WO2022257404A1

WO2022257404A1 - Battery test system and battery test method

Info

Publication number: WO2022257404A1
Application number: PCT/CN2021/138143
Authority: WO
Inventors: 郭师峰; 陈丹; 冯伟; 李叶海; 张树潇; 吕高龙; 王石; 吴新宇
Original assignee: 深圳先进技术研究院
Priority date: 2021-06-09
Filing date: 2021-12-15
Publication date: 2022-12-15
Also published as: CN113533989B; CN113533989A

Abstract

A battery test system, comprising: a test unit (101) used for testing a battery to be tested (20) to obtain a test signal, wherein the test signal comprises an ultrasonic signal, a temperature measurement signal, and a thickness measurement signal; and a processing unit (102) connected to the test unit (101), and used for receiving the test signal and analyzing the test signal by using a deep learning model to obtain a test result. In this way, the health state and charge state of a battery can be tested, and the accuracy is high. A battery test method is further provided.

Description

A battery detection system and battery detection method

technical field

The present application relates to the field of battery detection, in particular to a battery detection system and a battery detection method.

Background technique

A rechargeable battery, also commonly referred to as an accumulator or secondary battery, is a chemical battery that can be charged, discharged to a load, and cycled 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, etc. With the promulgation of the European Union's "Battery 2030+" plan and the mid-to-long term layout of lithium batteries and energy storage devices in countries such as China and the United States, traditional internal combustion engine vehicles will withdraw from the stage of history in the near future. However, shocking mobile phone explosions, car burning and other battery safety accidents occur frequently. For lithium-ion batteries with high energy density, problems such as overheating and runaway and safety failures seriously hinder the application of lithium-ion batteries in consumer electronics and electric vehicles. In order to ensure the safe use of the battery, it is usually necessary to configure a corresponding health management system (BMS) for the battery. Among them, state of charge (SoC) and state of health (SoH) are the two most important characteristic parameters of the battery health management system. From the perspective of battery research and development, there is an urgent need for a new technology that can realize the in-situ detection of the internal microstructure of the battery. Rapid in-situ monitoring provides an important analysis method for the development of new electrode materials and the inhibition of dendrite growth. From the perspective of battery safety, the operating temperature and depth of charge and discharge of the battery have a significant impact on its performance degradation. Conventional tests based on constant current test, constant power test, pulse test, impedance test, current integration, Kalman filter and neural network It is difficult to accurately predict the state of charge and state of health of the battery by other methods.

Traditional technologies such as transmission electron microscopy, scanning electron microscopy, X-ray imaging, and optical imaging can only be applied to specially designed batteries due to their limited detection depth, and their poor real-time performance is not applicable to actual batteries. In addition, although X-ray tomography technology can realize the characterization of electrode material structure, geometric parameters and mechanical properties, it cannot detect the infiltration of rechargeable battery electrolyte due to the insensitivity of X-rays to light elements, and the solid electrolyte interfacial film (SEI) The growth of the electrode and the micro bubbles on the surface of the electrode. In comparison, ultrasonic waves have strong penetrating ability and non-destructive properties, and can characterize the macroscopic properties of rechargeable batteries on a large scale. However, the traditional non-contact detection technology based on water immersion ultrasound and air-coupled ultrasound, as well as the contact ultrasonic detection technology based on piezoelectric ceramics, can effectively make up for the X-ray detection technology in terms of electrolyte wettability and electrode material aging loss. However, factors such as the use of couplant and sensor installation have seriously affected the receiving stability and signal consistency of ultrasonic signals, and the extremely low signal-to-noise ratio and poor signal consistency of air-coupled ultrasound make it difficult to accurately predict the state of charge of the battery. and health status are more difficult.

Contents of the invention

The present 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.

In order to solve the above technical problems, the first aspect of the present application provides a battery detection system, the battery detection system includes: a detection unit for detecting the battery under test to obtain a detection signal; wherein the detection signal includes Ultrasonic signal, temperature detection signal and thickness detection signal; processing unit, connected to the detection unit, the processing unit is used to receive the detection signal, and use a deep learning model to analyze the detection signal to predict the detected Test the state of charge and health of the battery.

In order to solve the above technical problems, the second aspect of the present application provides a battery detection method, including: monitoring the battery under test in real time, and obtaining detection signals; wherein, the detection signals include ultrasonic signals, temperature detection signals and thickness detection signals ; Input the detection signal into a pre-trained deep learning model to predict the state of charge and state of health of the battery under test.

