A new energy vehicle thermal management test system and method

Abstract

The application provides a new energy automobile thermal management test system and method, relates to the technical field of thermal management test, generates a first control signal and a second control signal based on power load parameters, and sends the first control signal and the second control signal to a heat source simulator; first thermal response data corresponding to the first control signal and second thermal response data corresponding to the second control signal are collected; a heat transfer delay characteristic value between a power total and a cooling pipeline is determined through a phase offset of the first control signal and the first thermal response data; a heat exchange loss factor between a battery pack and the cooling pipeline is determined through an amplitude attenuation of the second control signal and the second thermal response data; a thermal response abnormality coefficient of a thermal management system is determined in combination with the heat transfer delay characteristic value and the heat exchange loss factor, when the thermal response abnormality coefficient is greater than a preset threshold value, it is determined that the dynamic response of the thermal management system is abnormal and a test alarm is triggered. The application scheme can realize simulation test of thermal management strategies of new energy automobiles under various working conditions.

Description

Technical Field

[0001] This application relates to the field of thermal management testing technology, and more specifically, to a thermal management testing system and method for new energy vehicles. Background Technology

[0002] New energy vehicles generate a lot of heat during operation. If the heat cannot be conducted and dissipated in a timely and effective manner, it will directly affect battery life, vehicle performance and safety. Therefore, conducting testing and evaluation of the thermal management system has become a key part of the vehicle development process. By simulating the changes in heat load under actual working conditions, the adjustment capability and response characteristics of the thermal management strategy under different operating conditions can be comprehensively evaluated, thereby supporting the optimized design and reliability verification of the thermal management strategy.

[0003] Existing testing methods for thermal management in new energy vehicles generally rely on actual vehicle or system operating conditions. This approach is not only time-consuming and environmentally constrained, but also makes it difficult to accurately control heat source characteristics and heat transfer parameters. It is particularly inadequate in evaluating the response performance of thermal management systems. A key issue is the lack of a dynamic heat source simulation mechanism capable of simulating various power loads and thermal interactions. This makes it difficult to accurately quantify the heat transfer delay and energy attenuation characteristics between the cooling system and the heat source, thus hindering a quantitative assessment of the dynamic response capability of the thermal management system. Furthermore, this testing method fails to cover the boundary conditions of operating conditions, resulting in the inability to fully expose and verify the performance blind spots of thermal management strategies under these conditions. Therefore, how to achieve simulation testing of thermal management strategies for new energy vehicles under various operating conditions has become a significant challenge for the industry. Summary of the Invention

[0004] This application provides a thermal management testing system and method for new energy vehicles, which can realize the simulation testing of thermal management strategies of new energy vehicles under various operating conditions.

[0005] In a first aspect, this application provides a test method for thermal management of new energy vehicles. The thermal management test system for new energy vehicles includes a powertrain test bench, a heat source simulator, and cooling pipes. The test method comprises:

[0006] Receive power load parameters fed back from the powertrain test bench;

[0007] A first control signal and a second control signal are generated based on the power load parameters, and the first control signal and the second control signal are sent to the heat source simulator, wherein the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack.

[0008] Collect the first thermal response data corresponding to the first control signal and the second thermal response data corresponding to the second control signal;

[0009] The characteristic value of heat transfer delay between the power system and the cooling pipeline is determined by the phase offset between the first control signal and the first thermal response data.

[0010] The heat exchange loss factor between the battery pack and the cooling pipeline is determined by the amplitude attenuation of the second control signal and the second thermal response data.

[0011] The thermal response anomaly coefficient of the thermal management system is determined by combining the heat transfer delay characteristic value and the heat exchange loss factor. When the thermal response anomaly coefficient is greater than a preset threshold, the dynamic response of the thermal management system is determined to be abnormal and a test alarm is triggered.

[0012] In this embodiment, the power load parameters fed back from the powertrain test bench are received via the controller area network bus.

[0013] In this embodiment, generating the first control signal and the second control signal based on the power load parameters specifically includes:

[0014] The power load parameters are input into a pre-trained thermal characteristic prediction model, which establishes a mapping relationship between the power load and the heat source characteristics based on historical test data.

[0015] The heat generation and temperature change curves of the powertrain under the current load are output by the thermal characteristic prediction model, and a first control signal is generated for controlling the heat source simulator.

