Methods, apparatus, equipment, storage media, and procedures for detecting the internal resistance of batteries.

Abstract

This application provides a method, apparatus, device, storage medium, and program product for detecting the internal resistance of a battery. The method includes: acquiring historical charge and discharge data of the battery; determining the state of charge (SOC) of the battery's negative electrode at each data acquisition time based on the battery capacity at each data acquisition time; determining the open-circuit voltage at each data acquisition time based on the charging and discharging voltages at each data acquisition time; determining the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current; and determining the DC internal resistance value corresponding to different SOCs of the battery under different historical charge and discharge cycles based on the SOC and DC internal resistance value at each data acquisition time. This method does not rely on specific charge and discharge procedures or experimental conditions and can perform accurate internal resistance analysis using cloud data during actual battery operation.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and includes, but is not limited to, a method, apparatus, device, storage medium, and program product for detecting the internal resistance of a battery. Background Technology

[0002] Battery internal resistance is a crucial parameter for assessing battery performance and health, as changes in internal resistance directly impact power output and safety. During battery use, internal resistance gradually increases due to factors such as electrode material aging and electrolyte decomposition. Therefore, accurate detection of battery internal resistance is of paramount importance in fields such as electric vehicles, energy storage systems, and mobile devices.

[0003] Related technologies measure changes in voltage and current under specific conditions by controlling the charging and discharging process of a battery, and then calculate the internal resistance value accordingly. However, this method relies on a special charging and discharging process, including steps such as charging-resting-discharging, to obtain a stable voltage difference for internal resistance calculation, and is only suitable for the research and development stage.

[0004] Therefore, how to detect the internal resistance of a battery based on existing charge and discharge data without changing the original charge and discharge process has become an urgent problem to be solved. Summary of the Invention

[0005] To address the problems existing in related technologies, embodiments of this application provide a method, apparatus, device, storage medium, and program product for detecting the internal resistance of a battery. This method does not rely on specific charging and discharging processes or experimental conditions and can perform accurate internal resistance analysis through cloud data during actual battery operation.

[0006] In a first aspect, this application provides a method for detecting the internal resistance of a battery. The method includes: acquiring historical charge-discharge data of the battery; the historical charge-discharge data includes the charge-discharge voltage, charge-discharge current, and battery capacity at each data acquisition time for each historical charge-discharge cycle; determining the state of charge (SOC) of the battery's negative electrode at each data acquisition time based on the battery capacity at each data acquisition time; determining the open-circuit voltage at each data acquisition time based on the charging voltage and discharging voltage at each data acquisition time; determining the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current at each data acquisition time; and determining the DC internal resistance value corresponding to different SOCs of the battery under different historical charge-discharge cycle numbers based on the SOC and DC internal resistance value at each data acquisition time.

[0007] In the above embodiments, by acquiring historical charge and discharge data of the battery and combining it with the battery capacity at each data acquisition time, the state of charge of the battery's negative electrode is calculated. Then, the DC internal resistance value is calculated using the charge and discharge voltage and current, ultimately establishing the DC internal resistance distribution of the battery under different charge states at different cycle numbers. This approach does not rely on specific charge and discharge procedures or experimental conditions. It enables internal resistance analysis via cloud data during actual battery operation, thereby accurately capturing the aging characteristics of battery internal resistance over time. This provides a basis for parameter correction and lithium plating warning in the battery management system. Furthermore, it eliminates the need for additional battery testing procedures, achieving online detection of the battery's DC internal resistance value, demonstrating high practicality and adaptability.

[0008] In some embodiments, determining the state of charge (SOC) of the negative electrode of the battery at each data acquisition time based on the battery capacity at each data acquisition time includes: determining the SOC of the negative electrode of the battery at each data acquisition time during the charging process based on the rechargeable battery capacity and the rated capacity of the battery at each data acquisition time during the charging process; and determining the SOC of the negative electrode of the battery at each data acquisition time during the discharging process based on the discharging battery capacity and the rated capacity of the battery at each data acquisition time during the discharging process.

[0009] In the above embodiments, calculating the state of charge of the battery negative electrode based on capacity data from the charging and discharging processes respectively better reflects the true state of the battery and improves the reliability of the battery state of charge calculation.

[0010] In some embodiments, the internal resistance detection method further includes: in response to the difference between the number of charging data and the number of discharging data in each historical charge-discharge cycle, determining the charging data or discharging data corresponding to the smaller value between the number of charging data and the number of discharging data as reference data; performing data processing on target data based on the state of charge at each data acquisition time in the reference data so that the number of charging data is the same as the number of discharging data; the target data is charging data or discharging data that is different from the reference data.

[0011] In the above embodiments, by selecting reference data and matching another type of data, the states of charge of the two are consistent, which can effectively solve the problem of data imbalance caused by different data acquisition frequencies or time points, ensure the comparability of the data used in subsequent calculations, and thus improve the accuracy of DC internal resistance analysis results.

[0012] In some embodiments, data processing is performed on the target data based on the state of charge at each data acquisition time in the reference data, including: determining the target state of charge with the smallest difference between the target data and each state of charge based on the state of charge at each data acquisition time in the reference data; and determining each target state of charge and the charging data or discharging data at the corresponding data acquisition time as the target data after data processing.

[0013] In the above embodiments, effective filtering and matching of cloud-based charging and discharging data can be achieved. By finding the target data point that is closest to the state of charge in the reference data, it is ensured that the charging and discharging data in the sequential charging and discharging cycle has a high degree of matching in terms of battery state, reducing errors caused by differences in data volume and improving the accuracy of subsequent internal resistance calculation.

[0014] In some embodiments, the DC internal resistance value includes the charging internal resistance value and the discharging internal resistance value; determining the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current at each data acquisition time includes: determining the charging internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, and charging current at each data acquisition time; and determining the discharging internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, discharging voltage, and discharging current at each data acquisition time.

[0015] In the above embodiments, the internal resistance values ​​during the charging and discharging processes are calculated separately, which can provide a comprehensive understanding of the battery's performance under different operating conditions. This segmented calculation method can more accurately reflect the battery's true internal resistance performance under different operating conditions.

[0016] In some embodiments, determining the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current at each data acquisition time includes: in response to the difference between the charging internal resistance value and the discharging internal resistance value being less than a preset threshold, determining the DC internal resistance value of the battery at each data acquisition time based on the charging internal resistance value and the discharging internal resistance value.

[0017] In the above embodiments, no special charging and discharging operations are required to obtain the internal resistance value, making it more suitable for market-end vehicles, energy storage systems, and other scenarios where additional testing cannot be performed. This improves the battery's online monitoring capabilities and has good engineering practicality.

