Battery asset value evaluation and traceability management method and system

Abstract

The application discloses a kind of battery asset value evaluation and traceability management method and system, belong to battery asset management technical field, including the following contents: generate unique digital identity identification for each battery, write battery factory parameters into blockchain main chain, while creating corresponding data account in side chain;Collect battery operation data, environmental data and electrochemical impedance spectroscopy data, after pretreatment, chain storage to side chain according to period;Build multi-input fusion SOH evaluation model, output battery health state value;Build four-layer value evaluation model, get the final battery asset evaluation value;Adopt main side chain collaborative architecture to carry out whole life cycle traceability, realize cross-chain data credible authentication by hash check;Monitor main chain key node event and side chain SOH change, meet trigger condition when automatically trigger value revaluation, and write the latest evaluation value into main chain.The application uses the above method, improves the objectivity and reliability of battery asset evaluation result.

Description

Technical Field

[0001] This invention relates to the technical field of battery asset management, and in particular to a method and system for battery asset valuation and traceability management. Background Technology

[0002] With the rapid development of the new energy vehicle and electrochemical energy storage industries, the installed base of power batteries and energy storage batteries continues to grow. Throughout the entire lifecycle of batteries—from production, distribution, use, secondary utilization, to recycling and dismantling—the need for asset trading and pricing based on ownership is increasingly prominent. Currently, the battery asset management system still has many shortcomings. First, battery operating data is fragmented, with data from vehicle BMS and energy storage platforms isolated from each other, lacking a unified and reliable data carrier. Maintenance records, abnormal operating conditions, and accident information during the transfer process are easily lost or tampered with, making it difficult to form a complete and reliable traceability chain. Second, most existing battery health assessment methods rely solely on capacity or internal resistance to estimate state of health (SOH), failing to comprehensively consider multiple factors such as electrochemical characteristics and long-term operating damage, resulting in significant assessment errors. At the same time, battery value is generally determined using a linear depreciation method based on service life, considering only physical degradation and ignoring brand premiums, market supply and demand, and value fluctuations caused by historical risk events, leading to a significant deviation between the pricing results and the true market value.

[0003] Furthermore, the existing traceability system and value assessment module are independent of each other. Traceability events cannot automatically drive value updates. After an asset is traded or repaired, the battery value still needs to be manually recalculated, resulting in a significant lag. A single blockchain traceability solution suffers from insufficient throughput and high storage costs if it handles massive amounts of high-frequency data. On the other hand, centralized data platforms cannot meet the data trust requirements of multi-party participation scenarios. These problems lead to information asymmetry in the second-hand battery market, restricting standardized pricing for battery reuse and hindering the commercialization of battery assets in financial and residual value management scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for battery asset valuation and traceability management, improve the objectivity and credibility of battery asset valuation results, and support the standardized management of battery asset circulation, cascade utilization and residual value.

[0005] To achieve the above objectives, this invention provides a method for battery asset valuation and traceability management, comprising the following steps: S1. Generate a unique digital identity for each battery, write the battery's factory parameters into the main blockchain, and create a corresponding data account on the side chain. S2. Collect battery operation data, environmental data and electrochemical impedance spectroscopy data, and after preprocessing, upload and store the evidence to the side chain according to the cycle. S3. Construct a multi-input fusion SOH evaluation model, which integrates the features of four dimensions: capacity decay characteristics, internal resistance growth characteristics, EIS electrochemical characteristics, and operating condition degradation characteristics, and outputs the battery health status value. S4. Construct a four-layer value assessment model, and calculate the final battery asset valuation by sequentially passing through the basic residual value layer, performance degradation layer, market premium layer, and risk discount layer. S5. It adopts a main chain and side chain collaborative architecture for full lifecycle traceability. The main chain stores key flow node information, while the side chain carries high-frequency operation data. Cross-chain data trust verification is achieved through hash verification. S6. Monitor key node events on the main chain and changes in SOH on the side chain. When the triggering conditions are met, automatically trigger a value reassessment and write the latest assessment value to the main chain.