[0016] The characteristic value of the thermal coupling effect of the battery pack under the power load parameters is determined based on the physical positional relationship and heat conduction path between the powertrain and the battery pack.

[0017] A second control signal for controlling the heat source simulator is generated based on the characteristic value of the thermal coupling effect.

[0018] In this embodiment, determining the heat transfer delay characteristic value between the powertrain and the cooling pipes by the phase offset between the first control signal and the first thermal response data specifically includes:

[0019] Extract the temperature rise time point of the first control signal and the temperature peak time point of the first thermal response data;

[0020] Calculate the time difference between the temperature rise time point and the temperature peak time point;

[0021] The time difference is mapped to a quantitative index of the heat transfer delay characteristic value.

[0022] In this embodiment, determining the heat exchange loss factor between the battery pack and the cooling pipeline by the amplitude attenuation of the second control signal and the second thermal response data specifically includes:

[0023] The set temperature peak of the second control signal and the actual temperature peak of the second thermal response data are obtained;

[0024] Calculate the absolute value of the difference between the set peak temperature and the actual peak temperature;

[0025] The absolute value of the difference is normalized by combining the real-time flow data of the cooling pipeline to obtain the heat exchange loss factor between the battery pack and the cooling pipeline.

[0026] In this embodiment, determining the thermal response anomaly coefficient of the thermal management system by combining the heat transfer delay characteristic value and the heat exchange loss factor specifically includes:

[0027] A weighted summation operation is performed on the heat transfer delay characteristic value and the heat exchange loss factor;

[0028] A correction factor for the flow fluctuation of the cooling pipeline is introduced to compensate for the weighted summation result;

[0029] The compensation calculation results are used as the thermal response anomaly coefficient of the thermal management system.

[0030] In this embodiment, the thermal response anomaly coefficient is a characteristic index that measures the degree to which the overall thermal response of the thermal management system deviates from the normal state.

[0031] In this embodiment, the first thermal response data refers to the actual temperature change response data generated by the powertrain simulation area after receiving the first control signal.

[0032] In this embodiment, the second thermal response data refers to the actual temperature change response data generated by the simulated area of ​​the battery pack after receiving the second control signal.

[0033] Secondly, this application provides a new energy vehicle thermal management testing system for performing a new energy vehicle thermal management testing method, the new energy vehicle thermal management testing system comprising:

[0034] The acquisition module is used to receive power load parameters fed back from the powertrain test bench;

[0035] The feature processing module is used to generate a first control signal and a second control signal based on the power load parameters, and send the first control signal and the second control signal to the heat source simulator, wherein the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack.

[0036] The feature processing module is also used to collect first thermal response data corresponding to the first control signal and second thermal response data corresponding to the second control signal;

[0037] The feature processing module is further configured to determine the heat transfer delay feature value between the power unit and the cooling pipeline by the phase offset between the first control signal and the first thermal response data;

[0038] The feature processing module is also used to determine the heat exchange loss factor between the battery pack and the cooling pipeline by the amplitude attenuation of the second control signal and the second thermal response data.

[0039] The execution module is used to determine the thermal response anomaly coefficient of the thermal management system by combining the heat transfer delay characteristic value and the heat exchange loss factor. When the thermal response anomaly coefficient is greater than a preset threshold, the thermal management system is judged to have a dynamic response anomaly and a test alarm is triggered.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] First, the power load parameters fed back from the powertrain test bench are received; based on the power load parameters, a first control signal and a second control signal are generated and sent to the heat source simulator, wherein the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack; first thermal response data corresponding to the first control signal and second thermal response data corresponding to the second control signal are collected; the phase offset between the first control signal and the first thermal response data is used to determine the heat transfer delay characteristic value between the powertrain and the cooling pipeline; the amplitude attenuation between the second control signal and the second thermal response data is used to determine the heat exchange loss factor between the battery pack and the cooling pipeline; the thermal response anomaly coefficient of the thermal management system is determined by combining the heat transfer delay characteristic value and the heat exchange loss factor; when the thermal response anomaly coefficient is greater than a preset threshold, the dynamic response of the thermal management system is determined to be abnormal and a test alarm is triggered.

