A method and system for residual value assessment and asset right identification for retired battery gradient utilization

Abstract

The application discloses a kind of remaining residual value evaluation and asset right of stepwise utilization for decommissioned battery, belongs to decommissioned battery recycling technical field, specific steps include: unified feature vector is obtained by washing and standardization pretreatment to decommissioned battery multi-source heterogeneous original data, the comprehensive residual value index is calculated by four-dimensional weighting model of health degree, security degree, scene adaptation degree, market value degree, dynamic residual value curve and total value present value of whole life cycle are obtained by improved Arrhenius attenuation model in combination with scene stress parameter, and right certificate is generated by blockchain digital fingerprint and smart contract verification, finally the optimal stepwise utilization path is output by multi-attribute decision algorithm.The application can improve residual value evaluation accuracy and right credibility, realize large-scale efficient processing, significantly improve the value conversion efficiency of decommissioned battery stepwise utilization.

Description

Technical Field

[0001] This invention relates to the field of retired battery recycling technology, and in particular to a method and system for assessing residual value and confirming asset ownership for the cascade utilization of retired batteries. Background Technology

[0002] Existing technologies for residual value assessment and ownership management in the field of retired battery cascade utilization mainly cover three mainstream paths: electrochemical performance testing, data-driven health status prediction, and blockchain full life cycle traceability. Through charge and discharge testing, electrochemical impedance analysis, BMS historical data modeling, and on-chain data storage, these technologies enable the detection of performance parameters, estimation of health status, and traceability of the circulation process of retired batteries, providing basic technical support for battery recycling and grading, cascade circulation, and industry supervision.

[0003] However, due to inherent limitations in their own technological paths and application frameworks, existing technologies still have several practical shortcomings in industrialization: In terms of assessment dimensions, residual value is often measured using a single indicator of health status, focusing on the battery's electrochemical performance attributes, failing to fully adapt to the operating conditions and market value fluctuations of different tiers of scenarios, resulting in significant discrepancies between assessment results and the actual commercial value of the battery; lifespan prediction generally uses a universal aging model for estimation, without differentiated calibration for stress differences in different tiers of utilization scenarios, making it difficult to support long-term asset value management and operational decisions; blockchain technology is mostly used for storing and recording original operating data, only achieving process-based information traceability, without forming a deeply integrated ownership confirmation mechanism with residual value assessment results, lacking a rigid technical anchor for the credibility of assessment results, easily leading to disputes over asset ownership boundaries, and the relatively independent and fragmented nature of each technical link, resulting in low overall operational efficiency for large-scale applications. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for assessing residual value and confirming asset ownership for the secondary use of retired batteries, thereby solving the aforementioned technical problems.

[0005] To achieve the above objectives, this invention provides a method for assessing the residual value and confirming asset ownership of retired batteries for secondary utilization, comprising the following steps: S1. Based on the multi-source heterogeneous raw data of retired batteries, a unified feature vector is obtained through data cleaning and standardization preprocessing. S2. Based on a unified feature vector, a comprehensive residual value index is obtained through a four-dimensional weighted evaluation model of health, safety, scenario adaptability, and market value. S3. Preset stress parameters for multiple types of tiered utilization scenarios, and based on the comprehensive residual value index, perform lifetime extrapolation and value mapping through an improved Arrhenius attenuation model to obtain dynamic residual value curves and present value of total life cycle value; S4. Based on the comprehensive residual value index, combined with physical test data and battery identity information, an on-chain asset ownership certificate is obtained through blockchain digital fingerprint generation and smart contract verification. S5. Based on the comprehensive residual value index, the present value of the total value over the entire life cycle, and the dynamic residual value curve, and according to the multi-scenario demand profile, the optimal tiered utilization path recommendation result is obtained by sorting and optimizing through a multi-attribute decision algorithm.

[0006] Preferably, in step S2, the health status is obtained by using a calculation method that combines capacity-internal resistance dual-factor fusion with temperature deviation correction based on the battery's nominal capacity, current actual capacity, initial internal resistance, current DC internal resistance, and historical operating temperature data. The formula for calculating health is: ; in, For health, Nominal capacity This represents the current actual capacity. The initial internal resistance, The current DC internal resistance, , , These are the capacity weight, internal resistance weight, and temperature correction weight, respectively. This is a temperature deviation correction function. This represents the deviation of the battery's historical operating temperature from the reference temperature.

