CN112698232A - Battery health state tracking cloud system based on battery detection - Google Patents
Battery health state tracking cloud system based on battery detection Download PDFInfo
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Abstract
The invention discloses a battery health state tracking cloud system based on battery detection, and belongs to the technical field of battery detection. The battery health state tracking cloud system based on battery detection comprises a data server, an artificial intelligence reasoning server and a data acquisition module; the data acquisition module and the artificial intelligence reasoning server are in signal connection with the data server; the data server comprises an information receiving module, a judging module, a comparing module, a database and a regular pushing module; the database stores the battery related information of the tested battery X; the data acquisition module is used for acquiring the battery related information stored in the database; the information receiving module is used for receiving the battery related information acquired by the data acquisition module; the battery health state can be efficiently and accurately predicted and estimated, and warning information is pushed to a battery user under the condition that the battery health degree is lower than a preset value.
Description
Technical Field
The invention relates to the technical field of battery detection, in particular to a battery health state tracking cloud system based on battery detection.
Background
In recent years, with the development of electric vehicles, lithium batteries have been widely used. The lithium battery can be continuously aged in the recycling process, the internal resistance is increased, and the capacity is attenuated. The battery health represents the aging state of the battery, which affects the safety and reliability of the electric vehicle and is an important parameter monitored in a battery management system. Therefore, the method for rapidly and accurately monitoring the health degree of the battery has important significance for realizing long-term safe and effective operation of the lithium battery.
Most of the existing algorithms for SOH of electric vehicles judge the state of health of a battery according to the number of driving mileage, and the method does not consider the habit difference used by each user, so that the estimation accuracy of the SOH is not high. In addition, some methods predict the SOH of the battery by establishing an electrochemical model, an empirical model and the like, but the establishment of the model requires the introduction of a large number of parameters and a large number of experiments, is relatively complex, and is difficult to use in an actual electric vehicle.
In the prior art, when a power battery is managed, the whole-process data monitoring for the whole power battery vehicle is lacked, when the battery health is abnormal, the abnormal battery cannot be traced, the battery health cannot be dynamically evaluated, and the tracing of the data source of the whole power battery cannot be realized.
Disclosure of Invention
1. Technical problem to be solved
In view of the problems in the prior art, an object of the present invention is to provide a battery health status tracking cloud system based on battery detection, which can efficiently and accurately predict and estimate the battery health status, and push warning information to a battery user when the battery health is lower than a preset value.
2. Technical scheme
In order to solve the above problems, the present invention adopts the following technical solutions.
The battery health state tracking cloud system based on battery detection comprises a data server, an artificial intelligence reasoning server and a data acquisition module;
the data acquisition module and the artificial intelligence reasoning server are in signal connection with the data server;
the data server comprises an information receiving module, a judging module, a comparing module, a database and a regular pushing module;
the database stores the battery related information of the tested battery X;
the data acquisition module is used for acquiring the battery related information stored in the database;
the information receiving module is used for receiving the battery related information acquired by the data acquisition module;
the judging module is used for judging whether the detected battery X is damaged or not; the judging module transmits the judged information that the detected battery X is not damaged to the artificial intelligent reasoning server; the judging module transmits the judged damaged information of the tested battery X to the data acquisition module and stores the battery related information of the damaged tested battery X into a database;
the artificial intelligence reasoning server is used for predicting the service life S of the undamaged battery X to be tested; the artificial intelligence reasoning server can also regularly learn the relevant information of the damaged tested battery X;
the comparison module is used for analyzing and comparing the service life S of the battery X to be detected, which is predicted by the artificial intelligence reasoning server, with a set value, and storing the compared relevant information of the battery in a database;
the regular pushing module is used for regularly pushing early warning information that the service life S of the undamaged battery X exceeds a set value to a battery user within a set time.
Further, the battery related information stored in the database includes, but is not limited to, status data N of the battery X to be tested, operator identity C, battery user identity data D, battery device number J, time T, and battery code X.
Further, the set value is 3 months; when the service life S of the battery X to be detected is more than 3 months, the comparison module stores the relevant information of the battery into a database; and when the service life S of the battery X to be detected is less than or equal to 3 months, the comparison module sends out early warning information to the data acquisition module and stores the relevant information of the battery in a database.
Furthermore, the judging module judges whether the tested battery X is damaged or not by referring to the battery health parameter table and judging whether the tested battery X is damaged or not according to the service life N; the judging module judges that the tested battery X is damaged and transmits N to the artificial intelligent reasoning server; and the comparison module waits for the artificial intelligence reasoning server to send the predicted service life S of the tested battery X.
