Battery life evaluation method and system based on state of charge and internal resistance growth rate

Abstract

The invention discloses a battery life evaluation method and system based on a state of charge and an internal resistance growth rate, and belongs to the technical field of battery life prediction, and the method comprises the steps: obtaining the remaining electric quantity and the current internal resistance of a battery; determining the charge state and the internal resistance growth rate of the battery according to the residual electric quantity and the current internal resistance of the battery; selecting a battery life calibration curve corresponding to the state of charge of the battery; wherein the battery life calibration curve is a relation curve between the battery internal resistance growth rate and the battery life under the set state of charge of the battery; and determining the battery life according to the internal resistance growth rate of the battery and the selected battery life calibration curve. Accurate prediction and evaluation of the service life of the battery are realized, and the technical problem that the prediction is inaccurate when the service life of the battery is predicted purely depending on the state of charge of the battery at present is solved.

Description

Technical Field

[0001] This invention relates to the fields of battery management systems and battery health monitoring technology in electronic information technology, and particularly to a method and system for battery life assessment based on state of charge and internal resistance growth rate. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In distribution automation terminals (DTUs), backup power is used to maintain the normal operation of control equipment and communication systems during grid failures. Lead-acid batteries are the preferred backup power source due to their low cost, ease of maintenance, and high durability. However, with increasing usage time, the internal resistance of lead-acid batteries gradually increases, leading to a decrease in battery capacity and ultimately affecting the stability and reliability of the system. Therefore, accurately monitoring the health status of lead-acid batteries is crucial for ensuring the stable operation of the system.

[0004] Lead-acid battery life monitoring is one of the keys to ensuring reliable system operation. Currently, battery life is mainly predicted by measuring the battery's state of charge (SOC). However, as battery life decreases, the battery's internal resistance changes, and relying solely on SOC to assess battery life cannot guarantee the accuracy of the assessment. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a battery life assessment method and system based on state of charge and internal resistance growth rate, thereby improving the accuracy of battery life prediction.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a battery lifetime assessment method based on state of charge and internal resistance growth rate is proposed, including: Obtain the remaining battery power and current internal resistance; Determine the battery's state of charge and internal resistance growth rate based on the battery's remaining charge and current internal resistance. Select a battery life calibration curve corresponding to the battery's state of charge; wherein, the battery life calibration curve is the relationship curve between the battery's internal resistance growth rate and the battery life under the battery's set state of charge. The battery life is determined based on the battery's internal resistance growth rate and the selected battery life calibration curve.

[0007] Furthermore, the battery life, battery state of charge, and battery internal resistance growth rate were obtained when the battery was discharged with different discharge currents. Determine the relationship curve between battery life and battery internal resistance growth rate for each battery state of charge when discharging the battery with different discharge currents, and use it as the battery life prediction curve for each discharge current and battery state of charge. By fitting the predicted life curves of all batteries under the same state of charge, a battery life calibration curve is obtained.

[0008] Furthermore, the state of charge of the battery during discharge is determined by measuring the remaining charge of the battery during discharge and then calculating the state of charge based on the remaining charge. The rate of increase in the battery's internal resistance during discharge is obtained by measuring the battery's internal resistance during discharge and then calculating it based on the measured internal resistance. The effect of different discharge currents on battery life during discharge can be calculated by measuring the actual battery capacity during discharge, or predicted based on the battery's internal resistance growth rate and discharge current.

[0009] Furthermore, battery life is the ratio of the actual battery capacity to the rated capacity. The state of charge of a battery is the ratio of its remaining charge to its maximum charge. The battery's internal resistance growth rate is the ratio of the error between the battery's current internal resistance and its initial internal resistance to the battery's initial internal resistance.

[0010] Furthermore, based on the battery's internal resistance growth rate and discharge current, a battery life prediction model is used to predict the battery life when the battery is discharged at different discharge currents. The battery life prediction model is constructed using a multilayer perceptron neural network, with the battery's internal resistance growth rate and discharge current as inputs and the battery life as the output.