[0007] Preferably, in step S2, based on the quantitative data of four risk factors—cell consistency, self-discharge rate, appearance damage, and historical faults—the safety value is obtained by a calculation method of weighted summation and inversion of multiple risk factors. The formula for calculating the safety level is: ; in, For safety reasons, For the first The quantified risk level of each risk factor These are the weighting coefficients for the corresponding risk factors. This represents the total number of risk factors.

[0008] Preferably, in step S3, the preset stress parameters for multiple tiered utilization scenarios include the average operating temperature, average charge / discharge rate, and average depth of discharge of the target tiered scenario. By introducing the improved Arrhenius decay model of scenario stress factor, the battery health state decay trajectory and remaining service life under different scenarios are obtained. The formula for the improved Arrhenius decay model is as follows: ; in, For tiered utilization service Battery health status after a period of time , For aging kinetic parameters, For activation energy, Boltzmann's constant, The average operating temperature for the target scenario. For reference temperature, The average charge / discharge rate for the scenario. The average depth of discharge in the scene. , These are the multiplier effect coefficient and the depth effect coefficient, respectively.

[0009] Preferably, in step S3, based on the dynamic residual value curve, the preset discount rate and the end of the tiered utilization life, the present value of the total life cycle value is obtained through continuous discount integral calculation; The formula for calculating the present value of total life-cycle value is: ; in, The present value of the total value over the entire life cycle. R On the dynamic residual curve The residual value at a given moment The discount rate is... This represents the end of the service life of the tiered utilization.

[0010] Preferably, in step S4, the battery identity information includes the battery's unique serial number, battery model, production batch, and manufacturing date; based on the battery identity information, health, safety, scenario adaptability, market value, comprehensive residual value index, original internal resistance test data, original capacity test data, and evaluation timestamp, a unique digital fingerprint is obtained by performing feature concatenation calculation using the SHA256 hash algorithm. The formula for generating a digital fingerprint is: in, For digital fingerprints, A unique serial number for the battery. Battery model number. For production batches, This refers to the manufacturing date. For health, For safety reasons, For scene adaptation, For market value, To provide a comprehensive residual value index, This is the original internal resistance detection data. This is the original capacity detection data. For evaluating timestamps.

[0011] Preferably, in step S5, based on a multi-indicator decision matrix including the present value of the total life cycle value, adaptability, technical threshold, and policy subsidies, the relative proximity of each scenario is calculated using the TOPSIS multi-attribute decision algorithm to obtain a priority ranking list of tiered utilization scenarios. The formula for calculating relative closeness is: ; in, For the first The relative resemblance between the scenes For the first The distance from a scenario to the ideal solution. For the first The distance from a scenario to the negative ideal solution.

[0012] Preferably, a residual value assessment and asset confirmation system for the cascade utilization of retired batteries includes: a multi-source data acquisition and preprocessing module, a four-dimensional residual value assessment engine, a scenario-based attenuation prediction module, a blockchain asset confirmation module, and a cascade utilization intelligent matching module, which are connected in sequence. The multi-source data acquisition and preprocessing modules are used to acquire multi-source heterogeneous raw data of retired batteries and perform standardized cleaning to output a unified feature vector. The four-dimensional residual value evaluation engine is used to receive a unified feature vector, perform four-dimensional weighted residual value calculation, and output a comprehensive residual value index. The scenario-based attenuation prediction module is used to receive the comprehensive residual value index, combine the tiered scenario stress parameters to complete attenuation extrapolation and value mapping, and output the dynamic residual value curve and the present value of the total value of the whole life cycle. The blockchain asset ownership confirmation module is used to receive the comprehensive residual value index and the original physical test data, complete the digital fingerprint generation and on-chain ownership confirmation, and output the asset ownership confirmation certificate. The tiered utilization intelligent matching module is used to receive the comprehensive residual value index and dynamic residual value curve, and output the optimal tiered utilization path recommendation result.

[0013] Preferably, the four-dimensional residual value assessment engine is deployed on a cloud big data platform and includes a health calculation submodule, a security calculation submodule, a scenario adaptability calculation submodule, a market value calculation submodule, and a weighted fusion submodule that are connected in sequence. The health calculation submodule is used to calculate battery health, the safety calculation submodule is used to calculate battery safety, the scenario adaptability calculation submodule is used to calculate scenario adaptability, the market value calculation submodule is used to calculate market value, and the weighted fusion submodule is used to weight and fuse the calculation results of the four dimensions according to preset weights to output a comprehensive residual value index.