Further, the data acquisition module is the APP on the smart phone of the repair engineer corresponding to the tested battery X, and the repair engineer scans the X code X of the tested battery through the APP to acquire the X related information of the tested battery.
Further, repair engineer scans the two-dimensional code that battery equipment number J corresponds or inputs battery equipment number J through APP when detecting for the first time on the day that is detected battery X.
Further, the artificial intelligence reasoning server comprises a data receiving module, a second screening module, a CNN artificial intelligence reasoning module and a data pushing module; the mode of predicting the service life S of the undamaged battery X by the artificial intelligence reasoning server is as follows: the method comprises the following steps:
a1, receiving state data N of the battery X to be detected through a data receiving module;
a2, inputting data to a CNN artificial intelligence reasoning module;
a3, operating a CNN artificial intelligence reasoning module;
a4, obtaining a battery predicted service life S of the CNN artificial intelligence reasoning module;
and A5, sending the result of the predicted service life S of the battery to the data server through the data push module.
Further, the mode that the artificial intelligence reasoning server regularly learns the relevant information of the damaged tested battery X is as follows: the method comprises the following steps:
b1, receiving data of the data server;
b2, retaining the damaged data of the tested battery X after screening by the second screening module;
b3, making the CNN artificial intelligence reasoning module enter a learning mode, and using the data retained by B2 to perform unsupervised learning.
Further, the data server also comprises a first screening module; the process of pushing the early warning information to the battery user by the regular pushing module is as follows: the method comprises the following steps:
z1, screening the data in the database through a first screening module, and then reserving the data of the tested battery X which is not damaged;
Z2、S=S-TOADY（）-T；
z3, judging whether S is more than 3 months;
when Z4 and S are less than or equal to 3 months, pushing warning information to a battery user; when S is more than 3 months, executing Z5;
z5, updating S in the data strip processed by the flow in the database;
TOADY () is the date of detection of the current day within a specified time; t is a detection time point within a prescribed time.
Further, the step of pushing the warning information to the battery user is to push the warning information to a WeChat public number concerned by the smart phone of the battery user.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
(1) the scheme can efficiently and accurately predict and estimate the health state of the battery, and pushes warning information to a battery user under the condition that the health degree of the battery is lower than a preset value.
(2) The warning information is pushed to the battery user, so that the battery user can be effectively prevented from continuing to use under the condition of low battery health degree, and safety accidents can be effectively avoided.
(3) The artificial intelligence reasoning server can predict the service life of the battery, and can also perform unsupervised learning on the damaged battery state data so as to better and more accurately predict the service life of the battery.
Drawings
FIG. 1 is a block diagram of the modules of the present invention;
FIG. 2 is a flow chart of the artificial intelligence reasoning server of the present invention for regularly learning the relevant information of the damaged tested battery X;
fig. 3 is a flowchart illustrating the periodic pushing module of the present invention periodically pushes the warning message to the battery user.
Detailed Description
The drawings in the embodiments of the invention will be combined; the technical scheme in the embodiment of the invention is clearly and completely described; obviously; the described embodiments are only some of the embodiments of the invention; but not all embodiments, are based on the embodiments of the invention; all other embodiments obtained by a person skilled in the art without making any inventive step; all fall within the scope of protection of the present invention.
Referring to fig. 1-3, the battery health status tracking cloud system based on battery detection includes a data server, an artificial intelligence reasoning server, and a data collection module.
The data acquisition module and the artificial intelligence reasoning server are in signal connection with the data server.
The data server comprises an information receiving module, a judging module, a comparing module, a database and a regular pushing module.
The database stores the battery related information of the tested battery X;
the data acquisition module is in signal connection with the database and is used for acquiring the battery related information stored in the database;
the information receiving module is in signal connection with the data acquisition module and is used for receiving the battery related information acquired by the data acquisition module.
The judging module is used for judging whether the detected battery X is damaged or not; the judgment module is in signal connection with the artificial intelligent reasoning server and transmits the judged information that the tested battery X is not damaged to the artificial intelligent reasoning server; the judging module is in signal connection with the data acquisition module, transmits the judged damaged information of the tested battery X to the data acquisition module, and stores the damaged battery related information of the tested battery X into the database.
The artificial intelligence reasoning server is used for predicting the service life S of the undamaged battery X to be tested; the artificial intelligence reasoning server can also learn the relevant information of the damaged tested battery X regularly (the regular period can be one of the days of each month).
