A battery state of charge detection method and device based on self-excitation electromagnetic acoustic emission

Abstract

This invention provides a method and device for detecting the state of charge (SOC) of a battery based on self-excited electromagnetic acoustic emission. When the battery under test is subjected to transient current changes, the battery tabs generate a self-excited electromagnetic acoustic emission signal. This signal propagates inside the battery, is first collected by an acoustic emission sensor, then amplified, and finally collected and analyzed by a host computer. By recording historical data of the self-excited electromagnetic acoustic emission signals at different SOC states, a correlation model between the characteristics of the self-excited electromagnetic acoustic emission signal and the battery state is pre-established in the host computer. The signal features extracted from the real-time self-excited electromagnetic acoustic emission signals are substituted into the correlation model to solve for the current SOC state of the battery. This invention effectively balances the proactive nature of detection with the issues of equipment power consumption and cost.

Description

Technical Field

[0001] This invention relates to the field of acoustic nondestructive testing technology for batteries, and in particular to a method and device for detecting the state of charge of batteries based on self-excited electromagnetic acoustic emission. Background Technology

[0002] Monitoring the state of charge (SOC) of a battery allows for real-time assessment of its remaining capacity, thereby protecting battery safety, optimizing energy management, and extending battery life. Currently, battery status is typically characterized by monitoring parameters such as voltage, current, temperature, and electrochemical impedance spectroscopy. However, these methods struggle to directly detect changes in the battery's internal structure and early microscopic failures. In contrast, acoustic monitoring methods (such as active ultrasound and passive acoustic emission) leverage their high sensitivity and penetration to internal conditions like mechanical stress, gas production, and lithium plating, enabling in-situ, non-invasive direct detection.

[0003] The literature GOLD L, BACH T, VIRSIK W, et al. Probing lithium-ion batteries' state-of-charge using ultrasonic transmission – Concept and laboratory testing[J]. Journal of Power Sources, 2017, 343: 536-544. and LADPLI P, KOPSAFTOPOULOS F, CHANG F K. Estimating state of charge and health of lithium-ion batteries with guided waves using built-in piezoelectric sensors / actuators[J]. Journal of Power Sources, 2018, 384: 342-354. mention that active ultrasonic signals are correlated with SOC and can be used to monitor battery SOC. However, active ultrasound requires a power ultrasonic sensor to transmit the signal, which propagates inside the battery and is then received by another sensor. The entire process requires two sensors, which is not advantageous in terms of equipment power consumption and cost. Patent application number 202010072989.2 discloses a lithium-ion battery state detection system and method based on continuous acoustic emission signals. This method utilizes the acoustic emission signals generated by cracks or gas evolution in the internal electrodes of the battery to perform passive acoustic emission detection. Although only a single sensor is needed to receive the stress wave excited by changes in mechanical stress, the signal it collects can only be used to characterize the battery's health status, but not its state of charge. Therefore, no correlation has been found between the passive acoustic emission signal and the battery's SOC. Furthermore, traditional passive acoustic emission signals cannot achieve active detection, limiting their application in certain scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a method and device for detecting the state of charge of a battery based on self-excited electromagnetic acoustic emission, so as to solve the problem that it is difficult to balance the initiative of detection with the power consumption and cost of the device in the existing battery acoustic non-destructive testing technology.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical method: a battery state of charge detection method based on self-excited electromagnetic acoustic emission, which uses a battery state of charge detection device to detect the battery's state of charge. The device includes an acoustic emission sensor, an amplifier, a host computer, and a loading system that actively charges and discharges the battery under test and regulates its transient current. The loading system is connected to the battery under test to form a loop. The acoustic emission sensor is in close contact with the surface of the battery under test. One end of the amplifier is electrically connected to the acoustic emission sensor, and the other end is electrically connected to the host computer. The method includes: Step S1: The battery under test is placed in a working condition with transient current changes, and the battery tabs under test generate a self-excited electromagnetic acoustic emission signal. Step S2: The self-excited electromagnetic acoustic emission signal is collected by the acoustic emission sensor, amplified by the amplifier, and finally collected and analyzed by the host computer. Step S3: Collect and record historical data of self-excited electromagnetic acoustic emission signals of the battery under test at different states of charge, and establish a correlation model between the characteristics of self-excited electromagnetic acoustic emission signals and the state of charge of the battery in the host computer. Step S4: Real-time acquisition of the self-excited electromagnetic acoustic emission signal of the battery under test and extraction of signal features are then substituted into the correlation model to solve for the current state of charge of the battery under test.

