CN116467938A - Storage battery fault and service life prediction method based on artificial intelligence - Google Patents
Storage battery fault and service life prediction method based on artificial intelligence Download PDFInfo
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- CN116467938A CN116467938A CN202310367380.1A CN202310367380A CN116467938A CN 116467938 A CN116467938 A CN 116467938A CN 202310367380 A CN202310367380 A CN 202310367380A CN 116467938 A CN116467938 A CN 116467938A
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 17
- 230000032683 aging Effects 0.000 claims abstract description 16
- 238000004519 manufacturing process Methods 0.000 claims abstract description 13
- 230000005477 standard model Effects 0.000 claims description 10
- 238000007599 discharging Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 208000032953 Device battery issue Diseases 0.000 claims 7
- 239000002253 acid Substances 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention discloses a method for predicting faults and service lives of storage batteries based on artificial intelligence, which comprises the following steps of firstly, manufacturing a storage battery ageing and attenuation model, secondly, obtaining parameter information of the storage battery in the current state, thirdly, inputting the parameter information of the storage battery in the current state into the storage battery ageing and attenuation model, and fourthly, outputting results by the storage battery ageing and attenuation module so as to predict faults and service lives of the storage batteries. In the implementation process of the invention, the service life of the storage battery can be predicted according to the current parameter information of the storage battery, so that a user can conveniently grasp the service life of the storage battery, and the state of the battery can be grasped in time before the battery fails; meanwhile, the storage battery can also predict faults of the storage battery through measuring parameters of the storage battery, so that dangerous situations are avoided.
Description
Technical Field
The invention belongs to the technical field of data acquisition information, and particularly relates to a storage battery fault and service life prediction method based on artificial intelligence.
Background
At present, lead-acid storage batteries are still used in a large amount in a data center as a main backup power supply means, but due to the characteristics, insufficient production process, improper maintenance and the like of the lead-acid storage batteries, the conditions of liquid leakage, performance reduction and the like are easily generated after a certain service life, so that the usability of a storage battery system is reduced. The storage battery can be matched with a storage battery monitoring system to monitor relevant parameters (such as voltage, current, resistance, temperature and the like) of single storage batteries and groups of storage batteries, but the detection can only give an alarm under the condition that the storage battery itself fails, and the storage battery is invalid at the moment, so that the standby point safety is seriously influenced. Therefore, a method for pre-judging the failure time or the service life of the battery by adopting an artificial intelligence technology is needed to remind operation and maintenance personnel to search, replace or maintain the potential risk battery in advance.
Disclosure of Invention
Therefore, the invention provides a method for predicting the faults and the service lives of storage batteries based on artificial intelligence so as to solve the problems in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions:
according to a first aspect of the invention, a method for battery fault and life prediction based on artificial intelligence, comprises the steps of,
first, manufacturing an aging attenuation model of the storage battery,
second, obtaining parameter information of the accumulator in the current state,
third, inputting the parameter information of the current state of the storage battery into the aging attenuation model of the storage battery,
and step four, the storage battery aging attenuation module outputs a result so as to predict the fault and the service life of the storage battery.
Preferably, the manufacturing of the storage battery aging attenuation model comprises the following steps,
step one, obtaining a standard discharge curve of a storage battery, and a voltage, current and resistance change curve of the storage battery in the discharge process, manufacturing a storage battery standard model,
step two, changing the voltage, current and resistance of the storage battery, recording the service life of the storage battery to manufacture a storage battery service life model,
step three, collecting the storage battery used for different time, measuring the voltage, current and resistance value of the storage battery, and taking the measurement structure into a storage battery service life model to verify the storage battery,
and step four, outputting the verified storage battery service life model.
Preferably, when predicting the target storage battery, the voltage, current and resistance parameters of the target storage battery are measured, the parameter information is input into a storage battery service life model, and the storage battery service life model can output the service life of the storage battery in the same or similar state to the service life model so as to predict the service life of the target storage battery.
Preferably, the actual service life of the target storage battery is collected into a storage battery service life model so as to perfect the storage battery service life model.