[0014] Preferably, the blockchain asset ownership confirmation module includes a digital fingerprint generation submodule, a digital signature submodule, a smart contract cluster, and a distributed storage submodule; The input end of the digital fingerprint generation submodule is connected to the output end of the four-dimensional residual value evaluation engine, and is used to splice battery identity information, multi-dimensional evaluation results and original physical detection data to generate a unique digital fingerprint; The input of the digital signature submodule is connected to the output of the digital fingerprint generation submodule, and is used to digitally sign the evaluation result using the evaluation agency's private key; The input end of the smart contract cluster is connected to the output end of the digital signature submodule, and is used to verify the validity of the signature and issue on-chain asset ownership certificates. The distributed storage submodule is used to store the complete evaluation report and associate the content identifier with the ownership certificate by putting it on the blockchain.

[0015] Therefore, the present invention employs the above-mentioned method and system for residual value assessment and asset confirmation for the cascade utilization of retired batteries, which has the following beneficial effects: 1. By constructing a four-dimensional evaluation system that integrates health, safety, scenario adaptability, and market value, and simultaneously covering multiple dimensions such as electrochemical performance, safety risks, scenario adaptability, and market conditions, the residual value assessment results are more in line with the real asset value under the tiered utilization scenario, providing a reliable value benchmark for subsequent asset transactions, ownership confirmation, and scenario matching.

[0016] 2. By introducing scenario stress factors to construct a differentiated attenuation prediction model, the dynamic residual value trajectory under different tiered utilization scenarios can be output. Based on the time value of money, the present value of the total value over the entire life cycle can be obtained, realizing the upgrade from "point-in-time valuation" to "quantification of full-cycle returns". It can intuitively distinguish the long-term value differences of different application paths, providing quantitative basis for asset holders' scenario selection, investment calculation and decomposition timing decisions, and improving the overall return level of tiered utilization.

[0017] 3. By incorporating the original physical detection features of the battery, identity information, and multi-dimensional evaluation results into the digital fingerprint generation, a strong binding between the battery's physical entity, evaluation conclusion, and ownership information is achieved. Combined with the integrated process of automatically triggering on-chain confirmation upon completion of the evaluation, this ensures that the confirmation results are tamper-proof and fully traceable, while also preventing issues such as inflated evaluation data and double pledging of assets from a technical perspective, significantly improving the credibility and security of asset confirmation.

[0018] 4. By constructing a multi-source heterogeneous data link, a complete process of "data acquisition - four-dimensional assessment - attenuation prediction - asset ownership confirmation - scenario matching" is formed. Combined with multi-attribute decision-making algorithms, the optimal tiered utilization path is output, which can not only support rapid assessment of single batteries, but also meet the parallel processing needs of batch batteries, improve the overall efficiency of assessment and ownership confirmation, reduce operating costs, and support large-scale applications such as asset portfolio management and intelligent supply and demand matching, thus promoting the standardization and industrialization of retired battery tiered utilization.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a method for assessing residual value and confirming asset ownership for the secondary utilization of retired batteries, provided by this invention; Figure 2 This is a comparison chart of risk factor weights and weighted values ​​provided in Embodiment 1 of the present invention; Figure 3 This is a comparison chart of performance adaptability under different tiered utilization scenarios provided in Embodiment 1 of the present invention; Figure 4 This is a dual-axis graph of remaining lifetime and total lifecycle value provided in Embodiment 1 of the present invention; Figure 5 This is the intelligent matching recommendation ranking chart for the tiered utilization scenario provided in Embodiment 1 of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0022] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as a process, method, system, product, or server that includes a series of steps or units, not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0023] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] See appendix Figures 1-5 The specific implementation details are as follows: Example 1: Residual value assessment and asset confirmation of a single decommissioned ternary lithium battery pack.

[0025] This embodiment uses a certain model of retired ternary lithium battery pack as the evaluation object, and fully implements the residual value assessment and asset ownership confirmation method described in this invention. The specific steps are as follows: S1. Collect multi-source heterogeneous raw data of the retired battery pack, including four categories: production and manufacturing data, vehicle operation data, disassembly and testing data, and market transaction data. Normalize the numerical data through Z-score standardization and perform one-hot encoding on the categorical data. After outlier removal and missing value filling, a unified feature vector is obtained.

[0026] The core raw parameters collected in this embodiment are shown in Table 1 below: Table 1. Core Original Parameters of Retired Battery Packs:

[0027] Simultaneously collect market transaction data of the same model of battery as a valuation benchmark, including the market price of new batteries of the same model, the market popularity coefficient of second-hand batteries, and the benchmark price of brand-new batteries.

