CN113533989B - Battery detection system and battery detection method - Google Patents
Battery detection system and battery detection method Download PDFInfo
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- CN113533989B CN113533989B CN202110644424.1A CN202110644424A CN113533989B CN 113533989 B CN113533989 B CN 113533989B CN 202110644424 A CN202110644424 A CN 202110644424A CN 113533989 B CN113533989 B CN 113533989B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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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
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Abstract
The application discloses a battery detection system and a battery detection method. The method comprises the following steps: the battery detection system includes: the detection unit is used for detecting the battery to be detected to obtain a detection signal; wherein the detection signals comprise ultrasonic signals, temperature detection signals and thickness detection signals; and the processing unit is connected with the detection unit and is used for receiving the detection signals and analyzing the detection signals by utilizing a deep learning model so as to obtain detection results. By the mode, the application can realize the detection of the battery health state and the charge state, and has high accuracy.
Description
Technical Field
The present application relates to the field of battery detection, and in particular, to a battery detection system and a battery detection method.
Background
A rechargeable battery, also commonly referred to as a storage battery or secondary battery, is a chemical battery that can be charged, discharged to a load, and recycled multiple times. Currently common rechargeable batteries are: lead acid batteries, zinc-air batteries, nickel cadmium batteries, nickel metal hydride batteries, lithium ion batteries, lithium metal batteries, and the like. With the promulgation of the European Union 'battery 2030+', the middle and American countries and the like, the traditional internal combustion engine automobile will exit the history stage in the near future for the layout of lithium batteries and energy storage devices. However, battery safety accidents such as explosion of mobile phones, combustion of automobiles and the like frequently occur. For lithium ion batteries with high energy density, the problems of overheating runaway, safety failure and the like seriously prevent the application of the lithium ion batteries in the fields of consumer electronics and electric automobiles. To ensure safe use of the battery, it is generally necessary to configure a corresponding health management system (BMS) for the battery. Among these, state of charge (SoC) and state of health (SoH) are the two most important characteristics of a battery health management system. From the aspect of battery research and development, a new technology capable of realizing in-situ detection of the internal microstructure of the battery is urgently needed at present, and parameters such as equivalent rigidity, young modulus, lithium precipitation degree, porosity change and the like of an electrode in the process of charging and discharging the battery are rapidly monitored in situ, so that an important analysis means is provided for researching and developing a novel electrode material and inhibiting dendrite growth. From the aspect of the use safety of the battery, the working temperature and the charge and discharge depth of the battery have obvious influence on the performance degradation of the battery, and the charge state and the health state of the battery are difficult to accurately predict by conventional methods based on constant current test, constant power test, pulse test, impedance test, current integration, kalman filtering, neural network and the like.
The traditional transmission electron microscope, scanning electron microscope, X-ray imaging, optical imaging and other technologies can only be applied to batteries with special designs due to limited detection depth, and the poor instantaneity is not applicable to practical batteries. In addition, although the X-ray tomography technology can realize the characterization of the structure, geometric parameters and mechanical properties of the electrode material, the infiltration condition of the electrolyte of the rechargeable battery cannot be detected because the X-rays are insensitive to light elements, and the conditions such as the growth of a solid electrolyte interface film (SEI) and micro bubbles on the surface of the electrode can not be detected. In contrast, ultrasound has extremely strong penetration capability and non-invasive properties, and can characterize the macroscopic properties of rechargeable batteries over a large scale. However, the traditional non-contact detection technology based on water immersion ultrasound and air coupling ultrasound and the contact ultrasound detection technology based on piezoelectric ceramic plates can effectively overcome the defects of the X-ray detection technology in aspects of electrolyte wettability, electrode material aging loss and the like, but factors such as couplant use, sensor installation and the like seriously influence the receiving stability and signal consistency of ultrasound signals, and the problems of extremely low signal-to-noise ratio and poor signal consistency of air coupling ultrasound make accurate prediction of the charge state and the health state of a battery more difficult.
