Risk assessment method for large-scale gradient utilization of retired power batteries

Abstract

The invention provides a risk assessment method for large-scale gradient utilization of a decommissioned power battery, and the method comprises the steps: S1, classifying the decommissioned power battery into at least one battery unit, and screening out the battery units with obvious physical defects, S2, building a three-layer Bayesian network risk assessment model; s3, constructing a directed acyclic graph to represent a dependency relationship among the nodes; s4, collecting related data, and calculating risk probabilities of all levels of nodes in sequence based on the related data and the Bayesian network, and S5, judging whether gradient utilization is suitable or not based on the risk probabilities of a top layer target node and a middle layer risk node; s6, if it is judged that the battery units are not suitable for gradient utilization, key risk factors are identified, and according to risk sources of the key risk factors, disassembly and re-evaluation or material recovery of the battery units are decided; and S7, outputting a risk assessment result, and applying the risk assessment result to gradient utilization risk assessment of the new retired power battery. According to the invention, the scientificity and reliability of risk assessment can be improved.

Description

Risk assessment method for large-scale cascade utilization of retired power batteries Technical Field

[0001] This application relates to the field of battery technology, and in particular to a risk assessment method, system, equipment and storage medium for the large-scale cascade utilization of retired power batteries. Background Technology

[0002] The rapid development of the new energy vehicle industry has spurred a wave of retired power batteries. Properly handling these batteries is of profound significance for environmental protection, resource conservation, the development of a circular economy, and national strategic resource security. Cascaded utilization, as the optimal path to maximize their full life-cycle value, offers both environmental and economic benefits.

[0003] However, retired power batteries present challenges such as complex chemical systems, significant variations in operating conditions, and inconsistent health status, leading to risks in three categories: safety, performance, and economy. Current industry screening and evaluation methods have significant shortcomings, relying mainly on manual experience and single offline parameter testing. These methods suffer from limitations such as a lack of systematic scientific framework, a lack of quantitative decision-making support for evaluation conclusions, and a disconnect between risk assessment and disposal. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Firstly, this application proposes a risk assessment method for the large-scale tiered utilization of retired power batteries. The method includes: S1: classifying the retired power batteries into at least one battery unit according to their physical structure and integration level, judging the battery state of the battery unit, and filtering out battery units with obvious physical defects. The battery unit includes at least one of the following: single-cell battery unit, module-level battery unit, and cluster-level battery unit; S2: establishing a three-layer Bayesian network risk assessment model containing a top-level target node, a middle-level risk node, and a bottom-level observation node; S3: constructing a directed acyclic graph to represent the dependencies between the nodes, defining that the top-level target node depends on the middle-level risk node, and each middle-level risk node depends on the corresponding bottom-level observation node, and establishing a conditional probability table for each node; S4: collecting data from the retired power batteries. Based on historical operating data, current status data, and market economic data, the risk probability corresponding to the bottom-level observation node is calculated. Then, based on the risk probability corresponding to the bottom-level observation node and the conditional probability table, the risk probability of the middle-level risk node is calculated, and the risk probability of the top-level target node is further calculated. S5: Based on the calculated risk probabilities of the top-level target node and the middle-level risk node, it is determined whether the battery unit is suitable for cascade utilization. S6: If it is determined that it is not suitable for cascade utilization, reverse reasoning is performed to identify key risk factors, and the battery unit is disassembled and re-evaluated or its materials are recycled according to the risk source of the key risk factors. S7: The risk assessment result is output and used for the risk assessment of the cascade utilization of new retired power batteries.

[0006] In one implementation, calculating the risk probability corresponding to the bottom-level observation node based on the historical operating data, the current state data, and the market economic data includes: for each bottom-level observation node, determining the prior probability distribution of the bottom-level observation node based on the historical operating data; extracting feature indicators of the bottom-level observation node based on the current state data and the market economic data, and inputting the obtained feature parameter value dataset as evidence into the bottom-level observation node of the Bayesian network; constructing a likelihood function based on the current state data; and combining the prior probability distribution with the likelihood function according to Bayes' theorem to output the risk probability and uncertainty measure of each bottom-level observation node.

[0007] In one alternative implementation, the current state data follows a log-normal distribution, and the likelihood function is expressed as:

[0008] in, Represents the current state data. This represents the parameter to be estimated. This represents the log-standard deviation of a log-normal distribution.

[0009] Optionally, the step of combining the prior probability distribution with the likelihood function according to Bayes' theorem to output the risk probability and uncertainty of each of the bottom-level observation nodes includes: combining the prior probability distribution with the likelihood function to obtain the posterior probability; and obtaining the risk probability based on the posterior probability; wherein the expression for the posterior probability is:

[0010] in, Let be the mean of the posterior distribution. Let be the mean of the prior distribution. For properties with a priori distribution, Let V be the variance of the prior distribution. The current state data, Let be the variance of the likelihood function.

[0011] In one implementation, calculating the risk probability of the mid-level risk node based on the risk probability corresponding to the bottom-level observation node and the conditional probability table includes: inputting the risk probability corresponding to the bottom-level observation node as evidence into the corresponding mid-level risk node of the Bayesian network, and calculating the risk probability of the mid-level risk node by combining the conditional probability table corresponding to the bottom-level observation node and using a Bayesian network inference algorithm.

