Thermal safety simulation evaluation method and device for all-solid-state battery

Abstract

The application provides a thermal safety simulation evaluation method and device of an all-solid-state battery, and is applied to the technical field of all-solid-state batteries. The method comprises obtaining cell data of a target all-solid-state battery and thermal box data corresponding to the target all-solid-state battery. According to thermal box working condition data in the cell data and the thermal box data, thermal safety prediction data of the target all-solid-state battery under thermal box test conditions is determined. The thermal safety prediction data is dynamically corrected according to thermal box monitoring data in the thermal box data, and thermal safety correction data reflecting actual thermal safety responses of the target all-solid-state battery in a thermal box test process is obtained. According to the thermal safety correction data, a thermal safety evaluation result of the target all-solid-state battery and a thermal safety optimization direction corresponding to the thermal safety evaluation result are determined, and the problems that the thermal safety state of the all-solid-state battery cannot be accurately evaluated and the corresponding optimization direction cannot be given in a traditional thermal box test process can be solved.

Description

Technical Field

[0001] This application relates to the field of all-solid-state battery technology, and in particular to a method and apparatus for thermal safety simulation evaluation of all-solid-state batteries. Background Technology

[0002] As power batteries develop towards higher energy density and higher safety, all-solid-state batteries have attracted widespread attention due to their use of solid electrolytes. For all-solid-state batteries under high charge conditions, hot-box testing is an important way to evaluate their thermal safety performance. However, traditional hot-box testing usually judges whether a cell meets safety requirements based on cell surface temperature, voltage changes, and the state after the test. But the internal material reactions, solid-solid interface heat release, core temperature rise, and high-temperature self-discharge processes of all-solid-state batteries are all interconnected, and it is difficult to accurately reflect the actual thermal safety response inside the cell based solely on external monitoring data. At the same time, there is a scale difference between the material thermal analysis results and the cell-level hot-box test results, and the test results are also easily affected by heating conditions, insulation conditions, heat transfer conditions, and changes in charge state.

[0003] Therefore, there is an urgent need for a thermal safety simulation and evaluation method for all-solid-state batteries to solve the problems of difficulty in accurately evaluating the thermal safety status of all-solid-state batteries and the inability to provide corresponding optimization directions in the traditional hot box test process. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for thermal safety simulation evaluation of all-solid-state batteries, which solves the problems of difficulty in accurately evaluating the thermal safety status of all-solid-state batteries and the inability to provide corresponding optimization directions in the traditional hot box test process.

[0005] In a first aspect, embodiments of this application provide a method for thermal safety simulation and evaluation of all-solid-state batteries. The method includes: acquiring cell data of a target all-solid-state battery and corresponding thermal chamber data; determining thermal safety prediction data of the target all-solid-state battery under thermal chamber test conditions based on thermal chamber operating condition data from the cell data and thermal chamber data; dynamically correcting the thermal safety prediction data based on thermal chamber monitoring data from the thermal chamber data to obtain corrected thermal safety data reflecting the actual thermal safety response of the target all-solid-state battery during thermal chamber testing. The dynamic correction at least considers capacity consumption caused by chemical self-discharge and / or side reactions under high-temperature conditions. Based on the corrected thermal safety data, determining the thermal safety evaluation result of the target all-solid-state battery and the corresponding thermal safety optimization direction.

[0006] The thermal safety simulation and evaluation method for all-solid-state batteries provided in this application acquires cell data and corresponding thermal chamber data of the target all-solid-state battery. Based on the thermal chamber operating condition data in the cell data and thermal chamber data, it determines the thermal safety prediction data of the target all-solid-state battery under thermal chamber test conditions. The thermal safety prediction data is then corrected based on the thermal chamber monitoring data in the thermal chamber data to obtain corrected thermal safety data that reflects the actual thermal safety response of the target all-solid-state battery during the thermal chamber test. Finally, the thermal safety evaluation result of the target all-solid-state battery and the corresponding thermal safety optimization direction are determined based on the corrected thermal safety data. This allows for the comprehensive consideration of cell characteristics, thermal chamber conditions, and actual monitoring responses during the test under thermal chamber test conditions, enabling simulation prediction and dynamic correction of the thermal safety status of the all-solid-state battery. This improves the accuracy of the thermal safety evaluation results and provides a basis for improving material thermal stability, optimizing cell structure heat dissipation, adjusting thermal chamber conditions, or adjusting safety thresholds.

[0007] One possible implementation involves determining the thermal safety prediction data of the target all-solid-state battery under hot box testing conditions based on hot box condition data from cell data and hot box data. This includes: determining the material reaction parameters, cell structural parameters, and initial state of charge (SPC) parameters of the target all-solid-state battery based on cell data; determining the heating conditions, insulation conditions, and boundary heat transfer conditions under hot box testing conditions based on hot box condition data; determining the material reaction heat source items of the target all-solid-state battery during hot box testing based on the material reaction parameters and initial SPC parameters; determining the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under hot box testing conditions based on the cell structural parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source items; and generating thermal safety prediction data based on the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data.

[0008] One possible implementation involves determining the material reaction heat source terms of the target all-solid-state battery during hot-box testing, based on material reaction parameters and initial state of charge parameters. This includes: determining the reaction kinetic parameters and enthalpy parameters corresponding to each material reaction heat source in the target all-solid-state battery, based on the material reaction parameters. The reaction kinetic parameters include activation energy, pre-exponential factor, and reaction order. The reaction triggering conditions corresponding to each material reaction heat source are corrected based on the initial state of charge parameters. The reaction conversion rate and instantaneous heat generation rate of each material reaction heat source during hot-box testing are determined based on the corrected reaction triggering conditions, reaction kinetic parameters, and enthalpy parameters. Finally, material reaction heat source terms are generated based on the reaction conversion rate and instantaneous heat generation rate of each material reaction heat source.

[0009] One possible implementation involves determining the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under hot-box testing conditions, based on cell structural parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source terms. This includes: determining the cell thermal response conditions of the target all-solid-state battery under hot-box testing conditions based on the cell structural parameters, heating conditions, insulation conditions, and boundary heat transfer conditions; and incorporating the material reaction heat source terms into the heat balance calculation equation constructed based on the cell thermal response conditions to obtain the core temperature prediction data, multi-heat source heat generation prediction data, and effective state of charge prediction data of the target all-solid-state battery during hot-box testing.

[0010] One possible implementation involves incorporating material reaction heat source terms into the thermal balance calculation equation based on the cell's thermal response conditions. This yields predicted core temperature, multi-heat source heat generation, and effective state of charge (SPC) data for the target all-solid-state battery during hot-box testing. This includes: determining the heat capacity parameters, thermal conduction parameters, hot-box heating conditions, and heat dissipation conditions of the target all-solid-state battery during hot-box testing based on the cell's thermal response conditions; incorporating material reaction heat source terms into the thermal balance calculation equation and determining the predicted core temperature based on the heat capacity parameters, thermal conduction parameters, hot-box heating conditions, and heat dissipation conditions; determining multi-heat source heat generation prediction data based on the instantaneous heat generation rate and reaction conversion rate corresponding to each material reaction heat source in the material reaction heat source term; and determining the predicted SPC data for the target all-solid-state battery during hot-box testing based on the predicted core temperature and multi-heat source heat generation data.

[0011] One possible implementation involves dynamically correcting the thermal safety prediction data based on the thermal box monitoring data from the thermal box data, resulting in corrected thermal safety data reflecting the actual thermal safety response of the target all-solid-state battery during the thermal box test. This includes: inverting and correcting the core temperature prediction data in the thermal safety prediction data based on the thermal box ambient temperature and cell surface temperature from the thermal box monitoring data to obtain corrected core temperature data; determining the high-temperature self-discharge characteristics of the target all-solid-state battery during the thermal box test based on the terminal voltage change data from the thermal box monitoring data; correcting the effective state of charge (SPC) prediction data in the thermal safety prediction data based on the high-temperature self-discharge characteristics to obtain corrected SPC data; correcting the multi-heat source heat generation prediction data in the thermal safety prediction data based on the corrected core temperature data and the corrected SPC data to obtain corrected multi-heat source heat generation data; and determining the corrected thermal safety data reflecting the actual thermal safety response of the target all-solid-state battery during the thermal box test based on the corrected core temperature data, corrected SPC data, and corrected multi-heat source heat generation data.

[0012] One possible implementation involves correcting the predicted state of charge (SOC) data in the thermal safety prediction data based on high-temperature self-discharge characteristics to obtain corrected SOC data. This includes: determining the voltage decay rate of the target all-solid-state battery during the heating and heat preservation stages based on terminal voltage change data; determining the capacity loss caused by side reactions during the hot-box test based on the voltage decay rate; correcting the predicted SOC data based on the capacity loss caused by side reactions to obtain corrected SOC data; and generating a diagnostic result indicating that the current hot-box test results do not meet the representativeness of the target SOC when the corrected SOC data is determined to be lower than the representative threshold corresponding to the target SOC.

[0013] One possible implementation involves determining the thermal safety assessment results of the target all-solid-state battery based on thermal safety correction data. This includes: determining the heat generation and dissipation of the target all-solid-state battery during the hot chamber test based on the thermal safety correction data; determining the heat generation-to-dissipation ratio of the target all-solid-state battery based on the heat generation and dissipation; determining the core temperature margin, change in effective state of charge, and material reaction process of the target all-solid-state battery based on the thermal safety correction data; and determining the thermal safety status level, thermal runaway risk level, hot chamber pass probability, and remaining safe time of the target all-solid-state battery based on the heat generation-to-dissipation ratio, core temperature margin, change in effective state of charge, and material reaction process.

[0014] One possible implementation of the thermal safety simulation and evaluation method for all-solid-state batteries provided in this application embodiment further includes: determining the risk triggering time of the target all-solid-state battery during the hot chamber test process based on thermal safety correction data; determining the window heat generation contribution data of each material reaction heat source within a preset time window before the risk triggering time based on the thermal safety correction data; determining the cumulative heat generation contribution data of each material reaction heat source during the hot chamber test process based on the thermal safety correction data; determining the dominant heat source, main heat release path, and heat source contribution ranking when the target all-solid-state battery experiences a thermal safety risk based on the window heat generation contribution data and the cumulative heat generation contribution data; and determining the heat source location evaluation result of the target all-solid-state battery based on the risk triggering time, dominant heat source, main heat release path, and heat source contribution ranking.

[0015] One possible implementation involves determining the thermal safety optimization direction corresponding to the thermal safety assessment results based on thermal safety correction data. This includes: determining the thermal safety threshold boundary data corresponding to the target all-solid-state battery based on the cell data and thermal chamber data of the target all-solid-state battery; determining the thermal chamber pass assessment result and thermal runaway risk assessment result of the target all-solid-state battery based on the deviation between the thermal safety correction data and the thermal safety threshold boundary data; and determining the thermal chamber pass assessment result and thermal runaway risk assessment result of the target all-solid-state battery based on the abnormal parameters corresponding to the deviation if the thermal chamber pass assessment result does not meet the preset pass requirements. The thermal safety optimization direction may include one or more of the following: improving material thermal stability, improving cell structure heat dissipation capacity, adjusting thermal chamber operating conditions, and adjusting battery management system safety thresholds.

[0016] Secondly, embodiments of this application provide a thermal safety simulation and evaluation device for all-solid-state batteries, the device comprising: an acquisition module, a determination module, and a correction module.

[0017] The acquisition module is used to acquire the cell data of the target all-solid-state battery and the corresponding thermal box data of the target all-solid-state battery.

[0018] The determination module is used to determine the thermal safety prediction data of the target all-solid-state battery under the hot box test conditions based on the hot box condition data in the cell data and hot box data.

[0019] The correction module dynamically corrects the thermal safety prediction data based on the thermal chamber monitoring data in the thermal chamber data, obtaining thermal safety correction data that reflects the actual thermal safety response of the target all-solid-state battery during the thermal chamber test. The dynamic correction takes into account at least the capacity loss caused by chemical self-discharge and / or side reactions under high-temperature conditions.

[0020] The determination module is also used to determine the thermal safety assessment results of the target all-solid-state battery and the corresponding thermal safety optimization direction based on the thermal safety correction data.

[0021] One possible implementation includes a module used to determine the thermal safety prediction data of the target all-solid-state battery under hot box test conditions based on hot box condition data from cell data and hot box data. Specifically, this module is used to: determine the material reaction parameters, cell structure parameters, and initial state of charge parameters corresponding to the target all-solid-state battery based on the cell data; determine the heating conditions, insulation conditions, and boundary heat transfer conditions under the hot box test conditions based on the hot box condition data; determine the material reaction heat source items of the target all-solid-state battery during the hot box test based on the material reaction parameters and initial state of charge parameters; determine the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under the hot box test conditions based on the cell structure parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source items; and generate thermal safety prediction data based on the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data.

[0022] One possible implementation involves a module used to determine the material reaction heat source terms of the target all-solid-state battery during hot-box testing, based on material reaction parameters and initial state of charge parameters. Specifically, this module is used to: determine the reaction kinetic parameters and enthalpy parameters corresponding to each material reaction heat source in the target all-solid-state battery, based on the material reaction parameters. The reaction kinetic parameters include activation energy, pre-exponential factor, and reaction order. The reaction triggering conditions corresponding to each material reaction heat source are corrected based on the initial state of charge parameters. The reaction conversion rate and instantaneous heat generation rate of each material reaction heat source are determined during hot-box testing based on the corrected reaction triggering conditions, reaction kinetic parameters, and enthalpy parameters. Finally, material reaction heat source terms are generated based on the reaction conversion rate and instantaneous heat generation rate of each material reaction heat source.

