Battery internal resistance calculation method, electronic equipment and storage medium

Abstract

The invention relates to the technical field of energy storage, and provides a battery internal resistance calculation method, electronic equipment and a storage medium. The method comprises the following steps: acquiring voltage monitoring data and current monitoring data of a battery; determining current fluctuation data based on the current monitoring data, and determining voltage fluctuation data corresponding to the current fluctuation data based on the voltage monitoring data; and performing data fitting on the current fluctuation data and the voltage fluctuation data to determine the internal resistance of the battery. The method can reduce the calculation complexity of the internal resistance of the battery and relieve the scene limitation.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, specifically to a method for calculating battery internal resistance, an electronic device, and a storage medium. Background Technology

[0002] Battery internal resistance can be used to assess indicators such as battery health and performance status.

[0003] In related technologies, battery internal resistance is typically calculated using methods such as the DC internal resistance method, the AC impedance method, and the pulsed hybrid power method. However, these calculation methods not only require complex equipment, are time-consuming, and costly, but also cannot be used under dynamic operating conditions.

[0004] Therefore, reducing the complexity of calculating battery internal resistance and removing scenario limitations are crucial for improving the safety and performance of battery systems. Summary of the Invention

[0005] This application provides a method for calculating battery internal resistance, an electronic device, and a storage medium to solve the technical problem of how to reduce the computational complexity of battery internal resistance and remove scenario limitations.

[0006] The first aspect of this application provides a method for calculating the internal resistance of a battery. The method includes: acquiring voltage monitoring data and current monitoring data of the battery; determining current fluctuation data based on the current monitoring data, and determining voltage fluctuation data corresponding to the current fluctuation data based on the voltage monitoring data; and performing data fitting on the current fluctuation data and the voltage fluctuation data to determine the internal resistance of the battery.

[0007] This application embodiment can determine current fluctuation data based on current monitoring data, and determine the corresponding voltage fluctuation data based on the voltage monitoring data. Further data fitting of the current fluctuation data and the voltage fluctuation data allows direct determination of the battery's internal resistance. The entire calculation process is logically simple and has low complexity. Furthermore, this application embodiment is applicable to dynamic operating conditions with current fluctuations, overcoming the dependence on stable operating conditions with pulsed currents and increasing the opportunities for calculating the battery's internal resistance.

[0008] A second aspect of this application provides a battery internal resistance calculation device, the device comprising: an acquisition unit for acquiring voltage monitoring data and current monitoring data of the battery; a determination unit for determining current fluctuation data based on the current monitoring data, and determining voltage fluctuation data corresponding to the current fluctuation data based on the voltage monitoring data; the determination unit is further configured to perform data fitting on the current fluctuation data and the voltage fluctuation data to determine the internal resistance of the battery.

[0009] A third aspect of this application provides an electronic device, the electronic device comprising: a memory for storing program instructions; and a processor for reading and executing the program instructions stored in the memory, wherein when the program instructions are executed by the processor, the electronic device performs the battery internal resistance calculation method described in the first aspect.

[0010] A fourth aspect of this application provides a computer-readable storage medium storing program instructions that, when executed on an electronic device, cause the electronic device to perform the battery internal resistance calculation method described in the first aspect. Attached Figure Description

[0011] Figure 1 This is a schematic diagram illustrating an application scenario of the battery internal resistance calculation method provided in one embodiment of this application.

[0012] Figure 2 This is a schematic flowchart of a battery internal resistance calculation method provided in an embodiment of this application.

[0013] Figure 3 This is a schematic diagram of current monitoring data provided in one embodiment of this application.

[0014] Figure 4 This is a schematic diagram of battery current and battery voltage provided in one embodiment of this application.

[0015] Figure 5 This is a schematic diagram of a fitted straight line provided in an embodiment of this application.

[0016] Figure 6 This is a flowchart illustrating a battery internal resistance calculation method provided in another embodiment of this application.

[0017] Figure 7 This is a schematic diagram of current fluctuation data and corresponding voltage fluctuation data provided in an embodiment of this application.

[0018] Figure 8 This application provides an embodiment based on... Figure 7 The diagram shown illustrates the calculation of the battery's internal resistance based on current and voltage fluctuation data.

[0019] Figure 9 This is another embodiment of the application based on Figure 7 The diagram shown illustrates the calculation of the battery's internal resistance based on current and voltage fluctuation data.

