Graphene battery capacity monitoring method and system, electronic device and storage medium

Abstract

This invention discloses a method, system, electronic device, and storage medium for monitoring the capacity of graphene batteries, relating to the field of battery capacity monitoring technology. It acquires voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to a preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle; the current energy loss value of the graphene battery is calculated based on the power data; the energy loss value represents the unusable portion of the battery capacity; the voltage data is preprocessed to obtain input data, which is then input into a preset LSTM-FC model to obtain a predicted SOH; the predicted SOC is corrected using the energy loss value to obtain the real-time SOC of the graphene battery. By calculating the battery's energy loss value using the battery's power data and correcting the predicted SOH obtained from the voltage data to obtain the real-time SOC, the accuracy of SOC prediction is improved.

Description

Technical Field

[0001] This invention relates to the field of battery capacity monitoring technology, specifically to a graphene battery capacity monitoring method, system, electronic device, and storage medium. Background Technology

[0002] Most commonly used graphene batteries are made by adding graphene materials to lithium-ion batteries. The state of charge (SOC) of a lithium battery reflects its remaining capacity and is one of the important evaluation indicators. Accurate and stable SOC estimation (also known as SOC prediction) is crucial for extending battery life and ensuring the safe driving of electric vehicles. However, due to the influence of factors such as temperature, unknown noise, and uncertain outliers, accurate SOC estimation of lithium batteries is usually difficult.

[0003] CN114859249A discloses a method and apparatus for detecting battery capacity. It establishes a preset LSTM algorithm model group with a segmented LSTM algorithm model, matches the capacity detection data of the target battery pack with the LSTM algorithm model in the preset LSTM algorithm model group, and uses the matched target LSTM algorithm model to detect the capacity and battery capacity of a batch of battery packs to be tested.

[0004] In existing technologies, battery capacity is typically predicted using battery voltage data, resulting in low accuracy in battery capacity detection. Summary of the Invention

[0005] The purpose of this invention is to solve the problems mentioned above in the background technology and to propose an Internet of Things (IoT) data integration and analysis method.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for monitoring the capacity of a graphene battery, the method comprising:

[0008] Acquire voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to a preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle.

[0009] The current energy loss value of the graphene battery is calculated based on the power data; the energy loss value represents the unusable portion of the battery capacity.

[0010] Voltage data is preprocessed to obtain input data, which is then input into a preset LSTM-FC model to obtain the predicted SOH.

[0011] The predicted SOC is corrected using the energy loss value to obtain the real-time SOC of the graphene battery.

[0012] Optionally, the voltage data is the real-time voltage collected when the graphene battery is charged to 80% SOC.

[0013] Optionally, the current energy loss value of the graphene battery is calculated based on the power data as follows:

[0014]

[0015] E is the energy loss value, E0 is the battery capacity of the previous discharge cycle, P is the discharge power of the previous discharge cycle, α and β are preset constants, and T is the cycle duration of the previous discharge cycle.

[0016] Optionally, the default LSTM-FC network architecture consists of an input layer, an LSTM layer, an FC layer, and an output layer;

[0017] The voltage data is preprocessed to obtain the input data, which includes:

[0018] The voltage data corresponding to each cycle number of the graphene battery is determined by using a sliding window.

[0019] The cycle number and its corresponding voltage data are integrated as input data.

[0020] A second aspect of the present invention also provides a graphene battery capacity monitoring system, the system comprising a data acquisition module, a calculation module, a prediction module, and a correction module; wherein:

[0021] The data acquisition module is used to acquire voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to a preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle.

[0022] The calculation module is used to calculate the current energy loss value of the graphene battery based on the power data; the energy loss value represents the unusable portion of the battery capacity.

[0023] The prediction module is used to preprocess the voltage data to obtain input data, and input the input data into a preset LSTM-FC model to obtain the predicted SOH;

[0024] The correction module is used to correct the predicted SOC using the energy loss value to obtain the real-time SOC of the graphene battery.

[0025] Optionally, the voltage data is the real-time voltage collected when the graphene battery is charged to 80% SOC.

[0026] Optionally, the computing module is specifically used for:

[0027]

[0028] E is the energy loss value, E0 is the battery capacity of the previous discharge cycle, P is the discharge power of the previous discharge cycle, α and β are preset constants, and T is the cycle duration of the previous discharge cycle.

[0029] Optionally, the prediction module includes a preprocessing module and a preset LSTM-FC model; the preset LSTM-FC network architecture consists of an input layer, an LSTM layer, an FC layer, and an output layer; the preprocessing module includes a first processing module and a second processing module.

[0030] The first processing module is used to determine the voltage data corresponding to each cycle number of the graphene battery through a sliding window;

[0031] The second processing module is used to integrate the cycle number and its corresponding voltage data as input data.