[0011] Furthermore, by measuring the battery's internal resistance and actual capacity when discharging the battery with different discharge currents, and then using the internal resistance growth rate and battery life calculated based on the battery's internal resistance and actual capacity as training data, the constructed multilayer perceptron neural network is trained. After training is completed, a battery life prediction model is obtained.

[0012] Secondly, a battery life assessment system based on state of charge and internal resistance growth rate is proposed, including: The data acquisition unit acquires the remaining battery power and current internal resistance. The battery state calculation unit is used to determine the battery's state of charge and internal resistance growth rate based on the battery's remaining charge and current internal resistance. The battery life calibration curve determination unit is used to select the battery life calibration curve corresponding to the battery's state of charge; wherein, the battery life calibration curve is the relationship curve between the battery's internal resistance growth rate and the battery life under the battery's set state of charge. The battery life determination unit is used to determine the battery life based on the battery's internal resistance growth rate and the selected battery life calibration curve.

[0013] Thirdly, a computer device is proposed, the device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the battery life assessment method based on state of charge and internal resistance growth rate proposed in the first aspect.

[0014] Fourthly, a computer-readable storage medium is proposed, which stores a computer program adapted to be loaded by a processor and executed by the battery life assessment method based on state of charge and internal resistance growth rate proposed in the first aspect.

[0015] Fifthly, a computer program product is proposed, which includes a computer program that, when executed by a processor, implements the battery life assessment method based on state of charge and internal resistance growth rate proposed in the first aspect.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a battery life assessment method and system based on state of charge (SOC) and internal resistance growth rate. The method pre-constructs the relationship curve between the battery internal resistance growth rate and battery life for each SOC. When predicting and assessing battery life, only the SOC and internal resistance growth rate need to be obtained to achieve accurate prediction and assessment. The calculation is simple and efficient, improving prediction efficiency and reducing computational costs. Furthermore, since the battery life prediction comprehensively considers both the SOC and internal resistance growth rate, the accuracy of battery life assessment is guaranteed.

[0017] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0019] Figure 1The flowchart of the battery lifetime assessment method based on state of charge and internal resistance growth rate proposed in the example is shown below. Figure 2 The second-order Randles model and simplified equivalent circuit model (ECM) diagrams are presented for the embodiments. Figure 3 The relationship curve between the internal resistance growth rate and battery life at 70% SOC is presented in Table 1 for the example. Figure 4 The relationship curve between the internal resistance growth rate and battery life at 80% SOC is presented in Table 1 for the example. Figure 5 The battery life prediction curve and calibration curve obtained at 70% SOC are presented in the example. Figure 6 The battery life prediction curve and calibration curve obtained at SOC 80% are presented in the example. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0024] Example 1 First, the application scenarios of the battery life assessment method based on state of charge and internal resistance growth rate proposed in this embodiment will be explained.

[0025] The battery life assessment method based on state of charge and internal resistance growth rate proposed in this embodiment is applied to predict battery life.

[0026] The second-order Randle model is a classic electrochemical equivalent circuit model widely used to describe the electrochemical behavior inside batteries. In this model, chemical reactions such as electrode polarization, ohmic internal resistance, and capacitive effects can be equivalently simulated using circuit elements. This model can accurately characterize the dynamic response characteristics of batteries at different time scales, and therefore, it is widely used in the field of battery health monitoring.

[0027] The second-order Randles model includes the electrolyte resistance of the battery. positive and negative charge transfer resistors and and double-layer capacitors and To represent polarization reaction, diffusion resistance This represents a diffusion reaction. The combination of these components can effectively describe the rapid response of the battery in the short term and the changes in characteristics during long-term diffusion processes.

[0028] Figure 2 The second-order Randles model and its simplified equivalent circuit model (ECM) are presented. The components in this model are defined as follows: Indicates open-circuit voltage. Indicates the actual output voltage. Indicates the resistance of the electrolyte. , and , These represent the charge transfer resistance in the polarization reaction, , and , They represent capacitors, This represents the diffusion resistance.