[0020] Figure 10 This is a schematic diagram of a battery internal resistance calculation device provided in one embodiment of this application.

[0021] Figure 11This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0024] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0025] It should be noted that in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0026] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] In the field of energy storage devices, battery aging or internal short circuits can both cause an increase in battery internal resistance. High internal resistance can easily lead to heat generation during high-current discharge, and in extreme cases, may even cause safety risks such as expansion and fire. To improve the safety and performance of battery systems, the monitoring and calculation of battery internal resistance is crucial.

[0028] In related technologies, battery internal resistance is typically calculated using methods such as DC internal resistance, AC impedance, pulsed hybrid power, and model estimation. However, the DC internal resistance method is easily affected by polarization effects and temperature, causing real-time fluctuations in battery current, making it unsuitable for direct application. Methods such as AC impedance and pulsed hybrid power require complex equipment, are time-consuming, and costly. Model estimation relies on model accuracy and algorithm robustness, resulting in high computational complexity, making it unsuitable for online estimation of battery internal resistance.

[0029] Therefore, in order to reduce the complexity of calculating battery internal resistance and remove scenario limitations, this application provides a battery internal resistance calculation method with simple calculation logic that is applicable to dynamic operating conditions with current fluctuations. The following is in conjunction with... Figure 1 This illustration shows an application scenario of the battery internal resistance calculation method provided in one embodiment of this application.

[0030] like Figure 1 As shown, in some scenarios where the battery internal resistance calculation method of this application is applicable, the above-described battery internal resistance calculation method can be applied to electronic device 100, which can communicate with energy storage device 200. For example, electronic device 100 can communicate with energy storage device 200 through a wireless module and / or other communication modules to interact with energy storage device 200, such as obtaining voltage monitoring data and current monitoring data from energy storage device 200.

[0031] In some embodiments, the electronic device 100 may be an independent server or server cluster, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication security services, content delivery networks, and other basic cloud computing services. The electronic device 100 may also be a mobile phone, tablet computer, smart wearable device, augmented reality (AR) / virtual reality (VR) device, laptop computer, netbook, energy storage device, power distribution equipment, vehicle-mounted equipment, self-moving device, etc. This application embodiment does not impose any limitations on the specific type of electronic device.

[0032] In some embodiments, the energy storage device 200 can be applied to automobiles or self-moving devices including batteries, such as lawnmowers, sweepers, cruise control devices, etc. The energy storage device 200 can also be a mobile energy storage device, a home energy storage device, or other electronic devices including batteries or with energy storage capabilities. The energy storage device 200 can also connect to client devices, including but not limited to any electronic product that allows human-computer interaction with the user via a keyboard, mouse, remote control, touchpad, or voice control device, such as personal computers, tablets, smartphones, digital cameras, etc.

[0033] It should be noted that the energy storage device 200 is only an example. Other existing or future electronic products that are applicable to this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0034] In some embodiments, the energy storage device 200 is provided with one or more batteries connected in series and / or in parallel.

[0035] In other scenarios where the battery internal resistance calculation method of this application is applicable, the battery internal resistance calculation method can be applied to an electronic device 100 equipped with a battery. The electronic device 100 can monitor the electrical parameters of the battery in the device (e.g., battery voltage monitoring data and current monitoring data) and implement the battery internal resistance calculation method.

[0036] like Figure 2 The diagram shown is a flowchart illustrating a battery internal resistance calculation method according to an embodiment of this application. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0037] S201, acquire battery voltage monitoring data and current monitoring data.

[0038] In some embodiments, the energy storage device may have at least one built-in battery. The energy storage device can monitor the battery's electrical parameters in real time, which may include, but are not limited to, battery voltage monitoring data and current monitoring data. In one example, the energy storage device samples the battery's voltage and current monitoring data, and the electronic device can request the energy storage device to obtain the voltage and current monitoring data. In another example, after the energy storage device samples the battery's voltage and current monitoring data, it can actively send the voltage and current monitoring data to the electronic device.

[0039] In some embodiments, a preset time period can be set, and the electronic device can acquire the current monitoring data sampled by the energy storage device within the preset time period. The preset time period can be set and adjusted according to actual needs.