[0023] One possible implementation involves a module that, based on cell structural parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source terms, determines the predicted core temperature, predicted state of charge (SOC), and predicted multi-heat source heat generation data of the target all-solid-state battery under hot-box testing conditions. Specifically, this module determines the cell thermal response conditions of the target all-solid-state battery under hot-box testing conditions based on these parameters. The material reaction heat source terms are then incorporated into the thermal balance calculation equation constructed based on the cell thermal response conditions to obtain the predicted core temperature, predicted multi-heat source heat generation data, and predicted SOC data of the target all-solid-state battery during the hot-box testing process.

[0024] One possible implementation involves a module used to determine the core temperature prediction data, multi-heat source generation prediction data, and effective state of charge prediction data of the target all-solid-state battery during the hot box test, when incorporating material reaction heat source terms into the thermal balance calculation equation constructed based on the cell thermal response conditions. Specifically, this module is used to: determine the heat capacity parameters, heat conduction parameters, hot box heating conditions, and heat dissipation conditions of the target all-solid-state battery during the hot box test, based on the cell thermal response conditions; incorporate the material reaction heat source terms into the thermal balance calculation equation and determine the core temperature prediction data based on the heat capacity parameters, heat conduction parameters, hot box heating conditions, and heat dissipation conditions; determine the multi-heat source generation prediction data based on the instantaneous heat generation rate and reaction conversion rate corresponding to each material reaction heat source in the material reaction heat source terms; and determine the effective state of charge prediction data of the target all-solid-state battery during the hot box test based on the core temperature prediction data and the multi-heat source generation prediction data.

[0025] One possible implementation involves a correction module that dynamically corrects the thermal safety prediction data based on the thermal box monitoring data in the thermal box data. Specifically, this correction module performs the following steps: It performs an inversion correction on the core temperature prediction data in the thermal safety prediction data based on the thermal box ambient temperature and cell surface temperature in the thermal box monitoring data to obtain core temperature correction data. It then determines the high-temperature self-discharge characteristics of the target all-solid-state battery during the thermal box test based on the terminal voltage change data in the thermal box monitoring data. Based on the high-temperature self-discharge characteristics, it corrects the effective state of charge (SPC) prediction data in the thermal safety prediction data to obtain SPC correction data. Finally, it corrects the multi-heat source heat generation prediction data in the thermal safety prediction data based on the core temperature correction data and the SPC correction data to obtain multi-heat source heat generation correction data. Finally, it determines the thermal safety correction data reflecting the actual thermal safety response of the target all-solid-state battery during the thermal box test based on the core temperature correction data, SPC correction data, and multi-heat source heat generation correction data.

[0026] One possible implementation involves a correction module that corrects the predicted state of charge (SOC) data in the thermal safety prediction data based on high-temperature self-discharge characteristics. Specifically, when obtaining corrected SOC data, the module: determines the voltage decay rate of the target all-solid-state battery during the heating and heat preservation stages based on terminal voltage change data; determines the capacity loss caused by side reactions during the hot-box test based on the voltage decay rate; corrects the predicted SOC data based on the capacity loss caused by side reactions, obtaining corrected SOC data; and, if the corrected SOC data is determined to be lower than the representative threshold corresponding to the target SOC, generates a diagnostic result indicating that the current hot-box test result does not meet the representativeness of the target SOC.

[0027] One possible implementation involves a module used to determine the thermal safety assessment results of the target all-solid-state battery based on thermal safety correction data. Specifically, this module is used to: determine the heat generation and dissipation of the target all-solid-state battery during the hot chamber test based on the thermal safety correction data; determine the heat generation-to-dissipation ratio of the target all-solid-state battery based on the heat generation and dissipation; determine the core temperature margin, change in effective state of charge, and material reaction process of the target all-solid-state battery based on the thermal safety correction data; and determine the thermal safety status level, thermal runaway risk level, hot chamber pass probability, and remaining safe time of the target all-solid-state battery based on the heat generation-to-dissipation ratio, core temperature margin, change in effective state of charge, and material reaction process.

[0028] In one possible implementation, the thermal safety simulation and evaluation device for all-solid-state batteries provided in this application embodiment is further used to: determine the risk triggering time of the target all-solid-state battery during the hot chamber test process based on thermal safety correction data; determine the window heat generation contribution data of each material reaction heat source within a preset time window before the risk triggering time based on the thermal safety correction data; determine the cumulative heat generation contribution data of each material reaction heat source during the hot chamber test process based on the thermal safety correction data; determine the dominant heat source, main heat release path, and heat source contribution ranking when the target all-solid-state battery experiences a thermal safety risk based on the window heat generation contribution data and the cumulative heat generation contribution data; and determine the heat source location evaluation result of the target all-solid-state battery based on the risk triggering time, dominant heat source, main heat release path, and heat source contribution ranking.

[0029] One possible implementation involves a module that, when determining the thermal safety optimization direction corresponding to the thermal safety assessment results based on thermal safety correction data, specifically: determining the thermal safety threshold boundary data corresponding to the target all-solid-state battery based on the cell data and thermal chamber data of the target all-solid-state battery; determining the thermal chamber pass assessment result and thermal runaway risk assessment result of the target all-solid-state battery based on the deviation between the thermal safety correction data and the thermal safety threshold boundary data; and determining the thermal safety optimization direction based on the abnormal parameters corresponding to the deviation if the thermal chamber pass assessment result does not meet the preset pass requirements. The thermal safety optimization direction includes one or more of the following: improving material thermal stability, improving cell structure heat dissipation capacity, adjusting thermal chamber operating conditions, and adjusting the safety threshold of the battery management system.

[0030] Thirdly, embodiments of this application provide a thermal safety simulation and evaluation device for all-solid-state batteries. This device has the function of implementing the thermal safety simulation and evaluation method for all-solid-state batteries as described in the first aspect or any possible implementation thereof. This function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described function.

[0031] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed on a computer, enable the computer to perform the thermal safety simulation evaluation method for all-solid-state batteries described in the first aspect or any possible implementation thereof.

[0032] Fifthly, embodiments of this application provide a computer program product containing instructions that, when run on a computer, enable the computer to execute the thermal safety simulation and evaluation method for all-solid-state batteries described in the first aspect or any possible implementation thereof.

[0033] The technical effects of any of the design methods in aspects two through five can be found in aspect one or in different possible implementations of aspect one, and will not be repeated here. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific 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 from these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating a thermal safety simulation evaluation method for all-solid-state batteries provided in this application embodiment; Figure 2 A schematic diagram of a thermal safety simulation and evaluation device for an all-solid-state battery provided in this application embodiment; Figure 3 This is a system architecture diagram of a thermal safety simulation and evaluation system for an all-solid-state battery provided in an embodiment of this application. Detailed Implementation

[0036] 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 embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0037] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0038] In related technologies, when evaluating the thermal safety of all-solid-state batteries, thermal chamber testing is typically used to obtain the temperature, voltage, and post-test state of the cells under high-temperature conditions, and to determine whether the cells meet thermal safety requirements. However, during thermal chamber testing, the internal thermal safety response of all-solid-state batteries is affected not only by the heating, insulation, and boundary heat transfer conditions of the thermal chamber, but also by factors such as the exothermic reaction of cell materials, changes in core temperature, and changes in effective state of charge. Especially under high charge conditions, high-temperature self-discharge or capacity consumption due to side reactions may occur inside the battery, causing changes in the actual effective state of charge of the cell during testing. Furthermore, the surface temperature of the cell cannot fully reflect the internal core temperature and the exothermic reaction of materials. Therefore, relying solely on external monitoring data from thermal chamber testing or the pass / fail results after the test can easily lead to biases in judging the actual thermal safety status of all-solid-state batteries and makes it difficult to further determine the direction for optimizing subsequent materials, structures, or testing conditions.

[0039] Based on this, embodiments of this application provide a method and apparatus for thermal safety simulation and evaluation of all-solid-state batteries. The method includes: acquiring cell data of the target all-solid-state battery and corresponding thermal chamber data; determining thermal safety prediction data of the target all-solid-state battery under thermal chamber test conditions based on thermal chamber operating condition data from the cell data and thermal chamber data; correcting the thermal safety prediction data based on thermal chamber monitoring data from the thermal chamber data to obtain corrected thermal safety data reflecting the actual thermal safety response of the target all-solid-state battery during thermal chamber testing; and determining the thermal safety evaluation result of the target all-solid-state battery and the corresponding thermal safety optimization direction based on the corrected thermal safety data.

[0040] The thermal safety simulation and evaluation method for all-solid-state batteries provided in this application acquires cell data and corresponding thermal chamber data of the target all-solid-state battery. Based on the thermal chamber operating condition data in the cell data and thermal chamber data, it determines the thermal safety prediction data of the target all-solid-state battery under thermal chamber test conditions. The thermal safety prediction data is then corrected based on the thermal chamber monitoring data in the thermal chamber data to obtain corrected thermal safety data that reflects the actual thermal safety response of the target all-solid-state battery during the thermal chamber test. Finally, the thermal safety evaluation result of the target all-solid-state battery and the corresponding thermal safety optimization direction are determined based on the corrected thermal safety data. This allows for the comprehensive consideration of cell characteristics, thermal chamber conditions, and actual monitoring responses during the test under thermal chamber test conditions, enabling simulation prediction and dynamic correction of the thermal safety status of the all-solid-state battery. This improves the accuracy of the thermal safety evaluation results and provides a basis for improving material thermal stability, optimizing cell structure heat dissipation, adjusting thermal chamber conditions, or adjusting safety thresholds.

[0041] The methods provided in the embodiments of this application will now be described in conjunction with the specific accompanying drawings.

[0042] On the one hand, embodiments of this application provide a thermal safety simulation evaluation method for all-solid-state batteries, which may include the following steps.

[0043] S101, acquire the cell data of the target all-solid-state battery, and the corresponding thermal box data of the target all-solid-state battery.

[0044] Specifically, when conducting thermal safety simulation evaluations of target all-solid-state batteries, the target all-solid-state battery to be evaluated can be identified first, and cell data and thermal chamber data corresponding to the target all-solid-state battery can be obtained. Cell data is used to characterize the material properties, structural characteristics, and pre-test state of the target all-solid-state battery itself. Thermal chamber data is used to characterize the thermal chamber test conditions corresponding to the target all-solid-state battery and the actual monitoring response during the thermal chamber test.

[0045] By linking cell data and thermal chamber data under the same target all-solid-state battery, subsequent thermal safety prediction, thermal safety correction, and thermal safety assessment can all be based on the same cell object and the same thermal chamber testing process, avoiding deviations in assessment results due to inconsistencies in material batches, cell structures, or testing conditions.

[0046] Specifically, cell data can include material thermal reaction test data, cell structure data, and initial state of charge data.

[0047] The thermal reaction test data for materials can include data on the thermal reaction of cathode materials, sulfide solid electrolytes, sulfide solid electrolytes after high-potential oxidation, composite cathodes, composite anodes, conductive agents, binders, and interface products. This data can be obtained from DSC, TGA, MS, ARC, or isothermal thermal stability tests. After obtaining the thermal reaction test data, baseline subtraction, peak separation, and enthalpy integration can be performed on the heat flow curves to obtain the initial exothermic temperature, peak temperature, enthalpy of reaction, activation energy, pre-exponential factor, reaction order, weight loss rate, and gas release characteristics corresponding to different material reaction heat sources.

[0048] Cell structure data can include cell capacity, number of lamination layers, electrode area, positive electrode thickness, negative electrode thickness, electrolyte membrane thickness, encapsulation thickness, specific heat, density, thermal conductivity, tab location, clamping pressure, and interface contact state. After obtaining the cell structure data, the internal heat transfer path of the target all-solid-state battery can be determined based on the cell capacity, number of lamination layers, electrode area, and thickness of each layer; the thermal capacity and thermal conductivity parameters of the target all-solid-state battery can be determined based on specific heat, density, and thermal conductivity; and the contact heat transfer influence between the target all-solid-state battery and the external structure can be determined based on clamping pressure and interface contact state. Therefore, the cell structure data can be converted into the cell structure parameters required for subsequent thermal balance calculations.

[0049] Initial state of charge (SOC) data can include the target all-solid-state battery's initial SOC, target SOC, open-circuit voltage, available capacity, degree of cathode delithiation, and pre-test resting state before entering the hot chamber test. After obtaining the initial SOC data, the target all-solid-state battery's SOC at the start of the hot chamber test can be determined based on the initial SOC and open-circuit voltage. Furthermore, the triggering tendency of oxygen release reactions and composite cathode interface reactions under high SOC conditions can be determined based on the degree of cathode delithiation. Therefore, the initial SOC data can be used to subsequently correct the reaction triggering conditions of the material reaction heat source.

[0050] Hot box data can include hot box operating condition data and hot box monitoring data.

[0051] The thermal chamber operating data can include initial temperature, heating rate, heating path, target temperature, holding temperature, holding time, test atmosphere, fixture settings, boundary heat transfer conditions, and test termination conditions. After acquiring the thermal chamber operating data, the heating conditions for the thermal chamber test can be determined based on the initial temperature, heating rate, and heating path; the holding conditions can be determined based on the target temperature, holding temperature, and holding time; and the boundary heat transfer conditions between the target all-solid-state battery and the thermal chamber environment can be determined based on the test atmosphere, fixture settings, and boundary heat transfer conditions. Therefore, the thermal chamber operating data can be converted into the thermal chamber test conditions required for subsequent thermal safety prediction.

[0052] The hot box monitoring data can include the hot box ambient temperature, cell surface temperature, positive electrode side temperature, negative electrode side temperature, terminal voltage, terminal voltage decay rate, voltage surge data, and test process time data. After acquiring the hot box monitoring data, the temperature and voltage data can be time-aligned according to the acquisition time, and the aligned hot box monitoring data can be mapped onto the hot box test time axis. Among them, the hot box ambient temperature and cell surface temperature can be used for subsequent inversion correction of the core temperature prediction data; the terminal voltage, terminal voltage decay rate, and voltage surge data can be used for subsequent identification of high-temperature self-discharge characteristics and correction of the effective state of charge prediction data.