The beneficial effects of the present application are: different from the situation of the prior art, the present application detects the battery under test through the detection unit to obtain ultrasonic signals, temperature detection signals and thickness detection signals, and sends the obtained detection signals to the processing unit , to use the deep learning model to analyze the detection signal to obtain the battery state of charge and health state parameters. On the one hand, the application can use a variety of detection signals to detect the health state of the battery to improve the comprehensiveness and accuracy of battery detection On the other hand, the deep learning model can further improve the accuracy of signal analysis, making the detection results more reliable.

Description of drawings

Fig. 1 is a schematic structural diagram of an embodiment of the battery detection system of the present application;

Fig. 2 is a schematic structural diagram of another embodiment of the battery detection system of the present application;

Fig. 3 is a schematic structural diagram of another embodiment of the battery detection system 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 process of an embodiment of a battery detection method of the present application.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

The terms "first" and "second" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features shown. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. Furthermore, the terms "include" and "have", as well as any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally further includes For other steps or units inherent in these processes, methods, products or apparatuses.

Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The occurrences of this phrase 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. It is understood by those skilled in the art, both explicitly and implicitly, that the embodiments described herein can be combined with other embodiments.

Please refer to FIG. 1 . FIG. 1 is a schematic structural diagram of an embodiment of a battery testing system of the present application. The battery detection system includes: a detection unit 101 and a processing unit 102, the processing unit 102 is connected to the detection unit 101, and the detection unit 101 is used to detect the battery 20 under test to obtain a detection signal, wherein the detection signal includes an ultrasonic signal, a temperature detection signal And the thickness detection signal; the processing unit 102 is connected to the detection unit 101, and the processing unit 102 is used to receive the detection signal, and use the deep learning model to analyze the detection signal to obtain the detection result, so as to predict the state of charge and the thickness of the battery under test. health status.

Wherein, the detection unit 101 can realize laser ultrasonic detection, temperature detection and thickness detection of the battery under test 20, and after the above detection is performed on the battery under test 20, the ultrasonic signal, temperature detection signal and thickness detection signal can be respectively obtained.

Among them, the laser ultrasonic detection is to send a laser pulse signal to the battery under test 20 to generate an ultrasonic signal on the surface of the battery, and then use the signal receiving instrument to receive the ultrasonic signal, and then a detection signal with the internal structure information of the battery under test 20 can be obtained. The internal structure information of the battery under test 20 can be obtained by analyzing the detection signal; the temperature detection is to detect the temperature of the battery in real time, and the temperature detection signal can reflect the temperature change of the battery under test 20, which is beneficial to the health of the battery The state is detected; the thickness detection is to collect the thickness information of the battery under test 20 to obtain the thickness variation of the battery under test 20 . For example, a laser ultrasonic detection device can be used to perform laser ultrasonic detection to obtain an ultrasonic signal, a temperature sensor can be used to detect the temperature of the battery 20 under test to obtain a temperature detection signal, and a thickness sensor device or an image acquisition device can be used to measure the thickness of the battery 20 under test. Detection is performed to obtain a thickness sensing signal.

Wherein, the processing unit 102 can be a device or device with data processing capabilities such as computer equipment, and can process the detection signal to obtain the detection result of the battery under test 20. Specifically, the processing unit 102 will receive the ultrasonic signal, The temperature detection signal and the thickness detection signal are input into the trained deep learning model as characteristic parameters to directly output the detection results and predict the state of health and state of charge of the battery.

It can be understood that the battery testing system may include a loading platform 104, the loading platform 104 is used to place the battery under test 20, and the loading platform 104 is a three-axis movement mechanism, as shown in FIG. 1, the battery under test 20 is placed on After the loading platform 104 is loaded, the loading platform 104 can be controlled to move in three directions, so as to collect signals from different positions of the battery under test 20 .

Optionally, the loading platform 104 is driven by multiple motors to move, and the loading platform 104 is connected to the processing unit 102 through the control unit 105. When the loading platform 104 is to be controlled to move, the processing unit 102 sends a control command to the control unit 105, so that the control unit 105 controls the loading platform 104 to move along the preset path.

Different from the prior art, the application uses the detection unit to detect the battery under test 20 to obtain ultrasonic signals, temperature detection signals and thickness detection signals, and input the obtained detection signals into the deep learning model to output corresponding detection results, It can improve the accuracy of battery state of health and state of charge detection.

