Lithium battery performance test system and method

Abstract

The invention discloses a lithium battery performance test system and method, and belongs to the technical field of battery testing, and the method comprises the steps: obtaining the rated capacity and rated voltage of a target lithium battery, and the minimum pulse working current and pulse period parameters of a tested device; constructing a micro-power consumption pulse load model based on the parameters, and generating a target load control instruction sequence; controlling load application and voltage acquisition operation according to the instruction; in each pulse period, voltage drop amplitude and recovery time are collected in real time; calculating a dynamic internal resistance change characteristic value in a period according to the collected data; if the characteristic value exceeds a preset threshold value in a plurality of continuous periods, judging that the battery has a transient response decline risk under the micro-load; according to the invention, the dynamic response behavior of the battery in the operation scene of the low-power-consumption equipment can be truly restored, high-sensitivity and non-destructive performance test and early degradation identification are realized, and the method is suitable for the application fields with high requirements on power supply stability, such as intelligent wearing and Internet of Things terminals.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and specifically to a lithium battery performance testing system and method. Background Technology

[0002] As smart wearable devices, drones, and micro medical devices increasingly rely on micro lithium batteries, higher demands are being placed on lithium battery performance, especially the discharge stability testing of micro lithium batteries under extreme conditions. However, existing technologies for testing the performance of such batteries typically employ constant current discharge or stepped discharge methods, which struggle to accurately simulate the intermittent ultra-low power pulse load conditions in micro devices. This results in test data deviating from actual application scenarios, making it impossible to effectively assess the battery's transient response and recovery characteristics during actual operation.

[0003] Furthermore, traditional testing systems commonly employ analog loads or resistor network switching to control the load waveform, which suffers from problems such as large loading delays and poor sampling synchronization. During testing, when the battery is in an intermittent power supply state below the milliwatt level, if the load control and voltage acquisition are not synchronized, key pulse response characteristics will be lost, thus affecting the accuracy of the overall verification results. Therefore, there is an urgent need for a testing method that can accurately test lithium battery performance under low-power pulse conditions to meet the stringent requirements of battery performance verification for next-generation micro-devices. Summary of the Invention

[0004] The purpose of this invention is to provide a lithium battery performance testing system and method to address the shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a lithium battery performance testing method, comprising: Obtain the rated capacity and rated voltage of the target lithium battery, as well as the minimum pulse operating current and pulse period parameters of the device under test; Based on the minimum pulse operating current and pulse period, a low-power pulse load model is constructed, and a corresponding target load control command sequence is generated; Based on the generated target load control command sequence, control the load activation and voltage acquisition operations; Under the control of the target load control command, a low-power pulse load is applied to the target lithium battery, and the corresponding battery voltage drop amplitude and recovery time are collected in real time during each pulse cycle. Based on the collected voltage drop and recovery time data, the characteristic value of the dynamic internal resistance change of the battery in each cycle is calculated. If the dynamic internal resistance change characteristic value exceeds the preset critical threshold in multiple consecutive cycles, the target lithium battery is determined to have a risk of transient response degradation under micro-load.

[0006] Preferably, the step of real-time acquisition of the corresponding battery voltage drop amplitude and recovery time within each pulse cycle includes: When each target load control command is executed, the first voltage sampling point is set before the load is turned on to obtain the reference no-load voltage value. The second voltage sampling point is set to a preset delay time after the load is turned on, and the instantaneous voltage value is collected to calculate the voltage drop. Multiple recovery sampling points are set after the load is turned off, and the voltage recovery curve is continuously collected until the voltage change rate is lower than the set differential threshold. Based on the voltage drop magnitude and voltage recovery curve, the recovery time is determined as the time period required for the voltage to recover from the lowest point to the set percentage reference.

