An accident scrappage electric vehicle battery thermal runaway risk partition identification method

Abstract

The application relates to the technical field of scrapped electric vehicle batteries, in particular to a method for identifying a thermal runaway risk partition of an accident-scrapped electric vehicle battery. The method comprises the following steps: obtaining a water-related scrapped vehicle information set, analyzing internal electrochemical degradation characteristics of a lithium battery after a water-related accident based on the water-related scrapped vehicle information set, and obtaining an electrochemical information set; analyzing a current distribution unevenness and local heat production coupling relationship caused by electrochemical degradation according to the electrochemical information set, and obtaining an electrical-thermal coupling information set; obtaining a vehicle yard gas information set, distinguishing heat generation gas characteristics caused by water reaction of residual electrolyte from normal aging gas generation characteristics according to the vehicle yard gas information set, combining the electrical-thermal coupling information set, and obtaining a thermal runaway distinguishing information set; and generating and outputting an accident-scrapped electric vehicle battery thermal runaway risk partition identification strategy according to the thermal runaway distinguishing information set. The stability, reliability and safety of the storage and disposal process of the accident-scrapped battery are improved.

Description

Technical Field

[0001] This application relates to the field of end-of-life electric vehicle battery technology, and in particular to a method for identifying thermal runaway risk zones in accident-damaged electric vehicle batteries. Background Technology

[0002] With the continuous increase in the number of electric vehicles on the road, the disposal of vehicles scrapped due to accidents has become a critical aspect of environmental protection and safety, especially for vehicles scrapped due to water-related accidents, whose power battery packs have complex internal states and pose a potential risk of thermal runaway. Current technologies typically employ methods such as individual cell voltage and internal resistance measurements, conventional gas concentration monitoring, and infrared thermal imaging inspections for risk assessment of scrapped batteries. These methods determine the battery's health status by collecting open-circuit voltage and DC internal resistance data, or by installing sensors in the storage area to monitor hydrogen and volatile organic compound concentrations, or by periodically scanning the battery surface temperature distribution using infrared equipment to obtain information on the battery's current operating or storage status.

[0003] However, in the existing technology, there is a lack of targeted and in-depth analysis of the complex electrochemical degradation mechanism inside the battery after a water-related accident. It is difficult to distinguish between the sudden exothermic gas generation caused by the reaction of residual electrolyte with water and the normal aging gas evolution of the battery. As a result, it is impossible to make fine spatial zoning of the risk level of different areas inside the battery pack, which makes it difficult to meet the needs of graded disposal and differentiated management. Summary of the Invention

[0004] This application provides a method for identifying thermal runaway risk zones in accident-damaged electric vehicle batteries to address the aforementioned problems. The method includes: acquiring a set of information on water-damaged scrapped vehicles; analyzing the internal electrochemical degradation characteristics of lithium batteries after water-damaged accidents based on the water-damaged scrapped vehicle information set to obtain an electrochemical information set; analyzing the coupling relationship between uneven current distribution and local heat generation caused by electrochemical degradation based on the electrochemical information set to obtain an electrical-thermal coupling information set; acquiring a set of vehicle yard gas information; distinguishing between exothermic gas generation characteristics caused by the reaction of residual electrolyte with water and normal aging gas evolution characteristics based on the vehicle yard gas information set and the electrical-thermal coupling information set to obtain a thermal runaway differentiation information set; and generating and outputting a thermal runaway risk zone identification strategy for accident-damaged electric vehicle batteries based on the thermal runaway differentiation information set.

[0005] Through the aforementioned technical solutions, a complete closed-loop analysis chain was constructed, from electrochemical degradation mechanisms to electro-thermal coupling behavior, then to gas-heat source correlation verification, ultimately forming a spatialized risk zoning strategy. By combining the information set of water-damaged scrapped vehicles with electrochemical degradation characteristics, the problem of existing technologies being unable to deeply analyze the water-specific degradation mechanisms was solved. The introduction of the electro-thermal coupling information set enabled early warning of potential hotspots in stages without significant temperature rise. Cross-validation of the vehicle yard gas information set and the electro-thermal coupling information set effectively distinguished between electrolyte hydrolysis gas production and normal aging gas evolution, reducing the false alarm rate. Finally, the risk zoning identification strategy generated based on the thermal runaway differentiation information set transformed the multidimensional analysis results into an intuitive spatial control plan, enabling on-site personnel to implement differentiated monitoring and response measures. This multi-dimensional, correlated, and spatialized analysis method not only improved the accuracy and timeliness of thermal runaway risk identification but also optimized the allocation of emergency resources.

[0006] Optionally, the step of analyzing the internal electrochemical degradation characteristics of lithium batteries after a water-related accident based on the water-damaged scrapped vehicle information set to obtain an electrochemical information set includes: the water-damaged scrapped vehicle information set includes the vehicle battery model, water immersion duration, and battery pack sealing information; based on the vehicle battery model and the water immersion duration, analyzing the active material loss characteristics caused by the chemical reaction between water and electrode materials and electrolyte to obtain electrode component attenuation information; based on the battery pack sealing information and the water immersion duration, analyzing the local electrochemical corrosion and conductive path formation characteristics caused by water intrusion at the seal failure point to obtain insulation failure risk information; and integrating the electrode component attenuation information and the insulation failure risk information to construct the electrochemical information set.

[0007] The above technical solution achieves a complete mapping from macroscopic accident parameters to the microscopic internal state of the battery, covering both the capacity decay mechanism caused by chemical reactions and the short-circuit risk mechanism caused by physical intrusion. It provides detailed and targeted data support for the subsequent analysis of the coupling relationship between uneven current distribution and local heat generation, effectively avoiding misjudgments caused by single-dimensional evaluation.

[0008] Optionally, the process of constructing the electrode component decay information includes: analyzing the chemical stability of the positive electrode active material and electrolyte inside the cell according to the vehicle battery model, and determining the active site information that is prone to hydrolysis; analyzing the lithium ion consumption information and crystal structure collapse information during the side reaction process between water penetration and the active site information based on the water immersion duration; and analyzing the change in the proportion of effective material in the electrode layer that can participate in charge-discharge cycles based on the lithium ion consumption information and the crystal structure collapse information to obtain the electrode component decay information.

[0009] Through the above technical solution, electrode component degradation information was constructed. First, the chemically unstable active sites were identified by using the vehicle battery model, establishing the physical basis of the reaction. Second, the duration of wading was introduced as a kinetic variable to quantify the two core degradation indicators: lithium-ion consumption and crystal structure collapse. Finally, the two were analyzed together to derive the dynamic changes in the proportion of effective material. This progressive modeling, from material properties to reaction process to functional degradation, overcomes the shortcomings of existing technologies that cannot distinguish microscopic degradation mechanisms by relying solely on overall voltage or internal resistance measurements. By quantifying the degree of loss of active material, subsequent analysis of uneven current distribution can be based on the actual material degradation state, improving the early warning capability and spatial positioning accuracy of thermal runaway risk identification.

[0010] Optionally, the process of constructing the insulation failure risk information includes: locating the edge gaps and connector interfaces of the battery module based on the battery pack sealing information, and determining the preferred penetration path for moisture intrusion; analyzing the accumulation information of oxidation and corrosion products caused by moisture infiltrating along the preferred penetration path on the surface of the cell tabs and busbars, as well as the ion concentration change information after electrolyte dilution, based on the accumulation information of oxidation and corrosion products and the ion concentration change information, analyzing the ion conductivity bridging state formed on the surface of the metal components to obtain the insulation failure risk information.

[0011] The above technical solution enables refined modeling of insulation failure risks in water-damaged scrapped batteries. By locating the preferred penetration path based on the battery pack sealing information, the spatial range of water intrusion is limited, avoiding computational redundancy caused by full-scale scanning. Furthermore, based on the duration of water immersion, the accumulation information of oxidation corrosion products and changes in ion concentration are dynamically extrapolated, introducing the time dimension into the chemical degradation analysis, making the assessment results more consistent with actual accident scenarios. Finally, based on the coupled analysis of oxidation corrosion product accumulation information and ion concentration changes, the ion-conductive bridging state formed on the surface of metal components is identified, thereby obtaining high-confidence insulation failure risk information. This progressive analysis logic, from physical path location to chemical process evolution to electrical state determination, can not only detect micro-short circuit hazards before significant temperature rise, but also distinguish between insulation degradation caused by normal aging and the corrosion-induced conductive risks unique to water-damaged accidents, improving the accuracy and foresight of thermal runaway risk identification.

[0012] Optionally, the step of analyzing the coupling relationship between uneven current distribution and local heat generation caused by electrochemical degradation based on the electrochemical information set to obtain an electrical-thermal coupling information set includes: analyzing the characteristics of increased local internal resistance caused by the reduction of active materials based on the electrode component decay information to obtain local conductivity limitation information; analyzing the characteristics of abnormal parallel leakage paths caused by conductive bridging state based on the insulation failure risk information to obtain abnormal shunt path information; analyzing the density convergence of current in the limited area and the energy dissipation characteristics on the shunt path based on the local conductivity limitation information and the abnormal shunt path information to obtain a current-heat generation mapping relationship; and dividing the abnormal high temperature hazard area inside the battery module based on the current-heat generation mapping relationship to construct the electrical-thermal coupling information set.

[0013] The above technical solution enables cross-domain mapping from microscopic electrochemical degradation to macroscopic thermal behavior risks. Through the synergistic analysis of local conductivity limitation information and abnormal shunting path information, it can not only identify thermal risks caused by a single factor, but also capture the superposition effect produced when the two mechanisms overlap in space. This coupled analysis mechanism allows the system to lock in high-risk hidden danger areas inside the battery at an early stage before the battery shows a significant external temperature rise. It effectively solves the problem of missed detection caused by the lack of multi-dimensional coupled analysis in existing technologies, and provides a data foundation for the subsequent formulation of differentiated risk zoning control strategies.