[0018] In some embodiments, the internal resistance detection method further includes: determining the average DC internal resistance of the battery at each historical charge-discharge cycle number based on the DC internal resistance value corresponding to different states of charge of the battery at different historical charge-discharge cycle numbers; detecting abnormal abrupt changes in the average DC internal resistance based on the average DC internal resistance at each historical charge-discharge cycle number to obtain a detection result; and providing a lithium plating warning in response to the detection result indicating the presence of abnormal abrupt changes.

[0019] In the above embodiments, by monitoring the changing trend of the average DC internal resistance, the risk of battery aging or abnormalities can be detected in a timely manner. When the internal resistance shows an abnormal increase, it may mean that lithium plating has occurred in the battery. Triggering the early warning mechanism at this time helps to take maintenance measures in advance to prevent further deterioration, thereby improving the safety and service life of the battery system.

[0020] Secondly, embodiments of this application provide an internal resistance detection device for a battery cell. The internal resistance detection device includes: an acquisition module for acquiring historical charge-discharge data of the battery; the historical charge-discharge data includes the charge-discharge voltage, charge-discharge current, and battery capacity at each data acquisition time under each historical charge-discharge cycle number; a first determination module for determining the state of charge of the battery negative electrode at each data acquisition time based on the battery capacity at each data acquisition time; a second determination module for determining the open-circuit voltage at each data acquisition time based on the charging voltage and discharging voltage at each data acquisition time; a third determination module for determining the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current at each data acquisition time; and a fourth determination module for determining the DC internal resistance value of the battery corresponding to different states of charge under different historical charge-discharge cycle numbers based on the state of charge and DC internal resistance value at each data acquisition time.

[0021] Thirdly, embodiments of this application provide an internal resistance detection device, which includes: a memory for storing executable instructions; and a processor for implementing the above-described internal resistance detection method when executing the executable instructions stored in the memory.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing executable instructions, which, when executed by a processor, implement the aforementioned internal resistance detection method.

[0023] Fifthly, embodiments of this application provide a computer program product, which includes executable instructions stored in a computer-readable storage medium; when the processor of the internal resistance detection device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned internal resistance detection method is implemented.

[0024] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the internal resistance detection device provided in the embodiments of this application;

[0026] Figure 2 This is an optional flowchart of the internal resistance detection method provided in the embodiments of this application. Figure 1 ;

[0027] Figure 3 This is a schematic diagram of the trend model of battery internal resistance changing with the number of cycles provided in the embodiments of this application;

[0028] Figure 4 This is a schematic diagram of a single-cycle charge-discharge curve provided in an embodiment of this application;

[0029] Figure 5 This is a schematic diagram of the relationship between actual internal resistance and SOC provided in the embodiments of this application;

[0030] Figure 6 This is a schematic diagram of the actual internal resistance curves under different aging cycles provided in the embodiments of this application;

[0031] Figure 7 This is a schematic diagram comparing the actual internal resistance provided in the embodiments of this application with the internal resistance obtained from testing during the research and development stage. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] In the following description, references to "some embodiments" refer to a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. Unless otherwise defined, 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 to which the embodiments of this application pertain. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit the application.

[0034] With the widespread application of electric vehicles and energy storage systems, monitoring the health status of batteries has become extremely important. The DC internal resistance of a battery is a key indicator for assessing its health, reflecting its internal resistive characteristics.

[0035] Regarding internal resistance parameters, related technologies can only be tested during the R&D phase using standard direct current resistance (DCR) testing methods. For example, after allowing the battery to rest for 1 to 3 hours, it is charged or discharged with a current of 1 to 5C for 30 seconds, and the voltage difference before and after is calculated. Dividing this difference by the current gives the battery's DC internal resistance value. However, this method requires interrupting the battery's operating state for monitoring, making it impossible to monitor while the battery is operating normally. This method is not only cumbersome but also difficult to apply to practical products such as electric vehicles and energy storage systems.

[0036] The relevant technology is used to determine the current internal resistance value of lithium batteries during long-cycle and storage testing, as well as in product scenarios such as vehicles and energy storage boxes in the market.

[0037] To alleviate the problems existing in related technologies, the applicant proposes a method for calculating the DC internal resistance of a battery based on cloud-based charge and discharge data. This method acquires historical charge and discharge data of the battery, calculates the state of charge (SOC) at each data acquisition moment, and combines this with charge and discharge voltage and current information to determine the DC internal resistance value of the battery under different SOC states. This method achieves real-time calculation of battery internal resistance without requiring additional battery testing procedures. The calculation results are consistent with the test results of the national standard DCR scheme, and the results can be further used for early warning of risks such as lithium plating, showing broad application prospects.

[0038] Based on the above considerations, the inventors, through in-depth research, have provided a method for detecting the internal resistance of a battery, which can acquire historical charge-discharge data of the battery. This historical charge-discharge data includes the charge-discharge voltage, charge-discharge current, and battery capacity at each data acquisition moment for each historical charge-discharge cycle. Based on the battery capacity at each data acquisition moment, the state of charge (SOC) of the battery's negative electrode at each data acquisition moment is determined. Based on the charge-discharge voltage and charge-discharge current at each data acquisition moment, the DC internal resistance value of the battery at each data acquisition moment is determined. Based on the SOC and DC internal resistance value at each data acquisition moment, the DC internal resistance value corresponding to different SOCs of the battery under different historical charge-discharge cycle numbers is determined.

[0039] Thus, this embodiment of the application obtains historical charge and discharge data of the battery, calculates the state of charge (SOC) of the negative electrode of the battery by combining the battery capacity at each data acquisition time, and then calculates the DC internal resistance value using charge and discharge voltage and current. Finally, it establishes the DC internal resistance distribution of the battery under different SOCs at different cycle counts. Without relying on specific charge and discharge procedures or experimental conditions, it can perform internal resistance analysis through cloud data during actual battery operation, thereby accurately capturing the aging characteristics of battery internal resistance during use, providing a basis for BMS parameter correction and lithium plating early warning; and it can achieve online detection of battery DC internal resistance value without adding additional testing procedures to the battery, exhibiting high practicality and adaptability.

[0040] The application of new energy batteries in daily life and industry is becoming increasingly widespread. New energy batteries are not only used in energy storage power systems such as hydropower, thermal power, wind power, and solar power plants, but also widely used in electric vehicles such as electric bicycles, electric motorcycles, and electric cars, as well as in aerospace and other fields. With the continuous expansion of the application fields of power batteries, the market demand is also constantly increasing. In the embodiments of this application, the battery involved can be a battery cell, also known as a battery unit. A battery cell refers to a basic unit that can realize the mutual conversion of chemical energy and electrical energy, and can be used to make battery modules or battery packs to supply power to electrical devices. A battery cell can be a rechargeable battery, which refers to a battery cell that can be recharged after discharge to activate the active materials and continue to be used. Battery cells can be lithium-ion batteries, sodium-ion batteries, sodium-lithium-ion batteries, lithium metal batteries, sodium metal batteries, lithium-sulfur batteries, magnesium-ion batteries, nickel-metal hydride batteries, nickel-cadmium batteries, lead-acid batteries, etc., and the embodiments of this application are not limited to these.