Preferably, the service life model of the storage battery is compared with the standard model of the storage battery, and data of the service life of the storage battery in the service life model of the storage battery which is longer than the standard model of the storage battery are removed.
Preferably, temperature rise information of the storage battery during discharging is collected, a temperature rise threshold value is set, and a fault warning signal of the storage battery is output after the temperature rise of the storage battery is larger than the threshold value.
Preferably, the temperature rise information comprises temperature rise information of a shell of the storage battery and temperature rise information of the anode and the cathode of the storage battery,
when the temperature rise information of the storage battery shell is larger than a first threshold value, outputting alarm information that the storage battery shell is easy to crack,
and when the temperature rise information of the anode and the cathode of the storage battery is larger than a second threshold value, outputting alarm information that the anode and the cathode of the storage battery are easy to burn.
Preferably, the first threshold and the second threshold both comprise a primary early warning area and a high-level early warning area, the temperature rise information corresponding to the primary early warning area is just larger than the threshold information, the temperature rise information corresponding to the high-level early warning area is far larger than the threshold information, and the primary early warning area and the high-level early warning area are adjusted through preset parameters.
Compared with the prior art, the invention has the beneficial effects that:
when the method is implemented, the service life of the storage battery can be predicted according to the current parameter information of the storage battery, so that a user can conveniently grasp the service life of the storage battery, and the state of the battery can be grasped in time before the battery fails; meanwhile, the storage battery can also predict faults of the storage battery through measuring parameters of the storage battery, so that dangerous situations are avoided.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
A method for predicting the faults and service life of accumulator based on artificial intelligence includes such steps as,
the first step, a storage battery aging attenuation model is manufactured, based on the model, the scheme can realize intelligent screening in the implementation process, further realize the purpose of artificial intelligence,
a second step of obtaining parameter information of the current state of the storage battery, wherein the parameter information can be the weight of the storage battery, the type of the storage battery, the charge and discharge time of the storage battery and the like,
third, inputting the parameter information of the current state of the storage battery into the aging attenuation model of the storage battery,
and step four, the storage battery aging attenuation module outputs a result so as to predict the fault and the service life of the storage battery.
When the method is implemented, a user acquires the parameters of the storage battery, and after the acquisition is completed, the parameters corresponding to the storage battery are input into the storage battery aging attenuation model, so that the service life of the storage battery is predicted. And the parameters of the storage battery are obtained, so that the prediction result is more accurate.
The manufacturing of the storage battery aging attenuation model comprises the following steps of,
step one, obtaining a standard discharge curve of a storage battery, and a voltage, current and resistance change curve of the storage battery in the discharge process, manufacturing a storage battery standard model,
step two, changing the voltage, current and resistance of the storage battery, recording the service life of the storage battery to manufacture a storage battery service life model,
step three, collecting the storage battery used for different time, measuring the voltage, current and resistance value of the storage battery, and taking the measurement structure into a storage battery service life model to verify the storage battery,
and step four, outputting a verified storage battery service life model, and comparing the storage battery standard model with the storage battery service life model in the verification process, so that the storage battery service life model can be verified preliminarily, and when the data are inconsistent, the data in the storage battery service life model can be removed.
When the target storage battery is predicted, the voltage, current and resistance parameters of the target storage battery are measured, the parameter information is input into a storage battery service life model, and the storage battery service life model can output the service life of the storage battery in the same or similar state to the storage battery service life model so as to predict the service life of the target storage battery.
Of course, other parameters of the battery may be introduced when the present solution is implemented.
The actual service life of the target storage battery is collected into a storage battery service life model so as to perfect the storage battery service life model, and the model can be more and more accurate in the continuous verification and collection process, and finally a model capable of accurately predicting the service life of most storage batteries is formed.
And comparing the service life model of the storage battery with the standard model of the storage battery, and eliminating data of the service life of the storage battery in the service life model of the storage battery, which is longer than the standard model of the storage battery.
In the use of the storage battery, two problems are most frequently caused, namely, the storage battery is charged, the shell is cracked, the positive electrode and the negative electrode of the storage battery are overlarge in resistance, and the line head is burnt at the positive electrode and the negative electrode, so that the two faults are predicted according to the following technical scheme:
and collecting temperature rise information of the storage battery during discharging, setting a temperature rise threshold value, and outputting a fault alarm signal of the storage battery after the temperature rise of the storage battery is greater than the threshold value.