[0042] Therefore, this application, by constructing a controllable heat source simulation environment and collecting and analyzing first and second thermal response data, enables the simulation testing of thermal management strategies for new energy vehicles under various operating conditions, demonstrating significant overall benefits. Firstly, by inputting simulated thermal load signals into the battery pack simulation area, an adjustable power load and thermal interaction state is formed, effectively overcoming the lack of a dynamic heat source simulation mechanism in existing tests. This enables precise construction and control of different thermal load conditions, fundamentally improving the controllability and reproducibility of heat source input. Secondly, collecting first thermal response data comprehensively reflects the instantaneous response characteristics of the battery pack simulation area after receiving simulated thermal load signals, providing fundamental data support for evaluating the response speed and adjustment capability of the thermal management system to rapid thermal disturbances, significantly improving the difficulty in quantifying response performance in existing real-vehicle tests. Then, a second control signal is generated based on the first thermal response data to drive the thermal management system. This invention enables dynamic response testing of thermal management strategies under closed-loop control mechanisms, accurately simulating the actual operation of the thermal management system under real-world conditions and improving the test's ability to assess the adaptability of the strategy's control logic. Finally, by collecting second thermal response data and comparing it with the first thermal response data, the energy response delay and heat transfer efficiency of the thermal management system during the control process can be quantified, providing key technical support for evaluating the strategy's response sensitivity and steady-state control capability. Through the aforementioned closed-loop simulation and response analysis process, the limitations of traditional real-vehicle testing in terms of controllability, repeatability, and operating condition diversity can be effectively overcome, improving the flexibility and systematic nature of the testing process. Ultimately, this achieves systematic verification and optimized design of new energy vehicle thermal management strategies under complex operating conditions. In summary, this application's solution can achieve high-precision simulation testing of thermal management strategies under multiple operating conditions, improving the accuracy of system response characteristic evaluation and the adaptability verification capability of thermal management strategies. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of the thermal management test method for new energy vehicles provided in this application;

[0045] Figure 2 This is an exemplary flowchart of generating control signals according to the present application;

[0046] Figure 3 This is an exemplary flowchart for determining the heat exchange loss factor according to the present application;

[0047] Figure 4 This is a module structure diagram of the new energy vehicle thermal management test system provided in this application. Detailed Implementation

[0048] 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. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] This application provides a thermal management testing system and method for new energy vehicles. The core of the system is to generate a first control signal and a second control signal based on power load parameters and send them to a heat source simulator; collect first thermal response data corresponding to the first control signal and second thermal response data corresponding to the second control signal; determine the heat transfer delay characteristic value between the powertrain and cooling pipes by the phase offset between the first control signal and the first thermal response data; determine the heat exchange loss factor between the battery pack and the cooling pipes by the amplitude attenuation between the second control signal and the second thermal response data; and determine the thermal response anomaly coefficient of the thermal management system by combining the heat transfer delay characteristic value and the heat exchange loss factor. When the thermal response anomaly coefficient is greater than a preset threshold, the dynamic response of the thermal management system is determined to be abnormal, and a test alarm is triggered. This invention can realize the simulation testing of thermal management strategies for new energy vehicles under various operating conditions.

[0050] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a thermal management testing method for new energy vehicles according to this embodiment of the present application. The thermal management testing method for new energy vehicles includes the following steps:

[0051] In step S1, the power load parameters fed back from the powertrain test bench are received.

[0052] It should be noted that this application constructs a thermal management test system coupled with a powertrain test bench and a heat source simulator to simulate the thermal behavior of new energy vehicles under different operating loads. The system generates two control signals based on the power load parameters fed back from the powertrain test bench, which drive the powertrain heat source module and the battery pack heat exchange module in the heat source simulator, respectively, to simulate the heat generation and exchange process under real operating conditions. Then, thermal response data of the corresponding areas under the excitation of the two control signals are collected. The phase offset between the control signals and the thermal response data is used to quantify the heat transfer delay from the powertrain to the cooling system, thereby characterizing the thermal inertia and response efficiency of this path. Furthermore, the amplitude attenuation of the battery pack side thermal response data compared to the control signal is calculated to reflect the degree of energy loss during heat transfer. The two are constructed into a thermal response anomaly coefficient, which is used to determine the overall thermal management response consistency and dynamic stability of the system. When the coefficient exceeds the threshold, a fault alarm is triggered, thereby realizing accurate monitoring and early warning of the dynamic response capability of the thermal management system of new energy vehicles.