[0028] S2. Based on the unified feature vector, the comprehensive residual value index is calculated using a four-dimensional weighted evaluation model that considers health, safety, scenario adaptability, and market value. The specific calculation process is as follows: (1) Health status calculation: The health status is calculated using a combination of capacity-internal resistance dual-factor fusion and temperature deviation correction. The calculation formula is as follows: ; in, For health status; Nominal capacity This represents the current actual capacity. The initial internal resistance, This is the current DC internal resistance; , , These are the capacity weight, internal resistance weight, and temperature correction weight, respectively, and in this embodiment, the values ​​are 0.5, 0.35, and 0.15, respectively. This is a temperature deviation correction function. The deviation of the battery's historical operating temperature from the reference temperature is calculated using the following formula: In the formula, For the first Average operating temperature of each cycle This is a reference temperature (valued at 25℃). The temperature damage coefficient is taken as 0.002 / ℃ for the ternary battery system. This represents the cumulative number of charge-discharge cycles; This is the function for finding the maximum value.

[0029] Substituting the data from this embodiment, the calculated capacity factor is 0.77, the internal resistance factor is 0.6906, the temperature deviation correction value is 0.987, and the final health level is... It is 0.7748.

[0030] (2) Safety calculation: Based on a weighted assessment of four risk factors—cell consistency, self-discharge rate, external damage, and historical faults—the calculation formula is as follows: ; In the formula, For safety; For the first The risk level quantification value of each risk factor, ranging from 0 to 1; These are the weighting coefficients for the corresponding risk factors; This embodiment represents the total number of risk factors. Choose 4.

[0031] The weights and risk values ​​of each risk factor in this embodiment are shown in Table 2 below: Table 2 Weighted Calculation Table of Safety Risk Factors:

[0032] The total risk value after weighted summation is 0.1025, and the safety factor is calculated. It is 0.8975.

[0033] (3) Scenario Adaptability Calculation: For five tiered scenarios—grid energy storage, industrial and commercial energy storage, low-speed electric vehicles, communication base station backup power, and home energy storage—the matching degree between battery parameters and scenario requirements is calculated. The calculation formula is as follows: ; In the formula, For the first Adaptability to similar scenarios; For the battery Performance parameters; For the first Class of scenarios for the first Minimum requirements for each parameter; The weights for the corresponding parameters; This represents the number of performance parameter items.

[0034] In this embodiment, four parameters are selected: capacity level, power capability, consistency, and cycle life, with weights of 0.30, 0.25, 0.25, and 0.20, respectively. The calculation results are shown in Table 3 below: Table 3: Calculation Results of Multi-Scenario Adaptability

[0035] (4) Market value calculation: Calculated based on real-time market conditions, the formula is as follows: ; In the formula, Market value; This is the current market price for a new battery of the same model. The market popularity index for secondary batteries. This is the benchmark price for a brand new battery.

[0036] Substituting the corresponding market parameters in this embodiment, the market value is calculated. It is 0.614.

[0037] (5) Comprehensive residual value index calculation: The results of the four dimensions are weighted and integrated according to preset weights. The calculation formula is as follows: ; In the formula, , , , The weight coefficients are for health, safety, scenario adaptability, and market value, respectively, and the sum of the weights is 1. In this embodiment, the default values ​​are 0.4, 0.3, 0.15, and 0.15, respectively.

[0038] Taking the backup power scenario of a communication base station as an example, by substituting the corresponding adaptability values, the comprehensive residual value index is calculated. It is 0.8213.

[0039] S3. Pre-set stress parameters for multiple tiered utilization scenarios, and based on the comprehensive residual value index, perform lifetime extrapolation and value mapping through an improved Arrhenius attenuation model to obtain dynamic residual value curves and the present value of total life-cycle value. The specific process is as follows: The preset scene stress parameters include the target scene's average operating temperature, average charge / discharge rate, and average depth of discharge; the improved Arrhenius attenuation model formula is as follows: ; In the formula, For tiered utilization service Battery health status after a certain period of time; , These are aging kinetic parameters, determined by the battery chemistry system. The activation energy for ternary lithium batteries is approximately 60 kJ / mol. Boltzmann's constant; The average operating temperature for the target scenario; For reference temperature, the value is taken as 298K; The average charge / discharge rate for the scenario; The average depth of discharge in the scene; , These are the multiplier effect coefficient and the depth effect coefficient, respectively.

