Method and device for detecting battery performance, electronic equipment and storage medium

Abstract

The invention provides a method and a device for detecting battery performance, electronic equipment and a storage medium. The method comprises the following steps: acquiring noise information of an environment where a to-be-detected battery is located; when the noise information indicates that the noise interference of the environment is greater than a preset threshold value, detecting the performance of the to-be-detected battery based on an electrochemical impedance spectroscopy detection scheme; otherwise, detecting the performance of the to-be-detected battery based on a sound wave detection scheme, or detecting the performance of the to-be-detected battery based on a combined scheme of the sound wave detection scheme and the electrochemical impedance spectroscopy detection scheme; and evaluating the performance of the battery based on a detection result of the sound wave detection scheme and / or the electrochemical impedance spectroscopy detection scheme. By means of the method, the problem that real-time online precise health detection cannot be conducted on an existing vehicle-mounted battery is solved.

Description

A method, apparatus, electronic device, and storage medium for testing battery performance. Technical Field

[0001] This application relates to the field of battery health testing technology, and more specifically, to a method, apparatus, electronic device, and storage medium for testing battery performance. Background Technology

[0002] The health status of the power battery and critical onboard backup power sources (such as the backup battery of the telematics terminal TBOX) directly affects the vehicle's operational safety and system reliability. Accurate and timely testing and lifespan assessment of battery performance are crucial measures to prevent vehicle malfunctions caused by sudden battery failure and to ensure driving safety.

[0003] Currently, the industry generally relies on specialized offline testing equipment and methods for accurate assessment of battery state of health (SOH) and remaining useful life (RUL). A typical approach involves removing the battery from the vehicle and testing it in a laboratory or repair shop using sophisticated instruments (such as electrochemical workstations and impedance analyzers). While this type of offline testing can obtain relatively accurate internal battery state parameters, it has significant drawbacks: First, the testing process is cumbersome and lacks real-time capability, failing to reflect the dynamic performance changes and aging process of the battery under actual driving conditions; second, disassembly and testing are costly and time-consuming, making it unsuitable as a routine preventative maintenance method; and more importantly, it cannot provide real-time warnings of sudden performance degradation or potential faults that may occur during vehicle operation.

[0004] Therefore, an improvement solution is needed. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method, apparatus, electronic device, and storage medium for detecting battery performance, in order to solve the problem that existing vehicle batteries cannot perform real-time online accurate health detection.

[0006] In a first aspect, embodiments of this application provide a method for detecting battery performance, the method comprising: acquiring noise information of the environment in which the battery to be tested is located; when the noise information indicates that the noise interference of the environment is greater than a preset threshold, detecting the performance of the battery to be tested based on an electrochemical impedance spectroscopy (EIS) detection scheme; the EIS detection scheme is used to detect the electrochemical reaction of the battery under the action of AC signals of different frequencies; otherwise, detecting the performance of the battery to be tested based on an acoustic wave detection scheme, or detecting the performance of the battery to be tested based on a combination of the acoustic wave detection scheme and the EIS detection scheme; the acoustic wave detection scheme is used to determine the internal structure of the battery cell based on the reflection of acoustic wave signals by the battery to be tested; and evaluating the performance of the battery based on the detection results of the acoustic wave detection scheme and / or the EIS detection scheme.

[0007] In one feasible implementation, the battery to be tested is the backup battery of the vehicle telematics terminal TBOX, and the environment is the in-vehicle environment during vehicle operation.

[0008] In one feasible implementation, the performance of the battery under test is detected based on an electrochemical impedance spectroscopy (EIS) detection scheme, including: generating multiple AC excitation signals of different frequencies; sequentially applying the multiple AC excitation signals of different frequencies to the battery under test; acquiring the voltage response signal and current response signal of the battery under test under the action of the multiple AC excitation signals of different frequencies; and calculating the complex impedance of the battery under test at different frequencies based on the voltage response signal and the current response signal to generate the electrochemical impedance spectrum of the battery under test.

[0009] In one feasible implementation, the complex impedance of the battery under test at different frequencies includes the following characteristic information: ohmic impedance information obtained under a preset high-frequency AC excitation signal; the frequency range of the high-frequency AC excitation signal is 10 kHz to 1 MHz, and the ohmic impedance is used to characterize the ion and electron conduction capabilities of the electrolyte, electrode materials, and current collector in the battery under test; charge transfer impedance information obtained under a preset mid-frequency AC excitation signal; the frequency range of the mid-frequency AC excitation signal is 10 Hz to 10 kHz, and the charge transfer impedance is used to characterize the kinetic resistance of the battery under test during electrochemical reactions at the electrode or electrolyte interface; and diffusion impedance information obtained under a preset low-frequency AC excitation signal; the frequency range of the low-frequency AC excitation signal is less than 10 Hz, and the diffusion impedance is used to characterize the diffusion and migration capabilities of active ions inside the battery under test in the bulk phase of the electrode material.

[0010] In one feasible implementation, evaluating the performance of the battery includes: extracting at least one feature parameter from the ohmic impedance information, the charge transfer impedance information, and the diffusion impedance information to form an impedance feature set; inputting the impedance feature set into a preset battery evaluation model; obtaining the evaluation result output by the battery evaluation model; the evaluation result includes at least one of the health status score, aging stage, or remaining life prediction value of the battery under test.

[0011] In one feasible implementation, the performance of the battery under test is detected based on an acoustic wave detection scheme, comprising: controlling an acoustic wave emitting device to emit an incident acoustic wave signal of a specific frequency to the battery under test; controlling an acoustic wave receiving device to receive an echo acoustic wave signal formed by reflection and / or scattering from the battery under test; analyzing the incident acoustic wave signal and the echo acoustic wave signal to extract at least one characteristic parameter related to the internal physical structure of the battery under test; the analysis includes time domain analysis, frequency domain analysis, and / or phase analysis; and evaluating the internal structural state of the battery under test based on the at least one characteristic parameter; the internal structural state includes the density and cracks of the electrode material, the integrity of the electrode-electrolyte interface, and / or the uniformity of electrolyte distribution in the electrode pores.