[0006] Furthermore, in step S2, after the amplified self-excited electromagnetic acoustic emission signal is acquired by the host computer, it is first filtered by a filtering algorithm, and then time-domain and frequency-domain analysis is performed to extract signal features that are correlated with the battery state of charge.

[0007] Furthermore, the signal characteristic is the flight time characteristic of the self-excited electromagnetic acoustic emission signal of the tested battery, and the flight time is the time difference from the generation of the battery's self-excited electromagnetic acoustic emission signal to its acquisition by the acoustic emission sensor.

[0008] Furthermore, in step S3, a correlation model between the flight time characteristics of the self-excited electromagnetic acoustic emission signal of the tested battery and its state of charge is established using linear fitting.

[0009] Furthermore, in step S4, if it is necessary to detect the state of charge of the battery within a specified charge / discharge time window, the loading system is connected to the battery under test to form a loop, the battery under test is charged and discharged, and the loop current is regulated by the current control unit of the loading system to make the battery under test be in a working condition with transient current changes. The battery under test generates a self-excited electromagnetic acoustic emission signal. Then, the time-of-flight characteristics of the self-excited electromagnetic acoustic emission signal are extracted in step S2 and substituted into the correlation model to solve for the state of charge of the battery under test at the current moment.

[0010] Preferably, the loading system includes a battery charge / discharge tester, a constant current source, or an electronic load.

[0011] As another aspect of the present invention, a battery state-of-charge detection device based on self-excited electromagnetic acoustic emission includes an acoustic emission sensor, an amplifier, and a host computer. The acoustic emission sensor is in close contact with the surface of the battery under test. One end of the amplifier is electrically connected to the acoustic emission sensor, and the other end is electrically connected to the host computer. The device also includes a loading system for charging and discharging the battery under test and regulating its transient current. The loading system is connected to the battery under test to form a circuit. The device can detect the battery state of charge using the aforementioned battery state-of-charge detection method based on self-excited electromagnetic acoustic emission.

[0012] In another aspect of the present invention, a host computer includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the aforementioned battery state-of-charge detection method based on self-excited electromagnetic acoustic emission.

[0013] As another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the aforementioned battery state-of-charge detection method based on self-excited electromagnetic acoustic emission.

[0014] As another aspect of the present invention, a computer program product includes a computer program that, when executed by a processor, implements the aforementioned battery state-of-charge detection method based on self-excited electromagnetic acoustic emission.

[0015] The battery state-of-charge (SOC) detection device proposed in this invention utilizes the transient changes in current to generate an electromagnetic acoustic emission signal. It requires only one ultrasonic receiving sensor, reducing the complexity of traditional single-transmitter, single-receiver battery ultrasonic detection systems. Furthermore, active acoustic detection of the battery SOC can be achieved using a single sensor by actively controlling the current.

[0016] This invention proposes a battery state-of-charge (SOC) detection method based on self-excited electromagnetic acoustic emission (MEA). This method utilizes the correlation between MEA signals and the battery SOC to establish a correlation model for real-time SOC determination. Relying on the internal mechanical characteristics of the battery, this method complements traditional battery SOC detection methods based on electrical and thermal parameters, providing a multi-dimensional characterization of the battery's state.