[0041] In this application embodiment, a battery cell can refer to any shape, such as a square cell or a round cell. A battery cell typically refers to a battery cell, which is one of the basic units constituting a battery. The battery cell is the core component of a battery, responsible for storing and releasing electrical energy. A battery cell can be a lithium-ion battery cell (Li-ion Cell), a lithium-polymer battery cell (Li-polymer Cell), a nickel-metal hydride battery cell (NiMH Cell), etc. This application embodiment does not limit the type of battery cell; it can be selected according to the actual application scenario. In this application embodiment, the battery cell is the core component of a battery pack. A battery pack typically includes multiple battery cells, which are combined together to provide the required electrical capacity and voltage. The components of a battery pack include at least: individual battery cells, a battery management system (BMS), a casing, connecting harnesses, connectors, and interfaces. These components work together to combine the individual battery cells into a fully functional battery pack for various application scenarios. For example, battery packs can be used in electric vehicles, energy storage systems, portable electronic devices, solar energy systems, wind energy systems, emergency backup power supplies, power tools, or electric bicycles, etc. This application does not impose any limitations on these applications; specific applications can be selected based on actual usage scenarios.

[0042] It should be noted that the battery pack can use different types of battery cells, such as lithium-ion batteries, nickel-metal hydride batteries, lithium polymer batteries, etc., depending on the specific application requirements and performance specifications.

[0043] In this embodiment, the battery may also be a single physical module comprising one or more battery cells to provide higher voltage and capacity. When there are multiple battery cells, the multiple battery cells are connected in series, parallel, or mixed via a busbar.

[0044] The following describes an exemplary application of the internal resistance detection method according to the embodiments of this application. The internal resistance detection method provided in the embodiments of this application can be executed by a processor in an internal resistance detection device. Figure 1 This is a schematic diagram of the internal resistance detection device provided in the embodiments of this application. Figure 1 The internal resistance detection device 10 shown includes at least one processor 110, a memory 150, at least one network interface 120, and a user interface 130. The various components in the internal resistance detection device are coupled together via a bus system 140. It is understood that the bus system 140 is used to enable communication between these components. In addition to a data bus, the bus system 140 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 1 The general labeled all buses as Bus System 140.

[0045] The processor 110 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), 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, etc.

[0046] User interface 130 includes one or more output devices 131 that enable the presentation of media content, and one or more input devices 132.

[0047] Memory 150 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, etc. Memory 150 may optionally include one or more storage devices physically located remote from processor 110. Memory 150 may include volatile memory or non-volatile memory, or both. Non-volatile memory may be read-only memory (ROM), and volatile memory may be random access memory (RAM). The memory 150 described in this application embodiment is intended to include any suitable type of memory. In some embodiments, memory 150 is capable of storing data to support various operations, examples of which may include AI agents, programs, modules, and data structures or subsets or supersets thereof, as illustrated below.

[0048] Operating system 151 includes system programs for handling various basic system services and performing hardware-related tasks, such as the framework layer, core library layer, driver layer, etc., for implementing various basic business functions and handling hardware-based tasks;

[0049] The network communication module 152 is used to reach other computing devices via one or more (wired or wireless) network interfaces 120, exemplary network interfaces 120 including: Bluetooth, WiFi, and Universal Serial Bus (USB), etc.

[0050] The input processing module 153 is used to detect one or more inputs or interactions from one or more input devices 132.

[0051] In some embodiments, the apparatus provided in this application may be implemented in software. Figure 1An internal resistance detection device 154 stored in memory 150 is shown. This internal resistance detection device 154 can be an internal resistance detection device in an internal resistance detection method. It can be software in the form of programs and plug-ins, including the following software modules: an acquisition module 1541, a first determination module 1542, a second determination module 1543, a third determination module 1544, and a third determination module 1545. These modules can be logically linked and therefore can be arbitrarily combined or further divided according to the functions they implement. The functions of each module will be described below.

[0052] In other embodiments, the apparatus provided in this application can also be implemented in hardware. As an example, the apparatus provided in this application can be a processor in the form of a hardware decoding processor, which is programmed to execute the internal resistance detection method provided in this application. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0053] In some embodiments, the internal resistance detection method provided in this application can be executed by an internal resistance detection device. The internal resistance detection device can be any terminal with data processing capabilities, or it can be a server. That is, the internal resistance detection method in each embodiment of this application can be executed by a terminal or by a server. The server can be a physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0054] The technical solution of this application will now be described in detail with reference to the accompanying drawings.

[0055] Figure 2 This is an optional flowchart of the internal resistance detection method provided in the embodiments of this application. Figure 1 ,like Figure 2 As shown, the internal resistance detection method provided in this application embodiment can be implemented through steps S201 and S204:

[0056] Step S201: Obtain historical charge and discharge data of the battery; historical charge and discharge data includes charge and discharge voltage, charge and discharge current and battery capacity at each data acquisition time under each historical charge and discharge cycle.

[0057] In this embodiment, the historical charge / discharge data of the battery refers to data automatically collected and recorded during multiple charge / discharge operations, which may include the charge / discharge voltage, charge / discharge current, and battery capacity at each data collection point. This data can be used to analyze the trend of battery performance changes over time and as input information for calculating internal resistance. The historical charge / discharge data of the battery can be stored in a cloud database or a local database, such as a vehicle management system (BMS) or the monitoring platform of an energy storage system.

[0058] Here, the historical charge / discharge data of the battery in the cloud database can include the charge / discharge voltage, charge / discharge current, and battery capacity at various data collection points throughout multiple charge / discharge cycles since the battery's inception. The historical charge / discharge data can be sampled continuously or collected periodically, with the collection frequency depending on the system design and application scenario. For example, in electric vehicles, complete charge / discharge data is recorded each time the battery is charged or discharged for subsequent analysis.

[0059] In this embodiment of the application, historical charge and discharge data of the battery can be obtained efficiently by calling the cloud interface or reading data from the local database.

[0060] Step S202: Based on the battery capacity at each data acquisition time, determine the state of charge of the battery negative electrode at each data acquisition time.