[0006] Preferably, step S2 specifically includes the following steps: S21. Collect battery operation data through the battery management system at a preset sampling frequency, collect battery internal and external environmental conditions data synchronously through the supporting environmental sensing equipment, and collect electrochemical impedance spectroscopy detection data at fixed detection cycles or key circulation nodes throughout the entire life cycle. S22. Perform outlier removal, missing value imputation, time-series aligned resampling, degraded derivative feature calculation and numerical normalization on the collected multi-source heterogeneous data in sequence to obtain a standardized feature dataset in a unified format. S23. Package the standardized feature dataset into structured data packets carrying the battery's unique digital identity and the data generation node's digital signature according to a set period, write them into the blockchain sidechain to complete the notarization, and calculate the Merkle root hash value of all data packets in this batch, and synchronously anchor them to the corresponding battery digital identity account on the blockchain main chain for cross-chain data integrity verification.

[0007] Preferably, step S3 specifically includes the following steps: S31. The cumulative charge and discharge ampere-hours of the battery are calculated using the ampere-hour integration method. Combined with the Kalman filter algorithm, the cumulative SOC error is corrected, and the ratio of the current available capacity to the nominal capacity is calculated to obtain the capacity decay characteristic value. S32. Measure the current DC internal resistance of the battery using the DC pulse method. Based on the initial internal resistance and the internal resistance at the end of the battery's life, calculate the health characterization value corresponding to the internal resistance growth rate and obtain the internal resistance growth characteristic value. S33. Fit the collected electrochemical impedance spectroscopy data to an equivalent circuit model, extract the charge transfer internal resistance and SEI membrane impedance parameters, construct an electrochemical health index, and obtain EIS electrochemical characteristic values. S34. Based on historical operating data, the distribution of charge and discharge depth, the distribution of charge and discharge rate, and the cumulative duration of battery temperature deviation from the optimal operating range are statistically analyzed, and the cumulative degradation characteristic value of the operating condition is obtained by weighted calculation. S35. Input the feature values ​​of the above four dimensions into the attention fusion layer, adaptively allocate the dynamic weights of each feature and sum them up, and output the battery comprehensive health status SOH value with a value range of 0 to 1.

[0008] Preferably, in step S4, the calculation formula for the four-layer valuation model is as follows: ; in, This is the final valuation of the battery assets. Based on residual value, This is the performance degradation discount factor. This is the market premium coefficient. This is the risk discount factor.

[0009] Preferably, step S5 specifically includes the following steps: S51. The blockchain main chain is constructed using a consortium blockchain architecture, with multiple participants in the industry chain serving as consensus nodes. Information on six key circulation nodes in the entire battery life cycle—production, first sale, resale, maintenance, secondary use and transfer, and recycling and dismantling—is written into the main chain. Each node contains a timestamp, the digital signature of the operating entity, and a core state snapshot, forming an immutable traceability main chain. S52. A permissioned blockchain architecture is used to build a blockchain sidechain, which establishes an independent data account for each battery's digital identity and stores high-frequency battery operation data, environmental condition data, intermediate state assessment data, and general event data at fixed intervals, carrying data of the entire life cycle process. S53. After the sidechain completes the data writing for each cycle, it calculates the Merkle root hash value of all data in that batch and anchors the Merkle root hash, the corresponding time interval, and the sidechain block height to the state storage area of ​​the corresponding battery digital identity on the main chain. When retrieving sidechain data, it performs integrity verification through the anchored hash value stored on the main chain to verify whether the data has been tampered with. S54. When initiating a source tracing query, first read the time sequence link of key flow nodes from the main chain as the core skeleton of source tracing, support penetrating query of detailed side chain data in the corresponding time period, and mark the data with the trust level and display it in layers according to the hash verification results.

