Battery detection method and device for multifunctional mobile power supply and mobile power supply

Abstract

The invention is suitable for the technical field of data identification, and provides a battery detection method and device for a multifunctional mobile power supply and the mobile power supply, and the method comprises the steps: obtaining an initial calendar life and an average environment temperature obtained based on an aging acceleration test; obtaining the number of charge-discharge cycles, the average charging current, the average charging voltage and the number of deep discharge times collected by the mobile power supply equipment within a preset time period; calculating a correction factor according to the average environment temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage and the number of deep discharge times; and calculating a final calendar life according to the initial calendar life and the correction factor, and sending the final calendar life to the mobile power supply device. In conclusion, according to the battery detection method of the multifunctional mobile power supply, through multi-factor comprehensive consideration and dynamic real-time adjustment, the accuracy and the real-time performance of battery life prediction are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data recognition, and particularly relates to a battery detection method and device of a multifunctional mobile power supply and the mobile power supply. BACKGROUND

[0002] With the wide popularity of mobile devices, mobile power supplies have become one of the indispensable electronic devices in daily life. Users' requirements for mobile power supplies are not limited to their charging capacity and portability, but also hope that they have intelligent battery management functions to real-time understand the health status and remaining life of the battery. However, the mobile power supplies in the prior art still have many deficiencies in battery life detection.

[0003] Traditional mobile power supplies usually rely on simple voltage and current detection to estimate the remaining capacity and life of the battery. This method lacks comprehensive consideration of the battery usage environment and working conditions, resulting in inaccurate life prediction. Secondly, the existing life detection methods rarely consider the aging characteristics of the battery. The performance of the battery will gradually degrade during long-term use, and the aging degree will affect the actual life of the battery. Simply relying on the initial state parameters to estimate the life cannot accurately reflect the current health status and future availability of the battery. SUMMARY

[0004] Therefore, the embodiments of the present application provide a battery detection method and device of a multifunctional mobile power supply and the mobile power supply to solve the technical problem that simply relying on the initial state parameters to estimate the life cannot accurately reflect the current health status and future availability of the battery.

[0005] The first aspect of the embodiments of the present application provides a battery detection method of a multifunctional mobile power supply, which comprises:

[0006] obtaining an initial calendar life based on an aging acceleration test and an average ambient temperature;

[0007] obtaining the number of charge-discharge cycles, the average charging current, the average charging voltage and the number of deep discharge times collected by the mobile power supply device within a preset time length, wherein the preset time length is a preset correction period length;

[0008] calculating a correction factor according to the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage and the number of deep discharge times;

[0009] calculating a final calendar life according to the initial calendar life and the correction factor, and sending the final calendar life to the mobile power supply device; wherein the final calendar life is used to prompt the user about the battery life situation.

[0010] Further, the step of obtaining the initial calendar life based on the accelerated aging test and the average ambient temperature comprises:

[0011] obtaining the average ambient temperature corresponding to the external data set;

[0012] obtaining the capacity fade amount corresponding to each of the different test durations at different accelerated aging test temperatures;

[0013] substituting the test duration and the capacity fade amount at different accelerated aging test temperatures into the power law equation: denotes the test duration at the i-th accelerated aging test temperature corresponding capacity fade amount, denotes the decay rate coefficient, denotes the time index, denotes the test duration;

[0014] solving the decay rate coefficient and the time index in the power law equation based on the least squares method;

[0015] converting the power law equation to: ; wherein, is equivalent to in the power law equation, denotes the predicted calendar life at the i-th accelerated aging test temperature, is equivalent to in the power law equation, denotes the capacity fade amount corresponding to the end of battery life;

[0016] substituting the decay rate coefficient and the time index into the converted power law equation to obtain the predicted calendar life at the i-th accelerated aging test temperature;

[0017] calculating the initial calendar life according to the predicted calendar life at the i-th accelerated aging test temperature.

[0018] Further, the step of calculating the initial calendar life according to the predicted calendar life at the i-th accelerated aging test temperature comprises:

[0019] constructing a relationship model between calendar life and temperature: ; wherein, denotes the pre-exponential factor, denotes the activation energy, denotes the Boltzmann constant, denotes the i-th accelerated aging test temperature;

[0020] ​based on the known slope and , the relationship model is least square fitted to obtain a slope and an intercept ;

[0021] the relationship model is converted into: ; wherein, is equivalent to in the relationship model, represents an initial calendar life, is equivalent to in the relationship model, represents a reference value of a real environment temperature in which the user is located;

[0022] based on the known slope and the intercept , the initial calendar life is obtained by substituting into the converted relationship model.

[0023] Further, after the step of least square fitting the relationship model based on the known slope and , the slope and the intercept , the step further comprises:

[0024] the slope is multiplied by the Boltzmann constant to obtain an activation energy ;

[0025] if the activation energy is within a preset numerical range, subsequent steps are continued to be executed; wherein, the preset numerical range comprises 0.4 to 1.2;

[0026] if the activation energy is not within the preset numerical range, new aging acceleration test data is obtained, and a new activation energy is calculated based on the new aging acceleration test data until the new activation energy is within the preset numerical range.

[0027] Further, the step of calculating the correction factor according to the average environment temperature, the number of charge-discharge cycles, the average charge current, the average charge voltage and the number of deep discharge times comprises:

[0028] the average environment temperature, the number of charge-discharge cycles, the average charge current, the average charge voltage and the number of deep discharge times are substituted into a first function to obtain a correction factor output by the first function;

[0029] The first function is:

[0030]

[0031] wherein, represents a correction factor, represents an average ambient temperature, represents a reference temperature, represents a temperature sensitivity, represents a number of charge-discharge cycles, represents a cycle number sensitivity, represents an average charge current, represents a current normalization constant, represents a dynamic coupling factor.

[0032] Further, the dynamic coupling factor is calculated by a second function:

[0033] The second function is:

[0034]

[0035] wherein, represents a voltage offset sensitivity, represents an average charge voltage, represents a battery nominal voltage, represents a depth discharge sensitivity, represents a number of depth discharges, represents a number of depth discharges, represents a temperature-voltage cross-sensitivity.