[0053] In this embodiment, the power load parameters fed back from the powertrain test bench can be received via the controller area network bus. Specifically, speed sensors, torque sensors, and current sensors are installed at key locations on the powertrain test bench, and the physical quantities are converted into 0-5V analog signals. After filtering and signal amplification preprocessing, the signals are converted into digital signals by a 16-bit analog-to-digital converter (ADC) and sent to the bus via a transceiver. The test system listens to the bus, verifies the data, parses the frame data, restores the physical quantities, and stores them in a buffer for subsequent modules to call. It should be noted that the power load parameters specifically include physical quantities reflecting the current working load state of the powertrain, such as output torque, speed, current, voltage, and power.

[0054] In step S2, a first control signal and a second control signal are generated based on the power load parameters, and the first control signal and the second control signal are sent to the heat source simulator, wherein the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack.

[0055] In this embodiment, reference Figure 2 As shown, this figure is an exemplary flowchart of generating control signals in an embodiment of this application. In this embodiment, the generation of the first control signal and the second control signal based on the power load parameters can be achieved by the following steps:

[0056] In step S21, the power load parameters are input into a pre-trained thermal characteristic prediction model, which establishes a mapping relationship between power load and heat source characteristics based on historical test data.

[0057] In step S22, the heat generation and temperature change curve of the powertrain under the current load are output through the thermal characteristic prediction model to generate a first control signal for controlling the heat source simulator.

[0058] In step S23, the characteristic value of the thermal coupling effect of the battery pack under the power load parameters is determined based on the physical positional relationship and heat conduction path between the powertrain and the battery pack.

[0059] In step S24, a second control signal for controlling the heat source simulator is generated based on the thermal coupling effect characteristic value.

[0060] It should be noted that the thermal characteristic prediction model in this application is a mathematical model built based on historical test data. It is used to map the functional relationship between power load parameters and heat source characteristics. Its technical principle adopts a multiple linear regression algorithm, using parameters such as the speed, torque, and power of the powertrain as input features and the corresponding calorific value and temperature change curves as output labels. The regression equation is fitted by the least squares method to control the root mean square error between the model prediction value and the historical measured value within 5%. During training, the input parameters are normalized to the 0-1 interval to enhance the model's generalization ability. In practical applications, after receiving real-time load parameters, the model outputs the calorific value and temperature curves under the current operating conditions. These curves are converted into control signals through modulation technology to drive the heat source simulator to simulate the real thermal environment.

[0061] In addition, it should be noted that the thermal coupling effect characteristic value in this application is a characteristic value that measures the intensity of heat transfer and the degree of mutual thermal influence between the powertrain and the battery pack; furthermore, it should be noted that the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack.

[0062] In specific implementation, firstly, the thermal characteristic prediction model is constructed using a multiple linear regression algorithm. It uses historical test power load parameters (speed, torque, power) as input features and the powertrain's heat generation and temperature change curves as output labels. The mapping relationship is fitted using the least squares method, with a training objective of a root mean square error not exceeding 5%. Before inputting the current load parameters, normalization preprocessing is required to map the parameters to the 0-1 range. The model outputs the real-time heat generation and temperature curves under the current operating conditions. Secondly, the first control signal is generated using PWM (Pulse Width Modulation). The system employs a signal modulation technique to transform the temperature curve into a time-varying pattern of heating power. A 1 kHz PWM signal is generated by a microcontroller, with its duty cycle dynamically adjusted according to power demand. This controls the electric heating element of the heat source simulator, enabling the simulated area temperature to track the target curve with a temperature error controlled within ±2 degrees Celsius. Then, for the second control signal, the thermal coupling effect is calculated based on a thermal resistance network model. A thermal resistance network is constructed based on the physical distance between the powertrain and the battery pack, and the type of intermediate medium. The heat transfer ratio is calculated using the convective heat transfer coefficient, and converted into a thermal coupling effect characteristic value within the range of 0-1. The larger this characteristic value, the stronger the thermal influence of the powertrain on the battery pack. Finally, the second control signal is dynamically adjusted based on the characteristic value. For example, if the characteristic value is 0.3, it indicates that 30% of the powertrain's heat will be transferred to the battery pack. The battery pack heating module of the heat source simulator outputs heat according to this ratio. The required heating power is calculated based on the battery pack's specific heat capacity and converted into a 0-10V analog voltage signal to drive the heating unit to simulate the thermal interaction process, with the temperature response deviation controlled within ±3 degrees Celsius.

