CN211627771U - Battery fault monitoring system based on deep learning algorithm - Google Patents
Battery fault monitoring system based on deep learning algorithm Download PDFInfo
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- CN211627771U CN211627771U CN201922359788.6U CN201922359788U CN211627771U CN 211627771 U CN211627771 U CN 211627771U CN 201922359788 U CN201922359788 U CN 201922359788U CN 211627771 U CN211627771 U CN 211627771U
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Abstract
The utility model discloses a battery failure monitoring system based on degree of depth learning algorithm, concretely relates to storage battery technical field, including storage battery, the inside fixed a plurality of installation framves of grouping that are provided with of storage battery, the inside battery of having placed of installation frame of grouping, the fixed battery panel that is provided with of battery top end face, the installation frame inner wall both sides of grouping all are provided with installation splint, installation splint and the setting of contradicting on battery surface, fixed connection is provided with the spring between installation splint and the installation frame inner wall of grouping. The utility model discloses a divide into groups the installing frame and carry out the subregion to storage battery inside, divide into groups the battery and install, install splint and fix the battery, and temperature sensor, pressure sensor and voltage sensor cooperation treater constitute degree of depth study network, pinpoint the trouble battery, and judge the trouble type, make things convenient for the equipment and the trouble dimension of battery.
Description
Technical Field
The embodiment of the utility model provides a relate to storage battery technical field, concretely relates to battery fault monitoring system based on degree of depth learning algorithm.
Background
The battery is widely applied to various electronic products, and the working state of the battery has great influence on the reliability of the electronic products. According to the traditional technical scheme, the working state of the battery is monitored by adopting an integrated circuit, so that the electric energy storage technology for judging whether the battery fails is very important for realizing the basic function of a micro-grid, a high-performance energy storage system can guarantee the power supply quality and the continuous reliability of a power supply system, and the comprehensive utilization efficiency of electric energy is improved.
The prior art has the following defects: the charge-discharge process of lithium cell is an extremely complicated process, charge-discharge multiplying power and ambient temperature, and some other factors can all influence the decay and the life of group battery total capacity, the lithium cell still can appear the inflation in the use because of self reason in addition, if not in time handle and all can cause the potential safety hazard, current monitoring system adopts a single monitoring methods mostly, can not judge the trouble of battery, and the inconvenient accurate position of confirming trouble battery, influence subsequent maintenance.
SUMMERY OF THE UTILITY MODEL
For this, the embodiment of the utility model provides a battery failure monitoring system based on degree of depth learning algorithm, divide the storage battery inside through grouping the installing frame, divide the battery into groups and install, the installation splint are fixed the battery, a weighing sensor and a temperature sensor, pressure sensor and voltage sensor cooperation treater, constitute degree of depth learning network, it goes wrong to wait to detect the storage battery in the storage battery, the convenience is pinpointed the trouble battery, and judge the fault type, make things convenient for the equipment and the trouble dimension of battery, problem that the battery that leads to because inconvenient location trouble among the solution prior art.
In order to achieve the above object, the embodiment of the present invention provides the following technical solutions: a battery fault monitoring system based on a deep learning algorithm comprises a storage battery pack, wherein a plurality of grouping installation frames are fixedly arranged in the storage battery pack, a storage battery is placed in each grouping installation frame, a battery panel is fixedly arranged on the top end face of the storage battery, installation clamping plates are arranged on two sides of the inner wall of each grouping installation frame and are abutted against the surface of the storage battery, springs are fixedly connected between the installation clamping plates and the inner walls of the grouping installation frames, a temperature sensor is fixedly arranged on the surface of each installation clamping plate, a pressure sensor is fixedly arranged in the middle of each installation clamping plate and a bracket of the inner wall of each grouping installation frame, an electric power transmission line is fixedly arranged between the tops of a plurality of grouping installation frames, a first electric power output port is fixedly arranged on the top of the storage battery, a voltage sensor is fixedly arranged at the first power output port;
the storage battery pack is characterized in that a control panel is fixedly arranged at one end of the storage battery pack, the control panel comprises a processor used for calculating and processing data, the input end of the processor is connected with a data receiving module used for receiving monitoring data information, and the output ends of the temperature sensor, the pressure sensor and the voltage sensor are fixedly connected with the input end of the data receiving module.
Further, the number of the grouping installation frames is set to five, the top of the grouping installation frame is detachably arranged on the battery panel, the serial number label is fixedly arranged on the surface of the battery panel, the battery panel and the top end of the storage battery pack are horizontally arranged, and the storage battery is a lithium battery.