[0061] Here, State of Charge (SOC) refers to a parameter indicating the current level of charge / discharge of the battery, usually expressed as a percentage. SOC can be calculated by comparing the currently measured or discharged battery capacity with the battery's rated capacity. For example, during a charging process, if the battery's total capacity after a full charge is 50 Ah, and the currently measured battery capacity is 30 Ah, then the SOC is 60%.

[0062] The lithium-ion insertion or extraction status of the negative electrode material can reflect the charge and discharge state of the battery. Therefore, the battery capacity can be converted into the state of charge of the negative electrode. In this way, the state of charge can not only reflect the current energy state of the battery, but also characterize the state of the internal chemical reaction of the battery, especially the lithium-ion insertion or extraction status of the negative electrode material.

[0063] In some embodiments, the state of charge can be used as the horizontal axis to construct a curve relating the DC internal resistance value to the state of charge, which can intuitively determine the change of the DC internal resistance value during the charging and discharging process.

[0064] Step S203: Determine the open-circuit voltage at each data acquisition time based on the charging voltage and discharging voltage at each data acquisition time.

[0065] In the embodiments of this application, open circuit voltage (OCV) refers to the terminal voltage of a battery under no-load conditions (i.e., without charging or discharging operations). Open circuit voltage reflects the equilibrium state of the internal chemical reactions of the battery.

[0066] In some embodiments, under the same negative electrode state of charge, the voltage during charging is higher than the voltage during discharging because of the internal resistance in the battery. During charging, the charging voltage = open circuit voltage + internal resistance potential; during discharging, the discharging voltage = open circuit voltage - internal resistance potential. Combining these two formulas, we get the open circuit voltage = (charging voltage + discharging voltage) / 2.

[0067] Step S204: Based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current at each data acquisition time, determine the DC internal resistance value of the battery at each data acquisition time.

[0068] In some embodiments, the DC internal resistance value can refer to the resistive characteristics exhibited by the battery during DC charging and discharging, directly reflecting factors such as the contact resistance between internal battery materials and the resistance to electrode reactions. The smaller the DC internal resistance value, the better the battery conductivity and the stronger the power output capability.

[0069] In this embodiment, the charging internal resistance and discharging internal resistance can be calculated based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current, respectively, and then the DC internal resistance of the battery can be further calculated.

[0070] Here, the corresponding DC internal resistance can be calculated by comparing the charging and discharging voltage and current data under the same state of charge. For example, DC internal resistance = (charging voltage / charging current – ​​discharging voltage / discharging current) / 2. Because there is a positive polarization effect inside the battery during charging and a negative polarization effect during discharging, this calculation formula takes into account the phenomenon that the charging voltage is higher than the discharging voltage under the same state of charge. By using the formula for calculating DC internal resistance, the polarization effect can be eliminated, resulting in a result closer to the true DC internal resistance value.

[0071] The DC internal resistance value can be calculated by comparing the charging and discharging voltage and charging and discharging current data under the same state of charge. This allows for online detection of the battery's DC internal resistance value without the need for additional testing procedures, making it highly practical and adaptable.

[0072] Step S205: Based on the state of charge and DC internal resistance value at each data acquisition time, determine the DC internal resistance value of the battery corresponding to different states of charge under different historical charge-discharge cycle numbers.

[0073] In this embodiment, by matching the state of charge (SOC) with the DC internal resistance value at different cycle counts, a DC internal resistance-SOC relationship curve can be established. This curve reflects the changing trend of the battery's DC internal resistance at different usage stages and can be used to assess the battery's aging condition.

[0074] For example, in early cycles, the battery's DC internal resistance may be low and increase slowly, while in later cycles, due to material aging or lithium plating, the DC internal resistance may suddenly rise. By monitoring the trend of DC internal resistance changes, potential battery problems can be detected in a timely manner, and corresponding maintenance measures can be taken.

[0075] In practice, the aging process of the battery can be observed intuitively by plotting the DC internal resistance-state of charge curves under different cycle numbers.

[0076] In some embodiments, a warning threshold can be set. When the DC internal resistance exceeds the set range, a warning signal can be issued to alert the user that the battery may have lithium plating or similar abnormal conditions.

[0077] This application's embodiments acquire historical charge and discharge data of the battery, calculate the state of charge (SOC) of the battery's negative electrode by combining the battery capacity at each data acquisition time, and then calculate the DC internal resistance value using charge and discharge voltage and current. Finally, it establishes the DC internal resistance distribution of the battery under different SOCs at different cycle counts. Without relying on specific charge and discharge procedures or experimental conditions, it can perform internal resistance analysis via cloud data during actual battery operation, thereby accurately capturing the aging characteristics of battery internal resistance during use, providing a basis for BMS parameter correction and lithium plating early warning. Furthermore, it achieves online detection of the battery's DC internal resistance value without requiring additional battery testing procedures, demonstrating high practicality and adaptability.

[0078] Step S202 in the internal resistance detection method provided in this application embodiment can be implemented through steps S2021 and S2022:

[0079] Step S2021: Based on the battery capacity and rated capacity at each data acquisition moment during the charging process, determine the state of charge of the battery negative electrode at each data acquisition moment during the charging process.

[0080] In this embodiment of the application, the rechargeable battery capacity refers to the amount of electricity charged into the battery at a certain moment during the charging process, measured in ampere-hours (Ah). The rechargeable battery capacity reflects the cumulative energy input of the battery during the charging process.

[0081] Rated capacity is the maximum amount of electricity that a battery can store, representing the theoretical maximum capacity that a battery can fully charge and discharge once under standard conditions.

[0082] In this embodiment, the state of charge (SOC) of the battery negative electrode at each data acquisition moment during the charging process can be obtained by dividing the rechargeable battery capacity by the rated capacity. For example, if the charged amount is Qc1 and the rated capacity is Qn, then the SOC is Qc1 / Qn×100%.

[0083] Step S2022: Based on the discharge battery capacity and the battery's rated capacity at each data acquisition moment during the discharge process, determine the state of charge of the battery's negative electrode at each data acquisition moment during the discharge process.

[0084] In some embodiments, the discharge battery capacity may refer to the amount of electricity released at a certain moment during the discharge process. The discharge battery capacity reflects the actual output capability of the battery during use.

[0085] During discharge, lithium ions are extracted from the graphite layer, and the state of charge (SOC) of the battery's negative electrode gradually decreases. Based on the discharged battery capacity and rated capacity, the SOC of the battery's negative electrode at each data acquisition moment during the discharge process can be determined. For example, if the battery capacity for a certain discharge is Qd1, then the SOC is 1 - Qd1 / Qn × 100%.

