Energy storage thermal management test platform and method

Abstract

The application discloses an energy storage thermal management test platform and method, which sets initial environmental temperature and cooling liquid flow through a full-size energy storage tank thermal environment simulation system and a liquid cooling circulation system, and outputs multi-region environmental parameters; generates simulated thermal load based on the environmental parameters, adjusts thermal load intensity through a dynamic feedback mechanism of a central controller, and verifies spatial distribution uniformity; under the action of the thermal load, synchronously collects battery pack three-dimensional temperature, cooling liquid inlet and outlet temperature difference and flow data by using a multilevel temperature sensor array and a thermal imager, and constructs a space-time correlation data set with heat flow density mapping; reconstructs three-dimensional thermal field distribution based on the data set by using a spatial interpolation algorithm, calculates temperature rise rate and heat dissipation energy efficiency ratio, and outputs evaluation results containing a thermal field uniformity index; through multi-cycle iterative optimization of environmental temperature and thermal load parameters, dual improvement of test efficiency and energy efficiency evaluation accuracy is realized. The application can effectively solve the test problem of environmental-load dynamic coupling.

Description

Technical Field

[0001] This invention relates to the field of energy storage, and more particularly to an energy storage thermal management testing platform and method. Background Technology

[0002] Thermal management testing of energy storage systems faces challenges such as a disconnect between environmental temperature control and load simulation, making it difficult to reproduce real-world operating conditions; insufficient temperature acquisition accuracy leading to distorted monitoring of thermal field distribution; and simplistic cooling system control lacking dynamic response capabilities. Traditional testing platforms, due to the independent operation of each subsystem, cannot achieve coordinated changes in temperature and load, resulting in thermal management assessments that deviate from actual application scenarios. Furthermore, the lack of multi-dimensional data synchronous acquisition methods makes it impossible to construct a three-dimensional thermal field model, hindering the accurate identification of localized overheating risks. In addition, static testing modes are insufficient to verify the cooling system's ability to adjust under sudden changes in operating conditions, thus limiting the optimization of thermal management strategies. Summary of the Invention

[0003] To address the problems identified in existing technologies, this invention proposes a test method for energy storage thermal management, comprising:

[0004] S1. Set the initial ambient temperature and coolant flow rate of the liquid cooling circulation system through the thermal environment simulation system of the full-size energy storage box, and output environmental status parameters including environmental parameters of multiple areas.

[0005] S2. Generate simulated thermal load of battery pack based on environmental state parameters, adjust the thermal load intensity through dynamic feedback mechanism of thermal load intensity implemented by central controller, and output simulated thermal load of battery pack after spatial distribution uniformity verification.

[0006] S3. Under the simulated thermal load of the battery pack, the battery pack temperature data, the temperature difference between the inlet and outlet of the coolant, the ambient temperature and the coolant flow rate are collected synchronously by a multi-source sensing system that integrates multi-level temperature sensors and thermal imagers, and the spatiotemporal correlation multidimensional temperature dataset with heat flux density mapping is output.

[0007] S4. Based on the multidimensional temperature dataset, the three-dimensional thermal field distribution is reconstructed using a spatial interpolation algorithm. Temperature rise characteristic parameters including the temperature rise rate and heat dissipation energy efficiency evaluation parameters including the heat dissipation energy efficiency ratio are calculated. The thermal management performance evaluation results containing the thermal field uniformity index are output.

[0008] Furthermore, the method also includes: S5, dynamically adjusting the ambient temperature and thermal load intensity settings based on the thermal field uniformity index and heat dissipation efficiency evaluation parameters according to the performance evaluation results, and feeding them back to S1 and S2 for multi-cycle iterative closed-loop testing until the thermal management performance meets the standards.

[0009] Further, S1 specifically includes: S11, setting an initial ambient temperature that is independently controllable and defined with temperature difference thresholds for multiple regions through the temperature regulation device of the full-size energy storage tank; S12, calculating the coolant flow rate reference value based on the heat conduction equation of the predicted heat load value and the initial ambient temperature, and driving the liquid cooling circulation system to perform closed-loop regulation; S13, using a high-precision temperature sensor array to collect the ambient temperature and coolant flow rate of multiple regions in real time, calculating the temperature difference uniformity index, and iteratively adjusting the coolant flow rate through proportional-integral control until the standard is met; S14, outputting environmental state parameters including the measured values ​​of ambient temperature, measured values ​​of coolant flow rate, and standard deviation of regional temperature difference for multiple regions.

