Method and device for detecting internal resistance of battery

Abstract

The embodiment of the application provides a battery internal resistance detection method and a detection device. The detection method comprises the following steps: obtaining battery state data of the battery in a first specified time period; determining a total internal resistance value of the battery according to the battery state data; determining a sub-internal resistance proportion value of a battery sub-internal resistance according to the battery state data by using a relationship model; wherein the relationship model is used to indicate the sub-internal resistance proportion value of the battery sub-internal resistance corresponding to the battery state data; and obtaining the sub-internal resistance value of the battery sub-internal resistance according to the total internal resistance value and the sub-internal resistance proportion value. The battery internal resistance detection method and the detection device can conveniently obtain the internal resistance of the battery.

Description

Technical Field

[0001] This application relates to the field of energy technology, specifically to a method and apparatus for detecting the internal resistance of a battery. Background Technology

[0002] In existing technologies, the state of batteries in energy storage systems is monitored online to assess their health. For example, operational status data such as the battery's state of charge, voltage, current, and temperature can be detected.

[0003] Battery internal resistance is a crucial indicator of battery performance, reflecting its health and aging status. Current technologies primarily rely on offline experimental methods for detecting battery internal resistance, such as electro-impedance spectroscopy (EIS) testing or DC internal resistance measurement. However, these methods depend on laboratory conditions, making it difficult to measure battery internal resistance during operation within an energy storage system. Summary of the Invention

[0004] This application provides a method and apparatus for detecting the internal resistance of a battery, which can accurately determine the internal resistance of the battery.

[0005] In a first aspect, embodiments of this application provide a method for detecting the internal resistance of a battery, comprising: acquiring battery state data of the battery during a first specified time period; determining the total internal resistance value of the battery based on the battery state data; determining the percentage value of the subdivided internal resistance of the battery based on the battery state data using a relational model; wherein the relational model is used to indicate the percentage value of the subdivided internal resistance of the battery corresponding to the battery state data; and deriving the subdivided internal resistance value of the battery based on the total internal resistance value and the percentage value of the subdivided internal resistance.

[0006] Optionally, determining the total internal resistance of the battery based on the battery state data includes: determining the total internal resistance of the battery based on the battery state data when the current change of the battery within a second specified time period meets specified conditions; wherein the specified conditions include: the rate of change of the current is greater than a specified charging rate or a specified discharging rate, or the current value is greater than a specified current threshold; the second specified time period is a subset of the first specified time period.

[0007] Optionally, the first specified time period is selected from at least one of the following: the start period of battery charging, the end period of battery charging, the start period of battery discharging, or the end period of battery discharging.

[0008] Optionally, determining the percentage of subdivided internal resistance of the battery based on the battery state data using a relational model includes: matching the battery state data in the relational model to obtain the percentage of subdivided internal resistance corresponding to the battery state data.

[0009] Optionally, the relational model includes multiple sub-relational models, each corresponding to a specific internal resistance of a battery; the multiple battery state data included in the sub-relational model are used to form multiple operating conditions, and the sub-relational model records the experimentally measured percentage of the specific internal resistance for each operating condition.

[0010] Optionally, the state of charge (SCC) value, current value, and operating temperature value form an operating condition; wherein, at least one of the SCC value, current value, and operating temperature value is different between different operating conditions.

[0011] Optionally, the battery state data includes the voltage change and current change of the battery during the first specified time period; determining the total internal resistance of the battery based on the battery state data includes: calculating the total internal resistance of the battery based on the voltage change and the current change.

[0012] Secondly, embodiments of this application provide a battery internal resistance detection device, comprising: an acquisition module for acquiring battery state data of the battery during a first specified time period; a first determination module for determining the total internal resistance value of the battery based on the battery state data; a second determination module for determining the percentage value of the subdivided internal resistance of the battery based on the battery state data using a relational model; wherein the relational model is used to indicate the percentage value of the subdivided internal resistance of the battery corresponding to the battery state data; and a generation module for deriving the subdivided internal resistance value of the battery based on the total internal resistance value and the percentage value of the subdivided internal resistance.