[0086] By combining the state of charge (SOC) data during the discharge process with the SOC data during the charging process, a SOC-voltage relationship curve can be constructed for the battery under a complete charge-discharge cycle, which can accurately reflect the battery's true internal resistance characteristics.

[0087] The embodiments of this application calculate the state of charge of the battery negative electrode based on the capacity data of the charging and discharging processes respectively, which can better reflect the true state of the battery and improve the reliability of the battery state of charge calculation.

[0088] In some embodiments, the internal resistance detection method provided in this application may further include steps S1 and S2:

[0089] Step S1: In response to the difference between the number of charging data and the number of discharging data in each historical charge-discharge cycle, the charging data or discharging data corresponding to the smaller value between the number of charging data and the number of discharging data is determined as the baseline data.

[0090] In this embodiment, the amount of charging and discharging data may differ from the amount of discharging data due to factors such as ambient temperature, load conditions, and sampling frequency during the charging and discharging process. For example, in a certain cycle, 100 sets of voltage and current data were collected during the charging process, while only 80 sets were collected during the discharging process.

[0091] When the amount of charging data and discharging data is inconsistent, direct data comparison may introduce errors. Therefore, this application compares the amount of charging data and discharging data, and uses the smaller dataset from either the charging or discharging data as the benchmark data for data alignment, so that the amount of charging data equals the amount of discharging data.

[0092] Here, a set of data points with fewer data points from either the charging or discharging data can be selected as the baseline data to avoid calculation errors caused by misalignment of data. The baseline data allows the charging and discharging curves to be compared on the same time axis, eliminating interference caused by differences in data volume and providing a unified time scale for subsequent data processing.

[0093] Step S2: Based on the state of charge at each data acquisition time in the reference data, process the target data to make the number of charging data the same as the number of discharging data; the target data is charging data or discharging data that is different from the reference data.

[0094] Here, target data refers to another set of data in the charging and discharging data that corresponds to the reference data. For example, if the reference data is discharging data, then the target data is charging data. To align the two sets of data, the target data can be downsampled based on the state of charge information in the reference data, ensuring that the number of data points matches that of the reference data. This makes the charging and discharging data comparable within the same state of charge range.

[0095] In some embodiments, the state of charge (SOC) value at each data acquisition moment can be extracted from the reference data, and the closest SOC value can be found in the target data for correspondence. If a point corresponding to a certain SOC value is missing in the target data, a new data point can be generated using methods such as linear interpolation, thereby achieving consistency in the number of data points. This allows the difference between charging voltage and discharging voltage under the same SOC to be accurately calculated.

[0096] This application embodiment selects benchmark data and performs matching processing on another type of data to ensure that the states of charge of the two are consistent. This can effectively solve the problem of data imbalance caused by different data acquisition frequencies or time points, and ensure that the data used in subsequent calculations are comparable, thereby improving the accuracy of DC internal resistance analysis results.

[0097] In some embodiments, step S2 can be implemented by steps S21 and S22:

[0098] Step S21: Based on the state of charge at each data acquisition time in the reference data, determine the target state of charge with the smallest difference between the target data and each state of charge.

[0099] The target state of charge (SOC) can be the SOC value in the target data that is closest to the SOC at a certain data acquisition time in the reference data, thereby improving the accuracy of data matching.

[0100] For example, in a certain charge-discharge cycle, the reference data records the state of charge at multiple time points (such as 50.67%, 62.51%, and 77.63%), while the target data contains multiple states of charge (such as 50.23%, 61.32%, and 76.25%). The difference between each state of charge in the reference data and each state of charge in the target data will be calculated separately, and the state of charge corresponding to the smallest of these differences will be selected as the target state of charge.

[0101] Step S22: Determine the target state of charge and the charging or discharging data at the corresponding data acquisition time as the target data after data processing.

[0102] Charging data refers to parameters such as voltage, current, and capacity during the charging process; discharging data refers to parameters such as voltage, current, and capacity during the discharging process.

[0103] Once the target state of charge (SOC) is determined, the parameters such as voltage, current, and capacity corresponding to each SOC in the target data can be used as the processed target data. For example, when the target data is charging data, the charging voltage, discharging voltage, and rechargeable battery capacity corresponding to a target SOC of 60% can be used as a set of processed target data to calculate the true internal resistance value under a target SOC of 60%.

[0104] The embodiments of this application can effectively filter and match cloud-based charging and discharging data. By finding the target data point that is closest to the state of charge in the reference data, it ensures that the charging and discharging data in the sequential charging and discharging cycle has a high degree of matching in terms of battery state, reduces errors caused by differences in data volume, and improves the accuracy of subsequent internal resistance calculation.

[0105] In some embodiments, the charge / discharge voltage includes the charging voltage and the discharging voltage, and the charge / discharge current includes the charging current and the discharging current. The charging voltage, discharging voltage, charging current, and discharging current can be collected in real time by the BMS and uploaded to a cloud database or a local database.

[0106] In some embodiments, the DC internal resistance value may include a charging internal resistance value and a discharging internal resistance value. The charging internal resistance value reflects the voltage drop caused by the internal polarization effect during battery charging, while the discharging internal resistance value reflects the voltage rise caused by the same internal polarization effect during battery discharging. Distinguishing between charging and discharging internal resistance values ​​allows for a more accurate assessment of battery performance changes under different operating conditions.

[0107] Step S204 provided in this embodiment can be implemented through steps S31 and S32:

[0108] Step S31: Based on the open-circuit voltage, charging voltage, and charging current at each data acquisition time, determine the charging internal resistance value of the battery at each data acquisition time.

[0109] Here, the open-circuit voltage can be used as a reference and compared with real-time voltage and current data during the charging process to calculate the charging internal resistance. Charging internal resistance value. It can be determined using formula (1):

[0110] (1);

[0111] in, It is the charging voltage at each data acquisition moment; It is the open-circuit voltage at each data acquisition moment; It is the charging current at each data acquisition moment.

[0112] Step S32: Based on the open-circuit voltage, discharge voltage, and discharge current at each data acquisition time, determine the discharge internal resistance value of the battery at each data acquisition time.

[0113] In some embodiments, the discharge internal resistance value It can be determined using formula (2):

[0114] (2);

[0115] in, It is the discharge voltage at each data acquisition moment; It is the open-circuit voltage at each data acquisition moment; It is the discharge current at each data acquisition moment.

[0116] The embodiments of this application calculate the internal resistance value during the charging and discharging processes respectively, which can provide a comprehensive understanding of the battery's performance under different operating conditions. This segmented calculation method can more accurately reflect the battery's true internal resistance performance under different operating conditions.