[0040] In other embodiments, a first preset quantity can be set. This first preset quantity can be set and adjusted according to actual needs. For example, the first preset quantity can be a positive integer greater than or equal to 2. The electronic device can acquire the first preset quantity of current monitoring data most recently sampled by the energy storage device.

[0041] The embodiments of this application can obtain current monitoring data and voltage monitoring data under any operating conditions, and calculate the internal resistance of the battery based on the current monitoring data and voltage monitoring data, thereby increasing the opportunities for calculating the battery internal resistance.

[0042] S202, determine current fluctuation data based on current monitoring data, and determine voltage fluctuation data corresponding to the current fluctuation data based on voltage monitoring data.

[0043] In some embodiments, the electronic device acquires N adjacent current monitoring data from the current monitoring data.

[0044] In one example, a sliding window can be pre-set. The window length of the sliding window represents the interval between the data points at the beginning and end of the window. The window length can be set to N, which can be a preset positive integer greater than or equal to 2. The sliding step size represents the data points between two adjacent sliding intervals. The sliding step size can be set and adjusted according to actual needs. Electronic devices can use the sliding window to obtain N adjacent current monitoring data from current monitoring data. For example, if the current monitoring data includes 30A, 32A, 35A, 28A, 30A, and 31A, and the window length of the sliding window is 3, and the sliding step size is 2, then the first group of N adjacent current monitoring data is: 30A, 32A, 35A, and the second group of N adjacent current monitoring data is: 35A, 28A, 30A.

[0045] In another example, the electronic device obtains N adjacent current monitoring data points from the current monitoring data. No two adjacent sets of N current monitoring data points contain the same current monitoring data point. Continuing with the previous example, if N is 3, the electronic device can obtain the first set of N adjacent current monitoring data points as: 30A, 32A, 35A, and the second set as: 28A, 30A, 31A.

[0046] In some embodiments, the electronic device calculates the range among N adjacent current monitoring data points to obtain current fluctuation data. In this embodiment, the current fluctuation data can be the difference between the largest and smallest current monitoring data points among the N adjacent current monitoring data points. For example, if the N adjacent current monitoring data points are 30A, 32A, and 35A, the electronic device calculates the range among these N adjacent current monitoring data points to obtain the current fluctuation data as 35A - 30A = 5A. This embodiment reduces the computational threshold and complexity by calculating the range among the N adjacent current monitoring data points, thereby quickly determining the current fluctuation data.

[0047] In some embodiments, when N is 2, the electronic device calculates the difference between two adjacent current monitoring data to obtain current fluctuation data.

[0048] In other embodiments, the electronic device can calculate the range among N adjacent current monitoring data points to obtain candidate fluctuation data corresponding to the current monitoring data. The electronic device can set a preset fluctuation threshold for the current monitoring data. This preset fluctuation threshold can be set and adjusted according to actual needs; for example, it can be set to 2A, 3A, 5A, etc. To extract current data with large fluctuations, the electronic device filters data from the candidate fluctuation data corresponding to the current monitoring data that is greater than or equal to the preset fluctuation threshold as the current fluctuation data.

[0049] See Figure 3 As shown, Figure 3 This is a schematic diagram of current monitoring data provided in one embodiment of this application. Figure 3 As shown, the current monitoring data includes currents I1, I2, I3, I4, and I5. Calculations show that current I1 is related to the current monitoring data from the previous moment (…). Figure 3 The difference between current I1 and current I2 (not shown) (candidate fluctuation data ΔI corresponding to current I2) is greater than a preset fluctuation threshold; the difference between current I3 and current I2 (candidate fluctuation data ΔI corresponding to current I3) is less than a preset fluctuation threshold; the difference between current I4 and current I3 (candidate fluctuation data ΔI corresponding to current I4) is greater than a preset fluctuation threshold; and the difference between current I5 and current I4 (candidate fluctuation data ΔI corresponding to current I5) is greater than a preset fluctuation threshold. The electronic device can determine the candidate fluctuation data ΔI corresponding to current I1, current I2, current I4, and current I5 as current fluctuation data.

[0050] In this embodiment, since the voltage fluctuation characteristics under small current changes are not significant enough, a more significant internal resistance characteristic can be obtained by filtering the small current data.

[0051] In some embodiments, when the battery current changes, the battery voltage changes synchronously. See also Figure 4 As shown, Figure 4 This is a schematic diagram of battery current and battery voltage provided in one embodiment of this application. Figure 4 As shown, each current monitoring data point corresponds to a voltage monitoring data point. When a sudden change in current ΔI occurs (e.g., ... Figure 4 When the current changes abruptly, the voltage will change accordingly by ΔU.