The artificial intelligence reasoning server is in signal connection with the comparison module, the comparison module is in signal connection with the database, and the comparison module is used for analyzing and comparing the service life S of the battery X to be tested, which is predicted by the artificial intelligence reasoning server, with a set value and storing the compared relevant information of the battery in the database.
The regular pushing module is in signal connection with the artificial intelligence reasoning server, and is used for regularly pushing early warning information that the service life S of an undamaged battery X exceeds a set value to a battery user within a set time (the regular time can be the set time every day, such as 2 am every day), and pushing the warning information to the battery user to be warning information to a WeChat public number concerned by a smart phone of the battery user.
The battery related information stored in the database includes, but is not limited to, state data N of the battery X to be tested, an operator identity C, battery user identity data D, a battery device number J, a two-dimensional code corresponding to the battery device number J, time T, and a battery code X.
Setting value is 3 months; when the service life S of the battery X to be detected is more than 3 months, the comparison module stores the relevant information of the battery into a database; and when the service life S of the battery X to be detected is less than or equal to 3 months, the comparison module sends out early warning information to the data acquisition module and stores the relevant information of the battery in a database.
The judging module judges whether the tested battery X is damaged or not by referring to the battery health parameter table and judging whether the tested battery X is damaged or not according to the service life N; the judging module judges that the tested battery X is damaged and transmits N to the artificial intelligent reasoning server; and the comparison module waits for the artificial intelligence reasoning server to send the predicted service life S of the tested battery X.
The data acquisition module is the APP on the smart phone of the repair engineer corresponding to the tested battery X, and the repair engineer scans the X code X of the tested battery through the APP to acquire the related information of the tested battery X.
Repair the engineer and carry out the day that detects to being surveyed battery X, scan the two-dimensional code that battery equipment number J corresponds or input battery equipment number J through APP during the first time detection.
The artificial intelligence reasoning server comprises a data receiving module, a second screening module, a CNN artificial intelligence reasoning module and a data pushing module; the mode of predicting the service life S of the undamaged battery X by the artificial intelligence reasoning server is as follows: the method comprises the following steps:
a1, receiving state data N of the battery X to be detected through a data receiving module;
a2, inputting data to a CNN artificial intelligence reasoning module;
a3, operating a CNN artificial intelligence reasoning module;
a4, obtaining a battery predicted service life S of the CNN artificial intelligence reasoning module;
and A5, sending the result of the predicted service life S of the battery to the data server through the data push module.
The mode that the artificial intelligence reasoning server regularly learns the relevant information of the damaged tested battery X is as follows: the method comprises the following steps:
b1, receiving data of the data server;
b2, retaining the damaged data of the tested battery X after screening by the second screening module;
b3, making the CNN artificial intelligence reasoning module enter a learning mode, and using the data retained by B2 to perform unsupervised learning.
The data server also comprises a first screening module; the process of pushing the early warning information to the battery user by the regular pushing module is as follows: the method comprises the following steps:
z1, screening the data in the database through a first screening module, and then reserving the data of the tested battery X which is not damaged;
Z2、S=S-TOADY（）-T；
z3, judging whether S is more than 3 months;
when Z4 and S are less than or equal to 3 months, pushing warning information to a battery user; when S is more than 3 months, executing Z5;
z5, updating S in the data strip processed by the flow in the database;
TOADY () is the date of detection of the current day within a specified time; t is a detection time point within a prescribed time.
The battery health state can be efficiently and accurately predicted and estimated, warning information is pushed to a battery user under the condition that the battery health degree is lower than a preset value, the battery user can be effectively prevented from continuing to use under the condition that the battery health degree is lower, and therefore safety accidents can be effectively avoided.
The above; but are merely preferred embodiments of the invention; the scope of the invention is not limited thereto; any person skilled in the art is within the technical scope of the present disclosure; the technical scheme and the improved concept of the invention are equally replaced or changed; are intended to be covered by the scope of the present invention.