[0014] Optionally, the process of constructing the abnormal shunting path information includes: based on the preferred penetration path, analyzing the liquid accumulation area formed on the surface of the battery cell tabs and busbars after water intrusion to obtain local wetting state information; based on the local wetting state information and combined with the information on the accumulation of oxidation corrosion products, analyzing the distribution morphology of the non-uniform conductive layer formed on the surface of the metal components due to salt deposition and electrolyte residue to obtain surface conductivity difference information; based on the surface conductivity difference information and combined with the ion concentration change information, analyzing the direction of the low-impedance channel formed by the connection of the positive and negative electrodes and components with different potentials by the unexpected conductive medium to obtain the abnormal shunting path information.

[0015] The above technical solution addresses several key aspects. First, by determining the physical location of the accumulated liquid based on the preferred permeation path, it solves the problem of where the water is distributed. Second, by combining corrosion products and salt deposition analysis to identify surface conductivity differences, it reveals the non-uniform formation mechanism of the conductive medium at the microscale, solving the problem of the form of the leakage medium. Finally, by integrating ion concentration and conductivity differences to pinpoint the path of low-impedance channels, it achieves a mapping from static material properties to dynamic current paths. These three tightly coupled aspects enable the system to overcome the limitations of existing uniform corrosion models and identify hidden micro-short-circuit channels formed by local salt enrichment and electrolyte bridging. This high-resolution path prediction capability improves the accuracy of electrical-thermal coupling analysis, ensuring that subsequent current density convergence analysis and heat superposition effect calculations can reflect the dangerous state inside the battery, thus providing data support for early warning and zoning of thermal runaway risks.

[0016] Optionally, the process of constructing the current-heat generation mapping relationship includes: based on the local conductivity limitation information, analyzing the obstruction effect of the increased internal resistance region caused by the reduction of active material on current flow to obtain local current change information; based on the abnormal shunt path information, analyzing the local resistance difference caused by the non-uniform conductive layer distribution along the low impedance channel to obtain shunt energy change information; based on the local current change information and combined with the shunt energy change information, analyzing the spatial overlap area and heat superposition effect of the two on the electrode structure inside the battery module to obtain the current-heat generation mapping relationship.

[0017] The above technical solution realizes the mapping from microscopic electrochemical degradation to macroscopic thermal behavior. By using local conductivity-limited information and abnormal current shunting path information in synergy, it not only independently analyzes the current redistribution effect caused by increased internal resistance and the leakage current thermal effect caused by insulation failure, but more importantly, it reveals the spatial overlap mechanism of the two. On this basis, by using the thermal superposition effect analysis of the spatially overlapping region, it is possible to quantitatively evaluate the extreme heat generation scenario under the combined action of multiple degradation mechanisms. Furthermore, the generated current-heat generation mapping relationship provides a physical basis for the subsequent division of abnormal high temperature hazard areas, ensuring that the risk zoning identification strategy can locate the core hazard sources that are prone to thermal runaway due to complex coupling effects, thereby effectively improving the safety and early warning in the process of disposing of water-damaged scrapped electric vehicle batteries.

[0018] Optionally, the step of distinguishing between exothermic gas generation characteristics caused by the reaction of residual electrolyte with water and normal aging gas evolution characteristics, based on the vehicle yard gas information set and the electrical-thermal coupling information set, to obtain a thermal runaway differentiation information set, includes: the vehicle yard gas information set including gas component concentration information and gas temperature information; based on the gas component concentration information, analyzing the instantaneous release concentration characteristics of volatile organic compounds and hydrogen fluoride gas to obtain gas evolution information; based on the gas temperature information and the electrical-thermal coupling information set, analyzing the spatial correspondence and temperature rise rate matching characteristics between gas temperature and abnormal high temperature hazard areas to obtain gas generation heat source correlation information; based on the gas evolution information and the gas generation heat source correlation information, distinguishing between sudden exothermic gas generation areas caused by residual electrolyte hydrolysis and slowly changing gas generation areas caused by electrode stable gas evolution, to obtain the thermal runaway differentiation information set.

[0019] The above technical solution enables the identification of thermal runaway risks in water-damaged waste batteries. Specifically, a multi-dimensional sensing foundation is constructed by jointly acquiring gas component concentration and gas temperature information. Then, by utilizing the instantaneous release characteristics of volatile organic compounds and hydrogen fluoride gas, potential violent reactions are rapidly screened at the chemical mechanism level. Based on this, abnormally high-temperature hazard areas with concentrated electrical-thermal coupling information are introduced as spatial anchors. Through spatiotemporal matching verification of gas temperature and hazard areas, the true source and degree of danger of gas generation are confirmed at the physical heat source level. This dual mechanism of chemical fingerprint initial screening and spatiotemporal verification of heat source enables the system to distinguish between sudden exothermic gas generation characteristics caused by the reaction of residual electrolyte with water and normal aging gas evolution characteristics. The resulting thermal runaway differentiation information set not only solves the false alarm problem caused by the inability to distinguish gas sources in existing technologies but also defines the nature of risk areas, ensuring the pertinence and effectiveness of subsequent risk zoning identification strategies and improving the safety management capabilities at accident sites.

[0020] Optionally, the process of constructing the gas-generating heat source association information includes: based on the gas temperature information, analyzing the temperature gradient distribution of monitoring points at different height levels, determining the vertical accumulation position of high-temperature gas in the parking lot space, and obtaining gas spatial distribution information; based on the electrical-thermal coupling information set, combined with the gas spatial distribution information, analyzing the overlap relationship between the projection directly above the abnormal high-temperature hazard area and the vertical accumulation position of the gas, and determining the synchronous relationship between the temperature rise slope of the hazard area and the rising rhythm of the gas temperature, and obtaining the gas-generating heat source association information.

[0021] The above technical solution achieves a leap from macroscopic gas detection to microscopic heat source localization. By spatially projecting and matching the gradient distribution characteristics of gas temperature at different height levels with the abnormal high-temperature hazard area generated by the internal electrical-thermal coupling model of the battery, and combining the synchronicity judgment of the temperature rise slope and the gas temperature rise rhythm, not only is the existence of high-temperature gas confirmed, but the specific internal battery region where the gas is generated is also located. This synergistic effect of spatial overlap analysis and temporal synchronous verification effectively solves the technical problems in the prior art of being unable to distinguish the gas source and difficult to associate external gas concentration alarms with specific internal fault points. This ensures the accuracy and pertinence of subsequent risk zoning strategy formulation and provides data support for implementing differentiated emergency response.

[0022] Optionally, the step of generating and outputting a risk zoning identification strategy for thermal runaway of accident-damaged electric vehicle batteries based on the thermal runaway differentiation information set includes: analyzing the boundary characteristics of the spatial distribution of sudden heat release and gas generation areas and slowly changing gas generation areas based on the thermal runaway differentiation information set to obtain risk source location information; analyzing the differences in critical conditions for thermal runaway triggering in different areas based on the risk source location information and the gas generation heat source correlation information to obtain grading judgment benchmark information; formulating differentiated disposal priorities and monitoring frequencies corresponding to different risk levels based on the grading judgment benchmark information to obtain a preliminary control plan; and planning inspection routes and safety isolation ranges based on the preliminary control plan and the abnormal high temperature hazard areas to generate and output the risk zoning identification strategy for thermal runaway of accident-damaged electric vehicle batteries.

[0023] The aforementioned technical solutions transform multi-dimensional risk analysis results into actionable on-site management strategies. First, by analyzing the spatial boundaries of sudden and gradually changing gas-generating areas, the source of risk is located, solving the problem that existing methods cannot distinguish the specific location of risks. Second, a grading and judgment benchmark, based on information related to gas-generating heat sources, enables differentiated treatment of areas with different risk levels, avoiding overreaction or underresponse. Third, differentiated handling plans generated based on the grading benchmark optimize resource allocation, ensuring that high-risk areas receive the highest priority. Finally, the inspection routes and safety isolation ranges planned for areas with abnormally high temperature hazards provide on-site personnel with intuitive and safe operational guidelines. These technical features work together to form a complete closed loop from risk identification to decision support to action guidance, not only reducing safety hazards caused by false alarms and missed alarms but also improving the efficiency of emergency response and the level of management sophistication in the storage and disposal of water-damaged waste batteries. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of an application scenario provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for identifying risk zones of thermal runaway in an accident-damaged electric vehicle battery, as provided in one embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0029] With the increasing number of electric vehicles on the road, the disposal of vehicles scrapped due to accidents has become a major challenge in the fields of environmental protection and safety. Among them, vehicles scrapped due to water damage have extremely complex internal states of their power battery packs, posing a very high potential risk of thermal runaway. In current technologies, risk assessment of scrapped batteries mainly uses methods such as individual cell voltage and internal resistance measurement, conventional gas detection, or infrared thermal imaging. However, relying solely on electrical parameters or gas concentrations lacks multi-dimensional data coupling analysis, resulting in high false alarm and false negative rates. Furthermore, existing methods fail to deeply analyze the correlation between the electrochemical degradation characteristics and electrical-thermal behavior of the battery after water damage, leading to a lag in risk assessment. In addition, existing methods usually only provide an overall assessment and cannot finely categorize the risk levels of different areas within the entire battery pack, making it difficult to guide subsequent graded disposal and differentiated management.

[0030] Based on this, this application provides a method for identifying thermal runaway risk zones in electric vehicle batteries that have been scrapped due to accidents. It constructs a fully closed-loop analysis system, integrates multiple information sets from water-damaged scrapped vehicles, clarifies the unique degradation mechanism of water-damaged batteries, achieves early warning of hotspots and distinguishes gas generation types, and forms spatial risk zones based on multi-dimensional data to improve the efficiency of thermal runaway assessment and optimize emergency resource allocation.

[0031] Figure 1 This application provides an application scenario diagram. In the process of identifying the risk zoning of battery thermal runaway in vehicles scrapped due to water damage, the method provided in this application is applied to build a closed-loop analysis framework around the battery of vehicles scrapped due to water damage, from degradation mechanism to multi-field coupling verification. Relying on multi-source datasets, it overcomes a number of technical shortcomings and implements spatial zoning control.