[0012] In one feasible implementation, the method further includes: generating an alarm signal if the evaluation result indicates that the performance of the battery under test is lower than a preset warning threshold; and / or adjusting the charging and discharging strategy of the battery under test based on the evaluation result.

[0013] Secondly, embodiments of this application also provide an apparatus for detecting battery performance, the apparatus comprising: a noise acquisition module for acquiring noise information of the environment in which the battery to be tested is located; a first detection module for detecting the performance of the battery to be tested based on an electrochemical impedance spectroscopy (EIS) detection scheme when the noise information indicates that the noise interference of the environment is greater than a preset threshold; the EIS detection scheme is used to detect the electrochemical reaction of the battery under the action of AC signals of different frequencies; a second detection module for otherwise detecting the performance of the battery to be tested based on an acoustic detection scheme, or based on a combination of the acoustic detection scheme and the EIS detection scheme; the acoustic detection scheme is used to determine the internal structure of the battery cell based on the reflection of acoustic signals by the battery to be tested; and an evaluation module for evaluating the performance of the battery based on the detection results of the acoustic detection scheme and / or the EIS detection scheme.

[0014] In one feasible implementation, the battery to be tested is the backup battery of the vehicle telematics terminal TBOX, and the environment is the in-vehicle environment during vehicle operation.

[0015] In one feasible implementation, the first detection module is used to detect the performance of the battery under test based on an electrochemical impedance spectroscopy detection scheme, and is used to: generate multiple AC excitation signals of different frequencies; sequentially apply the multiple AC excitation signals of different frequencies to the battery under test; acquire the voltage response signal and current response signal of the battery under test under the action of the multiple AC excitation signals of different frequencies; and calculate the complex impedance of the battery under test at different frequencies based on the voltage response signal and the current response signal to generate the electrochemical impedance spectrum of the battery under test.

[0016] In one feasible implementation, the complex impedance of the battery under test at different frequencies includes the following characteristic information: ohmic impedance information obtained under a preset high-frequency AC excitation signal; the frequency range of the high-frequency AC excitation signal is 10 kHz to 1 MHz, and the ohmic impedance is used to characterize the ion and electron conduction capabilities of the electrolyte, electrode materials, and current collector in the battery under test; charge transfer impedance information obtained under a preset mid-frequency AC excitation signal; the frequency range of the mid-frequency AC excitation signal is 10 Hz to 10 kHz, and the charge transfer impedance is used to characterize the kinetic resistance of the battery under test during electrochemical reactions at the electrode or electrolyte interface; and diffusion impedance information obtained under a preset low-frequency AC excitation signal; the frequency range of the low-frequency AC excitation signal is less than 10 Hz, and the diffusion impedance is used to characterize the diffusion and migration capabilities of active ions inside the battery under test in the bulk phase of the electrode material.

[0017] In one feasible implementation, the evaluation module is used to evaluate the performance of the battery by: extracting at least one feature parameter from the ohmic impedance information, the charge transfer impedance information, and the diffusion impedance information to form an impedance feature set; inputting the impedance feature set into a preset battery evaluation model; and obtaining the evaluation result output by the battery evaluation model. The evaluation result includes at least one of the following: the health status score, aging stage, or remaining life prediction value of the battery under test.

[0018] In one feasible implementation, the second detection module is used to detect the performance of the battery under test based on an acoustic detection scheme, and is used to: control an acoustic wave emitting device to emit an incident acoustic wave signal of a specific frequency to the battery under test; control an acoustic wave receiving device to receive an echo acoustic wave signal formed by reflection and / or scattering from the battery under test; analyze the incident acoustic wave signal and the echo acoustic wave signal to extract at least one characteristic parameter related to the internal physical structure of the battery under test; the analysis includes time domain analysis, frequency domain analysis, and / or phase analysis; and evaluate the internal structural state of the battery under test based on the at least one characteristic parameter; the internal structural state includes the density and cracks of the electrode material, the integrity of the electrode-electrolyte interface, and / or the uniformity of electrolyte distribution in the electrode pores.

[0019] In one feasible implementation, the device further includes: an alarm module, configured to generate and output an alarm signal if the evaluation result indicates that the performance of the battery under test is lower than a preset warning threshold; and / or, a strategy adjustment module, configured to adjust the charging and discharging strategy of the battery under test according to the evaluation result.

[0020] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in any one of the first aspects.

[0021] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method as described in any one of the first aspects.

[0022] This application provides a method, apparatus, electronic device, and storage medium for detecting battery performance. It employs a dynamic dual-mode detection mechanism, using real-time acquisition of in-vehicle environmental noise information as the basis for detection mode selection. When noise interference is strong, the electrochemical impedance spectroscopy (EIS) detection mode, with its strong anti-interference capability, is prioritized. When noise conditions permit, a more comprehensive acoustic detection mode or a combination of both modes can be used. Finally, the battery performance is comprehensively evaluated based on the detection results of the selected mode. Compared with existing technologies that typically use only a single detection mode or a fixed combination of detection methods, this solution effectively addresses the problems of insufficient reliability and weak anti-interference capability in battery performance testing under complex and variable noise environments in vehicles, achieving environmentally adaptive and accurate detection of battery health status in in-vehicle scenarios.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 shows a flowchart of a method for detecting battery performance provided in an embodiment of this application.

[0026] Figure 2 shows a flowchart of another method for detecting battery performance provided in an embodiment of this application.

[0027] Figure 3 shows a schematic diagram of a device for detecting battery performance provided in an embodiment of this application.

[0028] Figure 4 shows a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0030] The health status of the power battery and critical onboard backup power sources (such as the backup battery of the telematics terminal TBOX) directly affects the vehicle's operational safety and system reliability. Accurate and timely testing and lifespan assessment of battery performance are crucial measures to prevent vehicle malfunctions caused by sudden battery failure and to ensure driving safety.