Please refer to FIG. 2 . FIG. 2 is a schematic structural diagram of another embodiment of the battery testing system of the present application. The battery testing system of this embodiment also includes a charging and discharging unit 103, which is used to connect the positive pole and the negative pole of the battery under test 20, so as to perform periodic charging and discharging operations on the battery under test 20, so that the battery under test The battery 20 can be tested in the state of charge and discharge, and the battery detection system of this embodiment can detect the state of charge and the state of health of the battery 20 under test.

Wherein, the charging and discharging unit 103 can be a charging and discharging instrument, after being connected to the positive and negative poles of the battery under test 20, the charging and discharging unit 103 can charge and discharge the battery under test, and the charging and discharging cycle can also be set to realize the charging and discharging of the battery under test 20. periodic charging and discharging.

In this embodiment, when the battery under test 20 is detected, the charging and discharging unit 103 can be started to periodically charge and discharge the battery under test 20, and when the battery is in a state of periodically charging and discharging, the battery under test 20 can be detected by laser ultrasonic testing, Temperature detection and thickness detection, obtain one or more sets of ultrasonic signals, temperature detection signals and thickness detection signals, use the deep learning model to analyze the obtained ultrasonic signals, temperature detection signals and thickness detection signals, directly obtain the detection results, and improve the detection efficiency High, moreover, this embodiment takes the temperature information and thickness information of the battery under test 20 into consideration, so that the information detection of the battery under test 20 is more comprehensive, and the accuracy of the detection result is greatly improved.

Please refer to FIG. 3 . FIG. 3 is a schematic structural diagram of another embodiment of the battery detection system of the present application. The detection unit 101 can also include a laser pulse transmitting unit 1011 and an ultrasonic signal receiving unit 1012, the laser pulse transmitting unit 1011 is used to transmit the first laser pulse to the battery under test 20, so that the surface of the battery under test 20 generates an ultrasonic signal; the ultrasonic signal receiving Unit 1012 is used for receiving ultrasonic signals.

Specifically, when the intensity of the laser beam irradiating the surface of the battery is not high, the laser power density is not enough to melt the surface of the battery, part of the energy of the laser is absorbed by the shallow surface of the solid, and the other part is reflected by the surface. The temperature of the shallow surface of the battery under test 20 rises rapidly due to absorbing the energy of the laser, and at the same time, the kinetic energy of the lattice inside the material also increases, but it is still within the elastic limit. Due to thermal expansion and contraction, thermoelastic expansion occurs, and the solid deforms. Therefore, the surface of the battery under test 20 generates ultrasonic signals due to the thermoelastic mechanism, and the ultrasonic signals propagate in the battery under test 20 and are transmitted through the other side, so that the ultrasonic signals carry information about the internal structure of the battery under test 20 .

It is worth noting that before using high-energy pulsed laser to generate high signal-to-noise ratio signals in the fluid-saturated porous multilayer composite structure, it is necessary to strictly control the laser energy to avoid ablation of the surface of the battery 20 under test.

Wherein, the synchronization 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 sends a laser pulse, a synchronous signal is sent to the ultrasonic signal receiving unit 1012 to trigger the ultrasonic signal receiving unit 1012 Receive ultrasonic signals synchronously.

Optionally, the laser pulse emitting unit 1011 is connected to the processing unit 102 through the laser controller 106, and the processing unit 102 sends a control signal, so that the laser controller 106 sends a trigger signal, triggering the laser pulse emitting unit 1011 to send a laser pulse to the battery under test 20 Signal.

Optionally, the ultrasonic signal receiving unit 1012 may be an ultrasonic sensor or an optical interferometer. Among them, when the ultrasonic sensor is used to receive the ultrasonic signal, the ultrasonic sensor needs to be in contact with the battery under test 20, which cannot highlight the superiority of the laser ultrasonic non-contact detection; while the optical interferometry uses a continuous laser to emit linearly polarized light, which passes through the half-wave plate To change its polarization direction, and use the polarization beam splitter to decompose it into two orthogonal beams, the reflected beam is used as the reference beam, and the beam passing through the polarization beam splitter is used as the signal beam, and the signal beam passes through another polarization beam splitter After the mirror, the lens group is focused on the surface of the object to be measured, and the particle vibration on the surface of the object to be measured will cause the phase of the reflected signal beam to change, which will form a dynamic interference pattern with the reference beam after passing through the polarization beam splitter in the photorefractive crystal , and finally the optical signal is converted into an electrical signal by the photodetector, which can realize completely non-contact detection.