[0012] In one implementation, determining whether the battery cell is suitable for cascade utilization based on the calculated risk probabilities of the top-level target node and the middle-level risk node includes: if ,and If the condition is met, the power battery is determined to be suitable for cascade utilization; otherwise, the power battery is determined to be unsuitable for cascade utilization. and For the preset threshold, This refers to the current state data.

[0013] In one implementation, if a battery cell is determined to be unsuitable for cascade utilization, reverse reasoning is performed to identify key risk factors, and the battery cell is disassembled for reassessment or recycled based on the risk source of the key risk factors. This includes: for battery cells determined to be unsuitable for cascade utilization, reverse reasoning is performed to obtain the top M bottom-level observation nodes with high risk contribution through sensitivity analysis, and the key risk factors are determined based on the top M bottom-level observation nodes with high risk contribution; if the key risk factors originate from structural defects or consistency issues at the module or cluster level and can be eliminated through disassembly, the battery cell is disassembled to the individual cell level, and step S4 is returned for re-inspection and evaluation; if the key risk factors originate from irreversible degradation of the individual battery cell itself, or irreparable defects exist in the module / cluster structure, the battery cell is determined to be unsuitable for cascade utilization and enters the material recycling process.

[0014] In one implementation, the historical operating data includes at least one of the following: battery capacity status, charge / discharge operating curve, capacity decay curve, operating record, and fault record; the current status data includes at least one of the following: voltage, internal resistance, capacity, and temperature; and the market economic data includes at least one of the following: material market price, labor cost, equipment depreciation cost, and product revenue forecast.

[0015] In one implementation, the mid-level risks include at least one of the following: safety risks, performance risks, and economic risks; the bottom-level observation nodes include at least one of the following: voltage inconsistency, internal resistance inconsistency, capacity inconsistency, shell expansion coefficient, charging and discharging temperature rise, self-discharge rate, remaining capacity, capacity decay rate, DC internal resistance, open-circuit voltage, cascade utilization cost, and cascade utilization benefit.

[0016] Secondly, this application proposes a risk assessment system for the large-scale tiered utilization of retired power batteries. The system includes: a first processing module, used to classify the retired power batteries into at least one battery unit according to their physical structure and integration level, determine the battery state of the battery unit, and screen out battery units with obvious physical defects. The battery unit includes at least one of the following: single-cell battery unit, module-level battery unit, and cluster-level battery unit; a second processing module, used to establish a three-layer Bayesian network risk assessment model containing a top-level target node, middle-level risk nodes, and a bottom-level observation node; a third processing module, used to construct a directed acyclic graph to represent the dependencies between the nodes, defining that the top-level target node depends on the middle-level risk node, each middle-level risk node depends on the corresponding bottom-level observation node, and establishing a conditional probability table for each node; and a fourth processing module, used to collect data on the retired power batteries. The system uses historical operating data, current status data, and market economic data of the power battery. Based on these data, it calculates the risk probability corresponding to the bottom-level observation node. Then, based on the risk probability corresponding to the bottom-level observation node and the conditional probability table, it calculates the risk probability of the middle-level risk node and further calculates the risk probability of the top-level target node. A fifth processing module determines whether the battery unit is suitable for cascade utilization based on the calculated risk probabilities of the top-level target node and the middle-level risk node. A sixth processing module, if determined to be unsuitable for cascade utilization, performs reverse reasoning to identify key risk factors and decides whether to disassemble and re-evaluate the battery unit or recycle materials based on the risk sources of these key risk factors. A seventh processing module outputs the risk assessment results and uses these results for risk assessment of the cascade utilization of new retired power batteries.

[0017] In one implementation, the fourth processing module can be used to: for each of the bottom-level observation nodes, determine the prior probability distribution of the bottom-level observation node based on the historical operating data; extract feature indicators of the bottom-level observation node based on the current state data and the market economic data, and input the obtained feature parameter value dataset as evidence into the bottom-level observation node of the Bayesian network; construct a likelihood function based on the current state data; and combine the prior probability distribution with the likelihood function according to Bayes' theorem to output the risk probability and uncertainty measure of each of the bottom-level observation nodes.

[0018] In one alternative implementation, the current state data follows a log-normal distribution, and the likelihood function is expressed as:

[0019] in, Represents the current state data. This represents the parameter to be estimated. This represents the log-standard deviation of a log-normal distribution.

[0020] Optionally, the fourth processing module can be used to: combine the prior probability distribution with the likelihood function to obtain the posterior probability; and obtain the risk probability based on the posterior probability; wherein the expression for the posterior probability is:

[0021] in, Let be the mean of the posterior distribution. Let be the mean of the prior distribution. For properties with a priori distribution, Let V be the variance of the prior distribution. The current state data, Let be the variance of the likelihood function.

[0022] In one implementation, the fourth processing module can be used to: input the risk probability corresponding to the bottom observation node as evidence into the corresponding mid-level risk node of the Bayesian network, and calculate the risk probability of the mid-level risk node by combining the conditional probability table corresponding to the bottom observation node and using a Bayesian network inference algorithm.

[0023] In one implementation, the fifth processing module can be used to: if ,and If the condition is met, the power battery is determined to be suitable for cascade utilization; otherwise, the power battery is determined to be unsuitable for cascade utilization. and For the preset threshold, This refers to the current state data.