Disclosure of Invention
The application mainly provides a battery detection system and a battery detection method, which can solve the problem of low battery detection accuracy in the prior art.
To solve the above technical problem, a first aspect of the present application provides a battery detection system, including: the detection unit is used for detecting the battery to be detected to obtain a detection signal; wherein the detection signals comprise ultrasonic signals, temperature detection signals and thickness detection signals; and the processing unit is connected with the detection unit and is used for receiving the detection signal and analyzing the detection signal by utilizing a deep learning model so as to predict the state of charge and the state of health of the battery to be detected.
In order to solve the above technical problem, a second aspect of the present application provides a battery detection method, including: monitoring a battery to be detected in real time to obtain a detection signal; wherein the detection signals comprise ultrasonic signals, temperature detection signals and thickness detection signals; and inputting the detection signal into a pre-trained deep learning model to predict the state of charge and the state of health of the battery to be tested.
The beneficial effects of the application are as follows: compared with the prior art, the application detects the detected battery through the detection unit to obtain the ultrasonic signal, the temperature detection signal and the thickness detection signal, and sends the obtained detection signal to the processing unit to analyze the detection signal by using the deep learning model to obtain the state of charge and the state of health parameters of the battery.
Drawings
FIG. 1 is a schematic diagram of a battery detection system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of another embodiment of a battery detection system of the present application;
FIG. 3 is a schematic view of a battery detection system according to another embodiment of the present application;
FIG. 4 is a schematic diagram of the time domain signal of the battery detection ultrasonic signal of the present application;
FIG. 5 is a schematic diagram of the frequency domain signal of the battery detection ultrasonic signal of the present application;
fig. 6 is a schematic block diagram of a flow chart of an embodiment of a battery detection method of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first" and "second" in the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features shown. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a battery detection system according to an embodiment of the application. The battery detection system includes: the detection unit 101 and the processing unit 102, the processing unit 102 is connected with the detection unit 101, the detection unit 101 is used for detecting the detected battery 20 to obtain detection signals, wherein the detection signals comprise ultrasonic signals, temperature detection signals and thickness detection signals; the processing unit 102 is connected with the detecting unit 101, and the processing unit 102 is used for receiving the detection signal and analyzing the detection signal by using a deep learning model to obtain a detection result, so as to facilitate the prediction of the state of charge and the state of health of the battery to be detected.
The detection unit 101 may perform laser ultrasonic detection, temperature detection, and thickness detection on the battery 20 to be detected, and may obtain an ultrasonic signal, a temperature detection signal, and a thickness detection signal, respectively, after performing the above detection on the battery 20 to be detected.
The laser ultrasonic detection is to send a laser pulse signal to the battery 20 to be detected to generate an ultrasonic signal on the surface of the battery, then receive the ultrasonic signal by using a signal receiving instrument to obtain a detection signal with internal structure information of the battery 20 to be detected, and analyze the detection signal to obtain the internal structure information of the battery 20 to be detected; the temperature detection is to detect the temperature of the battery in real time, and the temperature detection signal can reflect the temperature change condition of the battery 20 to be detected, thereby being beneficial to detecting the health state of the battery; the thickness detection is to collect thickness information of the measured battery 20 to obtain thickness variation of the measured battery 20. For example, laser ultrasonic detection may be performed using a laser ultrasonic detection device to obtain an ultrasonic signal, a temperature detection of the battery 20 to be measured using a temperature sensor to obtain a temperature detection signal, and a thickness detection of the battery 20 to be measured using a thickness sensing device or an image acquisition device to obtain a thickness sensing signal.
The processing unit 102 may be a device or equipment with data processing capability, such as a computer device, and may process the detection signal to obtain a detection result of the battery 20 to be tested, specifically, the processing unit 102 inputs the received ultrasonic signal, the temperature detection signal, and the thickness detection signal as characteristic parameters into a trained deep learning model to directly output the detection result, and predict the health state and the state of charge of the battery.