[0117] In some embodiments, after calculating the charging internal resistance and the discharging internal resistance, step S204 can be implemented through step S41:

[0118] Step S41: In response to the difference between the charging internal resistance value and the discharging internal resistance value being less than a preset threshold, determine the DC internal resistance value of the battery at each data acquisition time based on the charging internal resistance value and the discharging internal resistance value.

[0119] In some embodiments, when the difference between the calculated charging internal resistance and the discharging internal resistance is less than a preset threshold, it indicates that the battery's charging and discharging behavior under the current state of charge is relatively stable and not significantly affected by polarization effects. In this case, the battery's DC internal resistance at each data acquisition moment can be calculated using the charging and discharging internal resistance values.

[0120] When the charging internal resistance is equal to the discharging internal resistance, the right side of formula (1) can be considered equal to the right side of formula (2). Since the discharging current is more stable than the charging current, the discharging current can be used to replace the charging current. Therefore, the DC internal resistance R can be determined by formula (3):

[0121] (3);

[0122] In this embodiment, when the difference between the charging and discharging internal resistance values ​​is small and the battery is in a relatively stable state, the DC internal resistance value of the battery can be calculated based on the charging and discharging internal resistance values. This method does not require special charging and discharging operations to obtain the internal resistance value, making it more suitable for market-end applications such as vehicles and energy storage systems where additional testing is not feasible. It improves the battery's online monitoring capabilities and has good engineering practicality.

[0123] In some embodiments, the internal resistance detection method provided in this application may further include steps S51 to S53:

[0124] Step S51: Based on the DC internal resistance values ​​of the battery at different states of charge under different historical charge-discharge cycles, determine the average DC internal resistance of the battery under each historical charge-discharge cycle.

[0125] The embodiments of this application can reflect the aging degree of the battery's internal materials and the changing trend of power performance by calculating the DC internal resistance. This can be used to assess the battery's state of health (SOH) and predict potential failure risks.

[0126] Historical charge-discharge cycle count refers to the number of complete charge-discharge cycles a battery undergoes during use. Each cycle causes some degree of damage to the battery's internal structure, thus affecting its DC internal resistance. By analyzing the DC internal resistance values ​​from multiple historical charge-discharge cycles, a battery aging curve can be established, providing data support for subsequent battery anomaly detection.

[0127] Here, the average DC internal resistance refers to the statistical average of the DC internal resistance values ​​measured at multiple SOC points under a certain charge-discharge cycle. The average can be calculated using the DC internal resistance values ​​at 55% to 65% state of charge. The average DC internal resistance reflects the overall internal resistance level of the battery in a specific charge-discharge cycle, and helps to eliminate potential errors or fluctuations at individual measurement points.

[0128] By obtaining the average DC internal resistance under multiple historical charge-discharge cycles, a trend model of the battery internal resistance changing with the number of cycles can be established, thus providing a basis for subsequent abnormal change detection. Figure 3 This is a schematic diagram of the trend model of battery internal resistance changing with the number of cycles provided in the embodiments of this application. Curve 301 is a curve composed of the average DC internal resistance under different charge and discharge cycles.

[0129] Step S52: Based on the average DC internal resistance under each historical charge-discharge cycle, detect abnormal abrupt changes in the average DC internal resistance and obtain the detection results.

[0130] In this embodiment, the abnormal abrupt change point can refer to a phenomenon where the average DC internal resistance deviates significantly from the previous few cycles in a certain cycle. When the average DC internal resistance exhibits such a sudden change, it indicates that abnormal physical or chemical changes have occurred inside the battery, including lithium plating, electrolyte decomposition, and active material shedding.

[0131] The detection result can refer to whether abnormal mutation points are detected, and the detection result serves as the basis for subsequent early warning decisions.

[0132] Step S53: In response to the presence of abnormal mutation points in the detection results, a lithium plating warning is issued.

[0133] If an abnormal mutation point is detected, a warning signal indicating lithium plating behavior is returned. The warning signal may include the time when the lithium plating signal appears. If no abnormal mutation point is detected, no action is taken. Please continue to refer to [link / reference]. Figure 3 The abnormal mutation point 302 can be considered as the occurrence of an abnormal increase in the actual internal resistance signal at 600 cycles, at which point the battery exhibits interface lithium plating.

[0134] This application embodiment monitors the changing trend of the average DC internal resistance, which can promptly detect the risk of battery aging or abnormalities. When the internal resistance shows an abnormal increase, it may indicate that lithium plating has occurred in the battery. Triggering the early warning mechanism at this time helps to take maintenance measures in advance to prevent further deterioration, thereby improving the safety and service life of the battery system.

[0135] The following will describe an exemplary application of the embodiments of this application in a real-world application scenario.

[0136] To address the problems existing in related technologies, this application proposes a DC internal resistance calculation model based on cloud-based charging and discharging data, which can realize online calculation and early warning of the battery's true internal resistance without changing the original charging and discharging process.

[0137] The core inventive points of this application include two aspects: first, establishing a DC internal resistance calculation model applicable to conventional charge and discharge processes, the results of which are consistent with the test results of the national standard DCR scheme; second, using the calculated internal resistance data for real-time early warning of battery lithium plating risk. This effectively overcomes the problems of complex processes and poor applicability in related technologies' internal resistance calculation methods.

[0138] The internal resistance calculation method provided in this application embodiment can be implemented through steps S61 to S65:

[0139] Step S61: Read the charge and discharge voltage, current and capacity data of the battery in multiple cycle processes (i.e., the number of historical charge and discharge cycles) stored in the cloud.

[0140] Here, each charging and discharging process constitutes a cycle, covering the two operational phases of charging and discharging.

[0141] Step S62: Based on the capacity data, convert the charge and discharge amount of each cycle into the state of charge value (i.e., state of charge) of the negative electrode graphite (i.e., the negative electrode of the battery).

[0142] For example, during the first charge-discharge cycle, if the charge is Qa1 (which can be considered as the rated capacity under full charge), the SOC of graphite is 100%; if the discharge capacity is Qb1, the SOC is 1-Qb1 / Qa1.

[0143] Subsequently, the same operation was performed for each cycle number to establish the relationship curve between SOC and charge / discharge voltage. Figure 4 This is a schematic diagram of a single-cycle charge-discharge curve provided in an embodiment of this application, as shown below. Figure 4 As shown in Figure 401, under this cycle, the charging voltage varies with the graphite state of charge during the charging process, and the discharging voltage varies with the graphite state of charge during the charging process, as shown in Figure 402.

[0144] Step S63: Derive the true internal resistance using the voltage difference measured during the charging and discharging process.