[0024] In one implementation, the sixth processing module can be used to: perform reverse reasoning on the battery cells determined to be unusable for cascade utilization, obtain the top M bottom-level observation nodes with high risk contribution through sensitivity analysis, and determine the key risk factors based on the top M bottom-level observation nodes with high risk contribution; if the key risk factors originate from structural defects or consistency issues at the module or cluster level and can be eliminated through disassembly, then the battery cell is disassembled to the individual cell level and returned to step S4 for re-inspection and evaluation; if the key risk factors originate from irreversible degradation of the individual battery cell itself, or irreparable defects exist in the module / cluster structure, then the battery cell is determined to be unsuitable for cascade utilization and enters the material recycling process.

[0025] In one implementation, the historical operating data includes at least one of the following: battery capacity status, charge / discharge operating curve, capacity decay curve, operating record, and fault record; the current status data includes at least one of the following: voltage, internal resistance, capacity, and temperature; and the market economic data includes at least one of the following: material market price, labor cost, equipment depreciation cost, and product revenue forecast.

[0026] In one implementation, the mid-level risks include at least one of the following: safety risks, performance risks, and economic risks; the bottom-level observation nodes include at least one of the following: voltage inconsistency, internal resistance inconsistency, capacity inconsistency, shell expansion coefficient, charging and discharging temperature rise, self-discharge rate, remaining capacity, capacity decay rate, DC internal resistance, open-circuit voltage, cascade utilization cost, and cascade utilization benefit.

[0027] Thirdly, this application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the risk assessment method for the large-scale cascade utilization of retired power batteries as described in the first aspect.

[0028] Fourthly, this application proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect.

[0029] Fifthly, this application proposes a program product comprising at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method described in the first aspect.

[0030] The risk assessment method, system, equipment, and storage medium for the large-scale tiered utilization of retired power batteries provided in this application can achieve a synergistic quantitative assessment of safety, performance, and economic risks based on Bayesian networks and integrating multi-dimensional characteristic parameters of batteries. This overcomes the limitations of traditional methods that rely on single parameters and subjective experience, improving the scientific rigor and reliability of the assessment. It innovatively adopts a combined mechanism of forward reasoning and reverse tracing, outputting a quantitative comprehensive risk probability to provide a basis for tiered utilization decisions, while also accurately identifying the causes of risks. Through dynamic iterative optimization, the model achieves self-learning evolution by continuously collecting data and updating parameters, adapting to the performance degradation characteristics of retired batteries, and providing long-term reliable technical support for large-scale tiered utilization.

[0031] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0032] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 is a flowchart illustrating a risk assessment method for the large-scale cascade utilization of retired power batteries provided by an embodiment of this application; Figure 2 is an example diagram of a Bayesian network node provided by an embodiment of this application; Figure 3 is an example diagram of the conditional probability of a safety risk provided by an embodiment of this application; Figure 4 is an example diagram of the risk probability of a mid-level risk node provided by an embodiment of this application; Figure 5 is an example diagram of comprehensive risk state probability data provided by an embodiment of this application; Figure 6 is an example diagram of risk contribution factors provided by an embodiment of this application; Figure 7 is a structural schematic diagram of a risk assessment system for the large-scale cascade utilization of retired power batteries provided by an embodiment of this application; and Figure 8 is a structural schematic diagram of an electronic device provided by an embodiment of this application. Detailed Implementation

[0033] The embodiments of this application are described in detail below. 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. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The risk assessment method and system for the large-scale cascade utilization of retired power batteries according to embodiments of this application are described below with reference to the accompanying drawings.

[0035] Figure 1 is a flowchart illustrating a risk assessment method for the large-scale cascade utilization of retired power batteries provided in an embodiment of this application. As shown in Figure 1, the method may include, but is not limited to, the following steps: S1: According to the physical structure and integration level of the retired power batteries, classify the retired power batteries into at least one battery cell, determine the battery state of the battery cells, and screen out battery cells with obvious physical defects.

[0036] In the embodiments of this application, the battery unit includes at least one of the following: a single-cell battery unit, a module-level battery unit, and a cluster-level battery unit.

[0037] For example, the physical structure and integration level of retired power batteries are classified into single-cell level, module level or cluster level, and the battery status is judged. Battery cells with obvious physical defects such as appearance damage, leakage, deformation, and severe corrosion are screened out, and the remaining battery cells are used as target battery cells for risk assessment that require cascade utilization.

[0038] S2: Establish a three-layer Bayesian network risk assessment model that includes top-level target nodes, mid-level risk nodes, and bottom-level observation nodes.

[0039] In the embodiments of this application, the top-level target node represents the comprehensive risk level (R) of cascade utilization, which is used to characterize the overall risk level of the battery cell throughout its entire life cycle of cascade utilization.

[0040] In the embodiments of this application, the mid-level risk nodes include safety risk (S) nodes, performance risk (P) nodes, and economic risk (E) nodes; the safety risk (S) node represents the probability that the target battery will experience serious safety accidents such as thermal runaway under specific tiered utilization scenarios and operating conditions; the performance risk (P) node represents the probability that the key technical parameters of the target battery do not meet the preset tiered utilization requirements; and the economic risk (E) node represents the probability that the disassembly, reassembly, and testing costs of the target battery do not meet the expected returns.

[0041] In the embodiments of this application, the risk nodes of the underlying current state data include, but are not limited to: safety-related observation nodes: voltage inconsistency, internal resistance inconsistency, capacity inconsistency, shell expansion coefficient, charging and discharging temperature rise, and self-discharge rate; wherein at least one inconsistency risk node is used for evaluation at the module level and cluster level; performance-related observation nodes: remaining capacity, capacity decay rate, DC internal resistance, and open circuit voltage; and economic-related observation nodes: cascade utilization cost and cascade utilization benefit.