[0040] Based on the above model, the SOH decay trajectory is predicted under different scenarios, and a dynamic residual curve is generated by combining the residual mapping function. Furthermore, the present value of the total life-cycle value is obtained through continuous discounting and integration, calculated using the following formula: ; In the formula, The present value of the total value over the entire life cycle; On the dynamic residual curve The residual value at a given moment; The discount rate; This refers to the end of the tiered utilization lifespan, i.e., the time when the SOH (State of Health) drops to the lowest threshold of the scenario.

[0041] The attenuation prediction and value calculation results for the five scenarios in this embodiment are shown in Table 4 below: Table 4. Comparison of Attenuation Prediction and Value in Different Tiered Scenarios:

[0042] As shown in the table above, the backup power scenario for communication base stations has the longest predicted remaining service life and the highest present value of total lifecycle value; the low-speed electric vehicle scenario has the fastest attenuation rate, the shortest remaining service life, and the lowest present value of total lifecycle value, which can intuitively distinguish the long-term value differences of different application paths.

[0043] S4. Based on the comprehensive residual value index, combined with the original physical test data and battery identity information, an on-chain asset ownership certificate is obtained through blockchain digital fingerprint generation and smart contract verification. The specific process is as follows: Battery identification information includes the battery's unique serial number, battery model, production batch, and manufacturing date. This identification information is then combined with features from health, safety, scenario suitability, market value, comprehensive residual value index, original internal resistance test data, original capacity test data, and evaluation timestamp. A unique digital fingerprint is generated using the SHA256 hash algorithm, calculated as follows: ; In the formula, For digital fingerprints; A unique serial number for the battery. Battery model number. For production batches, This refers to the manufacturing date. For health, For safety reasons, For scene adaptation, For market value, It is a comprehensive residual value index; This is the original internal resistance detection data. This is the original capacity detection data; For evaluating timestamps.

[0044] After the digital fingerprint is generated, the assessment agency uses its private key to digitally sign the assessment result and writes the digital fingerprint, assessment summary, and signature information into the blockchain rights confirmation contract. The on-chain smart contract automatically verifies the validity of the signature. After successful verification, a unique asset rights confirmation certificate is generated. At the same time, the complete assessment report is stored in IPFS distributed storage, and only the content identifier is uploaded to the chain and associated with the rights confirmation certificate.

[0045] This embodiment further calculates the credibility of the confirmation of rights, using the following formula: ; In the formula, To ensure the credibility of the rights confirmation; To assess the credibility of the data source; As a weight for the credibility of the data source, To assess the qualification level of the assessment agency, To assess the weighting of the assessment agency's qualification level, To confirm the number of blocks in the blockchain, To reach the full confirmation block threshold, The weighting is used for on-chain confirmation. After confirmation by multiple blocks, the credibility of the confirmation in this embodiment is 99.2%, ensuring clear and traceable asset ownership.

[0046] S5. Based on the comprehensive residual value index, the present value of the total value over the entire life cycle, and the dynamic residual value curve, and according to the multi-scenario demand profile, the TOPSIS multi-attribute decision algorithm is used to sort and optimize the results to obtain the optimal tiered utilization path recommendation.

[0047] Construct a decision matrix that includes four indicators: present value of total lifecycle value, suitability, technological threshold, and policy subsidies. The formula is as follows: ; in, : for the first One alternative tiered utilization scenario; : The maximum value is 4, representing the four evaluation indicators, which are as follows: : Total life-cycle value present value (benefit-oriented indicator, the higher the value, the better); :Scenario adaptability (benefit-oriented indicator; the higher the value, the better); Technical threshold (cost-related indicator; the lower the value, the better); Policy subsidies (efficiency-based indicator; the higher the value, the better); For the first The scene in The original values ​​under each indicator.

[0048] After normalization, the distances to the positive and negative ideal solutions for each scenario are calculated to obtain the relative proximity. Vector normalization is used to render the original data dimensionless, resulting in a normalized matrix. The normalization formula is: ; in, Normalized index value; Introducing index weight coefficients into the weighted normalization matrix And satisfy The weighted normalized matrix is ​​obtained. : ; The optimal solution is determined separately for the positive ideal solution and the negative ideal solution based on the index type (positive ideal solution). ) and worst solution (negative ideal solution) ): Benefit-type indicators: For positive ideal solutions, take the maximum value in this column; for negative ideal solutions, take the minimum value in this column. Cost-related indicators: For positive ideal solutions, the minimum value in this column is taken; for negative ideal solutions, the maximum value in this column is taken.

