CN115172911A - Analysis and prediction system and warning method for automobile lithium battery - Google Patents
Analysis and prediction system and warning method for automobile lithium battery Download PDFInfo
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- CN115172911A CN115172911A CN202210983840.9A CN202210983840A CN115172911A CN 115172911 A CN115172911 A CN 115172911A CN 202210983840 A CN202210983840 A CN 202210983840A CN 115172911 A CN115172911 A CN 115172911A
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/482—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for several batteries or cells simultaneously or sequentially
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/488—Cells or batteries combined with indicating means for external visualization of the condition, e.g. by change of colour or of light density
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Abstract
The invention discloses an analysis and prediction system and a warning method for an automobile lithium battery, and the system comprises a lithium battery, wherein the input end of the lithium battery is bidirectionally and electrically connected with a detection module, the output end of the detection module is bidirectionally and electrically connected with a terminal processor, the output end of the terminal processor is respectively bidirectionally and electrically connected with a data analysis module and a database control module, the output end of the database control module is electrically connected with a long-term and short-term database, the output end of the long-term and short-term database is electrically connected with a model building module, the output end of the data analysis module is electrically connected with a neural network prediction model, the input end of the neural network prediction model is electrically connected with the output end of the model building module, and the output end of the neural network prediction model is electrically connected with a calculation and judgment module. The invention solves the problems that the existing lithium battery has a single monitoring mode, detection data cannot be displayed in an integrated mode, a user cannot conveniently and visually observe the state of the lithium battery, and the safety of a vehicle is seriously influenced.
Description
Technical Field
The invention relates to the technical field of automobile lithium batteries, in particular to an analysis and prediction system and a warning method for an automobile lithium battery.
Background
The lithium battery for the vehicle is a power battery of a hybrid electric vehicle and an electric vehicle, and has the advantages of high energy density, large capacity, no memory property and the like because some technical properties of the nickel-hydrogen battery, such as energy density, charge and discharge speed and the like, are close to theoretical limit values.
In order to guarantee new energy automobile's operation security, lithium cell for new energy automobile need monitor its running state at the operation in-process, but the monitoring mode of current lithium cell is comparatively single, and the unable integrated demonstration of data that detects, the person of not being convenient for carries out visual observation to the lithium cell state, has seriously influenced vehicle security.
Therefore, the design and the modification of the automobile lithium battery system are needed, and the lithium battery prediction and monitoring safety is effectively improved.
Disclosure of Invention
In order to solve the problems in the background art, the invention aims to provide an analysis and prediction system and a warning method for an automobile lithium battery, which have the advantage of predicting the state of the lithium battery and solve the problems that the existing lithium battery has a single monitoring mode, detection data cannot be displayed in an integrated manner, a user cannot observe the state of the lithium battery directly and conveniently, and the safety of a vehicle is seriously influenced.
In order to achieve the purpose, the invention provides the following technical scheme: an analysis and prediction system and a warning method for an automobile lithium battery comprise a lithium battery;
the input end of the lithium battery is bidirectionally and electrically connected with a detection module, the output end of the detection module is bidirectionally and electrically connected with a terminal processor, the output end of the terminal processor is respectively and bidirectionally and electrically connected with a data analysis module and a database control module, the output end of the database control module is electrically connected with a long-term and short-term database, the output end of the long-term and short-term database is electrically connected with a model building module, the output end of the data analysis module is electrically connected with a neural network prediction model, the input end of the neural network prediction model is electrically connected with the output end of the model establishing module, the output end of the neural network prediction model is electrically connected with a calculation and judgment module, the output end of the calculation and judgment module is electrically connected with a data output control module, the output end of the data output control module is electrically connected with a wireless transceiving module, and the output end of the wireless transceiving module is electrically connected with a vehicle instrument.
Preferably, the detection module comprises a voltage detection module, a current detection module, a temperature detection module and a humidity detection module.
Preferably, the input end of the terminal processor is electrically connected with a mobile terminal, and the mobile terminal is composed of a navigation app and route data.
Preferably, the output ends of the detection module and the mobile terminal are both electrically connected with a data preprocessing module in a bidirectional manner, and the output end of the data preprocessing module is electrically connected with the input end of the terminal processor.
Preferably, the model building module is composed of a training data set, a verification data set and a test data set.
Preferably, the vehicle instrument is composed of a broadcaster and a warning lamp.
Preferably, the output end of the wireless transceiver module is electrically connected to a cloud server, and the output end of the cloud server is electrically connected to the input end of the model building module.