The ultrasonic signal receiving unit 1012 of this embodiment is a laser interference receiving unit, and the laser interference receiving unit is used to receive the ultrasonic signal generated by the reflection of the laser pulse on the surface of the battery under test 20, and utilize the continuous reference laser and the continuous detection laser generated by the beam splitter Interference is performed to receive ultrasonic signals. Since there are obvious volume changes in the charging and discharging process of the battery, this embodiment can realize non-contact detection of the battery under test 20, and it is easy to obtain stable and consistent ultrasonic signals.

Optionally, before testing the battery 20 to be tested, the coaxial alignment of the laser pulse emitting unit 1011 and the laser interference receiving unit on the battery to be tested 20 is adjusted. Specifically, a metal/alloy plate whose thickness is close to the battery under test 20 or consistent with the thickness of the battery under test 20 can be used as a reference for optical path adjustment, and the laser emitted by the laser pulse emitting unit 1011 and the laser interference receiving unit can be adjusted so that both are focused On the front and back sides of the metal/alloy plate, mark the laser focus points on the front and back sides. If the two marking points are strictly centered with respect to the thickness direction of the battery, the optical path adjustment will be completed.

Different from the prior art, in this embodiment, the laser pulse transmitting unit 1011 is used to send laser pulses to the battery under test 20, so that the surface of the battery under test 20 generates ultrasonic signals, and the laser interference receiving unit is used as the ultrasonic signal receiving unit 1012 to perform ultrasonic signal detection. The entire laser detection process does not need to touch the battery under test 20, and can realize the battery in-situ detection of laser ultrasonic non-contact excitation and non-contact reception, and is non-destructive. Affecting the normal use of the battery under test 20 makes it easier to ensure the consistency and stability of the signal.

Wherein, the detection unit 101 may further include a temperature detection unit 1013, and the temperature detection unit 1013 is used to detect the temperature of the battery under test 20 in real time to obtain a temperature detection signal. Optionally, the temperature detection unit 101 is a patch temperature sensor, which can be attached to the surface of the battery under test 20 to detect the temperature of the surface of the battery under test 20 in real time.

Wherein, the detection unit 101 may further include a battery thickness collection unit 1014 for performing real-time thickness detection on the battery under test 20 to collect a thickness detection signal, which includes thickness information of the battery under test 20 . Wherein, the battery thickness acquisition unit 1014 can be an image acquisition unit, and the image acquisition unit acquires thickness detection signals in real time by collecting thickness images of the battery under test 20 in real time.

In one of the embodiments, the battery thickness acquisition unit 1014 includes an industrial camera, and the industrial camera is set directly opposite to the thickness section direction of the battery under test 20, so as to obtain image information in the thickness section direction of the battery in real time, and only need to process the acquired image. The thickness information of the battery under test 20 is obtained. For example, an image recognition method may be used for edge recognition and extraction to obtain thickness detection information of the battery 20 under test. Since the image recognition technology is quite mature nowadays, only a schematic description of the thickness detection is given here. Those skilled in the art can choose other ways to process the acquired image to obtain the thickness detection information of the battery, which will not be repeated here. repeat.

Different from the prior art, this embodiment can use the laser pulse emitting unit 1011 and the ultrasonic signal receiving unit 1012 to perform non-contact non-destructive testing under the charging state of the battery under test 20, obtain ultrasonic signals, and use the temperature detecting unit 1013 and the battery thickness The acquisition unit 1014 performs temperature detection and thickness detection on the battery under test 20 to obtain detection signals in multiple dimensions, making the battery detection more comprehensive and effectively improving the accuracy of battery detection.

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 of the battery under test 20 .

Please refer to FIG. 4 and FIG. 5. FIG. 4 is a schematic diagram of the time domain signal of the battery detection ultrasonic signal of the present application, and FIG. 5 is a schematic diagram of the frequency domain signal of the battery detection ultrasonic signal of the present application. Scan the image.

This scheme has been verified by previous experiments, and the signal-to-noise ratio of the ultrasonic signal received in the transmission mode is high, as shown in Figure 4 and Figure 5, according to the delay time of the transmitted wave signal relative to the reference signal (6.38μs) and the battery thickness (11.2 mm), the sound velocity of the longitudinal wave is calculated to be 1755m/s, which is consistent with the existing literature reports, which is enough to prove the feasibility of the method.