The temperature rise information comprises temperature rise information of a storage battery shell and temperature rise information of an anode and a cathode of the storage battery,
when the temperature rise information of the storage battery shell is larger than a first threshold value, outputting alarm information that the storage battery shell is easy to crack,
and when the temperature rise information of the anode and the cathode of the storage battery is larger than a second threshold value, outputting alarm information that the anode and the cathode of the storage battery are easy to burn.
The first threshold and the second threshold both comprise a primary early warning area and a high-level early warning area, the temperature rise information corresponding to the primary early warning area is just larger than the threshold information, the temperature rise information corresponding to the high-level early warning area is far larger than the threshold information, and the primary early warning area and the high-level early warning area are adjusted through preset parameters.
Claims (8)
1. A method for predicting storage battery faults and service lives based on artificial intelligence is characterized by comprising the following steps: comprises the steps of,
first, manufacturing an aging attenuation model of the storage battery,
second, obtaining parameter information of the accumulator in the current state,
third, inputting the parameter information of the current state of the storage battery into the aging attenuation model of the storage battery,
and step four, the storage battery aging attenuation module outputs a result so as to predict the fault and the service life of the storage battery.
2. The method for predicting battery failure and service life based on artificial intelligence of claim 1, wherein the method comprises the following steps: the manufacturing of the storage battery aging attenuation model comprises the following steps of,
step one, obtaining a standard discharge curve of a storage battery, and a voltage, current and resistance change curve of the storage battery in the discharge process, manufacturing a storage battery standard model,
step two, changing the voltage, current and resistance of the storage battery, recording the service life of the storage battery to manufacture a storage battery service life model,
step three, collecting the storage battery used for different time, measuring the voltage, current and resistance value of the storage battery, and taking the measurement structure into a storage battery service life model to verify the storage battery,
and step four, outputting the verified storage battery service life model.
3. The method for predicting battery failure and service life based on artificial intelligence of claim 1, wherein the method comprises the following steps: when the target storage battery is predicted, the voltage, current and resistance parameters of the target storage battery are measured, the parameter information is input into a storage battery service life model, and the storage battery service life model can output the service life of the storage battery in the same or similar state to the storage battery service life model so as to predict the service life of the target storage battery.
4. A method of battery failure and life prediction based on artificial intelligence as claimed in claim 3, wherein: and collecting the actual service life of the target storage battery into a storage battery service life model so as to perfect the storage battery service life model.
5. The method for predicting battery failure and service life based on artificial intelligence according to claim 2, wherein the method comprises the following steps: and comparing the service life model of the storage battery with the standard model of the storage battery, and eliminating data of the service life of the storage battery in the service life model of the storage battery, which is longer than the standard model of the storage battery.
6. The method for predicting battery failure and service life based on artificial intelligence of claim 1, wherein the method comprises the following steps: and collecting temperature rise information of the storage battery during discharging, setting a temperature rise threshold value, and outputting a fault alarm signal of the storage battery after the temperature rise of the storage battery is greater than the threshold value.
7. The method for predicting battery failure and service life based on artificial intelligence of claim 6, wherein the method comprises the following steps: the temperature rise information comprises temperature rise information of a storage battery shell and temperature rise information of an anode and a cathode of the storage battery,
when the temperature rise information of the storage battery shell is larger than a first threshold value, outputting alarm information that the storage battery shell is easy to crack,
and when the temperature rise information of the anode and the cathode of the storage battery is larger than a second threshold value, outputting alarm information that the anode and the cathode of the storage battery are easy to burn.
8. The method for predicting battery failure and service life based on artificial intelligence of claim 7, wherein: the first threshold and the second threshold both comprise a primary early warning area and a high-level early warning area, the temperature rise information corresponding to the primary early warning area is just larger than the threshold information, the temperature rise information corresponding to the high-level early warning area is far larger than the threshold information, and the primary early warning area and the high-level early warning area are adjusted through preset parameters.
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