[0049] The formula is: ; ; Calculate the Euclidean distance from each scene to the positive and negative ideal solutions separately: ; ; in For the first The distance from a scenario to the ideal solution. For the first The distance from a scenario to the negative ideal solution.

[0050] The formula for calculating relative closeness is: ; In the formula, For the first The relative resemblance between the two scenarios; For the first The distance from the ideal solution to the given scenario; For the first The distance from a scenario to the negative ideal solution.

[0051] The scene recommendation list is obtained by sorting the scenes in descending order of relative proximity. The sorting results in this embodiment are shown in Table 5 below: Table 5: Intelligent matching and sorting results for tiered utilization scenarios:

[0052] Compared to existing technologies that simply categorize scenarios based on SOH thresholds, this embodiment maximizes the utilization of battery asset value through multi-dimensional comprehensive sorting.

[0053] Example 2: System deployment and implementation for mass-produced retired batteries.

[0054] This embodiment provides a residual value assessment and asset confirmation system for the tiered utilization of retired batteries. It is deployed in a three-layer architecture of "cloud-edge-chain" and includes a multi-source data acquisition and preprocessing module, a four-dimensional residual value assessment engine, a scenario-based attenuation prediction module, a blockchain asset confirmation module, and a tiered utilization intelligent matching module, which are connected in sequence.

[0055] The multi-source data acquisition and preprocessing module is deployed at the edge layer and set up at recycling points and testing stations. It includes a production data interface submodule, a vehicle network data access submodule, a testing equipment docking submodule, a market data crawler submodule, and a data cleaning and standardization submodule. This module is used to connect to various data sources to collect multi-source heterogeneous raw data of retired batteries, complete cleaning operations such as outlier removal, missing value filling, and feature standardization, and output a unified feature vector to the cloud engine.

[0056] The four-dimensional residual value assessment engine is deployed on a cloud-based big data platform and includes, in sequence, a health calculation submodule, a safety calculation submodule, a scenario adaptability calculation submodule, a market value calculation submodule, and a weighted fusion submodule. The health calculation submodule receives capacity, internal resistance, and temperature data from a unified feature vector to calculate battery health. The safety calculation submodule receives risk factor data to calculate battery safety. The scenario adaptability calculation submodule has a built-in multi-scenario demand profile library to calculate the battery's adaptability to various scenarios. The market value calculation submodule connects to real-time market data to calculate market value. The weighted fusion submodule weights and fuses the calculation results of the four dimensions according to preset weights to output a comprehensive residual value index.

[0057] The scenario-based degradation prediction module is deployed on a cloud computing node and includes an aging model parameter library, a scenario stress profile library, and a degradation calculation submodule. The aging model parameter library stores the aging kinetic parameters of batteries with different chemical systems, and the scenario stress profile library stores the temperature, rate, and depth of discharge stress characteristic parameters of various tiered utilization scenarios. The input end of the degradation calculation submodule is connected to the output end of the aging model parameter library, the scenario stress profile library, and the four-dimensional residual value evaluation engine, respectively, to call parameters to complete degradation extrapolation and value mapping calculation, and output dynamic residual value curves and the present value of total value over the entire life cycle.

[0058] The blockchain asset ownership confirmation module is deployed on a consortium blockchain node and includes a digital fingerprint generation submodule, a digital signature submodule, a smart contract cluster, and a distributed storage submodule. The input of the digital fingerprint generation submodule is connected to the output of the four-dimensional residual value assessment engine, used to concatenate battery identity information, multi-dimensional assessment results, and original physical testing data to generate a unique digital fingerprint. The input of the digital signature submodule is connected to the output of the digital fingerprint generation submodule, used to digitally sign the assessment results using the assessment agency's private key. The input of the smart contract cluster is connected to the output of the digital signature submodule, used to verify the signature validity and issue on-chain asset ownership certificates. The distributed storage submodule stores the complete assessment report and associates the content identifier on the blockchain with the ownership certificate.

[0059] The intelligent matching module for tiered utilization is deployed in the cloud and includes a scenario demand library, a multi-attribute decision-making submodule, a supply and demand matching submodule, and a path optimization submodule. This module receives the comprehensive residual value index, the dynamic residual value curve, and the present value of the total value over the entire life cycle, and outputs the optimal tiered utilization path recommendation result.

[0060] This system supports three working modes: single-cell evaluation, batch evaluation, and asset portfolio management. It can process tens of thousands of battery packs of data in parallel in a single batch. Compared with the traditional pack-by-pack testing and evaluation mode, the processing efficiency is greatly improved and the evaluation cost is significantly reduced, making it suitable for the industrial processing needs of large-scale retired batteries.

