical data of the electric automobile in the cloud platform, a database of the direct current internal resistance of the battery system battery cell monomers is established, a multi-factor DCR prediction model with high correlation such as current, SOC, temperature, driving mileage, total voltage, a plurality of battery cell DCR values, a battery cell DCR average value and the like is established through a machine learning mechanism to predict the single DCR, the DCR change of the battery system is monitored on line, and then the safety state of the battery system is evaluated.
EXAMPLE six
A fifth embodiment of the present invention provides an electronic device for measuring direct current internal resistance of electric core monomers of electric vehicles, including:
at least one processor;
a memory communicatively linked to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by at least one of the processors to enable the at least one of the processors to:
acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
and inputting the single direct current internal resistance into a single direct current internal resistance prediction model of the battery cell to be tested to obtain the predicted single direct current internal resistance of the battery cell to be tested, which is returned by the single direct current internal resistance prediction model.
And acquiring the actual monomer direct current internal resistance of the battery cell to be tested, comparing the actual monomer direct current resistance with the predicted monomer direct current resistance, and giving an alarm if the comparison result meets the preset alarm condition.
In this embodiment, an alarm is given according to a comparison result between the actual monomer direct-current internal resistance and the predicted monomer direct-current internal resistance. Since the predicted individual direct-current internal resistance is obtained by prediction according to the historical data, it can be considered that the predicted individual direct-current internal resistance is the cell individual direct-current internal resistance in the fault-free state. And if the actual monomer direct current internal resistance obtained by actual measurement is greatly different from the predicted monomer direct current internal resistance, the fault can be considered to occur, and an alarm is given.
EXAMPLE seven
A seventh embodiment of the present invention provides a storage medium, where the storage medium stores computer instructions, and when a computer executes the computer instructions, the storage medium is configured to execute all the steps of the method for determining direct current internal resistance of a single electric core of an electric vehicle as described above.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (13)
1. A method for measuring direct current internal resistance of a battery cell monomer of an electric automobile is characterized by comprising the following steps:
acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
2. The method for measuring direct-current internal resistance of electric automobile battery cell monomers according to claim 1, wherein the monomer parameters include: and the cell direct current internal resistance of the adjacent cell in a preset range by taking the cell to be tested as the center, and/or the average cell direct current internal resistance of the adjacent cell.
3. The method for measuring direct-current internal resistance of electric automobile battery cell units according to claim 2, wherein the environmental parameters include: driving range, battery system charging current, battery system total voltage, battery system state of charge, and/or battery system temperature.
4. The method for measuring direct-current internal resistance of electric automobile battery cell monomer according to claim 1, further comprising:
acquiring historical environmental parameters, historical monomer parameters and historical direct current internal resistance calculation parameters of the electric core to be tested of the electric automobile from a cloud platform;
calculating a plurality of training monomer direct current internal resistances of the electric core to be tested according to the historical direct current internal resistance calculation parameters of the electric core to be tested, selecting a historical environment parameter corresponding to each training monomer direct current internal resistance as a training environment parameter from a cloud platform, and selecting a historical monomer parameter corresponding to each training monomer direct current internal resistance as a training historical monomer parameter;
and obtaining a single direct current internal resistance prediction model of the battery cell to be tested through machine learning training by taking the training environment parameters and the training single parameters as input and taking the corresponding training single direct current internal resistance as a response.
5. The method according to claim 4, wherein the calculating of the direct current internal resistance of the multiple training monomers of the electric core to be tested according to the historical direct current internal resistance calculation parameter of the electric core to be tested selects, from the cloud platform, a historical environmental parameter corresponding to each direct current internal resistance of the training monomers as a training environmental parameter, and selects a historical monomer parameter corresponding to each direct current internal resistance of the training monomers as a training historical monomer parameter, specifically includes:
the direct current internal resistance calculation parameters comprise: the method comprises the following steps of (1) identifying a cell voltage, a battery system charging current and a battery system charging state;
searching the moment when the charging state identifier of the battery system is converted from a preparation state before charging to a charging state as an initial moment from the historical direct current internal resistance calculation parameters;
for each initial moment, selecting the monomer voltage corresponding to the initial moment from the historical direct current internal resistance calculation parameters as an initial voltage, selecting the monomer voltage corresponding to the end moment as an end voltage by taking the moment when a preset measurement time period passes after the initial moment as an end moment, and calculating the training monomer direct current internal resistance corresponding to each end moment as the absolute value of the difference value of the initial voltage and the end voltage divided by the charging current of the battery system in the measurement time period after the initial moment;
and for the direct current internal resistance of the training monomer corresponding to each ending moment, selecting the historical environment parameter corresponding to the ending moment from the historical environment parameters of the electric core to be tested as the training environment parameter corresponding to the direct current internal resistance of the training monomer, and selecting the historical monomer parameter corresponding to the ending moment from the historical monomer parameters of the electric core to be tested as the training monomer parameter corresponding to the direct current internal resistance of the training monomer.