[0036] Further, the step of calculating a final calendar life from the initial calendar life and the correction factor, and sending the final calendar life to a mobile power supply device comprises:

[0037] multiplying the initial calendar life by the correction factor to obtain a correction amount;

[0038] subtracting the initial calendar life from the correction amount to obtain the final calendar life, and sending the final calendar life to a mobile power supply device.

[0039] A second aspect of the embodiments of the present application provides a battery detection device of a multifunctional mobile power supply, comprising:

[0040] a first obtaining unit configured to obtain an initial calendar life based on an aging acceleration test and an average ambient temperature;

[0041] The second acquisition unit is configured to acquire the number of charge-discharge cycles, the average charging current, the average charging voltage and the number of deep discharge times collected by the mobile power supply device within a preset time length, wherein the preset time length is a preset correction period time length.

[0042] The first calculation unit is configured to calculate a correction factor according to the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage and the number of deep discharge times.

[0043] The second calculation unit is configured to calculate a final calendar life according to the initial calendar life and the correction factor, and send the final calendar life to the mobile power supply device, wherein the final calendar life is used to prompt the user about the battery life condition.

[0044] The third aspect of the embodiment of the present application provides a mobile power supply, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps in the battery detection method of the multifunctional mobile power supply according to the first aspect of the present application when executing the computer program.

[0045] The fourth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps in the battery detection method of the multifunctional mobile power supply according to the first aspect of the present application when executed by a processor.

[0046] Compared with the prior art, the embodiment of the present application has the beneficial effects that: the traditional battery life prediction method usually only relies on a single parameter such as voltage and current, while the present application obtains multiple data such as initial calendar life, average ambient temperature, number of charge-discharge cycles, average charging current, average charging voltage and number of deep discharge times, and fully considers various influencing factors of the battery in the actual use environment. Through the comprehensive calculation of these parameters, the actual use condition of the battery can be more accurately reflected, and the accuracy of the battery life prediction is improved. The method of the present application can obtain and update the charge-discharge cycle data and environmental temperature information of the battery in real time within a preset correction period, and calculate a correction factor. In this way, the battery life prediction is not only a static analysis based on the initial state, but also dynamically reflects the performance change of the battery in different time periods, and provides personalized battery management. By sending the final calendar life to the mobile power supply device and timely prompting the user about the battery life condition, the user can know the health condition of the battery in real time. This real-time and personalized prompt function can help the user better manage the battery use and avoid the inconvenience caused by sudden failure of the battery. In summary, the battery detection method of the multifunctional mobile power supply of the present application considers multiple factors comprehensively and adjusts dynamically in real time, which improves the accuracy and real-time performance of the battery life prediction. BRIEF DESCRIPTION OF DRAWINGS

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A schematic flowchart of a battery testing method for a multifunctional mobile power bank provided by the present invention is shown.

[0049] Figure 2 A schematic diagram of a battery detection device for a multifunctional mobile power bank according to an embodiment of the present invention is shown.

[0050] Figure 3 A schematic diagram of a portable power bank provided according to an embodiment of the present invention is shown. Detailed Implementation

[0051] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0052] This invention provides a battery detection method and apparatus for a multifunctional mobile power bank to solve the technical problem that existing methods often cannot accurately locate the region of interest.

[0053] First, this invention provides a battery testing method for a multifunctional portable power bank. Please refer to [link / reference]. Figure 1 , Figure 1 A schematic flowchart of a battery testing method for a multifunctional mobile power bank provided by the present invention is shown. Figure 1 As shown, the battery detection method of this multi-functional power bank may include the following steps:

[0054] Step 101: Obtain the initial calendar lifetime and average ambient temperature based on the accelerated aging test;

[0055] Initial Calendar Life is the theoretical expected lifespan of a power bank battery under standard usage conditions (e.g., constant temperature at 25°C, standard charge and discharge modes) calculated by aging a sample of the same model of battery through accelerated aging tests in a laboratory environment.

[0056] The average ambient temperature is the average temperature value used to simulate the actual operating environment of the battery during accelerated aging testing. It represents the "ambient temperature level" corresponding to the test conditions. This data is important because it serves as the temperature reference for subsequent calibration. It's important to note that this is not the temperature of the user's actual operating environment.

[0057] Specifically, step 101 includes steps 1011 to 1017:

[0058] Step 1011: Obtain the average ambient temperature corresponding to the external dataset;

[0059] External datasets include, but are not limited to, publicly available weather data or data collected by organizations.

[0060] Step 1012: Obtain the capacity decay corresponding to different test durations at different accelerated aging test temperatures;

[0061] At each selected accelerated aging test temperature Under these conditions, long-term aging experiments (storage or microcycles) will be conducted. At different time points... (For example, 1 day, 7 days, 30 days, 90 days...) Measure the battery's capacity decay. . Indicates temperature Below, after a period of time The percentage or absolute value of the battery capacity loss relative to the initial capacity after aging (e.g., a 5% or 100mAh decrease).

[0062] Step 1013: Substitute the test duration and capacity decay at different accelerated aging test temperatures into the power-law equation: , This represents the test duration at the i-th accelerated aging test temperature. The corresponding capacity decay, Represents the decay rate coefficient. Indicates time index, Indicates the test duration;

[0063] It is related to temperature Strongly correlated parameters. The larger the value, the more time is required. The faster the content decays, the faster the aging rate. This comprehensively reflects temperature. Accelerating effect on aging rate.

[0064] The pattern representing the rate of capacity decay over time (0.5≤ ≤1). It is related to the battery chemistry or the main degradation mechanism and is relatively insensitive to temperature.

[0065] The power-law equation assumes a constant temperature Under these conditions, the battery capacity decay follows a power function relationship.

[0066] The power-law equation is constructed based on the principle that battery storage degradation mainly stems from the continuous growth of the SEI layer (consuming active lithium) and side reactions at the electrode / electrolyte interface. These processes are controlled by mass diffusion. . For SEI thickness, Diffusion coefficient. Capacity loss. and Proportional: Generalized to a power-law model: . =0.5 represents ideal diffusion; the actual value is determined through data fitting.

[0067] Step 1014: Solve for the attenuation rate coefficient in the power-law equation using the least squares method. and time index ;

[0068] For a specific temperature A series of data points obtained from the aging experiment conducted below ( , Using the least squares method, the optimal solution is found. and The goal is to make the power-law equation ( The calculated predicted value and the actual observed value The sum of squared errors between them is minimized. This process is performed for each test temperature. They all fit a specific set of parameters ( , ).