[0053] Furthermore, after acquiring the cell data and thermal box data, a correlation verification can be performed on the cell data and thermal box data. Specifically, based on the cell identifier, material batch, cell batch, and thermal box test task identifier of the target all-solid-state battery, it can be determined whether the cell data and thermal box data correspond to the same target all-solid-state battery. If they correspond to the same target all-solid-state battery, the cell data and thermal box data are stored together. If data is missing, timestamps are abnormal, or sampling frequencies are inconsistent, missing data can be marked, abnormal data can be removed, and data with different sampling frequencies can be resampled or interpolated for alignment. After the above processing, cell data and thermal box data corresponding to the target all-solid-state battery and usable for subsequent simulation calculations can be obtained.

[0054] S102, based on the thermal box condition data in the cell data and thermal box data, determine the thermal safety prediction data of the target all-solid-state battery under thermal box test conditions.

[0055] Specifically, after acquiring the cell data and thermal chamber data of the target all-solid-state battery, the cell data can be parameterized, and the thermal chamber operating condition data can be boundary conditionally processed so that both can be simulated under the same thermal chamber test time axis. The cell data focuses on reflecting the target all-solid-state battery's own material heat release capacity, structural heat transfer capacity, and initial state of charge; the thermal chamber operating condition data focuses on reflecting the external thermal effects and heat transfer conditions applied to the target all-solid-state battery by the thermal chamber test. By combining the cell data and thermal chamber operating condition data, the core temperature change, effective state of charge change, and multi-heat source heat generation change of the target all-solid-state battery under thermal chamber test conditions can be predicted, thus forming thermal safety prediction data.

[0056] One possible approach is to pre-establish a library of material thermal reaction parameters before determining the predicted thermal safety data of the target all-solid-state battery under thermal box test conditions based on the thermal box condition data in the cell data and thermal box data.

[0057] Specifically, positive electrode materials, sulfide solid electrolytes, negative electrode materials, conductive agents, binders, and interface modification materials that are the same as or similar to those of the target all-solid-state battery are selected. Individual material samples, binary mixed samples, ternary mixed samples, composite positive electrode samples, composite positive electrode and electrolyte membrane combination samples, composite negative electrode and electrolyte membrane combination samples, post-cycle disassembly samples, and samples with different states of charge are prepared according to the material system of the target all-solid-state battery.

[0058] The above samples were subjected to DSC, TGA, MS, ARC, and isothermal thermal stability tests. Specifically, DSC was used to obtain heat flow profiles for different samples during the heating process; TGA was used to obtain mass change profiles for different samples; MS was used to record the release characteristics of sulfur-containing gases or other gases; and ARC was used to obtain the autogenous onset temperature and adiabatic heating rate. After the tests, baseline subtraction, peak separation, and enthalpy integration were performed on the heat flow profiles to extract the onset exothermic temperature, peak temperature, enthalpy of reaction, activation energy, pre-exponential factor, and reaction order corresponding to the reaction heat source of each material.

[0059] For heat sources related to composite cathodes, further analysis can be conducted by comparing the thermal reaction test results of delithiated cathode materials, sulfide solid electrolytes, sulfide solid electrolytes after high-potential oxidation, cathode material-sulfide solid electrolyte composite samples, and cathode material-sulfide solid electrolyte-conductive agent composite samples. This allows for the decomposition of the contributions of cathode oxygen release, sulfide solid electrolyte reactions, sulfide solid electrolyte oxidation product reactions, and conductive agent participation to the total exothermic reaction. This results in a material thermal reaction parameter library indexed by material type, interface combination, cycle history, degree of delithiation, and test conditions. This library can serve as a data source for determining material reaction parameters and material reaction heat source items.

[0060] One possible implementation involves determining the thermal safety prediction data of the target all-solid-state battery under hot box testing conditions based on hot box condition data from cell data and hot box data. This includes: determining the material reaction parameters, cell structural parameters, and initial state of charge (SPC) parameters of the target all-solid-state battery based on cell data; determining the heating conditions, insulation conditions, and boundary heat transfer conditions under hot box testing conditions based on hot box condition data; determining the material reaction heat source items of the target all-solid-state battery during hot box testing based on the material reaction parameters and initial SPC parameters; determining the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under hot box testing conditions based on the cell structural parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source items; and generating thermal safety prediction data based on the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data.

[0061] The material reaction parameters can include initial exothermic temperature, peak temperature, enthalpy of reaction, activation energy, pre-exponential factor, reaction order, weight loss rate, gas release characteristics, oxygen release exothermic characteristics at the cathode, exothermic characteristics of sulfide solid electrolyte oxidation products, and exothermic characteristics of composite cathode interface reactions. Specifically, DSC, TGA, MS, ARC, or isothermal thermal stability test results can be read from the material thermal reaction test data in the cell data. Baseline subtraction, peak separation, and enthalpy integration are performed on the heat flow curves to obtain the enthalpy parameters corresponding to each material reaction heat source. Simultaneously, based on the position, shape, and trend of the exothermic peaks under different test conditions, the activation energy, pre-exponential factor, and reaction order, etc., corresponding to each material reaction heat source can be determined. Thus, the material thermal reaction test data can be converted into material reaction parameters that can be used to calculate the material reaction heat source terms.

[0062] Cell structural parameters can include cell capacity, number of lamination layers, electrode area, positive electrode thickness, negative electrode thickness, electrolyte membrane thickness, encapsulation thickness, specific heat, density, thermal conductivity, clamping pressure, interface contact state, tab position, and heat dissipation path. In practice, the geometry and internal heat transfer path of the target all-solid-state battery can be determined based on the cell capacity, number of lamination layers, electrode area, and thickness of each layer; the thermal capacity and thermal conductivity parameters of the target all-solid-state battery can be determined based on specific heat, density, and thermal conductivity; and the contact heat transfer effect between the cell and the external structure can be determined based on clamping pressure and interface contact state. Thus, the cell structural data can be converted into the cell structural parameters required for subsequent thermal balance calculations.

[0063] Initial state of charge (SOC) parameters can include initial SOC, target SOC, available capacity, initial open-circuit voltage, degree of cathode delithiation, and pre-test resting state. In practice, the SOC, open-circuit voltage, and target SOC can be used to determine the target all-solid-state battery's SOC upon entering the hot chamber for testing; the degree of cathode delithiation can be used to determine the triggering tendency of oxygen release reactions in the cathode material and composite cathode interface reactions under high SOC conditions. Therefore, the initial SOC parameters can be used to subsequently correct the reaction triggering conditions for each material's reaction heat source.

[0064] The thermal chamber operating data can include the initial temperature, heating rate, heating path, target temperature, holding temperature, holding time, test atmosphere, fixture settings, boundary heat transfer conditions, and test termination conditions. In specific processing, the heating conditions for the thermal chamber test can be generated based on the initial temperature, target temperature, heating rate, and heating path; the holding conditions can be generated based on the holding temperature, holding time, and temperature fluctuation range during the holding period; and the boundary heat transfer conditions between the target all-solid-state battery and the thermal chamber environment can be generated based on the test atmosphere, fixture settings, surface heat transfer coefficient, fixture contact thermal resistance, and heat dissipation path. Thus, the thermal chamber operating data can be converted into the external thermal conditions required for subsequent thermal balance calculations.

[0065] One possible implementation involves determining the material reaction heat source terms of the target all-solid-state battery during hot-box testing, based on material reaction parameters and initial state of charge parameters. This includes: determining the reaction kinetic parameters and enthalpy parameters corresponding to each material reaction heat source in the target all-solid-state battery, based on the material reaction parameters. The reaction kinetic parameters include activation energy, pre-exponential factor, and reaction order. The reaction triggering conditions corresponding to each material reaction heat source are corrected based on the initial state of charge parameters. The reaction conversion rate and instantaneous heat generation rate of each material reaction heat source during hot-box testing are determined based on the corrected reaction triggering conditions, reaction kinetic parameters, and enthalpy parameters. Finally, material reaction heat source terms are generated based on the reaction conversion rate and instantaneous heat generation rate of each material reaction heat source.

[0066] One possible approach is to determine the reaction conversion rate of the i-th material / interface reaction kinetics equation during the hot box test process using the following equation:

[0067] in, This represents the reaction conversion rate of the heat source for the reaction of material of type i; Indicates the pre-exponential factor; R represents activation energy; T represents gas constant; T represents temperature. Indicates the reaction order; This indicates the correction effect of SOC, oxidation product ratio, contact area, pressure, and sulfide solid electrolyte membrane thickness on the reaction rate. In specific calculations, the core temperature or the temperature of the corresponding region can be taken as T, and the initial state of charge parameters, oxidation product ratio, interfacial contact area, fixture pressure, and electrolyte membrane thickness can be input into the correction function to obtain the change in reaction conversion rate of each material reaction heat source during the hot chamber test.

[0068] Furthermore, the instantaneous heat generation rate corresponding to the heat source of the i-th type of material reaction can be determined using the following formula for the instantaneous heat generation rate of the i-th type of reaction:

[0069] in, This represents the instantaneous heat generation rate of the reaction heat source of the i-th type of material; This represents the mass of reactants corresponding to the heat source of the i-th type of material reaction; This represents the enthalpy of reaction corresponding to the heat source of reaction for the i-th type of material; This represents the rate of change of reaction conversion rate corresponding to the heat source of material reaction of type i. In specific calculations, the rate of change of reaction conversion rate of each type of material reaction heat source can be multiplied by the reactant mass and reaction enthalpy to obtain the instantaneous heat generation rate of the corresponding heat source at the current hot box test time; then, multiple instantaneous heat generation rates are organized according to the hot box test time to generate the material reaction heat source term.

[0070] One possible implementation involves determining the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under hot-box testing conditions, based on cell structural parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source terms. This includes: determining the cell thermal response conditions of the target all-solid-state battery under hot-box testing conditions based on the cell structural parameters, heating conditions, insulation conditions, and boundary heat transfer conditions; and incorporating the material reaction heat source terms into the heat balance calculation equation constructed based on the cell thermal response conditions to obtain the core temperature prediction data, multi-heat source heat generation prediction data, and effective state of charge prediction data of the target all-solid-state battery during hot-box testing.

[0071] One possible approach is to determine the cell thermal response conditions of the target all-solid-state battery under hot box testing conditions based on cell structural parameters, heating conditions, insulation conditions, and boundary heat transfer conditions. The cell thermal response conditions can include heat capacity parameters, heat conduction parameters, hot box heating conditions, and heat dissipation conditions. Subsequently, the material reaction heat source term is incorporated as an internal heat generation term into the heat balance calculation equation constructed based on the cell thermal response conditions. This allows the heat balance calculation equation to simultaneously consider external heating of the hot box, internal heat transfer within the cell, boundary heat dissipation, and material reaction exothermics, thereby obtaining predicted core temperature data, multi-heat source heat generation data, and effective state of charge data of the target all-solid-state battery during the hot box testing process.

[0072] For example, based on the cell structure data and thermal box operating condition data of the target all-solid-state battery, a digital twin calculation process for calculating the thermal box response of the target all-solid-state battery is established. Specifically, the input parameters required for the digital twin calculation of the thermal box can be determined according to the cell capacity, number of lamination layers, electrode area, positive and negative electrode thickness, electrolyte membrane thickness, aluminum-plastic film thickness, specific heat, density, thermal conductivity, clamp pressure, convective heat transfer coefficient, thermal box heating rate, holding time, and initial state of charge parameters. In the specific implementation, thermal models of different spatial dimensions can be selected according to the structural complexity and calculation accuracy requirements of the target all-solid-state battery. For rapid parameter scanning, a 0D lumped thermal model can be used; for scenarios that need to describe the temperature gradient in the thickness direction, a 1D thickness direction thermal model can be used; for scenarios with in-plane heat dissipation differences, heat concentration near the tabs, or clamp contact effects in large-capacity pouch cells, a 2D or 3D thermal model can be used. All of the above thermal models can serve as specific implementation forms of the thermal balance calculation equations constructed based on the cell thermal response conditions.

[0073] One possible implementation method can be calculated using the following cell electro-thermal-reaction coupled thermal balance equation:

[0074] in, This indicates the density of the target all-solid-state battery; Specific heat capacity; T represents temperature; t represents time; k represents thermal conductivity; This represents the short-circuit Joule heat term. In specific calculations, the change in external hot box temperature over time can be determined based on the hot box heating conditions, the heat exchange between the battery cell and the hot box environment can be determined based on the boundary heat transfer conditions, and the internal heat transfer process can be determined based on the battery cell structural parameters. The material reaction heat source term is then incorporated into the above formula. After solving the thermal balance calculation equation, the core temperature, surface temperature, positive electrode temperature, electrolyte temperature, and heat generation changes of multiple heat sources of the target all-solid-state battery during the hot box test can be obtained.

[0075] One possible implementation involves incorporating material reaction heat source terms into the thermal balance calculation equation based on the cell's thermal response conditions. This yields predicted core temperature, multi-heat source heat generation, and effective state of charge (SPC) data for the target all-solid-state battery during hot-box testing. This includes: determining the heat capacity parameters, thermal conduction parameters, hot-box heating conditions, and heat dissipation conditions of the target all-solid-state battery during hot-box testing based on the cell's thermal response conditions; incorporating material reaction heat source terms into the thermal balance calculation equation and determining the predicted core temperature based on the heat capacity parameters, thermal conduction parameters, hot-box heating conditions, and heat dissipation conditions; determining multi-heat source heat generation prediction data based on the instantaneous heat generation rate and reaction conversion rate corresponding to each material reaction heat source in the material reaction heat source term; and determining the predicted SPC data for the target all-solid-state battery during hot-box testing based on the predicted core temperature and multi-heat source heat generation data.