[0010] Preferably, step S6 specifically includes the following steps: S61. Deploy an on-chain event listening engine to monitor on-chain events of key circulation nodes corresponding to the digital identity of the battery on the main chain in real time. At the same time, pull the battery health status (SOH) value stored in the side chain at fixed intervals and calculate the cumulative decrease. When a new key circulation node is added to the main chain, or the cumulative decrease of SOH exceeds the preset threshold and meets the minimum trigger interval, the battery asset value revaluation process is automatically triggered. S62. Identify the type of triggering event and match the corresponding correction coefficient in the four-layer value assessment model according to the event type; S63. Based on the adjusted coefficient parameters, call the four-layer value assessment model to complete the automatic recalculation, generate the triggering reason, parameter adjustment details and calculation process log of this recalculation, and archive and retain the historical value version by timestamp; S64. Package the latest asset valuation obtained from the revaluation, the revaluation timestamp, and the associated identifier of the triggering event into a value state snapshot, write it into the state storage area of ​​the corresponding battery digital identity on the main blockchain, and simultaneously update the latest value identifier in the side chain to achieve consistency of the value state between the main chain and the side chain.

[0011] Preferably, in step S62, the market premium coefficient is adjusted for ownership transfer events, the risk discount coefficient is adjusted for maintenance and abnormal accident events, the performance degradation discount coefficient is adjusted for SOH degradation events, and the recycling and dismantling events are switched to the recycled material value benchmark.

[0012] A battery asset valuation and traceability management system, comprising: The digital identity generation module is used to generate a unique DID for each battery and complete the on-chain anchoring of basic information; The data acquisition and preprocessing module is used to interface with the BMS system and environmental sensing devices to complete multi-source data acquisition, cleaning and time-series alignment. The SOH fusion evaluation module has a built-in multi-input fusion evaluation model for outputting the current health status of the battery. The layered value calculation module has a built-in four-layer value assessment model to output the current asset value of the battery. The main chain and side chain notarization module is used for notarization of key nodes on the main chain, on-chaining of high-frequency data from the side chain, and cross-chain hash verification. The linkage update module is used to monitor on-chain events and SOH changes, triggering automatic value reassessment.

[0013] Preferably, it also includes a visual interactive module to display the entire battery traceability chain and value change curve, and to support asset retrieval and verification queries.

[0014] Therefore, the present invention, employing the above-described method and system for battery asset valuation and traceability management, has the following advantages: (1) In this invention, a four-dimensional feature attention fusion SOH evaluation scheme is adopted, which integrates capacity decay, internal resistance growth, EIS electrochemistry, and operating condition degradation multi-dimensional features to adaptively calculate the battery health status. This overcomes the defect of large error in traditional single-parameter evaluation, and can more realistically reflect the degree of long-term cumulative damage and internal electrochemical decay of the battery, effectively improving the accuracy of battery health status evaluation.

[0015] (2) In this invention, a four-layer progressive battery asset valuation model is constructed, which completes the value correction from multiple dimensions such as basic residual value, performance degradation, market premium and risk discount. At the same time, it combines blockchain traceability records to quantify historical risks, breaks through the limitations of the traditional linear depreciation model, takes into account battery physical performance, market environment and historical risk factors, and the valuation results are more in line with the real transaction scenario of battery assets.

[0016] (3) In this invention, a main chain and side chain collaborative blockchain architecture is adopted to carry out full life cycle traceability. The main chain stores the key node information related to ownership, the side chain carries massive high-frequency operation data, and cross-chain hash verification ensures data credibility. It takes into account the immutable characteristics of blockchain evidence storage and the performance requirements of massive time-series data storage, and builds a battery full life cycle traceability link that can be trusted by multiple parties.