[0042] As an example, please refer to Figure 2, which is an example diagram of a Bayesian network node provided in an embodiment of this application.

[0043] As an example, please refer to Table 1, which is an example table of underlying observation node definition and risk factor classification provided in the embodiments of this application.

[0044] Table 1. Example of definition of bottom-level observation nodes and risk factor classification

[0045] S3: Construct a directed acyclic graph to represent the dependencies between nodes, define the top-level target node as dependent on the middle-level risk nodes, and define each middle-level risk node as dependent on the corresponding bottom-level observation node, and establish a conditional probability table for each node.

[0046] Based on the nodes defined in S2, the dependencies between nodes are clarified: the top-level node R depends on the three middle-level nodes S, P, and E; the middle-level node S depends on the bottom-level observation nodes X1, X2, X3, X4, X5, and X6; the middle-level node P depends on the bottom-level observation nodes X7, X8, X9, and X10; and the middle-level node E depends on the bottom-level observation nodes X11 and X12.

[0047] A conditional probability table (CPT) is established for each node to quantitatively characterize the influence of the parent node's state on the conditional probability distribution of the child nodes. Taking the conditional probability table of safety risk (S) as an example, safety risk (S) is jointly determined by the states of voltage inconsistency (X1), internal resistance inconsistency (X2), capacity inconsistency (X3), shell expansion coefficient (X4), charge / discharge temperature rise (X5), and self-discharge rate charge / discharge temperature rise (X6). Therefore, this CPT has a 6*6*6 matrix, i.e., 216 rows. Each parent node has three states: high (H), medium (M), and low (L). Table 2 shows the conditional probability tables of some safety risks (S).

[0048] As an example, please refer to Figure 3 and Table 2. Figure 3 is an example diagram of the conditional probability of a security risk provided by an embodiment of this application. Table 2 is an example table of the conditional probability of a security risk provided by an embodiment of this application.

[0049] Table 2. Examples of Conditional Probabilities of Security Risks

[0050] S4: Collect historical operating data, current status data, and market economic data of retired power batteries. Calculate the risk probability corresponding to the bottom-level observation nodes based on the historical operating data, current status data, and market economic data. Then, calculate the risk probability of the middle-level risk nodes based on the risk probability of the bottom-level observation nodes and the conditional probability table, and further calculate the risk probability of the top-level target nodes.

[0051] As an example, please refer to Table 3, which is an example table of test and estimation data for a low-level observation node provided in an embodiment of this application.

[0052] Table 3. Example of test and estimation data for bottom-level observation nodes

[0053] In one implementation, the risk probability corresponding to the bottom observation node is calculated based on historical operating data, current status data and market economic data, which may include, but is not limited to, the following steps: S41: For each bottom observation node, the prior probability distribution of the bottom observation node is determined based on historical operating data.

[0054] For example, for each underlying observation node, a corresponding prior probability distribution is determined based on historical operating data of similar LFP modules.

[0055] In some embodiments, the prior probabilities of the aforementioned bottom-level nodes conform to a log-normal distribution, which can be represented as follows:

[0056] in, =0.04V (the mean of ln(ΔU)).

[0057] As an example, taking voltage inconsistency as the underlying observation node, based on historical operating data of similar LFP modules in the historical database, the prior distribution of voltage inconsistency ΔU conforms to the above log-normal distribution, where... =0.04V (the mean of ln(ΔU)). =0.8 (This is the standard deviation of ln(ΔU)). Convert back To understand this, the median of the prior distribution of voltage inconsistency is e. -3.219 ≈0.04V means that there is a 50% probability that the voltage difference of this module is lower than 0.04V and a 50% probability that it is higher than 0.04V, with an uncertainty of 0.8.

[0058] S42: Extract feature indicators from the bottom-level observation nodes based on the current state data and market economy data, and input the obtained feature parameter value dataset as evidence into the bottom-level observation nodes of the Bayesian network.

[0059] As an example, taking voltage inconsistency at the bottom observation node as an example, the static voltage of the battery cell is measured, and the difference between the maximum and minimum voltage values ​​of the individual cells is extracted as a feature index to obtain the current state data corresponding to the voltage inconsistency. =0.08V.

[0060] S43: Construct a likelihood function based on the current state data.

[0061] In some embodiments, the measurement errors of the bottom-level observation nodes follow a log-normal distribution, then the above likelihood function can be expressed as:

[0062] in, Represents the current state data. This represents the parameter to be estimated. This represents the log-standard deviation of a log-normal distribution.

[0063] S44: Based on Bayes' theorem, the prior probability distribution is combined with the likelihood function to output the risk probability and uncertainty measure of each bottom-level observation node.

[0064] For example, according to Bayes' theorem, the prior probability distribution is combined with the likelihood function to output the posterior distribution of each bottom observation node, and the corresponding risk probability is obtained based on the probability integral of the posterior distribution falling into each preset probability interval.

[0065] It should be noted that, in the embodiments of this application, the probability intervals corresponding to different underlying observation nodes may be the same or different.