[0017] Compared to traditional active ultrasonic testing methods, this invention uses transient current changes during battery charging and discharging to excite stress wave signals, eliminating the need for an additional high-power ultrasonic probe, thus reducing power consumption, cost, and simplifying sensor placement. Furthermore, compared to traditional passive acoustic emission testing methods, this invention actively monitors the battery's internal state by controlling transient current changes, enabling the detection of battery state of charge in various situations. In summary, this invention effectively balances the proactive nature of battery state of charge detection with the power consumption and cost considerations of the testing equipment. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the battery state-of-charge detection device based on self-excited electromagnetic acoustic emission proposed in this invention. Figure 2 This is a flowchart of the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission proposed in this invention; Figure 3 This is a graph of the self-excited electromagnetic acoustic emission signal in this invention; Figure 4 These are comparison diagrams of the time-domain and frequency-domain curves of the self-excited electromagnetic acoustic emission signal before and after filtering in this invention (where (a) is a comparison diagram of the time-domain curve of the self-excited electromagnetic acoustic emission signal before filtering; (b) is a comparison diagram of the frequency-domain curve of the self-excited electromagnetic acoustic emission signal before filtering; (c) is a comparison diagram of the time-domain curve of the self-excited electromagnetic acoustic emission signal after filtering; and (d) is a comparison diagram of the frequency-domain curve of the self-excited electromagnetic acoustic emission signal after filtering). Figure 5 This is a graph showing the variation of the flight time characteristics of the self-excited electromagnetic acoustic emission signal under different battery conditions in this invention. Detailed Implementation

[0019] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0020] Before describing this invention, it should be noted that batteries experience numerous transient current changes during normal operation, such as when the battery starts or stops working, switching between inactive and active states, and in the hybrid pulse power characteristic (HPPC) test conditions of electric vehicles, where the battery current changes frequently depending on the operating conditions. These transient current changes induce electromagnetic forces at the battery terminals due to electromagnetic induction, thereby generating electromagnetic acoustic emission signals. These signals carry a large amount of battery state information and can propagate through the battery to the acoustic emission sensor. Because these transient current changes in the battery naturally generate electromagnetic acoustic emission signals, these signals are also called self-excited electromagnetic acoustic emission signals. Based on this principle, this invention proposes a battery state-of-charge detection method and device based on self-excited electromagnetic acoustic emission.

[0021] like Figure 1 The diagram shown is a schematic of the battery state-of-charge detection device based on self-excited electromagnetic acoustic emission proposed in this invention. The device includes a loading system, an acoustic emission sensor, an amplifier, and a host computer. The loading system forms a circuit with the battery under test for charging and discharging the battery. The loading system includes a current control unit composed of power electronic devices to control the circuit current, causing transient current changes in the battery to excite electromagnetic acoustic emission signals. Preferably, the loading system is a commercially available integrated device, such as a battery charge / discharge tester, a constant current source, or an electronic load.

[0022] An acoustic emission sensor is placed on the battery surface to receive self-excited electromagnetic acoustic emission signals. One end of an amplifier is connected to the acoustic emission sensor to amplify the weak self-excited electromagnetic acoustic emission signals. A host computer is connected to the other end of the amplifier to acquire the self-excited electromagnetic acoustic emission signals and analyze their characteristics, thereby characterizing the battery's internal state based on the battery state detection method based on self-excited electromagnetic acoustic emission.

[0023] like Figure 2 The diagram shows a flowchart of the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission proposed in this invention, which mainly includes the following steps.

[0024] Step S1: Adjust the transient current to excite the electromagnetic acoustic emission signal.

[0025] In practical applications, there are various testing scenarios. In most cases, the battery's state of charge (SOC) can be detected by utilizing the self-excited electromagnetic acoustic emission signal generated by the transient current changes during battery operation, such as when the battery starts or stops working, or during the HPPC test conditions of an electric vehicle. In these situations, the device proposed in this invention operates in passive detection mode. The battery under test does not need to be connected to a loading system; it only needs to be in a normal operating condition with transient current changes. The battery tabs will generate self-excited electromagnetic acoustic emission signals. In these situations, compared to traditional active ultrasonic testing methods, this invention excites stress wave signals by utilizing the transient current changes during battery charging and discharging, eliminating the need for an additional power ultrasonic probe to excite the ultrasonic signal, thus reducing power consumption, cost, and simplifying sensor placement.