It is understood that the battery detection system may include a loading table 104, where the loading table 104 is used for placing the battery 20 to be detected, and the loading table 104 is a three-axis moving mechanism, as shown in fig. 1, after the battery 20 to be detected is placed on the loading table 104, the loading table 104 is controlled to move in three directions to perform signal acquisition on different positions of the battery 20 to be detected.
Alternatively, the loading platform 104 is moved by a multi-motor drive, and the loading platform 104 is connected to the processing unit 102 by the control unit 105, and when the loading platform 104 is to be controlled to move, the processing unit 102 issues a control instruction to the control unit 105, so that the control unit 105 controls the loading platform 104 to move along a preset path.
In contrast to the prior art, the present application utilizes the detection unit to detect the detected battery 20 to obtain the ultrasonic signal, the temperature detection signal and the thickness detection signal, and inputs the obtained detection signals into the deep learning model to output the corresponding detection results, so as to improve the accuracy of battery state of health and state of charge detection.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a battery detection system according to another embodiment of the application. The battery detection system of the present embodiment further includes a charge and discharge unit 103, where the charge and discharge unit 103 is configured to connect the positive electrode and the negative electrode of the battery 20 to be detected, so as to perform periodic charge and discharge operations on the battery 20 to be detected, so that the battery 20 to be detected can be detected in a charge and discharge state, and the battery detection system of the present embodiment can perform charge state and health state detection on the battery 20 to be detected.
The charge and discharge unit 103 may be a charge and discharge instrument, and after being connected to the positive electrode and the negative electrode of the battery 20 to be tested, the charge and discharge unit 103 may perform charge and discharge operations on the battery to be tested, and may further set a charge and discharge cycle to implement periodic charge and discharge of the battery 20 to be tested.
In this embodiment, when the detected battery 20 is detected, the charge-discharge unit 103 may be started to periodically charge and discharge the detected battery 20, and in the state that the battery is periodically charged and discharged, laser ultrasonic detection, temperature detection and thickness detection are performed on the detected battery 20, one or more groups of ultrasonic signals, temperature detection signals and thickness detection signals are obtained, and the obtained ultrasonic signals, temperature detection signals and thickness detection signals are analyzed by using the deep learning model, so that the detection result is directly obtained.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a battery detection system according to another embodiment of the application. The detection unit 101 may further include a laser pulse transmitting unit 1011 and an ultrasonic signal receiving unit 1012, where the laser pulse transmitting unit 1011 is configured to transmit a first laser pulse to the battery 20 to be tested, so that an ultrasonic signal is generated on the surface of the battery 20 to be tested; the ultrasonic signal receiving unit 1012 is configured to receive an ultrasonic signal.
Specifically, when the intensity of the laser beam irradiated to the battery surface is not high, the laser power density is insufficient to melt the battery surface, and a part of the energy of the laser is absorbed by the shallow surface of the solid and another part is reflected by the surface. The shallow surface of the battery 20 to be measured rapidly rises due to the absorption of the energy of the laser, and the lattice kinetic energy in the material is increased, but within the elastic limit range, the thermal expansion and contraction generate thermal elastic expansion and the deformation of the solid occur, so that the surface of the battery 20 to be measured generates an ultrasonic signal due to the thermal elastic mechanism, and the ultrasonic signal propagates in the battery 20 to be measured and is transmitted out through the other side, so that the ultrasonic signal carries the information of the internal structure of the battery 20 to be measured.
Notably, prior to the use of high energy pulsed lasers to generate high signal-to-noise signals in porous multilayer composite structures with fluid saturation, strict control of laser energy is required to avoid ablating the surface of the cell 20 under test.