[0010] Furthermore, S2 specifically includes: S21, calculating the baseline value of thermal load intensity based on the measured values ​​of ambient temperature in multiple regions using a heat transfer model; S22, generating a simulated thermal load of the battery pack with spatial thermal distribution configuration using a programmable PTC load simulation device according to the calculated value; S23, monitoring the uniformity of spatial distribution of thermal load in real time using an infrared thermal imager and feeding it back to the central controller; S24, dynamically adjusting the PTC power according to a gradient step algorithm when the heat flux density deviation exceeds the threshold until the simulated thermal load of the battery pack with the standard deviation of the output heat flux density meets the standard.

[0011] Furthermore, S3 specifically includes: S31, acquiring three-dimensional temperature distribution data inside the battery pack through an embedded multi-level temperature sensor layout; S32, acquiring surface heat flux density distribution data of the battery pack through a high frame rate thermal imaging acquisition device; S33, recording real-time data from the coolant inlet and outlet temperature difference sensor, the ambient temperature sensor array, and the high-precision flow meter with millisecond-level synchronization accuracy; S34, aligning and fusing the internal temperature distribution data, surface heat distribution data, temperature difference data, ambient temperature data, and flow data of the battery pack according to spatiotemporal coordinates into a multi-dimensional temperature dataset and outputting it.

[0012] Furthermore, S4 specifically includes: S41, reconstructing the three-dimensional thermal field distribution of the battery pack using the Kriging interpolation algorithm based on the multidimensional temperature dataset; S42, calculating the temperature rise rate through time differentiation operations; S43, calculating the heat dissipation efficiency ratio according to the thermodynamic efficiency formula of the heat load input power and the heat dissipation of the coolant; S44, outputting performance evaluation results including the thermal field uniformity index, the temperature rise warning level based on the temperature rise rate classification, and the energy efficiency optimization range optimized by combining historical data.

[0013] Furthermore, S5 specifically includes: S51, generating an ambient temperature adjustment amount based on the thermal field uniformity index and temperature rise warning level in the performance evaluation results using a fuzzy control algorithm; S52, generating a heat load intensity adjustment amount based on the deviation between the heat dissipation efficiency ratio and the energy efficiency optimization range using a PID control law; S53, using the ambient temperature adjustment amount as the temperature difference threshold correction input feedback for S1; S54, using the heat load intensity adjustment amount as the heat distribution configuration parameter feedback for S2; S55, determining compliance when the standard deviation of the thermal field uniformity index is ≤ the set threshold, there is no high-risk alarm for the temperature rise warning level, and the heat dissipation efficiency ratio is within the optimization range for three consecutive iterations.

[0014] The present invention also proposes an energy storage thermal management test platform for implementing the above-mentioned energy storage thermal management test method.

[0015] This invention proposes an energy storage thermal management testing platform and method. Through thermal environment simulation and load dynamic coupling mechanism, it realistically recreates the temperature-load interaction scenario during charging and discharging, improving the reliability of the operating condition simulation. Secondly, by integrating multi-level temperature sensing and thermal imaging technologies, it achieves millisecond-level three-dimensional thermal field reconstruction, accurately locating abnormal temperature rise areas. Simultaneously, a closed-loop feedback control strategy is adopted, enabling the cooling system to respond to thermal load fluctuations in real time, significantly enhancing dynamic adjustment capabilities. Finally, performance evaluation results drive multi-cycle iterative optimization, automatically converging to the optimal combination of thermal management parameters. This improves testing efficiency and reduces errors in heat dissipation efficiency assessment. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of a thermal management testing method for energy storage proposed in this invention;

[0017] Figure 2 This is a partial flowchart of a thermal management testing method for energy storage proposed in this invention;

[0018] Figure 3 This is a partial flowchart of a thermal management testing method for energy storage proposed in this invention;

[0019] Figure 4 This is a partial flowchart of a thermal management testing method for energy storage proposed in this invention;

[0020] Figure 5 This is a partial flowchart of a thermal management testing method for energy storage proposed in this invention;

[0021] Figure 6 This is a partial flowchart of a thermal management testing method for energy storage proposed in this invention. Detailed Implementation

[0022] refer to Figures 1-6 This invention proposes a test method for energy storage thermal management, comprising:

[0023] S1. The initial ambient temperature and coolant flow rate of the liquid cooling circulation system are set through the thermal environment simulation system of the full-size energy storage box, and the environmental status parameters including environmental parameters of multiple areas are output.