[0026] In a few cases, it is required to detect the state of charge (SOC) of a battery within a specified charge / discharge time window. In such scenarios, the device proposed in this invention operates in active detection mode. The loading system needs to be connected to the battery under test to form a loop, charging and discharging the battery. The current control unit of the loading system regulates the loop current, placing the battery under test in a condition with transient current changes. The battery tabs then generate a self-excited electromagnetic acoustic emission signal. Since the transient current changes and the electromagnetic acoustic emission signal are generated synchronously, the electromagnetic acoustic emission signal can be indirectly controlled by adjusting the current signal. Therefore, this invention can achieve an active detection effect that traditional acoustic emission battery state detection methods lack, thus enriching its application scenarios.

[0027] Step S2: The self-excited electromagnetic acoustic emission signal propagating inside the battery under test is collected by an acoustic emission sensor, and the weak self-excited electromagnetic acoustic emission signal is amplified by an amplifier and then collected and analyzed by the host computer.

[0028] like Figure 3 The figure shows the amplified electromagnetic acoustic emission signal after amplifier enhancement. As can be seen from the figure, when the current in the charging / discharging circuit of the tested battery undergoes a transient change (the falling edge of the current in the figure), an electromagnetic acoustic emission signal is generated inside the battery. Therefore, this characteristic can be used to replace the ultrasonic signal generating sensor in traditional ultrasonic battery state detection methods, reducing power consumption and simplifying sensor setup.

[0029] To reduce mechanical and electromagnetic interference during signal transmission, the host computer first filters the amplified self-excited electromagnetic emission signal using a filtering algorithm after acquiring it. Then, it performs time-domain and frequency-domain analysis on the filtered self-excited electromagnetic emission signal to extract signal features that are correlated with the battery's state of charge.

[0030] like Figure 4 The image shows a comparison of the self-excited electromagnetic acoustic emission signal before and after filtering. After bandpass filtering, low-frequency signals below 20kHz and high-frequency signals above 40kHz are significantly removed. Therefore, filtering can effectively reduce low-frequency mechanical interference and high-frequency electromagnetic interference encountered in practical applications, which is beneficial for accurately extracting signal features related to the battery's state of charge.

[0031] like Figure 5The figure shows the curves illustrating the variation of the characteristics of the self-excited electromagnetic acoustic emission signal with the state of charge (SOC). It can be seen from the figure that there is a clear linear relationship between the time-of-flight (TOF) characteristic of the self-excited EAM signal and the SOC; that is, the TOF characteristic of the self-excited EAM signal gradually decreases as the SOC increases. Therefore, the TOF characteristic of the self-excited EAM signal can be used to characterize the state of charge of the battery. By establishing a correlation model based on this relationship, the battery state can be solved in real time.

[0032] Step S3: Collect and record historical data of self-excited electromagnetic acoustic emission signals of the tested battery under different states of charge using the methods in steps S1-S2. Establish a correlation model between the TOF characteristics of the self-excited electromagnetic acoustic emission signals and the state of charge of the battery using linear fitting in the host computer.

[0033] Step S4: The self-excited electromagnetic acoustic emission signal of the battery under test is acquired in real time using the methods in steps S1-S2, and the extracted TOF features are substituted into the correlation model constructed in step S3 to solve for the state of charge of the battery under test at the current time.

[0034] On the other hand, based on the same principle as the battery state-of-charge detection method based on self-excited electromagnetic emission described in the above embodiments, the present invention also provides a host computer, which includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the battery state-of-charge detection method based on self-excited electromagnetic emission described in the above embodiments. Specifically, the host computer can be an electronic computer or tablet computer, the processor can be a CPU, GPU, etc., and the memory can be RAM, ROM, EEPROM, CDROM, disk storage medium, or any other medium that can be used to carry or store the computer program and can be read by a computer, without limitation herein.

[0035] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission described in the above embodiments. Specifically, the computer-readable storage medium may be RAM, ROM, EEPROM, SSD, CDROM, DVD, USB flash drive, or any other medium capable of carrying or storing the computer program and capable of being read by a computer.

[0036] On the other hand, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission as described in the above embodiments.