[0069] The least squares method solution process is existing technology and will not be described in detail here.

[0070] Step 1015: Transform the power-law equation into: ;in, Equivalent to the power-law equation , This indicates the prediction of calendar lifetime at the i-th accelerated aging test temperature. Equivalent to the power-law equation , This indicates the amount of capacity decay at the end of the battery's lifespan.

[0071] For the original power-law equation ( Perform algebraic transformations to solve for time t:

[0072]

[0073]

[0074] This represents the capacity degradation at the end of the battery's lifespan. This is a preset threshold (e.g., capacity degradation to 80% of initial capacity, i.e.) = 20%). This threshold represents the standard by which a battery is considered "failed" or "end of life".

[0075] Will Substitute into the transformed equation: It is at a constant aging accelerated test temperature Below, the predicted battery degradation to the end of its lifespan. The required time, i.e., at that specific high temperature Predicted calendar lifespan under certain conditions.

[0076] Step 1016: The attenuation rate coefficient and the time index Substituting into the transformed power-law equation, we obtain the predicted calendar lifetime at the i-th accelerated aging test temperature;

[0077] Using the temperature-specific data obtained from the least squares fitting in step 1014... parameters and and the preset capacity decay at the end of the lifespan. Substitute into the formula Calculate the high temperature Predicted calendar lifespan .this These are the predicted lifetime values ​​under accelerated conditions.

[0078] Step 1017: Calculate the initial calendar lifetime based on the predicted calendar lifetime at the i-th accelerated aging test temperature.

[0079] In the embodiments corresponding to steps 1011 to 1017, the least squares method is used for parameter fitting to ensure that the model parameters ( , The determination of the "end of life" is mathematically optimal and objective. The "end of life" is defined as reaching the preset capacity decay rate. (e.g., 80% capacity retention) provides a clear and quantifiable standard for lifetime prediction. This is achieved by independently fitting the model at each temperature and predicting the lifetime at that temperature. Furthermore, by combining the temperature-lifetime model extrapolation, the accelerated aging effect of high temperature is scientifically quantified, thus enabling the accelerated test results to be reliably converted to actual operating temperature conditions.

[0080] Specifically, step 1017 includes steps A1 to A4:

[0081] Step A1: Construct a model of the relationship between calendar lifespan and temperature: ;in, Indicates pre-exponential factor, Indicates activation energy. Represents Boltzmann's constant. This represents the temperature of the i-th accelerated aging test;

[0082] This equation describes the relationship between the chemical reaction rate constant and temperature. In the field of battery calendar aging, it is widely believed that the rate of battery capacity decay (aging) (such as SEI growth, electrolyte decomposition, etc.) and temperature follows an Arrhenius form. Therefore, the reciprocal (1 / t) of the time t required to reach a given endpoint (i.e., lifetime) and temperature also follows an Arrhenius form, and it can be derived that the natural logarithm of lifetime t, ln(t), is linearly related to the reciprocal (1 / T) of absolute temperature.

[0083] The natural logarithm of the pre-exponential factor. A is a constant whose physical meaning is related to the frequency factor. ln(A) is the intercept of the linear model. It is the activation energy of the aging reaction, usually measured in eV or J / mol. This is a key physical quantity for measuring the temperature sensitivity of the aging reaction. The higher the value, the more significant the effect of temperature on the aging rate (lifespan). It is the Boltzmann constant, a known physical constant. ≈8.617333262145×10⁻ 5 eV / K). This is the slope of the linear model. The slope value is directly proportional to the activation energy. .

[0084] Step A2: Based on known information and The slope is obtained by performing a least-squares fit on the relationship model. and intercept ;

[0085] The data obtained from multiple accelerated aging test points (at least two, usually three or more different temperature points) are compiled as follows:

[0086] Independent variable (X): The reciprocal of each test temperature (Unit: K⁻¹).

[0087] Dependent variable (Y): Predicted calendar lifetime at various test temperatures natural logarithm .

[0088] Using the least squares method, the data points ( , Input linear model Y = Intercept + Slope * X (i.e.) The least squares method automatically calculates and returns the optimal slope. (obtained by fitting) and intercept (obtained through fitting);

[0089] The fitting process utilizes data points from the high-temperature acceleration zone ( , The parameters of the model describing the lifetime versus temperature relationship across the entire temperature range (especially the low-temperature region requiring extrapolation) were determined. ) and . (or( () is a core parameter reflecting the temperature dependence of battery aging mechanism.

[0090] As an optional embodiment of this application, after step A2, steps B1 to B3 are further included:

[0091] Step B1: The slope With the Boltzmann constant Multiplying them together yields the activation energy. ;

[0092] Step B2: If the activation energy If the value is within the preset range, the subsequent steps continue; wherein the preset range includes 0.4 to 1.2.

[0093] Verify the calculated activation energy Whether it is within a physically reasonable and acceptable range. Preset numerical range: 0.4 eV to 1.2 eV. This range is determined based on extensive research and experimental experience in the field of lithium-ion batteries.

[0094] The main calendar aging mechanisms of lithium-ion batteries, such as the continuous growth of the solid electrolyte interphase (SEI) film and the oxidative decomposition of the electrolyte, typically have activation energies in the range of 0.4 eV - 1.2 eV.

[0095] A value below 0.4 eV may indicate: serious errors in testing or fitting (such as improper temperature selection, insufficient testing time leading to high data noise, or fitting failure); or abnormal aging mechanisms (such as mechanical failure as the primary cause, rather than electrochemical side reactions).

[0096] A value above 1.2 eV generally does not conform to the activation energy level of the known main aging mechanism of lithium-ion batteries, and also strongly suggests a problem with the testing or fitting process.

[0097] The calculated The value is compared with a preset reasonable range [0.4 eV, 1.2 eV]. If If the activation energy is within the range of 0.4–1.2 eV, it is considered physically reasonable, and the calculated slope / intercept is reliable. Therefore, the process continues with subsequent steps (i.e., step A3 and its follow-up steps).

[0098] Step B3: If the activation energy If the value is not within the preset range, new accelerated aging test data is obtained, and a new activation energy is calculated based on the new accelerated aging test data. Until a new activation energy is generated. It is within the preset value range.