[0032] Specifically, the method provided in this application can be applied to any server. The server interacts with the scrapped vehicle inspection platform and the gas environment monitoring sensor to obtain the information set of water-damaged scrapped vehicles provided by the scrapped vehicle inspection platform and the information set of vehicle yard gas provided by the gas environment monitoring sensor. A closed-loop research link is established, and the problem of water-damaged power battery degradation assessment is solved by relying on multi-dimensional data fusion. The method generates and outputs a risk zoning identification strategy for thermal runaway of accident-damaged electric vehicle batteries to scrapped vehicle service stations, implements spatial risk zoning management, and effectively improves the accuracy of thermal runaway identification.

[0033] The specific implementation method can be referred to in the following embodiments, wherein the data mentioned in the embodiments are only for reference and examples, so that relevant personnel can better understand them.

[0034] Figure 2 This is a flowchart illustrating a method for identifying thermal runaway risk zones in an accident-damaged electric vehicle battery, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above scenarios. Figure 2 As shown, the method includes: Example 1: S201. Obtain information set of vehicles scrapped after being submerged in water. Based on the information set of vehicles scrapped after being submerged in water, analyze the internal electrochemical degradation characteristics of lithium batteries after water-related accidents to obtain electrochemical information set.

[0035] The water-damaged scrapped vehicle information set refers to a data collection recording the basic attributes and water-damaged conditions of the accident vehicle. Specifically, it includes the vehicle battery model, water immersion duration, and battery pack sealing information, with the scrapped vehicle inspection platform as the data source. The vehicle battery model is used to determine the chemical system (e.g., ternary lithium, lithium iron phosphate) and physical structure parameters of the battery cells; the water immersion duration refers to the cumulative time the battery pack is submerged in water, which directly determines the depth of water penetration and the degree of chemical reaction; the battery pack sealing information describes the integrity of the battery pack casing, sealing strips, and connectors. The electrochemical information set is obtained by simulating the interaction between water and the internal materials of the battery. Its purpose is to quantify the internal damage unique to water-damaged accidents, including the degree of loss of active materials and the decline in insulation performance. Specifically, the system analyzes the side reactions that occur between water penetration and the positive electrode active material based on the vehicle battery model and the duration of water immersion, calculating the consumption of active sites and the collapse ratio of the crystal structure to obtain electrode component degradation information. Simultaneously, based on the battery pack sealing information, it locates the preferred penetration paths of water intrusion (such as edge gaps and electrode tab interfaces), analyzes the accumulation of oxidation and corrosion products along these paths and the ion conductivity bridging state caused by electrolyte dilution, thus obtaining insulation failure risk information. Integrating the above electrode component degradation information and insulation failure risk information constructs an electrochemical information set reflecting the microscopic chemical and physical changes inside the battery. For example, when input data shows that a certain vehicle model's battery has been submerged for 2 hours and the sealing strip is damaged, the system can calculate that the effective lithium-ion concentration of the positive electrode material decreases by approximately 15%, and the insulation resistance at the busbar connection drops from 20MΩ to 50kΩ, forming an electrochemical information set containing specific numerical spectra. Through this mechanism-based degradation characteristic analysis, the system can capture the hidden damage caused by water immersion accidents, providing accurate initial boundary conditions for subsequent thermal risk prediction.

[0036] S202. Based on the electrochemical information set, analyze the coupling relationship between uneven current distribution caused by electrochemical degradation and local heat generation to obtain the electro-thermal coupling information set.

[0037] The electrical-thermal coupling information set refers to a dataset describing the spatial mapping relationship between abnormal current flow and heat accumulation within the battery. This information set is further derived from the aforementioned electrochemical information set, and its function is to transform microscopic chemical degradation into macroscopic electrical and thermal risk indicators. Specifically, the system first analyzes the characteristics of increased local internal resistance caused by the reduction of active materials based on electrode component decay information, identifies regions where current flow is obstructed, and obtains local conductivity-limited information. Second, based on insulation failure risk information, it analyzes abnormal parallel leakage paths caused by conductive bridging states, identifies unexpected low-impedance channels, and obtains abnormal shunt path information. On this basis, the system performs spatial superposition analysis of the local conductivity-limited information and the abnormal shunt path information, calculates the density convergence of current in the limited area and the energy dissipation characteristics on the shunt path, thereby establishing a current-heat generation mapping relationship. Based on this mapping relationship, the system can quantify the heat accumulation rate and temperature peak of different regions, thereby delineating abnormal high-temperature hazard areas inside the battery module, and finally constructing an electrical-thermal coupling information set that includes risk level classification. For example, if analysis reveals a low-impedance channel formed by corrosion products on the surface of a busbar, the system can simulate a parasitic current density along that path that is five times the normal value, with a localized heat generation power as high as 10 W / cm². 2 The system predicts that the temperature in the area will rise within one hour, thus marking the area as a Class A high-risk zone. Through coupled analysis of current distribution and heat generation effects, potential hotspots can be identified in advance, before the battery shows a significant temperature rise, achieving a leap from phenomenon monitoring to mechanism early warning.

[0038] S203. Obtain the vehicle yard gas information set. Based on the vehicle yard gas information set and the electrical-thermal coupling information set, distinguish between the exothermic gas generation characteristics caused by the reaction of residual electrolyte with water and the normal aging gas evolution characteristics to obtain the thermal runaway differentiation information set.

[0039] The vehicle yard gas information set specifically includes gas component concentration information (such as the concentrations of volatile organic compounds (VOCs), hydrogen fluoride (HF), and hydrogen) and gas temperature information, with gas environment monitoring sensors as the data source. The thermal runaway differentiation information set is obtained by fusing external gas monitoring data with an internal electrical-thermal coupling model. Its core function is to identify the gas source and eliminate normal aging interference. Specifically, based on the gas component concentration information, the system analyzes the instantaneous release concentration characteristics of volatile organic compounds and hydrogen fluoride gases to identify whether there is explosive gas release information. At the same time, based on the gas temperature information, combined with the abnormal high temperature hazard areas in the generated electrical-thermal coupling information set, the system analyzes the spatial distribution of gas temperature (such as vertical accumulation position) and the projection overlap relationship between the hazard area and the gas temperature rise rate, and determines the synchronicity between the gas temperature rise rate and the temperature rise slope of the hazard area, thereby obtaining the gas generation heat source correlation information. Based on a comprehensive assessment of gas evolution information and the correlation information of gas generation heat sources, the system can distinguish between two types of gas generation areas: one is a sudden exothermic gas generation area caused by the hydrolysis of residual electrolyte, characterized by the release of high concentrations of HF / VOC that are spatiotemporally synchronized with the internal high-temperature zone; the other is a slowly varying gas generation area caused by stable electrode gas evolution, characterized by gradual gas release without corresponding high-temperature abrupt changes. For example, when the sensor detects a surge in VOC concentration within 30 seconds and a synchronous rapid rise in gas temperature above, and this location corresponds to the aforementioned identified busbar micro-short circuit area, the system determines this to be a sudden exothermic gas generation; conversely, if the gas concentration fluctuates slowly without a synchronous temperature response, it is determined to be normal aging gas evolution. Through this multi-dimensional feature differentiation, false alarms caused by normal aging gases are reduced, ensuring the capture of real thermal runaway risks.

[0040] S204. Based on the thermal runaway differentiation information set, generate and output the thermal runaway risk zone identification strategy for accident-damaged electric vehicle batteries.

[0041] The risk zoning identification strategy for thermal runaway of electric vehicle batteries involved in accidents can be described as a spatialized control scheme to guide on-site safety handling. This strategy is generated based on the thermal runaway differentiation information set obtained above, and its function is to transform abstract risk data into actionable instructions. Specifically, the system first analyzes the boundary characteristics of the spatial distribution of sudden heat release and gas generation areas and slowly changing gas generation areas to accurately locate the core risk source. Then, combined with the gas generation heat source correlation information, it analyzes the differences in the critical conditions for triggering thermal runaway in different areas (such as temperature threshold, gas concentration threshold, and temperature rise rate threshold) and formulates classification judgment benchmark information. Subsequently, based on the classification judgment benchmark, it formulates differentiated handling priorities and monitoring frequencies for different risk levels to form a preliminary control plan, such as implementing forced cooling and high-frequency monitoring in high-risk areas, and strengthening ventilation and regular inspections in medium-risk areas. Finally, combined with the specific coordinates of abnormally high temperature hazard areas, it plans specific inspection routes and safety isolation ranges, generates a visualized identification strategy, and outputs it. For example, the system can output an electronic map, marking the busbar micro-short circuit area as a red high-risk zone and delineating a 2-meter isolation circle, planning a security patrol route that bypasses this area, and simultaneously sending a command to the monitoring terminal to report the red zone data every 5 minutes. By generating a comprehensive strategy that includes risk source location, danger boundary, graded handling, and patrol path, resource deployment is achieved, effectively preventing the spread of local thermal runaway to the entire storage area and improving the efficiency and safety of accident handling.

[0042] Example 2: In some embodiments, based on the information set of water-damaged scrapped vehicles, the internal electrochemical degradation characteristics of lithium batteries after water-damage accidents are analyzed to obtain an electrochemical information set. The method further includes the following steps: Step 1: Information collection for water-damaged scrapped vehicles includes vehicle battery model, duration of water immersion, and battery pack sealing information; The information set of water-damaged scrapped vehicles serves as the primary data foundation for subsequent electrochemical degradation analysis. Vehicle battery model information determines the cell's chemical system (e.g., ternary lithium, lithium iron phosphate), nominal capacity, and design IP protection rating. Different chemical systems exhibit variations in electrode material sensitivity to moisture and reaction products. Water immersion duration refers to the cumulative time the battery pack is submerged in water or in a high-humidity environment; this parameter directly determines the depth of water intrusion and the extent of chemical reactions. Battery pack sealing information includes the physical integrity of the battery pack casing, the aging degree of the sealing strips, the locking status of connectors, and records of known damage locations. This information defines the physical boundaries of water intrusion. For example, for an electric vehicle nominally IP67 but with collision marks on the underbody protection plate, its battery pack sealing information will be marked as having a failed bottom seal. Combined with a water immersion duration of 2 hours, the system can determine that a large amount of water has intruded through the bottom gaps. By integrating the above multi-dimensional macroscopic vehicle information, the accident scene environment can be reconstructed, providing accurate input conditions for microscopic electrochemical state estimation.