[0145] Since the charging voltage is higher than the discharging voltage, there is a potential difference between the charging and discharging voltages caused by the internal resistance. During charging, the charging voltage = true voltage (i.e., open-circuit voltage) + true internal resistance potential (i.e., internal resistance potential), and the discharging voltage = true voltage – true internal resistance potential, where the true internal resistance potential = charging / discharging current * true internal resistance. Therefore, the true internal resistance value can be estimated using the formula "(charging voltage / charging current – ​​discharging voltage / discharging current) / 2".

[0146] Step S64: Process the above data using a Python algorithm and plot the relationship curve between the actual internal resistance and the SOC.

[0147] Figure 5 This is a schematic diagram of the relationship between actual internal resistance and SOC provided in the embodiments of this application, such as... Figure 5 As shown, curve 501 is the relationship curve between the actual internal resistance and the state of charge (SOC) under a certain cycle.

[0148] Subsequently, by performing the same operation for each number of aging cycles, the actual internal resistance change under different aging cycles can be obtained. Figure 6 This is a schematic diagram of the actual internal resistance curves under different aging cycles provided in the embodiments of this application, such as... Figure 6 As shown, curve 601 is the relationship between the actual internal resistance and the state of charge (SOC) in the first cycle; curve 602 is the relationship between the actual internal resistance and the SOC in the 100th cycle; curve 603 is the relationship between the actual internal resistance and the SOC in the 500th cycle; curve 604 is the relationship between the actual internal resistance and the SOC in the 1000th cycle; and curve 605 is the relationship between the actual internal resistance and the SOC in the 1500th cycle.

[0149] Considering that the DCR test specified in the national standard is usually carried out when the SOC is about 60%, the average internal resistance in the range of 55% to 65% can be selected as the battery aging characteristic index to compare the actual internal resistance of this application with the DCR obtained by the test during the R&D stage. Figure 7 This is a schematic diagram comparing the actual internal resistance provided in the embodiments of this application with the internal resistance obtained from testing during the R&D stage, as shown in the figure. Figure 7 As shown, curve 701 represents the internal resistance obtained during the R&D phase testing under different cycle counts, and curve 702 represents the internal resistance value obtained in the embodiments of this application under different cycle counts. The internal resistance value obtained in this application shows the same trend as the internal resistance obtained during the R&D phase testing, indicating a high degree of confidence.

[0150] The embodiments of this application are based on cloud-based charge and discharge data. Data processing can obtain the actual internal resistance aging situation equivalent to the research and development DCR data, which can be used to correct the BMS internal resistance coefficient and provide a more accurate and safer system design.

[0151] Step S65: Based on the calculated actual internal resistance data and the trend of the actual internal resistance data with the number of cycles, determine whether lithium plating occurs.

[0152] Under normal circumstances, the actual internal resistance increases slowly with the number of cycles; however, when lithium plating occurs, the actual internal resistance increases suddenly and significantly, such as... Figure 3 As shown. By capturing the characteristic of a sudden and significant increase in the actual internal resistance value, an early warning signal can be issued, prompting the user to take measures to prevent further damage to the battery.

[0153] The embodiments of this application can perform real-time calculation of DC internal resistance based on charge and discharge data in the cloud; based on the calculated DC internal resistance data, real-time early warning of risks such as lithium plating can be provided.

[0154] The embodiments of this application can also be extended to application scenarios of multiple battery types, and are applicable to batteries with different power levels and structural forms. This not only solves the shortcomings of related technologies in terms of process limitations and applicability, but also realizes online monitoring and risk warning of battery internal resistance, possessing broad practical application value and promising prospects for promotion.

[0155] Please continue to refer to Figure 1 The internal resistance detection device 154 provided in this application embodiment may include an acquisition module 1541, a first determination module 1542, a second determination module 1543, a third determination module 1544, and a fourth determination module 1545. The acquisition module 1541 is used to acquire historical charge-discharge data of the battery; the historical charge-discharge data includes the charge-discharge voltage, charge-discharge current, and battery capacity at each data acquisition time for each historical charge-discharge cycle. The first determination module 1542 is used to determine the battery negative electrode capacity at each data acquisition time based on the battery capacity at each data acquisition time. The first determination module 1543 is used to determine the open-circuit voltage at each data acquisition time based on the charging voltage and discharging voltage at each data acquisition time; the second determination module 1544 is used to determine the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current and discharging current at each data acquisition time; the third determination module 1545 is used to determine the DC internal resistance value of the battery corresponding to different states of charge under different historical charge and discharge cycles based on the state of charge and DC internal resistance value at each data acquisition time.

[0156] In some embodiments, the first determining module 1542 is further configured to determine the state of charge of the battery negative electrode at each data acquisition time during the charging process based on the charging battery capacity and the battery's rated capacity at each data acquisition time during the charging process; and to determine the state of charge of the battery negative electrode at each data acquisition time during the discharging process based on the discharging battery capacity and the battery's rated capacity at each data acquisition time during the discharging process.

[0157] In some embodiments, the internal resistance detection device further includes: a fifth determining module, configured to determine the charging data or discharging data corresponding to the smaller value between the number of charging data and the number of discharging data in each historical charge-discharge cycle as the reference data in response to the difference between the number of charging data and the number of discharging data in each historical charge-discharge cycle; and a data processing module, configured to perform data processing on the target data based on the state of charge at each data acquisition time in the reference data, so that the number of charging data is the same as the number of discharging data; the target data is charging data or discharging data that is different from the reference data.

[0158] In some embodiments, the data processing module is further configured to determine the target state of charge with the smallest difference between the target data and each state of charge based on the state of charge at each data acquisition time in the reference data; and to determine each target state of charge and the charging data or discharging data at the corresponding data acquisition time as the target data after data processing.

[0159] In some embodiments, the third determining module 1544 is further configured to determine the charging internal resistance value of the battery at each data acquisition time based on the open circuit voltage, charging voltage and charging current at each data acquisition time; and to determine the discharging internal resistance value of the battery at each data acquisition time based on the open circuit voltage, discharging voltage and discharging current at each data acquisition time.

[0160] In some embodiments, the third determining module 1544 is further configured to determine the DC internal resistance value of the battery at each data acquisition moment based on the charging internal resistance value and the discharging internal resistance value in response to the difference between the charging internal resistance value and the discharging internal resistance value being less than a preset threshold.

[0161] In some embodiments, the internal resistance detection device further includes: a sixth determining module, configured to determine the average DC internal resistance of the battery at each historical charge-discharge cycle number based on the DC internal resistance values ​​corresponding to different states of charge at different historical charge-discharge cycle numbers; a detection module, configured to detect abnormal abrupt changes in the average DC internal resistance based on the average DC internal resistance at each historical charge-discharge cycle number, and obtain a detection result; and an early warning module, configured to issue a lithium plating early warning in response to the detection result indicating the presence of abnormal abrupt changes.