[0007] Preferably, the step of calculating the characteristic value of the dynamic internal resistance change of the battery in each cycle based on the collected voltage drop amplitude and recovery time data includes: Within each pulse cycle, based on the collected voltage drop amplitude and the corresponding low-power pulse load current value, the initial value of the instantaneous equivalent internal resistance of the cycle is calculated. The initial value of the instantaneous equivalent internal resistance is defined as the ratio of the voltage drop amplitude to the pulse load current. Based on the determined recovery time, the initial value of the instantaneous equivalent internal resistance is corrected by time weighting to obtain a periodically corrected internal resistance value that reflects the influence of the voltage recovery speed. The internal resistance change sequence is constructed by arranging the periodically corrected internal resistance values ​​within multiple consecutive pulse cycles in chronological order, and the rate of change of internal resistance between adjacent cycles is calculated. The characteristic value of dynamic internal resistance change is determined based on the internal resistance change rate.

[0008] Preferably, the steps for obtaining the minimum pulse operating current and pulse period parameters of the device under test include: sampling the current during the instantaneous wake-up process of the device under test when it is in the lowest power consumption operating state, and determining the minimum pulse operating current and the corresponding pulse period parameters based on the sampling results.

[0009] Preferably, the step of constructing a low-power pulse load model includes: constructing a periodic rectangular pulse current waveform using a time-domain discrete function based on the minimum pulse operating current, pulse period, and device wake-up duration, wherein the pulse amplitude corresponds to the minimum pulse operating current, and the pulse period corresponds to the pulse period parameter.

[0010] Preferably, the step of generating the target load control command sequence includes: based on the low-power pulse load model, encapsulating the pulse start time, pulse end time, load current amplitude, and voltage sampling time into a control command sequence arranged in chronological order.

[0011] The present invention also provides a lithium battery performance testing system, comprising: Parameter acquisition module: Acquires the rated capacity and rated voltage of the target lithium battery, as well as the minimum pulse operating current and pulse period parameters of the device under test; Instruction generation module: Based on the minimum pulse operating current and pulse period, construct a low-power pulse load model and generate a corresponding target load control instruction sequence; Command execution control module: Based on the generated target load control command sequence, it controls the load start-up and voltage acquisition operations; Synchronous sampling module: Under the control of the target load control command, a low-power pulse load is applied to the target lithium battery, and the corresponding battery voltage drop amplitude and recovery time are collected in real time during each pulse cycle; Dynamic internal resistance calculation module: Based on the collected voltage drop amplitude and recovery time data, calculate the characteristic value of the dynamic internal resistance change of the battery in each cycle; Degradation Detection Module: If the characteristic value of dynamic internal resistance change exceeds the preset critical threshold in multiple consecutive cycles, the target lithium battery is determined to have a risk of transient response degradation under micro-load.

[0012] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention constructs a load control command sequence based on a low-power pulse model and achieves high-precision load application and synchronous voltage acquisition. It can completely reproduce the battery's operating state under microampere-level short-cycle load conditions, significantly improving the testing accuracy of lithium battery dynamic response capabilities. Compared with traditional constant current or simplified waveform testing methods, this invention not only achieves finer-grained capture of voltage drop and recovery processes but also adaptively adjusts test parameters for different battery models and application scenarios, demonstrating good versatility and scalability.

[0013] 2. This invention constructs a periodic dynamic internal resistance variation characteristic model and introduces an anomaly identification threshold and sliding window judgment mechanism to achieve early identification of lithium battery performance degradation trends under micro-load conditions. It is particularly suitable for power health assessment in extremely low-power applications such as smart wearables and sensors. This method possesses the advantages of non-destructive, quantifiable, and automated testing, providing important technical support for battery quality grading, lifespan prediction, and product reliability management. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0015] Figure 1 This is a flowchart of the method of the present invention.

[0016] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown in this embodiment, a lithium battery performance testing method includes: Obtain the rated capacity and rated voltage of the target lithium battery, as well as the minimum pulse operating current and pulse period parameters of the device under test.

[0019] In this embodiment of the invention, the basic electrical performance parameters of the target lithium battery and the typical load characteristics of the device under test used with it under actual operating conditions are first obtained. Specifically, this includes the following: Rated capacity of the target lithium battery (unit: mAh): This describes the total charge output of the battery under standard discharge conditions. This parameter is usually provided by the battery manufacturer in the specifications, or it can be measured using a conventional constant current discharge method. This capacity parameter is used in subsequent load modeling to determine whether the battery has the ability to continuously supply power, and serves as one of the important bases for battery health analysis.