[0043] Step 2: Based on the vehicle battery model and the duration of wading, analyze the characteristics of active material loss caused by the chemical reaction between water and electrode materials and electrolyte to obtain electrode component attenuation information; The electrode component degradation information quantifies the loss of effective active materials inside the battery caused by water immersion accidents. Specifically, the system first calls the corresponding material chemistry characteristic library based on the vehicle battery model to identify the chemical stability of the positive electrode active material (such as transition metal ions in lithium nickel cobalt manganese oxide) and electrolyte solvent in the presence of water, and determines the active sites that are prone to hydrolysis. Then, based on the duration of water immersion, it simulates the kinetic process of water penetrating into the battery cell and reacting with the above-mentioned active sites, calculating the irreversible consumption of lithium ions and the collapse ratio of the crystal structure. This process not only focuses on the reduction of the total amount, but also emphasizes the analysis of lattice distortion and active particle pulverization caused by water erosion, thereby obtaining the change in the proportion of effective materials in the electrode layer that can participate in charge-discharge cycles. For example, when the duration of water immersion exceeds a threshold, the HF generated by the reaction of water and lithium hexafluorophosphate will accelerate the dissolution of the positive electrode material, causing the proportion of active material to drop from the initial 98% to 85%. This change is recorded as electrode component degradation information. This information is closely coupled with the vehicle battery model, ensuring the accuracy of battery degradation assessment for different chemical systems and providing a material basis for subsequent judgment of increased internal resistance.

[0044] Step 3: Based on the battery pack sealing information and the duration of water immersion, analyze the characteristics of local electrochemical corrosion and conductive path formation caused by moisture intrusion at the sealing failure point to obtain insulation failure risk information; The insulation failure risk information describes the decline in internal insulation performance and the formation of abnormal conductive paths caused by moisture intrusion. Specifically, the system locates weak points such as edge gaps, tab leads, and high-voltage connector interfaces of the battery module based on battery pack sealing information, determining the preferred penetration path of moisture. Then, based on the duration of immersion, it analyzes the oxidation and corrosion reactions caused by moisture infiltrating along this path on the surfaces of metal components such as cell tabs and busbars, simulating the accumulation morphology of corrosion products (such as metal oxides and hydroxides) and the changes in ion concentration distribution after electrolyte dilution. Based on this, it analyzes the ion conductivity bridging state formed on the surface of metal components due to corrosion product deposition and high-concentration ion solution bridging, quantifying the leakage impedance between positive and negative electrodes or between components at different potentials. For example, if the sealing information shows that the connector seal ring has failed and the immersion time is long, moisture may form a continuous electrolyte film on the copper busbar surface, causing low-impedance channels in the originally insulated areas. This state is characterized as insulation failure risk information. This information reveals the unique short-circuit hazard of water immersion accidents, which is different from the uniform increase in internal resistance caused by conventional cyclic aging.

[0045] Step 4: Integrate electrode component attenuation information and insulation failure risk information to construct an electrochemical information set.

[0046] The electrochemical information set is a comprehensive encapsulation of the analysis results from the two aforementioned dimensions, fully reflecting the electrochemical health status of the water-damaged batteries. The system integrates the electrode component attenuation information reflecting the degree of active material loss with the insulation failure risk information reflecting insulation performance degradation and leakage path characteristics in a structured data format. This set includes not only static parameters of each individual cell or module region (such as the proportion of remaining active material and local insulation resistance values) but also dynamic evolution characteristics (such as corrosion rate and ion migration trends).

[0047] Example 3: In some embodiments, the process of constructing electrode component attenuation information specifically includes the following steps: Step 1: Based on the vehicle battery model, analyze the chemical stability of the positive electrode active material and electrolyte inside the cell to determine the active site information that is prone to hydrolysis. The vehicle battery model refers to the specific specifications of the power battery installed in the scrapped electric vehicle, which includes key parameters such as the cell's chemical system (e.g., ternary lithium NCM, lithium iron phosphate LFP), positive and negative electrode material formulations, and electrolyte composition. Analyzing the chemical stability of the positive electrode active material and electrolyte within the cell can involve using a material property database corresponding to the battery model and calling thermodynamic data to calculate the Gibbs free energy changes of the positive electrode lattice structure and electrolyte solvent molecules in a water-containing environment, thereby assessing their reaction tendency when in contact with water. Information on active sites prone to hydrolysis can refer to the specific atomic positions or crystal plane regions in the positive electrode material's crystal structure that have low chemical bond energies due to doping elements or defects, making them highly susceptible to substitution or decomposition reactions with water molecules. For example, in lithium nickel cobalt manganese oxide (NCM811) batteries, the high nickel content makes the surface lattice oxygen extremely unstable when exposed to water, with active sites mainly concentrated on highly active crystal planes and grain boundaries on the particle surface; while for materials containing aluminum dopant, active sites may be more distributed in areas with concentrated lattice defects. This analysis, based on the intrinsic properties of materials, can pinpoint the origin of chemical reactions in water-related accidents, providing a clear physical object for subsequent quantification of the degree of degradation.

[0048] Step 2: Based on the duration of immersion, analyze the lithium ion consumption and crystal structure collapse information during the side reaction process between water infiltration and active site information. The duration of immersion refers to the cumulative time the vehicle battery pack is submerged in water or in a high-humidity environment. This parameter directly determines the depth of water penetration and the extent of side reactions. Lithium-ion consumption information refers to the quantitative data showing a reduction in the amount of reversible lithium during side reactions, caused by water molecules attacking active sites, displacing lithium ions from the crystal lattice, or reacting with acidic substances (such as HF) to form insoluble lithium salts. Crystal structure collapse information describes the degree to which the original layered or olivine structure of the cathode material is distorted, disordered, or even transformed into a rock salt phase as lithium ions are lost and transition metal ions dissolve. Specifically, the longer the immersion time, the more thorough the contact between water and active sites, and the more severe the side reactions. For example, when the immersion time is 2 hours and the water depth submerges the bottom of the battery pack, water seeps in along the sealed micro-gap and reacts with the active sites on the surface of the NCM material, potentially causing a decrease in lithium-ion concentration within a depth of 5-10 micrometers of the surface layer by about 15%, accompanied by a decrease in the c / a ratio of the crystal lattice parameter, indicating localized collapse of the layered structure. This correlation analysis between time and reaction depth transforms the macroscopic duration of an accident into a microscopic indicator of material damage.

[0049] Step 3: Based on lithium ion consumption information and crystal structure collapse information, analyze the change in the proportion of effective materials that can participate in charge-discharge cycles in the electrode layer to obtain electrode component decay information; The change in the proportion of effective material refers to the change in the mass or volume of material in the electrode sheet that still maintains complete electrochemical activity and can normally insert / extract lithium ions, relative to the total initial amount after the water-related side reactions. The analysis process involves a weighted coupling calculation of the aforementioned lithium ion consumption and the degree of crystal structure damage: irreversible loss of lithium ions directly reduces the upper limit of the battery capacity, while the collapse of the crystal structure destroys the electron conduction network and ion diffusion channels, leading to the formation of dead zones that, although not completely deactivated, cannot participate in effective cycling. For example, if the analysis shows that the lithium ion consumption rate in a certain area is 10%, and the collapse of the crystal structure in that area results in a 20% breakage area in the conductive network, then the overall determination is that the decrease in the proportion of effective material in that area is greater than the sum of any single indicator, possibly reaching more than 25%. The electrode component attenuation information obtained from this not only includes an estimate of the remaining capacity but also clarifies the spatial distribution and mechanism type of active material failure, providing input basis for subsequent judgment of local internal resistance increase and uneven current distribution.

[0050] Example 4: In some embodiments, the process of constructing insulation failure risk information further includes: Step 1: Based on the battery pack sealing information, locate the edge gaps and connector interfaces of the battery module to determine the preferred penetration path of moisture intrusion; The battery pack sealing information refers to a data set recording the physical sealing state of the battery pack, specifically including the integrity data of the sealing strip, the locking status of the connectors, and the gap dimensions of the shell mating surfaces. This step aims to identify the weakest point in the battery module structure by analyzing the above sealing information, thereby determining the preferred penetration path of water in a water-related accident. Specifically, the system first extracts the structural drawings corresponding to the vehicle battery model and maps the defect coordinates (such as the broken point of the sealing strip, the incomplete closure of the connector) in the battery pack sealing information onto the 3D model. Due to gravity and capillary action, water often enters the battery first along these structural gaps. For example, when permanent compression deformation is detected in the sealing ring at a connector interface, the system marks the area within a 5mm radius around the interface as a high-probability water ingress point and defines it as the starting segment of the preferred penetration path. This physical structure-based positioning method avoids blindly guessing the water ingress location and provides spatial constraints for subsequent analysis of corrosion product accumulation.

[0051] Step 2: Based on the duration of immersion, analyze the accumulation of oxidation and corrosion products caused by water that seeps in along the preferred penetration path on the surface of the battery cell tabs and busbars, as well as the changes in ion concentration after electrolyte dilution. The duration of immersion refers to the cumulative time the battery pack is submerged in water or in a humid environment, a key time variable determining the extent of chemical reactions. This step quantifies the electrochemical side reactions triggered by water intrusion based on the established preferred penetration path. Specifically, after water reaches the cell tabs and busbar surfaces along the preferred penetration path, it reacts with metal components (such as copper and aluminum) to form oxide or hydroxide precipitates, i.e., information on the accumulation of oxidation corrosion products. Simultaneously, the infiltrated water mixes with leaked or residual electrolyte inside the battery, causing local electrolyte dilution and resulting in changes in ion concentration. For example, if the immersion duration is 2 hours, the system calculates, based on the Arrhenius equation and metal corrosion rate model, that the accumulation thickness of copper oxide (CuO) or iron hydroxide (Fe(OH)3) in a specific area on the busbar surface is approximately 10-50 micrometers. Furthermore, the conductivity of the electrolyte in this area exhibits a non-linear trend of first increasing and then decreasing due to water dilution (initially increasing conductivity due to increased ion dissociation, then decreasing due to excessive concentration dilution). This process not only generates depositional information describing the morphology of the corrosion layer, but also obtains ion concentration distribution data reflecting changes in the local electrolyte environment. Together, these constitute the fundamental data source for assessing insulation performance degradation, reflecting the specific impact of immersion time on the battery's internal microenvironment.