[0017] (4) In this invention, a value linkage update mechanism driven by traceability events and battery status is established. The value reassessment is automatically triggered by monitoring the main chain circulation events and side chain SOH changes. The latest assessment results are stored on the chain for evidence, thus opening up the data loop between traceability management and value assessment, solving the problem of value update lag, realizing the dynamic update of battery asset value with the evolution of the life cycle, and reducing the information asymmetry problem in asset transactions.

[0018] 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

[0019] Figure 1 This is a flowchart illustrating a battery asset valuation and traceability management method according to the present invention. Figure 2 A comparison chart of the mean absolute error of SOH evaluation provided in the embodiments of the present invention; Figure 3 This is a comparison chart of the average relative error of asset valuation provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Specific model specifications need to be selected and determined according to the actual specifications of the device, etc. The specific selection calculation method adopts existing technology in the art, and therefore will not be described in detail.

[0021] Example like Figure 1As shown, this invention provides a method for battery asset valuation and traceability management, including the following steps: S1. Generate a unique digital identity for each battery, write the battery's factory parameters into the main blockchain, and create a corresponding data account on the side chain. S2. Collect battery operation data, environmental data and electrochemical impedance spectroscopy data, and after preprocessing, upload and store the evidence to the side chain according to the cycle. S21. During battery operation, the battery management system continuously collects electrical parameters, temperature parameters, and SOC information at a sampling cycle of 10 seconds; the environmental sensing device simultaneously collects temperature and humidity data inside and outside the battery pack; every 3 months or at key circulation nodes such as transactions and maintenance, the electrochemical workstation collects electrochemical impedance spectroscopy data under standard test conditions.

[0022] S22. After receiving multi-source heterogeneous raw data in the cloud, the following processes are performed in sequence: abnormal data exceeding physical thresholds and statistical outliers are removed; short-term missing data are filled in using interpolation; and data is uniformly aligned to hourly time granularity for resampling. Based on the cleaned time-series data, the deterioration-derived features of the operating conditions, such as charge / discharge depth distribution, rate distribution, and temperature deviation, are calculated and normalized to output a standardized feature dataset.

[0023] S23. Using natural days as the cycle, encapsulate the standardized feature dataset of the day into structured data packets. Each data packet embeds a unique battery DID identifier and the digital signature of the data generation gateway node. These data packets are then batch-written to the blockchain sidechain for persistent storage. A Merkle tree is constructed for all data packets of the day, and the Merkle root hash value is calculated. The main chain smart contract is then invoked to write the Merkle root and corresponding time interval into the account storage area corresponding to the battery DID on the main chain. When subsequently retrieving historical data from the sidechain for SOH evaluation and traceability verification, integrity verification is performed using the hash value stored on the main chain to determine whether the sidechain data has been tampered with.

[0024] S3. Construct a multi-input fusion SOH evaluation model, which integrates the features of four dimensions: capacity decay characteristics, internal resistance growth characteristics, EIS electrochemical characteristics, and operating condition degradation characteristics, and outputs the battery health status value. S31. The cumulative ampere-hours of battery charge and discharge are calculated using the ampere-hour integration method. Combined with the Kalman filter algorithm to correct the cumulative SOC error, the ratio of the current usable capacity to the nominal capacity is calculated to obtain the capacity decay characteristic value: ; ; in, Current available capacity This refers to the initial usable capacity of the battery upon leaving the factory. For battery coulomb efficiency, for The charging and discharging current at any given time, This is the correction amount for the cumulative error of the ampere-hour integral by the Kalman filter. This is the characteristic value of capacity decay. This refers to the battery's nominal capacity. S32. Measure the current DC internal resistance of the battery using the DC pulse method. Based on the initial internal resistance and the end-of-life internal resistance of the battery, calculate the health characterization value corresponding to the internal resistance growth rate, and obtain the internal resistance growth characteristic value: ; ; in, The current DC internal resistance, This represents the voltage surge at the instant the pulse current is applied. This represents the amplitude of the sudden change in pulse current. This is the characteristic value of internal resistance growth. This refers to the initial DC internal resistance of the battery at the factory. The internal resistance threshold corresponding to the end of battery life (usually 1.5-2 times the initial internal resistance, depending on the cell type). S33. Fit the collected electrochemical impedance spectroscopy data to an equivalent circuit model, extract the charge transfer internal resistance and SEI film impedance parameters, construct an electrochemical health index, and obtain the EIS electrochemical characteristic values: ; in, EIS electrochemical characteristic value, and These are the current charge transfer internal resistance and SEI film impedance, respectively. and These are the initial charge transfer resistance at the factory and the SEI film impedance, respectively. and These are the charge transfer internal resistance threshold and the SEI film impedance threshold corresponding to the end of the lifetime, respectively. and These are the weighting coefficients; S34. Based on historical operating data, the distribution of charge and discharge depth, the distribution of charge and discharge rate, and the cumulative duration of battery temperature deviation from the optimal operating range are statistically analyzed, and the cumulative degradation characteristic value of the operating condition is obtained by weighted calculation. S35. Input the feature values ​​of the above four dimensions into the attention fusion layer, adaptively allocate the dynamic weights of each feature and sum them up, and output the battery comprehensive health status SOH value with a value range of 0 to 1.