[0032] According to a third aspect of the present invention, an electronic device is also provided, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0033] Memory, used to store computer programs;

[0034] When a processor executes a program stored in memory, it implements any of the steps described above.

[0035] In a fourth aspect, the present invention also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which, when executed by a processor, implements any of the steps described above.

[0036] The beneficial effects of this invention are:

[0037] This invention provides a method for monitoring the capacity of a graphene battery. The method includes: acquiring voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to a preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle; calculating the current energy loss value of the graphene battery based on the power data; the energy loss value represents the unusable portion of the battery capacity; preprocessing the voltage data to obtain input data, inputting the input data into a preset LSTM-FC model to obtain a predicted SOH; and using the energy loss value to correct the predicted SOC to obtain the real-time SOC of the graphene battery. By calculating the battery's energy loss value using the battery's power data and correcting the predicted SOH obtained from the voltage data to obtain the real-time SOC, the accuracy of SOC prediction is improved. Attached Figure Description

[0038] The invention will now be further described with reference to the accompanying drawings.

[0039] Figure 1 A flowchart of a graphene battery capacity monitoring method provided in an embodiment of the present invention;

[0040] Figure 2 A system block diagram of a graphene battery capacity monitoring system is provided for an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] This invention provides a method for monitoring the capacity of graphene batteries. (See also...) Figure 1 , Figure 1 A flowchart illustrating a graphene battery capacity monitoring method provided in this embodiment of the invention. The method includes:

[0044] S101, acquire voltage and power data of graphene battery.

[0045] S102 calculates the current energy loss of the graphene battery based on the power data.

[0046] S103: Preprocess the voltage data to obtain input data, input the input data into the preset LSTM-FC model, and obtain the predicted SOH.

[0047] S104 uses energy loss values ​​to correct the predicted SOC and obtain the real-time SOC of the graphene battery.

[0048] The voltage data is the real-time voltage collected when the graphene battery is charged to the preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle; the energy loss value represents the unusable portion of the battery capacity.

[0049] The graphene battery capacity monitoring method provided by this invention can calculate the battery's energy loss value using the battery's power data, and correct the predicted SOH obtained from the voltage data to obtain the real-time SOC, thereby improving the accuracy of SOC prediction.

[0050] In one implementation, the predicted SOC does not take into account the energy loss of the battery. The predicted SOC is usually greater than the real-time SOC, which may lead to an overestimation of the remaining driving range based on the predicted SOC. The real-time SOC can be obtained by subtracting the energy loss from the predicted SOC, which improves the accuracy of SOC prediction and the accuracy of driving range estimation.

[0051] In one embodiment, the voltage data is the real-time voltage collected when the graphene battery is charged to 80% SOC.

[0052] In one implementation, based on battery charge-discharge statistics, as the number of cycles increases, the battery voltage data shows the strongest correlation with battery capacity when charged to 80% SOC, exhibiting a monotonic increase. When the fault threshold is reached, all battery characteristics at 80% SOC are close to the upper limit of the cutoff voltage and have a statistical confidence interval of less than 0.01V, thus effectively reflecting the degradation process.

[0053] In one embodiment, the current energy loss value of the graphene battery is calculated based on the power data as follows:

[0054]

[0055] E is the energy loss value, E0 is the battery capacity of the previous discharge cycle, P is the discharge power of the previous discharge cycle, α and β are preset constants, and T is the cycle duration of the previous discharge cycle.

[0056] In one embodiment, the default LSTM-FC network architecture consists of an input layer, an LSTM layer, an FC layer, and an output layer;

[0057] The voltage data is preprocessed to obtain the input data, which includes:

[0058] The voltage data corresponding to each cycle number of the graphene battery is determined by using a sliding window.

[0059] The cycle number and its corresponding voltage data are integrated as input data.

[0060] This invention provides a graphene battery capacity monitoring system, see [link to relevant documentation]. Figure 2 , Figure 2 This is a system block diagram of a graphene battery capacity monitoring system provided in an embodiment of the present invention. The system includes a data acquisition module, a calculation module, a prediction module, and a correction module; wherein:

[0061] The data acquisition module is used to acquire voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to the preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle.

[0062] The calculation module is used to calculate the current energy loss value of the graphene battery based on the power data; the energy loss value represents the unusable portion of the battery capacity.

[0063] The prediction module is used to preprocess voltage data to obtain input data, and then input the input data into a preset LSTM-FC model to obtain the predicted SOH.

[0064] The correction module is used to correct the predicted SOC using the energy loss value to obtain the real-time SOC of the graphene battery.

[0065] The graphene battery capacity monitoring method provided by this invention can calculate the battery's energy loss value using the battery's power data, and correct the predicted SOH obtained from the voltage data to obtain the real-time SOC, thereby improving the accuracy of SOC prediction.