[0052] Step 3: Based on the information on the accumulation of oxidation and corrosion products and the information on changes in ion concentration, analyze the ion conductivity bridging state formed on the surface of the metal component to obtain information on the risk of insulation failure. The ion-conductive bridging state refers to a low-resistance conductive path formed by the combined action of corrosion products and diluted electrolyte on the surface or between originally insulating metal components. This step aims to combine the corrosion accumulation morphology and ion concentration distribution obtained in the previous two steps to determine whether a conductive bridge sufficient to trigger a micro-short circuit has been formed, thereby generating insulation failure risk information. Specifically, the system performs spatial superposition analysis on the conductive particle distribution (such as certain metal oxides having semiconductor properties or forming a conductive film after adsorbing electrolyte) in the information on oxidation corrosion product accumulation and the high conductivity region in the information on ion concentration changes. When corrosion products form a continuous coverage between the positive and negative electrodes or between the busbar and the shell, and the ion concentration within this coverage layer is sufficient to maintain charge migration, an ion-conductive bridging state is determined to have been formed. For example, on the surface of the insulating pad between the positive and negative electrodes of the busbar, if a continuous corrosion product accumulation band is detected, and the electrolyte ion concentration on this band is higher than a critical threshold (such as 0.1 mol / L), the system determines that an abnormal leakage path with a resistance possibly as low as several thousand ohms has been formed here. The resulting insulation failure risk information not only includes a qualitative assessment of the existence of short-circuit risk but also quantifies the specific location of the leakage path, the estimated impedance value, and the potential energy dissipation capacity. This result provides crucial input parameters for subsequent analysis of the coupling relationship between uneven current distribution and localized heat generation, effectively solving the problem that existing technologies cannot identify hidden micro-short circuits caused by water immersion.

[0053] Example 5: In some embodiments, based on the electrochemical information set, the relationship between uneven current distribution caused by electrochemical degradation and local heat generation coupling is analyzed to obtain the electro-thermal coupling information set. The method further includes the following steps: Step 1: Based on the electrode component decay information, analyze the characteristics of the local internal resistance increase caused by the reduction of active material to obtain information on local conductivity limitation; The electrode component degradation information is derived from the quantitative analysis of internal chemical changes in the battery after a water immersion accident, specifically including data on the reduction in effective lithium-ion concentration and the change in the proportion of active material. The increased local internal resistance refers to the physical phenomenon where the effective area for electrochemical reactions in the positive electrode active material decreases due to hydrolysis or crystal structure collapse, resulting in a significant increase in charge transfer impedance. Local conductivity limitation information is used to characterize the current flow obstruction areas formed inside the battery module due to material failure. Specifically, the processor calculates the equivalent resistance value of each micro-region based on the material characteristic curve corresponding to the vehicle battery model and the active site loss ratio calculated from the duration of water immersion. For example, when the layered structure of the positive electrode material in a certain region collapses due to long-term immersion, and the proportion of effective active material decreases from 95% to 70%, the local internal resistance of that region may increase from the initial 2mΩ to 8mΩ, forming a significant conductivity bottleneck. Through this resistance mapping based on the degree of chemical degradation, it is possible to locate the areas inside the battery where current flow is difficult due to material damage, providing basic data for subsequent analysis of current density distribution.

[0054] Step 2: Based on the insulation failure risk information, analyze the characteristics of abnormal parallel leakage paths caused by conductive bridging to obtain abnormal shunt path information; The insulation failure risk information is derived from the analysis of moisture intrusion paths and corrosion product accumulation at the battery pack sealing failure points. Specifically, it includes the coordinates of preferred penetration paths and ionic conductivity bridging parameters. Abnormal parallel leakage path characteristics refer to the low-impedance, unexpected channels formed between originally insulated positive and negative electrode components or between components at different potentials after moisture intrusion, due to the combined action of the electrolyte dilution-formed ionic solution and metal corrosion products (such as copper oxide and iron hydroxide). Abnormal shunt path information describes the spatial orientation, impedance magnitude, and potential shunt capacity of these bypass channels. Specifically, based on the aforementioned determined preferred penetration paths, the system analyzes the liquid accumulation areas formed along the tabs and busbar surfaces, and, combined with the surface conductivity differences caused by salt deposition, simulates and calculates the specific orientation of the low-impedance channels. For example, if a corrosion product accumulation thickness of 0.5 mm is detected at the edge gap of the battery module, accompanied by a high concentration of electrolyte residue, the system can determine that an abnormal shunt path with an impedance of approximately 10 kΩ has formed here. This path will cause some operating current to bypass the normal load and be directly discharged. By identifying such abnormal current shunting paths, we can effectively capture the hidden leakage risks caused by insulation failure and prevent thermal runaway caused by abnormal local energy dissipation.

[0055] Step 3: Based on the information of local conductivity limitation and combined with the information of abnormal current shunting paths, analyze the degree of current density concentration in the limited area and the energy dissipation characteristics on the current shunting paths to obtain the current-heat generation mapping relationship. The current density concentration in a confined area can refer to the phenomenon where, in a locally conductive confined area, the increased impedance of the normal path forces the current to concentrate in adjacent low-resistance areas or areas with remaining effective active material, resulting in a local current density far exceeding the average level. The energy dissipation characteristics on the shunt path can refer to the energy dissipation characteristics when current flows through an abnormal parallel leakage path, according to Joule's law (Q=I...). 2 The current-heat generation mapping relationship is a mathematical model or dataset describing the correspondence between the current distribution state and the heat generation power at a spatial location within the battery module. Specifically, the local internal resistance distribution map and the abnormal current shunt path map obtained above are spatially superimposed for analysis. Using finite element simulation or equivalent circuit model, the current vector and the corresponding heat generation power in each grid cell are calculated. For example, in a certain electrode layer, if there is severe active material degradation (high internal resistance) in the left region, and a low-resistance current shunt path formed by corrosion products exists on the right, the current will converge in large quantities to the right current shunt path and the high conductivity area on the left edge, causing the heat generation power per unit volume in these regions to increase sharply, possibly reaching more than 5 times the normal value. By constructing this multi-dimensional mapping relationship, it is possible to reveal how electrochemical degradation is transformed into specific thermal risks and achieve quantitative prediction of potential hotspots.

[0056] Step 4: Based on the current-heat generation mapping relationship, divide the abnormal high temperature potential areas inside the battery module and construct an electrical-thermal coupling information set; The abnormal high-temperature hazard area refers to the internal space of the battery where the predicted temperature rise rate or steady-state temperature exceeds a preset safety threshold, calculated based on the current-heat generation mapping relationship. The electrical-thermal coupling information set is a comprehensive dataset integrating local conductivity limitation information, abnormal current shunting path information, current-heat generation mapping relationship, and the delineation of abnormal high-temperature hazard areas. It is used to comprehensively characterize the electrothermal safety risks of water-damaged, scrapped batteries. Specifically, the system sets a temperature threshold (e.g., 60℃) or a temperature rise rate threshold (e.g., 2℃ / min), traverses all spatial nodes in the current-heat generation mapping relationship, marks continuous areas that meet the conditions as abnormal high-temperature hazard areas, and classifies them according to risk level (e.g., high-risk, medium-risk). For example, if the simulation shows that the predicted temperature at a bus connection will exceed 80℃ within 30 minutes, then this area and its surrounding 2cm range are classified as a Class A high-risk hazard area; if the predicted temperature rise in the middle region of an electrode is slower but the final temperature slightly exceeds the threshold, then it is classified as a Class B medium-risk hazard area. The final electrical-thermal coupling information set not only includes the spatial coordinates of each potential hazard area, but also links its causes (whether it is due to the decay of active materials or insulation failure) and evolution trend, providing a physical heat source basis for subsequent differentiation of thermal runaway by combining gas characteristics.

[0057] Example 6: In some embodiments, the process of constructing abnormal traffic routing path information further includes the following steps: Step 1: Based on the preferred penetration path, analyze the liquid accumulation area formed on the surface of the cell tabs and busbars after water intrusion to obtain local wetting status information; The preferred penetration path refers to the specific channel through which moisture enters the battery module from the location of the battery pack's sealing failure, as determined above. This typically includes structural weak points such as module edge gaps, connector interfaces, and the base of the tabs. Local wetting information refers to the non-uniform liquid distribution on the surface of metal components such as cell tabs and busbars after moisture penetrates along the aforementioned path, influenced by gravity, surface tension, and component geometry. Specifically, moisture does not uniformly cover the entire metal surface but tends to accumulate in low-lying areas or areas with significant capillary action, such as grooves, welds, and bolt connections, forming liquid accumulation zones. This information is obtained by simulating or detecting the wetting angle and flow trajectory of moisture on specific metal surfaces, characterizing the spatial residence characteristics of the electrolyte diluent at the microscale. For example, when moisture penetrates along the connector interface, due to the difference in oxide roughness on the surface of the busbar copper bus, moisture preferentially accumulates at the contact gap between the copper bus and the insulating support, forming a local liquid film with a depth of approximately 0.5 mm to 2 mm, while adjacent flat areas remain relatively dry. This refined analysis of the liquid accumulation area can pinpoint the high-incidence zones of subsequent electrochemical reactions, providing basic spatial data for constructing a conductive network model.

[0058] Step 2: Based on the local wetting state information and the accumulation information of oxidation and corrosion products, analyze the distribution morphology of the non-uniform conductive layer formed on the surface of the metal part due to salt deposition and electrolyte residue, and obtain the surface conductivity difference information. The information on the accumulation of oxidation and corrosion products refers to the deposition of oxides, hydroxides, and their complex salts on the metal surface after water intrusion and electrochemical reactions with metal components (such as copper, aluminum, and nickel). The information on differences in surface conductivity refers to the spatial distribution pattern of conductivity differences in different areas of the metal component surface due to the non-uniformity of local wetting and the randomness of corrosion product distribution. Specifically, in areas where liquid accumulates, residual electrolyte solutes (such as fluorides produced by the decomposition of lithium hexafluorophosphate) precipitate salts during water evaporation or concentration. These salts, together with metal corrosion products, form a non-uniform conductive layer with ionic conductivity. The thickness, density, and ion concentration of this conductive layer vary greatly at different locations, causing the local resistivity of the metal surface to no longer be a constant value but a function of spatial location. For example, in a liquid accumulation area on the surface of a busbar, due to long-term immersion leading to the accumulation of a mixture of verdigris (basic copper carbonate) and copper fluoride, the surface conductivity of this area may be 2 to 3 orders of magnitude higher than the surrounding dry area, forming a significant high-conductivity island. By analyzing the distribution morphology of this non-uniform conductive layer, we can identify micro-regions that are difficult to detect with the naked eye but whose electrical properties have undergone abrupt changes, thereby quantifying the conductive heterogeneity of the surface.