[0031] Currently, the industry generally relies on specialized offline testing equipment and methods for accurate assessment of battery state of health (SOH) and remaining useful life (RUL). A typical approach involves removing the battery from the vehicle and testing it in a laboratory or repair shop using sophisticated instruments (such as electrochemical workstations and impedance analyzers). While this type of offline testing can obtain relatively accurate internal battery state parameters, it has significant drawbacks: First, the testing process is cumbersome and lacks real-time capability, failing to reflect the dynamic performance changes and aging process of the battery under actual driving conditions; second, disassembly and testing are costly and time-consuming, making it unsuitable as a routine preventative maintenance method; and more importantly, it cannot provide real-time warnings of sudden performance degradation or potential faults that may occur during vehicle operation.

[0032] Based on this, embodiments of this application provide a method, apparatus, electronic device, and storage medium for detecting battery performance, which are described below through embodiments.

[0033] To facilitate understanding of this embodiment, a method for detecting battery performance disclosed in this application will first be described in detail. As shown in Figure 1, the method includes the following steps: Step 101, obtaining noise information of the environment in which the battery to be tested is located.

[0034] This step aims to perceive the environmental conditions during detection, providing a basis for subsequent decision-making regarding the selection of the most suitable detection mode. Here, "noise information" is a broad concept, referring to environmental factors that may interfere with the detection signal and affect detection accuracy. For acoustic wave detection, the main focus is on acoustic noise in the environment, such as vibrations and sounds generated by vehicle engines, air conditioning fans, and road bumps. This type of information can be obtained by directly acquiring raw signals through microphones and vibration sensors and analyzing their amplitude or spectrum.

[0035] Meanwhile, considering that electrochemical impedance spectroscopy detection may also be affected by electrical noise in specific frequency bands, "noise information" can also be extended to an assessment of the overall electromagnetic environment or operating conditions. For example, by acquiring engine speed, vehicle speed, and the start-stop status of high-power loads (such as air conditioning) through the vehicle's CAN bus, the interference level of the current environment can be indirectly determined. In some implementations, the detection system itself can also perform a rapid background signal acquisition before performing formal detection to assess the signal-to-noise ratio of the current environment.

[0036] In short, the core of this step is to identify and quantify the level of environmental interference. This can be achieved through direct physical signal measurement or through indirect logical judgment combined with vehicle status information. The purpose is to lay the foundation for the next step—adaptively selecting a detection mode with strong anti-interference capability or higher accuracy.

[0037] Step 102: When the noise information indicates that the noise interference in the environment is greater than a preset threshold, the performance of the battery under test is detected based on the electrochemical impedance spectroscopy detection scheme; the electrochemical impedance spectroscopy detection scheme is used to detect the electrochemical reaction of the battery under the action of AC signals at different frequencies.

[0038] This step completes the transition from environmental perception to detection decision-making. When the acquired noise information indicates that environmental interference exceeds a preset threshold, it means that the current environmental conditions may limit some detection methods. Especially for detection methods that rely on high signal-to-noise ratios, excessive environmental noise may directly overwhelm or severely distort the target signal being measured, leading to the inability to obtain effective measurement results or misjudgment.

[0039] Based on this assessment, the electrochemical impedance spectroscopy (EIS) detection scheme will be selected. This scheme's detection mechanism possesses a natural ability to suppress random noise and vibration interference in the environment. It does not rely on the absolute amplitude of the signal in a pristine environment, but rather uses specific signal generation and synchronous demodulation techniques to lock and extract the response component that is strictly correlated with the known excitation signal. Even in situations with strong background interference, as long as the interfering signal and the excitation signal do not have a strong correlation in frequency and phase, their influence can be largely separated and weakened, thus ensuring the validity of the core measurement data.

[0040] There are various possible implementations for this step. In one typical implementation, the frequency domain response of the battery might be obtained by applying a series of AC excitation signals of different frequencies. In another design, detection can be accomplished by applying a sweep signal of a specific frequency or a composite frequency signal of a specific form. Regardless of the specific form of the excitation signal, the essential purpose is to obtain the battery's impedance characteristic spectrum or its equivalent characteristic information, and use this as the basis for evaluating battery performance.

[0041] Step 103, otherwise, the performance of the battery under test is detected based on an acoustic detection scheme or a combination of the acoustic detection scheme and the electrochemical impedance spectroscopy detection scheme; the acoustic detection scheme is used to determine the internal structure of the battery cell based on the reflection of acoustic signals by the battery under test.

[0042] When the environmental noise information indicates that the noise interference does not exceed the preset threshold, it means that the current environment is relatively quiet and the background noise level that poses a major threat to acoustic detection is low. At this time, the acoustic detection scheme can operate reliably.

[0043] Under these favorable conditions, two more optimized detection path options are provided to maximize the detection value: the first path is to use the acoustic detection scheme alone.

[0044] This approach utilizes the properties of sound waves propagating, reflecting, or scattering within the battery to acquire structural information. Due to low ambient noise, the emitted sound wave signals and their weak echoes are not easily drowned out by background interference, thus clearly capturing the physical structural changes caused by aging within the battery, such as electrode material cracking, interface delamination, or uneven electrolyte distribution. This approach focuses on assessing battery health from the perspective of physical structural integrity, and its detection sensitivity and accuracy are fully utilized in low-noise environments.

[0045] The second approach is to use a combination of acoustic wave and electrochemical impedance spectroscopy detection.

[0046] This is an enhancement and expansion of the first approach. When environmental conditions permit, two detection methods based on different principles can be performed continuously or in parallel. For example, acoustic detection can be performed first to obtain structural information, followed or simultaneously by electrochemical impedance spectroscopy to obtain electrochemical state information. This combination is not a simple sequential superposition; its core value lies in the integration of multi-dimensional information. By correlating and comprehensively analyzing acoustic characteristic parameters reflecting physical structure with impedance characteristic parameters reflecting electrochemical processes, a more comprehensive and three-dimensional portrait of battery health can be constructed. The blind spots or risks of misjudgment that may exist with a single detection method can be significantly reduced with the corroboration of information from another dimension, thus achieving a more accurate and reliable lifetime assessment than any single approach.

[0047] In short, this step embodies the method's optimization and synergistic approach to resource utilization. Once environmental constraints are removed, not only is the use of acoustic detection, which is more sensitive to noise but possesses unique information dimensions, permitted, but the possibility of multi-technology fusion detection is further unlocked, aiming to pursue higher-order evaluation accuracy and reliability through information complementarity.