[0025] S4. Construct a four-layer value assessment model, and calculate the final battery asset valuation by sequentially passing through the basic residual value layer, performance degradation layer, market premium layer, and risk discount layer. The calculation formula for the four-level valuation model is as follows: ; in, This is the final valuation of the battery assets. Based on residual value, This is the performance degradation discount factor. This is the market premium coefficient, which takes into account three external market factors: brand, application scenario, and market supply and demand. It reflects how market attributes adjust the value of batteries. This is the risk discount factor; The basic residual value is the initial value benchmark calculated by combining the battery's brand new benchmark price with its initial depreciation rate. ; in, This represents the current market price per unit capacity of brand new batteries of the same model. For the rated capacity of the battery to be evaluated, The initial condition factor is 1 for brand new batteries and slightly adjusted for inventory batteries based on storage years and storage conditions. The performance degradation discount factor is obtained by weighted fusion of the current overall SOH and the normalized value of remaining cycle life, reflecting the impact of battery physical performance degradation on value: ; in, This is the predicted value of the remaining cycle life of the battery. This refers to the battery's nominal total cycle life as stated at the manufacturer. and These are the weighting coefficients; The risk discount factor is quantitatively calculated based on three types of risk events—abnormalities, maintenance, and accidents—from the full lifecycle traceability record, reflecting the impact of historical risks on the battery's value discount. ; in, , , These represent the cumulative number of occurrences of abnormal alarm events, maintenance events, and safety incidents in the traceability records, respectively. , , The base discount rate for a single corresponding event is determined according to the severity level of the event, such as minor alerts. Moderate structural repair Major water-related / collision accidents .

[0026] S5. It adopts a main chain and side chain collaborative architecture for full lifecycle traceability. The main chain stores key flow node information, while the side chain carries high-frequency operation data. Cross-chain data trust verification is achieved through hash verification. S51. The blockchain main chain is constructed using a consortium blockchain architecture, with multiple participants in the industry chain serving as consensus nodes. Information on six key circulation nodes in the entire battery life cycle—production, first sale, resale, maintenance, secondary use and transfer, and recycling and dismantling—is written into the main chain. Each node contains a timestamp, the digital signature of the operating entity, and a core state snapshot, forming an immutable traceability main chain. S52. A permissioned blockchain architecture is used to build a blockchain sidechain, which establishes an independent data account for each battery's digital identity and stores high-frequency battery operation data, environmental condition data, intermediate state assessment data, and general event data at fixed intervals, carrying data of the entire life cycle process. S53. After the sidechain completes the data writing for each cycle, it calculates the Merkle root hash value of all data in that batch and anchors the Merkle root hash, the corresponding time interval, and the sidechain block height to the state storage area of ​​the corresponding battery digital identity on the main chain. When retrieving sidechain data, it performs integrity verification through the anchored hash value stored on the main chain to verify whether the data has been tampered with. S54. When initiating a source tracing query, first read the time sequence link of key flow nodes from the main chain as the core skeleton of source tracing, support penetrating query of detailed side chain data in the corresponding time period, and mark the data with the trust level and display it in layers according to the hash verification results.