[0099] If the activation energy If the value is not within the preset range, return to step 1011 and subsequent steps.

[0100] In the embodiments corresponding to steps B1 to B3, activation energy for the core physical parameter is introduced. The validity check is crucial because... The rationality of the model directly determines its reliability. The preset range of 0.4 - 1.2 eV is not arbitrarily set, but is based on extensive experimental research on the activation energy of mainstream lithium-ion battery aging mechanisms (SEI growth, electrolyte oxidation). This makes the verification scientifically grounded. When verification fails, instead of simply reporting an error or using questionable results, a closed-loop feedback mechanism is designed to automatically optimize the model by acquiring new experimental data and recalculating. This significantly improves the robustness and reliability of the entire "initial calendar lifetime" prediction process. By adding verification and iterative optimization at key nodes (after fitting, before extrapolation), the model parameters (T_initial) used for calculating T_initial are ensured to the greatest extent possible. )and It is physically reasonable and statistically reliable.

[0101] Step A3: Transform the relational model into: ;in, Equivalent to the relational model , Indicates the initial calendar lifespan. Equivalent to the relational model , This represents a reference value indicating the actual ambient temperature where the user is located;

[0102] For the relational model in step A1 Take the exponent (exp) on both sides and solve for the solution. : ;

[0103] The transformed equation: This is used to calculate at any specified temperature. The final equation for predicting calendar lifespan is derived.

[0104] In the equation and These are the known parameters obtained through fitting in step A2.

[0105] Step A4: Based on the known slope and intercept ,Will Substituting the transformed relational model, the initial calendar lifespan is obtained.

[0106] The reference value represents standard test conditions or typical ambient temperature for the user, most commonly 25°C (298K). Substitute the following known quantities into the equation obtained in step A3. :

[0107] The intercept obtained from the fitting in step A2 ;

[0108] The slope obtained from the fitting in step A2 ;

[0109] Selected target reference temperature (Unit: Kelvin K) (e.g., 25°C = 298K);

[0110] The calculated value This refers to the initial calendar life. It represents the lifespan under constant, specified reference ambient temperature. At a temperature of 25°C, the theoretically expected time required for the battery to degrade to the preset end-of-life capacity decay amount ΔC_end. This T_initial will be used in subsequent correction processes.

[0111] In the embodiments corresponding to steps A1 to A4, the lifetime data (T_i, t_i*) from the accelerated high-temperature test points are used to fit the model parameters through linear regression, and finally extrapolated to the target reference temperature. This is a scientific process. It's a standard and reliable method for translating accelerated test results into expected lifespan under real-world usage conditions. The slope obtained through fitting ( This actually quantifies the activation energy of the aging process. . This is a key battery aging characteristic parameter, reflecting the sensitivity of its aging mechanism to temperature. "Initial calendar life" is defined as the initial calendar life at a user's real-world reference temperature. The predicted lifetime (typically at a standardized 25°C) provides a clear benchmark for the correction factor (whose "average ambient temperature" corresponds to this). ).

[0112] To more intuitively understand the technical effect of the algorithm in the specific embodiment corresponding to step 101, please refer to the following experimental data:

[0113] The accelerated testing conditions are shown in Table 1:

[0114] Table 1:

[0115] Parameter Group 1 Group 2 Group 3 Test temperature 45℃,55℃,65℃ 40℃,50℃,60℃ 50℃,60℃,70℃ SOC range 80-100% 50-70% 90-100% Data points / temperature 12 points 10 points 15 points

[0116] Table 2 shows a comparison of the prediction accuracy of this algorithm with that of the traditional algorithm:

[0117] Table 2:

[0118] Battery group Actual life (years) Algorithm of this embodiment Error Error of traditional model First type battery group 8.2 7.9 -3.70% 22.10% Second type battery group 12.5 13.1 4.80% -18.30% Second type battery group 9 8.7 -3.30% 15.90%

[0119] The algorithm in this embodiment has an average error of ≤4%, which is significantly better than the traditional method (average error >18%).

[0120] Step 102: Obtain the number of charge / discharge cycles, average charging current, average charging voltage, and number of deep discharge cycles collected by the power bank device within a preset time period; wherein, the preset time period is a preset correction cycle duration;

[0121] The preset duration is a pre-defined time period called the calibration cycle (e.g., 1 month, 3 months, 6 months). The system will continuously collect data within this cycle. Within the preset calibration cycle, the power bank device records the number of charge-discharge cycles, average charging current, average charging voltage, and number of deep discharges.

[0122] The number of charge-discharge cycles refers to the process of a battery going through a complete cycle from being fully discharged (or to a specific depth, such as 80%) and then fully charged again. A higher number of cycles indicates more frequent use.

[0123] Average charging current refers to the average current flowing into the battery during the charging process of the power bank (whether it is charging itself or charging a mobile phone). High-current charging generally puts more stress on the battery than low-current "trickle" charging.

[0124] Average charging voltage refers to the average voltage across the battery terminals during the charging process. The longer it takes to approach or reach the full charge voltage (such as 4.2V or 4.4V, depending on the battery chemistry), the greater the stress on the battery's lifespan.

[0125] Deep discharge cycles refer to the number of times a battery is discharged to a very low remaining capacity (e.g., below 20% or 10%). Deep discharge is generally more damaging to lithium-ion batteries than shallow discharge.

[0126] These parameters comprehensively reflect users' real-world habits when using power banks (charging frequency, depth of discharge) and the electrical stress (current, voltage) they experience, as well as environmental factors (indirect effects, such as high temperatures causing changes in charging and discharging efficiency; however, ambient temperature is not directly collected here, but rather reflected in usage behavior). This actual usage data is a key basis for correcting the initial predictions.

[0127] Step 103: Calculate the correction factor based on the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharge cycles;

[0128] By taking into account the effects of all input parameters, a single numerical correction factor is calculated, which accurately represents the correction ratio between actual user use and theoretical lifespan.

[0129] Specifically, step 103 includes: substituting the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharge cycles into a first function to obtain a correction factor output by the first function;

[0130] The first function is:

[0131]

[0132] in, Indicates the correction factor. Indicates the average ambient temperature. Indicates reference temperature. Indicates temperature sensitivity. Indicates the number of charge-discharge cycles. Indicates sensitivity to the number of iterations. Indicates the average charging current. This represents the normalization constant of the current. This represents the dynamic coupling factor.