[0063] In this embodiment, sending the first control signal and the second control signal to the heat source simulator means that the test control system transmits the generated instruction signals used to simulate the heat source characteristics of the powertrain and the thermal interaction characteristics of the battery pack to the heat source simulator to drive it to accurately reproduce the thermal behavior of the corresponding components. Specifically, the first control signal is a PWM signal, which controls the output power of the heating unit in the powertrain simulation area by adjusting the pulse duty cycle, so that the temperature in that area fluctuates according to the predicted temperature change curve. The second control signal is an analog voltage signal, which determines the heating power of the battery pack simulation area based on the characteristic value of the thermal coupling effect, and adjusts the heating intensity of the heating unit in that area by changing the voltage amplitude, thereby simulating the thermal interaction between the powertrain and the battery pack. Both are sent through a dedicated signal transmission line to ensure signal stability and timely response.

[0064] In step S3, the first thermal response data corresponding to the first control signal and the second thermal response data corresponding to the second control signal are collected.

[0065] In this embodiment, the acquisition of the first thermal response data corresponding to the first control signal and the second thermal response data corresponding to the second control signal can be achieved in the following way: thermocouples and resistance sensors are deployed in the powertrain and battery pack simulation areas of the heat source simulator to acquire real-time temperature signals. After signal conditioning and 16-bit ADC conversion, the test control system reads the data at 100ms intervals, records the temperature and rate of change, and stores the data in the local database with timestamps to form a thermal response dataset corresponding to the timing of the control signals.

[0066] It should be noted that the first thermal response data in this application refers to the actual temperature change response data generated by the powertrain simulation area after receiving the first control signal; the second thermal response data refers to the actual temperature change response data generated by the battery pack simulation area after receiving the second control signal.

[0067] In step S4, the characteristic value of heat transfer delay between the power supply and the cooling pipeline is determined by the phase offset between the first control signal and the first thermal response data.

[0068] In this embodiment, determining the heat transfer delay characteristic value between the powertrain and the cooling pipes by the phase offset between the first control signal and the first thermal response data can be achieved through the following steps:

[0069] Extract the temperature rise time point of the first control signal and the temperature peak time point of the first thermal response data;

[0070] Calculate the time difference between the temperature rise time point and the temperature peak time point;

[0071] The time difference is mapped to a quantitative index of the heat transfer delay characteristic value.

[0072] It should be noted that the heat transfer delay characteristic value in this application is a characteristic index that measures the degree of lag in heat transfer between the powertrain and the cooling pipes. Specifically, in implementation, firstly, key time points are extracted. For the first control signal, an edge detection algorithm, such as threshold-based jump detection technology, is used to record the moment when the signal power first rises from the reference value to the 5% threshold as the temperature rise time point. For the first thermal response data, a sliding window peak detection method is used, where the window size is set to 1 second. The data sequence is traversed to find the moment when the temperature value first reaches a local maximum value, which is recorded as the temperature peak time point. Then… Next, the time difference is calculated. Based on the timestamps of the two time points synchronized by the system's unified clock, the absolute time difference between the two is obtained through subtraction. For example, if the temperature peak occurs 3.2 seconds after the control signal rises, the difference is 3200ms. Finally, a piecewise linear mapping rule is used to divide the time difference into 5 intervals according to the historical normal range (e.g., 0-5 seconds). That is, 0-1 seconds corresponds to a characteristic value of 0.2, 1-2 seconds corresponds to 0.4, and so on. More than 5 seconds corresponds to 1.0. The time difference is converted into a heat transfer delay characteristic value in the range of 0-1 by a lookup table method. The larger the value, the more significant the delay.

[0073] In step S5, the heat exchange loss factor between the battery pack and the cooling pipeline is determined by the amplitude attenuation of the second control signal and the second thermal response data.