[0020] Rated voltage of the target lithium battery (unit: V): This reflects the standard output voltage of the battery under normal operating conditions, typically 3.7V (for common single-cell lithium-ion batteries). This voltage value provides a reference for low-power pulse load designs, ensuring that the applied load current is compatible with the voltage and avoiding over-discharge or false triggering of protection mechanisms.

[0021] Minimum pulse operating current of the device under test (unit: μA ~ mA): This is the current value required for the device to be instantaneously woken up in its lowest operating state or extreme energy-saving mode. This current typically has pulsed and intermittent characteristics. Because the duration of this type of current is extremely short, it is difficult to capture using conventional average current testing methods and must be acquired through a high-bandwidth current probe or synchronous load simulation. This parameter determines the minimum amplitude resolution of the output load required by the test system.

[0022] The pulse period parameter of the device under test (unit: ms ~ s): This refers to the typical wake-up period or communication interval of the device in continuous low-power operation. For example, the wake-up period of a certain type of Bluetooth Low Energy sensor may be 500ms, with each wake-up lasting less than 10ms. The purpose of obtaining this period parameter is to construct a load waveform that matches the actual application scenario for high-fidelity simulation of the device's operating state.

[0023] The accurate extraction and recording of the aforementioned information not only provides the necessary basic parameter support for the subsequent construction of a low-power pulse load model, but also ensures the consistency between the established test environment and the actual application scenario, avoiding test result distortion due to load setting deviations. Especially under microampere-level intermittent current conditions, traditional testing methods often neglect the restoration of pulse details. This invention, by introducing this parameter acquisition step, effectively solves the key problem of the disconnect between the current test scenario and actual application in micro-battery testing.

[0024] Based on the minimum pulse operating current and pulse period, a low-power pulse load model is constructed, and a corresponding target load control command sequence is generated.

[0025] After extracting the rated capacity and voltage parameters of the target lithium battery, as well as the minimum pulse operating current and pulse period of the device under test, the present invention further performs the following steps to realize the dynamic load behavior of the battery under realistic simulation of low power consumption application scenarios.

[0026] The obtained minimum pulse operating current and pulse period parameters are input into the pulse load modeling module, which constructs the target pulse current waveform based on a time-domain discrete function.

[0027] In this step, the minimum pulse current value (in microamps to milliamps) and its operating pulse period (in milliseconds to seconds) of the device under test in low-power operation are first imported as input parameters to build the model. The pulse current waveform is established using a standard time-domain discrete modeling method, specifically in the form of a periodic rectangular pulse function, where: The pulse amplitude is set to the minimum pulse operating current obtained; The pulse width is set to the device's typical wake-up duration; The pulse period is set as the device's wake-up cycle.

[0028] This pulse function outputs current only within a preset short time window in each cycle, and remains in a zero-current state for the rest of the time, forming a periodic and intermittent load pattern. Expressed through a time-domain function, it can be represented as follows: at the beginning of each full cycle, a set current amplitude is output, which remains at zero for several sampling intervals, completing the waveform description of one cycle.

[0029] The target pulse current waveform is amplitude modulated and period corrected to meet the stable operation requirements within the target lithium battery voltage range.

[0030] Because different types of lithium batteries have different voltage-current response characteristics, directly applying the theoretically calculated pulse current may cause the voltage to drop beyond the allowable threshold, thereby triggering the protection circuit. Therefore, in this step, the initial pulse waveform is corrected through a voltage feedback mechanism.

[0031] The specific implementation method is as follows: In the initial stage of the experiment, a trial waveform is applied, and the voltage change at the output terminal of the lithium battery is monitored in real time. If the voltage drop exceeds the minimum operating voltage (such as 3.0V) defined in the target battery's manufacturer's specifications, the pulse current amplitude is automatically reduced or the pulse period interval is extended to give the battery sufficient recovery time.