6. The method for measuring direct current internal resistance of electric automobile battery cell monomers according to any one of claims 1 to 5, further comprising:
and acquiring the actual monomer direct current internal resistance of the battery cell to be tested, comparing the actual monomer direct current resistance with the predicted monomer direct current resistance, and giving an alarm if the comparison result meets the preset alarm condition.
7. The utility model provides an electric automobile electricity core monomer direct current internal resistance survey electronic equipment which characterized in that includes:
at least one processor; and the number of the first and second groups,
a memory communicatively linked to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by at least one of the processors to enable the at least one of the processors to:
acquiring environmental parameters and monomer parameters of a to-be-detected battery cell of a battery pack of an electric vehicle in a cloud platform;
inputting the environmental parameters and the monomer parameters of the electric core to be tested of the electric automobile into a monomer direct current internal resistance prediction model of the electric core to be tested, and obtaining the predicted monomer direct current internal resistance of the electric core to be tested, which is returned by the monomer direct current internal resistance prediction model.
8. The electric vehicle battery cell direct-current internal resistance measurement electronic device of claim 7, wherein the cell parameters include: and the cell direct current internal resistance of the adjacent cell in a preset range by taking the cell to be tested as the center, and/or the average cell direct current internal resistance of the adjacent cell.
9. The electric vehicle battery cell direct-current internal resistance measurement electronic device of claim 8, wherein the environmental parameters include: driving range, battery system charging current, battery system total voltage, battery system state of charge, and/or battery system temperature.
10. The electric vehicle battery cell direct-current internal resistance measurement electronic device of claim 7, wherein the processor is further configured to:
acquiring historical environmental parameters, historical monomer parameters and historical direct current internal resistance calculation parameters of the electric core to be tested of the electric automobile from a cloud platform;
calculating a plurality of training monomer direct current internal resistances of the electric core to be tested according to the historical direct current internal resistance calculation parameters of the electric core to be tested, selecting a historical environment parameter corresponding to each training monomer direct current internal resistance as a training environment parameter from a cloud platform, and selecting a historical monomer parameter corresponding to each training monomer direct current internal resistance as a training historical monomer parameter;
and obtaining a single direct current internal resistance prediction model of the battery cell to be tested through machine learning training by taking the training environment parameters and the training single parameters as input and taking the corresponding training single direct current internal resistance as a response.
11. The electronic device for determining direct-current internal resistance of electric vehicle battery cells according to claim 10, wherein the calculating of the multiple training direct-current internal resistances of the battery cell to be tested according to the historical direct-current internal resistance calculation parameter of the battery cell to be tested selects, from a cloud platform, a historical environmental parameter corresponding to each training direct-current internal resistance as a training environmental parameter, and selects a historical monomer parameter corresponding to each training direct-current internal resistance as a training historical monomer parameter, specifically includes:
the direct current internal resistance calculation parameters comprise: the method comprises the following steps of (1) identifying a cell voltage, a battery system charging current and a battery system charging state;
searching the moment when the charging state identifier of the battery system is converted from a preparation state before charging to a charging state as an initial moment from the historical direct current internal resistance calculation parameters;
for each initial moment, selecting the monomer voltage corresponding to the initial moment from the historical direct current internal resistance calculation parameters as an initial voltage, selecting the monomer voltage corresponding to the end moment as an end voltage by taking the moment when a preset measurement time period passes after the initial moment as an end moment, and calculating the training monomer direct current internal resistance corresponding to each end moment as the absolute value of the difference value of the initial voltage and the end voltage divided by the charging current of the battery system in the measurement time period after the initial moment;
and for the direct current internal resistance of the training monomer corresponding to each ending moment, selecting the historical environment parameter corresponding to the ending moment from the historical environment parameters of the electric core to be tested as the training environment parameter corresponding to the direct current internal resistance of the training monomer, and selecting the historical monomer parameter corresponding to the ending moment from the historical monomer parameters of the electric core to be tested as the training monomer parameter corresponding to the direct current internal resistance of the training monomer.
12. The electric vehicle battery cell direct-current internal resistance measurement electronic device according to any one of claims 7 to 11, wherein the processor is further configured to:
and acquiring the actual monomer direct current internal resistance of the battery cell to be tested, comparing the actual monomer direct current resistance with the predicted monomer direct current resistance, and giving an alarm if the comparison result meets the preset alarm condition.
13. A storage medium, which stores computer instructions, and when the computer instructions are executed by a computer, is used for executing all the steps of the electric vehicle battery cell direct current internal resistance measurement method according to any one of claims 1 to 6.
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