[0029] In the second-order Randles model, the model structure considers the response characteristics at different time scales: within an extremely short time range (e.g., 0.1 seconds), the capacitance... and The effect of diffusion resistance is dominant, while over longer time periods (e.g., more than 10 seconds), diffusion resistance... The effects gradually become apparent. However, during short-term battery discharge (e.g., 5 seconds), the effects of double-layer capacitance and diffusion impedance are negligible, and the electrolyte resistance is negligible at this time. and charge transfer resistance and This becomes a major factor determining battery performance. Therefore, under short-duration discharge conditions, the second-order Randles model can be simplified to an equivalent circuit model (ECM) that only includes electrolyte resistance and charge transfer resistance.

[0030] Formula (1) describes the output voltage under short-term battery discharge. Calculation method:

[0031] The formula above shows the battery output voltage. Electrolyte resistance and charge transfer resistance and The influence of electrolyte resistance during battery aging. Typically remains stable, while charge transfer resistance and It will gradually increase, causing the battery output voltage to... The voltage decreases. Therefore, the output voltage of the battery under different current conditions can be estimated by measuring the resistance value. And further infer the battery's lifespan.

[0032] Currently, battery life assessment primarily relies on measuring the battery's state of charge (SOC) to predict its lifespan. However, as battery life decreases, the battery's internal resistance changes. Simply relying on SOC to assess battery life cannot guarantee accuracy. Furthermore, while neural network models are typically used to predict battery life using SOC, this method is computationally complex and incurs significant computational costs. Secondly, it requires a large amount of training data for the neural network model, but the available training data for battery life prediction is limited, making it difficult to guarantee the accuracy of the neural network model training and consequently affecting the accuracy of battery life prediction.

[0033] Based on the above application scenarios, in order to improve the accuracy of battery life prediction while reducing computational costs, this embodiment proposes a battery life assessment method based on state of charge and internal resistance growth rate.

[0034] like Figure 1 As shown, the battery life assessment method based on state of charge and internal resistance growth rate disclosed in this embodiment includes: Obtain the remaining battery power and current internal resistance; Determine the battery's state of charge and internal resistance growth rate based on the battery's remaining charge and current internal resistance. Select a battery life calibration curve corresponding to the battery's state of charge; wherein, the battery life calibration curve is the relationship curve between the battery's internal resistance growth rate and the battery life under the battery's set state of charge. The battery life is determined based on the battery's internal resistance growth rate and the selected battery life calibration curve.

[0035] The battery life assessment method based on state of charge and internal resistance growth rate proposed in this embodiment pre-constructs the relationship curve between the battery internal resistance growth rate and battery life for each battery state of charge. When predicting and assessing battery life, only the battery state of charge and internal resistance growth rate need to be obtained to achieve accurate prediction and assessment of battery life. The calculation is simple and efficient, improving the efficiency of prediction and reducing the computational cost. Furthermore, since the battery life prediction comprehensively considers the battery state of charge and internal resistance growth rate, the accuracy of battery life assessment is guaranteed.

[0036] The process of pre-constructing battery life calibration curves for each battery state of charge in this embodiment includes: Obtain the battery life, battery state of charge, and battery internal resistance growth rate when the battery is discharged with different discharge currents; Determine the relationship curve between battery life and battery internal resistance growth rate for each battery state of charge when discharging the battery with different discharge currents, and use it as the battery life prediction curve for each discharge current and battery state of charge. By fitting the predicted life curves of all batteries under the same state of charge, a battery life calibration curve is obtained.

[0037] Among them, the state of charge of the battery during discharge is determined by measuring the remaining charge of the battery during discharge and then calculating it based on the remaining charge of the battery. The rate of increase in the battery's internal resistance during discharge is obtained by measuring the battery's internal resistance during discharge and then calculating it based on the measured internal resistance. The effect of different discharge currents on battery life during discharge can be calculated by measuring the actual battery capacity during discharge, or predicted based on the battery's internal resistance growth rate and discharge current.