[0024] Specifically, this includes:

[0025] S11. Set the initial ambient temperature for multiple independently controllable zones with defined temperature difference thresholds using the temperature regulation equipment of the full-size energy storage tank. This step independently controls the ambient temperature of different zones using the temperature regulation equipment of the full-size energy storage tank, while presetting the maximum allowable temperature difference threshold between each zone, thereby ensuring the accuracy and safety of the environmental simulation. By setting the temperature difference threshold, the system can automatically warn of abnormal temperature distribution risks, improving the reliability of environmental parameter settings.

[0026] S12. Based on the predicted heat load and the initial ambient temperature, the heat conduction equation is used to calculate the reference value of the coolant flow rate and drive the liquid cooling circulation system to perform closed-loop regulation. This step accurately calculates the reference value of the coolant flow rate using the heat conduction equation, comprehensively considering the heat exchange relationship between the initial ambient temperature and the predicted heat load, thereby improving the accuracy of flow rate regulation. Pressure fluctuations are monitored simultaneously when driving the liquid cooling circulation system to perform closed-loop regulation to avoid pipeline vibration caused by sudden flow changes, thus enhancing system stability.

[0027] S13. A high-precision temperature sensor array is used to collect ambient temperature and coolant flow rate in multiple areas in real time. The temperature difference uniformity index is calculated, and the coolant flow rate is iteratively adjusted using proportional-integral control until the standard is met. This step uses a high-precision temperature sensor array to collect data in real time and automatically identifies local hot spots by calculating the temperature difference uniformity index. A proportional-integral control algorithm is used to dynamically fine-tune the coolant flow rate, automatically accelerating or decelerating the adjustment rate according to changes in temperature gradient, thereby shortening the convergence time of environmental parameters.

[0028] S14. Output environmental condition parameters including measured ambient temperature values, measured coolant flow rates, and standard deviations of temperature differences in multiple regions. This step outputs environmental condition parameters containing measured values ​​and standard deviations of temperature differences in different regions. The standard deviation calculation process simultaneously eliminates interference from outlier sensor values, thereby improving the reliability of the parameters. Adding data validity indicators, such as "Verification Passed / Failed," provides a basis for decision-making in subsequent steps.

[0029] S2. Generate simulated thermal load on the battery pack based on environmental state parameters. Adjust the thermal load intensity through a dynamic feedback mechanism for thermal load intensity implemented by the central controller, and output the simulated thermal load on the battery pack after verifying the uniformity of spatial distribution.

[0030] Specifically, this includes:

[0031] S21. Based on measured ambient temperature values ​​from multiple regions, calculate the baseline value of the heat load intensity using a heat transfer model. In this step, when calculating the heat load intensity using the heat transfer model, the real-time fluctuation characteristics of the measured ambient temperature values ​​are considered simultaneously, and the heat conduction delay effect is automatically compensated, thereby improving the timeliness of heat load prediction. A dynamic response curve of heat load and ambient temperature is established to optimize the efficiency of the baseline value calculation.

[0032] S22. Generate a simulated thermal load of the battery pack with spatial thermal distribution configuration using a programmable PTC load simulation device based on the calculated values. In this step, when generating the thermal load using the programmable PTC device, the thermal distribution configuration parameters are automatically decomposed to each PTC unit according to the calculated values. By using preset spatial thermal distribution templates, such as centrosymmetric / edge-reinforced modes, different battery pack configurations can be adapted to improve the realism of the simulation.

[0033] S23. Real-time monitoring of the spatial distribution uniformity of the heat load using an infrared thermal imager and feedback to the central controller. This step employs multispectral analysis technology during infrared thermal imager scanning to distinguish between actual thermal radiation and reflected interference, thereby improving monitoring accuracy. The heat load distribution data is converted into a heat flux density cloud map for real-time display, assisting operators in quickly identifying areas of abnormal uniformity.