[0074] In this embodiment, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the heat exchange loss factor in an embodiment of this application. In this embodiment, determining the heat exchange loss factor between the battery pack and the cooling pipeline by the amplitude attenuation of the second control signal and the second thermal response data can be achieved by the following steps:

[0075] In step S51, the set temperature peak value of the second control signal and the actual temperature peak value of the second thermal response data are obtained;

[0076] In step S52, the absolute value of the difference between the set temperature peak value and the actual temperature peak value is calculated;

[0077] In step S53, the absolute value of the difference is normalized by combining the real-time flow data of the cooling pipeline to obtain the heat exchange loss factor between the battery pack and the cooling pipeline.

[0078] It should be noted that the heat exchange loss factor in this application is a characteristic indicator that measures the degree to which the actual peak temperature deviates from the set value due to energy loss during the heat exchange process between the battery pack and the cooling pipeline.

[0079] In specific implementation, firstly, the peak temperature parameter is obtained. The set peak temperature is extracted from the second control signal, and the preset target temperature maximum value in the signal is read using the instruction parsing method. The actual peak temperature is extracted from the second thermal response data, and the maximum value search algorithm is used to traverse the data sequence to locate the value corresponding to the maximum temperature. Then, the absolute difference between the set peak temperature and the actual peak temperature is obtained through subtraction. Finally, normalization processing is performed. Real-time flow data of the cooling pipeline is introduced, which is collected by an electromagnetic flowmeter with a sampling frequency of 10Hz. Then, the normalization factor is calculated using the flow correction coefficient method. That is, the ratio of the real-time flow to the standard flow is used as the correction coefficient (for example, the correction coefficient is 2 / 3 when the real-time flow is 2L / min). The temperature difference is then multiplied by the correction coefficient (i.e., 5×2 / 3≈3.3) to obtain the value in the 0-10 range. The obtained value is then mapped to the 0-1 range, which is the heat exchange loss factor in this application. The larger the value, the lower the heat exchange efficiency and the more significant the loss between the battery pack and the cooling pipeline.

[0080] In step S6, the thermal response anomaly coefficient of the thermal management system is determined by combining the heat transfer delay characteristic value and the heat exchange loss factor. When the thermal response anomaly coefficient is greater than a preset threshold, the dynamic response of the thermal management system is determined to be abnormal and a test alarm is triggered.

[0081] In this embodiment, the thermal response anomaly coefficient of the thermal management system can be determined by combining the heat transfer delay characteristic value and the heat exchange loss factor using the following steps:

[0082] A weighted summation operation is performed on the heat transfer delay characteristic value and the heat exchange loss factor;

[0083] A correction factor for the flow fluctuation of the cooling pipeline is introduced to compensate for the weighted summation result;

[0084] The compensation calculation results are used as the thermal response anomaly coefficient of the thermal management system.

[0085] It should be noted that the thermal response anomaly coefficient in this application is a characteristic indicator that measures the degree to which the overall thermal response of the thermal management system deviates from the normal state.

[0086] In specific implementation, firstly, during the weighted summation process, the weights of the heat transfer delay characteristic value and the heat exchange loss factor are determined using the analytic hierarchy process (AHP). In this application, a judgment matrix is ​​constructed based on historical experience data, comparing the impact of heat transfer delay and heat exchange loss on system anomalies. The weight of the heat transfer delay characteristic value is set to 0.6, and the weight of the heat exchange loss factor is set to 0.4. Then, the two are multiplied by their respective weights and summed to obtain the weighted summation result. Next, in calculating the correction factor for the cooling pipe flow fluctuation rate, the following method can be used: firstly, flow data within one minute is continuously collected at a sampling interval of 100ms. Then, the fluctuation rate is calculated using the standard deviation analysis method: fluctuation rate = flow standard deviation / average flow rate. Then, based on the exponential function, i.e., correction factor = exp(0.5 × fluctuation rate), the fluctuation rate is converted into a correction factor. Finally, the correction factor is multiplied by the weighted summation result to obtain the compensation calculation result. Finally, the compensation calculation result is normalized, and the normalized value is used as the thermal response anomaly coefficient of the thermal management system.