[0133] (Average ambient temperature) refers to the average temperature of the user's actual operating environment (unit: °C or K, must be consistent with T_ref). This reflects thermal stress. The higher the temperature ( > ), accelerates aging.

[0134] (Reference temperature) refers to the standard reference ambient temperature (e.g., 25°C) on which the "initial calendar lifetime" calculation is based.

[0135] Temperature sensitivity is a positive constant parameter. It quantifies temperature. Relative to reference temperature The degree of sensitivity to the effects of deviation on aging rate. The larger the value, the weaker the effect of temperature changes on aging (lower sensitivity). The smaller the value, the stronger the effect of temperature changes on aging (higher sensitivity). It usually needs to be determined through experiments or experience.

[0136] (Charge / discharge cycle count) refers to the actual number of complete charge / discharge cycles performed by the user within a preset calibration period. This reflects the intensity of cyclic usage. The more you consume, the faster you age.

[0137] (Cycle Count Sensitivity) is a positive constant parameter. It quantifies the number of charge-discharge cycles. Sensitivity to the effects of aging rate. The larger the value, the weaker the influence of the number of iterations; The smaller the value, the stronger the influence of the number of iterations. This needs to be determined through experiments or experience.

[0138] Average charging current refers to the average current (unit: A or mA) experienced by the power bank battery during charging (whether charging itself or charging a device) within a preset calibration period. This reflects the charging current stress. Larger batteries (especially fast charging) may accelerate aging.

[0139] (Current normalization constant): is a positive constant parameter (unit: ...). (Same). It defines a characteristic scale or threshold for the effect of charging current. Used to normalize current. The impact.

[0140] The dynamic coupling factor is a variable factor (usually a positive number). Its core function is to dynamically adjust the overall correction strength. The correction factor was magnified or reduced by the parentheses (...) portion (i.e., the combined stress term) within the entire exponential function. The extent of the impact.

[0141] The global form of the first function is: F = exp[ - (S) *Γ], where S = This is an exponentially decaying function.

[0142] S (Comprehensive Stress Term) This part quantifies the comprehensive "accelerated aging stress" under actual user conditions relative to reference conditions. It consists of two added terms:

[0143] (T - T_ref) / kT (Temperature stress term) measures the accelerated aging effect caused by temperature deviation from the reference temperature. Its form is similar to a linearized Arrhenius term or empirical temperature factor.

[0144] Behavior: > This item is positive (accelerates aging); < This item is negative (it slows down aging, but the overall S and F calculations need to be considered).

[0145] (Coupled Cyclic and Current Stress Terms): Molecules Loop count The effect is introduced as a squared term, reflecting that cyclic aging is typically non-linear (e.g., the damage from 100 cycles is far greater than 100 times that from 1 cycle). The denominator... In Adjust the overall size (sensitivity) of this item directly.

[0146] This is a variant of the S-shaped function, with values ​​ranging from 1 to 2. This term represents the charging current. Impact and number of cycles Coupled together, it dynamically adjusts the contribution of cycle count to aging. Specifically, fast charging ( (Large) will increase the number of loops. The negative impact on aging (the denominator becomes smaller, and this term becomes larger), while slow charging ( (Small) will reduce the number of loops. The negative impact on aging (the denominator becomes larger, and this term becomes smaller). This reflects the phenomenon that fast charging usually exacerbates the damage caused by cyclic aging.

[0147] The larger the S value, the greater the overall accelerated aging stress.

[0148] Exponential terms and The combined stress term S multiplied by the dynamic coupling factor The result, after taking the negative sign, is used as the exponent of exp.

[0149] Index - S * The exponential term is negative (because S >= 0, > 0), and its absolute value |S * The larger the value, the stronger the aging acceleration effect.

[0150] The influence of the comprehensive stress S on the final correction factor F was amplified. The larger the value of S, the more likely the same value of S will result in exp(-S*). The smaller the F value (i.e., the smaller the F value, the stronger the acceleration effect), as mentioned earlier, This is the primary, and even the only, way to introduce the influence of average charge voltage and deep discharge cycles. For example:

[0151] V high-> Increased F -> decreased F (shortened lifespan), reflecting damage from high-pressure stress.

[0152] D more-> Increased F -> decreased F (shortened lifespan), reflecting deep discharge damage.

[0153] When S = 0 and In finite time (ideal reference conditions), F = exp(0) = 1. When actual usage conditions deviate from the reference conditions (S > 0), F is always less than 1 (0 < F < 1). S* The larger the value, the closer F is to 0, indicating a more significant acceleration in aging.

[0154] F < 1 means that the actual usage conditions are more stringent than the laboratory reference conditions, resulting in a shorter predicted actual lifetime T_final = T_initial * F than the initial theoretical lifetime T_initial. The smaller F is, the greater the shortening.

[0155] The first function defines an ingeniously structured exponential function (the first function) that integrates various practical stresses and their interactions, serving as the core method for calculating the correction factor F. It successfully incorporates factors such as temperature, cycling, current, voltage, and deep discharge, and utilizes dynamic coupling factors... The current-cycle coupling term reflects complex aging interaction effects.

[0156] The derivation principle of the first function:

[0157] Construction of the main attenuation path: ;

[0158] Global Coupling and Boundary Control: ;

[0159] Exponential decay mechanism: when (Ideal state): ;

[0160] when (Extreme stress): ;

[0161] Innovative coupling methods: The temperature derivative includes both direct decay and cross-effect components.

[0162] The dynamic coupling factor is calculated using a second function, which is:

[0163]

[0164] in, Indicates voltage offset sensitivity. This represents the average charging voltage. Indicates the battery's nominal voltage. Indicates deep discharge sensitivity. Indicates the number of deep discharges. Indicates the number of deep discharges. This indicates temperature-voltage cross-sensitivity.

[0165] (Average Charging Voltage): The average voltage experienced by the power bank battery during charging (whether it is charging itself or charging a device) within a preset calibration period. This reflects the charging voltage stress.

[0166] (Battery nominal voltage) is the standard rated voltage of a power bank battery (e.g., a single lithium-ion battery is typically 3.6V or 3.7V). This is the benchmark for measuring voltage deviation.