[0034] S24. When the heat flux density deviation exceeds the threshold, the PTC power is dynamically adjusted using a gradient step algorithm until the standard deviation of the output heat flux density meets the battery pack's simulated thermal load. In this step, when adjusting the PTC power using the gradient step algorithm, an upper limit is set for the power change rate to prevent thermal shock. Once the standard deviation of the heat flux density meets the target, a thermal distribution calibration report is automatically generated and appended to the output parameters, providing a basis for iterative optimization.

[0035] S3. Under simulated thermal load on the battery pack, a multi-source sensing system integrating multi-level temperature sensors and a thermal imager synchronously collects battery pack temperature data, coolant inlet and outlet temperature difference, ambient temperature, and coolant flow rate, outputting a spatiotemporally correlated multidimensional temperature dataset with heat flux density mapping. Specifically, this includes:

[0036] S31. Acquire three-dimensional temperature distribution data inside the battery pack using an embedded multi-level temperature sensor layout. In this step, self-compensation technology is employed to eliminate thermal conductivity errors in the wires when acquiring three-dimensional temperature data using embedded sensors. The sensor layout density automatically adjusts with the temperature gradient; the greater the gradient, the higher the density, thereby obtaining more refined thermal field data in key areas.

[0037] S32. Acquire surface heat flux density distribution data of the battery pack using a high-frame-rate thermal imaging acquisition device. In this step, while capturing surface heat flux density using the high-frame-rate thermal imaging device, ambient light intensity parameters are recorded simultaneously and their influence is automatically compensated for. A mapping correlation model between surface heat distribution and the internal temperature field is established to improve the spatial correlation of the dataset.

[0038] S33. Record real-time data from the coolant inlet / outlet temperature difference sensor, ambient temperature sensor array, and high-precision flow meter with millisecond-level synchronization accuracy. In this step, when integrating multi-source data using millisecond-level synchronization technology, a spatiotemporal coordinate label is attached to each data point. A timestamp verification mechanism is used to eliminate signal delay interference, ensuring the timing accuracy of the dataset.

[0039] S34. Align and fuse the battery pack's internal temperature distribution data, surface heat distribution data, temperature difference data, ambient temperature data, and flow rate data according to spatiotemporal coordinates into a multidimensional temperature dataset and output it. During data fusion in this step, a Kalman filter algorithm is used to eliminate sensor noise. The heat flux density distribution data is converted into a matrix form and embedded into the dataset to construct a standardized data structure that can be directly used for 3D reconstruction.

[0040] S4. Based on the multidimensional temperature dataset, the three-dimensional thermal field distribution is reconstructed using a spatial interpolation algorithm. Temperature rise characteristic parameters including the temperature rise rate and heat dissipation energy efficiency evaluation parameters including the heat dissipation energy efficiency ratio are calculated. The thermal management performance evaluation results containing the thermal field uniformity index are output.

[0041] Specifically, this includes:

[0042] S41. Reconstruct the three-dimensional thermal field distribution of the battery pack using the Kriging interpolation algorithm based on the multidimensional temperature dataset. In this step, the interpolation weight parameters are automatically optimized according to the spatial distribution of the temperature sensors. Boundary condition constraints, such as adiabatic boundary treatment, are used to avoid edge distortion in the thermal field reconstruction and improve model fidelity.

[0043] S42. Calculate the temperature rise rate through time differentiation. In this step, an adaptive time window algorithm is used for time differentiation (narrower window for faster temperature rise, wider window for slower temperature rise) to balance calculation accuracy and noise sensitivity. An output temperature rise rate distribution cloud map is generated, visually displaying the battery pack's thermal runaway risk area.

[0044] S43. Calculate the heat dissipation efficiency ratio (EDR) based on the thermodynamic efficiency formula for input power and coolant heat dissipation. During this step, the changes in coolant properties with temperature are recorded simultaneously, and the heat dissipation calculation formula is dynamically adjusted. A transient efficiency factor is introduced to evaluate the cooling system performance under short-term overload conditions, thus improving the energy efficiency assessment dimensions.