The synchronous output port of the laser pulse transmitting unit 1011 is connected to the reference signal channel of the ultrasonic signal receiving unit 1012, so that when the laser pulse transmitting unit 1011 transmits a laser pulse, a synchronous signal is transmitted to the ultrasonic signal receiving unit 1012, and the ultrasonic signal receiving unit 1012 is triggered to synchronously receive an ultrasonic signal.
Alternatively, the laser pulse transmitting unit 1011 is connected to the processing unit 102 through the laser controller 106, and the processing unit 102 transmits a control signal, so that the laser controller 106 transmits a trigger signal, and the laser pulse transmitting unit 1011 is triggered to transmit a laser pulse signal to the battery 20 under test.
Alternatively, the ultrasonic signal receiving unit 1012 may be an ultrasonic sensor or an optical interferometer. When receiving an ultrasonic signal by using an ultrasonic sensor, the ultrasonic sensor is required to be in contact with the battery 20 to be tested, so that the superiority of laser ultrasonic non-contact detection cannot be highlighted; the optical interference principle is that a continuous laser emits linearly polarized light, the linearly polarized light passes through a half-wave plate to change the polarization direction of the linearly polarized light, the linearly polarized light is decomposed into two orthogonal beams by utilizing a polarization spectroscope, the reflected beam is used as a reference beam, the beam passing through the polarization spectroscope is used as a signal beam, the signal beam is focused on the surface of an object to be detected by a lens group after passing through another polarization spectroscope, the particle vibration on the surface of the object to be detected can cause the phase change of the reflected signal beam, the dynamic interference pattern is formed in a photorefractive crystal by the polarized spectroscope and the reference beam, and finally, the optical signal is converted into an electric signal by a photoelectric detector, so that the complete non-contact detection can be realized.
The ultrasonic signal receiving unit 1012 in this embodiment is a laser interference receiving unit, which is configured to receive an ultrasonic signal generated by reflecting a laser pulse on the surface of the battery 20 to be tested, and to utilize continuous reference laser and continuous detection laser generated by a spectroscope to interfere with each other, so as to receive the ultrasonic signal.
Optionally, before the battery 20 to be tested is tested, the laser pulse transmitting unit 1011 and the laser interference receiving unit are adjusted to be centered on the same axis of the battery 20 to be tested. Specifically, the thickness of the metal/alloy plate close to the measured battery 20 or the thickness of the metal/alloy plate consistent with the thickness of the measured battery 20 is used as a reference object for adjusting the optical path, the laser emitted by the laser pulse emitting unit 1011 and the laser interference receiving unit are adjusted to focus the laser on the front and rear surfaces of the metal/alloy plate, the laser focusing points on the front and rear surfaces are marked, and if the two marking points are strictly centered relative to the thickness direction of the battery, the optical path adjustment is completed.
Compared with the prior art, the laser pulse transmitting unit 1011 is used for transmitting laser pulses to the battery 20 to be tested, so that ultrasonic signals are generated on the surface of the battery 20 to be tested, the laser interference receiving unit is used as the ultrasonic signal receiving unit 1012 for receiving the ultrasonic signals, the whole laser detection process does not need to contact the battery 20 to be tested, the non-contact excitation of laser ultrasonic and the in-situ detection of the battery which is received in a non-contact manner can be realized, the battery 20 to be tested is not damaged, special treatment is not needed, the normal use of the battery 20 to be tested is not influenced, and the consistency and the stability of signals are easier to ensure.
The detecting unit 101 may further include a temperature detecting unit 1013, where the temperature detecting unit 1013 is configured to detect the temperature of the battery 20 to be detected in real time to obtain a temperature detection signal. Alternatively, the temperature detecting unit 101 is a patch type temperature sensor, and is attached to the surface of the battery 20 to be detected, so that the surface of the battery 20 to be detected can be detected in real time.