[0013] Optionally, the first determining module includes a first sub-module; the first sub-module is used to determine the total internal resistance of the battery based on the battery state data when the current change of the battery within a second specified time period meets specified conditions; wherein, the specified conditions include: the current change rate is greater than a specified charging rate or a specified discharging rate, or the current value is greater than a specified current threshold; the second specified time period is a subset of the first specified time period.

[0014] Optionally, the second determining module includes a second sub-module; the second sub-module matches the battery state data in the relational model to obtain the subdivided internal resistance ratio value corresponding to the battery state data; wherein, the relational model is a dataset that records different battery state data and the corresponding subdivided internal resistance ratio values.

[0015] Thirdly, embodiments of this application also provide a computer device, the computer device including a memory and a processor, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to implement the battery internal resistance detection method as described above.

[0016] Fourthly, embodiments of this application also provide a computer-readable storage medium storing at least one computer program, which, when executed by a processor, can implement the battery internal resistance detection method described above.

[0017] Fifthly, embodiments of this application also provide a computer program product, which, when executed by a processor, implements the aforementioned method for detecting battery internal resistance.

[0018] In several embodiments provided in this application, by acquiring battery state data within a first specified time period and calculating the total internal resistance and the proportion of subdivided internal resistances based on the battery state data, the composition characteristics of the battery internal resistance can be evaluated in real time at critical moments during the battery charging and discharging process. This enables online detection and dynamic evaluation of ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance, thereby achieving accurate acquisition of the battery internal resistance. Furthermore, it can also improve the evaluation efficiency of battery operating status in the energy storage system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart of a method for detecting the internal resistance of a battery, provided as an embodiment of this application.

[0021] Figure 2 This is a schematic diagram of the voltage and current relationship in a DC internal resistance measurement method, provided as an embodiment of this application.

[0022] Figure 3 This is a schematic diagram of a battery internal resistance detection device provided in one embodiment of this application.

[0023] Figure 4 This is a schematic diagram of a module of an electronic device provided in one embodiment of this application. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0025] In the description of the embodiments of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] In related technologies, energy storage systems typically monitor battery health by collecting battery state data (e.g., state of charge (SOC), voltage, current, and temperature). However, battery internal resistance, as an important indicator of battery performance, is primarily measured using offline experimental methods, such as battery impedance spectroscopy (EIS) testing or DC internal resistance measurement. These methods require laboratory conditions and are difficult to implement for real-time monitoring of internal resistance during battery operation.

[0027] Battery internal resistance includes various types such as ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance. Changes in each type of internal resistance affect battery performance. During the dynamic process of battery charging and discharging, changes in the state data of each battery cause dynamic changes in internal resistance. After the battery is applied to an energy system, it is difficult to measure the battery internal resistance using the aforementioned offline experimental methods.

[0028] Therefore, it is necessary to provide a method for detecting battery internal resistance, which can obtain the values ​​of total internal resistance and subdivided internal resistance of the battery during battery operation, so as to improve the accuracy of battery operating status detection in energy storage systems.

[0029] This application provides a method for detecting the internal resistance of a battery, which can be applied to a detection device. The detection device can be an electronic device with certain data processing capabilities, and it can obtain feedback from sensors in the energy system via wired or wireless means. Of course, in some embodiments, the detection device can also be understood as a program module running in an electronic device.

[0030] Please see Figure 1 The method for detecting the internal resistance of a battery may include the following steps.

[0031] Step S110: Obtain battery status data of the battery during a first specified time period.

[0032] Step S120: Determine the total internal resistance of the battery based on the battery status data.

[0033] Step S130: Determine the percentage of the subdivided internal resistance of the battery based on the battery state data using a relational model; wherein, the relational model is used to indicate the percentage of the subdivided internal resistance of the battery corresponding to the battery state data.

[0034] Step S140: Based on the total internal resistance value and the percentage of the subdivided internal resistance, the subdivided internal resistance value of the battery is obtained.