[0038] This embodiment uses different discharge currents to conduct charge-discharge cycle tests on the battery, and records the actual battery capacity at different aging stages under different discharge currents. Remaining battery power and current internal resistance The battery life is calculated according to formulas (2)-(4). SOH Battery state of charge SOC and the growth rate of battery internal resistance ; obtain batteries in different SOC Table 1 shows the battery internal resistance growth rate and battery life data at (70%) and (80%). Based on the data in Table 1, plots were generated for different currents and... SOC Below is a curve showing the relationship between the battery's internal resistance growth rate and battery life (expressed in terms of battery capacity), as follows: Figure 2 and Figure 3As shown, it can be seen that under different discharge currents, the internal resistance growth rate gradually increases with the gradual decline of battery life, showing a significant trend of increasing internal resistance growth rate during battery aging. However, due to the limited amount of measured data and the lack of accurate data between two discrete points, the curve of internal resistance growth rate and battery life obtained by connecting discrete points is inaccurate.

[0039] Battery capacity is one of the important indicators for measuring battery life, reflecting the current health of the battery. Therefore, battery capacity is used to characterize battery life. Battery life is the ratio of the actual battery capacity to the rated capacity, defined as follows: (2) in, This indicates the actual battery capacity obtained through a discharge test. This is the battery's rated capacity.

[0040] The state of charge (SOC) of a battery is also closely related to its lifespan. The SOC is the ratio of the battery's remaining capacity to its maximum capacity, defined as follows: (3) in, This indicates the actual battery capacity obtained through a discharge test. This is the battery's rated capacity.

[0041] As batteries age, their capacity gradually decreases, leading to a reduction in State of Charge (SOC) and consequently, a decline in their discharge capability. Therefore, real-time SOC monitoring is crucial for assessing battery lifespan. The internal resistance growth rate describes the increase in battery internal resistance over time. It is defined as the ratio of the error between the current internal resistance and the initial internal resistance to the initial internal resistance, as follows: (4) in, It is the current internal resistance of the battery. It is the initial internal resistance of the battery.

[0042] Table 1. Lifetime and internal resistance growth rate of lead-acid batteries under different SOC and discharge current conditions.

[0043] In this embodiment, the battery life prediction model is used to predict the battery life when the battery is discharged at different discharge currents based on the battery's internal resistance growth rate and discharge current. The battery life prediction model is constructed using a multilayer perceptron neural network, with the battery's internal resistance growth rate and discharge current as inputs and the battery life as the output.

[0044] In this process, the battery internal resistance and actual battery capacity are measured when the battery is discharged with different discharge currents. The internal resistance growth rate and battery life calculated based on the internal resistance and actual battery capacity are then used as training data to train the constructed multilayer perceptron neural network. Once training is complete, a battery life prediction model is obtained.

[0045] Specifically, in this embodiment, a normalization function is called to normalize the discharge current, battery internal resistance growth rate, and battery life during the battery charge-discharge cycle test. The normalized discharge current, battery internal resistance growth rate, and battery life are divided into a training set and a test set. The discharge current and internal resistance growth rate in the training set are used as inputs to a multilayer perceptron (MLP) neural network, and the battery life is used as the output. The MLP neural network is trained to obtain a trained MLP neural network model, which is then used as a battery life prediction model. The discharge current and internal resistance growth rate in the test set are then input into the trained MLP neural network model to output the predicted battery life. Based on the predicted battery life and the actual battery life, the accuracy of the model is tested. The results show that the accuracy is 97.6% at 70% SOC and 98.2% at 80% SOC.

[0046] The multilayer perceptron (MLP) neural network consists of an input layer, two hidden layers, and an output layer. The input layer receives normalized discharge current and internal resistance growth rate data. The first hidden layer has 64 neurons, and the second hidden layer has 32 neurons. The ReLU activation function is used to increase the non-linear expressive power of the model. The output layer generates the predicted value of battery life.

[0047] To ensure the model's generalization ability, 80% of the normalized discharge current, battery internal resistance growth rate, and battery life were used for model training, and the remaining 20% ​​for testing. During model training, the mean squared error (MSE) was used as the loss function, and the Adam optimizer was used for weight adjustment. The model was iterated 100 times until the loss function converged. After training, the predicted battery life could be output by inputting the normalized discharge current and resistance growth rate.