Furthermore, the number of the springs is four, the springs are arranged at four corners corresponding to the installation clamping plates, and the grouping installation frame is made of stainless steel materials.
Furthermore, the fixed display screen that is provided with of control panel surface, the display screen input is connected with the treater output, the display screen has touch display screen to make.
Furthermore, battery panel fixed surface is provided with the pilot lamp, pilot lamp quantity sets up to three, three the data monitoring of temperature sensor, pressure sensor and voltage sensor is corresponded to the pilot lamp.
Furthermore, a turnover handle is fixedly arranged on the surface of the battery panel, and the turnover handle is U-shaped.
Furthermore, an output control panel is fixedly arranged at the other end of the storage battery pack, a second power output port is fixedly arranged on the surface of the output control panel, and the power transmission line is electrically connected with the second power output port.
The embodiment of the utility model provides a have following advantage:
1. the utility model discloses a set up the sub-district of carrying out of group installing frame to storage battery inside, install the battery in groups, utilize the spring to promote installation splint, press from both sides tightly the battery both sides, fix the battery, utilize temperature sensor to detect the service temperature of battery simultaneously, utilize pressure sensor to detect the appearance of battery, detect whether the battery takes place the inflation, utilize voltage sensor to detect the service voltage of battery, utilize the treater to carry out data processing, constitute degree of depth study network, wait to detect to hold battery trouble in the storage battery, can carry out specific dismantlement installation to the battery, for current monitoring system, the convenience is pinpointed fault type to the trouble battery, and judge the fault type, make things convenient for the equipment and the fault maintenance of battery.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structure, ratio, size and the like shown in the present specification are only used for matching with the content disclosed in the specification, so as to be known and read by people familiar with the technology, and are not used for limiting the limit conditions which can be implemented by the present invention, so that the present invention has no technical essential significance, and any structure modification, ratio relationship change or size adjustment should still fall within the scope which can be covered by the technical content disclosed by the present invention without affecting the efficacy and the achievable purpose of the present invention.
Fig. 1 is a schematic view of the overall structure provided by the present invention;
fig. 2 is a schematic sectional view of the storage battery according to the present invention;
fig. 3 is a schematic view of the structure of part a of fig. 2 according to the present invention;
fig. 4 is a schematic overall sectional structure diagram provided by the present invention;
fig. 5 is a schematic diagram of a system provided by the present invention;
fig. 6 is a schematic side view of the output control panel according to the present invention;
in the figure: the device comprises a storage battery pack 1, a grouping installation frame 2, a storage battery 3, a battery panel 4, an installation clamping plate 5, a spring 6, a temperature sensor 7, a pressure sensor 8, a power transmission line 9, a power output port 10, a voltage sensor 11, a control panel 12, a processor 13, a data receiving module 14, a serial number plate 15, a display screen 16, an indicator light 17, an overturning handle 18, an output control panel 19 and a power output port 20.
Detailed Description
The present invention is described in terms of specific embodiments, and other advantages and benefits of the present invention will become apparent to those skilled in the art from the following disclosure. Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative work belong to the protection scope of the present invention.
Referring to the attached drawings 1-6 in the specification, the battery failure monitoring system based on the deep learning algorithm comprises a storage battery pack 1, a plurality of grouping installation frames 2 are fixedly arranged in the storage battery pack 1, storage batteries 3 are placed in the grouping installation frames 2, battery panels 4 are fixedly arranged on the top end faces of the storage batteries 3, installation clamping plates 5 are arranged on two sides of the inner walls of the grouping installation frames 2, the installation clamping plates 5 are abutted to the surfaces of the storage batteries 3, springs 6 are fixedly connected between the installation clamping plates 5 and the inner walls of the grouping installation frames 2, temperature sensors 7 are fixedly arranged on the surfaces of the installation clamping plates 5, pressure sensors 8 are fixedly arranged between the middle parts of the installation clamping plates 5 and supports on the inner walls of the grouping installation frames 2, electric power transmission lines 9 are fixedly arranged between the tops of the grouping installation frames 2, a first power output port 20 is fixedly arranged at the top of the storage battery 3, the power transmission line 9 is electrically connected with the first power output port 20, and a voltage sensor 11 is fixedly arranged at the first power output port 20;
one end of the storage battery pack 1 is fixedly provided with a control panel 12, the control panel 12 comprises a processor 13 used for calculating and processing data, the input end of the processor 13 is connected with a data receiving module 14 used for receiving monitoring data information, and the output ends of the temperature sensor 7, the pressure sensor 8 and the voltage sensor 11 are fixedly connected with the input end of the data receiving module 14.