[0035] In this embodiment, the detection device can acquire battery state data during a first specified time period. The battery state data can be used to represent the actual operating state of the battery. For example, the battery state data may include, but is not limited to, the battery's state of charge (SOC), current, voltage, and temperature.

[0036] In this embodiment, the total internal resistance of the battery refers to the combined impedance formed by the various sub-resistances of the battery components during battery operation. The total internal resistance of the battery can include various sub-resistances such as ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance. Ohmic internal resistance is formed by the combined resistance of the battery's internal materials, the ion transport impedance in the electrolyte, the separator impedance, and the contact impedance between the electrodes and the current collector. Ohmic internal resistance exhibits purely resistive characteristics and is unrelated to the battery's chemical reactions. Electrochemical polarization internal resistance refers to the impedance caused by the limited electron or ion transfer rates of reactants and products during the electrochemical reaction at the battery electrode interface. Concentration polarization internal resistance refers to the impedance caused by the ion concentration gradient (concentration difference) near the electrode interface during battery operation.

[0037] In some embodiments, a relationship model between the battery's internal resistance and battery state data can be constructed through offline experiments. This model reflects the changes in the internal resistance under different operating conditions and can be deployed within a detection device, allowing it to indicate the percentage of the battery's internal resistance corresponding to the battery state data. Furthermore, at the start or end of battery charging and discharging, instantaneous voltage changes ΔU and current changes ΔI are collected, and the data is calculated according to formula R. 总 =ΔU / ΔI, calculate the total internal resistance of the battery. Furthermore, the detection device can obtain the percentage of each subdivided internal resistance by inputting battery state data into the above relationship model. This percentage represents the relative proportions of the ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance within the total internal resistance.

[0038] In this embodiment, the specific values ​​of the internal resistance of each battery segment are further calculated based on the total internal resistance value and the percentage of segmented internal resistance. For example, if the total internal resistance value is known to be R, and the percentages of segmented internal resistance can be α, β, and γ, then the specific values ​​of the ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance can be calculated respectively: Ohmic internal resistance R 欧姆 =α*R, electrochemical polarization internal resistance R 电化学 =β*R, concentration polarization internal resistance R 浓差 =γ*R.

[0039] In several embodiments provided in this application, by acquiring battery state data within a first specified time period and determining the total internal resistance and the proportion of subdivided internal resistances based on the battery state data, the composition characteristics of the battery internal resistance can be evaluated in real time at critical moments during the battery charging and discharging process. This enables online detection and dynamic evaluation of ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance, thereby achieving accurate acquisition of the battery internal resistance. Furthermore, it can also improve the evaluation efficiency of battery operating status in the energy storage system.

[0040] In some embodiments, the first specified time period is selected from at least one of the following: the start period of battery charging, the end period of battery charging, the start period of battery discharging, or the end period of battery discharging.

[0041] In this embodiment, the battery voltage and / or current undergo significant changes during the start and end of charging or discharging. Thus, within a first specified time period, the dynamic changes in battery state data accurately reflect the total internal resistance characteristics and the composition characteristics of the battery's individual internal resistances. For example, at the start of charging, the current gradually increases from its initial quiescent state, leading to a significant change in voltage response. Similarly, at the end of discharging, the current gradually decreases to zero, also resulting in a significant voltage change. Furthermore, by collecting the instantaneous voltage change ΔU and current change ΔI within the first specified time period, the total internal resistance of the battery can be calculated relatively accurately. Moreover, by combining the relationship model between battery state data and individual internal resistances, the percentage of individual internal resistances can be obtained from the collected battery state data (e.g., SOC, current, temperature, etc.). In summary, the selection range of the first specified time period exhibits significant current and voltage changes, thereby improving the accuracy of battery internal resistance detection.

[0042] In some implementations, the detection device can determine the total internal resistance of the battery based on the battery state data if the current change of the battery within a second specified time period meets specified conditions; wherein the specified conditions include: the rate of change of the current is greater than a specified charging rate or a specified discharging rate, or the current value is greater than a specified current threshold; and the second specified time period is a subset of the first specified time period.