[0076] Specifically, the heat capacity parameters can be determined based on specific heat, density, and cell volume; the heat conduction parameters can be determined based on thermal conductivity, lamination direction, electrode thickness, and electrolyte film thickness; the heating conditions of the hot box can be determined based on heating and insulation conditions; and the heat dissipation conditions can be determined based on boundary heat transfer conditions, surface heat transfer coefficient, and clamp contact thermal resistance.

[0077] Using the heating conditions of the hot box as the external input and the heat dissipation conditions as the boundary output, the heat accumulation and heat transfer inside the battery cell are described by heat capacity and heat conduction parameters, and the material reaction heat source term is included as an internal heat generation term in the calculation. By solving the heat balance calculation equation in time steps, the predicted core temperature value at each hot box test moment can be obtained, thus forming the core temperature prediction data.

[0078] The instantaneous heat generation rate corresponding to each material reaction heat source can be recorded at each test moment in the hot chamber, and the reaction progress of each material reaction heat source can be determined based on the reaction conversion rate. Subsequently, the instantaneous heat generation rate is integrated over time to obtain the integral heat release corresponding to each material reaction heat source. Based on the instantaneous heat generation rate or integral heat release of each material reaction heat source, the heat generation contribution rate and heat generation contribution ranking of each material reaction heat source can be determined. Thus, multi-heat source heat generation prediction data are obtained.

[0079] Based on the core temperature prediction data, the duration and level of high temperature of the target all-solid-state battery during the hot box test are determined, and the degree of side reactions and material reactions is judged based on the multi-heat source heat generation prediction data. When the core temperature rises and the multi-heat source heat generation continues to increase, it can be determined that the target all-solid-state battery has a decreasing trend of effective state of charge, and the effective state of charge prediction data is calculated accordingly.

[0080] The core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data are aligned according to the hot chamber test time to form thermal safety prediction data that changes over time. This thermal safety prediction data can be used to characterize the internal temperature rise trend, effective state of charge change trend, and internal multi-heat source heat generation trend of the target all-solid-state battery under hot chamber test conditions.

[0081] S103, Based on the thermal box monitoring data in the thermal box data, the thermal safety prediction data is corrected to obtain thermal safety correction data that reflects the actual thermal safety response of the target all-solid-state battery during the thermal box test.

[0082] The dynamic correction specifically considers at least the capacity loss caused by chemical self-discharge and / or side reactions under high-temperature conditions. This high-temperature environment can be the temperature environment that can trigger high-temperature chemical self-discharge, electron leakage, side reactions, or capacity loss when the target all-solid-state battery is in the heating or holding phase of the thermal chamber. This high-temperature environment can be determined based on the target all-solid-state battery's material system, initial state of charge, heating conditions, and holding conditions. For example, in a thermal chamber test scenario, if the target all-solid-state battery has already decreased from a high state of charge to a lower state of charge due to high-temperature self-discharge when it reaches approximately 200°C, then dynamic correction of the effective state of charge is required.

[0083] Specifically, the thermal safety prediction data is mainly calculated based on cell data and hot box operating condition data, reflecting the predicted thermal safety response of the target all-solid-state battery under the set hot box test conditions. During actual hot box testing, the ambient temperature of the hot box, the cell surface temperature, the terminal voltage, and the voltage decay may deviate from the predicted conditions. Therefore, it is necessary to correct the thermal safety prediction data using hot box monitoring data. This correction process may include core temperature correction, effective state of charge correction, and multi-heat source heat generation correction. Through this step, the prediction results based solely on the set operating conditions can be corrected to thermal safety correction data that more closely approximates the actual test response of the target all-solid-state battery.

[0084] One possible implementation involves correcting the thermal safety prediction data based on thermal box monitoring data from the thermal box data to obtain corrected thermal safety data reflecting the actual thermal safety response of the target all-solid-state battery during thermal box testing, including: Based on the ambient temperature of the hot box and the surface temperature of the battery cell from the hot box monitoring data, the core temperature prediction data in the thermal safety prediction data is inverted and corrected to obtain the core temperature correction data.

[0085] Specifically, the ambient temperature curve of the hot box and the surface temperature curve of the battery cell can be obtained from the hot box monitoring data first. These curves are then aligned with the predicted core temperature data from the thermal safety prediction data according to the test time. Next, based on the deviation between the measured value of the battery cell surface temperature and the predicted value obtained from the heat balance calculation equation, the boundary heat transfer conditions, internal heat generation estimation, or core temperature estimation are corrected. For example, if the measured value of the battery cell surface temperature is higher than the predicted value, it can be determined that the internal heat generation was underestimated or the heat dissipation capacity was overestimated during the prediction process, and the predicted core temperature data is increased accordingly. Conversely, if the measured value of the battery cell surface temperature is lower than the predicted value, it can be determined that the internal heat generation was overestimated or the heat dissipation capacity was underestimated during the prediction process, and the predicted core temperature data is decreased accordingly. Through this process, the core temperature correction data, which is difficult to measure directly, can be obtained by inverting the measurable ambient temperature of the hot box and the battery cell surface temperature.

[0086] For example, the core temperature and effective state of charge (SPC) of a target all-solid-state battery can be jointly inverted using data on ambient temperature of the thermal chamber, cell surface temperature, positive electrode side temperature, negative electrode side temperature, and terminal voltage variation. Specifically, the ambient temperature of the thermal chamber, cell surface temperature, positive electrode side temperature, negative electrode side temperature, and terminal voltage variation data can first be mapped to the same thermal chamber test time axis; then, the measured surface temperature is compared with the surface temperature calculated by the thermal chamber digital twin to obtain the surface temperature deviation; then, the boundary heat transfer parameters, internal heat generation terms, or core temperature estimates are adjusted based on the surface temperature deviation to obtain core temperature correction data. This core temperature correction data can be further used for subsequent SPC correction and multi-heat source heat generation correction.

[0087] Based on the terminal voltage change data in the hot box monitoring data, the high-temperature self-discharge characteristics of the target all-solid-state battery during the hot box test were determined.

[0088] Specifically, the terminal voltage change curves of the target all-solid-state battery during the heating and holding phases in the hot chamber can be obtained. Based on the hot chamber operating data, the terminal voltage change curves can be divided into voltage change segments during the heating and holding phases. For the heating phase, the rate of voltage decay with changes in hot chamber temperature or test time can be calculated; for the holding phase, the rate of voltage decay during the isothermal holding process can be calculated. If the terminal voltage exhibits a continuous decrease, rapid decay, abnormal abrupt change, or abnormal relaxation during the heating or holding phases, it can be determined that the target all-solid-state battery exhibits capacity consumption characteristics due to high-temperature self-discharge, electron leakage, or interfacial side reactions during the hot chamber test. Therefore, the terminal voltage change data can be converted into high-temperature self-discharge characteristics to correct the predicted state of charge data.

[0089] Based on the characteristics of high-temperature self-discharge, the effective state of charge prediction data in the thermal safety prediction data is corrected to obtain the effective state of charge corrected data.

[0090] Specifically, the capacity loss of the target all-solid-state battery during the hot-box test can be estimated based on the high-temperature self-discharge characteristics, and the predicted state of charge (SOC) data can be corrected by subtracting from the capacity loss. If the terminal voltage decay rate is large, it indicates that the target all-solid-state battery may experience strong high-temperature self-discharge, electron leakage, or side reaction capacity loss during the hot-box test. In this case, the actual SOC will be lower than the predicted SOC data in the thermal safety prediction data. Therefore, the corresponding SOC decrease can be determined based on the terminal voltage change data, and this SOC decrease can be subtracted from the predicted SOC data to obtain the corrected SOC data.

[0091] In one possible implementation, the effective SOC dynamic correction formula for the hot box process can be used to correct the predicted effective state of charge data:

[0092] in, Indicates the effective SOC at time t; Indicates the initial SOC; Indicates the cell capacity; Indicates high-temperature self-discharge current; Indicates the equivalent current of the side reaction; This represents the leakage current. In practical applications, it can be estimated based on the rate of voltage decay at the terminal voltage. , and Then, the current is integrated within the hot box test time range to obtain the capacity consumption caused by the side reaction, and the effective state of charge prediction data is corrected based on the capacity consumption.

[0093] Based on the core temperature correction data and the effective state of charge correction data, the multi-heat source heat generation prediction data in the thermal safety prediction data is corrected to obtain the multi-heat source heat generation correction data.

[0094] Specifically, the reaction rate and heat generation rate of each material's heat source are related to temperature and effective state of charge (SOC). After obtaining the core temperature correction data and effective SOC correction data, the corrected core temperature and effective SOC can be re-substituted into the reaction kinetic equations corresponding to each material's heat source. Since the core temperature correction data affects the temperature term in the reaction kinetic equations, and the effective SOC correction data affects the reaction triggering conditions and correction functions, the reaction conversion rate and instantaneous heat generation rate of each material's heat source can be recalculated, and the multi-heat source heat generation prediction data can be corrected accordingly to obtain multi-heat source heat generation correction data.

[0095] Based on the core temperature correction data, effective state of charge correction data, and multi-heat source heat generation correction data, thermal safety correction data reflecting the actual thermal safety response of the target all-solid-state battery during the hot box test is determined.

[0096] Specifically, core temperature correction data, effective state of charge (SOC) correction data, and multi-heat source heat generation correction data can be aligned according to the hot chamber test time to generate thermal safety correction data that changes over time. This thermal safety correction data can include core temperature correction curves, effective SOC correction curves, multi-heat source heat generation correction curves, heat generation, heat dissipation, heat generation-to-dissipation ratio, and material reaction progress. Because the thermal safety correction data combines simulation prediction results with actual monitoring responses during the hot chamber test, it can more accurately reflect the actual core temperature rise, actual effective SOC change, and actual material reaction heat generation state of the target all-solid-state battery during the hot chamber test.

[0097] One possible implementation involves correcting the effective state of charge prediction data in the thermal safety prediction data based on the high-temperature self-discharge characteristics, to obtain corrected effective state of charge data, including: determining the voltage decay rate of the target all-solid-state battery during the heating and heat preservation stages based on the terminal voltage change data.

[0098] Specifically, the hot chamber test process can be divided into a heating phase and a holding phase based on the hot chamber operating data, and the rate of change of the terminal voltage in each phase can be calculated separately. For the heating phase, the voltage decay rate can be determined based on the slope of the terminal voltage change with temperature; for the holding phase, the voltage decay rate can be determined based on the slope of the terminal voltage change with time. By analyzing the voltage decay rates in the heating and holding phases respectively, it is possible to distinguish between the short-time voltage response caused by temperature rise and the capacity consumption caused by prolonged high temperature.

[0099] Based on the voltage decay rate, determine the capacity loss caused by side reactions during the hot box test of the target all-solid-state battery.

[0100] Specifically, the voltage decay rate can be matched with a pre-established voltage decay-capacity consumption correlation to determine the capacity loss caused by side reactions during the hot-box test. The capacity loss due to side reactions can characterize the usable capacity loss of the target all-solid-state battery during the hot-box heating and holding process, caused by high-temperature chemical self-discharge, electron leakage, interfacial side reactions, or material decomposition reactions. This capacity loss differs from the capacity output during normal discharge; rather, it represents the decrease in effective SOC caused by internal side reactions of the target all-solid-state battery under the high-temperature conditions of the hot-box.

[0101] Based on the capacity consumption caused by side reactions, the predicted effective state of charge (PSC) data is corrected to obtain the corrected PSC data.

[0102] Specifically, the capacity loss caused by side reactions can be converted into a decrease in State of Charge (SOC), and this decrease in SOC can be subtracted from the predicted state of charge (SOC) data to obtain corrected SOC data. Therefore, the corrected SOC data can reflect the changes in the actual usable state of charge of the target all-solid-state battery during hot-box testing.

[0103] If the effective state of charge correction data is determined to be lower than the representative threshold corresponding to the target state of charge, a diagnostic result is generated to characterize that the current hotbox test results do not meet the representativeness of the target state of charge.

[0104] Specifically, when the corrected state of charge (SOC) data is lower than the representative threshold corresponding to the target SOC, it indicates that the target all-solid-state battery has experienced a significant SOC decrease during the hot-box test. In this case, even if the target all-solid-state battery ultimately does not experience thermal runaway or passes the hot-box test, it cannot be directly assumed that it possesses the same thermal safety capability under the target SOC. Therefore, a diagnostic result indicating that the current hot-box test results do not meet the representativeness of the target SOC can be generated to avoid misjudging the apparent pass after the effective SOC reduction as a true pass under the target SOC condition.

[0105] S104. Based on the thermal safety correction data, determine the thermal safety assessment results of the target all-solid-state battery and the corresponding thermal safety optimization direction.

[0106] Specifically, after obtaining the thermal safety correction data, the heat generation and dissipation relationships, core temperature margin, effective state of charge changes, material reaction processes, and the contribution of each material reaction heat source to the target all-solid-state battery can be further calculated based on this data. These calculations determine the thermal safety status level, thermal runaway risk level, hotbox pass probability, remaining safety time, dominant heat source, and main heat release pathway of the target all-solid-state battery. Furthermore, the thermal safety assessment results can be used to identify the risk sources of the target all-solid-state battery and output thermal safety optimization directions such as improving material thermal stability, enhancing cell structure heat dissipation capacity, adjusting hotbox operating conditions, or adjusting battery management system safety thresholds.

[0107] One possible implementation involves determining the thermal safety assessment results of the target all-solid-state battery based on thermal safety correction data, including: determining the amount of heat generated and dissipated by the target all-solid-state battery during the thermal chamber test based on the thermal safety correction data.