[0052] In some embodiments, the electronic device can determine the voltage fluctuation data corresponding to the current fluctuation data based on voltage monitoring data. Combined with... Figure 3 Explain how voltage fluctuation data was determined, such as... Figure 3 As shown, the current monitoring data includes currents I1, I2, I3, I4, and I5; correspondingly, the voltage monitoring data includes voltages U1, U2, U3, U4, and U5. Current fluctuation data includes: candidate fluctuation data ΔI corresponding to current I1 (the difference between current I1 and the current monitoring data at the previous moment), candidate fluctuation data ΔI corresponding to current I2 (the difference between current I2 and current I1), candidate fluctuation data ΔI corresponding to current I4 (the difference between current I4 and current I3), and candidate fluctuation data ΔI corresponding to current I5 (the difference between current I5 and current I4). Therefore, voltage fluctuation data can include: voltage fluctuation data ΔU corresponding to voltage U1 (the difference between voltage U1 and the voltage monitoring data at the previous moment), voltage fluctuation data ΔU corresponding to voltage U2 (the difference between voltage U2 and voltage U1), voltage fluctuation data ΔU corresponding to voltage U4 (the difference between voltage U4 and voltage U3), and voltage fluctuation data ΔU corresponding to voltage U5 (the difference between voltage U5 and voltage U4).

[0053] In other embodiments, the electronic device can calculate the range of N adjacent voltage monitoring data to obtain voltage fluctuation data.

[0054] In this embodiment of the application, the electronic device calculates the range among N adjacent voltage monitoring data to obtain candidate fluctuation data corresponding to the voltage monitoring data.

[0055] In some embodiments, the electronic device can set a preset fluctuation threshold for the voltage monitoring data. This preset fluctuation threshold can be set and adjusted according to actual needs; for example, it can be set to 10mV. The electronic device then selects data from the candidate fluctuation data corresponding to the voltage monitoring data that is greater than or equal to the preset fluctuation threshold as voltage fluctuation data.

[0056] In this embodiment, the electronic device determines the current fluctuation data corresponding to the voltage fluctuation data based on current monitoring data. In one example, the electronic device determines N target current monitoring data corresponding to the voltage fluctuation data from the current monitoring data. The electronic device calculates the range among the N target current monitoring data to obtain the current fluctuation data. In another example, the electronic device determines N target current monitoring data corresponding to the voltage fluctuation data from the current monitoring data. The electronic device calculates the range among the N target current monitoring data to obtain candidate fluctuation data corresponding to the current monitoring data, and the electronic device selects data greater than or equal to a preset fluctuation threshold from the candidate fluctuation data corresponding to the current monitoring data as the current fluctuation data.

[0057] S203 performs data fitting on current fluctuation data and voltage fluctuation data to determine the battery's internal resistance.

[0058] In some embodiments, during the process of determining the internal resistance of a battery, the electronic device uses current fluctuation data as an independent variable and the corresponding voltage fluctuation data as a dependent variable, and performs linear regression analysis on the current fluctuation data and voltage fluctuation data to determine the internal resistance of the battery.

[0059] In this embodiment, the electronic device performs linear fitting on current fluctuation data and voltage fluctuation data to obtain a fitted straight line. Based on the regression coefficients of the fitted straight line, the electronic device determines the battery's internal resistance; the regression coefficients can be the slope of the fitted straight line. In one example, this is based on Ohm's law. We can obtain ΔI = R × ΔI. By performing linear regression analysis on the current fluctuation data and voltage fluctuation data, the slope of the fitted line can be used as the internal resistance of the battery.

[0060] See Figure 5 As shown, Figure 5 This is a schematic diagram of a fitted straight line provided in one embodiment of this application. Figure 5 As shown, by taking the current fluctuation data ΔI as the independent variable and the corresponding voltage fluctuation data ΔU as the dependent variable, we can obtain... Figure 5 The coordinate points represented by the solid gray circles are used to perform linear regression fitting on these coordinate points, and the fitted line can be: Figure 5 The dashed line shown represents the fitted straight line. Figure 5The slope corresponding to the dashed line shown is taken as the internal resistance of the battery.

