A method, system, device and medium for evaluating residual value of a power lithium battery

Abstract

The application discloses a kind of power lithium battery residual value evaluation method, system, equipment and medium, comprising: the remaining capacity V of prediction battery c ;The value O that battery can possibly generate in future n (p i ) is calculated;According to the remaining capacity V of the battery c And the value O that battery can possibly generate in future n (p i ) battery dynamic value V n It is calculated, and the method, system, equipment and medium can more accurately estimate the residual value of power lithium battery.

Description

Technical Field

[0001] This invention belongs to the field of power lithium battery aging and battery residual value assessment, and relates to a method, system, equipment and medium for assessing the residual value of power lithium batteries. Background Technology

[0002] Lithium-ion batteries, as an important type of lithium-ion battery, are widely used in electric vehicles, energy storage systems, and portable devices. With the rapid development and increasing demand in the electric vehicle market, assessing the residual value of lithium-ion batteries has become particularly important for users and investors. Accurately assessing the residual value of lithium-ion batteries can help decision-makers optimize battery usage, maintenance, and replacement strategies, reduce costs, and improve resource utilization efficiency. However, existing methods for assessing the residual value of lithium-ion batteries have some problems, such as a lack of accuracy, reliability, and comprehensiveness. These methods are often based solely on the monitoring and estimation of battery capacity degradation, ignoring other influencing factors such as battery cost, project operating cycle, charge / discharge power, and the market value created by the battery, thus resulting in poor estimation accuracy. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and medium for assessing the residual value of power lithium batteries. This method, system, device and medium can estimate the residual value of power lithium batteries more accurately.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In one aspect, the present invention provides a method for assessing the residual value of a power lithium battery, comprising:

[0006] Predict the remaining battery capacity V c ;

[0007] Calculate the potential future value of batteries O n (p i );

[0008] According to the remaining capacity V of the battery c and the potential future value of batteries O n (p i Calculate the dynamic value V of the battery. n .

[0009] The predicted remaining capacity V of the battery c The process is as follows:

[0010] Obtain basic information about power lithium batteries;

[0011] The basic information of the power lithium battery is input into the trained deep degradation network to predict the remaining capacity V of the battery. c .

[0012] The basic information of the power lithium battery includes the battery model, capacity, cycle life, voltage and current information.

[0013] The potential value of batteries in the future n (p i )for:

[0014]

[0015] Where n is the number of years the project lasts, n∈{1, 2, ..., N}, P n Let P be the power output of the battery in year n. n ∈[0, P], N=(Tt) / M is the number of segments to be sampled at the end of the project cycle or the end of the battery life, T is the project end time, t is the battery usage time, and M is the sampling step size.

[0016] Battery dynamic value V n for:

[0017] V n =O n (p i )+γV c .

[0018] Also includes:

[0019] Calculate the residual value of the battery, Vnf = max{O n (p i )}.

[0020] In a second aspect, the present invention provides a system for assessing the residual value of a power lithium battery, comprising:

[0021] Prediction module, used to predict the remaining battery capacity V c ;

[0022] The first calculation module is used to calculate the potential future value of the battery. n (p i );

[0023] The second calculation module is used to calculate based on the remaining capacity V of the battery. c and the potential future value of batteries O n (p i Calculate the dynamic value V of the battery. n .

[0024] The prediction module includes:

[0025] The acquisition module is used to acquire basic information about the power lithium battery.

[0026] The third calculation module is used to input the basic information of the power lithium battery into the trained deep degradation network to predict the remaining capacity V of the battery. c .

[0027] In three aspects, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for assessing the residual value of a power lithium battery.

[0028] In four aspects, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for assessing the residual value of a power lithium battery.

[0029] The present invention has the following beneficial effects:

[0030] The residual value assessment method, system, equipment, and medium for power lithium batteries described in this invention, in specific operation, not only consider the aging factors of the battery's own parameters, but also the economic benefits that the battery may generate through discharge. It proposes a dynamic value assessment method for batteries to improve the accuracy of residual value assessment, provide data support for the subsequent assessment and classification of waste batteries, and provide a scientific basis for the reuse and recycling of waste batteries, thereby promoting the development of the battery recycling industry and reducing environmental impact. Attached Figure Description

[0031] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0032] Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] The present invention will now be described in further detail with reference to the accompanying drawings:

[0036] Example 1

[0037] The method for assessing the residual value of power lithium batteries according to the present invention includes the following steps:

[0038] 1) Obtain basic information about the power lithium battery, including the battery model, capacity, cycle count, voltage, and current information;

[0039] 2) Input the basic information of the power lithium battery into the trained deep degradation network (DDN) to predict the remaining capacity V of the battery. c ;

[0040] 3) Calculate the potential future value of the battery. n (p i );

[0041] 4) Based on the remaining capacity V of the battery c and the potential future value of batteries O n (p i Calculate the dynamic value V of the battery. n ,in:

[0042] V n =O n (p i )+γV c

[0043]

[0044] Where n is the number of years the project lasts, n∈{1, 2, ..., N}, P n Let P be the power output of the battery in year n. n∈[0, P], where P is the rated power of the new battery, γ is the discount rate (constant), N = (Tt) / M is the number of sampling segments at the end of the project cycle or the end of the battery's lifespan, T is the project end time, t is the battery's usage time, and M is the sampling step size of 24 hours; λ n This represents the market price per kilowatt-hour.