[0045] S44. Output performance evaluation results including a thermal field uniformity index, temperature rise warning levels based on temperature rise rate classification, and an energy efficiency optimization range optimized using historical data. This step automatically generates an energy efficiency optimization trend chart by linking to the historical database when outputting the performance evaluation results. The energy efficiency optimization range threshold is dynamically updated using machine learning algorithms, allowing the evaluation criteria to adaptively adjust as the system ages.

[0046] S5. Based on the performance evaluation results, analyze the thermal field uniformity index and heat dissipation energy efficiency evaluation parameters to dynamically adjust the ambient temperature and thermal load intensity settings, and feed them back to S1 and S2 for multi-cycle iterative closed-loop testing until the thermal management performance meets the standards.

[0047] Specifically, this includes:

[0048] S51. Based on the thermal field uniformity index and temperature rise warning level from the performance evaluation results, an ambient temperature adjustment amount is generated using a fuzzy control algorithm. In this step, when generating the adjustment amount using the fuzzy control algorithm, the weight of the temperature rise warning level is set higher than that of the uniformity index (prioritizing cooling during high-risk warnings), thereby optimizing the safety of the control strategy. The confidence evaluation value of the adjustment amount is output to avoid over-adjustment.

[0049] S52. Based on the deviation between the heat dissipation efficiency ratio and the energy efficiency optimization range, a PID control law is applied to generate the thermal load intensity adjustment. In this step, when applying the PID control law, the proportional coefficient is dynamically adjusted according to the rate of change of energy efficiency deviation (the adjustment intensity is increased when the deviation is large), thereby accelerating the system convergence speed. An effective delay time parameter is added to the thermal load intensity adjustment to match the thermal inertia characteristics.

[0050] S53. Use the ambient temperature adjustment as the temperature difference threshold correction input for S1. S54. Use the heat load intensity adjustment as the heat distribution configuration parameter feedback for S2. In this step, when feeding back the ambient temperature adjustment, it is automatically broken down into independent correction parameters for each region. A failure protection mechanism (such as automatic isolation in case of single-region failure) is added to the S1 temperature difference threshold to improve the system's fault tolerance.

[0051] S54. Feedback the thermal load intensity adjustment as the thermal distribution configuration parameter from S2. This step automatically converts the thermal load intensity adjustment into a spatial thermal distribution configuration parameter, driving the programmable PTC device to update the power allocation scheme of each unit in real time, thereby improving the accuracy of the thermal load simulation. By dynamically adapting the adjustment input through a preset thermal distribution template (such as a gradient decay mode), it ensures that the simulated thermal load always conforms to the actual configuration of the battery pack, enhancing the response efficiency of closed-loop optimization.

[0052] S55. When the standard deviation of the thermal field uniformity index is less than or equal to the set threshold, there are no high-risk alarms in the temperature rise warning level, and the heat dissipation efficiency ratio remains within the optimization range for three consecutive iterations, the system is deemed to have met the standard. Upon determining compliance in this step, a test report containing elements such as the three-dimensional thermal field distribution and energy efficiency change curves is simultaneously generated. Digital signature technology ensures the report is tamper-proof, providing a reliable basis for thermal management system certification.

[0053] This embodiment also proposes an energy storage thermal management test platform for implementing the above-described energy storage thermal management test method. Its technical effects are similar to the test method described above, and will not be repeated here.