[0087] In some embodiments, determining that the dynamic response of the thermal management system is abnormal and triggering a test alarm when the thermal response abnormality coefficient is greater than a preset threshold means that when the thermal response abnormality coefficient exceeds the preset threshold, the system determines that the dynamic thermal response of the thermal management system deviates from the normal range and starts a test alarm mechanism to indicate the abnormal state.

[0088] Therefore, this application, by constructing a controllable heat source simulation environment and collecting and analyzing first and second thermal response data, enables the simulation testing of thermal management strategies for new energy vehicles under various operating conditions, demonstrating significant overall benefits. Firstly, by inputting simulated thermal load signals into the battery pack simulation area, an adjustable power load and thermal interaction state is formed, effectively overcoming the lack of a dynamic heat source simulation mechanism in existing tests. This enables precise construction and control of different thermal load conditions, fundamentally improving the controllability and reproducibility of heat source input. Secondly, collecting first thermal response data comprehensively reflects the instantaneous response characteristics of the battery pack simulation area after receiving simulated thermal load signals, providing fundamental data support for evaluating the response speed and adjustment capability of the thermal management system to rapid thermal disturbances, significantly improving the difficulty in quantifying response performance in existing real-vehicle tests. Then, a second control signal is generated based on the first thermal response data to drive the thermal management system. This invention enables dynamic response testing of thermal management strategies under closed-loop control mechanisms, accurately simulating the actual operation of the thermal management system under real-world conditions and improving the test's ability to assess the adaptability of the strategy's control logic. Finally, by collecting second thermal response data and comparing it with the first thermal response data, the energy response delay and heat transfer efficiency of the thermal management system during the control process can be quantified, providing key technical support for evaluating the strategy's response sensitivity and steady-state control capability. Through the aforementioned closed-loop simulation and response analysis process, the limitations of traditional real-vehicle testing in terms of controllability, repeatability, and operating condition diversity can be effectively overcome, improving the flexibility and systematic nature of the testing process. Ultimately, this achieves systematic verification and optimized design of new energy vehicle thermal management strategies under complex operating conditions. In summary, this application's solution can achieve high-precision simulation testing of thermal management strategies under multiple operating conditions, improving the accuracy of system response characteristic evaluation and the adaptability verification capability of thermal management strategies.

[0089] Example 2: This application provides a new energy vehicle thermal management testing system, referring to... Figure 4 As shown in the figure, this is a schematic diagram of a new energy vehicle thermal management test system according to this embodiment of the present application. The new energy vehicle thermal management test system includes:

[0090] The acquisition module 100 is used to receive power load parameters fed back from the powertrain test bench;

[0091] The feature processing module 200 is used to generate a first control signal and a second control signal based on the power load parameters, and send the first control signal and the second control signal to the heat source simulator, wherein the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack.

[0092] The feature processing module 200 is also used to collect first thermal response data corresponding to the first control signal and second thermal response data corresponding to the second control signal;

[0093] The feature processing module 200 is further configured to determine the heat transfer delay feature value between the power unit and the cooling pipeline by the phase offset between the first control signal and the first thermal response data;

[0094] The feature processing module 200 is further configured to determine the heat exchange loss factor between the battery pack and the cooling pipeline by the amplitude attenuation of the second control signal and the second thermal response data.

[0095] The execution module 300 is used to determine the thermal response anomaly coefficient of the thermal management system by combining the heat transfer delay characteristic value and the heat exchange loss factor. When the thermal response anomaly coefficient is greater than a preset threshold, the thermal management system is judged to have a dynamic response anomaly and a test alarm is triggered.