[0027] S6. Monitor key node events on the main chain and changes in SOH on the side chain. When the triggering conditions are met, automatically trigger a value reassessment and write the latest assessment value to the main chain. S61. Deploy an on-chain event listening engine to monitor on-chain events of key circulation nodes corresponding to the digital identity of the battery on the main chain in real time. At the same time, pull the battery health status (SOH) value stored in the side chain at fixed intervals and calculate the cumulative decrease. When a new key circulation node is added to the main chain, or the cumulative decrease of SOH exceeds the preset threshold and meets the minimum trigger interval, the battery asset value revaluation process is automatically triggered. S62. Identify the type of triggering event and match the corresponding correction coefficient in the four-layer value assessment model according to the event type; adjust the market premium coefficient for ownership transfer events, adjust the risk discount coefficient for maintenance and abnormal accident events, adjust the performance degradation discount coefficient for SOH degradation events, and switch the recycling and dismantling events to the value benchmark of recycled materials. S63. Based on the adjusted coefficient parameters, call the four-layer value assessment model to complete the automatic recalculation, generate the triggering reason, parameter adjustment details and calculation process log of this recalculation, and archive and retain the historical value version by timestamp; S64. Package the latest asset valuation obtained from the revaluation, the revaluation timestamp, and the associated identifier of the triggering event into a value state snapshot, write it into the state storage area of ​​the corresponding battery digital identity on the main blockchain, and simultaneously update the latest value identifier in the side chain to achieve consistency of the value state between the main chain and the side chain.

[0028] This embodiment provides a battery asset valuation and traceability management system, including: The digital identity generation module is used to generate a unique DID for each battery and complete the on-chain anchoring of basic information; The data acquisition and preprocessing module is used to interface with the BMS system and environmental sensing devices to complete multi-source data acquisition, cleaning and time-series alignment. The SOH fusion evaluation module has a built-in multi-input fusion evaluation model for outputting the current health status of the battery. The layered value calculation module has a built-in four-layer value assessment model to output the current asset value of the battery. The main chain and side chain notarization module is used for notarization of key nodes on the main chain, on-chaining of high-frequency data from the side chain, and cross-chain hash verification. The linkage update module is used to monitor on-chain events and SOH changes, and trigger automatic value reassessment; It also includes a visualization and interactive module to display the entire battery traceability chain and value change curve, and supports asset retrieval and verification queries.

[0029] like Figures 2-3 As shown, to verify the technical effectiveness of this invention, 30 samples of lithium iron phosphate power batteries with different service lives and varying chemical conditions were selected for comparative testing, comparing the existing traditional battery asset management solutions with the solution of this invention. Using the true state of health (SOH) obtained from third-party standard charge-discharge testing as the reference value, the average absolute error of SOH assessment using traditional methods was 6.21%, while the average absolute error of the solution of this invention was reduced to 2.37%, significantly improving the accuracy of health status assessment. Using the actual transaction price in the battery resale market as a reference, the average relative error of asset assessment using traditional methods was 15.60%, while the average relative error of the four-layer value model of this invention was only 7.50%, making the valuation results more consistent with the actual market situation.

[0030] Therefore, the present invention adopts the above-mentioned battery asset valuation and traceability management method and system, which completes the reliable traceability of data throughout the entire life cycle by leveraging the main-side chain collaborative architecture, integrates multi-dimensional features to achieve high-precision assessment of battery health status, quantifies battery asset value using a hierarchical value model, and automatically triggers value revaluation by combining on-chain monitoring mechanism, forming a closed loop of traceability and valuation linkage, which improves the objectivity and credibility of asset valuation results, and is applicable to scenarios of power battery, energy storage battery circulation, cascade utilization and residual value management.