[0054] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A test method for energy storage thermal management, characterized in that, include: S1. Set the initial ambient temperature and coolant flow rate of the liquid cooling circulation system through the thermal environment simulation system of the full-size energy storage box, and output environmental status parameters including environmental parameters of multiple areas. S2. Generate simulated thermal load of battery pack based on environmental state parameters, adjust the thermal load intensity through dynamic feedback mechanism of thermal load intensity implemented by central controller, and output simulated thermal load of battery pack after spatial distribution uniformity verification. S3. Under the simulated thermal load of the battery pack, the battery pack temperature data, the temperature difference between the inlet and outlet of the coolant, the ambient temperature and the coolant flow rate are collected synchronously by a multi-source sensing system that integrates multi-level temperature sensors and thermal imagers, and the spatiotemporal correlation multidimensional temperature dataset with heat flux density mapping is output. S4. Based on the multidimensional temperature dataset, the three-dimensional thermal field distribution is reconstructed through spatial interpolation algorithm. Temperature rise characteristic parameters including temperature rise rate and heat dissipation energy efficiency evaluation parameters including heat dissipation energy efficiency ratio are calculated. The thermal management performance evaluation results including thermal field uniformity index are output. S1 specifically includes: S11. Set the initial ambient temperature for multiple areas to be independently controllable and defined with temperature difference thresholds through the temperature regulation device of the full-size energy storage box; S12. Based on the predicted heat load and the initial ambient temperature, the heat conduction equation is used to calculate the reference value of coolant flow and drive the liquid cooling circulation system to perform closed-loop regulation. S13. A high-precision temperature sensor array is used to collect ambient temperature and coolant flow rate in multiple areas in real time, calculate the temperature difference uniformity index, and iteratively adjust the coolant flow rate through proportional-integral control until the standard is met. S14. Outputs environmental condition parameters including measured values ​​of ambient temperature in multiple regions, measured values ​​of coolant flow rate, and standard deviation of temperature difference in each region. S2 specifically includes: S21. The benchmark value of heat load intensity is calculated based on the measured values ​​of ambient temperature in multiple regions using a heat transfer model. S22. Generate a simulated thermal load of the battery pack with spatial thermal distribution configuration according to the calculated value using a programmable PTC load simulation device; S23. Monitor the uniformity of spatial distribution of heat load in real time using an infrared thermal imager and feed it back to the central controller; S24. When the heat flux density deviation exceeds the threshold, the PTC power is dynamically adjusted according to the gradient step algorithm until the standard deviation of the output heat flux density meets the battery pack's simulated thermal load.

2. The energy storage thermal management test method according to claim 1, characterized in that, The method further includes: S5. Based on the performance evaluation results, analyze the thermal field uniformity index and heat dissipation energy efficiency evaluation parameters to dynamically adjust the ambient temperature and thermal load intensity settings, and feed them back to S1 and S2 for multi-cycle iterative closed-loop testing until the thermal management performance meets the standards.

3. The energy storage thermal management test method according to claim 1, characterized in that, S3 specifically includes: S31. Collect three-dimensional temperature distribution data inside the battery pack by using an embedded multi-level temperature sensor layout; S32. Obtain heat flux density distribution data on the surface of the battery pack using a high frame rate thermal imaging acquisition device; S33. Records real-time data from the coolant inlet and outlet temperature difference sensor, ambient temperature sensor array, and high-precision flow meter with millisecond-level synchronization accuracy; S34. Align and fuse the internal temperature distribution data, surface heat distribution data, temperature difference data, ambient temperature data, and flow rate data of the battery pack according to spatiotemporal coordinates into a multidimensional temperature dataset and output it.

4. The energy storage thermal management test method according to claim 1, characterized in that, S4 specifically includes: S41. Reconstruct the three-dimensional thermal field distribution of the battery pack using the Kriging interpolation algorithm based on a multidimensional temperature dataset; S42. Calculate the temperature rise rate through time differential operation; S43. Calculate the heat dissipation efficiency ratio based on the thermodynamic efficiency formula of the input power of the heat load and the heat dissipation of the coolant. S44. Output includes a thermal field uniformity index, a temperature rise warning level based on the temperature rise rate classification, and a performance evaluation result of an energy efficiency optimization range optimized by combining historical data.

5. The energy storage thermal management test method according to claim 2, characterized in that, S5 specifically includes: S51. Based on the thermal field uniformity index and temperature rise warning level in the performance evaluation results, the ambient temperature adjustment amount is generated through a fuzzy control algorithm. S52. Based on the deviation between the heat dissipation efficiency ratio and the energy efficiency optimization range, a PID control law is applied to generate the heat load intensity adjustment amount. S53, Use the ambient temperature adjustment amount as the temperature difference threshold correction input feedback for S1; S54. The thermal load intensity adjustment amount is used as the thermal distribution configuration parameter feedback of S2; S55. When the standard deviation of the thermal field uniformity index is less than or equal to the set threshold, there is no high-risk alarm for the temperature rise warning level, and the heat dissipation efficiency ratio is in the optimization range for three consecutive iterations, it is determined to meet the standard.

6. An energy storage thermal management test platform, characterized in that, Used to implement the energy storage thermal management test method according to any one of claims 1-5.

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