Further, 2 quantity of grouping installing frames set up to five, the setting can be dismantled with battery panel 4 at 2 tops of grouping installing frame, 4 fixed surfaces of battery panel are provided with serial number sign 15, battery panel 4 personally submits the level setting with 1 top of storage battery, battery 3 sets up to the lithium cell, conveniently through pulling down battery panel 4, draws out grouping installing frame 2 with battery 3, makes things convenient for the installation and the trouble maintenance of battery 3.
Further, 6 quantity of spring sets up to four, four 6 corresponding installation splint 5 four corners settings of spring, group's installing frame 2 is made by stainless steel material, utilizes 6 promotion installation splint 5 of spring to press from both sides tight installation to battery 3, and it is simultaneously that the inflation takes place at the battery, compression spring 6 to 8 weak children of extrusion pressure sensor detect whether the inflation takes place for battery 3.
Further, control panel 12 fixed surface is provided with display screen 16, the 16 input ends of display screen are connected with treater 13 output, display screen 16 has touch display screen to make, conveniently shows the monitoring condition of battery 3, conveniently confirms the in service behavior of battery 3.
Further, battery panel 4 fixed surface is provided with pilot lamp 17, pilot lamp 17 quantity sets up to three, three pilot lamp 17 corresponds temperature sensor 7, pressure sensor 8 and voltage sensor 11's data monitoring, and every pilot lamp 17 corresponds a monitoring suggestion, makes things convenient for maintenance personal to carry out the maintenance that corresponds.
Furthermore, the surface of the battery panel 4 is fixedly provided with a turnover handle 18, the turnover handle 18 is U-shaped, the storage battery 3 is convenient to put forward, and the maintenance efficiency is improved.
Further, an output control board 19 is fixedly arranged at the other end of the storage battery pack 1, a second power output port 20 is fixedly arranged on the surface of the output control board 19, the power transmission line 9 is electrically connected with the second power output port 20, power output of the storage battery pack 1 is facilitated, a control switch is mounted on the output control board 19, and the power output is conveniently closed during maintenance.
The implementation scenario is specifically as follows: when the utility model is used for monitoring the faults of the storage batteries, each storage battery 3 is correspondingly arranged in a grouping installation frame 2, the grouping installation frame 2 divides the interior of a storage battery pack 1, a spring 6 pushes an installation clamping plate 5 to clamp the two sides of the storage battery 3, the storage battery 3 is fixed, a temperature sensor 7 detects the service temperature of the batteries, a pressure sensor 8 detects the appearance of the storage battery 3 to detect whether the storage battery 3 is expanded or not, if the storage battery is expanded, the spring 6 is compressed, so that the pressure sensor 8 is extruded to detect whether the storage battery 3 is expanded or not, a voltage sensor 11 detects the service voltage of the storage battery 3, a processor 13 processes data to form a deep learning network, the problem of the storage battery 3 in the storage battery pack 1 is detected, and the monitoring condition of the storage battery 3 is conveniently displayed, the in service behavior of battery 3 is confirmed to the convenience, and through setting up three pilot lamp 17, every pilot lamp 17 corresponds a monitoring suggestion, makes things convenient for maintenance personal to carry out the maintenance that corresponds, can carry out specific dismantlement installation to battery 3, conveniently pinpoints trouble battery 3, and judges the fault type, makes things convenient for the equipment and the fault maintenance of battery.
The working principle is as follows:
referring to the attached figures 1-6, when the system is used for monitoring the faults of the storage batteries, each storage battery 3 is correspondingly arranged in a grouping installation frame 2, the grouping installation frame 2 divides the interior of the storage battery pack 1, a spring 6 pushes an installation clamping plate 5, clamping two sides of a storage battery 3, fixing the storage battery 3, detecting the service temperature of the battery by a temperature sensor 7, detecting the appearance of the storage battery 3 by a pressure sensor 8, detecting whether the storage battery 3 swells or not, detecting the service voltage of the storage battery 3 by a voltage sensor 11, processing data by a processor 13 to form a deep learning network, and detecting that the storage battery 3 in the storage battery pack 1 has a problem, can carry out specific dismantlement installation to battery 3, conveniently pinpoint trouble battery 3, and judge the fault type, make things convenient for the equipment and the fault maintenance of battery.