The battery detection system proposed in this application can be applied to energy fields such as lithium-ion power and energy storage batteries, next-generation lithium-sulfur batteries, lithium-air batteries, sodium-ion batteries, and solid oxide fuel cells. It is an effective means to accurately predict the state and health state, and it is also an important tool to realize the in-situ real-time monitoring of the internal microstructure of the battery, so it has important scientific research value and market application prospects in the future.

Please refer to FIG. 6 . FIG. 6 is a schematic flow diagram of an embodiment of a battery detection method of the present application. The present application also provides a battery detection method, comprising the following steps:

S70: Perform real-time detection on the battery under test to obtain a detection signal; wherein, the detection signal includes an ultrasonic signal, a temperature detection signal, and a 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-mentioned embodiments can be used to detect the battery to obtain ultrasonic signals, temperature detection signals, and thickness detection signals. repeat.

S80: Input the detection signal into the pre-trained deep learning model, and output the prediction results of the state of charge and state of health of the battery.

In this step, input the multi-dimensional characteristic parameters in the received ultrasonic signal, temperature detection signal, and thickness detection signal into the trained deep learning model to directly output the detection results and predict the health status and charge state of the battery.

This method can also control the laser controller 106 and the control unit 105 to send a control signal to trigger the laser pulse emitting unit 1011 to send a laser pulse to perform laser ultrasonic testing on the battery under test 20, and control the movement of the loading platform 104 to perform a laser ultrasonic test on the battery under test 20. Scanning detection; this method can also use the received ultrasonic signal to reconstruct the image of the battery under test 20 to obtain a two-dimensional image of the battery under test 20 . For the specific implementation of the method, please refer to the descriptions of the above-mentioned embodiments of the battery detection system, which will not be repeated here.

The above is only an embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent process conversion made by using the specification and drawings of the application, or directly or indirectly used in other related technologies fields, are all included in the scope of patent protection of this application in the same way.

Claims

A battery detection system, characterized in that the battery detection system comprises:

a detection unit, configured to detect the battery under test to obtain a detection signal; wherein the detection signal includes an ultrasonic signal, a temperature detection signal, and a thickness detection signal;

The processing unit is connected to the detection unit, and the processing unit is used to receive the detection signal and analyze the detection signal by using a deep learning model to predict the state of charge and state of health of the battery under test.
The battery detection system according to claim 1, wherein the detection unit comprises:

a laser pulse emitting unit, configured to emit laser pulses to the battery under test, so that the surface of the battery under test generates ultrasonic signals;

The ultrasonic signal receiving unit is used for receiving the ultrasonic signal.
The battery detection system according to claim 2, wherein,

The ultrasonic signal receiving unit is a laser interference receiving unit, which is used to receive the ultrasonic signal generated by the laser pulse on the surface of the battery under test, and utilizes the interference of the reference beam and the signal beam in the photorefractive crystal to receive the ultrasonic signal Ultrasonic signal.
The battery detection system according to claim 2, wherein,

The synchronization output port of the laser pulse transmitting unit is connected to the reference signal channel of the ultrasonic signal receiving unit, so as to trigger the ultrasonic signal receiving unit to receive the ultrasonic signal synchronously when the laser pulse transmitting unit sends out the laser pulse.
The battery testing system according to claim 2, wherein the processing unit is further configured to perform imaging processing according to the received ultrasonic signal, so as to perform two-dimensional imaging of the battery under test.
The battery detection system according to claim 1, wherein the detection unit comprises:

A temperature detection unit is connected to the processing unit, and the temperature detection unit is used to detect the temperature of the battery under test in real time to obtain the temperature detection signal.
The battery detection system according to claim 1, wherein the detection unit comprises:

The battery thickness acquisition unit is configured to detect the thickness of the battery under test to obtain the thickness detection signal.
The battery detection system according to claim 7, wherein the battery thickness acquisition unit is an image acquisition unit, and the image acquisition unit collects the thickness image of the battery under test in real time to obtain the thickness detection signal .
The battery detection system according to claim 1, wherein the detection system further comprises:

A charging and discharging unit, the charging and discharging unit is used to connect the positive pole and the negative pole of the battery under test, so as to perform periodic charge and discharge operations on the battery under test when the battery under test is in a testing state.
A battery detection method, characterized in that the battery detection method comprises:

Real-time monitoring of the battery under test to obtain a detection signal; wherein the detection signal includes an ultrasonic signal, a temperature detection signal and a thickness detection signal;

The detection signal is input into the pre-trained deep learning model, and the prediction results of the state of charge and state of health of the battery are output.