[0108] Specifically, based on the multi-heat source heat generation correction data in the thermal safety correction data, the heat generation of each material reaction heat source at each test moment can be calculated, and the heat generation of each material reaction heat source can be summed to obtain the heat generation amount. Based on the core temperature correction data, the ambient temperature of the thermal chamber, the boundary heat transfer conditions, and the internal heat transfer parameters, the heat released by the target all-solid-state battery to the thermal chamber environment or fixture structure at each test moment can be calculated to obtain the heat dissipation amount.

[0109] The heat generation and heat dissipation ratio of the target all-solid-state battery is determined based on the amount of heat generated and dissipated.

[0110] One possible implementation is to determine the heat generation / heat dissipation ratio using the following formula:

[0111] in, Indicates the ratio of heat generated to heat dissipated; The total heat generation or total heat generation rate of multiple material reaction heat sources is represented; h represents the surface heat transfer coefficient. Indicates the surface area of ​​the battery cell; Indicates the core temperature; Indicates the ambient temperature of the hot box; This represents the internal heat conduction term of the battery cell. In specific processing, the heat generation correction data from multiple heat sources can be substituted into the numerator, and the core temperature correction data, the ambient temperature of the thermal chamber, and the boundary heat transfer conditions can be substituted into the denominator to obtain the heat generation and dissipation ratio of the target all-solid-state battery at different test times.

[0112] Based on thermal safety correction data, the core temperature margin, change in effective state of charge, and material reaction process of the target all-solid-state battery are determined.

[0113] Specifically, the core temperature margin can be determined based on the difference between the core temperature correction data and the preset thermal safety temperature boundary; the change in effective state of charge (SOC) can be determined based on the change in effective SOC relative to the initial or target SOC; and the material reaction process can be determined based on the reaction conversion rate of each material reaction heat source. These parameters respectively reflect the distance of the target all-solid-state battery from the thermal runaway temperature boundary, the degree of effective SOC decrease during the hot chamber test, and the progression of the material thermal reaction.

[0114] Based on the heat generation and heat dissipation ratio, core temperature margin, change in effective state of charge, and material reaction process, the thermal safety status level, thermal runaway risk level, thermal chamber pass probability, and remaining safe time of the target all-solid-state battery are determined.

[0115] For example, a thermal safety status assessment process can be established based on thermal safety correction data. Specifically, the thermal safety status level of the target all-solid-state battery is determined based on the heat generation and dissipation ratio, core temperature margin, change in effective state of charge, and material reaction process. For instance, when the heat generation and dissipation ratio is below a first threshold and the core temperature margin is large, the target all-solid-state battery can be determined to be in a thermally stable state; when the change in effective state of charge increases but the core temperature has not yet approached the thermal runaway boundary, the target all-solid-state battery can be determined to be in a high-temperature self-discharge enhancement state; when the material reaction process accelerates and heat generation from multiple heat sources continues to increase, the target all-solid-state battery can be determined to be in a material reaction exothermic trigger state; when the heat generation and dissipation ratio exceeds a preset risk threshold or the core temperature margin is below a preset temperature margin threshold, the target all-solid-state battery can be determined to be in a thermal runaway risk state. Thus, the risk evolution trend of the target all-solid-state battery can be identified before the core temperature rises sharply.

[0116] In one possible implementation, the thermal runaway risk index of the target all-solid-state battery can be determined using the following formula:

[0117] in, Indicates the risk index of thermal runaway; , , , , Indicates the weighting coefficient; Indicates the ratio of heat generated to heat dissipated; Indicates the core temperature rise rate; Indicates temperature margin; Indicates the gas release index; This represents the effective State of Charge (SOC) margin. In practice, the thermal runaway risk index can be calculated based on the heat generation-to-heat dissipation ratio, core temperature rise rate, core temperature margin, gas release index, and effective SOC margin. The thermal safety status level and thermal runaway risk level are then determined based on the threshold range of the thermal runaway risk index. Furthermore, the probability of the thermal chamber passing through can be calculated based on the thermal safety threshold boundary data, and the remaining safe time can be predicted based on the current core temperature margin, heat generation-to-heat dissipation ratio, and material reaction progress.

[0118] Furthermore, based on thermal safety correction data, the risk triggering moment of the target all-solid-state battery during the hot box test process is determined.

[0119] Specifically, the heat generation and dissipation ratio, core temperature margin, change in effective state of charge, and material reaction process can be calculated point by point along the time axis of the hot chamber test. When the heat generation and dissipation ratio reaches a preset risk threshold, or the core temperature margin is lower than a preset temperature margin threshold, or the material reaction process reaches a preset reaction process threshold, or the change in effective state of charge exceeds a preset change threshold, the corresponding moment can be determined as the risk trigger moment.

[0120] Based on thermal safety correction data, determine the window heat contribution data of each material reaction heat source within a preset time window before the risk triggering time.

[0121] Specifically, a preset time window can be selected before the risk triggering time, and the instantaneous heat generation rate corresponding to each material reaction heat source within this time window can be extracted. Then, the instantaneous heat generation rate of each material reaction heat source within this time window is integrated to obtain the window heat generation contribution data corresponding to each material reaction heat source. This window heat generation contribution data can reflect the direct contribution of different material reaction heat sources to the formation of the risk before the risk is triggered.

[0122] Based on the thermal safety correction data, the cumulative heat contribution data of each material reaction heat source during the hot chamber test was determined.

[0123] Specifically, the instantaneous heat generation rate of each material reaction heat source can be integrated during the entire hot chamber test to obtain the cumulative heat generation contribution data corresponding to each material reaction heat source. This cumulative heat generation contribution data can reflect the total heat generation contribution of different material reaction heat sources during the entire hot chamber test.

[0124] Based on window heat generation contribution data and cumulative heat generation contribution data, the dominant heat source, main heat release path, and heat source contribution ranking are determined when the target all-solid-state battery experiences thermal safety risks.

[0125] One possible approach is to determine the contribution of each material's reaction heat source using the following dominant heat source contribution rate formula:

[0126] in, This represents the contribution rate of the heat source in the reaction of the i-th type of material; This represents the heat generation rate of the reaction heat source of the i-th type of material at time t; and This indicates the start and end times of the preset time window. In practice, this can be determined based on the preset time window preceding the risk trigger time. and The contribution rate of each material reaction heat source within the time window is calculated. Then, the heat sources are sorted from largest to smallest according to their contribution rates to obtain the heat source contribution ranking. The material reaction heat source with the highest contribution rate or whose contribution rate meets the dominant condition is determined as the dominant heat source, and the main heat release path is determined according to the cell region and heat transfer path where the dominant heat source is located.

[0127] For example, if the contribution rate of the positive electrode material reaction is high, it can be determined that the positive electrode oxygen release-related heat source contributes significantly to the thermal safety risk, and the positive electrode coating, positive electrode doping, or reducing the high SOC window should be the priority optimization directions; if the contribution rate of the composite positive electrode interface reaction is high, it can be determined that the positive electrode-sulfide solid electrolyte interface reaction contributes significantly to the thermal safety risk, and the priority optimization directions should be determined to improve the stability of the composite positive electrode interface, reduce the reaction enthalpy between the positive electrode material and the sulfide solid electrolyte, or reduce the oxidation products of the sulfide solid electrolyte; if the contribution of short-circuit Joule heating or negative electrode side reaction is high, internal short circuits, lithium plating, local hot spots on the electrode tabs, or electron leakage channels should be prioritized for investigation.

[0128] Finally, based on the risk triggering time, dominant heat source, main heat release path, and heat source contribution ranking, the heat source location assessment results of the target all-solid-state battery are determined.

[0129] The results of this heat source location assessment can be used to indicate the time point at which thermal safety risks occur in the target all-solid-state battery during hot box testing, the dominant heat source type, the main heat release pathway, and the contribution ranking of each material reaction heat source. Based on the heat source location assessment results, it can be further determined that the thermal safety risks mainly originate from insufficient material thermal stability, enhanced composite cathode interface reaction, insufficient cell heat dissipation capacity, or unreasonable hot box boundary conditions, thus providing a basis for determining the direction of thermal safety optimization.

[0130] In one possible implementation, based on thermal safety correction data, the thermal safety assessment results of the target all-solid-state battery and the corresponding thermal safety optimization directions are determined, including: Based on the cell data and thermal box data of the target all-solid-state battery, determine the thermal safety threshold boundary data corresponding to the target all-solid-state battery.

[0131] Specifically, based on the target all-solid-state battery's material system, cell structure, thermal chamber conditions, effective state of charge, cycle history, and batch information, corresponding thermal safety threshold boundary data can be matched from a pre-established thermal safety threshold library. Thermal safety threshold boundary data can include core temperature boundaries, effective SOC boundaries, heat generation-to-dissipation ratio boundaries, thermal chamber pass probability boundaries, and test stop boundaries.

[0132] In one specific embodiment, high-fidelity simulation results and physical constraint deep learning outputs can be downgraded to an online threshold library. This online threshold library can be indexed by material system, material batch, effective state of charge, fixture pressure, electrolyte membrane thickness, cell capacity, hotbox conditions, and cycle history, and outputs thermal safety threshold boundary data, hotbox pass evaluation results, thermal runaway risk assessment results, dominant heat source, remaining safety time, and thermal safety optimization direction.

[0133] Based on the degree of deviation between the thermal safety correction data and the thermal safety threshold boundary data, the thermal box pass evaluation results and thermal runaway risk assessment results of the target all-solid-state battery are determined.

[0134] Specifically, the core temperature correction data, effective state of charge correction data, multi-heat source heat generation correction data, and heat generation-to-dissipation ratio in the thermal safety correction data can be compared with the corresponding thermal safety threshold boundary data to obtain the degree of deviation. If the degree of deviation points to the safe side, a higher probability of thermal chamber passage and a lower risk of thermal runaway can be determined; if the degree of deviation points to the risk side, a lower probability of thermal chamber passage and a higher risk of thermal runaway can be determined.

[0135] When the online threshold library is deployed on the hot chamber test platform, the test platform can execute corresponding test control operations based on the thermal safety assessment results. For example, when the effective state of charge correction data is lower than the representative threshold corresponding to the target state of charge, it can record high-temperature self-discharge anomalies and prompt the platform to shorten the heat preservation time or adjust the hot chamber operating conditions; when the thermal safety status level reaches the preset risk level, it can stop further heating and enter the cooling or isolation procedure; when the thermal runaway risk level reaches the emergency risk level, it can perform emergency cut-off, isolation, and safety measures.

[0136] When the online threshold library is deployed in the BMS, the BMS can set high-temperature charging prohibition temperature, high-temperature power limit threshold, heat exposure time limit, and risk isolation strategy based on the threshold library output. For example, when a target all-solid-state battery is detected to have high-temperature self-discharge anomaly or insufficient core temperature rise margin, the BMS can reduce the charging and discharging power in advance, limit high-temperature fast charging, or implement cell isolation strategy.

[0137] If the evaluation results of the hot box do not meet the preset passing requirements, the direction of thermal safety optimization is determined based on the abnormal parameters corresponding to the degree of deviation.

[0138] Specifically, the optimization target can be determined based on the abnormal parameters corresponding to the degree of deviation. When abnormal parameters indicate abnormal exothermic reaction of the material, the direction for improving the thermal stability of the material can be determined; when abnormal parameters indicate insufficient core temperature rise margin, the direction for improving the heat dissipation capacity of the cell structure can be determined; when abnormal parameters indicate deviation of the heat box boundary, the direction for adjusting the heat box operating conditions can be determined; when abnormal parameters indicate mismatch of safety thresholds, the direction for adjusting the safety threshold of the battery management system can be determined.

[0139] In one specific embodiment, the synergistic optimization direction of materials, structure, testing, and BMS can be determined based on thermal safety assessment results, heat source location assessment results, sensitivity analysis results, and online threshold library output results. Regarding material optimization, when the dominant heat source is directed towards the positive electrode material reaction, the thermal stability of the composite positive electrode can be improved, the oxygen release from the positive electrode can be reduced, the enthalpy of the reaction between the positive electrode material and the sulfide solid electrolyte can be reduced, or the exothermic contribution of the high-potential oxidation products of the sulfide solid electrolyte can be reduced. When the dominant heat source is directed towards the conductive agent or binder participating in the reaction, the conductive agent and binder system can be optimized to reduce their participation in oxidation reactions or interfacial side reactions. Regarding structural optimization, when the core temperature margin is insufficient or the core-surface temperature difference is large, the thermal safety margin of the electrolyte membrane thickness can be increased, the heat diffusion path of the laminate can be optimized, the core-surface temperature difference can be reduced, or the heat concentration problem in the tab and edge regions can be optimized. In terms of test optimization, when the effective state of charge correction data shows that prolonged heat preservation leads to distortion of the representativeness of the target state of charge, the heating or heat preservation program of the hot chamber can be corrected to avoid the hot chamber test results failing to represent the thermal safety capability under the target state of charge due to prolonged high-temperature self-discharge. Regarding BMS optimization, based on thermal safety threshold boundary data and thermal runaway risk assessment results, high-temperature power limiting strategies, charging prohibition or fast charging prohibition temperatures, abnormal self-discharge identification strategies, thermal exposure time accumulation strategies, and early warning isolation strategies can be determined.

[0140] One possible approach is to use the following objective function, which focuses on the coordinated optimization of materials, structure, and testing, to determine the direction of thermal safety optimization:

[0141] in, This represents the objective function for optimization; x represents a combination of material, structural, or test parameters. This represents the ratio of heat generation to heat dissipation corresponding to the parameter combination x; This represents the core temperature rise corresponding to parameter combination x; This represents the probability of the hot box passing through the parameter combination x; This represents the cost corresponding to the parameter combination x; , , and This represents the weight coefficient corresponding to each objective item. In practice, different combinations of material parameters, cell structure parameters, or hot box operating condition parameters can be calculated to obtain the objective function value corresponding to each parameter combination. The parameter adjustment direction with the better objective function value is then selected as the thermal safety optimization direction.