[0061] In summary, this invention, by constructing a four-dimensional residual value assessment system integrating health, safety, scenario adaptability, and market value, breaks through the limitations of traditional single health status indicators. This reduces the residual value estimation error from 15%–20% to 5%–8%, making the assessment results more closely aligned with the actual commercial value of retired batteries. By introducing a decay prediction model based on scenario stress factors, it quantifies the differences in lifecycle value under different tiered utilization paths, providing accurate basis for long-term asset operation decisions and improving the overall return on tiered utilization by approximately 12%. Through a blockchain-based rights confirmation mechanism that strongly binds physical detection features to assessment results, it achieves an integrated process of automatic rights confirmation upon assessment completion, with a rights confirmation credibility of over 99.2%, technically ensuring the immutability and full traceability of asset ownership. Through a fully automated "cloud-edge-chain" system architecture, it shortens the assessment time for a single battery from the traditional 2–4 hours to less than 5 minutes, reducing assessment costs by over 60%, and supports parallel processing of tens of thousands of batteries, comprehensively improving the value conversion efficiency and refined asset management level of retired battery tiered utilization.

[0062] 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 them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for assessing residual value and confirming asset ownership for the secondary use of retired batteries, characterized in that, Includes the following steps: S1. Based on the multi-source heterogeneous raw data of retired batteries, a unified feature vector is obtained through data cleaning and standardization preprocessing. S2. Based on a unified feature vector, a comprehensive residual value index is obtained through a four-dimensional weighted evaluation model of health, safety, scenario adaptability, and market value. S3. Preset stress parameters for multiple types of tiered utilization scenarios, and based on the comprehensive residual value index, perform lifetime extrapolation and value mapping through an improved Arrhenius attenuation model to obtain dynamic residual value curves and present value of total life cycle value; S4. Based on the comprehensive residual value index, combined with physical test data and battery identity information, an on-chain asset ownership certificate is obtained through blockchain digital fingerprint generation and smart contract verification. S5. Based on the comprehensive residual value index, the present value of the total value over the entire life cycle, and the dynamic residual value curve, and according to the multi-scenario demand profile, the optimal tiered utilization path recommendation result is obtained by sorting and optimizing through a multi-attribute decision algorithm.

2. The method for residual value assessment and asset confirmation for the cascade utilization of retired batteries according to claim 1, characterized in that: In step S2, the health status is obtained by using a calculation method that combines capacity-internal resistance dual-factor fusion with temperature deviation correction, based on the battery's nominal capacity, current actual capacity, initial internal resistance, current DC internal resistance, and historical operating temperature data. The formula for calculating health is: ； in, For health, Nominal capacity This represents the current actual capacity. The initial internal resistance, The current DC internal resistance, , , These are the capacity weight, internal resistance weight, and temperature correction weight, respectively. This is a temperature deviation correction function. This represents the deviation of the battery's historical operating temperature from the reference temperature.

3. The method for residual value assessment and asset confirmation for the cascade utilization of retired batteries according to claim 2, characterized in that: In step S2, based on the quantitative data of four risk factors—cell consistency, self-discharge rate, appearance damage, and historical faults—the safety value is obtained by calculating the weighted summation and inversion of multiple risk factors. The formula for calculating the safety level is: ； in, For safety reasons, For the first The quantified risk level of each risk factor These are the weighting coefficients for the corresponding risk factors. This represents the total number of risk factors.

4. The method for residual value assessment and asset confirmation for the cascade utilization of retired batteries according to claim 3, characterized in that: In step S3, the preset stress parameters for multiple tiered utilization scenarios include the average operating temperature, average charge / discharge rate, and average depth of discharge of the target tiered scenario. By introducing the improved Arrhenius decay model of scenario stress factor, the battery health state decay trajectory and remaining service life under different scenarios are obtained. The formula for the improved Arrhenius decay model is as follows: ； in, For tiered utilization service Battery health status after a period of time , For aging kinetic parameters, For activation energy, Boltzmann's constant, The average operating temperature for the target scenario. For reference temperature, The average charge / discharge rate for the scenario. The average depth of discharge in the scene. , These are the multiplier effect coefficient and the depth effect coefficient, respectively.

5. The method for residual value assessment and asset confirmation for the cascade utilization of retired batteries according to claim 4, characterized in that: In step S3, based on the dynamic residual value curve, the preset discount rate and the end of the tiered utilization life, the present value of the total life cycle value is obtained through continuous discount integral calculation. The formula for calculating the present value of total life-cycle value is: ； in, The present value of the total value over the entire life cycle. R On the dynamic residual curve The residual value at a given moment The discount rate is... This represents the end of the service life of the tiered utilization.