[0061] This embodiment improves the robustness of internal resistance by performing linear regression analysis on current fluctuation data and voltage fluctuation data.

[0062] In some embodiments, the electronic device can determine the health status of the battery based on its internal resistance. For example, the electronic device can set an internal resistance threshold; if the battery's internal resistance is greater than or equal to the threshold, the electronic device determines that the battery is malfunctioning. If the battery's internal resistance is less than the threshold, the electronic device determines that the battery is not malfunctioning.

[0063] In some embodiments, when a battery malfunctions, the electronic device may output a prompt message to indicate the battery malfunction. The prompt message may take one or more forms, including voice, light, and text.

[0064] Using the aforementioned battery internal resistance calculation method, current fluctuation data can be determined based on current monitoring data, and voltage fluctuation data corresponding to the current fluctuation data can be determined based on voltage monitoring data. Further data fitting of the current and voltage fluctuation data allows for direct determination of the battery's internal resistance. The entire calculation process is logically simple and has low complexity. Furthermore, the embodiments of this application are applicable to dynamic operating conditions with current fluctuations, overcoming the dependence on stable operating conditions with pulsed currents and increasing the opportunities for calculating battery internal resistance.

[0065] like Figure 6 The diagram shown is a flowchart of a battery internal resistance calculation method provided in another embodiment of this application. The order of the steps in the flowchart can be changed or some can be omitted depending on different needs.

[0066] S601 acquires battery voltage and current monitoring data.

[0067] S602 determines current fluctuation data based on current monitoring data, and determines voltage fluctuation data corresponding to the current fluctuation data based on voltage monitoring data.

[0068] For details of steps S601-S602, please refer to the above text. Figure 2 The detailed description of steps S201-S202 is not repeated here.

[0069] S603, the number of detected current fluctuation data is greater than or equal to the second preset number.

[0070] In some embodiments, in order to improve the accuracy of the battery's internal resistance, the electronic device may set a second preset number. The second preset number can be set and adjusted according to actual needs. For example, the second preset number can be set to values ​​such as 10, 15, and 20.

[0071] In some embodiments, if the number of current fluctuation data is greater than or equal to the second preset number, step S604 is executed; if the number of current fluctuation data is less than the second preset number, step S605 is executed.

[0072] S604 performs data fitting on current fluctuation data and voltage fluctuation data to determine the battery's internal resistance.

[0073] In some embodiments, based on Ohm's law, we can obtain Voltage fluctuation data is directly proportional to current fluctuation data. Due to the influence of depolarization, temperature, and other conditions, the internal resistance obtained from a single calculation has large fluctuations, resulting in multiple calculated internal resistances. Therefore, electronic devices perform data fitting on current and voltage fluctuation data to obtain the battery's internal resistance.

[0074] Combination Figures 7 to 9 Explain the process of determining the internal resistance of a battery. Figure 7 The data shows the battery's current fluctuations and the corresponding voltage fluctuations.

[0075] Figure 8 Based on an embodiment of this application Figure 7 The diagram shown illustrates the calculation of battery internal resistance based on current and voltage fluctuation data. Figure 8 As shown, the electronic device calculates the ratio of each voltage fluctuation data to the corresponding current fluctuation data to obtain the internal resistance corresponding to each voltage fluctuation data. The fluctuation range of the calculated internal resistances is from 0mΩ to 17mΩ.

[0076] Figure 9 Provided for another embodiment of this application based on Figure 7 The diagram shown illustrates the calculation of battery internal resistance based on current and voltage fluctuation data. Figure 9 As shown, the electronic device performs data fitting on current fluctuation data and voltage fluctuation data, using current fluctuation data as the independent variable and the corresponding voltage fluctuation data as the dependent variable. Linear regression analysis is then performed on the current fluctuation data and voltage fluctuation data to obtain the following results: Figure 9 The fitted line shown is calculated, assuming the fitted line is obtained ( Figure 9 If the slope of the dashed line shown is 1.38, then the internal resistance of the battery can be determined to be 1.38mΩ.

[0077] For details of step S604, please refer to the above text. Figure 2The detailed description of step S203 is provided in the previous section and will not be repeated here.

[0078] S605 does not calculate the internal resistance of the battery.