[0059] Step 3: Based on the surface conductivity difference information and the ion concentration change information, analyze the low impedance channel formed by the connection of the positive and negative electrodes and components with different potentials by the unexpected conductive medium to obtain the abnormal shunting path information; Among these, the information on changes in ion concentration can refer to the free-moving ions (such as Li) in the solution as water intrusion and electrolyte dilution occur. + PF6 - F -The dynamic spatial distribution data of the concentration of ions (e.g., ions) directly determines the volume resistivity of the medium. Abnormal shunting path information refers to the specific spatial trajectory of a low-impedance current channel connecting the positive and negative electrodes of the battery or components at different potentials (such as the high-voltage busbar and the casing), formed by the aforementioned non-uniform conductive layer (surface leakage) and high-ion-concentration solution (volume leakage). Specifically, the system performs three-dimensional superposition analysis of surface conductivity difference information and ion concentration change information to find the continuous path with the lowest resistivity. When a highly conductive surface deposited layer and a high-concentration ion solution are spatially connected and bridge two components with a potential difference, an abnormal shunting path is formed. This path is often not a straight line, but rather meanders along the area with the most severe corrosion, the richest liquid accumulation, and the densest salt deposition. For example, if a surface conductivity difference at a certain point on the positive side of the busbar is detected as indicating a high conductivity state, and the ion concentration at that point remains above 1 mol / L due to electrolyte residue, and this area is connected to the adjacent negative electrode support via a suspended electrolyte droplet, the algorithm will determine that there exists a low-impedance channel from the positive busbar through the surface deposition layer and the droplet bridging to the negative electrode support, with an equivalent impedance possibly as low as below 10 kΩ. The resulting abnormal shunt path information not only includes the start and end points of the channel but also describes the channel's geometry, cross-sectional size, and real-time impedance value, providing topological input for subsequent calculations of parasitic current magnitude and heat generation distribution.

[0060] Example 7: In some embodiments, the process of constructing the current-heat generation mapping relationship further includes the following steps: Step 1: Based on the information on localized limited conductivity, analyze the hindering effect of the increased internal resistance region caused by the reduction of active material on current flow, and obtain information on localized current changes. The information on localized conductivity limitation originates from the aforementioned analysis of electrode component decay, specifically characterizing the physical state within the battery caused by moisture intrusion leading to the loss of positive electrode active material and the collapse of the crystal structure. Localized current variation information refers to the density redistribution data that occurs when current flows within the battery module under this state. This information is calculated by substituting the characteristics of increased local internal resistance into a circuit simulation model or equivalent circuit network. Its function is to quantitatively identify high-impedance bottleneck regions where current cannot flow smoothly, and the current paths that are forced to detour or converge as a result. Specifically, when the proportion of active material decreases, the electronic conductivity and ion diffusion rate of this region decrease simultaneously, manifesting as an increase in localized ohmic and polarization resistance. For example, if the active lithium content in a certain cell region decreases by 20% due to hydrolysis, the equivalent internal resistance of this region may increase from the initial 2mΩ to 5mΩ, resulting in a 40% reduction in the normal charge / discharge current density flowing through this region, while the current density in adjacent healthy regions increases accordingly to maintain total current conservation.

[0061] Step 2: Based on the abnormal shunting path information, analyze the local resistance differences caused by the non-uniform conductive layer distribution along the low impedance channel to obtain the shunting energy change information; The abnormal shunt path information originates from the aforementioned analysis of insulation failure risk, specifically characterizing the unexpected low-resistance conductive bridging state formed by the accumulation of oxidation corrosion products and electrolyte dilution. The shunt energy change information refers to the additional heat dissipation data generated by the Joule effect when current flows through these abnormally low-resistance channels. This information is calculated by analyzing the spatial distribution of the non-uniform conductive layer and its corresponding resistance value, combined with the magnitude of the leakage current flowing through the path. Its function is to assess the potential for local temperature rise caused by parasitic currents resulting from insulation failure. Specifically, the salt deposit layer formed on the electrode or busbar surface after moisture intrusion has a low resistivity, forming a bypass channel in parallel with the normal load. For example, when an ionic conductive bridging path with an impedance of approximately 10kΩ is formed on the busbar surface, even in a quiescent state, a small potential difference will drive a continuous leakage current. If the current on this path reaches 50mA, continuous Joule heat power will be generated at this tiny contact point. This analysis can detect hidden heat sources caused by micro-short circuits, which are often located in internal gaps that are difficult to reach with conventional detection.

[0062] Step 3: Based on the local current change information and the shunt energy change information, analyze the spatial overlap area and heat superposition effect of the two on the electrode structure inside the battery module to obtain the current-heat generation mapping relationship. The current-heat generation mapping relationship is a multi-dimensional spatiotemporal data set used to describe the heat generation rate and temperature evolution trend at different locations inside the battery under the current electrochemical degradation state. This relationship is obtained by spatially registering and superimposing the aforementioned local current change field and the aforementioned shunt energy dissipation field in a three-dimensional battery structure model. Its function is to reveal the combined thermal runaway risk that cannot be reflected by a single mechanism, that is, to identify those dual high-risk areas where both current congestion and abnormal leakage exist. Specifically, when local current change information shows an abnormal current density convergence in a certain area, and this area happens to be a low-resistance corrosion zone indicated by abnormal shunt path information, the thermal effects of the two will be nonlinearly superimposed, causing the temperature rise rate at this point to be much higher than the predicted value when considering either factor alone. For example, if an electrode edge region is both a current convergence point caused by active material loss (heat generation increases by 30%) and the starting point of a leakage path formed by corrosion product accumulation (heat generation increases by 50%), the synergistic effect of the two may cause the actual heat generation power in this area to surge by more than 100%, rapidly exceeding the critical temperature for thermal runaway. This coupling analysis can more realistically simulate the thermal evolution process inside the battery, improve the accuracy and reliability of identifying potential high-temperature zones, and avoid overlooking the risk of sudden thermal runaway caused by the coupling of multiple factors.

[0063] Example 8: In some embodiments, based on the vehicle yard gas information set and combined with the electrical-thermal coupling information set, the exothermic gas generation characteristics caused by the reaction of residual electrolyte with water are distinguished from the normal aging gas evolution characteristics to obtain a thermal runaway differentiation information set. The method further includes the following steps: Step 1: The vehicle yard gas information set includes gas component concentration information and gas temperature information; The "vehicle yard gas information set" refers to the multi-dimensional data collection from environmental monitoring systems deployed in the storage area (vehicle yard) for accident-damaged and scrapped electric vehicles. Specifically, gas component concentration information includes real-time concentration values ​​of key gases such as volatile organic compounds (VOCs), hydrogen fluoride (HF), and hydrogen (H2). This data comes from electrochemical sensor arrays installed at different heights above the battery pack. Gas temperature information refers to the ambient temperature data corresponding to the gas concentration monitoring points, used to characterize the thermal state of the gas itself. The role of gas component concentration information is to capture characteristic products released by chemical reactions inside the battery, while the role of gas temperature information is to provide thermodynamic basis for subsequent judgment on whether the gas generation process is accompanied by severe exothermic reactions. For example, when an electrolyte hydrolysis reaction occurs in a certain area, the HF concentration in the gas component concentration information may surge from the background value of 0.1 ppm to 50 ppm in a short period of time, while the gas temperature information at that point shows a significant temperature rise trend; in contrast, normal aging gas evolution usually only shows a slow linear increase in H2 concentration, and the gas temperature does not fluctuate significantly. By acquiring the above two types of information, we can provide basic data support for distinguishing between sudden dangerous gas production and slow-changing safe gas evolution.

[0064] Step 2: Based on the gas component concentration information, analyze the instantaneous release concentration characteristics of volatile organic compounds and hydrogen fluoride gas to obtain gas evolution information; Gas evolution information refers to a dataset that quantitatively describes the release behavior of specific hazardous gas components over time. Volatile organic compounds (VOCs) and hydrogen fluoride are typical characteristic products of the violent hydrolysis reaction that occurs when lithium battery electrolytes (mainly composed of lithium hexafluorophosphate and organic carbonate solvents) come into contact with water. The specific process for analyzing instantaneous release concentration characteristics includes: calculating the rate of change of VOC and HF concentrations per unit time (i.e., concentration gradient) and identifying whether there are explosive growth peaks. If an exponential increase in HF concentration is detected in a short period of time, accompanied by a specific proportion of VOC release, it is judged to have a sudden characteristic; if the gas concentration changes slowly and is dominated by hydrogen, it is judged to have a slow-changing characteristic. This step aims to identify the reaction properties through chemical fingerprinting, thereby initially screening out high-risk gas generation events. For example, a threshold is set for an increase in HF concentration exceeding 10 ppm within 30 seconds. Once the monitoring data meets this condition, the system immediately marks this time period as an abnormal release window and generates gas evolution information including the release start time, peak concentration, and duration. This analysis, based on the instantaneous behavior of characteristic gases, can effectively eliminate interference from ambient background gases and pinpoint the chemical crisis caused by electrolyte leakage.