[0048] Subsequently, a battery life prediction model was used to predict the battery life of batteries for which only the internal resistance growth rate and discharge current were calculated in the cyclic charge-discharge test, but the battery life was not measured. This filled the gap in the test data and obtained the battery life corresponding to all internal resistance growth rates. Based on this data, the relationship curve between the battery life and the battery internal resistance growth rate for each battery state of charge under different discharge currents was plotted, which is the battery life prediction curve for each discharge current and battery state of charge.

[0049] Figure 5 and Figure 6 The solid lines represent the predicted battery life curves at SOC 70% and SOC 80%. This demonstrates that the neural network model provides more comprehensive and abundant data on internal resistance growth rate and battery life compared to experimental data. Figure 3 and Figure 4 The battery life prediction curve is smoother and more accurate.

[0050] To achieve more universal battery life prediction, this embodiment further employs a polynomial regression method to fit the battery life prediction curves under the same SOC but different discharge currents, thereby generating a unified battery life calibration curve applicable to all discharge current conditions. Specifically: (1) Collect data on the internal resistance growth rate and battery life under different current conditions, and let the internal resistance growth rate be the independent variable x and the battery life be the dependent variable y; (2) The internal resistance growth rate of the independent variable is extended to a polynomial feature by using the polynomial regression function (PolynomialFeatures). The extended polynomial feature is fitted by the regression model LinearRegression. The regression model captures the complex nonlinear relationship between the internal resistance growth rate and the battery life, and fits a general regression model applicable to all currents. (3) Finally, the general regression model of step (2) is used to predict the battery life under all internal resistance growth rates, and a battery life calibration curve suitable for all discharge currents is plotted based on the newly obtained internal resistance growth rate and battery life.

[0051] Polynomial regression can not only capture the nonlinear relationship between internal resistance growth and battery life, but also reflect the aging trend of the battery under different discharge conditions, so that the final calibration curve has strong adaptability and robustness under various operating conditions.

[0052] The generated calibration curve is an important tool for assessing battery health. Subsequently, by monitoring the battery's internal resistance growth rate and state of charge (SOC), a battery life calibration curve is determined based on the SOC. Using the battery's internal resistance growth rate and the calibration curve, battery life can be directly estimated, avoiding the complexity of continuously monitoring discharge current in practical applications. This calibration curve captures the non-linear relationship between internal resistance growth and battery life, ensuring that the final battery life calibration curve comprehensively reflects the battery's aging trend under different discharge conditions. Figure 5 and Figure 6 The dashed lines represent the battery life calibration curves at SOC 70% and SOC 80%, respectively. The calibration curves show consistency under different SOC conditions, further validating the applicability and reliability of the method.

[0053] The battery life assessment method based on state of charge and internal resistance growth rate disclosed in this embodiment provides a scientific basis for battery health management and simplifies the battery life prediction process. In practical applications, this battery life calibration curve can effectively reduce the cost of battery state monitoring and provide efficient technical support for battery management systems. The stability and accuracy of the calibration curve ensure the prediction of battery life under various usage conditions and has important application value.

[0054] Example 2 In this embodiment, a battery life assessment system based on state of charge and internal resistance growth rate is disclosed, including: The data acquisition unit acquires the remaining battery power and current internal resistance. The battery state calculation unit is used to determine the battery's state of charge and internal resistance growth rate based on the battery's remaining charge and current internal resistance. The battery life calibration curve determination unit is used to select the battery life calibration curve corresponding to the battery's state of charge; wherein, the battery life calibration curve is the relationship curve between the battery's internal resistance growth rate and the battery life under the battery's set state of charge. The battery life determination unit is used to determine the battery life based on the battery's internal resistance growth rate and the selected battery life calibration curve.

[0055] The system disclosed in this embodiment also includes a remote display unit; the remote display unit is used to remotely display the corresponding battery life.

[0056] The data acquisition unit includes a WO2 monitoring module. The positive and negative terminals of the lead-acid battery are connected to the corresponding WO2 monitoring module through independent interfaces. All WO2 monitoring modules are connected in series via a UART bus to the battery status calculation unit. The battery status calculation unit, the battery life calibration curve determination unit, the battery life determination unit, and the remote display unit are connected in sequence.