[0079] Using the aforementioned battery internal resistance calculation method, current fluctuation data can be determined based on current monitoring data, and voltage fluctuation data corresponding to the current fluctuation data can be determined based on voltage monitoring data. When the number of current fluctuation data is greater than or equal to a second preset number, data fitting of the current and voltage fluctuation data can directly yield the battery's internal resistance. The entire calculation process is logically simple and has low complexity, making it suitable for deployment in embedded devices. Furthermore, data fitting with a large amount of data can improve the accuracy of the battery's internal resistance. When the number of current fluctuation data is less than the second preset number, the battery's internal resistance is not calculated to avoid large errors in the calculated internal resistance. In addition, the embodiments of this application are applicable to dynamic operating conditions with current fluctuations, overcoming the dependence on stable operating conditions with pulse currents and increasing the opportunities for calculating the battery's internal resistance.

[0080] Moreover, as can be seen from the various embodiments of this application, the battery internal resistance calculation method of this application can complete the dynamic monitoring of battery internal resistance by monitoring the battery voltage monitoring data and current monitoring data, without actively affecting the battery, and therefore does not require various complex auxiliary equipment / circuits (such as pulse generation circuits, etc.), which can effectively reduce the cost of detecting battery internal resistance.

[0081] like Figure 10 The diagram shown is a schematic representation of a battery internal resistance calculation device according to an embodiment of this application. The battery internal resistance calculation device 1001 operates within an electronic device 100 (e.g., Figure 11 The processor 1101 shown. The battery internal resistance calculation device 1001 includes an acquisition unit 1010 and a determination unit 1011.

[0082] In some embodiments, the acquisition unit 1010 is used to acquire voltage monitoring data and current monitoring data of the battery; the determination unit 1011 is used to determine current fluctuation data based on the current monitoring data, and to determine voltage fluctuation data corresponding to the current fluctuation data based on the voltage monitoring data; the determination unit 1011 is also used to perform data fitting on the current fluctuation data and voltage fluctuation data to determine the internal resistance of the battery.

[0083] In some embodiments, the current monitoring data is current monitoring data sampled within a preset time period, or the latest sampled first preset number of current monitoring data.

[0084] In some embodiments, the determining unit 1011 is specifically used to: calculate the range among N adjacent current monitoring data to obtain current fluctuation data; N is a preset positive integer greater than or equal to 2.

[0085] In some embodiments, the determining unit 1011 is further configured to: calculate the range among N adjacent current monitoring data to obtain candidate fluctuation data; and select data from the candidate fluctuation data that are greater than or equal to a preset fluctuation threshold as current fluctuation data.

[0086] In some embodiments, the determining unit 1011 is further configured to: calculate the difference between two adjacent current monitoring data respectively to obtain current fluctuation data.

[0087] In some embodiments, the determining unit 1011 is further configured to: perform data fitting on the current fluctuation data and voltage fluctuation data to determine the internal resistance of the battery when the number of current fluctuation data is greater than or equal to a second preset number.

[0088] In some embodiments, the determining unit 1011 is further configured to: not calculate the internal resistance of the battery when the number of current fluctuation data is less than a second preset number.

[0089] In some embodiments, the determining unit 1011 is further configured to: perform linear regression analysis on the current fluctuation data and the corresponding voltage fluctuation data as independent variables to determine the internal resistance of the battery.

[0090] For detailed information on the functions of each module / unit, please refer to the above text. Figure 2 , Figure 6 The detailed description will not be repeated here.

[0091] This application embodiment can determine current fluctuation data based on current monitoring data, and determine the corresponding voltage fluctuation data based on voltage monitoring data. Further data fitting of the current and voltage fluctuation data allows direct determination of the battery's internal resistance. The entire calculation process is logically simple and has low complexity. Furthermore, this application embodiment is applicable to dynamic operating conditions with current fluctuations, overcoming the dependence on stable operating conditions with pulsed currents and increasing the opportunities for calculating the battery's internal resistance.

[0092] like Figure 11 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application.

[0093] In some embodiments, the electronic device 100 includes, but is not limited to, a processor 1101, a memory 1102, and program instructions stored in the memory 1102 and executable on the processor 1101, such as a battery internal resistance calculation program.

[0094] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device 100 may also include input / output devices, network access devices, buses, etc.

[0095] Processor 1101 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. Processor 1101 is the computing core and control center of electronic device 100, connecting various parts of electronic device 100 through various interfaces and lines, and executing the operating system of electronic device 100 and various installed application programs and program code.