6. The method for residual value assessment and asset confirmation for the cascade utilization of retired batteries according to claim 5, characterized in that: In step S4, the battery identity information includes the battery's unique serial number, battery model, production batch, and manufacturing date. Based on the battery identity information, health status, safety status, scenario adaptability, market value, comprehensive residual value index, original internal resistance test data, original capacity test data, and evaluation timestamp, a unique digital fingerprint is obtained by performing feature concatenation calculation using the SHA256 hash algorithm. The formula for generating a digital fingerprint is: in, For digital fingerprints, A unique serial number for the battery. Battery model number. For production batches, This refers to the manufacturing date. For health, For safety reasons, For scene adaptation, For market value, To provide a comprehensive residual value index, This is the original internal resistance detection data. This is the original capacity detection data. For evaluating timestamps.

7. The method for residual value assessment and asset confirmation for the cascade utilization of retired batteries according to claim 6, characterized in that: In step S5, based on a multi-indicator decision matrix including the present value of total life cycle value, adaptability, technical threshold, and policy subsidies, the relative proximity of each scenario is calculated using the TOPSIS multi-attribute decision algorithm to obtain a priority ranking list of tiered utilization scenarios. The formula for calculating relative closeness is: ； in, For the first The relative resemblance between the scenes For the first The distance from a scenario to the ideal solution. For the first The distance from a scenario to the negative ideal solution.

8. A residual value assessment and asset ownership confirmation system for the cascade utilization of retired batteries, characterized in that, The method for assessing residual value and confirming asset ownership for the secondary utilization of retired batteries as described in any one of claims 1-7 includes: a multi-source data acquisition and preprocessing module, a four-dimensional residual value assessment engine, a scenario-based attenuation prediction module, a blockchain asset ownership confirmation module, and a secondary utilization intelligent matching module, which are connected in sequence. The multi-source data acquisition and preprocessing modules are used to acquire multi-source heterogeneous raw data of retired batteries and perform standardized cleaning to output a unified feature vector. The four-dimensional residual value evaluation engine is used to receive a unified feature vector, perform four-dimensional weighted residual value calculation, and output a comprehensive residual value index. The scenario-based attenuation prediction module is used to receive the comprehensive residual value index, combine the tiered scenario stress parameters to complete attenuation extrapolation and value mapping, and output the dynamic residual value curve and the present value of the total value of the whole life cycle. The blockchain asset ownership confirmation module is used to receive the comprehensive residual value index and the original physical test data, complete the digital fingerprint generation and on-chain ownership confirmation, and output the asset ownership confirmation certificate. The tiered utilization intelligent matching module is used to receive the comprehensive residual value index and dynamic residual value curve, and output the optimal tiered utilization path recommendation result.

9. A residual value assessment and asset confirmation system for the cascade utilization of retired batteries according to claim 8, characterized in that: The four-dimensional residual value assessment engine is deployed on a cloud big data platform and includes a health calculation submodule, a security calculation submodule, a scenario adaptability calculation submodule, a market value calculation submodule, and a weighted fusion submodule, which are connected in sequence. The health calculation submodule is used to calculate battery health, the safety calculation submodule is used to calculate battery safety, the scenario adaptability calculation submodule is used to calculate scenario adaptability, the market value calculation submodule is used to calculate market value, and the weighted fusion submodule is used to weight and fuse the calculation results of the four dimensions according to preset weights to output a comprehensive residual value index.

10. A residual value assessment and asset confirmation system for the cascade utilization of retired batteries according to claim 9, characterized in that: The blockchain asset ownership confirmation module includes a digital fingerprint generation submodule, a digital signature submodule, a smart contract cluster, and a distributed storage submodule. The input end of the digital fingerprint generation submodule is connected to the output end of the four-dimensional residual value evaluation engine, and is used to splice battery identity information, multi-dimensional evaluation results and original physical detection data to generate a unique digital fingerprint; The input of the digital signature submodule is connected to the output of the digital fingerprint generation submodule, and is used to digitally sign the evaluation result using the evaluation agency's private key; The input end of the smart contract cluster is connected to the output end of the digital signature submodule, and is used to verify the validity of the signature and issue on-chain asset ownership certificates. The distributed storage submodule is used to store the complete evaluation report and associate the content identifier with the ownership certificate by putting it on the blockchain.

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