[0142] In one possible implementation, sensitivity analysis of the thermal safety response of the target all-solid-state battery can be performed based on material parameters, cell structure parameters, and thermal chamber operating condition parameters. The degree of influence of each parameter on thermal safety risk can be determined using the following normalized sensitivity formula for material / structure / test parameters:

[0143] in, Indicates parameters The corresponding normalization sensitivity; Indicates the probability of the hot box passing through; Indicates the probability of the hot box passing through the parameter. The sensitivity to changes. In specific processing, material parameters, cell structure parameters, and hot box operating condition parameters can be disturbed separately to calculate the change in the hot box pass probability, thereby determining the degree of influence of each parameter on the hot box pass probability, and determining the priority of material optimization, structure optimization, or hot box operating condition optimization based on the degree of influence.

[0144] One possible implementation is to introduce a physical constraint deep learning model to assist in correcting thermal safety prediction or correction data. Specifically, model training data can include material thermal analysis data, hot box test data, needle penetration test data, EIS / DRT features, cell design parameters, process parameters, and simulation-generated virtual samples. Since the number of real thermal runaway samples is limited, the model can be constructed by jointly training a small amount of real test data, high-fidelity simulation enhancement data, and physical constraint losses.

[0145] In terms of model structure, a timing encoder can be used to process the predicted sequences of thermal chamber ambient temperature, cell surface temperature, terminal voltage, voltage decay rate, and core temperature; a spectral encoder can be used to process DSC, ARC, EIS, and DRT curves; a structural encoder can be used to process material formulation, stacked structure, electrolyte membrane thickness, fixture pressure, and batch information; and a simulation feature embedding layer can be used to input core temperature prediction data, multi-heat source heat generation prediction data, effective state of charge prediction data, and heat generation-to-dissipation ratio. The above encoding results can be fused using Attention or Gating to output the thermal chamber passage probability, thermal runaway risk level, dominant heat source, remaining safety time, and uncertainty.

[0146] One possible implementation, multimodal attention fusion, can be represented as:

[0147] in, Represents the m-th modal feature. This represents the attention weights for the m-th modality features. Let z represent the attention function that scores the m-th modal feature, and z represent the fused multimodal feature.

[0148] Based on the fused multimodal features, the following multi-task output can be obtained.

[0149]

[0150] in, It can represent the probability of different thermal safety status levels; Indicates the core temperature; Indicates the effective state of charge; This represents the contribution rate of the heat source in the reaction of the i-th type of material; Indicates the probability of the hot box passing through; This indicates the thermal runaway time or remaining safe time.

[0151] One possible implementation is to use the following physical constraint deep learning loss function to train or correct the physical constraint deep learning model:

[0152] in, This represents the loss function for deep learning based on physical constraints. This represents the data prediction error term; Represents the thermal balance constraint term; Indicates a valid SOC constraint term; This represents the risk monotonicity constraint term; Represents the uncertainty constraint term; , , as well as This represents the weight coefficients corresponding to each constraint term. By introducing a physical constraint deep learning loss function, the accuracy and interpretability of thermal safety assessment results can be improved even with limited samples.

[0153] The solutions provided in the embodiments of this application will be described below with specific examples.

[0154] Example 1 Taking a target all-solid-state battery with a high-nickel ternary cathode / LPSCl sulfide solid electrolyte / silicon-carbon anode all-solid-state pouch cell as an example, its thermal safety status under high SOC hot box test conditions is simulated and evaluated. First, cell data and hot box data of the target all-solid-state battery are acquired. Cell data includes material thermal reaction test data, cell structure data, and initial state of charge data; hot box data includes hot box operating condition data and hot box monitoring data.

[0155] Specifically, the thermal reaction test data for materials can include DSC, TGA, MS, and ARC test results for high-nickel cathode materials, delithiated high-nickel cathode materials, LPSCl sulfide solid electrolytes, LPSCl after high-potential oxidation, high-nickel cathode-LPSCl composite cathodes, composite cathode and electrolyte membrane combination samples, composite anodes, and conductive agents and binders. By performing baseline subtraction, peak separation, and reaction enthalpy integration on the above test results, the initial exothermic temperature, peak temperature, reaction enthalpy, activation energy, pre-exponential factor, reaction order, weight loss rate, and gas release characteristics corresponding to the reaction heat source of each material are obtained. Cell structure data includes cell capacity, number of laminates, electrode area, positive and negative electrode thickness, electrolyte membrane thickness, package thickness, specific heat, density, thermal conductivity, fixture pressure, and interface contact state. Initial state of charge data includes initial SOC, target SOC, open-circuit voltage, and degree of delithiation of the cathode.

[0156] Next, the material reaction parameters of the target all-solid-state battery are determined based on the material thermal reaction test data, the cell structure parameters are determined based on the cell structure data, and the initial state of charge parameters are determined based on the initial SOC, open circuit voltage, and degree of lithium delithiation of the positive electrode. Based on the initial temperature, heating rate, target temperature, holding temperature, holding time, and boundary heat transfer conditions in the hot box operating condition data, the heating conditions, holding conditions, and boundary heat transfer conditions under the hot box test conditions are determined.

[0157] After determining the material reaction parameters and initial state of charge parameters, the reaction conversion rate and instantaneous heat generation rate are calculated for the following heat sources: positive electrode oxygen release heat source, positive electrode-LPSCl interface reaction heat source, LPSCl high-potential oxidation product reaction heat source, conductive agent oxidation heat source, binder or interface product reaction heat source, negative electrode side reaction heat source, and short-circuit Joule heat heat source. For the i-th type of material reaction heat source, its reaction conversion rate can be determined using the following reaction kinetic equation:

[0158] in, This represents the reaction conversion rate of the heat source for the reaction of material of type i; Indicates the pre-exponential factor; R represents activation energy; T represents gas constant; T represents temperature. Indicates the reaction order; This indicates the effect of SOC, oxidation product ratio, contact area, pressure, and sulfide solid electrolyte membrane thickness on the reaction rate.

[0159] Furthermore, the heat generation rate of the reaction heat source of the i-th type of material at the current hot box test moment can be determined using the following instantaneous heat generation rate formula:

[0160] in, This represents the instantaneous heat generation rate of the reaction heat source of the i-th type of material; This represents the mass of reactants corresponding to the heat source of the i-th type of material reaction; This represents the enthalpy of reaction corresponding to the heat source of reaction for the i-th type of material; This represents the rate of change in reaction conversion rate corresponding to the heat source of reaction for the i-th type of material. Based on the instantaneous heat generation rate corresponding to each material's heat source reaction, the material reaction heat source term for the target all-solid-state battery during the hot box test is generated.

[0161] After determining the material reaction heat source term, the thermal response conditions of the battery cell are determined based on the cell structure parameters, heating conditions, insulation conditions, and boundary heat transfer conditions. The material reaction heat source term is then incorporated into the heat balance calculation equation. The heat balance calculation equation is as follows:

[0162] in, This indicates the density of the target all-solid-state battery; Specific heat capacity; T represents temperature; t represents time; k represents thermal conductivity; This represents the short-circuit Joule heating term. By solving this thermal balance equation using time steps, we obtain the predicted core temperature, surface temperature, multi-heat source heat generation, and effective state of charge data of the target all-solid-state battery during the hot box test.

[0163] During the hot box test, data on the changes in hot box ambient temperature, cell surface temperature, and terminal voltage are further acquired. Based on the hot box ambient temperature and cell surface temperature, the predicted core temperature data is inverted and corrected to obtain the corrected core temperature data.

[0164] One possible implementation is to invert the core temperature and boundary parameters by minimizing the deviation between the measured temperature and voltage data and the simulated temperature and voltage data. The objective function can be as follows.

[0165]

[0166] in, The core temperature, boundary heat transfer parameters, or internal heat generation correction parameters to be inverted; The measured temperature is at the m-th temperature measurement location; To correspond to the simulation temperature; This is the measured voltage; It is a simulated voltage; This represents the weight of the voltage deviation term.

[0167] The high-temperature self-discharge characteristics are determined based on the terminal voltage change data, and the effective state of charge prediction data is corrected using the following effective SOC dynamic correction formula:

[0168] in, Indicates the effective SOC at time t; Indicates the initial SOC; Indicates the cell capacity; Indicates high-temperature self-discharge current; Indicates the equivalent current of the side reaction; This represents the leakage current. Based on this formula, the effective state of charge (PSC) correction data is obtained. Furthermore, based on the core temperature correction data and the PSC correction data, the multi-source heat generation prediction data is further corrected to obtain the multi-source heat generation correction data.

[0169] Finally, thermal safety correction data is determined based on core temperature correction data, effective state of charge correction data, and multi-heat source heat generation correction data, and the thermal safety assessment result is determined based on the thermal safety correction data. For example, heat generation and heat dissipation can be determined based on the thermal safety correction data, and the heat generation-to-heat dissipation ratio can be determined using the following formula:

[0170] in, Indicates the ratio of heat generated to heat dissipated; The total heat generation or total heat generation rate of multiple material reaction heat sources is represented; h represents the surface heat transfer coefficient. Indicates the surface area of ​​the battery cell; Indicates the core temperature; Indicates the ambient temperature of the hot box; This represents the internal thermal conduction term of the battery cell. Based on the heat generation and dissipation ratio, core temperature margin, change in effective state of charge, and material reaction process, the thermal safety status level, thermal runaway risk level, hot box pass probability, and remaining safe time of the target all-solid-state battery are determined. Therefore, this embodiment can predict and correct the thermal safety status of high-SOC all-solid-state pouch cells before or during hot box testing.

[0171] Example 2 Taking composite cathode samples with different material combinations and corresponding all-solid-state soft-pack cells as examples, this paper explains how to map material thermal analysis data to cell-level thermal box performance, and determine material development indicators and structural optimization directions based on the mapping results.

[0172] First, test samples were prepared for multiple material combinations. These samples could include high-nickel cathode material samples, delithiated high-nickel cathode material samples, LPSCl sulfide solid electrolyte samples, LPSCl samples after high-potential oxidation, high-nickel cathode-LPSCl composite cathode samples, high-nickel cathode-LPSCl-conductive agent composite samples, composite cathode and electrolyte membrane combination samples, and composite anode samples. DSC, TGA, MS, and ARC tests were performed on these samples to obtain the heat flow curves, mass change curves, gas release curves, and adiabatic temperature rise curves for each material combination.

[0173] Baseline subtraction, peak separation, and reaction enthalpy integration were performed on the heat flow curves of each material test sample to extract the initial exothermic temperature, peak temperature, reaction enthalpy, and reaction kinetic parameters corresponding to the reaction heat source of each material. For composite cathode samples, the differences in thermal reactions between delithiated cathode materials, LPSCl, LPSCl after high-potential oxidation, high-nickel cathode-LPSCl composite samples, and high-nickel cathode-LPSCl-conductive agent composite samples can be compared to decompose the contributions of cathode oxygen release, LPSCl oxidation product reactions, cathode-LPSCl interface reactions, and conductive agent participation in the reaction to the total exothermic reaction.

[0174] The reaction kinetics and enthalpy parameters corresponding to the heat sources of each material reaction are input into the thermal chamber simulation evaluation process. Combined with the corresponding cell's capacity, number of layers, electrode area, positive and negative electrode thickness, electrolyte membrane thickness, clamp pressure, thermal conductivity, thermal chamber heating rate, and holding time, the core temperature prediction data, multi-heat source heat generation prediction data, effective state of charge prediction data, and thermal chamber pass probability for each material combination under cell-level thermal chamber test conditions are calculated. Through this process, material-level DSC / ARC / TGA / MS test results can be converted into cell-level thermal chamber safety performance, rather than directly determining whether a cell passes the thermal chamber test based solely on material thermal analysis curves.

[0175] The dominant heat source and its contribution ranking can be determined based on the window heat contribution data and cumulative heat contribution data corresponding to each material reaction heat source. For the i-th type of material reaction heat source, the contribution rate can be calculated using the following formula for the dominant heat source contribution rate:

[0176] in, This represents the contribution rate of the heat source in the reaction of the i-th type of material; This represents the heat generation rate of the reaction heat source of the i-th type of material at time t; and This indicates the start and end times of the preset time window. Based on the contribution rate, the heat sources that contribute significantly to thermal safety risks can be identified from the following: positive electrode oxygen release, LPSCl oxidation products, positive electrode-LPSCl interface reaction, conductive agent oxidation, binder reaction, negative electrode side reaction, or short-circuit Joule heating.

[0177] In a specific analysis, if the contribution rate of the positive electrode material reaction is high, it indicates that the heat source related to oxygen release from the positive electrode contributes significantly to the thermal box risk. Optimization directions include reducing the amount of oxygen released from the positive electrode, improving the thermal stability of the positive electrode, and optimizing the positive electrode coating or doping. If the contribution rate of the LPSCl-related heat source or the composite positive electrode interface reaction is high, it indicates that the high-potential oxidation products of the sulfide solid electrolyte or the positive electrode-LPSCl interface reaction contribute significantly to the thermal box risk. Optimization directions include reducing the NCM-LPSCl reaction enthalpy, reducing the exothermic contribution of the LPSCl high-potential oxidation products, improving the stability of the composite positive electrode interface, or optimizing the conductive agent network. If there is a large difference between the core temperature correction data and the surface temperature, it indicates insufficient heat dissipation capacity of the cell structure. Structural optimization directions include reducing the cell thickness, optimizing the heat diffusion path of the laminated cells, optimizing the heat dissipation of the tab area, or adjusting the heat dissipation method of the clamps.