[0096] For example, program instructions can be divided into one or more modules / units, one or more of which are stored in memory 1102 and executed by processor 1101 to complete this application. One or more modules / units can be a series of program instruction segments capable of performing a specific function, which describe the execution process of the program instructions in processor 1101. For example, program instructions can be divided into an acquisition unit 1010 and a determination unit 1011.

[0097] The memory 1102 can be used to store program instructions and / or modules. The processor 1101 implements various functions by running or executing the program instructions and / or modules stored in the memory 1102 and by calling the data stored in the memory 1102. The memory 1102 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function, etc.; the data storage area may store data created according to the use of the electronic device, etc. The memory 1102 may include non-volatile and volatile memory, such as: hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other storage devices.

[0098] The memory 1102 can be the external memory and / or internal memory of the processor 1101. Furthermore, the memory 1102 can be a physical memory, such as a memory stick, a TF card (Trans-flash Card), etc.

[0099] If the integrated modules / units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by instructing related hardware through program instructions. The program instructions can be stored in a computer-readable storage medium, and when executed by a processor, they can implement the steps of the various method embodiments described above.

[0100] Program instructions include program instruction code, which can be in the form of source code, object code, executable file, or some intermediate form. Computer-readable storage media can include: any entity or device capable of carrying program instruction code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), and random access memory (RAM).

[0101] Combination Figure 2 , Figure 6 The memory 1102 in the electronic device 100 stores program instructions, and the processor 1101 can execute the program instructions stored in the memory 1102 to implement the battery internal resistance calculation method as shown in any of the above method embodiments.

[0102] Specifically, the specific implementation method of the processor 1101 for the above program instructions can be found in the description of the relevant steps in the above method embodiments, and will not be repeated here.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0104] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0106] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0107] Furthermore, it is clear that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for calculating the internal resistance of a battery, characterized in that, The method includes: Acquire battery voltage and current monitoring data; The current fluctuation data is determined based on the current monitoring data, and the voltage fluctuation data corresponding to the current fluctuation data is determined based on the voltage monitoring data. The internal resistance of the battery is determined by performing data fitting on the current fluctuation data and the voltage fluctuation data.

2. The method as described in claim 1, characterized in that, The current monitoring data is the current monitoring data sampled within a preset time period, or the latest sampled first preset number of current monitoring data.

3. The method as described in claim 1, characterized in that, The determination of current fluctuation data based on the current monitoring data includes: Calculate the range among N adjacent current monitoring data to obtain current fluctuation data; N is a preset positive integer greater than or equal to 2.

4. The method as described in claim 3, characterized in that, The calculation of the range among N adjacent current monitoring data points to obtain current fluctuation data includes: Calculate the range among N adjacent current monitoring data to obtain candidate fluctuation data; Data that is greater than or equal to a preset fluctuation threshold is selected from the candidate fluctuation data as the current fluctuation data.

5. The method as described in claim 3, characterized in that, When N is 2, the calculation of the range among N adjacent current monitoring data to obtain current fluctuation data includes: The difference between two adjacent current monitoring data points is calculated to obtain the current fluctuation data.

6. The method as described in claim 1, characterized in that, The step of performing data fitting on the current fluctuation data and the voltage fluctuation data to determine the internal resistance of the battery includes: If the number of current fluctuation data is greater than or equal to a second preset number, the current fluctuation data and the voltage fluctuation data are fitted together to determine the internal resistance of the battery.

7. The method as described in claim 1, characterized in that, The method further includes: If the number of current fluctuation data is less than the second preset number, the internal resistance of the battery is not calculated.

8. The method as described in claim 1, characterized in that, The step of performing data fitting on the current fluctuation data and the voltage fluctuation data to determine the internal resistance of the battery includes: Using the current fluctuation data as the independent variable and the corresponding voltage fluctuation data as the dependent variable, a linear regression analysis is performed on the current fluctuation data and the voltage fluctuation data to determine the internal resistance of the battery.

9. An electronic device, characterized in that, include: Memory, used to store program instructions; and A processor is configured to read and execute the program instructions stored in the memory, wherein when the program instructions are executed by the processor, the electronic device performs the battery internal resistance calculation method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions that, when executed on an electronic device, cause the electronic device to perform the battery internal resistance calculation method as described in any one of claims 1 to 8.

Images