[0066] In some embodiments, if both the aforementioned prior probability distribution and measurement error conform to a log-normal distribution, then the corresponding posterior distribution is also approximately a log-normal distribution. The posterior distribution can be solved using conjugate prior properties or numerical methods, and can be specifically expressed as follows:

[0067] =

[0068] in, Let be the mean of the posterior distribution. Let be the mean of the prior distribution. For properties with a priori distribution, Let V be the variance of the prior distribution. This is the current state data. Let be the variance of the likelihood function.

[0069] For example, taking the aforementioned voltage inconsistency as an example of the underlying observation node, then . 0.00984, that is 0.0992. Therefore, the corresponding posterior distribution can be expressed as: P(ΔU|X) ~ Log-Normal(-2.536, 0.0992²). The probability intervals corresponding to voltage inconsistency can be: High (H): ΔU>0.1V, Medium (M): 0.05V≤ΔU≤0.1V, Low (L): ΔU<0.05V. The probability integral of the posterior distribution falling within each interval, calculated based on the above probability intervals, can be expressed as follows:

[0070]

[0071]

[0072] That is, for X1 (voltage inconsistency) observed as ΔU = 0.08 V, P(X1=H) = P(X1=M)= P(X1=L)= .

[0073] As an example, please refer to Table 4, which is an example table of risk probabilities for a bottom-level observation node provided in the embodiments of this application.

[0074]

[0075] In one implementation, calculating the risk probability of a mid-level risk node based on the risk probability and conditional probability table corresponding to the bottom-level observation node may include the following steps: inputting the risk probability corresponding to the bottom-level observation node as evidence into the corresponding mid-level risk node of the Bayesian network, combining it with the conditional probability table corresponding to the bottom-level observation node, and calculating the risk probability of the mid-level risk node through a Bayesian network inference algorithm.

[0076] For example, the risk state probabilities of each bottom-level observation node calculated in S44 are used as input evidence. The conditional probability table (CPT) defined in the Bayesian network is used to perform reasoning using the connection tree algorithm to calculate the posterior probabilities of each middle-level node (security risk S, performance risk P, economic risk E).

[0077] As an example, please refer to Figure 4 and Table 5. Figure 4 is an example diagram of the risk probability of a mid-level risk node provided in an embodiment of this application, and Table 5 is an example table of the risk state probability of a mid-level node risk point provided in an embodiment of this application. The data in Table 5 is calculated based on the risk probability of each bottom-level observation node in Table 4.

[0078] Table 5. Example Table of Risk State Probabilities for Mid-Level Risk Nodes

[0079] In some embodiments, the method for calculating the risk probability of the bottom-level target node is the same as the method for calculating the risk probability of the middle-level risk node. That is, the posterior probability distribution of the calculated middle-level nodes (S, P, E) is used as input evidence, and the connection tree algorithm is used again for precise reasoning based on the conditional probability table (CPT) of the top-level node R to calculate the posterior probability distribution of the top-level node (the comprehensive risk level R of the tiered utilization).

[0080] For example, based on the final calculation of the risk state probability of each intermediate risk node shown in Table 5, the posterior probability distribution of the comprehensive risk level (R) of cascade utilization is as follows: P(R=high)=0.045P(R=medium)=0.412P(R=low)=0.543S5: Based on the calculated risk probabilities of the top target node and intermediate risk nodes, determine whether the battery unit is suitable for cascade utilization.

[0081] For example, a comprehensive risk high-risk probability threshold and a safety risk high-risk probability threshold are set to compare the risk probabilities of the top-level target node and the middle-level risk node with the corresponding probability thresholds, and to determine whether the battery cell is suitable for cascade utilization based on the comparison results.

[0082] As an example, the high-risk probability threshold for comprehensive risk is set at α = 0.85 (the median of the range 0.8 to 0.9), and the high-risk probability threshold for safety risk is set at β = 0.03 (the median of the range 0.01 to 0.05). The following tiered utilization decision is then applied: If... ,and If the condition is met, the battery is deemed suitable for tiered utilization; otherwise, it is deemed unsuitable for tiered utilization. The decision rule can be specifically expressed as follows: Condition 1: P(R=high)=0.045<α(0.85) → Satisfied; Condition 2: P(S=high)=0.102>β(0.03) → Not satisfied. Taking the data provided in the aforementioned tables as an example, since Condition 2 is not satisfied (the probability of a high safety risk level exceeds the acceptable high-risk probability threshold), according to the decision rule "otherwise, it is deemed unsuitable for tiered utilization," it is determined that the battery is not suitable for tiered utilization.

[0083] The judgment result indicates that although the overall comprehensive risk of this module is at the lowest level (54.3%), its safety risk is at the highest level (10.2%), which exceeds the system's strict tolerance limit for safety (β=3%). There are unacceptable safety hazards such as thermal runaway, so it is not approved.

[0084] As an example, please refer to Figure 5, which is an example diagram of comprehensive risk state probability data provided in an embodiment of this application. As shown in Figure 5, taking safety risk as an example, a high-risk probability threshold and a safety risk high-risk probability threshold can be set for the comprehensive risk state probability data. If the high-risk probability in the comprehensive risk state probability data is less than the high-risk probability threshold, and the high-risk probability of safety risk is less than the safety risk high-risk probability threshold, then the target battery structure unit is determined to be usable in a tiered manner; if the high-risk probability in the comprehensive risk state probability data is greater than or equal to the high-risk probability threshold, and the safety risk high-risk probability is less than the safety risk high-risk probability threshold, then the target battery structure unit is determined not to be usable in a tiered manner; if the high-risk probability in the comprehensive risk state probability data is less than the high-risk probability threshold, and the safety risk high-risk probability is greater than or equal to the safety risk high-risk probability threshold, then the target battery structure unit is determined not to be usable in a tiered manner.