[0037] To verify the effectiveness and accuracy of this invention, an EVE Energy commercial soft-pack lithium-ion battery was selected as the test battery. The state-of-charge (SOC) of the battery was detected using the method and equipment proposed in this invention. Specifically, the battery has Li(NiCoMn)O2 as the positive electrode, graphite as the negative electrode, a rated capacity of 50.5 Ah, and dimensions of 301.5 mm × 99.7 mm × 11.6 mm. The battery was connected to an ITECH IT8513C+ programmable DC electronic load. A discharge pulse current of 100 A and a pulse width of 0.5 s were applied to the battery in dynamic mode to excite an electromagnetic acoustic emission (EAM) signal. The EAM signal was received using a Huarun VS45H acoustic emission piezoelectric ceramic sensor. Vacuum sealing grease was used as a coupling agent between the sensor and the battery surface, providing good thermal and oxidation stability. The EAM signal was amplified by 40 dB using a Huarun AEP5 preamplifier and then received and recorded using a Tektronix MDO34 oscilloscope with a sampling rate of 10 MHz. The amplified self-excited EAM signal effect is shown in [the image / description]. Figure 3 Next, a Butterworth filter is used to perform a 20kHz-40kHz bandpass filter on the electromagnetic acoustic emission signal to reduce the influence of low-frequency mechanical interference and high-frequency electromagnetic interference. The specific effects of this process are as follows: Figure 4 As shown. Next, time-domain and frequency-domain analyses are performed on the filtered self-excited electromagnetic acoustic emission signal, and the time-of-flight characteristics related to the battery's state of charge are extracted. The time of flight is the time difference between the generation of the battery's self-excited electromagnetic acoustic emission signal and its acquisition by the acoustic emission sensor, and its calculation formula is as follows: (1) in, Indicates flight time; The moment when the acoustic emission sensor collects the self-excited electromagnetic acoustic emission signal. This indicates the moment when the battery current undergoes a transient change, which is the moment when the battery generates a self-excited electromagnetic acoustic emission signal.

[0038] Subsequently, self-excited electromagnetic acoustic emission (MEA) signal data of the tested battery under different states of charge (SOC) were recorded. Taking the acquisition of MEA signal data at different SOCs as an example, the battery was charged and discharged at room temperature (25℃) using a Xinwei CT-4004-5V100A battery charge / discharge tester, with the SOC controlled to decrease from 100% to 0% in 10% increments. Constant current and constant voltage charging were used during charging, with the battery charged at a constant current of 1C until the voltage reached 4.2V, followed by constant voltage charging until the current dropped to C / 10 to complete the charging process. During the discharging phase, the battery underwent constant current discharge at 1C until the voltage dropped to 2.5V to complete the discharge. The battery was allowed to rest for 5 minutes between charging and discharging. At each different SOC, five MEA signals were recorded, and the time-of-flight signal characteristics were extracted. The variation patterns of the time-of-flight characteristics of the MEA signals at different SOCs are as follows: Figure 5 As shown ( Figure 5 The black dots represent the average of five flight times under different states of charge, displayed as error bars. A clear linear relationship exists between the flight time characteristics and the State of Charge (SOC). Utilizing this relationship, a linear fitting model is used to establish the correlation between the flight time characteristics and SOC, as shown in the following equation: (2) in, SOC and TOF The units are % and microsecond, respectively.

[0039] By acquiring the self-excited electromagnetic acoustic emission signal of the battery under test in real time and extracting the flight time signal features using the aforementioned method, and substituting the real-time calculated flight time features into the pre-established correlation model, the state of charge of the battery under test at the current moment can be solved, thereby achieving the purpose of characterizing the battery's state of charge.

[0040] Compared to traditional active ultrasonic detection methods, this invention uses transient current changes during battery charging and discharging to excite stress wave signals, eliminating the need for an additional power ultrasonic probe to excite ultrasonic signals, thus reducing power consumption and simplifying sensor placement.

[0041] It is worth mentioning that, compared with traditional passive acoustic emission detection methods, this invention can actively monitor the state of charge of the battery by adjusting the transient change current. At the same time, during the current stable period, like traditional passive acoustic emission detection methods, it can also be used to passively monitor the stress wave signals generated inside the battery due to phenomena such as gas evolution and electrode particle breakage, thereby achieving passive monitoring of the battery's health status. This further improves the applicability of the detection method.

[0042] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present technical solution are within the protection scope of the present invention.