[0045] It should be noted that in solving O n (p i When calculating the battery electricity price in a given region over a recent period, the solution is obtained by using the price change curve of the battery electricity in that region. Specifically, the sampling step size M is set to 24 hours, and λ is set to... n Let P be the average price of battery energy over 24 hours, and let P be the battery's charging and discharging power. Since the battery's charging and discharging power changes very little over 24 hours, P is treated as a constant in the calculation. Finally, the value generated by battery discharge in each segment is calculated separately and summed to obtain O. n (p i ).

[0046] Assuming the battery has a resale value S while still under valid manufacturer warranty, the Vc of the NMC battery can be measured using the DDN model. Substituting this value into the formula, S can be obtained, and subsequently, the residual value Vnf of the NMC battery can be calculated as max{O n (p i )}.

[0047] Batteries are classified according to their residual value. Specifically, decision algorithms are used to classify used batteries based on factors such as the residual value of power lithium batteries and their failure rate. A multi-level classification system can be employed to categorize used batteries into different classes as needed. For example, classifications could include reusable, resale, and safe disposal levels to meet different processing requirements and procedures.

[0048] This invention has the following characteristics:

[0049] This invention optimizes traditional dynamic value assessment models. Traditional assessment methods focus on the battery's structure, defining its value based on its parameters and structural aging. This invention's dynamic value assessment model considers not only the aging of battery parameters but also the potential economic benefits of battery discharge, proposing a novel algorithm for dynamic battery value. This algorithm calculates economic benefits not by using the battery's remaining lifespan as the total time but by the duration of the battery project. The local electricity price is considered constant per unit time. The economic value generated by the battery per unit time is calculated by multiplying the electrical energy discharged per unit time by the value of that energy. Summing these values ​​per unit time yields the total value generated by the battery over the total time.

[0050] This invention differs from other models in its calculation of battery aging parameters. Instead of dynamic programming, it directly reads the battery capacity change dataset predicted by DDN (Digital Data Network). This effectively leverages DDN's high accuracy to reduce evaluation errors and enables dynamic value prediction.

[0051] Example 2

[0052] refer to Figure 1 The residual value assessment system for power lithium batteries of the present invention includes:

[0053] Prediction module, used to predict the remaining battery capacity V c ;

[0054] The first calculation module is used to calculate the potential future value of the battery. n (p i );

[0055] The second calculation module is used to calculate based on the remaining capacity V of the battery. c and the potential future value of batteries O n (p i Calculate the dynamic value V of the battery. n .

[0056] In one embodiment of the present invention, the prediction module includes:

[0057] The acquisition module is used to acquire basic information about the power lithium battery.

[0058] The third calculation module is used to input the basic information of the power lithium battery into the trained deep degradation network to predict the remaining capacity V of the battery. c .

[0059] Example 3

[0060] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for assessing the residual value of a power lithium battery. The memory may include main memory, such as high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc. The bus may be categorized as an address bus, a data bus, a control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0061] Example 4

[0062] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for assessing the residual value of a power lithium battery. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0063] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0065] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the residual value of a power lithium battery, characterized in that, include: Predicting the remaining battery capacity ; Calculate the potential future value of batteries ; Based on the remaining capacity of the battery and the potential value of batteries in the future Calculate the dynamic value of a battery ; The predicted remaining capacity of the battery The process is as follows: Obtain basic information about power lithium batteries; The basic information of the power lithium battery is input into the trained deep degradation network to predict the remaining capacity of the battery. ; The potential value of batteries in the future for: Where n is the number of years the project lasts, n∈{1,2,……N}, P i In the first i The battery's output power per year. P i ∈[0,P], N=(Tt) / M is the number of segments to be sampled at the end of the project cycle or the end of the battery's lifespan, T is the project end time, t is the battery usage time, M is the sampling step size, λ i The price per kilowatt-hour in the market. P The rated power of the new battery; dynamic value of batteries for: Where γ is the discount rate; Also includes: Calculate the residual value of the battery, Vnf = max{ } 2. The method for assessing the residual value of power lithium batteries according to claim 1, characterized in that, The basic information of the power lithium battery includes the battery model, capacity, cycle life, voltage and current information.

3. A residual value assessment system for power lithium batteries, said assessment system being applicable to the residual value assessment method for power lithium batteries according to any one of claims 1-2, characterized in that, include: Prediction module, used to predict the remaining capacity of the battery. ; The first calculation module is used to calculate the potential future value of the battery. ; The second calculation module is used to calculate based on the remaining capacity of the battery. and the potential value of batteries in the future Calculate the dynamic value of a battery ; The prediction module includes: The acquisition module is used to acquire basic information about the power lithium battery. The third calculation module is used to input the basic information of the power lithium battery into the trained deep degradation network to predict the remaining capacity of the battery. .

4. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the residual value assessment method for power lithium batteries as described in any one of claims 1-2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the residual value assessment method for power lithium batteries as described in any one of claims 1-2.

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