[0085] S6: If it is determined that the battery cell is not suitable for cascade utilization, reverse reasoning is performed to identify key risk factors, and the battery cell is disassembled and re-evaluated or the material is recycled based on the source of the risk of the key risk factors.

[0086] For example, if a volume is deemed unsuitable for utilization, reverse reasoning is required to trace the key risk factors that led to the decision and formulate corresponding solutions.

[0087] As an example, using the data provided in the aforementioned tables, the contribution of each bottom-level observation node to the top-level "Comprehensive Risk Level R = High" and the middle-level "Safety Risk S = High" states is quantified. Analysis reveals that the nodes contributing the most to safety risk (S) are: internal resistance inconsistency (X2) and charging / discharging temperature rise (X5). Safety risk (S) is the dominant factor in triggering the determination of "High" comprehensive risk (R), with its contribution far exceeding that of performance risk (P) and economic risk (E). For example, please refer to Figure 6, which is an example diagram of risk contribution factors provided in an embodiment of this application.

[0088] For example, using the data provided in the aforementioned tables, the key risk factors for the battery module are concentrated on module-level consistency issues (X2 shows significant internal resistance inconsistency) and thermal characteristic issues (X5 shows high temperature rise). These problems stem from varying degrees of degradation in individual cells and potential anomalies in the internal connections of the module, but not all cells themselves have irreversible fatal defects. Therefore, according to the handling logic in S6: the module is disassembled to the cell level, and all disassembled individual cells are returned to step S4 for re-inspection and evaluation. After disassembly, each cell can be independently evaluated, and cells with good performance can be selected to be reassembled into new, more consistent modules, while severely degraded cells are discarded and recycled.

[0089] S7: Output the risk assessment results and use them for risk assessment of the cascade utilization of new retired power batteries.

[0090] For example, the risk assessment results may include, but are not limited to: comprehensive risk assessment results: P(R=high)=4.5%, P(R=medium)=41.2%, P(R=low)=54.3%; risk judgment conclusion: "not applicable to module-level tiered utilization, it is recommended to disassemble to individual units for reassessment"; key risk factor diagnosis: clearly pointing out that internal resistance inconsistency (X2) and charging / discharging temperature rise (X5) are the main reasons for this rejection decision; handling recommendation: carry out disassembly.

[0091] All data from this round of evaluation (especially the data related to key risk factors) will be included in the historical database. When evaluating similar modules in the future, the mean of the prior distribution of the underlying observation nodes related to key risk factors will be appropriately increased, enabling the model to issue early warnings of high inconsistency risks more quickly and achieve continuous model optimization.

[0092] By implementing the embodiments of this application, a multi-level, multi-dimensional risk assessment model based on Bayesian networks can be established. This model effectively integrates the multi-dimensional characteristic parameters of batteries, achieving a synergistic quantitative assessment of safety, performance, and economic risks. It overcomes the limitations of traditional methods that rely on single parameters and subjective experience, significantly improving the scientific rigor and reliability of the assessment results. Furthermore, it innovatively employs a mechanism combining forward reasoning and reverse tracing. This not only outputs a quantitative comprehensive risk probability, providing a clear and operable basis for tiered utilization decisions, but also accurately identifies the causes of risks while determining them. This provides targeted guidance for subsequent disposal schemes at the individual, module, and cluster levels, forming a closed-loop management system of "assessment-decision-disposal," greatly enhancing the practical application value of the assessment results. A dynamically iterative assessment optimization system is also established. By continuously collecting assessment data and updating prior distribution parameters, the model possesses self-learning and evolution capabilities, continuously adaptively optimizing assessment accuracy as data accumulates. This is particularly suitable for the characteristics of continuous performance degradation in retired batteries, providing long-term and reliable technical support for large-scale tiered utilization.

[0093] Please refer to Figure 7, which is a schematic diagram of the structure of a risk assessment system for the large-scale cascade utilization of retired power batteries provided in an embodiment of this application. As shown in Figure 7, the system 700 includes: a first processing module 701, used to classify retired power batteries into at least one battery unit according to their physical structure and integration level, determine the battery state of the battery unit, and screen out battery units with obvious physical defects. The battery unit includes at least one of the following: single-cell battery unit, module-level battery unit, and cluster-level battery unit; a second processing module 702, used to establish a three-layer Bayesian network risk assessment model containing a top-level target node, a middle-level risk node, and a bottom-level observation node; a third processing module 703, used to construct a directed acyclic graph to represent the dependencies between nodes, define that the top-level target node depends on the middle-level risk node, and each middle-level risk node depends on the corresponding bottom-level observation node, and establish a conditional probability table for each node; and a fourth processing module 704, used to collect historical data of retired power batteries. The system uses operational data, current status data, and market economic data to calculate the risk probability corresponding to the bottom-level observation nodes. Then, based on the risk probabilities of the bottom-level observation nodes and the conditional probability table, it calculates the risk probability of the middle-level risk nodes and further calculates the risk probability of the top-level target node. The fifth processing module 705 is used to determine whether the battery cell is suitable for cascade utilization based on the calculated risk probabilities of the top-level target node and the middle-level risk nodes. The sixth processing module 706 is used to perform reverse reasoning to identify key risk factors if it is determined that the battery cell is not suitable for cascade utilization, and decide whether to disassemble and re-evaluate the battery cell or recycle the materials based on the risk source of the key risk factors. The seventh processing module 707 is used to output the risk assessment results and use the risk assessment results for the cascade utilization risk assessment of new retired power batteries.