An analysis prediction system and a warning method for an automobile lithium battery comprise the following steps:
s1: the detection module detects the voltage, the current, the temperature and the humidity of the lithium battery and transmits data to the data preprocessing module, and meanwhile, an operator sets route data in the mobile terminal navigation app and synchronously transmits the route data to the data preprocessing module;
s2: the terminal processor extracts data and transmits the data to the database control module, and the model building module extracts data in the long-term and short-term database and processes data samples fitted by the model;
s3: the verification data set is a sample set reserved independently in the model training process, can be used for adjusting the hyper-parameters of the model and carrying out primary evaluation on the capability of the model, and generates a neural network prediction model through simulation;
s4: the terminal processor transmits the single prediction data to the neural network prediction model for modeling processing, the calculation and judgment module collects the prediction data of the neural network prediction model and transmits the prediction data to the wireless transceiver module by using the data output control module, and the wireless transceiver module controls a broadcast device and an alarm lamp in the vehicle instrument to send out warning signals.
Compared with the prior art, the invention has the following beneficial effects:
1. the operation data of the lithium battery is detected through the detection module and is transmitted to the data preprocessing module, the terminal processor extracts the data and transmits the data to the database control module, the model establishing module extracts the data in the long-term and short-term database, processes the data sample fitted by the model and generates a neural network prediction model through simulation, the terminal processor transmits the single prediction data to the neural network prediction model for modeling, the calculation and judgment module collects the neural network prediction model prediction data and transmits the neural network prediction model prediction data to the wireless transceiving module through the data output control module, and the wireless transceiving module controls the vehicle instrument to send out a warning signal.
2. According to the invention, by arranging the voltage detection module, the current detection module, the temperature detection module and the humidity detection module, the data monitoring range can be improved, and the model prediction accuracy is improved.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions 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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the analysis and prediction system for an automotive lithium battery provided by the invention comprises a lithium battery;
the output end of the neural network prediction model is electrically connected with a calculation determination module, the output end of the calculation determination module is electrically connected with a data output control module, the output end of the data output control module is electrically connected with a wireless transceiving module, and the output end of the wireless transceiving module is electrically connected with a vehicle instrument.
Referring to fig. 1, the detection module includes a voltage detection module, a current detection module, a temperature detection module, and a humidity detection module.
As a technical optimization scheme of the invention, the voltage detection module, the current detection module, the temperature detection module and the humidity detection module are arranged, so that the data monitoring range can be enlarged, and the model prediction accuracy can be improved.
Referring to fig. 1, the input terminal of the terminal processor is electrically connected with a mobile terminal, which is composed of a navigation app and route data.
As a technical optimization scheme of the invention, the mobile terminal, the navigation app and the route data are set, and the route data can be used for assisting model connection, so that the prediction requirements of different driving data are met.
Referring to fig. 1, the output ends of the detection module and the mobile terminal are both electrically connected with a data preprocessing module in a bidirectional manner, and the output end of the data preprocessing module is electrically connected with the input end of the terminal processor.
As a technical optimization scheme of the invention, the data preprocessing module is arranged, so that the data can be pre-cached, and the operating pressure of the terminal processor is reduced.
Referring to FIG. 1, the model building module consists of a training dataset, a validation dataset, and a test dataset.
As a technical optimization scheme of the invention, the training data set, the verification data set and the test data set are set, so that the model connection efficiency can be improved, and the model can be verified for many times.
Referring to fig. 1, a vehicle meter is composed of a announcer and a warning lamp.
As a technical optimization scheme of the invention, the prompt effect and the warning response speed of a user can be improved by arranging the broadcaster and the warning lamp.
Referring to fig. 1, the output end of the wireless transceiver module is electrically connected to the cloud server, and the output end of the cloud server is electrically connected to the input end of the model building module.
According to the technical optimization scheme, the cloud server is arranged, so that the lithium battery data of multiple automobiles can be stored in an integrated mode, and the model building efficiency and stability are greatly improved.
Referring to fig. 1, an analysis prediction warning method for an automotive lithium battery includes the following steps:
s1: the detection module detects the voltage, the current, the temperature and the humidity of the lithium battery and transmits data to the data preprocessing module, and meanwhile, an operator sets route data in the mobile terminal navigation app and synchronously transmits the route data to the data preprocessing module;
s2: the terminal processor extracts data and transmits the data to the database control module, and the model building module extracts data in the long-term and short-term database and processes data samples fitted by the model;
s3: the verification data set is a sample set reserved in the model training process, can be used for adjusting the hyper-parameters of the model and carrying out preliminary evaluation on the capability of the model, and generates a neural network prediction model through simulation;
s4: the terminal processor transmits the single prediction data to the neural network prediction model for modeling processing, the calculation and judgment module collects the prediction data of the neural network prediction model and transmits the prediction data to the wireless transceiver module by using the data output control module, and the wireless transceiver module controls a broadcast device and an alarm lamp in the vehicle instrument to send out warning signals.