[0043] In this embodiment, to further improve the accuracy of calculating the total internal resistance and the percentage of subdivided internal resistance, the detection device can determine the total internal resistance and the percentage of subdivided internal resistance based on battery state data only when the battery current change meets specified conditions within a second specified time period. The second specified time period is a subset of the first specified time period, including portions of the battery charging start period, battery charging end period, battery discharging start period, or battery discharging end period. For example, the first specified time period is 20 seconds long, and the second specified time period is 1 second long.

[0044] The specified conditions include, but are not limited to, one of the following: the rate of change of battery current is greater than a specified charging rate or a specified discharging rate, or the battery current value exceeds a specified current threshold. For example, at the beginning of battery charging, if the rate of increase of the battery charging current exceeds a set specified charging rate, such as 0.1 C / s, or the charging current value exceeds a specified current threshold, such as 10 A, then the specified condition is met, and the detection device begins to determine the total internal resistance value and the percentage of subdivided internal resistance values ​​of the battery based on the collected battery state data. Similarly, at the end of battery discharging, if the rate of decrease of the discharging current is greater than a specified discharging rate, or the discharging current is lower than a certain specified current threshold, such as close to 0 A, then the specified condition is considered to be met.

[0045] In summary, by identifying a second specified time period that meets the specified conditions within a first specified time period, and determining the total internal resistance and the proportion of subdivided internal resistance based on the battery state data within the second specified time period, the accuracy of internal resistance detection can be improved.

[0046] In some embodiments, the detection device can match the battery state data in the relational model to obtain the subdivided internal resistance ratio value corresponding to the battery state data; wherein, the relational model is a dataset that records different battery state data and the corresponding subdivided internal resistance ratio values.

[0047] In this embodiment, the detection device can match the battery state data in a relational model to obtain a detailed internal resistance percentage corresponding to the battery state data. The detailed internal resistance percentage can be used to represent the relative proportions of the battery's ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance in the total internal resistance.

[0048] The relationship model can include the relationship between different battery state data constructed through experiments and the subdivided internal resistance percentage values. Specifically, the relationship model can be constructed based on experimental data measured under different operating conditions, such as battery state parameters, current values, and temperature values. For example, under different state parameters such as 20%, 50%, or 80%, current values ​​such as 10A or 50A, and temperature values ​​such as 10℃, 25℃, or 40℃, the subdivided internal resistance percentage values ​​corresponding to each combination of state parameters can be calculated, and these data can be stored in a dataset to form the relationship model.

[0049] Furthermore, during battery operation, the detection device can collect battery state data in real time and match the collected battery state data with the state parameter combinations stored in the relational model to output the corresponding detailed internal resistance percentage values. For example, when the real-time collected battery state data is 50% state of charge, 20A current, and 25℃ temperature, the detection device can match the corresponding detailed internal resistance percentage values ​​in the relational model, such as ohmic internal resistance percentage α = 0.4, electrochemical polarization internal resistance percentage β = 0.3, and concentration polarization internal resistance percentage γ = 0.3.

[0050] In summary, by matching the subdivided internal resistance percentages based on the relational model, the subdivided battery internal resistance values ​​for each type of battery can be further derived by combining the previously calculated total internal resistance value. The detection device, through the use of the relational model, effectively improves the accuracy and real-time performance of calculating the subdivided internal resistance percentages, thus providing reliable data support for online detection and evaluation of battery internal resistance. This detection method is applicable to various energy storage system scenarios and meets the real-time monitoring needs under different battery operating conditions.

[0051] In some embodiments, the relational model includes multiple sub-relational models, each sub-relational model corresponding to a specific internal resistance of a battery; the multiple battery state data included in the sub-relational model are used to form multiple operating conditions, and the sub-relational model records the experimentally measured percentage of the specific internal resistance for each operating condition.

[0052] In this embodiment, the relationship model may include multiple sub-relationship models. Each sub-relationship model corresponds to a specific internal resistance of the battery, and is used to record the percentage of the specific internal resistance measured experimentally under different operating conditions.