[0167] Voltage offset sensitivity is a constant parameter (it can be positive or negative, but is usually positive). It quantifies the charging voltage. Relative to nominal voltage Deviation from (i.e., the degree of sensitivity to the effects of aging.) > 0, > This will lead to Increase (accelerate aging) < This will lead to Reduce (slow down) aging.

[0168] (Number of deep discharge cycles) is the number of times a battery is discharged to a very low remaining capacity (e.g., below 20% or 10%) within a preset calibration period. This reflects the deep discharge stress.

[0169] (Deep discharge sensitivity) is a positive constant parameter. It quantifies the number of deep discharges. right (i.e., the degree of sensitivity to the effects of aging.) The higher the value, the stronger the effect of deep discharge.

[0170] (Average ambient temperature) is the average temperature of the user's actual operating environment (unit: °C or K, needs to be compared with...). Consistent).

[0171] (Reference temperature) is the standard reference ambient temperature (e.g., 25°C) on which the "Initial Calendar Life" calculation is based.

[0172] The temperature-voltage cross term coefficient is a constant parameter (usually positive). It quantifies temperature. and voltage The intensity of the additional effects of synergistic effects on aging. > 0 indicates that high temperature and high voltage will exacerbate aging damage.

[0173] (Temperature-voltage cross sensitivity) is a positive constant parameter. It is used to normalize or adjust the temperature-voltage cross term. The magnitude, controlling the impact of this item The size of the contribution. The larger the value, the smaller the impact of the cross term.

[0174] This is the voltage offset stress term. Quantization of the average charging voltage. Deviation from nominal voltage The linear effect. > (Overfilling tendency) is a positive contribution. < For making a negative contribution.

[0175] This is the deep discharge stress term. The number of deep discharges. The effect is introduced by the square term, reflecting the nonlinear cumulative characteristics of deep discharge damage (frequent deep discharge exacerbates the harm). Always non-negative, > 0 guarantees that this item is correct. It always increases (accelerates aging).

[0176] This is the temperature-voltage coupled stress term (cross term). This is the key innovation of this function:

[0177] The temperature deviation relative to the reference temperature.

[0178] The deviation of voltage from the nominal voltage.

[0179] The product coupling effect of quantified temperature stress and voltage stress is explained. Its physical meaning is:

[0180] when > and > When (high temperature + high voltage), if this term is positive and large, it will produce a significant additional aging acceleration effect (synergistic effect).

[0181] when < and < When the temperature is low and the voltage is low, this item is also positive, resulting in a (relatively small) slowing-down effect on aging.

[0182] when and When the offset directions are opposite (such as one high and one low), this term can be positive or negative, but its absolute value is usually smaller than the offset in the same direction.

[0183] / Normalize or scale the cross term to match its magnitude with the other two terms, and this can be achieved through... Adjust its sensitivity.

[0184] The coefficient amplifies or reduces the coupling effect. > 0 indicates recognition and reinforcement of the synergistic effect of high temperature and high voltage in accelerating aging.

[0185] Add the above three items together: Sum: .

[0186] Applying the tanh function: .

[0187] The tanh function is a sigmoid function with a range of (-1, 1). When |x| is large (when |Sum| is large), tanh(Sum) approaches -1 or 1 (the sign depends on Sum).

[0188] When x = 0 (Sum = 0), tanh(0) = 0. The function is approximately linear near x = 0. The reason for choosing tanh is:

[0189] Map any Sum value to the range (-1, 1) to prevent subsequent calculations of F = exp(-S * (Exponential term -S*) An excessively large value will cause F to be too small or result in calculation overflow. This ensures... Numerical stability.

[0190] The function output value is the dynamic coupling factor. It is a value between (-1, 1). Under typical design and input conditions, it is expected that... >= 0.

[0191] pass This directly quantifies the impact of charging voltage deviation from the nominal voltage (usually too high) on aging. High-voltage charging (approaching or reaching the full-charge cutoff voltage) is a significant factor leading to capacity degradation. The nonlinear, cumulative damaging effect of deep discharge cycles on aging is quantified using squared terms. Deep discharge accelerates battery degradation. The tanh function is used as follows: It provides a bounded output (-1, 1), which enhances the entire model (especially F = exp(-S*). The numerical stability and robustness of tanh are also enhanced. The nonlinear properties of tanh also help in fitting complex relationships.

[0192] Loop count The attenuation is affected by the current Dynamic modulation: at high current ( The cyclic decay rate increased to The attenuation weakens at low currents.

[0193] temperature With voltage Through cross terms A synergistic effect is generated, and the lifespan decay is significantly accelerated under high temperature and high pressure.

[0194] Voltage offset, deep discharge, and temperature-voltage crossover are constrained within the range of [-1, 1] to prevent excessive decay of the correction factor under extreme conditions.

[0195] Cross-item passing Dynamic weighting ensures the nonlinear controllability of multi-parameter coupling. The outer exponential function integrates the temperature-cycle main decay path with the dynamic coupling factor. The output range is (0, 1).

[0196] Calculation example: Suppose a battery is used under the following conditions: , , , , .

[0197] Fitting parameters: .

[0198] Calculation steps:

[0199] Calculate the main attenuation path: ;

[0200] Calculate the dynamic coupling factor: ;

[0201] Calculate the correction factor: .

[0202] To more intuitively understand the technical effect of the algorithm in the specific embodiment corresponding to step 103, please refer to the following experimental data:

[0203] The test condition matrix is ​​shown in Table 3:

[0204] Table 3:

[0205] Stress factor Horizontal setting Temperature (T) 35℃, 45℃, 50℃ Cycle number (N) 50, 100, 200, 300 Charging current (I) 0.5C, 1C, 1.5C (C is rated capacity) Charging voltage (V) Nominal value, +0.1V, +0.2V Depth discharge number (D) 0, 10, 20, 40 (20% in total cycles)

[0206] Measurement indicators: ① Number of cycles to reduce actual capacity to 80% (used to calculate actual lifetime degradation rate), ② Correction factor benchmark: F_actual = actual lifetime / nominal lifetime (25℃, 0.5C, nominal voltage, no deep discharge).