[0048] Step 104: Evaluate the performance of the battery based on the detection results of the acoustic wave detection scheme and / or the electrochemical impedance spectroscopy detection scheme.

[0049] This step relies on the raw detection data from one or both sources obtained in step 102 or step 103.

[0050] The "AND / OR" logic here directly corresponds to the detection scheme selected in the previous steps. If only acoustic detection or only electrochemical impedance spectroscopy was performed previously, this step will analyze and evaluate based on a single detection result. If a combination of both was performed previously, this step will provide a comprehensive evaluation based on the detection results from both sources.

[0051] The specific evaluation process involves comparing and analyzing the battery status information reflected in the test results with pre-set evaluation standards or knowledge models. For example, changes in acoustic signal characteristics can indicate whether there is physical damage or degradation in the internal structure; characteristic parameters of electrochemical impedance spectroscopy can indicate whether the electrochemical reaction process is healthy, whether internal resistance has increased, or whether ion diffusion capability has decreased. These judgments can be quantified as a health status score, percentage of lifespan degradation, or simple "normal / warning / fault" levels.

[0052] Regardless of whether a single result or a fusion result is used, and regardless of the specific evaluation algorithm, this step ultimately outputs a valid conclusion about the current performance state of the battery. This conclusion can be used to trigger subsequent maintenance operations, such as issuing warning messages, adjusting battery management strategies, or recording historical state data, thereby completing a full closed loop from data collection to value decision-making.

[0053] This application provides a method, apparatus, electronic device, and storage medium for detecting battery performance. It employs a dynamic dual-mode detection mechanism, using real-time acquisition of in-vehicle environmental noise information as the basis for detection mode selection. When noise interference is strong, the electrochemical impedance spectroscopy (EIS) detection mode, with its strong anti-interference capability, is prioritized. When noise conditions permit, a more comprehensive acoustic detection mode or a combination of both modes can be used. Finally, the battery performance is comprehensively evaluated based on the detection results of the selected mode. Compared with existing technologies that typically use only a single detection mode or a fixed combination of detection methods, this solution effectively addresses the problems of insufficient reliability and weak anti-interference capability in battery performance testing under complex and variable noise environments in vehicles, achieving environmentally adaptive and accurate detection of battery health status in in-vehicle scenarios.

[0054] In one feasible implementation, the battery to be tested is the backup battery of the vehicle telematics terminal TBOX, and the environment is the in-vehicle environment during vehicle operation.

[0055] This implementation plan applies the aforementioned method to a specific and important real-world scenario—monitoring the health status of a vehicle's TBOX backup battery.

[0056] In this scenario, the battery under test specifically refers to the backup battery that provides emergency power to the vehicle telematics unit (TBOX). This battery is a critical component in the vehicle's communication and safety network, and its reliability directly affects whether core safety functions such as emergency calls and automatic accident alarms can be activated normally when the vehicle experiences a main power failure. Therefore, accurate and timely evaluation of its performance has special safety significance.

[0057] It is important to note that "vehicle operation" here includes, but is not limited to, the vehicle being in motion. It refers to all operating conditions of the vehicle after it is powered on, such as: the self-check phase before starting the vehicle, the engine idling, the vehicle in normal driving, the delayed power supply phase after the vehicle is turned off, and the parking monitoring state where the vehicle is stationary but some electrical systems are still working.

[0058] Accordingly, "vehicle environment" here encompasses the physical and electrical environment in which the battery exists under all the aforementioned vehicle operating conditions. The sources of noise interference are therefore diverse: they include continuous vibrations and acoustic noise from the engine, road surface, and wind noise when the vehicle is in motion, as well as intermittent interference generated by equipment such as the air conditioning compressor, cooling fan, and audio system when the vehicle is stationary, and transient electromagnetic interference caused by the vehicle's electrical network during various load switching.

[0059] When the aforementioned method is applied to this specific scenario, its technical value and adaptability become apparent. The complexity of the in-vehicle environment and the unpredictability of interference amplify the necessity of the adaptive mechanism of "dynamically selecting the detection mode based on noise." For example, when the vehicle is stationary and parked quietly, the ambient noise is usually low. In this case, prioritizing or supplementing with acoustic detection can effectively detect early aging of the battery's internal physical structure. However, when the vehicle is in motion and the engine is running at high speed, the mechanical vibration and noise levels increase sharply. At this time, the method can automatically switch to the more interference-resistant electrochemical impedance spectroscopy detection mode to ensure the continuity of the monitoring process and the reliability of data acquisition. This scenario-driven intelligent switching is the key to achieving continuous, effective, and in-situ health status management of TBOX backup batteries throughout the real and complex vehicle usage cycle.

[0060] In other words, specifying the object to be tested as the TBOX backup battery and limiting the application scenario to the real-world in-vehicle environment under this broad definition is to emphasize the practical application value and relevance of this method. The performance and reliability of the TBOX backup battery are directly related to the availability of safety functions such as emergency calls. Accurate assessment of its health status must be conducted in a real, complex, and variable vehicle environment, rather than under ideal laboratory conditions. The mechanism in this method that dynamically selects the detection mode based on environmental noise is precisely to address the challenges of variable interference sources and dynamic fluctuations in the signal-to-noise ratio in the in-vehicle environment. This ensures that under various typical and atypical vehicle operating conditions, the most feasible technical path can be selected to obtain effective battery status information, thereby achieving uninterrupted, adaptive status monitoring and lifespan assessment of this critical component.

[0061] In one feasible implementation, as shown in Figure 2, the performance of the battery under test is detected based on an electrochemical impedance spectroscopy detection scheme, including: step 201, generating multiple AC excitation signals of different frequencies.

[0062] In this implementation scheme, the detection process begins with generating an AC excitation signal covering a preset frequency range. This step can be achieved using a programmable signal generator, a direct digital frequency synthesis circuit, or a microcontroller with a digital-to-analog converter. The generated signal is typically a sine wave, with its frequency preset or dynamically adjusted according to the detection requirements. For example, it can start from a starting frequency and increase or decrease in specific steps or sequences to form a frequency sweep sequence. The signal amplitude remains constant and sufficiently small to ensure that the battery measurement falls within the "perturbation" range, without affecting the battery's normal operating state or causing significant electrochemical reaction deviations.