[0053] In the relational model, the operating conditions in each sub-relational model are formed by combining multiple battery state data. Battery state data can include state of charge (SOC), current, and temperature values. For example, the sub-relational model for ohmic internal resistance can record the percentage of different SOC values ​​(e.g., 20%, 50%, 80%), different current values ​​(e.g., 10A, 20A, 50A), and different temperatures (e.g., 10℃, 25℃, 40℃).

[0054] In some embodiments, the sub-relationship model is obtained by detecting the detailed internal resistance ratio corresponding to each operating condition under the conditions of simulating the multiple operating conditions using experimental resources. Specifically, during the experiment, the sub-relationship model can be constructed by performing offline measurements on the battery.

[0055] In one specific embodiment, a method for constructing a relational model may be provided, which may include the following steps.

[0056] Step S210: Determine the type of internal resistance of the battery subdivision.

[0057] Step S220: Select operating condition.

[0058] Step S230: Calculate the percentage of internal resistance under each operating condition.

[0059] Step S240: Construct the relational model.

[0060] Specifically, the type of internal resistance of the battery can be selected first, such as ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance. Further, multiple operating conditions can be formed by combining different battery state data, and then the corresponding operating conditions can be simulated using experimental resources. For example, an operating condition with a state of charge of 50%, a current of 20A, and a temperature of 25°C can be simulated using experimental resources, and the percentages of ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance can be experimentally measured to be 0.4, 0.3, and 0.3 respectively. These percentages of internal resistance can be stored in corresponding sub-relationship models and associated with the corresponding operating conditions. In this way, all sub-relationship models obtained from the simulation of each operating condition constitute the overall relationship model.

[0061] Specifically, the sub-relationship model can include multiple data tables, each corresponding to a state of charge (SOC) value and a type of subdivided internal resistance of the battery. This allows the data tables to record the corresponding subdivided internal resistance percentage under the given current and temperature values ​​for that SOC value. For example, the sub-relationship model could include a data table showing the values ​​of the ohmic internal resistance percentage α when the SOC value is 20%, as shown in Table 1.

[0062] Table 1. Data on α values ​​under a state of charge of 20%

[0063]

[0064] In some embodiments, the first specified time period is divided into multiple sub-time periods; the subdivided internal resistance value of the battery is calculated based on the voltage and current changes within the corresponding sub-time period; wherein, the correspondence between the battery subdivided internal resistance and the sub-time period is established experimentally. Specifically, the ohmic internal resistance mainly originates from the conductivity and current flow resistance within the battery. During the initial charging, initial discharging, and nearing the end of charging or discharging, the current typically increases or decreases rapidly, and the voltage and current changes in these sub-time periods better reflect the ohmic internal resistance value of the battery. Electrochemical polarization internal resistance is related to the electrochemical reaction rate at the battery electrode interface. When the battery is charging or discharging, the reaction occurring at the electrode interface causes a certain impedance, which typically increases gradually with changes in current. Electrochemical polarization internal resistance requires a certain amount of time for the electrochemical reaction to occur, causing the sub-time period corresponding to the electrochemical polarization internal resistance to lag behind the sub-time period corresponding to the ohmic internal resistance. Concentration polarization internal resistance is mainly related to the change in ion concentration gradient near the electrode interface during charging or discharging. During charging or discharging, the ion concentration near the electrodes may change gradients, causing a lag in the battery's response to current changes. These gradient changes in ion concentration near the electrodes may take a relatively long time, resulting in the sub-period corresponding to concentration polarization resistance lagging behind the sub-period corresponding to electrochemical polarization resistance. For example, please refer to... Figure 2 The voltage change ΔV1 and current change ΔI1 within the sub-period 0 to 0.1 s correspond to the ohmic internal resistance R. 欧姆 The voltage change ΔV2 and current change ΔI2 during the sub-period 0.1–1 s correspond to the electrochemical polarization internal resistance R. 电化学 The voltage change ΔV3 and current change ΔI3 during sub-periods 1–20S or 1–30S correspond to the concentration polarization internal resistance R. 浓差 By calculating the values ​​of ΔV / ΔI in the corresponding sub-periods, the corresponding subdivided battery internal resistance values ​​are obtained. Furthermore, the total internal resistance R of the battery is calculated. 总 =R 欧姆 +R 电化学 +R 浓差 After determining the total internal resistance of the battery, the percentage of each individual internal resistance can be calculated. For example, the percentage of ohmic internal resistance is α = R. 欧姆 / R 总 The proportion of electrochemical polarization internal resistance β = R 电化学 / R 总 The proportion of concentration polarization internal resistance γ = R 浓差 / R 总 .