[0207] The test results (48 sets of independent validation data) are shown in Table 4:

[0208] Table 4:

[0209] Evaluation index This model Traditional model MAE (correction factor) 0.041 0.087 RMSE 0.053 0.112 Maximum absolute error 0.128 0.231 RMSE in high temperature (45℃) area 0.061 0.143 MAE in large current (1.5C) area 0.047 0.103

[0210] Under combined high temperature and high pressure conditions, the error of this embodiment is 60% lower than that of the traditional model. Overfitting is effectively limited by the tanh function (maximum error <15% under extreme conditions).

[0211] Step 104: Calculate the final calendar life based on the initial calendar life and the correction factor, and send the final calendar life to the power bank device; wherein, the final calendar life is used to inform the user of the battery life status.

[0212] The calculated final calendar lifespan represents a revised prediction of the remaining usable lifespan of the power bank battery, taking into account the specific usage habits of individual users. This prediction is then sent back to the power bank device.

[0213] Once the power bank receives its final calendar lifespan, it will use this information to inform the user of the current battery status. This helps users understand the battery's condition and consider replacing it or adjusting their usage habits as needed.

[0214] Specifically, step 104 includes steps 1041 to 1042:

[0215] Step 1041: Multiply the initial calendar lifetime by the correction factor to obtain the correction amount;

[0216] The correction factor quantifies the reduction in theoretical lifespan due to actual usage conditions relative to standard reference conditions.

[0217] Step 1042: Subtract the initial calendar lifetime from the correction amount to obtain the final calendar lifetime, and send the final calendar lifetime to the power bank device.

[0218] The actual lifespan loss caused by use is calculated, and the user's perceived remaining usable lifespan (T_final) is obtained and fed back to the device.

[0219] The calculated T_final is the final calendar lifespan. It represents how long the battery is predicted to continue operating normally (until it reaches the end of its lifespan ΔC_end) at the current point in time, considering the user's historical actual usage patterns.

[0220] In the embodiments corresponding to steps 101 to 104, traditional battery life prediction methods typically rely only on single parameters such as voltage and current. However, this invention fully considers various influencing factors in the actual usage environment of the battery by acquiring multiple data points, including initial calendar life, average ambient temperature, charge / discharge cycle count, average charging current, average charging voltage, and deep discharge count. Through comprehensive calculation of these parameters, the actual usage of the battery can be more accurately reflected, improving the accuracy of battery life prediction. The method of this invention can acquire and update battery charge / discharge cycle data and ambient temperature information in real time within a preset correction period, and calculate correction factors. Thus, battery life prediction is not only a static analysis based on the initial state, but also dynamically reflects the performance changes of the battery over different time periods, providing personalized battery management. By sending the final calendar life to the power bank device and promptly alerting the user to the battery life status, the user can understand the battery's health status in real time. This real-time and personalized alert function helps users better manage battery usage and avoid inconvenience caused by sudden battery failure. In summary, the battery detection method of the multifunctional power bank of this invention, through comprehensive consideration of multiple factors and dynamic real-time adjustment, not only improves the accuracy and real-time performance of battery life prediction.

[0221] likeFigure 2 This invention provides a battery testing device for a multifunctional portable power bank. Please refer to [link / reference]. Figure 2 , Figure 2 A schematic diagram of a battery detection device for a multifunctional portable power bank provided by the present invention is shown, as follows: Figure 2 The battery testing device for a multifunctional portable power bank shown includes:

[0222] The first acquisition unit 21 is used to acquire the initial calendar lifetime and average ambient temperature based on the accelerated aging test.

[0223] The second acquisition unit 22 is used to acquire the number of charge-discharge cycles, average charging current, average charging voltage, and number of deep discharge cycles collected by the mobile power supply device within a preset time period; wherein, the preset time period is a preset correction cycle duration;

[0224] The first calculation unit 23 is used to calculate a correction factor based on the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharge cycles.

[0225] The second calculation unit 24 calculates the final calendar life based on the initial calendar life and the correction factor, and sends the final calendar life to the power bank device; wherein the final calendar life is used to inform the user of the battery life status.

[0226] This invention provides a battery testing device for a multifunctional power bank. Traditional battery life prediction methods typically rely on single parameters such as voltage and current. However, this invention fully considers various influencing factors in the actual usage environment by acquiring multiple data points, including initial calendar life, average ambient temperature, charge / discharge cycle count, average charging current, average charging voltage, and deep discharge count. Through comprehensive calculation of these parameters, the actual usage of the battery can be more accurately reflected, improving the accuracy of battery life prediction. The method of this invention can acquire and update battery charge / discharge cycle data and ambient temperature information in real time within a preset correction period, and calculate correction factors. Thus, battery life prediction is not only a static analysis based on the initial state but also dynamically reflects the battery's performance changes over different time periods, providing personalized battery management. By sending the final calendar life to the power bank device and promptly alerting the user to the battery's status, the user can understand the battery's health condition in real time. This real-time and personalized alert function helps users better manage battery usage and avoid inconvenience caused by sudden battery failure. In summary, the battery testing method for a multifunctional power bank of this invention, through comprehensive consideration of multiple factors and dynamic real-time adjustment, not only improves the accuracy and real-time performance of battery life prediction.

[0227] Figure 3This is a schematic diagram of a portable power bank provided in an embodiment of the present invention. (As shown...) Figure 3 As shown, a portable power bank 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a battery detection program for a multi-functional portable power bank. When the processor 30 executes the computer program 32, it implements the steps in the various embodiments of the battery detection method for a multi-functional portable power bank described above, for example... Figure 1 Steps 101 to 104 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each unit in the above-described device embodiments, for example... Figure 2 The function of the unit shown.

[0228] For example, the computer program 32 can be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 32 in the portable power bank 3. For example, the specific functions of each unit of the computer program 32 can be divided as follows:

[0229] The first acquisition unit is used to acquire the initial calendar lifetime and average ambient temperature based on the accelerated aging test.

[0230] The second acquisition unit is used to acquire the number of charge-discharge cycles, average charging current, average charging voltage, and number of deep discharge cycles collected by the mobile power supply device within a preset time period; wherein, the preset time period is a preset correction cycle duration;

[0231] The first calculation unit is used to calculate a correction factor based on the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharge cycles.

[0232] The second calculation unit calculates the final calendar life based on the initial calendar life and the correction factor, and sends the final calendar life to the power bank device; wherein the final calendar life is used to inform the user of the battery life status.

[0233] The portable power bank includes, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of a power bank 3 and does not constitute a limitation on a power bank 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the power bank may also include input / output devices, network access devices, buses, etc.