[0057] The present invention also discloses a computer device, the device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the battery life assessment method based on state of charge and internal resistance growth rate disclosed in Embodiment 1.

[0058] The present invention also discloses a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by a processor to perform the battery life assessment method based on state of charge and internal resistance growth rate disclosed in Example 1.

[0059] The present invention also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the battery life assessment method based on state of charge and internal resistance growth rate disclosed in Embodiment 1.

[0060] The method disclosed in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0061] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments 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 implementation should not be considered beyond the scope of this application.

[0062] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A battery lifetime assessment method based on state of charge and internal resistance growth rate, characterized in that, include: Obtain the remaining battery power and current internal resistance; Determine the battery's state of charge and internal resistance growth rate based on the battery's remaining charge and current internal resistance. Select a battery life calibration curve corresponding to the battery's state of charge; wherein, the battery life calibration curve is the relationship curve between the battery's internal resistance growth rate and the battery life under the battery's set state of charge. The battery life is determined based on the battery's internal resistance growth rate and the selected battery life calibration curve.

2. The battery life assessment method based on state of charge and internal resistance growth rate as described in claim 1, characterized in that, Obtain the battery life, battery state of charge, and battery internal resistance growth rate when the battery is discharged with different discharge currents; Determine the relationship curve between battery life and battery internal resistance growth rate for each battery state of charge when discharging the battery with different discharge currents, and use it as the battery life prediction curve for each discharge current and battery state of charge. By fitting the predicted life curves of all batteries under the same state of charge, a battery life calibration curve is obtained.

3. The battery life assessment method based on state of charge and internal resistance growth rate as described in claim 2, characterized in that, The state of charge of a battery during discharge is determined by measuring the remaining charge of the battery during discharge and then calculating the state of charge based on the remaining charge. The rate of increase in the battery's internal resistance during discharge is obtained by measuring the battery's internal resistance during discharge and then calculating it based on the measured internal resistance. The effect of different discharge currents on battery life during discharge can be calculated by measuring the actual battery capacity during discharge, or predicted based on the battery's internal resistance growth rate and discharge current.

4. The battery life assessment method based on state of charge and internal resistance growth rate as described in claim 3, characterized in that, Battery life is the ratio of the battery's actual capacity to its rated capacity. The state of charge of a battery is the ratio of its remaining charge to its maximum charge. The battery's internal resistance growth rate is the ratio of the error between the battery's current internal resistance and its initial internal resistance to the initial internal resistance of the battery.

5. The battery life assessment method based on state of charge and internal resistance growth rate as described in claim 3, characterized in that, Based on the battery's internal resistance growth rate and discharge current, a battery life prediction model is used to predict the battery life when the battery is discharged at different discharge currents. The battery life prediction model takes the battery's internal resistance growth rate and discharge current as inputs and the battery life as output, and is constructed using a multilayer perceptron neural network.

6. The battery life assessment method based on state of charge and internal resistance growth rate as described in claim 5, characterized in that, By discharging the battery with different discharge currents, the internal resistance and actual battery capacity of the battery are measured. The internal resistance growth rate and battery life calculated based on the internal resistance and actual battery capacity are then used as training data to train the constructed multilayer perceptron neural network. Once training is complete, a battery life prediction model is obtained.

7. A battery life assessment system based on state of charge and internal resistance growth rate, characterized in that, include: The data acquisition unit acquires the remaining battery power and current internal resistance. The battery state calculation unit is used to determine the battery's state of charge and internal resistance growth rate based on the battery's remaining charge and current internal resistance. The battery life calibration curve determination unit is used to select the battery life calibration curve corresponding to the battery's state of charge; wherein, the battery life calibration curve is the relationship curve between the battery's internal resistance growth rate and the battery life under the battery's set state of charge. The battery life determination unit is used to determine the battery life based on the battery's internal resistance growth rate and the selected battery life calibration curve.

8. An electronic device, characterized in that, The device includes: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the battery life assessment method based on state of charge and internal resistance growth rate as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed by the battery life assessment method based on state of charge and internal resistance growth rate as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the battery life assessment method based on state of charge and internal resistance growth rate as described in any one of claims 1-6.

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