[0065] In actual testing, the testing device can match the sub-relationship models in the relationship model based on the collected battery state data and obtain the detailed internal resistance ratio values ​​that match the current operating conditions. For example, when the real-time collected battery state data is SOC 50%, current 20A, and temperature 25℃, the testing device can match a ratio value of 0.4 in the ohmic internal resistance sub-relationship model, a ratio value of 0.3 in the electrochemical polarization internal resistance sub-relationship model, and a ratio value of 0.3 in the concentration polarization internal resistance sub-relationship model.

[0066] In summary, by setting up multiple sub-relationship models through offline experiments to correspond to different sub-resistances of the battery, the efficiency of the detection device in determining the proportion of sub-resistances under complex working conditions can be improved.

[0067] In some embodiments, the state of charge (SCC) value, current value, and operating temperature value form an operating condition; wherein, at least one of the SCC value, current value, and operating temperature value is different between different operating conditions.

[0068] In this embodiment, specifying the state of charge (SOC), current, and operating temperature values ​​can form an operating condition. The operating condition represents a combination of state parameters of the battery under specific operating conditions. Each operating condition can be stored in a sub-relational model of the relational model for matching and calculating the detailed internal resistance percentage.

[0069] Furthermore, each operating condition consists of a combination of at least one state of charge (SOC), current, and operating temperature value. For example, operating condition A may consist of a SOC of 50%, a current of 20A, and a temperature of 25°C, while operating condition B may consist of a SOC of 80%, a current of 10A, and a temperature of 40°C. Between different operating conditions, at least one parameter value must be different; for example, operating conditions A and B may have different SOC values, thus forming two independent operating conditions.

[0070] During the experiment, corresponding sub-relationship models can be constructed based on the subdivided internal resistance percentages measured under different operating conditions. For example, under operating conditions of 50% state of charge, 20A current, and 25℃, the experimentally measured percentages of ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance are 0.4, 0.3, and 0.3, respectively. Under operating conditions of 80% state of charge, 10A current, and 40℃, the experimentally measured percentages of ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance are 0.35, 0.4, and 0.25, respectively. These operating conditions and their corresponding subdivided internal resistance percentages can be stored in the sub-relationship models within the overall relational model.

[0071] In actual testing, the testing device can match the real-time acquired battery state data with the operating conditions recorded in the relational model. For example, when the battery state data acquired by the testing device is 50% state of charge, 20A current, and 25℃, it can match operating condition A and output the corresponding detailed internal resistance percentage values, such as ohmic internal resistance percentage of 0.4, electrochemical polarization internal resistance percentage of 0.3, and concentration polarization internal resistance percentage of 0.3. Similarly, when the battery state data acquired by the testing device is 80% state of charge, 10A current, and 40℃, it can match operating condition B and output the corresponding detailed internal resistance percentage values.

[0072] In this embodiment, by setting the combination of state of charge (SCC), current, and operating temperature as the operating condition, and ensuring that at least one of these SCC, current, and operating temperature is different between different operating conditions, the matching accuracy of the subdivided internal resistance percentage can be effectively improved. Simultaneously, setting the operating condition simplifies the data structure of the relational model, providing higher efficiency for internal resistance detection and evaluation under complex battery operating scenarios.