[0096] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0098] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for testing the thermal management of new energy vehicles, wherein the new energy vehicle thermal management testing system includes a powertrain test bench, a heat source simulator, and cooling pipes, characterized in that, The testing method includes: Receive power load parameters fed back from the powertrain test bench; A first control signal and a second control signal are generated based on the power load parameters, and the first control signal and the second control signal are sent to the heat source simulator, wherein the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack. Collect the first thermal response data corresponding to the first control signal and the second thermal response data corresponding to the second control signal; The characteristic value of heat transfer delay between the power system and the cooling pipeline is determined by the phase offset between the first control signal and the first thermal response data. The heat exchange loss factor between the battery pack and the cooling pipeline is determined by the amplitude attenuation of the second control signal and the second thermal response data. The thermal response anomaly coefficient of the thermal management system is determined by combining the heat transfer delay characteristic value and the heat exchange loss factor. When the thermal response anomaly coefficient is greater than a preset threshold, the dynamic response of the thermal management system is determined to be abnormal and a test alarm is triggered. Specifically, determining the characteristic value of heat transfer delay between the powertrain and cooling pipes by the phase offset between the first control signal and the first thermal response data includes: Extract the temperature rise time point of the first control signal and the temperature peak time point of the first thermal response data; Calculate the time difference between the temperature rise time point and the temperature peak time point; The time difference is mapped to a quantitative index of the heat transfer delay characteristic value.

2. The method as described in claim 1, characterized in that, The system receives power load parameters from the powertrain test bench via the controller area network bus.

3. The method as described in claim 1, characterized in that, Generating the first and second control signals based on the power load parameters specifically includes: The power load parameters are input into a pre-trained thermal characteristic prediction model, which establishes a mapping relationship between the power load and the heat source characteristics based on historical test data. The heat generation and temperature change curves of the powertrain under the current load are output by the thermal characteristic prediction model, and a first control signal is generated for controlling the heat source simulator. The characteristic value of the thermal coupling effect of the battery pack under the power load parameters is determined based on the physical positional relationship and heat conduction path between the powertrain and the battery pack. A second control signal for controlling the heat source simulator is generated based on the characteristic value of the thermal coupling effect.

4. The method as described in claim 1, characterized in that, Determining the heat exchange loss factor between the battery pack and cooling pipes by measuring the amplitude attenuation of the second control signal and the second thermal response data specifically includes: The set temperature peak of the second control signal and the actual temperature peak of the second thermal response data are obtained; Calculate the absolute value of the difference between the set peak temperature and the actual peak temperature; The absolute value of the difference is normalized by combining the real-time flow data of the cooling pipeline to obtain the heat exchange loss factor between the battery pack and the cooling pipeline.

5. The method as described in claim 1, characterized in that, Determining the thermal response anomaly coefficient of the thermal management system by combining the heat transfer delay characteristic value and the heat exchange loss factor specifically includes: A weighted summation operation is performed on the heat transfer delay characteristic value and the heat exchange loss factor; A correction factor for the flow fluctuation of the cooling pipeline is introduced to compensate for the weighted summation result; The compensation calculation results are used as the thermal response anomaly coefficient of the thermal management system.

6. The method as described in claim 1, characterized in that, The thermal response anomaly coefficient is a characteristic indicator that measures the degree to which the overall thermal response of the thermal management system deviates from the normal state.

7. The method as described in claim 1, characterized in that, The first thermal response data refers to the actual temperature change response data generated in the powertrain simulation area after receiving the first control signal.

8. The method as described in claim 1, characterized in that, The second thermal response data refers to the actual temperature change response data generated by the simulated area of ​​the battery pack after receiving the second control signal.

9. A thermal management testing system for new energy vehicles, used to execute the thermal management testing method for new energy vehicles as described in any one of claims 1 to 8, characterized in that, The new energy vehicle thermal management testing system includes: The acquisition module is used to receive power load parameters fed back from the powertrain test bench; The feature processing module is used to generate a first control signal and a second control signal based on the power load parameters, and send the first control signal and the second control signal to the heat source simulator, wherein the first control signal is used to simulate the heat source characteristics of the powertrain, and the second control signal is used to simulate the thermal interaction characteristics of the battery pack. The feature processing module is also used to collect first thermal response data corresponding to the first control signal and second thermal response data corresponding to the second control signal; The feature processing module is further configured to determine the heat transfer delay feature value between the power unit and the cooling pipeline by the phase offset between the first control signal and the first thermal response data; The feature processing module is also used to determine the heat exchange loss factor between the battery pack and the cooling pipeline by the amplitude attenuation of the second control signal and the second thermal response data. The execution module is used to determine the thermal response anomaly coefficient of the thermal management system by combining the heat transfer delay characteristic value and the heat exchange loss factor. When the thermal response anomaly coefficient is greater than a preset threshold, the thermal management system is judged to have a dynamic response anomaly and a test alarm is triggered.

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