[0031] 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 battery asset valuation and traceability management, characterized in that: Includes the following steps: S1. Generate a unique digital identity for each battery, write the battery's factory parameters into the main blockchain, and create a corresponding data account on the side chain. S2. Collect battery operation data, environmental data and electrochemical impedance spectroscopy data, and after preprocessing, upload and store the evidence to the side chain according to the cycle. S3. Construct a multi-input fusion SOH evaluation model, which integrates the features of four dimensions: capacity decay characteristics, internal resistance growth characteristics, EIS electrochemical characteristics, and operating condition degradation characteristics, and outputs the battery health status value. S4. Construct a four-layer value assessment model, and calculate the final battery asset valuation by sequentially passing through the basic residual value layer, performance degradation layer, market premium layer, and risk discount layer. S5. It adopts a main chain and side chain collaborative architecture for full lifecycle traceability. The main chain stores key flow node information, while the side chain carries high-frequency operation data. Cross-chain data trust verification is achieved through hash verification. S6. Monitor key node events on the main chain and changes in SOH on the side chain. When the triggering conditions are met, automatically trigger a value reassessment and write the latest assessment value to the main chain.

2. The battery asset valuation and traceability management method according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21. Collect battery operation data through the battery management system at a preset sampling frequency, collect battery internal and external environmental conditions data synchronously through the supporting environmental sensing equipment, and collect electrochemical impedance spectroscopy detection data at fixed detection cycles or key circulation nodes throughout the entire life cycle. S22. Perform outlier removal, missing value imputation, time-series aligned resampling, degraded derivative feature calculation and numerical normalization on the collected multi-source heterogeneous data in sequence to obtain a standardized feature dataset in a unified format. S23. Package the standardized feature dataset into structured data packets carrying the battery's unique digital identity and the data generation node's digital signature according to a set period, write them into the blockchain sidechain to complete the notarization, and calculate the Merkle root hash value of all data packets in this batch, and synchronously anchor them to the corresponding battery digital identity account on the blockchain main chain for cross-chain data integrity verification.

3. The battery asset valuation and traceability management method according to claim 2, characterized in that: Step S3 specifically includes the following steps: S31. The cumulative charge and discharge ampere-hours of the battery are calculated using the ampere-hour integration method. Combined with the Kalman filter algorithm, the cumulative SOC error is corrected, and the ratio of the current available capacity to the nominal capacity is calculated to obtain the capacity decay characteristic value. S32. Measure the current DC internal resistance of the battery using the DC pulse method. Based on the initial internal resistance and the internal resistance at the end of the battery's life, calculate the health characterization value corresponding to the internal resistance growth rate and obtain the internal resistance growth characteristic value. S33. Fit the collected electrochemical impedance spectroscopy data to an equivalent circuit model, extract the charge transfer internal resistance and SEI membrane impedance parameters, construct an electrochemical health index, and obtain EIS electrochemical characteristic values. S34. Based on historical operating data, the distribution of charge and discharge depth, the distribution of charge and discharge rate, and the cumulative duration of battery temperature deviation from the optimal operating range are statistically analyzed, and the cumulative degradation characteristic value of the operating condition is obtained by weighted calculation. S35. Input the feature values ​​of the above four dimensions into the attention fusion layer, adaptively allocate the dynamic weights of each feature and sum them up, and output the battery comprehensive health status SOH value with a value range of 0 to 1.

4. The battery asset valuation and traceability management method according to claim 3, characterized in that: In step S4, the calculation formula for the four-layer valuation model is as follows: ； in, This is the final appraised value of the battery assets. Based on residual value, This is the performance degradation discount factor. This is the market premium coefficient. This is the risk discount factor.