The working principle and the using process of the invention are as follows: when the device is used, the voltage, the current, the temperature and the humidity of the lithium battery are detected through the detection module, the data are transmitted to the data preprocessing module, meanwhile, an operator sets route data in a mobile terminal navigation app and synchronously transmits the route data to the data preprocessing module, the terminal processor extracts the data and transmits the data to the database control module, the model building module extracts the data in the long-term and short-term database and processes data samples fitted by the model, the data sets are verified to be sample sets reserved independently in the model training process, the super-parameters of the model can be adjusted, the capability of the model can be preliminarily evaluated, the neural network prediction model is generated through simulation, the terminal processor transmits the single prediction data to the neural network prediction model for modeling processing, the calculation and judgment module collects the neural network prediction data and transmits the neural network prediction data to the wireless transceiving module through the data output control module, and the wireless transceiving module controls a broadcast device and an alarm lamp in a vehicle instrument to send out warning signals.
In conclusion: the analysis and prediction system for the lithium battery of the automobile and the warning method thereof detect the operation data of the lithium battery through a detection module and transmit the data to a data preprocessing module, a terminal processor extracts the data and transmits the data to a database control module, a model establishing module extracts the data in a long-term and short-term database, processes a data sample fitted by a model and generates a neural network prediction model through simulation, the terminal processor transmits the single prediction data to the neural network prediction model for modeling, a calculation and judgment module collects the neural network prediction model prediction data and transmits the neural network prediction model prediction data to a wireless transceiver module by using a data output control module, and the wireless transceiver module controls a vehicle instrument to send a warning signal.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An analysis and prediction system for an automobile lithium battery comprises a lithium battery;
the method is characterized in that: the output end of the neural network prediction model is electrically connected with a calculation and judgment module, the output end of the calculation and judgment module is electrically connected with a data output control module, the output end of the data output control module is electrically connected with a wireless transceiving module, and the output end of the wireless transceiving module is electrically connected with a vehicle instrument.
2. The analysis and prediction system for the lithium battery of the automobile of claim 1, wherein: the detection module comprises a voltage detection module, a current detection module, a temperature detection module and a humidity detection module.
3. The analysis and prediction system for the lithium battery of the automobile of claim 1, wherein: the input end of the terminal processor is electrically connected with a mobile terminal, and the mobile terminal is composed of navigation app and route data.
4. The analysis and prediction system for the lithium battery of the automobile of claim 3, wherein: the output ends of the detection module and the mobile terminal are both electrically connected with a data preprocessing module in a bidirectional mode, and the output end of the data preprocessing module is electrically connected with the input end of the terminal processor.
5. The analysis and prediction system for the lithium battery of the automobile according to claim 1, wherein: the model building module is composed of a training data set, a verification data set and a test data set.
6. The analysis and prediction system for the lithium battery of the automobile according to claim 1, wherein: the vehicle instrument is composed of a broadcaster and an alarm lamp.
7. The analysis and prediction system for the lithium battery of the automobile according to claim 1, wherein: the output end of the wireless transceiver module is electrically connected with a cloud server, and the output end of the cloud server is electrically connected with the input end of the model building module.
8. The analysis prediction warning method for the lithium battery of the automobile according to claim 1, characterized in that: the method comprises the following steps:
s1: the detection module detects the voltage, the current, the temperature and the humidity of the lithium battery and transmits data to the data preprocessing module, and meanwhile, an operator sets route data in the mobile terminal navigation app and synchronously transmits the route data to the data preprocessing module;
s2: the terminal processor extracts data and transmits the data to the database control module, and the model building module extracts data in the long-term and short-term database and processes data samples fitted by the model;
s3: the verification data set is a sample set reserved in the model training process, can be used for adjusting the hyper-parameters of the model and carrying out preliminary evaluation on the capability of the model, and generates a neural network prediction model through simulation;
s4: the terminal processor transmits the single prediction data to the neural network prediction model for modeling processing, the calculation and judgment module collects the prediction data of the neural network prediction model and transmits the prediction data to the wireless transceiver module by using the data output control module, and the wireless transceiver module controls a broadcast device and an alarm lamp in the vehicle instrument to send out warning signals.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116317037A (en) * | 2023-05-23 | 2023-06-23 | 广东天圣网络科技有限公司 | Intelligent management method and system for charging cabinet |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116317037A (en) * | 2023-05-23 | 2023-06-23 | 广东天圣网络科技有限公司 | Intelligent management method and system for charging cabinet |
| CN116317037B (en) * | 2023-05-23 | 2023-08-29 | 广东天圣网络科技有限公司 | Intelligent management method and system for charging cabinet |
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