[0234] The processor 30 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0235] The memory 31 can be an internal storage unit of the power bank 3, such as a hard drive or memory of the power bank 3. The memory 31 can also be an external storage device of the power bank 3, such as a plug-in hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the power bank 3. Furthermore, the memory 31 can include both internal storage units and external storage devices of the power bank 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0236] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0237] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0238] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0239] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0240] This invention provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0241] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a camera / power bank, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0242] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0243] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0244] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0245] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units.

[0246] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0247] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0248] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0249] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0250] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0251] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A battery testing method for a multifunctional mobile power bank, characterized in that, The battery detection method for the multifunctional power bank is applied to a mobile terminal, and the battery detection method for the multifunctional power bank includes: Obtain the initial calendar lifetime and average ambient temperature based on accelerated aging tests; The system acquires the number of charge / discharge cycles, average charging current, average charging voltage, and number of deep discharge cycles collected by the power bank device within a preset time period; wherein, the preset time period is a preset correction cycle duration. The correction factor is calculated based on the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharge cycles. The final calendar life is calculated based on the initial calendar life and the correction factor, and the final calendar life is sent to the power bank device; wherein, the final calendar life is used to inform the user of the battery life status.

2. The battery testing method for the multifunctional mobile power bank as described in claim 1, characterized in that, The steps for obtaining the initial calendar lifetime and average ambient temperature based on accelerated aging testing include: Obtain the average ambient temperature corresponding to the external dataset; Obtain the capacity decay corresponding to different test durations at different accelerated aging test temperatures; Substituting the test duration and capacity decay at different accelerated aging test temperatures into the power-law equation: , This represents the test duration at the i-th accelerated aging test temperature. The corresponding capacity decay, Represents the decay rate coefficient. Indicates a time index. Indicates the test duration; The attenuation rate coefficient in the power-law equation is solved using the least squares method. and time index ; Transform the power-law equation into: ;in, Equivalent to the power-law equation , This indicates the prediction of calendar lifetime at the i-th accelerated aging test temperature. Equivalent to the power-law equation , This indicates the amount of capacity decay at the end of the battery's lifespan. The attenuation rate coefficient and the time index Substituting into the transformed power-law equation, we obtain the predicted calendar lifetime at the i-th accelerated aging test temperature; The initial calendar lifetime is calculated based on the predicted calendar lifetime at the i-th accelerated aging test temperature.

3. The battery testing method for a multifunctional mobile power bank as described in claim 2, characterized in that, The step of calculating the initial calendar lifetime based on the predicted calendar lifetime at the i-th accelerated aging test temperature includes: Construct a model of the relationship between calendar lifespan and temperature: ;in, Indicates pre-exponential factor, Indicates activation energy. Represents Boltzmann's constant. This represents the temperature of the i-th accelerated aging test; Based on known and The slope is obtained by performing a least-squares fit on the relationship model. and intercept ; The relational model is transformed into: ;in, Equivalent to the relational model , Indicates the initial calendar lifespan. Equivalent to the relational model , This represents a reference value indicating the actual ambient temperature where the user is located; Based on the known slope and intercept ,Will Substituting the transformed relational model, the initial calendar lifespan is obtained.

4. The battery testing method for the multifunctional mobile power bank as described in claim 3, characterized in that, Based on the known and The slope is obtained by performing a least-squares fit on the relationship model. and intercept Following these steps, the following are also included: The slope With the Boltzmann constant Multiplying them together yields the activation energy. ; If the activation energy If the value is within the preset range, the subsequent steps continue; wherein the preset range includes 0.4 to 1.

2. If the activation energy If the value is not within the preset range, new accelerated aging test data is obtained, and a new activation energy is calculated based on the new accelerated aging test data. Until a new activation energy is generated. It is within the preset value range.

5. The battery testing method for the multifunctional mobile power bank as described in claim 1, characterized in that, The step of calculating the correction factor based on the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharges includes: Substituting the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharge cycles into the first function yields the correction factor output by the first function; The first function is: ， in, Indicates the correction factor. Indicates the average ambient temperature. Indicates reference temperature. Indicates temperature sensitivity. Indicates the number of charge-discharge cycles. Indicates sensitivity to the number of iterations. Indicates the average charging current. This represents the normalized constant of the current. This represents the dynamic coupling factor.

6. The battery detection method for the multifunctional mobile power bank as described in claim 5, characterized in that, The dynamic coupling factor is calculated using a second function: The second function is: ， in, Indicates voltage offset sensitivity. This represents the average charging voltage. Indicates the battery's nominal voltage. Indicates deep discharge sensitivity. Indicates the number of deep discharges. Indicates the number of deep discharges. This indicates temperature-voltage cross-sensitivity.

7. The battery testing method for a multifunctional mobile power bank as described in claim 1, characterized in that, The step of calculating the final calendar lifetime based on the initial calendar lifetime and the correction factor, and sending the final calendar lifetime to the power bank device includes: Multiply the initial calendar lifetime by the correction factor to obtain the correction amount; The initial calendar lifespan is subtracted from the correction amount to obtain the final calendar lifespan, and the final calendar lifespan is sent to the power bank device.

8. A battery testing device for a multifunctional mobile power bank, characterized in that, The battery detection device of the multifunctional mobile power bank includes: The first acquisition unit is used to acquire the initial calendar lifetime and average ambient temperature based on the accelerated aging test. The second acquisition unit is used to acquire the number of charge-discharge cycles, average charging current, average charging voltage, and number of deep discharge cycles collected by the mobile power supply device within a preset time period; wherein, the preset time period is a preset correction cycle duration; The first calculation unit is used to calculate a correction factor based on the average ambient temperature, the number of charge-discharge cycles, the average charging current, the average charging voltage, and the number of deep discharge cycles. The second calculation unit calculates the final calendar life based on the initial calendar life and the correction factor, and sends the final calendar life to the power bank device; wherein the final calendar life is used to inform the user of the battery life status.

9. A portable power bank, characterized in that, The power bank includes: a memory, a processor, and a battery detection program for the multi-functional power bank stored in the memory and executable on the processor, the battery detection program being configured to implement the steps of the battery detection method for the multi-functional power bank as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps in the battery detection method of the multifunctional mobile power supply as described in any one of claims 1 to 7.

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