[0066] In one embodiment, the voltage data is the real-time voltage collected when the graphene battery is charged to 80% SOC.

[0067] In one embodiment, the computing module is specifically used for:

[0068]

[0069] E is the energy loss value, E0 is the battery capacity of the previous discharge cycle, P is the discharge power of the previous discharge cycle, α and β are preset constants, and T is the cycle duration of the previous discharge cycle.

[0070] In one embodiment, the prediction module includes a preprocessing module and a preset LSTM-FC model; the preset LSTM-FC network architecture consists of an input layer, an LSTM layer, an FC layer, and an output layer; the preprocessing module includes a first processing module and a second processing module.

[0071] The first processing module is used to determine the voltage data corresponding to each cycle number of the graphene battery through a sliding window;

[0072] The second processing module is used to integrate the cycle number and its corresponding voltage data as input data.

[0073] This invention also provides an electronic device, such as... Figure 3 As shown, it includes a processor 301, a communication interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304.

[0074] Memory 303 is used to store computer programs;

[0075] When processor 301 executes a program stored in memory 303, it performs the following steps:

[0076] Acquire voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to a preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle.

[0077] The current energy loss value of the graphene battery is calculated based on the power data; the energy loss value represents the unusable portion of the battery capacity.

[0078] Voltage data is preprocessed to obtain input data, which is then input into a preset LSTM-FC model to obtain the predicted SOH.

[0079] The predicted SOC is corrected using the energy loss value to obtain the real-time SOC of the graphene battery.

[0080] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0081] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0082] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0083] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be 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, or discrete hardware components.

[0084] In another embodiment of the present invention, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of any of the graphene battery capacity monitoring methods described above.

[0085] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the graphene battery capacity monitoring methods described in the above embodiments.

[0086] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover 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 limitations, 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 said element.

[0088] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, computer-readable storage media, and computer program products are basically similar to the method embodiments, and therefore the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0089] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method of monitoring the capacity of a graphene battery, characterized by, The method includes: Acquire voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to a preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle. The current energy loss of the graphene battery is calculated based on power data; this energy loss represents the unusable portion of the battery capacity; the specific calculation formula is as follows: This represents the energy loss value. This represents the battery capacity from the previous discharge cycle. This represents the discharge power of the previous discharge cycle. and As a preset constant, The duration of the previous discharge cycle; Voltage data is preprocessed to obtain input data, which is then input into a preset LSTM-FC model to obtain the predicted SOC. The predicted SOC is corrected using the energy loss value to obtain the real-time SOC of the graphene battery.

2. The graphene battery capacity monitoring method according to claim 1, characterized in that, The voltage data is the real-time voltage collected when the graphene battery is charged to 80% SOC.

3. The graphene battery capacity monitoring method according to claim 1, characterized in that, The default LSTM-FC network architecture consists of an input layer, an LSTM layer, an FC layer, and an output layer. The voltage data is preprocessed to obtain the input data, which includes: The voltage data corresponding to each cycle number of the graphene battery is determined by using a sliding window. The cycle number and its corresponding voltage data are integrated as input data.

4. A graphene battery capacity monitoring system, characterized in that, The system includes a data acquisition module, a calculation module, a prediction module, and a correction module; wherein: The data acquisition module is used to acquire voltage and power data of the graphene battery; the voltage data is the real-time voltage collected when the graphene battery is charged to a preset SOC; the power data is the discharge power of the graphene battery in the previous discharge cycle. The calculation module is used to calculate the current energy loss value of the graphene battery based on the power data; the energy loss value represents the unusable portion of the battery capacity; the specific calculation formula is as follows: This represents the energy loss value. This represents the battery capacity from the previous discharge cycle. This represents the discharge power of the previous discharge cycle. and As a preset constant, The duration of the previous discharge cycle; The prediction module is used to preprocess the voltage data to obtain input data, and input the input data into a preset LSTM-FC model to obtain the predicted SOC; The correction module is used to correct the predicted SOC using the energy loss value to obtain the real-time SOC of the graphene battery.

5. The graphene battery capacity monitoring system according to claim 4, characterized in that, The voltage data is the real-time voltage collected when the graphene battery is charged to 80% SOC.

6. The graphene battery capacity monitoring system according to claim 4, characterized in that, The prediction module includes a preprocessing module and a preset LSTM-FC model; the preset LSTM-FC network architecture consists of an input layer, an LSTM layer, an FC layer, and an output layer; the preprocessing module includes a first processing module and a second processing module. The first processing module is used to determine the voltage data corresponding to each cycle number of the graphene battery through a sliding window; The second processing module is used to integrate the cycle number and its corresponding voltage data as input data.

7. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-3.

Images