[0063] Step 202: The multiple AC excitation signals of different frequencies are sequentially applied to the battery under test.

[0064] This step involves effectively coupling the generated AC excitation signal into the battery test circuit. Specifically, each frequency of excitation signal is sequentially and individually applied to both ends of the battery under test via a drive circuit or power amplifier. During the application process, it is necessary to ensure the purity and stability of the excitation signal and to ensure that the battery circuit is in a defined, interference-controlled state during the application of the excitation signal, such as the battery being in an open-circuit resting state or in a stable phase of constant current charging / discharging.

[0065] Step 203: Collect the voltage response signal and current response signal of the battery under test under the action of multiple AC excitation signals of different frequencies.

[0066] For each AC excitation signal of a specific frequency applied to the battery under test, the battery's AC response signal to the excitation must be acquired synchronously or quasi-synchronously. This includes, but is not limited to, accurately measuring the voltage response signal across the battery terminals caused by the excitation, and the current response signal flowing through the battery circuit. To achieve accurate measurement, a high-precision differential amplifier is typically used to extract the weak voltage response signal, and a precision current sensing element (such as a sampling resistor or current transformer) is used to measure the current response signal. The acquisition process needs to be synchronized with the AC excitation signal of the corresponding frequency to ensure accurate capture of the amplitude and phase information of the response. This step acquires a set of paired voltage and current data for each frequency point.

[0067] Step 204: Based on the voltage response signal and the current response signal, calculate the complex impedance of the battery under test at different frequencies to generate the electrochemical impedance spectrum of the battery under test.

[0068] This step is the core processing step for the measurement data, which aims to convert the acquired voltage and current response signals into impedance information that can characterize the internal state of the battery.

[0069] The "voltage response signal" and "current response signal" here refer to the AC electrical signals actually collected from the two ends and the circuit of the battery under test after it is subjected to an externally applied AC excitation signal. They contain information about the battery's internal state in response to the AC excitation signal.

[0070] Specifically, for each applied AC excitation signal of a specific frequency: the AC excitation signal is a known, artificially injected "cause"—for example, a sinusoidal current signal of a specific frequency and amplitude.

[0071] The voltage response signal is the "result" of the battery being excited by this signal—its waveform frequency is the same as the excitation signal, but its magnitude (amplitude) and the start time (phase) of the waveform will change due to the internal impedance characteristics of the battery.

[0072] The current response signal also contains information about the battery and circuit status, and its precise magnitude and waveform start time also need to be acquired synchronously and accurately.

[0073] The calculation process involves analyzing the change in the "effect" (response signal) relative to the "cause" (excitation signal). By comparing the magnitudes of the voltage response signal and the current response signal, the impedance at that frequency can be obtained. By determining the difference between the start times of the voltage response signal and the current response signal waveforms, the phase angle of the impedance can be obtained. This physical quantity, determined by both the impedance magnitude and the phase angle, is the complex impedance of the battery under test at that frequency. It describes the battery's resistance to alternating current at that frequency and includes both energy dissipation and storage characteristics.

[0074] Finally, the complex impedance values ​​calculated at each frequency point are arranged and recorded with frequency as the abscissa, thus forming the electrochemical impedance spectrum of the battery. This spectrum is a comprehensive representation of various physicochemical processes inside the battery (such as ohmic conduction, charge transfer, and mass diffusion) in the frequency domain, and serves as a direct basis for subsequent battery state assessment.

[0075] In an optional implementation, the complex impedance of the battery under test at different frequencies contains characteristic information, including: This implementation provides a specific way to interpret the characteristic information contained in the electrochemical impedance spectroscopy, clarifying the correspondence between impedance information in different frequency bands and specific physicochemical processes inside the battery. This correspondence provides a direct theoretical basis and analytical path for battery performance evaluation based on impedance spectroscopy.

[0076] (1) Ohmic impedance information obtained under the action of a preset high-frequency AC excitation signal; the frequency range of the high-frequency AC excitation signal is 10kHz to 1MHz, and the ohmic impedance is used to characterize the ion and electron conduction capabilities of the electrolyte, electrode material and current collector in the battery under test.

[0077] The frequency range of the high-frequency AC excitation signal here is typically set between 10 kHz and 1 MHz. At this high frequency, the slower-responding electrochemical processes inside the battery (such as interfacial reactions and ion diffusion) cannot respond quickly enough to changes in the excitation signal. Therefore, the measured impedance mainly reflects the series sum of various purely resistive components. This impedance is called ohmic impedance. It is mainly used to characterize aspects of the battery directly related to conductivity, including: the conductivity of ions in the electrolyte, the electronic conductivity of the electrode active material particles themselves, and the electronic conductivity of current collectors (such as aluminum foil and copper foil). Changes in ohmic impedance can directly reflect whether the internal conductive network of the battery is unobstructed and whether the contact resistance has increased.

[0078] (2) Charge transfer impedance information obtained under the action of a preset intermediate frequency AC excitation signal; the frequency range of the intermediate frequency AC excitation signal is 10 Hz to 10 kHz, and the charge transfer impedance is used to characterize the kinetic resistance of the battery under test when an electrochemical reaction occurs at the electrode or electrolyte interface.

[0079] The frequency range of the intermediate-frequency AC excitation signal here is typically set between 10 Hz and 10 kHz. Within this frequency range, the charge transfer reactions (i.e., electrochemical reactions involving the gain and loss of electrons) occurring at the electrode-electrolyte interface can keep up with changes in the excitation signal, becoming the main contributor to the impedance. This portion of the impedance is called charge transfer impedance. It is mainly used to characterize the kinetic resistance present when electrochemical reactions occur at the electrode surface or the electrode-electrolyte interface. The magnitude of the charge transfer impedance directly reflects the ease or difficulty of the interfacial electrochemical reaction; its increase usually indicates a decrease in electrode activity, a deterioration of the reaction interface, or a thickening of the interfacial film.