[0032] The correction method employs a closed-loop gain control strategy, setting a maximum voltage drop limit based on the target voltage threshold, for example, setting the maximum voltage drop to no more than 0.3V. After each pulse loading, the actual voltage change is compared with the voltage drop threshold. If the limit is exceeded, the pulse current amplitude is reduced proportionally or the period interval is adjusted until the waveform output meets the stability requirements.

[0033] A load control command sequence is generated based on the corrected pulse waveform. The command sequence includes pulse start and end times, current amplitude, and sampling time information.

[0034] After pulse waveform correction, the system generates a corresponding sequence of digital control instructions based on the discrete waveform data. Each instruction explicitly specifies: Pulse start time (relative to reference clock); Pulse end time (determines the load switching time); The specific amplitude of the applied current; The synchronous voltage sampling point during load application.

[0035] The instruction sequence is encapsulated in a structured data format and arranged chronologically to ensure the predictability and repeatability of the subsequent loading process. Simultaneously, the sampling interval must be less than one-fifth of the pulse width to ensure that voltage changes during current loading are fully captured, resulting in a high-fidelity response curve.

[0036] The load control command sequence is imported into the real-time control module for subsequent synchronous load loading and voltage sampling operations.

[0037] After the instruction sequence is generated, it is sent to a control unit equipped with a high-precision clock source and parallel execution capabilities for real-time scheduling and control. The control unit executes the instructions one by one according to the preset instruction sequence: The electronic load is turned on at a specified time point to apply current to the target lithium battery; The sampling circuit is started synchronously to collect voltage data at multiple points before and after the load is switched on and off. Record all timing, current, and voltage data for subsequent calculations and analysis.

[0038] By employing the above methods, we can ensure that the load application and voltage acquisition processes are aligned within the nanosecond error range, thereby improving the time consistency of the data and the accuracy of the test results.

[0039] Based on the generated target load control command sequence, control the load activation and voltage acquisition operations.

[0040] After generating and importing the target load control command sequence, the invention enters the crucial execution phase, namely, precisely controlling the load activation and voltage acquisition operations according to the command sequence to achieve dynamic performance testing under low-power pulse conditions. This step includes: Initiating the high-precision timing control process: The control unit initializes the synchronous timing process based on an internally configured high-precision oscillation clock (accuracy better than ±10ppm). The execution time of all instructions is based on this reference clock, achieving cross-instruction timing consistency and precise alignment of event triggering, avoiding load waveform drift and sampling offset problems caused by clock offset in traditional control systems.

[0041] The load switch action is scheduled according to the target load control command sequence: the control unit analyzes the pulse start and end times and current amplitude parameters in the target load control command sequence one by one, and controls the precision programmable electronic load circuit to conduct at the set start time point, applying a current load of the set amplitude to the target lithium battery. The application process adopts high-speed current feedback control technology to ensure that the current output is stable within ±2% of the target value.

[0042] When the set pulse end time is reached, the control unit immediately controls the load to shut off, terminates the current loading, restores the battery to its natural no-load state, and ensures the consistency between the test conditions and the pulse model.

[0043] Synchronous voltage sampling is performed during load switching: In order to obtain the response characteristics of the battery during pulse current loading, the present invention presets multiple voltage sampling points in each control command, respectively covering: the baseline voltage before the load is turned on; the voltage drop at the beginning of the load application; and the voltage recovery process after the pulse application ends.

[0044] The control unit activates a high-speed analog-to-digital converter (sampling rate no less than 1MS / s) to collect real-time voltage data from the lithium battery output, based on the sampling time points defined in the instructions. All voltage sampling data, along with corresponding timestamps, are stored for subsequent dynamic analysis and calculations.

[0045] Establishing a three-dimensional current-voltage-time response dataset: Through the above process, a set of corresponding current application data, voltage response data, and precise time information are generated within each pulse cycle, forming a complete three-dimensional test dataset. This dataset reflects the dynamic operating behavior of lithium batteries under low-power pulse loads and serves as the basis for subsequent dynamic internal resistance calculations, voltage recovery assessments, and performance degradation judgments.

[0046] The precision and execution timing of this step directly affect the accuracy and reproducibility of the test results. By adopting a command-driven precision control method, this invention significantly improves the synchronous control capability of the test system under low load and short cycle conditions, providing reliable data support for evaluating the performance of lithium batteries under intermittent load conditions.