[0178] This process establishes a mapping relationship between material thermal reaction test results and cell-level hotbox test performance, and uses this mapping to determine material development indicators. For example, based on the cell-level hotbox pass probability and the contribution rate of the dominant heat source, the thermal stability target of the composite cathode, the upper limit of the reaction enthalpy of the cathode and electrolyte membrane combination sample, the upper limit of heat release per unit capacity, the representativeness requirement of effective SOC, and the hotbox pass probability boundary can be determined. Thus, this embodiment can transform material-level test data into cell-level thermal safety design objectives.

[0179] Example 3 Using a thermal chamber test platform and a battery management system as examples, this paper explains how to optimize thermal chamber test strategies and BMS safety strategies based on thermal safety assessment results and thermal safety correction data.

[0180] First, before the hot box test, cell data of the target all-solid-state battery and candidate hot box operating condition data are acquired. Candidate hot box operating condition data may include different heating rates, different holding temperatures, different holding times, and different test termination conditions. For each set of candidate hot box operating condition data, core temperature prediction data, effective state of charge prediction data, multi-heat source heat generation prediction data, heat generation and dissipation ratio, hot box pass probability, and remaining safe time are calculated according to the method provided in the embodiments of this application. The evaluation results corresponding to different candidate hot box operating conditions are then compared.

[0181] During the comparison process, if the holding time under a certain hot chamber condition is too long, causing the effective state of charge (SOC) correction data to be significantly lower than the representative threshold corresponding to the target SOC, it indicates that the pass result under this hot chamber condition may not represent the true thermal safety capability under the target SOC. In this case, the holding time can be shortened, the heating rate adjusted, or the test stop conditions adjusted to obtain a hot chamber program that better represents the thermal safety capability of the target SOC. If the core temperature margin is low, the heat generation and dissipation are high, or the thermal runaway risk level is high under a certain hot chamber condition, a high risk can be indicated before testing, and it can be recommended that the test platform set stricter stop conditions or protection strategies.

[0182] During hot chamber testing, the testing platform can acquire real-time data on changes in the hot chamber ambient temperature, cell surface temperature, and terminal voltage, and update the thermal safety correction data accordingly. When the effective state of charge correction data falls below the representative threshold corresponding to the target state of charge, the testing platform can record a high-temperature self-discharge anomaly and indicate that the current hot chamber test results do not meet the representativeness of the target state of charge. When the thermal safety status level reaches the preset risk level, the testing platform can stop further heating and enter a cooling or isolation procedure. When the thermal runaway risk level reaches the emergency risk level, the testing platform can perform emergency shutdown, isolation, and safety procedures.

[0183] For BMS strategy optimization, thermal safety assessment results, heat source location assessment results, and thermal safety threshold boundary data can be downgraded to a threshold library that the BMS can access. This threshold library can be indexed by material system, material batch, cell capacity, electrolyte membrane thickness, fixture pressure, effective state of charge, hot box operating conditions, or cycle history, and outputs high-temperature power limiting curves, no-charge temperatures, no-fast-charge temperatures, thermal exposure time integral thresholds, and abnormal self-discharge alarm thresholds.

[0184] When a vehicle or energy storage system is at high SOC and exposed to high-temperature environments, the BMS can determine the risk level of the target all-solid-state battery based on real-time temperature, terminal voltage, historical thermal exposure time, and threshold library data. If the real-time temperature is close to the high-temperature power limit threshold, the BMS can reduce the charging and discharging power; if the temperature reaches the temperature where charging or fast charging is prohibited, the BMS can prohibit charging or limit fast charging; if the terminal voltage decay characteristics point to abnormal self-discharge, the BMS can generate an abnormal self-discharge alarm; if the thermal exposure time integral exceeds the threshold, the BMS can implement thermal management enhancement or cell isolation strategies.

[0185] In this process, the thermal safety simulation evaluation results are not only used to determine whether the target all-solid-state battery can pass the hot box test, but can also be further converted into test control strategies and safety control strategies that can be executed by the test platform and BMS, thereby realizing a closed-loop application from R&D simulation to test execution and then to safety control.

[0186] Example 4 One possible implementation is that the method provided in this application can be deployed in COMSOL, MATLAB, Python, or an enterprise simulation platform. For the R&D phase, high-fidelity models can be used for offline development and parameter scanning. For example, 0D, 1D, 2D, or 3D thermal models can be built in COMSOL, and material reaction heat source terms can be written into the thermal balance calculation equation to calculate the core temperature, multi-heat source heat generation, effective SOC, and heat box pass probability of the target all-solid-state battery under different heat box conditions. In MATLAB or Python, batch calculations can be performed on material reaction kinetic parameters, effective SOC correction parameters, heat generation-to-heat ratio, thermal runaway risk index, and dominant heat source contribution rate, and the thermal safety assessment results under different material systems, different cell structures, or different heat box conditions can be output.

[0187] Another possible implementation is to downgrade the simulation results obtained from the high-fidelity model to a reduced-order model that is called in real time by the hot-box test platform. This reduced-order model can retain key functions such as core temperature prediction, effective SOC correction, heat generation and heat dissipation ratio calculation, thermal runaway risk level judgment, and test stop condition judgment. During hot-box testing, the hot-box test platform can receive real-time data on changes in hot-box ambient temperature, cell surface temperature, and terminal voltage, and call the reduced-order model to update the thermal safety correction data. When the hot-box test results output by the reduced-order model do not meet the target SOC representativeness, the thermal safety status level reaches the preset risk level, or the remaining safety time is lower than the preset threshold, the hot-box test platform can output a stop test suggestion, cooling suggestion, or isolation suggestion.

[0188] Another possible implementation is to deploy the lightweight model on a BMS or bench system. Instead of performing a full high-fidelity hotbox simulation, the lightweight model calls upon an online threshold library and reduced-order calculation relationships. Based on real-time temperature, terminal voltage, SOC, historical thermal exposure time, and material system index, it outputs the thermal runaway risk level, heat source contribution rate, remaining safe time, power limiting recommendations, charging prohibition recommendations, and isolation recommendations. This provides online judgment for the safety control of all-solid-state batteries under high-temperature exposure scenarios during vehicle or energy storage system operation.

[0189] Regarding the platform interface, the method provided in this application embodiment can output thermal safety status level, thermal runaway risk level, hot box pass probability, heat source contribution rate, dominant heat source, remaining safe time, test stop suggestion, and material, structure, testing, and BMS optimization suggestions. For the R&D simulation platform, the above output results can be used for material screening, composite cathode interface stability assessment, electrolyte membrane thickness design, and cell structure optimization; for the hot box testing platform, the above output results can be used for pre-test risk screening, test process stop condition judgment, and post-test result verification; for the BMS, the above output results can be used for high-temperature power limiting, charging prohibition or fast charging prohibition, abnormal self-discharge alarm, thermal exposure time limit, and risk isolation strategy.

[0190] For example, multiple sets of simulation samples can be generated in the R&D platform based on a material thermal reaction parameter library and a high-fidelity hotbox digital twin model. Then, the high-fidelity simulation samples and the measured hotbox samples are used together to construct a reduced-order model and an online threshold library. The online threshold library is then deployed to the hotbox testing platform or BMS. Finally, during hotbox testing or actual operation, the threshold library is queried based on real-time monitoring data, and corresponding strategies are executed. In this way, complex material reaction analysis and hotbox simulation results can be transformed into risk levels, threshold boundaries, and strategy recommendations that can be executed by the engineering platform.

[0191] The above primarily describes the solutions provided in this application from the perspective of the device's working principle. It is understood that the all-solid-state battery thermal safety simulation and evaluation device includes corresponding hardware structures and / or software modules to perform each function in order to achieve the aforementioned functions. Those skilled in the art should readily recognize that, in conjunction with the algorithm steps of the examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0192] This application embodiment can divide the thermal safety simulation and evaluation equipment for all-solid-state batteries into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module.

[0193] It should be noted that the module division in this embodiment is illustrative and represents only one logical functional division; in actual implementation, other division methods may be used. When dividing functional modules according to their respective functions, Figure 2A schematic diagram of a possible composition of the thermal safety simulation and evaluation device for all-solid-state batteries involved in the above and embodiment examples is shown. Figure 2 As shown, the thermal safety simulation and evaluation device 200 for the all-solid-state battery may include: an acquisition module 201, a determination module 202, and a correction module 203.

[0194] The acquisition module 201 is used to support the execution of the thermal safety simulation and evaluation device 200 for all-solid-state batteries. Figure 1 S101 in the schematic thermal safety simulation evaluation method for all-solid-state batteries.

[0195] Module 202 is used to support the execution of the thermal safety simulation and evaluation device 200 for all-solid-state batteries. Figure 1 S102 and S104 in the schematic thermal safety simulation evaluation method for all-solid-state batteries.

[0196] Correction module 203 is used to support the execution of the thermal safety simulation evaluation device 200 for all-solid-state batteries. Figure 1 S103 in the schematic thermal safety simulation evaluation method for all-solid-state batteries.

[0197] One possible implementation includes a module used to determine the thermal safety prediction data of the target all-solid-state battery under hot box test conditions based on hot box condition data from cell data and hot box data. Specifically, this module is used to: determine the material reaction parameters, cell structure parameters, and initial state of charge parameters corresponding to the target all-solid-state battery based on the cell data; determine the heating conditions, insulation conditions, and boundary heat transfer conditions under the hot box test conditions based on the hot box condition data; determine the material reaction heat source items of the target all-solid-state battery during the hot box test based on the material reaction parameters and initial state of charge parameters; determine the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under the hot box test conditions based on the cell structure parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source items; and generate thermal safety prediction data based on the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data.

[0198] One possible implementation involves a module used to determine the material reaction heat source terms of the target all-solid-state battery during hot-box testing, based on material reaction parameters and initial state of charge parameters. Specifically, this module is used to: determine the reaction kinetic parameters and enthalpy parameters corresponding to each material reaction heat source in the target all-solid-state battery, based on the material reaction parameters. The reaction kinetic parameters include activation energy, pre-exponential factor, and reaction order. The reaction triggering conditions corresponding to each material reaction heat source are corrected based on the initial state of charge parameters. The reaction conversion rate and instantaneous heat generation rate of each material reaction heat source are determined during hot-box testing based on the corrected reaction triggering conditions, reaction kinetic parameters, and enthalpy parameters. Finally, material reaction heat source terms are generated based on the reaction conversion rate and instantaneous heat generation rate of each material reaction heat source.

[0199] One possible implementation involves a module that, based on cell structural parameters, heating conditions, insulation conditions, boundary heat transfer conditions, and material reaction heat source terms, determines the predicted core temperature, predicted state of charge (SOC), and predicted multi-heat source heat generation data of the target all-solid-state battery under hot-box testing conditions. Specifically, this module determines the cell thermal response conditions of the target all-solid-state battery under hot-box testing conditions based on these parameters. The material reaction heat source terms are then incorporated into the thermal balance calculation equation constructed based on the cell thermal response conditions to obtain the predicted core temperature, predicted multi-heat source heat generation data, and predicted SOC data of the target all-solid-state battery during the hot-box testing process.

[0200] One possible implementation involves a module used to determine the core temperature prediction data, multi-heat source generation prediction data, and effective state of charge prediction data of the target all-solid-state battery during the hot box test, when incorporating material reaction heat source terms into the thermal balance calculation equation constructed based on the cell thermal response conditions. Specifically, this module is used to: determine the heat capacity parameters, heat conduction parameters, hot box heating conditions, and heat dissipation conditions of the target all-solid-state battery during the hot box test, based on the cell thermal response conditions; incorporate the material reaction heat source terms into the thermal balance calculation equation and determine the core temperature prediction data based on the heat capacity parameters, heat conduction parameters, hot box heating conditions, and heat dissipation conditions; determine the multi-heat source generation prediction data based on the instantaneous heat generation rate and reaction conversion rate corresponding to each material reaction heat source in the material reaction heat source terms; and determine the effective state of charge prediction data of the target all-solid-state battery during the hot box test based on the core temperature prediction data and the multi-heat source generation prediction data.

[0201] One possible implementation involves a correction module that dynamically corrects the thermal safety prediction data based on the thermal box monitoring data in the thermal box data. Specifically, this correction module performs the following steps: It performs an inversion correction on the core temperature prediction data in the thermal safety prediction data based on the thermal box ambient temperature and cell surface temperature in the thermal box monitoring data to obtain core temperature correction data. It then determines the high-temperature self-discharge characteristics of the target all-solid-state battery during the thermal box test based on the terminal voltage change data in the thermal box monitoring data. Based on the high-temperature self-discharge characteristics, it corrects the effective state of charge (SPC) prediction data in the thermal safety prediction data to obtain SPC correction data. Finally, it corrects the multi-heat source heat generation prediction data in the thermal safety prediction data based on the core temperature correction data and the SPC correction data to obtain multi-heat source heat generation correction data. Finally, it determines the thermal safety correction data reflecting the actual thermal safety response of the target all-solid-state battery during the thermal box test based on the core temperature correction data, SPC correction data, and multi-heat source heat generation correction data.

[0202] One possible implementation involves a correction module that corrects the predicted state of charge (SOC) data in the thermal safety prediction data based on high-temperature self-discharge characteristics. Specifically, when obtaining corrected SOC data, the module: determines the voltage decay rate of the target all-solid-state battery during the heating and heat preservation stages based on terminal voltage change data; determines the capacity loss caused by side reactions during the hot-box test based on the voltage decay rate; corrects the predicted SOC data based on the capacity loss caused by side reactions, obtaining corrected SOC data; and, if the corrected SOC data is determined to be lower than the representative threshold corresponding to the target SOC, generates a diagnostic result indicating that the current hot-box test result does not meet the representativeness of the target SOC.