[0080] (3) Diffusion impedance information obtained under the action of a preset low-frequency AC excitation signal; the frequency range of the low-frequency AC excitation signal is less than 10 Hz, and the diffusion impedance is used to characterize the diffusion and migration ability of active ions inside the battery under test in the bulk phase of the electrode material.

[0081] The frequency range of the low-frequency AC excitation signal here is typically set to less than 10 Hz. At this low frequency, the excitation signal changes slowly, sufficient to excite and measure the diffusion and migration process of active ions (such as lithium ions) within the electrode material particles (bulk phase) or in the porous electrolyte. This impedance is called diffusion impedance (or Warburg impedance). It is mainly used to characterize the diffusion and migration capability of active ions within the battery in the bulk phase of the electrode material. The characteristic changes in diffusion impedance are closely related to the microstructure of the electrode material, the availability of the active material, and the unobstructedness of the ion diffusion path, and are important indicators for evaluating battery capacity decay and power performance degradation.

[0082] This implementation scheme establishes an analytical bridge from raw complex impedance data to specific internal state parameters of the battery by dividing the impedance spectrum into frequency bands and assigning physical meaning. This allows subsequent performance evaluation to be based not on abstract data comparison, but on quantitative diagnosis of key physicochemical processes inside the battery, thereby improving the scientific rigor and accuracy of the evaluation.

[0083] In one optional implementation, evaluating the battery performance includes: extracting at least one feature parameter from the ohmic impedance information, the charge transfer impedance information, and the diffusion impedance information to form an impedance feature set; inputting the impedance feature set into a preset battery evaluation model; obtaining the evaluation result output by the battery evaluation model; the evaluation result includes at least one of the following: the health status score, aging stage, or remaining life prediction value of the battery under test.

[0084] This implementation scheme uses ohmic impedance, charge transfer impedance, and diffusion impedance—three types of information representing different physical processes—as the basic input for evaluation. First, parameters that effectively reflect changes in battery state are extracted from this information, such as impedance values ​​at specific frequencies, characteristic shape parameters of spectral curves, or values ​​of key components in equivalent circuit models, thus forming a multi-dimensional impedance feature set. Subsequently, this impedance feature set is input into a pre-established battery evaluation model. This model encapsulates the quantitative or qualitative relationships between these feature parameters and actual battery performance (such as capacity decay, internal resistance growth, and cycle life). It may be a statistical model trained on a large amount of experimental data, a computational model derived from electrochemical mechanisms, or a combination of both. The model analyzes and calculates the input feature set, ultimately outputting a specific evaluation conclusion that directly points to the actual performance state of the battery. This conclusion can take the form of a quantified health status score (e.g., expressed as a percentage), a determination of the battery's current aging stage (e.g., early, middle, or declining), or a predicted value for its remaining service life (e.g., remaining cycle count or expected service time). This process achieves an automated and quantitative conversion from raw impedance data to performance evaluation results that can be directly used for decision-making.

[0085] In one optional implementation, the performance of the battery under test is detected based on an acoustic detection scheme, including: first, controlling an acoustic emitting device to emit an incident acoustic wave signal of a specific frequency to the battery under test; and controlling an acoustic receiving device to receive an echo acoustic wave signal formed by reflection and / or scattering from the battery under test.

[0086] This step involves signal excitation and acquisition. Under the control of a drive circuit, the acoustic wave emitting device (usually a piezoelectric transducer) emits a beam of incident acoustic wave signals with a specific frequency and waveform towards the battery under test. After entering the battery, this acoustic wave signal is reflected at different medium interfaces (such as between electrode material particles, between the electrode and electrolyte, and between the current collector and coating), and may also be scattered due to internal inhomogeneities (such as cracks and pores). The acoustic wave receiving device (which can be the same transducer or a separate sensor) is responsible for capturing these echo acoustic wave signals carrying information about the internal structure.

[0087] Next, the incident acoustic wave signal and the echo acoustic wave signal are analyzed to extract at least one feature parameter related to the internal physical structure of the battery under test; the analysis includes time domain analysis, frequency domain analysis, and / or phase analysis.

[0088] This step is the core of signal processing and feature extraction. By comparing the original incident signal and the received echo signal, changes in the sound wave during propagation can be analyzed. The analysis method is not limited to a single type: time-domain analysis focuses on the time delay of the echo signal, changes in pulse width, and the degree of waveform attenuation. For example, the delay in echo arrival time may reflect changes in sound speed, which is related to material density; waveform broadening may be related to internal scattering (such as cracks).

[0089] Frequency domain analysis: By converting a time-domain signal to the frequency domain, the spectral changes of the echo signal can be observed, such as the attenuation of specific frequency components, the shift of the center frequency, or the appearance of new frequency components. This can reveal information related to the microstructure of the material, such as porosity and interface properties.

[0090] Phase analysis: compares the phase difference between the incident wave and the echo, and is very sensitive to minute changes in the interface or the elastic modulus of the material.

[0091] Through one or more of the above analyses, one or more characteristic parameters can be extracted, such as echo amplitude attenuation rate, propagation time, center frequency shift, spectral energy distribution, and phase change. Changes in these parameters are closely related to alterations in the internal physical structure of the battery.

[0092] Finally, based on the at least one characteristic parameter, the internal structural state of the battery under test is evaluated; the internal structural state includes the density and cracks of the electrode material, the integrity of the electrode-electrolyte interface, and / or the uniformity of electrolyte distribution in the electrode pores.

[0093] The extracted feature parameters are correlated with the known internal structural states of the battery to assess its condition. Specifically, the assessed internal structural states may include: the density and cracks of the electrode materials: a decrease in material density or the appearance of microcracks usually leads to a slowdown in the propagation speed of sound waves, increased attenuation, and may generate additional reflection or scattering signals in the time domain waveform.

[0094] Integrity of the electrode-electrolyte interface: Problems such as interface delamination, excessive growth or peeling of the solid electrolyte interface film can change the acoustic impedance of the interface, thereby affecting the amplitude and phase characteristics of the reflected wave.

[0095] Uniformity of electrolyte distribution in electrode pores: Insufficient or uneven electrolyte wetting will change the effective acoustic characteristics of porous electrodes and may show characteristic changes in the spectrum.