[0043] To facilitate understanding by those skilled in the art of the improvements of this invention over the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted from this application. Those skilled in the art should realize that these omitted elements may also constitute the content of this invention.

Claims

1. A method for detecting the state of charge of a battery based on self-excited electromagnetic acoustic emission, characterized in that, A battery state of charge detection device is used to detect the state of charge of a battery. The device includes an acoustic emission sensor, an amplifier, a host computer, and a loading system that actively charges and discharges the battery under test and regulates its transient current. The loading system is connected to the battery under test to form a circuit. The acoustic emission sensor is in close contact with the surface of the battery under test. One end of the amplifier is electrically connected to the acoustic emission sensor and the other end is electrically connected to the host computer. The method includes: Step S1: The battery under test is placed in a working condition with transient current changes, and the battery tabs under test generate a self-excited electromagnetic acoustic emission signal. Step S2: The self-excited electromagnetic acoustic emission signal is collected by the acoustic emission sensor, amplified by the amplifier, and finally collected and analyzed by the host computer. Step S3: Collect and record historical data of self-excited electromagnetic acoustic emission signals of the battery under test at different states of charge, and establish a correlation model between the characteristics of self-excited electromagnetic acoustic emission signals and the state of charge of the battery in the host computer. Step S4: Real-time acquisition of the self-excited electromagnetic acoustic emission signal of the battery under test and extraction of signal features are then substituted into the correlation model to solve for the current state of charge of the battery under test.

2. The battery state-of-charge detection method based on self-excited electromagnetic acoustic emission according to claim 1, characterized in that: In step S2, after the amplified self-excited electromagnetic acoustic emission signal is acquired by the host computer, it is first filtered by a filtering algorithm, and then analyzed in the time and frequency domains to extract signal features that are correlated with the battery state of charge.

3. The battery state-of-charge detection method based on self-excited electromagnetic acoustic emission according to claim 2, characterized in that: The signal characteristic is the flight time characteristic of the self-excited electromagnetic acoustic emission signal of the tested battery, and the flight time is the time difference from the generation of the battery's self-excited electromagnetic acoustic emission signal to its acquisition by the acoustic emission sensor.

4. The battery state-of-charge detection method based on self-excited electromagnetic acoustic emission according to claim 3, characterized in that: In step S3, a correlation model between the flight time characteristics of the self-excited electromagnetic acoustic emission signal of the tested battery and the state of charge is established by linear fitting.

5. The battery state-of-charge detection method based on self-excited electromagnetic acoustic emission according to claim 4, characterized in that: In step S4, if it is necessary to detect the state of charge of the battery within a specified charge / discharge time window, the loading system is connected to the battery under test to form a loop, the battery under test is charged and discharged, and the loop current is regulated by the current control unit of the loading system to make the battery under test be in a working condition with transient current changes. The battery under test generates a self-excited electromagnetic acoustic emission signal. Then, the time-of-flight characteristics of the self-excited electromagnetic acoustic emission signal are extracted using step S2 and substituted into the pre-built correlation model to solve for the state of charge of the battery under test at the current time.

6. The battery state-of-charge detection method based on self-excited electromagnetic acoustic emission according to claim 5, characterized in that: The loading system includes a battery charge / discharge tester, a constant current source, or an electronic load.

7. A battery state-of-charge detection device based on self-excited electromagnetic acoustic emission, comprising an acoustic emission sensor, an amplifier, and a host computer, wherein the acoustic emission sensor is in close contact with the surface of the battery under test, one end of the amplifier is electrically connected to the acoustic emission sensor, and the other end is electrically connected to the host computer, characterized in that: It also includes a loading system for charging and discharging the battery under test and regulating its transient current. The loading system is connected to the battery under test to form a circuit. The device can detect the state of charge of the battery by using the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission as described in any one of claims 1 to 6.

8. A host computer, comprising a processor and a memory, wherein the memory stores a computer program, characterized in that: The processor executes the computer program to implement the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that: When executed by a processor, the computer program implements the battery state-of-charge detection method based on self-excited electromagnetic acoustic emission as described in any one of claims 1 to 6.

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