[0047] Under the control of the target load control command, a low-power pulse load is applied to the target lithium battery, and the corresponding battery voltage drop amplitude and recovery time are collected in real time during each pulse cycle.

[0048] Under the control of the target load control command, this invention applies a low-power pulse load to the target lithium battery and executes a customized sampling process within each pulse cycle to collect and record the dynamic changes in the battery's response voltage in real time, thereby obtaining key characteristic parameters reflecting its operating performance: voltage drop amplitude and recovery time. This process includes the following specific steps: The first voltage sampling point is set before the load is turned on, in order to obtain the reference no-load voltage value.

[0049] Before the start of each pulse cycle, the control unit first schedules the voltage sampler to perform a reference sampling operation when the load is not yet turned on. The time interval between this point and the load being turned on does not exceed 10 microseconds, ensuring that the collected no-load voltage value is not affected by any current interference and is representative.

[0050] The acquired no-load voltage value is recorded as the reference voltage for that cycle, used for subsequent calculations of voltage drop and recovery. Throughout the test, the no-load voltage is independently acquired for each cycle to avoid affecting test accuracy due to reference voltage shifts caused by gradual battery discharge.

[0051] The second voltage sampling point is set to a preset delay time after the load is turned on, and the instantaneous voltage value is collected to calculate the voltage drop.

[0052] After the target load is turned on according to the control command, the battery port voltage will drop rapidly due to the instantaneous internal resistance. This invention avoids instantaneous spike interference and accurately obtains the stable load voltage by setting a fixed delay time, such as 50 microseconds, after the load is turned on and then delaying the voltage sampler by this time.

[0053] The difference between the instantaneous voltage value under load and the no-load voltage value recorded in the first step is calculated, and the result is the voltage drop amplitude within that cycle. The voltage drop amplitude reflects the battery's instantaneous load-bearing capacity under pulsed load impact and is closely related to its internal electrochemical response speed.

[0054] Multiple recovery sampling points are set after the load is turned off, and the voltage recovery curve is continuously collected until the voltage change rate is lower than the set differential threshold.

[0055] After the pulsed load is applied and then turned off, the battery port voltage will undergo a gradual recovery from a low point to a steady state. To capture the dynamic characteristics of this process, the control unit initiates continuous voltage sampling after the load is turned off, with the sampling interval set to a fixed value, such as 20 microseconds, until the slope of the voltage recovery curve drops below a set differential threshold.

[0056] This differential threshold is defined as the lower limit of the voltage change rate per unit time, for example, 10 millivolts per millisecond, as the termination condition for determining that the voltage tends to stabilize. The sampling process forms a complete voltage recovery curve, which is used for subsequent evaluation of the battery recovery speed and stability.

[0057] Based on the voltage drop magnitude and voltage recovery curve, the recovery time is determined as the time period required for the voltage to recover from the lowest point to the set percentage reference.

[0058] In the voltage recovery curve obtained from sampling, the lowest voltage point within that cycle is first identified. Then, based on the no-load reference voltage value recorded in the first step, the voltage recovery target value is set. For example, the recovery reference is set to recover to 95% of the no-load voltage, which is the reference voltage multiplied by 0.95.

[0059] Recovery time is defined as the time interval during which the voltage recovers from its lowest point to the baseline recovery value. This time is obtained by calculating the difference between sampling time points and serves as an indicator of the battery's self-recovery capability after short-cycle loads. A shorter recovery time indicates stronger transient power supply stability, making it suitable for intermittent, load-intensive applications.

[0060] Based on the collected voltage drop and recovery time data, the characteristic value of the dynamic internal resistance change of the battery in each cycle is calculated.

[0061] To further evaluate the periodic dynamic performance changes of the target lithium battery under low-power pulse load conditions, this invention, based on obtaining the voltage drop amplitude and recovery time within each pulse cycle, performs the following calculation steps to extract the dynamic internal resistance change characteristic value reflecting the changing trend of the battery's response capability: Within each pulse cycle, the initial value of the instantaneous equivalent internal resistance of the cycle is calculated based on the collected voltage drop amplitude and the corresponding low-power pulse load current value.