[0096] By comprehensively analyzing these characteristic parameters, the health status of the battery's internal structure can be qualitatively or semi-quantitatively assessed. For example, it can be determined whether there is obvious physical damage or whether aging has affected the structural integrity, providing key physical dimension information for the overall performance evaluation of the battery.

[0097] In an optional implementation, the method further includes generating and outputting an alarm signal if the evaluation result indicates that the performance of the battery under test is lower than a preset warning threshold.

[0098] An early warning mechanism based on the assessment results has been added here. The preset early warning threshold is a pre-defined performance boundary, which can be set based on battery safety requirements, functional assurance requirements, or historical statistical data. For example, when the assessed health status score is lower than a certain threshold, or the predicted remaining lifespan is shorter than the minimum requirement, it is determined that the battery performance has entered a range requiring attention or vigilance. At this time, this embodiment of the application will trigger and generate an alarm signal. This signal can be output in various forms, such as: locally through indicator light flashing, display pop-up windows, or sound prompts; or through a network communication module, sending information containing specific assessment results and alarm levels to a remote monitoring center or user terminal (such as a mobile APP). This step realizes the transformation from implicit assessment to explicit early warning, which can promptly remind relevant personnel or systems to pay attention to the battery status, buying time for preventive maintenance or safety intervention.

[0099] And / or, based on the evaluation results, adjust the charging and discharging strategy of the battery under test.

[0100] This embodies the concept of state-based adaptive management. Traditional battery charging and discharging strategies are often fixed, while this implementation allows for dynamic optimization of the battery's operating mode based on real-time performance status assessments. Specifically, assessment results (such as current health status, aging stage, and internal resistance growth) are used as the basis for adjusting the charging and discharging strategy. For example, for batteries assessed as early-stage aging, their maximum charging current or cutoff charging voltage can be appropriately reduced to slow down the aging process; for batteries with significantly increased internal resistance, pre-compensation or power allocation adjustments can be made in advance when performing high-power discharge tasks (such as TBOX emergency calls) to ensure voltage stability and functional reliability; even combined with remaining life prediction, a more moderate usage strategy can be planned to extend its overall service life. Through this adjustment, the usage pattern of the battery under test can be matched with its actual health status, thereby extending the effective lifespan of the battery while ensuring functionality, and improving the overall economy and reliability of the system.

[0101] Based on the same technical concept, this application embodiment also provides a device for detecting battery performance, as shown in FIG3. The device includes: a noise acquisition module 301, used to acquire noise information of the environment in which the battery to be tested is located.

[0102] The first detection module 302 is used to detect the performance of the battery under test based on an electrochemical impedance spectroscopy detection scheme when the noise information indicates that the noise interference of the environment is greater than a preset threshold; the electrochemical impedance spectroscopy detection scheme is used to detect the electrochemical reaction of the battery under the action of AC signals at different frequencies.

[0103] The second detection module 303 is used to detect the performance of the battery under test based on an acoustic detection scheme or a combination of the acoustic detection scheme and the electrochemical impedance spectroscopy detection scheme, otherwise; the acoustic detection scheme is used to determine the internal structure of the battery cell based on the reflection of acoustic signals by the battery under test.

[0104] Evaluation module 304 is used to evaluate the performance of the battery based on the detection results of the acoustic detection scheme and / or the electrochemical impedance spectroscopy detection scheme.

[0105] In one feasible implementation, the battery to be tested is the backup battery of the vehicle telematics terminal TBOX, and the environment is the in-vehicle environment during vehicle operation.

[0106] In one feasible implementation, the first detection module is used to detect the performance of the battery under test based on an electrochemical impedance spectroscopy detection scheme, and is used to generate multiple AC excitation signals of different frequencies.

[0107] The multiple AC excitation signals of different frequencies are sequentially applied to the battery under test.

[0108] The voltage response signal and current response signal of the battery under test are collected under the action of multiple AC excitation signals of different frequencies.

[0109] Based on the voltage response signal and the current response signal, the complex impedance of the battery under test at different frequencies is calculated to generate the electrochemical impedance spectrum of the battery under test.

[0110] In one feasible implementation, the complex impedance of the battery under test at different frequencies contains characteristic information including: ohmic impedance information obtained under the action of a preset high-frequency AC excitation signal; the frequency range of the high-frequency AC excitation signal is 10kHz to 1MHz, and the ohmic impedance is used to characterize the ion and electron conduction capabilities of the electrolyte, electrode materials and current collector in the battery under test.

[0111] The charge transfer impedance information is obtained under the action of a preset intermediate frequency AC excitation signal; the frequency range of the intermediate frequency AC excitation signal is 10Hz to 10kHz, and the charge transfer impedance is used to characterize the kinetic resistance of the battery under test when an electrochemical reaction occurs at the electrode or electrolyte interface.

[0112] The diffusion impedance information is obtained under the action of a preset low-frequency AC excitation signal; the frequency range of the low-frequency AC excitation signal is less than 10Hz, and the diffusion impedance is used to characterize the diffusion and migration ability of active ions inside the battery under test in the bulk phase of the electrode material.

[0113] In one feasible implementation, the evaluation module is used to evaluate the performance of the battery by: extracting at least one feature parameter from the ohmic impedance information, the charge transfer impedance information, and the diffusion impedance information to form an impedance feature set.

[0114] The impedance feature set is input into a preset battery evaluation model.

[0115] Obtain the evaluation results output by the battery evaluation model; the evaluation results include at least one of the health status score, aging stage, or remaining life prediction value of the battery under test.

[0116] In one feasible implementation, the second detection module is used to detect the performance of the battery under test based on an acoustic detection scheme, and is used to: control the acoustic emitting device to emit an incident acoustic wave signal of a specific frequency to the battery under test.

[0117] The control device receives the echo acoustic signal formed by the reflection and / or scattering of the battery under test.

[0118] The incident acoustic wave signal and the echo acoustic wave signal are analyzed to extract at least one feature parameter related to the internal physical structure of the battery under test; the analysis includes time domain analysis, frequency domain analysis, and / or phase analysis.