The detecting unit 101 may further include a battery thickness collecting unit 1014, where the battery thickness collecting unit 1014 is configured to perform real-time thickness detection on the measured battery 20 to collect a thickness detection signal, and the thickness detection signal includes thickness information of the measured battery 20. The battery thickness acquisition unit 1014 may be an image acquisition unit, which acquires a thickness detection signal in real time by acquiring a thickness image of the battery 20 to be measured in real time.
In one embodiment, the battery thickness acquisition unit 1014 includes an industrial camera, which is disposed opposite to the thickness section direction of the battery 20 to be measured, so as to acquire image information of the thickness section direction of the battery in real time, and process the acquired image to obtain the thickness information of the battery 20 to be measured. For example, edge recognition and extraction may be performed using an image recognition method to obtain thickness detection information of the battery 20 under test. Since image recognition technology is now well established, only thickness detection is schematically illustrated, and other ways of processing the acquired image to obtain thickness detection information of the battery may be selected by those skilled in the art, and will not be described here.
Unlike the prior art, the present embodiment can perform non-contact nondestructive testing by using the laser pulse transmitting unit 1011 and the ultrasonic signal receiving unit 1012 to obtain an ultrasonic signal, and perform temperature detection and thickness detection on the battery 20 to be tested by using the temperature detecting unit 1013 and the battery thickness collecting unit 1014 to obtain detection signals with multiple dimensions, so that the detection on the battery is more comprehensive, and the accuracy of the battery detection is effectively improved.
Optionally, the processing unit 102 is further configured to perform imaging processing according to the received ultrasonic signal, so as to perform two-dimensional imaging on the battery 20 under test.
Referring to fig. 4 and 5, fig. 4 is a schematic time-domain signal diagram of the ultrasonic signal detected by the battery according to the present application, and fig. 5 is a schematic frequency-domain signal diagram of the ultrasonic signal detected by the battery according to the present application, and the scanned image of the battery 20 to be detected can be reconstructed by using the time-domain signal.
The scheme has been verified by earlier experiments, the signal-to-noise ratio of the ultrasonic signal received by adopting the transmission mode is higher, as shown in fig. 4 and 5, the longitudinal wave sound velocity is 1755m/s according to the delay time (6.38 mu s) of the transmitted wave signal relative to the reference signal and the thickness (11.2 mm) of the battery, and the method is consistent with the report of the existing literature, so that the feasibility of the method is proved.
The battery detection system provided by the application can be applied to the energy fields of lithium ion power and energy storage batteries, next-generation lithium sulfur batteries, lithium-air batteries, sodium ion batteries, solid oxide fuel batteries and the like, is an effective means for accurately predicting the charge state and the health state of a rechargeable battery, and is also an important tool for realizing in-situ real-time monitoring of the internal microstructure of the battery, so that the battery detection system has important scientific research value and market application prospect in the future.
Referring to fig. 6, fig. 6 is a schematic block diagram illustrating a battery detection method according to an embodiment of the present application. The application also provides a battery detection method, which comprises the following steps:
s70: detecting a detected battery in real time to obtain a detection signal; wherein, the detection signal includes ultrasonic signal, temperature detection signal and thickness detection signal.
The laser pulse transmitting unit 1011, the ultrasonic signal receiving unit 1012, the temperature detecting unit 1013, and the battery thickness collecting unit 1014 in the above embodiments may be used to detect the battery to obtain an ultrasonic signal, a temperature detecting signal, and a thickness detecting signal, which are not described again.
S80: and inputting the detection signal into a pre-trained deep learning model, and outputting a predicted result of the state of charge and the state of health of the battery.
The method comprises the steps of inputting multidimensional characteristic parameters in the received ultrasonic signals, temperature detection signals and thickness detection signals into a trained deep learning model to directly output detection results and predict the health state and the charge state of a battery.