[0065] Step 3: Based on the gas temperature information and combined with the electrical-thermal coupling information set, analyze the spatial correspondence between gas temperature and the abnormal high temperature hazard area and the matching characteristics of the temperature rise rate to obtain the gas-generating heat source correlation information; The gas-generating heat source correlation information refers to a dataset that establishes a logical mapping relationship between the thermal state of the gas detected within the vehicle yard and the predicted hot spots inside the battery pack. The abnormal high-temperature hazard area originates from the high-risk physical locations inside the battery identified above based on electrochemical degradation and uneven current distribution. Analyzing spatial correspondence characteristics involves determining whether the monitoring point of the gas temperature sensor is located above the vertical projection of the abnormal high-temperature hazard area or on the airflow diffusion path; analyzing temperature rise rate matching characteristics involves comparing the consistency of the gas temperature rise slope with the simulated temperature rise slope of the hazard area on the time axis. Specifically, if an abnormal high-temperature hazard area will reach its temperature peak at time t, and the gas temperature at the monitoring point directly above it also shows a synchronous and rapid increase around time t, then a strong correlation is determined between the two. Through this dual verification mechanism, it can be confirmed that the observed abnormal gas indeed originates from the thermochemical reaction of this specific hazard area, rather than interference from other distant heat sources or environmental factors. For example, when a manifold corrosion area is identified as a potential area for abnormally high temperatures, if the gas sensor above it detects that the temperature is rising at a rate of 5°C / min, and this rate matches the heat generation rate of the short-circuit model inside the area, then a high-confidence gas-generating heat source association information is generated.

[0066] Step 4: Based on the gas evolution information and combined with the gas generation heat source correlation information, distinguish between the sudden exothermic gas generation region caused by residual electrolyte hydrolysis and the slowly changing gas generation region caused by electrode stable gas evolution, and obtain the thermal runaway differentiation information set. The thermal runaway differentiation information set is the final result of classifying the risk attributes of the space surrounding the battery pack, clearly identifying which areas are undergoing dangerous exothermic reactions and which areas are only in a normal aging and gas emission state. The differentiation process adopts a two-level judgment logic: the first level uses the burst characteristics of characteristic gases (VOC, HF) in the gas emission information to eliminate hydrogen emission that is simply produced by normal aging; the second level uses the spatiotemporal synchronicity in the gas generation heat source correlation information to further confirm whether the sudden gas generation is accompanied by localized violent exothermic reactions. Only areas that simultaneously meet the two conditions of instantaneous high-concentration release of characteristic gases and synchronous temperature rise with the temperature of the potential danger area are classified as sudden exothermic gas generation areas caused by residual electrolyte hydrolysis; conversely, if there is only slow gas release and no significant temperature rise correlation, it is classified as a slowly changing gas generation area caused by stable electrode gas evolution. The overall explanation of this step is that, through cross-verification of chemical and thermal characteristics, the qualitative identification of the precursors to thermal runaway is achieved. For example, at a given monitoring point, if a surge in HF concentration is detected but the gas temperature does not rise with the temperature of the potential hazard area (possibly due to leakage and diffusion from a distant location), or if the gas temperature rises but no characteristic gas is released (possibly due to external heating), it is not considered a sudden exothermic gas production area; only when both conditions are met is it identified as a high-risk area. The resulting thermal runaway differentiation information set not only identifies the location of the risk but also clarifies its nature, providing a decisive basis for developing differentiated emergency response strategies. This effectively avoids over-response due to misjudging normal aging gas evolution or safety accidents caused by omitting hydrolysis reactions.

[0067] Example 9: In some embodiments, the process of constructing gas-generating heat source association information further includes: Step 1: Based on the gas temperature information, analyze the temperature gradient distribution at different heights of the monitoring points to determine the vertical accumulation location of high-temperature gas in the parking lot space and obtain the gas spatial distribution information. The gas temperature information refers to the real-time temperature data collected by an array of temperature sensors deployed at different vertical heights within the battery storage facility. This step aims to utilize the physical principle that hot gases, being less dense than cold air, naturally rise, and to infer the spatial location of the heat source by analyzing temperature differences along the vertical direction. Specifically, the system first acquires data from monitoring points positioned at multiple levels, such as 0.5 meters, 1.5 meters, and 2.5 meters above the ground, and calculates the temperature difference between adjacent levels to construct a temperature gradient distribution model. When the temperature at a certain series of monitoring points exhibits a significant non-linear increasing trend with increasing height, it is determined that there is a vertical accumulation of high-temperature gas in that area. For example, if the temperature at 1.5 meters above a battery pack is 8°C higher than at 0.5 meters, and the temperature at 2.5 meters further increases to a peak, then the horizontal coordinate range where this peak is located is determined as the vertical accumulation location of the high-temperature gas. Through this multi-level gradient analysis, the interference of ground ambient temperature fluctuations can be effectively eliminated, and the core distribution area of ​​the hot gas mass generated by the internal reaction of the battery in three-dimensional space can be identified, thereby obtaining the spatial distribution information of the gas.

[0068] Step 2: Based on the electrical-thermal coupling information set and combined with the gas spatial distribution information, analyze the overlap relationship between the projection directly above the abnormal high temperature hazard area and the vertical gas accumulation position, and determine the synchronous relationship between the temperature rise slope of the hazard area and the rising rhythm of the gas temperature to obtain the gas-generating heat source correlation information.

[0069] The electrical-thermal coupling information set includes the spatial coordinates and predicted temperature rise curves of the abnormally high temperature hazard area inside the battery module identified above; the gas spatial distribution information provides the location of the externally observed hot gas mass. The core of this step is to establish a causal mapping between the internal hazard and the external phenomenon. Specifically, the system projects the abnormally high temperature hazard area inside the battery pack vertically upwards onto the vehicle space plane and calculates the percentage of overlap between the projected area and the determined vertical gas accumulation location. If the overlap exceeds a preset threshold (e.g., 80%), a preliminary spatial correlation is determined. Further, the system extracts the predicted temperature rise slope (i.e., the rate of temperature change per unit time) of the hazard area and compares it with the actual temperature rise rhythm of the vertical gas accumulation location in time. Only when the acceleration of the temperature rise in the internal hazard area and the sudden increase in the external gas temperature are highly synchronized on the time axis (e.g., the time deviation is less than 30 seconds) and the change trends are consistent, is it finally confirmed that the gas originates from the specific abnormally high temperature hazard area. For example, if a simulation shows that the temperature rise rate in a busbar micro-short-circuit region abruptly changes from 0.5℃ / min to 5℃ / min at time T0, and simultaneously the gas sensor directly above it detects a synchronously accelerated temperature rise at time T0+10s, then the two are determined to be strongly correlated. This dual verification mechanism of spatial overlap and temporal synchronization can eliminate misjudgments caused by interference from nearby batteries or environmental heat sources, constructing gas-generating heat source correlation information that reflects the true causal relationship.

[0070] Example 10: In some embodiments, a thermal runaway risk zoning identification strategy for accident-damaged electric vehicle batteries is generated and output based on a thermal runaway differentiation information set. The method further includes the following steps: Step 1: Based on the thermal runaway differentiation information set, analyze the boundary characteristics of the spatial distribution between the sudden exothermic gas production area and the slowly changing gas production area to obtain the risk source location information; The thermal runaway differentiation information set is the dataset obtained by distinguishing between the exothermic gas generation characteristics of residual electrolyte reacting with water and the gas evolution characteristics of normal aging. Sudden exothermic gas generation areas refer to the spatial regions where electrolyte leakage and violent hydrolysis occur due to water contact, accompanied by rapid temperature rise and the release of high concentrations of volatile organic compounds (VOCs) and hydrogen fluoride (HF). Slow-changing gas generation areas refer to the spatial regions where hydrogen or small amounts of organic solvent gases are slowly released during normal aging or slight corrosion of the battery. Boundary feature analysis refers to using image processing algorithms or spatial clustering algorithms to identify the interface between these two types of areas in three-dimensional space, determining the geometric contour of the transition from high-risk to low-risk. Risk source location information specifically includes the three-dimensional coordinates of the core hazard point, the volume of the hazardous area, and the relative position of the area to the physical structure of the battery module. For example, when monitoring data shows that the VOC concentration in the upper right corner of a battery pack surges within 30 seconds and the temperature rises synchronously, while the gas release in adjacent areas remains stable, the system identifies this rapidly changing interface as a boundary, thus locking the approximately 5cm × 5cm area in the upper right corner as a risk source. Through this detailed analysis of boundary features, the true thermal runaway trigger point can be identified, avoiding the generalization of the entire battery pack as a danger zone, and providing precise spatial anchors for subsequent graded disposal.

[0071] Step 2: Based on the risk source location information and the gas production heat source correlation information, analyze the differences in the critical conditions for thermal runaway triggering in different regions to obtain the classification judgment benchmark information; The information on gas-generating heat sources includes the spatial correspondence between gas temperature and areas with abnormally high internal temperatures within the battery, as well as the synchronicity of temperature rise rates. Differences in critical conditions can refer to the different temperature thresholds, gas concentration thresholds, or temperature rise rate thresholds required for uncontrollable thermal chain reactions to occur in areas of different risk levels. The classification benchmark information is a set of standards used to quantify risk levels, typically including a red high-risk benchmark, a yellow medium-risk benchmark, and a green low-risk benchmark. Specifically, for areas identified as core risk sources, the critical conditions are set more sensitively; for example, a temperature rise rate exceeding 1℃ / s or an HF concentration exceeding 50ppm is set as the trigger red line. For areas with only slow-changing gas evolution, the critical conditions are relatively lenient, possibly set as a sustained temperature above 60℃ without significant gas explosion. For example, if an area is identified as a sudden exothermic gas-generating area and spatially overlaps with an internal micro-short-circuit hotspot, its classification benchmark is set to the immediate response level, requiring any minute parameter fluctuations to be considered a precursor to loss of control; conversely, if it is only a slowly changing area, the benchmark is set to the observation level. By combining the location of the risk source with the correlation characteristics of the heat source, the system can dynamically adjust the alarm threshold of each area to ensure that the judgment criteria match the actual physical risks.