[0073] In some embodiments, the battery state data includes the voltage change and current change of the battery during the first specified time period; the detection device can calculate the total internal resistance of the battery based on the voltage change and the current change.

[0074] In this embodiment, the battery state data includes the changes in battery voltage and current during a first specified time period. The changes in battery voltage can be obtained by monitoring the dynamic changes in battery voltage in real time during the first specified time period; for example, the maximum difference in instantaneous battery voltage values ​​during this time period is represented as ΔU. Similarly, the changes in battery current can be obtained by monitoring the dynamic changes in current during the first specified time period; for example, the maximum difference in instantaneous battery current values ​​during this time period is represented as ΔI.

[0075] Furthermore, the detection device can calculate the total internal resistance of the battery based on the voltage change ΔU and current change ΔI. The total internal resistance of the battery can be calculated using the formula R = ΔU / ΔI, where R represents the total internal resistance. In this way, the detection device can evaluate the overall impedance characteristics of the battery in real time with high accuracy.

[0076] For example, if the instantaneous voltage of the battery changes from 12.5V to 12.0V within the first specified time period, and the corresponding instantaneous current changes from 5A to 10A, then the voltage change ΔU is 0.5V and the current change ΔI is 5A. At this time, according to the formula R=ΔU / ΔI=0.5 / 5=0.1Ω, the total internal resistance of the battery is 0.1Ω.

[0077] Please see Figure 3 One embodiment of this application provides a device for detecting the internal resistance of a battery. The device may include an acquisition module, a determination module, and a generation module.

[0078] The acquisition module can be used to acquire battery status data of the battery during a first specified time period.

[0079] The first determining module can be used to determine the total internal resistance of the battery based on the battery state data.

[0080] The second determining module is used to determine the percentage of the subdivided internal resistance of the battery based on the battery state data using a relational model; wherein the relational model is used to indicate the percentage of the subdivided internal resistance of the battery corresponding to the battery state data.

[0081] The generation module can be used to derive the subdivided internal resistance value of the battery based on the total internal resistance value and the subdivided internal resistance ratio value.

[0082] The specific functions and effects of the battery internal resistance detection device can be explained by referring to the foregoing embodiments, and will not be repeated here.

[0083] In some embodiments, the first determining module includes a first sub-module; the first sub-module is used to determine the total internal resistance of the battery based on the battery state data when the current change of the battery within a second specified time period meets specified conditions; wherein, the specified conditions include: the current change rate is greater than a specified charging rate or a specified discharging rate, or the current value is greater than a specified current threshold; the second specified time period is a subset of the first specified time period.

[0084] The specific implementation functions and effects of the first submodule can be explained by referring to the aforementioned embodiments, and will not be repeated here.

[0085] In some embodiments, the second determining module includes a second sub-module; the second sub-module matches the battery state data in the relational model to obtain the subdivided internal resistance ratio value corresponding to the battery state data; wherein, the relational model is a dataset that records different battery state data and the corresponding subdivided internal resistance ratio values.

[0086] The specific implementation functions and effects of the second submodule can be explained by referring to the aforementioned embodiments, and will not be repeated here.

[0087] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the battery internal resistance detection method as described in the foregoing embodiments.

[0088] This application also provides a computer program product, wherein when the computer program software is executed by a processor, it implements the battery internal resistance detection method as described in the foregoing embodiments.

[0089] Please see Figure 4 This description describes an embodiment that can provide a computer device, the computer device including: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by the one or more processors, the instructions being executed by the one or more processors to cause the one or more processors to implement the battery internal resistance detection method as described above.

[0090] In some embodiments, the computer device may include a processor connected to a system bus, a non-volatile storage medium, internal memory, a communication interface, a display device, and an input device. The non-volatile storage medium may store an operating system and related computer programs.

[0091] It is understood that the term "connection" in the following embodiments should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc. can transmit electrical signals or data to each other.

[0092] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the embodiments of this application, and are not intended to limit the scope of the invention.

[0093] It is understood that in the various embodiments of this application, the sequence number of each process 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 this application.