[0119] Based on the at least one characteristic parameter, the internal structural state of the battery under test is evaluated; the internal structural state includes the density and cracks of the electrode material, the integrity of the electrode-electrolyte interface, and / or the uniformity of electrolyte distribution in the electrode pores.

[0120] In one feasible implementation, the device further includes an alarm module for generating and outputting an alarm signal if the evaluation result indicates that the performance of the battery under test is lower than a preset warning threshold.

[0121] And / or, a strategy adjustment module is used to adjust the charging and discharging strategy of the battery under test based on the evaluation results.

[0122] Figure 4 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, including: a processor 401, a storage medium 402 and a bus 403. The storage medium 402 stores machine-readable instructions that can be executed by the processor 401. When the electronic device runs the method for detecting battery performance as in the embodiment, the processor 401 communicates with the storage medium 402 through the bus 403, and the processor 401 executes the machine-readable instructions to perform the steps as in the embodiment.

[0123] In this embodiment, the storage medium 402 may also execute other machine-readable instructions to perform other methods as described in the embodiment. For details on the specific execution steps and principles, please refer to the description of the embodiment, which will not be repeated here.

[0124] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to perform the steps as described in the embodiments.

[0125] In this embodiment, the computer program, when run by the processor, can also execute other machine-readable instructions to perform other methods as described in the embodiments. For details on the specific execution steps and principles, please refer to the description of the embodiments, which will not be repeated here.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0127] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0129] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0130] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for testing battery performance, characterized in that, The method includes: acquiring noise information of the environment in which the battery under test is located; when the noise information indicates that the noise interference of the environment is greater than a preset threshold, detecting the performance of the battery under test based on an electrochemical impedance spectroscopy (EIS) detection scheme; the EIS detection scheme is used to detect the electrochemical reaction of the battery under the action of AC signals at different frequencies; otherwise, detecting the performance of the battery under test based on an acoustic detection scheme, or based on a combination of the acoustic detection scheme and the EIS detection scheme; the acoustic detection scheme is used to determine the internal structure of the battery cell based on the reflection of acoustic signals by the battery under test; and evaluating the performance of the battery based on the detection results of the acoustic detection scheme and / or the EIS detection scheme.

2. The method according to claim 1, characterized in that, The battery to be tested is the backup battery of the vehicle telematics terminal TBOX, and the environment is the in-vehicle environment when the vehicle is running.

3. The method according to claim 1, characterized in that, The method for detecting the performance of the battery under test based on electrochemical impedance spectroscopy includes: generating multiple AC excitation signals of different frequencies; sequentially applying the multiple AC excitation signals of different frequencies to the battery under test; acquiring the voltage response signal and current response signal of the battery under test under the action of the multiple AC excitation signals of different frequencies; and calculating the complex impedance of the battery under test at different frequencies based on the voltage response signal and the current response signal to generate the electrochemical impedance spectrum of the battery under test.

4. The method according to claim 3, characterized in that, The complex impedance of the battery under test at different frequencies includes the following characteristic information: ohmic impedance information obtained under a preset high-frequency AC excitation signal; the frequency range of the high-frequency AC excitation signal is 10 kHz to 1 MHz, and the ohmic impedance is used to characterize the ion and electron conduction capabilities of the electrolyte, electrode material, and current collector in the battery under test; charge transfer impedance information obtained under a preset mid-frequency AC excitation signal; the frequency range of the mid-frequency AC excitation signal is 10 Hz to 10 kHz, and the charge transfer impedance is used to characterize the kinetic resistance of the battery under test during electrochemical reactions at the electrode or electrolyte interface; and diffusion impedance information obtained under a preset low-frequency AC excitation signal; the frequency range of the low-frequency AC excitation signal is less than 10 Hz, and the diffusion impedance is used to characterize the diffusion and migration capabilities of active ions inside the battery under test in the bulk phase of the electrode material.

5. The method according to claim 4, characterized in that, Evaluating the performance of the battery includes: extracting at least one feature parameter from the ohmic impedance information, the charge transfer impedance information, and the diffusion impedance information to form an impedance feature set; inputting the impedance feature set into a preset battery evaluation model; obtaining the evaluation result output by the battery evaluation model; the evaluation result includes at least one of the following: the health status score, aging stage, or remaining life prediction value of the battery under test.

6. The method according to claim 1, characterized in that, The performance testing of the battery under test based on an acoustic wave detection scheme includes: controlling an acoustic wave emitting device to emit an incident acoustic wave signal of a specific frequency to the battery under test; controlling an acoustic wave receiving device to receive an echo acoustic wave signal formed by reflection and / or scattering from the battery under test; analyzing the incident acoustic wave signal and the echo acoustic wave signal to extract at least one characteristic parameter related to the internal physical structure of the battery under test; the analysis includes time domain analysis, frequency domain analysis, and / or phase analysis; and evaluating the internal structural state of the battery under test based on the at least one characteristic parameter; the internal structural state includes the density and cracks of the electrode material, the integrity of the electrode-electrolyte interface, and / or the uniformity of electrolyte distribution in the electrode pores.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: generating and outputting an alarm signal if the evaluation result indicates that the performance of the battery under test is lower than a preset warning threshold; and / or adjusting the charging and discharging strategy of the battery under test according to the evaluation result.

8. A device for detecting battery performance, characterized in that, The device includes: a noise acquisition module for acquiring noise information of the environment in which the battery under test is located; a first detection module for detecting the performance of the battery under test based on an electrochemical impedance spectroscopy (EIS) detection scheme when the noise information indicates that the noise interference in the environment is greater than a preset threshold; the EIS detection scheme is used to detect the electrochemical reaction of the battery under the action of AC signals at different frequencies; a second detection module for otherwise detecting the performance of the battery under test based on an acoustic detection scheme, or based on a combination of the acoustic detection scheme and the EIS detection scheme; the acoustic detection scheme is used to determine the internal structure of the battery cell based on the reflection of acoustic signals by the battery under test; and an evaluation module for evaluating the performance of the battery based on the detection results of the acoustic detection scheme and / or the EIS detection scheme.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for detecting battery performance as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for detecting battery performance as described in any one of claims 1 to 7.

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