[0072] Step 3: Based on the tiered judgment benchmark information, formulate differentiated handling priorities and monitoring frequencies corresponding to different risk levels to obtain a preliminary control plan; The differentiated handling priority refers to the urgency ranking of intervention measures for areas with different risk levels, while the monitoring frequency refers to the time interval for data collection in a specific area. The preliminary control plan is a set of specific execution instructions based on the above priorities and frequencies. Specifically, for areas meeting the red high-risk benchmark, the handling priority is the highest, requiring immediate activation of forced cooling, power isolation, and preparation of fire extinguishing media, with the monitoring frequency set to a high-frequency real-time mode (e.g., once per second or per minute); for areas meeting the yellow medium-risk benchmark, the handling priority is medium, requiring measures such as enhanced ventilation and reduced storage density, with the monitoring frequency set to a regular inspection mode (e.g., once per hour); for areas meeting the green low-risk benchmark, the handling priority is low, maintaining normal storage conditions, with the monitoring frequency set to a low-frequency spot check mode (e.g., once per day). For example, for the aforementioned locked busbar micro-short circuit high-risk area, the plan generation module will automatically generate instructions to activate local air cooling and report thermal imaging data every 5 minutes; while for electrode areas with no surrounding abnormalities, instructions for daily routine checks will be generated. This differentiated configuration approach allows limited emergency resources and monitoring computing power to be concentrated on the most critical areas, avoiding resource waste or response delays caused by a one-size-fits-all approach.

[0073] Step 4: Based on the preliminary control plan and the areas with abnormal high temperature hazards, plan the inspection route and safety isolation range, and generate and output the risk zone identification strategy for thermal runaway of electric vehicle batteries that have been scrapped due to accidents.

[0074] The abnormally high temperature hazard area refers to the physical space within the battery module where overheating risk exists, as defined in the aforementioned electrical-thermal coupling analysis. Planning the inspection path refers to designing the optimal movement route for on-site personnel or inspection robots that covers all monitoring points while avoiding high-risk radiation areas. The safety isolation range refers to the radius of the area requiring physical barriers or prohibited entry, calculated based on thermal radiation and gas diffusion models, centered on the risk source. Generating and outputting the system involves integrating the aforementioned paths, ranges, and control measures into a visualized electronic map, instruction list, or multimedia guidance information, and sending it to the terminal device. Specifically, the system will mark a red high-risk area with a prohibited entry circle (e.g., with a radius of 2 meters) in the digital twin model and plan a serpentine inspection path around it, ensuring that inspection personnel can observe medium- and low-risk areas from a safe distance. For example, the output strategy can be represented as a thermal runaway risk zone identification map overlaid on the parking lot floor plan. Different colors mark each risk zone, dashed lines indicate recommended inspection routes, and text bubbles indicate specific handling requirements for each point (e.g., Zone A: Do not approach, remote monitoring; Zone B: Wear a gas mask, hourly inspection). This spatialized strategy output achieves a direct transformation from abstract data analysis to concrete on-site actions, improving the organizational efficiency and personnel safety at the accident scene.

[0075] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying thermal runaway risk zones in batteries of accident-damaged and scrapped electric vehicles, characterized in that, include: Obtain a set of information on vehicles scrapped due to water damage, and based on this set of information, analyze the internal electrochemical degradation characteristics of lithium batteries after water damage accidents to obtain an electrochemical information set. Based on the electrochemical information set, the coupling relationship between uneven current distribution caused by electrochemical degradation and local heat generation is analyzed to obtain the electro-thermal coupling information set; Obtain the vehicle yard gas information set, and based on the vehicle yard gas information set and the electrical-thermal coupling information set, distinguish between the exothermic gas generation characteristics caused by the reaction of residual electrolyte with water and the normal aging gas evolution characteristics to obtain the thermal runaway differentiation information set. Based on the thermal runaway differentiation information set, a risk zone identification strategy for thermal runaway of electric vehicle batteries that have been scrapped due to accidents is generated and output.

2. The method according to claim 1, characterized in that, Based on the information set of water-damaged scrapped vehicles, the internal electrochemical degradation characteristics of lithium batteries after water-damage accidents are analyzed to obtain an electrochemical information set, including: The information set of water-damaged scrapped vehicles includes vehicle battery model, duration of water immersion, and battery pack sealing information; Based on the vehicle battery model and the duration of wading, the characteristics of active material loss caused by the chemical reaction between water and electrode materials and electrolyte are analyzed to obtain electrode component attenuation information. Based on the battery pack sealing information and the duration of water immersion, the characteristics of local electrochemical corrosion and conductive path formation caused by water intrusion at the sealing failure point are analyzed to obtain insulation failure risk information. The electrochemical information set is constructed by integrating the electrode component decay information and the insulation failure risk information.

3. The method according to claim 2, characterized in that, The process of constructing the electrode component attenuation information includes: Based on the vehicle battery model, the chemical stability of the positive electrode active material and electrolyte inside the cell was analyzed to determine the information of active sites that are prone to hydrolysis. Based on the duration of immersion in water, analyze the lithium ion consumption and crystal structure collapse information during the side reaction process between water infiltration and the active site information; Based on the lithium-ion consumption information and the crystal structure collapse information, the change in the proportion of effective materials that can participate in charge-discharge cycles in the electrode layer is analyzed to obtain the electrode component decay information.

4. The method according to claim 2, characterized in that, The process of constructing the insulation failure risk information includes: Based on the battery pack sealing information, locate the edge gaps and connector interfaces of the battery module to determine the preferred penetration path of moisture intrusion. Based on the duration of immersion, analyze the accumulation of oxidation and corrosion products caused by water seeping along the preferred penetration path on the surface of the battery cell tabs and busbars, as well as the changes in ion concentration after electrolyte dilution. Based on the information on the accumulation of oxidation and corrosion products, combined with the information on changes in ion concentration, the ion conductivity bridging state formed on the surface of the metal component is analyzed to obtain the information on the risk of insulation failure.

5. The method according to claim 4, characterized in that, Based on the electrochemical information set, the relationship between uneven current distribution caused by electrochemical degradation and local heat generation coupling is analyzed to obtain an electro-thermal coupling information set, including: Based on the electrode component attenuation information, the characteristics of increased local internal resistance caused by the reduction of active material are analyzed to obtain information on local conductivity limitation. Based on the insulation failure risk information, the abnormal parallel leakage path characteristics caused by the conductive bridging state are analyzed to obtain abnormal shunt path information; Based on the local conductivity restriction information and the abnormal current shunting path information, the density concentration of current in the restricted area and the energy dissipation characteristics on the current shunting path are analyzed to obtain the current-heat generation mapping relationship. Based on the current-heat generation mapping relationship, the abnormal high temperature potential area inside the battery module is divided, and the electrical-thermal coupling information set is constructed.

6. The method according to claim 5, characterized in that, The process of constructing the abnormal traffic routing path information includes: Based on the preferred penetration path, the liquid accumulation area formed on the surface of the cell tabs and busbars after water intrusion is analyzed to obtain local wetting status information. Based on the local wetting state information and the accumulation information of oxidation corrosion products, the distribution morphology of the non-uniform conductive layer formed on the surface of the metal component due to salt deposition and electrolyte residue is analyzed to obtain surface conductivity difference information. Based on the surface conductivity difference information and the ion concentration change information, the path of the low impedance channel formed by the connection of the positive and negative electrodes and components with different potentials by the unexpected conductive medium is analyzed to obtain the abnormal shunt path information.

7. The method according to claim 5, characterized in that, The process of constructing the current-heat generation mapping relationship includes: Based on the aforementioned information on limited local conductivity, the hindering effect of the region with increased internal resistance due to the reduction of active material on current flow is analyzed, and information on local current changes is obtained. Based on the abnormal shunting path information, the local resistance difference caused by the non-uniform conductive layer distribution along the low impedance channel is analyzed to obtain the shunting energy change information. Based on the local current change information and the shunt energy change information, the spatial overlap area and heat superposition effect of the two on the electrode structure inside the battery module are analyzed to obtain the current-heat generation mapping relationship.

8. The method according to claim 5, characterized in that, Based on the vehicle yard gas information set and the electrical-thermal coupling information set, the exothermic gas generation characteristics caused by the reaction of residual electrolyte with water are distinguished from the normal aging gas evolution characteristics, resulting in a thermal runaway differentiation information set, including: The gas information set for the parking lot includes gas component concentration information and gas temperature information; Based on the gas component concentration information, the instantaneous release concentration characteristics of volatile organic compounds and hydrogen fluoride gas are analyzed to obtain gas evolution information; Based on the gas temperature information and combined with the electrical-thermal coupling information set, the spatial correspondence and temperature rise rate matching characteristics between the gas temperature and the abnormal high temperature hazard area are analyzed to obtain the gas-generating heat source correlation information. Based on the gas evolution information and the gas generation heat source correlation information, the sudden exothermic gas generation region caused by residual electrolyte hydrolysis and the slowly changing gas generation region caused by electrode stable gas evolution are distinguished, thus obtaining the thermal runaway differentiation information set.

9. The method according to claim 8, characterized in that, The process of constructing the gas-generating heat source association information includes: Based on the gas temperature information, the temperature gradient distribution at different heights of the monitoring points is analyzed to determine the vertical accumulation location of high-temperature gas in the parking lot space, thus obtaining gas spatial distribution information. Based on the electrical-thermal coupling information set and the gas spatial distribution information, the overlap relationship between the projection directly above the abnormal high temperature hazard area and the vertical gas accumulation position is analyzed, and the synchronous relationship between the temperature rise slope of the hazard area and the rising rhythm of the gas temperature is determined, thereby obtaining the gas-generating heat source association information.

10. The method according to claim 9, characterized in that, The step of generating and outputting a risk zoning identification strategy for thermal runaway of electric vehicle batteries in the event of an accident and subsequent scrapping, based on the thermal runaway differentiation information set, includes: Based on the thermal runaway differentiation information set, the boundary characteristics of the spatial distribution of sudden exothermic gas production areas and slowly changing gas production areas are analyzed to obtain risk source location information. Based on the risk source location information and the gas-generating heat source correlation information, the differences in the critical conditions for thermal runaway triggering in different regions are analyzed to obtain the classification judgment benchmark information. Based on the aforementioned classification and judgment criteria, differentiated handling priorities and monitoring frequencies corresponding to different risk levels are formulated to obtain preliminary control plans; Based on the preliminary control plan and the abnormal high temperature hazard area, the inspection route and safety isolation range are planned, and the thermal runaway risk zone identification strategy for the accident-damaged electric vehicle battery is generated and output.

Images