[0094] It is understood that the various embodiments described in this application can be implemented individually or in combination, and the embodiments of this application are not limited in this respect.

[0095] It is understood that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0096] It is understood that the storage medium in the embodiments of this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0097] Unless otherwise stated, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0098] 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 implementation should not be considered beyond the scope of this application.

[0099] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0100] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 apparatuses or units may be electrical, mechanical, or other forms.

[0101] 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; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for detecting the internal resistance of a battery, characterized in that, include: Obtain battery status data of the battery during a first specified time period; The total internal resistance of the battery is determined based on the battery status data. The percentage of the subdivided internal resistance of the battery is determined using a relational model based on the battery state data; wherein, the relational model is used to indicate the percentage of the subdivided internal resistance of the battery corresponding to the battery state data. The subdivided internal resistance value of the battery is obtained based on the total internal resistance value and the subdivided internal resistance ratio value.

2. The method according to claim 1, characterized in that, Determining the total internal resistance of the battery based on the battery state data includes: If the current change of the battery within a second specified time period meets specified conditions, the total internal resistance of the battery is determined based on the battery state data; wherein, the specified conditions include: the rate of change of the current is greater than a specified charging rate or a specified discharging rate, or the current value is greater than a specified current threshold; the second specified time period is a subset of the first specified time period.

3. The method according to claim 1, characterized in that, The first specified time period is selected from at least one of the following: the start period of battery charging, the end period of battery charging, the start period of battery discharging, or the end period of battery discharging.

4. The method according to claim 1, characterized in that, Based on the battery state data, the percentage of each subdivided internal resistance is determined using a relational model, including: Based on the matching of the battery state data in the relational model, the detailed internal resistance ratio corresponding to the battery state data is obtained.

5. The method according to claim 4, characterized in that, The relational model includes multiple sub-relational models, each corresponding to a specific internal resistance of a battery. The multiple battery state data included in the sub-relational models are used to form multiple operating conditions, and the experimentally measured percentage of the specific internal resistance is recorded in each operating condition in the sub-relational model.

6. The method according to claim 5, characterized in that, A state of charge (SCC), current, and operating temperature constitute an operating condition; among different operating conditions, at least one of the SCC, current, and operating temperature values ​​must be different.

7. The method according to claim 1, characterized in that, The battery status data includes the voltage and current changes of the battery during the first specified time period; determining the total internal resistance of the battery based on the battery status data includes: Based on the voltage change and the current change, the total internal resistance of the battery is calculated.

8. A device for detecting the internal resistance of a battery, characterized in that, include: The acquisition module is used to acquire battery status data of the battery during a first specified time period; The first determining module is used to determine the total internal resistance of the battery based on the battery state data; The second determining module is used to determine the percentage of the subdivided internal resistance of the battery based on the battery state data using a relational model; wherein, the relational model is used to indicate the percentage of the subdivided internal resistance of the battery corresponding to the battery state data. The generation module is used to derive the subdivided internal resistance value of the battery based on the total internal resistance value and the subdivided internal resistance ratio value.

9. The detection device according to claim 8, characterized in that, The first determining module includes a first sub-module; The first submodule is used to determine the total internal resistance of the battery based on the battery state data when the current change of the battery within a second specified time period meets specified conditions; wherein, the specified conditions include: the current change rate is greater than a specified charging rate or a specified discharging rate, or the current value is greater than a specified current threshold; the second specified time period is a subset of the first specified time period.

10. The detection device according to claim 8, characterized in that, The second determining module includes a second sub-module; The second submodule matches the battery state data in the relational model to obtain the subdivided internal resistance ratio value corresponding to the battery state data; wherein, the relational model is a dataset that records different battery state data and the corresponding subdivided internal resistance ratio values.

11. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the battery internal resistance detection method as described in any one of claims 1 to 7.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which, when executed by a processor, enables the detection method for battery internal resistance as described in any one of claims 1 to 7.

13. A computer program product, characterized in that, When the computer program product is executed by a processor, it implements the battery internal resistance detection method as described in any one of claims 1 to 7.

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