[0094] In one implementation, the fourth processing module 704 can be used to: determine the prior probability distribution of each bottom observation node based on historical operating data; extract feature indicators of the bottom observation node based on current state data and market economic data, and input the obtained feature parameter value dataset as evidence into the bottom observation node of the Bayesian network; construct a likelihood function based on the current state data; and combine the prior probability distribution with the likelihood function according to Bayes' theorem to output the risk probability and uncertainty measure of each bottom observation node.

[0095] In one alternative implementation, the current state data follows a log-normal distribution, and the likelihood function is expressed as:

[0096] in, Represents the current state data. This represents the parameter to be estimated. This represents the log-standard deviation of a log-normal distribution.

[0097] Optionally, the fourth processing module can be used to: combine the prior probability distribution with the likelihood function to obtain the posterior probability; and obtain the risk probability based on the posterior probability; wherein the expression for the posterior probability is:

[0098]

[0099] in, Let be the mean of the posterior distribution. Let be the mean of the prior distribution. For properties with a priori distribution, Let V be the variance of the prior distribution. This is the current state data. Let be the variance of the likelihood function.

[0100] In one implementation, the fourth processing module 704 can be used to: input the risk probability corresponding to the bottom observation node as evidence into the corresponding mid-level risk node of the Bayesian network, and calculate the risk probability of the mid-level risk node by combining the conditional probability table corresponding to the bottom observation node and using the Bayesian network inference algorithm.

[0101] In one implementation, the fifth processing module 705 can be used to: if ,and If the condition is met, the power battery is deemed suitable for cascade utilization; otherwise, it is deemed unsuitable for cascade utilization. and For the preset threshold, This represents the current state data.

[0102] In one implementation, the sixth processing module 706 can be used to: perform reverse reasoning on battery cells determined to be unusable for cascade utilization, obtain the top M bottom-level observation nodes with high risk contribution through sensitivity analysis, and determine key risk factors based on the top M bottom-level observation nodes with high risk contribution; if the key risk factors originate from structural defects or consistency issues at the module or cluster level and can be eliminated through disassembly, then the battery cell is disassembled to the individual cell level and returned to step S4 for re-inspection and evaluation; if the key risk factors originate from irreversible degradation of the individual battery cell itself, or irreparable defects exist in the module / cluster structure, then the battery cell is determined to be unsuitable for cascade utilization and enters the material recycling process.

[0103] In one implementation, historical operating data includes at least one of the following: battery capacity status, charge / discharge operating curve, capacity decay curve, operating record, and fault record; current status data includes at least one of the following: voltage, internal resistance, capacity, and temperature; and market economic data includes at least one of the following: material market price, labor cost, equipment depreciation cost, and product revenue forecast.

[0104] In one implementation, the mid-level risks include at least one of the following: safety risks, performance risks, and economic risks; the bottom-level observation nodes include at least one of the following: voltage inconsistency, internal resistance inconsistency, capacity inconsistency, shell expansion coefficient, charging and discharging temperature rise, self-discharge rate, remaining capacity, capacity decay rate, DC internal resistance, open-circuit voltage, cascade utilization cost, and cascade utilization benefit.

[0105] The system implemented in this application allows for the establishment of a multi-level, multi-dimensional risk assessment model based on Bayesian networks. This model effectively integrates multi-dimensional characteristic parameters of batteries, achieving a synergistic quantitative assessment of safety, performance, and economic risks. It overcomes the limitations of traditional methods that rely on single parameters and subjective experience, significantly improving the scientific rigor and reliability of the assessment results. Furthermore, it innovatively employs a mechanism combining forward reasoning and reverse tracing. This not only outputs a quantitative comprehensive risk probability, providing a clear and operable basis for tiered utilization decisions, but also accurately identifies the causes of risks while determining them. This provides targeted guidance for subsequent disposal plans at the individual, module, and cluster levels, forming a closed-loop management system of "assessment-decision-disposal," greatly enhancing the practical application value of the assessment results. A dynamically iterative assessment optimization system is also established. By continuously collecting assessment data and updating prior distribution parameters, the model possesses self-learning and evolution capabilities, continuously adaptively optimizing assessment accuracy as data accumulates. This is particularly suitable for the characteristics of continuous performance degradation in retired batteries, providing long-term and reliable technical support for large-scale tiered utilization.

[0106] It should be noted that the explanation of the aforementioned risk assessment method embodiment for the large-scale cascade utilization of retired power batteries also applies to the risk assessment system for the large-scale cascade utilization of retired power batteries in this embodiment, and will not be repeated here.

[0107] To implement the above embodiments, this application also proposes an electronic device. Please refer to FIG8, which is a schematic diagram of the structure of the electronic device provided in the embodiment of this application. As shown in FIG8, the electronic device 800 includes: a processor 801, and a memory 802 communicatively connected to the processor 801; the memory 802 stores computer execution instructions; the processor 801 executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0108] To implement the above embodiments, this application also proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods provided in the foregoing embodiments.

[0109] To implement the above embodiments, this application also proposes a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method provided in the foregoing embodiments.