Claims (10)
1. Battery health state based on battery detection tracks cloud system, its characterized in that: the system comprises a data server, an artificial intelligence reasoning server and a data acquisition module;
the data acquisition module and the artificial intelligence reasoning server are in signal connection with the data server;
the data server comprises an information receiving module, a judging module, a comparing module, a database and a regular pushing module;
the database stores the battery related information of the tested battery X;
the data acquisition module is used for acquiring the battery related information stored in the database;
the information receiving module is used for receiving the battery related information acquired by the data acquisition module;
the judging module is used for judging whether the detected battery X is damaged or not; the judging module transmits the judged information that the detected battery X is not damaged to the artificial intelligent reasoning server; the judging module transmits the judged damaged information of the tested battery X to the data acquisition module and stores the battery related information of the damaged tested battery X into a database;
the artificial intelligence reasoning server is used for predicting the service life S of the undamaged battery X to be tested; the artificial intelligence reasoning server can also regularly learn the relevant information of the damaged tested battery X;
the comparison module is used for analyzing and comparing the service life S of the battery X to be detected, which is predicted by the artificial intelligence reasoning server, with a set value, and storing the compared relevant information of the battery in a database;
the regular pushing module is used for regularly pushing early warning information that the service life S of the undamaged battery X exceeds a set value to a battery user within a set time.
2. The battery state of health tracking cloud system based on battery detection as claimed in claim 1, wherein: the battery related information stored in the database includes, but is not limited to, state data N of the battery X to be tested, an operator identity C, battery user identity data D, a battery device number J, a two-dimensional code corresponding to the battery device number J, time T, and a battery code X.
3. The battery state of health tracking cloud system based on battery detection as claimed in claim 1, wherein: setting value is 3 months; when the service life S of the battery X to be detected is more than 3 months, the comparison module stores the relevant information of the battery into a database; and when the service life S of the battery X to be detected is less than or equal to 3 months, the comparison module sends out early warning information to the data acquisition module and stores the relevant information of the battery in a database.
4. The battery state of health tracking cloud system based on battery detection as claimed in claim 1, wherein: the judging module judges whether the tested battery X is damaged or not by referring to the battery health parameter table and judging whether the tested battery X is damaged or not according to the service life N; the judging module judges that the tested battery X is damaged and transmits N to the artificial intelligent reasoning server; and the comparison module waits for the artificial intelligence reasoning server to send the predicted service life S of the tested battery X.
5. The battery state of health tracking cloud system based on battery detection as claimed in claim 1, wherein: the data acquisition module is the APP on the smart phone of the repair engineer corresponding to the tested battery X, and the repair engineer scans the X code X of the tested battery through the APP to acquire the related information of the tested battery X.
6. The battery state of health tracking cloud system based on battery detection as claimed in claim 5, wherein: repair the engineer and carry out the day that detects to being surveyed battery X, scan the two-dimensional code that battery equipment number J corresponds or input battery equipment number J through APP during the first time detection.
7. The battery state of health tracking cloud system based on battery detection as claimed in claim 1, wherein: the artificial intelligence reasoning server comprises a data receiving module, a second screening module, a CNN artificial intelligence reasoning module and a data pushing module; the mode of predicting the service life S of the undamaged battery X by the artificial intelligence reasoning server is as follows: the method comprises the following steps:
a1, receiving state data N of the battery X to be detected through a data receiving module;
a2, inputting data to a CNN artificial intelligence reasoning module;
a3, operating a CNN artificial intelligence reasoning module;
a4, obtaining a battery predicted service life S of the CNN artificial intelligence reasoning module;
and A5, sending the result of the predicted service life S of the battery to the data server through the data push module.
8. The battery state of health tracking cloud system based on battery detection as claimed in claim 7, wherein: the mode that the artificial intelligence reasoning server regularly learns the relevant information of the damaged tested battery X is as follows: the method comprises the following steps:
b1, receiving data of the data server;
b2, retaining the damaged data of the tested battery X after screening by the second screening module;
b3, making the CNN artificial intelligence reasoning module enter a learning mode, and using the data retained by B2 to perform unsupervised learning.
9. The battery state of health tracking cloud system based on battery detection as claimed in claim 1, wherein: the data server also comprises a first screening module; the process of pushing the early warning information to the battery user by the regular pushing module is as follows: the method comprises the following steps:
z1, screening the data in the database through a first screening module, and then reserving the data of the tested battery X which is not damaged;
Z2、S＝S-TOADY()-T；
z3, judging whether S is more than 3 months;
when Z4 and S are less than or equal to 3 months, pushing warning information to a battery user; when S is more than 3 months, executing Z5;
z5, updating S in the data strip processed by the flow in the database;
TOADY () is the date of detection of the current day within a specified time; t is a detection time point within a prescribed time.
10. The battery state of health tracking cloud system based on battery detection as claimed in claim 1, wherein: the step of pushing the warning information to the battery user is to push the warning information to a WeChat public number concerned by the smart phone of the battery user.
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