[0062] First, in each pulse cycle, the voltage drop amplitude within that cycle is extracted, i.e., the difference between the no-load voltage and the instantaneous applied voltage; at the same time, the pulse load current amplitude set in the control command is retrieved as the load current for that cycle.

[0063] Based on Ohm's law, dividing the voltage drop by the pulse load current yields the initial instantaneous equivalent internal resistance for that cycle, which describes the battery's transient voltage response under that specific pulse load. The calculation formula can be described as follows: Initial value of instantaneous equivalent internal resistance = voltage drop amplitude ÷ pulse load current value.

[0064] To ensure calculation accuracy, the voltage drop is measured in volts, the current in amperes, and the internal resistance in ohms.

[0065] Based on the determined recovery time, the initial value of the instantaneous equivalent internal resistance is corrected by time weighting to obtain a periodically corrected internal resistance value that reflects the influence of the voltage recovery speed.

[0066] Considering that relying solely on instantaneous voltage drop cannot fully reflect the battery response quality, this invention further introduces recovery time as a weighting factor to dynamically correct the initial value of instantaneous equivalent internal resistance.

[0067] The specific correction method is as follows: Set the weighting factor to "1 + recovery time × weighting coefficient", where the recovery time is in milliseconds, and the weighting coefficient is an empirically set value (e.g., 0.01), representing the proportion of the recovery time's influence on the internal resistance estimate. Then, multiply the initial instantaneous equivalent internal resistance value by this weighting factor to obtain the periodically corrected internal resistance value. The calculation description is as follows: Periodic correction internal resistance value = instantaneous equivalent internal resistance initial value × (1 + recovery time × weighting coefficient).

[0068] This correction value can comprehensively reflect the battery's recovery rate after being subjected to load, thereby improving the accuracy and robustness of the cycle resistance judgment.

[0069] The internal resistance change sequence is constructed by arranging the periodically corrected internal resistance values ​​over multiple consecutive pulse cycles in chronological order, and the rate of change of internal resistance between adjacent cycles is calculated.

[0070] The periodically corrected internal resistance values ​​corresponding to each pulse cycle are arranged sequentially in time to form an internal resistance variation sequence containing multiple time-series samples. To further analyze the changing trend of the battery's internal resistance, the difference in internal resistance values ​​between each adjacent cycle in this sequence is normalized to obtain the internal resistance change rate.

[0071] Specifically, let the internal resistance value be Rn in the nth period and Rn+1 in the (n+1)th period. Then, the rate of change of internal resistance is defined as: Rate of change of internal resistance = (Rn+1 - Rn) ÷ Rn. This value can be positive or negative. A positive value indicates an increase in internal resistance, and a negative value indicates a decrease in internal resistance. It is a sensitive indicator of battery performance fluctuations under micro-load conditions.

[0072] The characteristic value of dynamic internal resistance change is determined based on the internal resistance change rate.

[0073] To extract the overall performance trend characteristics of the battery, this invention performs statistical processing on the rate of change of internal resistance over multiple cycles, and calculates one or more of the following characteristic parameters as dynamic internal resistance change characteristic values: The average rate of change of internal resistance is used to reflect the overall trend of change; The standard deviation of the rate of change of internal resistance is used to assess stability; The maximum single-cycle internal resistance jump value is used to identify extreme abnormal fluctuations.

[0074] The aforementioned feature values ​​are used as output indicators for subsequent performance evaluation and lifetime prediction. They can be correlated through formula modeling or machine learning models to improve the ability to determine battery reliability in low-power scenarios.

[0075] If the dynamic internal resistance change characteristic value exceeds the preset critical threshold in multiple consecutive cycles, the target lithium battery is determined to have a risk of transient response degradation under micro-load.

[0076] To achieve early identification of performance degradation trends in lithium batteries under low-power pulsed load conditions, this invention, based on the constructed dynamic internal resistance change characteristic value, further performs a response degradation risk assessment step to determine whether the target lithium battery is at risk of short-cycle performance stability degradation. This process includes the following key technical aspects: Set a critical threshold for the characteristic value of dynamic internal resistance change.