5. The battery asset valuation and traceability management method according to claim 4, characterized in that: Step S5 specifically includes the following steps: S51. The blockchain main chain is constructed using a consortium blockchain architecture, with multiple participants in the industry chain serving as consensus nodes. Information on six key circulation nodes in the entire battery life cycle—production, first sale, resale, maintenance, secondary use and transfer, and recycling and dismantling—is written into the main chain. Each node contains a timestamp, the digital signature of the operating entity, and a core state snapshot, forming an immutable traceability main chain. S52. A permissioned blockchain architecture is used to build a blockchain sidechain, which establishes an independent data account for each battery's digital identity and stores high-frequency battery operation data, environmental condition data, intermediate state assessment data, and general event data at fixed intervals, carrying data of the entire life cycle process. S53. After each cycle of data writing is completed, the Merkle root hash value of all data in that batch is calculated, and the Merkle root hash, the corresponding time interval, and the sidechain block height are anchored to the state storage area of ​​the corresponding battery digital identity in the main chain. When retrieving sidechain data, integrity verification is performed through the anchored hash value stored in the main chain to verify whether the data has been tampered with. S54. When initiating a source tracing query, first read the time sequence link of key flow nodes from the main chain as the core skeleton of source tracing, support penetrating query of detailed side chain data in the corresponding time period, and mark the data with the trust level and display it in layers according to the hash verification results.

6. The battery asset valuation and traceability management method according to claim 5, characterized in that: Step S6 specifically includes the following steps: S61. Deploy an on-chain event listening engine to monitor on-chain events of key circulation nodes corresponding to the digital identity of the battery on the main chain in real time. At the same time, pull the battery health status (SOH) value stored in the side chain at fixed intervals and calculate the cumulative decrease. When a new key circulation node is added to the main chain, or the cumulative decrease of SOH exceeds the preset threshold and meets the minimum trigger interval, the battery asset value revaluation process is automatically triggered. S62. Identify the type of triggering event and match the corresponding correction coefficient in the four-layer value assessment model according to the event type; S63. Based on the adjusted coefficient parameters, call the four-layer value assessment model to complete the automatic recalculation, generate the triggering reason, parameter adjustment details and calculation process log of this recalculation, and archive and retain the historical value version by timestamp. S64. Package the latest asset valuation obtained from the revaluation, the revaluation timestamp, and the associated identifier of the triggering event into a value state snapshot, write it into the state storage area of ​​the corresponding battery digital identity on the main blockchain, and simultaneously update the latest value identifier in the side chain to achieve consistency of the value state between the main chain and the side chain.

7. The battery asset valuation and traceability management method according to claim 6, characterized in that: In step S62, the market premium coefficient is adjusted for ownership transfer events, the risk discount coefficient is adjusted for maintenance and abnormal accident events, the performance degradation discount coefficient is adjusted for SOH degradation events, and the recycling and dismantling events are switched to the recycled material value benchmark.

8. A battery asset valuation and traceability management system, used in the battery asset valuation and traceability management method according to any one of claims 1-7, characterized in that: include: The digital identity generation module is used to generate a unique DID for each battery and complete the on-chain anchoring of basic information; The data acquisition and preprocessing module is used to interface with the BMS system and environmental sensing devices to complete multi-source data acquisition, cleaning and time-series alignment. The SOH fusion evaluation module has a built-in multi-input fusion evaluation model for outputting the current health status of the battery. The layered value calculation module has a built-in four-layer value assessment model to output the current asset value of the battery. The main chain and side chain notarization module is used for notarization of key nodes on the main chain, on-chaining of high-frequency data from the side chain, and cross-chain hash verification. The linkage update module is used to monitor on-chain events and SOH changes, triggering automatic value reassessment.

9. A battery asset valuation and traceability management system according to claim 8, characterized in that: It also includes a visualization and interactive module to display the entire battery traceability chain and value change curve, and supports asset retrieval and verification queries.

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