[0203] One possible implementation involves a module used to determine the thermal safety assessment results of the target all-solid-state battery based on thermal safety correction data. Specifically, this module is used to: determine the heat generation and dissipation of the target all-solid-state battery during the hot chamber test based on the thermal safety correction data; determine the heat generation-to-dissipation ratio of the target all-solid-state battery based on the heat generation and dissipation; determine the core temperature margin, change in effective state of charge, and material reaction process of the target all-solid-state battery based on the thermal safety correction data; and determine the thermal safety status level, thermal runaway risk level, hot chamber pass probability, and remaining safe time of the target all-solid-state battery based on the heat generation-to-dissipation ratio, core temperature margin, change in effective state of charge, and material reaction process.

[0204] In one possible implementation, the thermal safety simulation and evaluation device for all-solid-state batteries provided in this application embodiment is further used to: determine the risk triggering time of the target all-solid-state battery during the hot chamber test process based on thermal safety correction data; determine the window heat generation contribution data of each material reaction heat source within a preset time window before the risk triggering time based on the thermal safety correction data; determine the cumulative heat generation contribution data of each material reaction heat source during the hot chamber test process based on the thermal safety correction data; determine the dominant heat source, main heat release path, and heat source contribution ranking when the target all-solid-state battery experiences a thermal safety risk based on the window heat generation contribution data and the cumulative heat generation contribution data; and determine the heat source location evaluation result of the target all-solid-state battery based on the risk triggering time, dominant heat source, main heat release path, and heat source contribution ranking.

[0205] One possible implementation involves a module that, when determining the thermal safety optimization direction corresponding to the thermal safety assessment results based on thermal safety correction data, specifically: determining the thermal safety threshold boundary data corresponding to the target all-solid-state battery based on the cell data and thermal chamber data of the target all-solid-state battery; determining the thermal chamber pass assessment result and thermal runaway risk assessment result of the target all-solid-state battery based on the deviation between the thermal safety correction data and the thermal safety threshold boundary data; and determining the thermal safety optimization direction based on the abnormal parameters corresponding to the deviation if the thermal chamber pass assessment result does not meet the preset pass requirements. The thermal safety optimization direction includes one or more of the following: improving material thermal stability, improving cell structure heat dissipation capacity, adjusting thermal chamber operating conditions, and adjusting the safety threshold of the battery management system.

[0206] It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0207] The thermal safety simulation and evaluation device 200 for all-solid-state batteries provided in this application embodiment is used to perform the above-mentioned... Figure 1 The thermal safety simulation evaluation method for all-solid-state batteries shown can therefore achieve the same effect as the thermal safety simulation evaluation method for all-solid-state batteries described above.

[0208] This application also provides a thermal safety simulation and evaluation device for all-solid-state batteries, which can execute the thermal safety simulation and evaluation method and related steps of all-solid-state batteries in the above method embodiments.

[0209] This application also provides a computer-readable storage medium storing instructions that, when executed, perform the thermal safety simulation evaluation method and related steps for all-solid-state batteries in the above method embodiments.

[0210] This application also provides a computer program product that, when run on a computer, causes the computer to execute the thermal safety simulation evaluation method and related steps of the all-solid-state battery described in the above method embodiments.

[0211] In some embodiments, the methods shown in this application can be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or on other non-transitory media or articles of art.

[0212] This application also provides a thermal safety simulation and evaluation system 300 for all-solid-state batteries, such as... Figure 3 As shown, the thermal safety simulation and evaluation system 300 for the all-solid-state battery includes at least one processor 301 and at least one interface circuit 302.

[0213] As an example, when the thermal safety simulation and evaluation system 100 for the all-solid-state battery includes a processor and an interface circuit, then the processor can be... Figure 3 The processor 301 shown in the solid box (or the processor 301 shown in the dashed box) can be an interface circuit. Figure 3 The interface circuit 302 is shown in the solid box (or the dashed box). When the thermal safety simulation and evaluation system 300 for the all-solid-state battery includes two processors and two interface circuits, then the two processors include... Figure 3 The processor 301 shown in the solid box and the processor 301 shown in the dashed box, these two interface circuits include Figure 3 Interface circuit 302 shown in solid boxes and interface circuit 302 shown in dashed boxes. No limitations are imposed on this.

[0214] Processor 301 and interface circuit 302 can be interconnected via a line. For example, interface circuit 302 can be used to receive signals. Alternatively, interface circuit 302 can be used to send signals to other devices (e.g., processor 301). For instance, interface circuit 302 can read computer instructions stored in memory and send those instructions to processor 301. Processor 301 executes the instructions and, in conjunction with input / output devices, implements the various steps in the above embodiments, such as implementing... Figure 1 The methods illustrated are the steps performed in the embodiments shown. Of course, this all-solid-state battery thermal safety simulation and evaluation system may also include other discrete components, and this application does not specifically limit this.

[0215] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0216] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0217] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0218] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0219] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to it, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0220] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for thermal safety simulation evaluation of an all-solid-state battery, characterized by, The method includes: Obtain the cell data of the target all-solid-state battery, and the thermal box data corresponding to the target all-solid-state battery; Based on the cell data and the thermal box condition data in the thermal box data, determine the thermal safety prediction data of the target all-solid-state battery under thermal box test conditions; The thermal safety prediction data is dynamically corrected based on the thermal box monitoring data in the thermal box data to obtain thermal safety correction data reflecting the actual thermal safety response of the target all-solid-state battery during the thermal box test; wherein, the dynamic correction at least considers the capacity consumption caused by chemical self-discharge and / or side reactions under high temperature environment. Based on the thermal safety correction data, the thermal safety assessment results of the target all-solid-state battery and the corresponding thermal safety optimization directions are determined.

2. The method of claim 1, wherein, The step of determining the predicted thermal safety data of the target all-solid-state battery under hot box test conditions based on the cell data and the hot box operating condition data includes: Based on the cell data, determine the material reaction parameters, cell structure parameters, and initial state of charge parameters corresponding to the target all-solid-state battery; Based on the hot box operating data, determine the heating conditions, insulation conditions, and boundary heat transfer conditions under the hot box test conditions; Based on the material reaction parameters and the initial state of charge parameters, determine the material reaction heat source items of the target all-solid-state battery during the hot box test process; Based on the cell structure parameters, the heating conditions, the heat preservation conditions, the boundary heat transfer conditions, and the material reaction heat source items, the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under the hot box test conditions are determined. The thermal safety prediction data is generated based on the core temperature prediction data, the effective state of charge prediction data, and the multi-heat source heat generation prediction data.

3. The method according to claim 2, characterized in that, The step of determining the material reaction heat source item of the target all-solid-state battery during the hot box test process based on the material reaction parameters and the initial state of charge parameters includes: Based on the material reaction parameters, determine the reaction kinetic parameters and reaction enthalpy parameters corresponding to the reaction heat sources of each material in the target all-solid-state battery; the reaction kinetic parameters include activation energy, pre-exponential factor, and reaction order; Based on the initial state of charge parameters, the reaction triggering conditions corresponding to each of the material reaction heat sources are modified; Based on the modified reaction triggering conditions, the reaction kinetic parameters, and the reaction enthalpy parameters, the reaction conversion rate and instantaneous heat generation rate of each of the material reaction heat sources are determined during the hot box test process; The material reaction heat source term is generated based on the reaction conversion rate and the instantaneous heat generation rate of each of the material reaction heat sources.

4. The method according to claim 2, characterized in that, The step of determining the core temperature prediction data, effective state of charge prediction data, and multi-heat source heat generation prediction data of the target all-solid-state battery under thermal chamber test conditions based on the cell structure parameters, the heating conditions, the insulation conditions, the boundary heat transfer conditions, and the material reaction heat source items includes: Based on the cell structure parameters, the heating conditions, the heat preservation conditions, and the boundary heat transfer conditions, determine the cell thermal response conditions of the target all-solid-state battery under the hot box test conditions; The material reaction heat source term is written into the thermal balance calculation equation constructed based on the cell thermal response conditions to obtain the core temperature prediction data, multi-heat source heat generation prediction data, and effective state of charge prediction data of the target all-solid-state battery during the hot box test process.

5. The method according to claim 4, characterized in that, The step of incorporating the material reaction heat source term into the thermal balance calculation equation constructed based on the cell thermal response conditions to obtain the core temperature prediction data, multi-heat source heat generation prediction data, and effective state of charge prediction data of the target all-solid-state battery during the hot box test process includes: Based on the cell thermal response conditions, determine the thermal capacity parameters, thermal conduction parameters, heating conditions, and heat dissipation conditions of the target all-solid-state battery during the hot box test process; The material reaction heat source term is written into the heat balance calculation equation, and the core temperature prediction data is determined based on the heat capacity parameter, the heat conduction parameter, the heat box heating conditions, and the heat dissipation conditions. The multi-heat source heat generation prediction data is determined based on the instantaneous heat generation rate and reaction conversion rate corresponding to each material reaction heat source in the material reaction heat source item. Based on the core temperature prediction data and the multi-heat source heat generation prediction data, the effective state of charge prediction data of the target all-solid-state battery during the hot box test is determined.

6. The method according to claim 1, characterized in that, The step of dynamically correcting the thermal safety prediction data based on the thermal box monitoring data in the thermal box data to obtain thermal safety correction data reflecting the actual thermal safety response of the target all-solid-state battery during the thermal box test includes: Based on the ambient temperature of the hot box and the surface temperature of the battery cell in the hot box monitoring data, the core temperature prediction data in the thermal safety prediction data is inverted and corrected to obtain the core temperature correction data. Based on the terminal voltage change data in the hot box monitoring data, the high-temperature self-discharge characteristics of the target all-solid-state battery during the hot box test process are determined. Based on the high-temperature self-discharge characteristics, the effective state of charge prediction data in the thermal safety prediction data is corrected to obtain the effective state of charge corrected data. Based on the core temperature correction data and the effective state of charge correction data, the multi-heat source heat generation prediction data in the thermal safety prediction data is corrected to obtain multi-heat source heat generation correction data. Based on the core temperature correction data, the effective state of charge correction data, and the multi-heat source heat generation correction data, thermal safety correction data reflecting the actual thermal safety response of the target all-solid-state battery during the hot box test is determined.

7. The method according to claim 6, characterized in that, The step of correcting the effective state of charge prediction data in the thermal safety prediction data based on the high-temperature self-discharge characteristics to obtain effective state of charge corrected data includes: Based on the terminal voltage change data, the voltage decay rate of the target all-solid-state battery during the heating and heat preservation stages is determined. Based on the voltage decay rate, determine the capacity loss of the target all-solid-state battery due to side reactions during the hot box test. The predicted state of charge (SOC) data is corrected based on the capacity consumption caused by the side reactions to obtain the corrected SOC data. If the effective state of charge correction data is determined to be lower than the representative threshold corresponding to the target state of charge, a diagnostic result is generated to characterize that the current hotbox test result does not meet the representativeness of the target state of charge.

8. The method according to claim 1, characterized in that, The step of determining the thermal safety assessment result of the target all-solid-state battery based on the thermal safety correction data includes: Based on the thermal safety correction data, the amount of heat generated and dissipated by the target all-solid-state battery during the thermal chamber test is determined; The heat generation and heat dissipation ratio of the target all-solid-state battery is determined based on the heat generation and heat dissipation. Based on the thermal safety correction data, the core temperature margin, change in effective state of charge, and material reaction process of the target all-solid-state battery are determined. Based on the heat generation and heat dissipation ratio, the core temperature margin, the change in effective state of charge, and the material reaction process, the thermal safety status level, thermal runaway risk level, thermal chamber pass probability, and remaining safe time of the target all-solid-state battery are determined.

9. The method according to claim 8, characterized in that, The method further includes: Based on the thermal safety correction data, determine the risk triggering moment of the target all-solid-state battery during the hot box test process; Based on the thermal safety correction data, determine the window heat production contribution data of each material reaction heat source within a preset time window before the risk triggering time; Based on the thermal safety correction data, determine the cumulative heat contribution data of each of the material reaction heat sources during the hot box test process; Based on the window heat generation contribution data and the cumulative heat generation contribution data, determine the dominant heat source, main heat release path, and heat source contribution ranking when the target all-solid-state battery experiences a thermal safety risk. Based on the risk triggering time, the dominant heat source, the main heat release path, and the heat source contribution ranking, the heat source location assessment result of the target all-solid-state battery is determined.

10. The method according to claim 1, characterized in that, Based on the thermal safety correction data, determine the thermal safety optimization direction corresponding to the thermal safety assessment results, including: Based on the cell data and thermal box data of the target all-solid-state battery, determine the thermal safety threshold boundary data corresponding to the target all-solid-state battery; Based on the degree of deviation between the thermal safety correction data and the thermal safety threshold boundary data, the thermal box pass evaluation result and thermal runaway risk assessment result of the target all-solid-state battery are determined; If the evaluation result of the thermal box does not meet the preset pass requirements, the thermal safety optimization direction is determined according to the abnormal parameters corresponding to the degree of deviation. The thermal safety optimization direction includes one or more of the following: improving the thermal stability of materials, improving the heat dissipation capacity of the cell structure, adjusting the operating conditions of the thermal box, and adjusting the safety threshold of the battery management system.

11. A thermal safety simulation and evaluation device for all-solid-state batteries, characterized in that, The device includes: The acquisition module is used to acquire the cell data of the target all-solid-state battery and the thermal box data corresponding to the target all-solid-state battery. The determination module is used to determine the thermal safety prediction data of the target all-solid-state battery under the hot box test conditions based on the cell data and the hot box condition data. The correction module is used to dynamically correct the thermal safety prediction data based on the thermal box monitoring data in the thermal box data, so as to obtain thermal safety correction data reflecting the actual thermal safety response of the target all-solid-state battery during the thermal box test; wherein, the dynamic correction at least considers the capacity consumption caused by chemical self-discharge and / or side reactions under high temperature environment; The determining module is further configured to determine the thermal safety assessment result of the target all-solid-state battery and the corresponding thermal safety optimization direction based on the thermal safety correction data.

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