[0077] The critical threshold is a boundary condition used to determine abnormal changes in battery state, representing the maximum permissible internal resistance fluctuation range of the target lithium battery within its normal operating range. This threshold can be set in one of the following two ways: Static setting method: Based on the maximum working internal resistance range (e.g., not exceeding 200 milliohms) indicated in the target lithium battery's manufacturer's specifications and historical experimental data, a fixed threshold range is set. For example, if the absolute value of the internal resistance change rate exceeds 0.15, it is considered abnormal. Dynamic learning method: Collect the characteristic values ​​of internal resistance change over multiple cycles under normal use conditions, calculate their statistical mean and standard deviation, and set the threshold as the mean plus twice the standard deviation.

[0078] The critical threshold is configurable, adapting to the accuracy requirements of different battery types and application scenarios.

[0079] Determine whether the characteristic value of dynamic internal resistance change in multiple consecutive cycles exceeds the critical threshold.

[0080] This invention sets up a judgment window, and performs a threshold comparison operation on the dynamic internal resistance change characteristic value of each cycle within multiple consecutive pulse cycles (e.g., 5). If it is detected that within this window, there are 3 or more cycles where the dynamic internal resistance change characteristic value is greater than the set critical threshold, it is considered that the internal resistance fluctuation is frequent and the amplitude exceeds the limit, indicating that the battery's response characteristics tend to deteriorate under short-cycle load stimulation. This judgment method adopts a sliding window mode, and the window content is updated with each acquisition cycle, realizing online and continuous performance degradation detection capability.

[0081] The target lithium battery was determined to have a risk of transient response degradation under micro-load.

[0082] When the judgment result meets the above abnormal conditions, the system outputs the judgment result, believing that the current test sample has "transient response degradation risk under micro-load". This risk is defined as: under the action of low amplitude, short cycle pulse load, the lithium battery port voltage exhibits a continuous and irreversible slowdown behavior, specifically manifested as performance degradation characteristics such as increased internal resistance, prolonged recovery time, and decreased adaptability to load fluctuations.

[0083] The risk assessment results can be used as part of a battery health assessment to guide application scenario adaptation; as a reference for battery replacement or maintenance; and for battery batch consistency screening or early anomaly screening.

[0084] Through this step, the present invention not only achieves high-precision monitoring of lithium battery voltage response capability, but also provides a risk identification mechanism for practical applications, providing important technical support for the power reliability of micro terminal devices during long-term operation.

[0085] Example 2, please refer to Figure 2 As shown in this embodiment, a lithium battery performance testing system includes: Parameter acquisition module: Acquires the rated capacity and rated voltage of the target lithium battery, as well as the minimum pulse operating current and pulse period parameters of the device under test; Instruction generation module: Based on the minimum pulse operating current and pulse period, construct a low-power pulse load model and generate a corresponding target load control instruction sequence; Command execution control module: Based on the generated target load control command sequence, it controls the load start-up and voltage acquisition operations; Synchronous sampling module: Under the control of the target load control command, a low-power pulse load is applied to the target lithium battery, and the corresponding battery voltage drop amplitude and recovery time are collected in real time during each pulse cycle; Dynamic internal resistance calculation module: Based on the collected voltage drop amplitude and recovery time data, calculate the characteristic value of the dynamic internal resistance change of the battery in each cycle; Degradation Detection Module: If the dynamic internal resistance change characteristic value exceeds the preset critical threshold in multiple consecutive cycles, the target lithium battery is determined to have a risk of transient response degradation under micro-load.

[0086] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A method for testing the performance of lithium batteries, characterized in that: include: Obtain the rated capacity and rated voltage of the target lithium battery, as well as the minimum pulse operating current and pulse period parameters of the device under test; Based on the minimum pulse operating current and pulse period, a low-power pulse load model is constructed, and a corresponding target load control command sequence is generated; Based on the generated target load control command sequence, control the load activation and voltage acquisition operations; Under the control of the target load control command, a low-power pulse load is applied to the target lithium battery, and the corresponding battery voltage drop amplitude and recovery time are collected in real time during each pulse cycle. Based on the collected voltage drop and recovery time data, the characteristic value of the dynamic internal resistance change of the battery in each cycle is calculated. If the dynamic internal resistance change characteristic value exceeds the preset critical threshold in multiple consecutive cycles, the target lithium battery is determined to have a risk of transient response degradation under micro-load.