[0110] It should be noted that the acquisition, transmission, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0111] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0112] It is worth noting that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0113] In the description of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0114] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0116] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0118] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0121] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A risk assessment method for the large-scale cascade utilization of retired power batteries, characterized in that, include: S1: According to the physical structure and integration level of the retired power battery, the retired power battery is classified into at least one battery unit, the battery status of the battery unit is judged, and battery units with obvious physical defects are screened out. The battery unit includes at least one of the following: single cell level battery unit, module level battery unit, and cluster level battery unit. S2: Establish a three-layer Bayesian network risk assessment model containing a top-level target node, a middle-level risk node, and a bottom-level observation node; S3: Construct a directed acyclic graph to represent the dependencies between the nodes, define that the top-level target node depends on the middle-level risk node, and each middle-level risk node depends on the corresponding bottom-level observation node, and establish a conditional probability table for each node. S4: Collect historical operating data, current status data, and market economic data of the retired power battery; calculate the risk probability corresponding to the bottom observation node based on the historical operating data, current status data, and market economic data; then calculate the risk probability of the middle risk node based on the risk probability corresponding to the bottom observation node and the conditional probability table; and further calculate the risk probability of the top target node. S5: Based on the calculated risk probabilities of the top-level target node and the middle-level risk node, determine whether the battery unit is suitable for cascade utilization; S6: If it is determined that the battery cell is not suitable for cascade utilization, reverse reasoning is performed to identify key risk factors, and the battery cell is disassembled for re-evaluation or material recycling is decided based on the risk source of the key risk factors. S7: Output the risk assessment results and use the risk assessment results for the risk assessment of the cascade utilization of new retired power batteries.

2. The method according to claim 1, characterized in that, The step of calculating the risk probability corresponding to the bottom observation node based on the historical operating data, the current state data, and the market economic data includes: for each bottom observation node, determining the prior probability distribution of the bottom observation node based on the historical operating data; extracting feature indicators of the bottom observation node based on the current state data and the market economic data, and inputting the obtained feature parameter value dataset as evidence into the bottom observation node of the Bayesian network; constructing a likelihood function based on the current state data; and combining the prior probability distribution with the likelihood function according to Bayes' theorem to output the risk probability and uncertainty measure of each bottom observation node.

3. The method according to claim 2, characterized in that, The current state data follows a log-normal distribution, and the expression for the likelihood function is: in, Represents the current state data. This represents the parameter to be estimated. This represents the log-standard deviation of a log-normal distribution.

4. The method according to claim 3, characterized in that, The step of combining the prior probability distribution with the likelihood function according to Bayes' theorem to output the risk probability and uncertainty of each bottom-level observation node includes: combining the prior probability distribution with the likelihood function to obtain the posterior probability; and obtaining the risk probability based on the posterior probability; wherein the expression for the posterior probability is: in, Let be the mean of the posterior distribution. Let be the mean of the prior distribution. For properties with a priori distribution, Let V be the variance of the prior distribution. The current state data, Let be the variance of the likelihood function.

5. The method according to claim 1, characterized in that, The step of calculating the risk probability of the mid-level risk node based on the risk probability corresponding to the bottom-level observation node and the conditional probability table includes: inputting the risk probability corresponding to the bottom-level observation node as evidence into the corresponding mid-level risk node of the Bayesian network, and calculating the risk probability of the mid-level risk node by combining the conditional probability table corresponding to the bottom-level observation node and using a Bayesian network inference algorithm.

6. The method according to claim 1, characterized in that, The determination of whether a battery cell is suitable for cascade utilization based on the calculated risk probabilities of the top-level target node and the middle-level risk node includes: if ,and If the condition is met, the power battery is determined to be suitable for cascade utilization; otherwise, the power battery is determined to be unsuitable for cascade utilization. and For the preset threshold, This refers to the current state data.

7. The method according to claim 1, characterized in that, If a battery cell is determined to be unsuitable for cascade utilization, reverse reasoning is performed to identify key risk factors. Based on the risk source of these key risk factors, a decision is made to disassemble and re-evaluate the battery cell or recycle the materials. This includes: for battery cells determined to be unsuitable for cascade utilization, reverse reasoning is performed to obtain the top M bottom-level observation nodes with high risk contribution through sensitivity analysis, and the key risk factors are determined based on these top M bottom-level observation nodes; if the key risk factors originate from structural defects or consistency issues at the module or cluster level and can be eliminated through disassembly, the battery cell is disassembled to the individual cell level, and step S4 is returned for re-inspection and evaluation; if the key risk factors originate from irreversible degradation of the individual battery cell itself, or from irreparable defects in the module / cluster structure, the battery cell is determined to be unsuitable for cascade utilization and enters the material recycling process.

8. The method according to claim 1, characterized in that, The historical operating data includes at least one of the following: battery capacity status, charge / discharge operating curve, capacity decay curve, operating record, and fault record; the current status data includes at least one of the following: voltage, internal resistance, capacity, and temperature; the market economic data includes at least one of the following: material market price, labor cost, equipment depreciation cost, and product revenue forecast.

9. The method according to claim 1, characterized in that, The mid-level risks include at least one of the following: safety risks, performance risks, and economic risks; the bottom-level observation nodes include at least one of the following: voltage inconsistency, internal resistance inconsistency, capacity inconsistency, shell expansion coefficient, charging and discharging temperature rise, self-discharge rate, remaining capacity, capacity decay rate, DC internal resistance, open-circuit voltage, cascade utilization cost, and cascade utilization benefit.

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