[0162] It should be noted that this application Figure 1 The description of the device embodiments is similar to that of the method embodiments described above, and has similar beneficial effects, therefore, it will not be repeated. For technical details not disclosed in this device embodiment, please refer to the description of the method embodiments of this application for understanding.

[0163] It should be noted that, in the embodiments of this application, if the above-mentioned internal resistance detection method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware, software, or firmware, or any combination of hardware, software, and firmware.

[0164] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements some or all of the steps in the above-described method. The computer-readable storage medium can be transient or non-transient.

[0165] This application provides a computer program including computer-readable code. When the computer-readable code is run in a computer device, the processor in the computer device performs some or all of the steps in the above-described method.

[0166] This application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, it implements some or all of the steps in the above-described method. This computer program product can be implemented specifically through hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium; in other embodiments, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc.

[0167] It should be noted that the descriptions of the various embodiments above tend to emphasize the differences between them, while their similarities or commonalities can be referred to interchangeably. The descriptions of the above embodiments of the device, storage medium, computer program, and computer program product are similar to the descriptions of the above method embodiments and have similar beneficial effects. For technical details not disclosed in the embodiments of the device, storage medium, computer program, and computer program product of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0168] The aforementioned computer storage media / memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc.; or it can be various terminals that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0169] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above steps / processes do not imply a sequential order of execution; the execution order of each step / 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. The sequence numbers of the above embodiments of this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0170] This application uses terms such as "upper," "lower," "top," "bottom," "front," "back," "inner," and "outer" to indicate orientation or positional relationships. This is only for the convenience of describing this application and is not intended to indicate or imply that the device referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation on the scope of protection of this application.

[0171] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application depending on the specific circumstances.

[0172] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0174] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this application may all be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the integrated unit may be implemented in hardware or in a combination of hardware and software functional units.

[0175] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A method for detecting the internal resistance of a battery, characterized in that, The internal resistance detection method includes: Acquire historical charge and discharge data of the battery; the historical charge and discharge data includes the charge and discharge voltage, charge and discharge current and battery capacity at each data acquisition time for each historical charge and discharge cycle; Based on the battery capacity at each data acquisition time, the state of charge of the battery negative electrode at each data acquisition time is determined. Based on the charging voltage and discharging voltage at each data acquisition time, the open-circuit voltage at each data acquisition time is determined. Based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current at each data acquisition time, the DC internal resistance value of the battery at each data acquisition time is determined. Based on the state of charge and DC internal resistance at each data acquisition time, the DC internal resistance of the battery at different states of charge is determined for different historical charge-discharge cycles.

2. The internal resistance detection method according to claim 1, characterized in that, Determining the state of charge of the battery negative electrode at each data acquisition time based on the battery capacity at each data acquisition time includes: Based on the battery capacity and the battery's rated capacity at each data acquisition moment during the charging process, the state of charge of the battery's negative electrode at each data acquisition moment during the charging process is determined. Based on the discharge battery capacity and the rated capacity of the battery at each data acquisition time during the discharge process, the state of charge of the negative electrode of the battery at each data acquisition time during the discharge process is determined.

3. The internal resistance detection method according to claim 1, characterized in that, The internal resistance detection method further includes: In response to the difference between the number of charging data and the number of discharging data in each historical charge-discharge cycle, the charging data or discharging data corresponding to the smaller value between the number of charging data and the number of discharging data is determined as the baseline data. Based on the state of charge at each data acquisition time in the reference data, the target data is processed to make the number of charging data the same as the number of discharging data; the target data is charging data or discharging data that is different from the reference data.

4. The internal resistance detection method according to claim 3, characterized in that, The data processing of the target data based on the state of charge at each data acquisition time in the reference data includes: Based on the state of charge at each data acquisition time in the reference data, the target state of charge with the smallest difference between each state of charge is determined in the target data. The target state of charge and the charging or discharging data at the corresponding data acquisition time are determined as the target data after data processing.

5. The internal resistance detection method according to any one of claims 1 to 4, characterized in that, The DC internal resistance value includes the charging internal resistance value and the discharging internal resistance value; determining the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current at each data acquisition time includes: Based on the open-circuit voltage, charging voltage, and charging current at each data acquisition time, the charging internal resistance value of the battery at each data acquisition time is determined. Based on the open-circuit voltage, discharge voltage, and discharge current at each data acquisition time, the discharge internal resistance value of the battery at each data acquisition time is determined.

6. The internal resistance detection method according to claim 5, characterized in that, The determination of the DC internal resistance value of the battery at each data acquisition time based on the open-circuit voltage, charging voltage, discharging voltage, charging current, and discharging current includes: In response to the difference between the charging internal resistance value and the discharging internal resistance value being less than a preset threshold, the DC internal resistance value of the battery at each data acquisition time is determined based on the charging internal resistance value and the discharging internal resistance value.

7. The internal resistance detection method according to any one of claims 1 to 4, characterized in that, The internal resistance detection method further includes: Based on the DC internal resistance values ​​of the battery at different states of charge under different historical charge-discharge cycles, the average DC internal resistance of the battery under each historical charge-discharge cycle is determined. Based on the average DC internal resistance under each historical charge-discharge cycle, abnormal abrupt changes in the average DC internal resistance are detected, and the detection results are obtained. A lithium plating warning is issued in response to the presence of abnormal mutation points in the detection results.

8. A battery internal resistance detection device, characterized in that, The internal resistance detection device includes: The acquisition module is used to acquire historical charge and discharge data of the battery; the historical charge and discharge data includes the charge and discharge voltage, charge and discharge current and battery capacity at each data acquisition time for each historical charge and discharge cycle. The first determining module is used to determine the state of charge of the battery negative electrode at each data acquisition time based on the battery capacity at each data acquisition time. The second determining module is used to determine the open-circuit voltage at each data acquisition time based on the charging voltage and discharging voltage at each data acquisition time. The third determining module is used to determine the DC internal resistance value of the battery at each data acquisition time based on the open circuit voltage, charging voltage, discharging voltage, charging current and discharging current at each data acquisition time. The fourth determining module is used to determine the DC internal resistance value of the battery corresponding to different states of charge under different historical charge-discharge cycles, based on the state of charge and DC internal resistance value at each data acquisition time.

9. A battery internal resistance testing device, characterized in that, The internal resistance detection device includes: A memory for storing executable instructions; a processor for executing the executable instructions stored in the memory to implement the internal resistance detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The device stores executable instructions for causing a processor to execute the executable instructions to implement the internal resistance detection method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the internal resistance detection device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, it implements the internal resistance detection method according to any one of claims 1 to 7.

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