Although the invention has been described in detail with respect to the general description and the specific embodiments, it will be apparent to those skilled in the art that modifications and improvements can be made based on the invention. Therefore, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (7)
1. A battery fault monitoring system based on a deep learning algorithm comprises a storage battery pack (1), and is characterized in that: the storage battery pack is characterized in that a plurality of grouping installation frames (2) are fixedly arranged inside the storage battery pack (1), the storage battery (3) is placed inside the grouping installation frames (2), a battery panel (4) is fixedly arranged on the top end face of the storage battery (3), installation clamping plates (5) are arranged on two sides of the inner wall of each grouping installation frame (2), the installation clamping plates (5) are abutted to the surface of the storage battery (3), springs (6) are fixedly connected between the installation clamping plates (5) and the inner wall of each grouping installation frame (2), temperature sensors (7) are fixedly arranged on the surfaces of the installation clamping plates (5), pressure sensors (8) are fixedly arranged on the middle portions of the installation clamping plates (5) and the inner wall supports of the grouping installation frames (2), a plurality of power transmission lines (9) are fixedly arranged between the tops of the grouping installation frames (2), and a first power output port (10, the power transmission line (9) is electrically connected with a first power output port (10), and a voltage sensor (11) is fixedly arranged at the first power output port (10);
the storage battery pack is characterized in that a control panel (12) is fixedly arranged at one end of the storage battery pack (1), the control panel (12) comprises a processor (13) used for calculating and processing data, the input end of the processor (13) is connected with a data receiving module (14) used for receiving monitoring data information, and the output ends of the temperature sensor (7), the pressure sensor (8) and the voltage sensor (11) are fixedly connected with the input end of the data receiving module (14).
2. The battery fault monitoring system based on the deep learning algorithm as claimed in claim 1, wherein: group installing frame (2) quantity sets up to five, group installing frame (2) top can be dismantled with battery panel (4) and set up, battery panel (4) fixed surface is provided with serial number sign (15), battery panel (4) personally submits the level setting with storage battery (1) top, battery (3) set up to the lithium cell.
3. The battery fault monitoring system based on the deep learning algorithm as claimed in claim 1, wherein: the number of the springs (6) is four, the springs (6) are arranged at four corners of the corresponding mounting clamping plate (5), and the grouping mounting frame (2) is made of stainless steel materials.
4. The battery fault monitoring system based on the deep learning algorithm as claimed in claim 1, wherein: control panel (12) fixed surface is provided with display screen (16), display screen (16) input is connected with treater (13) output, display screen (16) have touch display screen to make.
5. The battery fault monitoring system based on the deep learning algorithm as claimed in claim 1, wherein: battery panel (4) fixed surface is provided with pilot lamp (17), pilot lamp (17) quantity sets up to three, three pilot lamp (17) correspond the data monitoring of temperature sensor (7), pressure sensor (8) and voltage sensor (11).
6. The battery fault monitoring system based on the deep learning algorithm as claimed in claim 1, wherein: the surface of the battery panel (4) is fixedly provided with a turnover handle (18), and the turnover handle (18) is U-shaped.
7. The battery fault monitoring system based on the deep learning algorithm as claimed in claim 1, wherein: the storage battery pack is characterized in that an output control board (19) is fixedly arranged at the other end of the storage battery pack (1), a second power output port (20) is fixedly arranged on the surface of the output control board (19), and the power transmission line (9) is electrically connected with the second power output port (20).
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| Application Number | Priority Date | Filing Date | Title |
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| CN201922359788.6U CN211627771U (en) | 2019-12-25 | 2019-12-25 | Battery fault monitoring system based on deep learning algorithm |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201922359788.6U CN211627771U (en) | 2019-12-25 | 2019-12-25 | Battery fault monitoring system based on deep learning algorithm |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112557709A (en) * | 2020-12-09 | 2021-03-26 | 安徽浩瀚星宇新能源科技有限公司 | Charging and discharging mechanism for battery research and development test system |
| CN112762806A (en) * | 2020-12-23 | 2021-05-07 | 北京国电光宇机电设备有限公司 | Wisdom lithium cell of health degree self-test |
| CN114801877A (en) * | 2022-06-23 | 2022-07-29 | 深圳市菲尼基科技有限公司 | Monitoring system of electric vehicle power battery pack and electric vehicle |
-
2019
- 2019-12-25 CN CN201922359788.6U patent/CN211627771U/en active Active
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112557709A (en) * | 2020-12-09 | 2021-03-26 | 安徽浩瀚星宇新能源科技有限公司 | Charging and discharging mechanism for battery research and development test system |
| CN112762806A (en) * | 2020-12-23 | 2021-05-07 | 北京国电光宇机电设备有限公司 | Wisdom lithium cell of health degree self-test |
| CN114801877A (en) * | 2022-06-23 | 2022-07-29 | 深圳市菲尼基科技有限公司 | Monitoring system of electric vehicle power battery pack and electric vehicle |
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