The method can also control the laser controller 106 and the control unit 105 to send out control signals to trigger the laser pulse sending unit 1011 to send out laser pulses to carry out laser ultrasonic detection on the battery 20 to be detected, and control the loading platform 104 to move to carry out scanning detection on the battery 20 to be detected; the method can also reconstruct an image of the battery 20 to be measured by using the received ultrasonic signals to obtain a two-dimensional image of the battery 20 to be measured. The specific implementation manner of the method is specifically referred to the description of each embodiment of the above battery detection system, and will not be repeated here.
The foregoing description is only illustrative of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present application.
Claims (3)
1. A battery detection system, the battery detection system comprising:
the charging and discharging unit is used for connecting the positive electrode and the negative electrode of the battery to be tested, and periodically charging and discharging the battery to be tested when the battery to be tested is detected;
the detection unit is used for detecting the detected battery in real time to obtain a detection signal; wherein the detection signals comprise ultrasonic signals, temperature detection signals and thickness detection signals; the device comprises a three-axis moving mechanism, a battery to be tested, a signal acquisition unit and a signal acquisition unit, wherein the battery to be tested is placed on the three-axis moving mechanism, and the three-axis moving mechanism can move in three directions so as to acquire signals of different positions of the battery to be tested;
the processing unit is connected with the detection unit and is used for receiving the detection signals, analyzing the detection signals by utilizing a deep learning model so as to predict the charge state and the health state of the battery to be detected in the periodic charge-discharge operation, and performing imaging processing according to the received ultrasonic signals so as to perform two-dimensional imaging on the battery to be detected;
wherein the detection unit includes:
the laser pulse transmitting unit is used for transmitting laser pulses to the battery to be tested, so that the surface of the battery to be tested generates ultrasonic signals in a non-contact mode;
the ultrasonic signal receiving unit is a laser interference receiving unit, and the laser interference receiving unit and the laser pulse transmitting unit are coaxially aligned in the thickness direction of the battery to be tested and are used for receiving ultrasonic signals generated by the laser pulse on the surface of the battery to be tested, and non-contact receiving of the ultrasonic signals is realized by utilizing interference of a reference beam and a signal beam in a photorefractive crystal;
the temperature detection unit is connected with the processing unit and is used for detecting the temperature of the battery to be detected in real time so as to obtain the temperature detection signal;
the battery thickness acquisition unit is an image acquisition unit and acquires the thickness detection signal by acquiring the thickness image of the battery to be detected in real time.
2. The battery detection system of claim 1, wherein,
and the synchronous output port of the laser pulse transmitting unit is connected with the reference signal channel of the ultrasonic signal receiving unit so as to trigger the ultrasonic signal receiving unit to synchronously receive the ultrasonic signal when the laser pulse transmitting unit transmits the laser pulse.
3. A battery detection method, characterized in that the battery detection method comprises:
monitoring a battery to be detected in real time to obtain a detection signal; wherein the detection signals comprise ultrasonic signals, temperature detection signals and thickness detection signals;
inputting the detection signal into a pre-trained deep learning model, and outputting a prediction result of the state of charge and the state of health of the battery;
wherein the battery detection method is applied to the battery detection system according to any one of claims 1 to 2.
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| CN202110644424.1A CN113533989B (en) | 2021-06-09 | 2021-06-09 | Battery detection system and battery detection method |
| PCT/CN2021/138143 WO2022257404A1 (en) | 2021-06-09 | 2021-12-15 | Battery test system and battery test method |
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| CN113533989B (en) * | 2021-06-09 | 2023-08-18 | 深圳先进技术研究院 | Battery detection system and battery detection method |
| CN114415035B (en) * | 2022-03-30 | 2022-06-21 | 华北电力大学 | Lead storage battery capacity online measurement device and method based on reflected ultrasound |
| CN115291122B (en) * | 2022-08-24 | 2024-04-19 | 华中科技大学 | Method for acquiring internal information of lithium ion battery based on ultrasonic reflection image |
| CN115343623B (en) * | 2022-08-31 | 2023-06-16 | 中国长江三峡集团有限公司 | Online detection method and device for faults of electrochemical energy storage battery |
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