2. The lithium battery performance testing method according to claim 1, characterized in that: The step of acquiring the corresponding battery voltage drop magnitude and recovery time in real time within each pulse cycle includes: When each target load control command is executed, the first voltage sampling point is set before the load is turned on to obtain the reference no-load voltage value. The second voltage sampling point is set to a preset delay time after the load is turned on, and the instantaneous voltage value is collected to calculate the voltage drop. Multiple recovery sampling points are set after the load is turned off, and the voltage recovery curve is continuously collected until the voltage change rate is lower than the set differential threshold. Based on the voltage drop magnitude and voltage recovery curve, the recovery time is determined as the time period required for the voltage to recover from the lowest point to the set percentage reference.

3. The lithium battery performance testing method according to claim 1, characterized in that: The step of calculating the characteristic value of the dynamic internal resistance change of the battery in each cycle based on the collected voltage drop amplitude and recovery time data includes: Within each pulse cycle, based on the collected voltage drop amplitude and the corresponding low-power pulse load current value, the initial value of the instantaneous equivalent internal resistance of the cycle is calculated. The initial value of the instantaneous equivalent internal resistance is defined as the ratio of the voltage drop amplitude to the pulse load current. Based on the determined recovery time, the initial value of the instantaneous equivalent internal resistance is corrected by time weighting to obtain a periodically corrected internal resistance value that reflects the influence of the voltage recovery speed. The internal resistance change sequence is constructed by arranging the periodically corrected internal resistance values ​​within multiple consecutive pulse cycles in chronological order, and the rate of change of internal resistance between adjacent cycles is calculated. The characteristic value of dynamic internal resistance change is determined based on the internal resistance change rate.

4. The lithium battery performance testing method according to claim 1, characterized in that: The steps for obtaining the minimum pulse operating current and pulse period parameters of the device under test include: sampling the current during the instantaneous wake-up process of the device under test when it is in the lowest power consumption operating state, and determining the minimum pulse operating current and the corresponding pulse period parameters based on the sampling results.

5. The lithium battery performance testing method according to claim 1, characterized in that: The steps for constructing a low-power pulse load model include: based on the minimum pulse operating current, pulse period, and device wake-up duration, constructing a periodic rectangular pulse current waveform using a time-domain discrete function, wherein the pulse amplitude corresponds to the minimum pulse operating current, and the pulse period corresponds to the pulse period parameter.

6. The lithium battery performance testing method according to claim 5, characterized in that: The step of generating the target load control command sequence includes: based on the low-power pulse load model, encapsulating the pulse start time, pulse end time, load current amplitude, and voltage sampling time into a control command sequence arranged in chronological order.

7. A lithium battery performance testing system for implementing the lithium battery performance testing method according to any one of claims 1-6, characterized in that: include: Parameter acquisition module: Acquires the rated capacity and rated voltage of the target lithium battery, as well as the minimum pulse operating current and pulse period parameters of the device under test; Instruction generation module: Based on the minimum pulse operating current and pulse period, construct a low-power pulse load model and generate a corresponding target load control instruction sequence; Command execution control module: Based on the generated target load control command sequence, it controls the load start-up and voltage acquisition operations; Synchronous sampling module: Under the control of the target load control command, a low-power pulse load is applied to the target lithium battery, and the corresponding battery voltage drop amplitude and recovery time are collected in real time during each pulse cycle; Dynamic internal resistance calculation module: Based on the collected voltage drop amplitude and recovery time data, calculate the characteristic value of the dynamic internal resistance change of the battery in each cycle; Degradation Detection Module: If the characteristic value of dynamic internal resistance change exceeds the preset critical threshold in multiple consecutive cycles, the target lithium battery is determined to have a risk of transient response degradation under micro-load.

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