System and method for testing heat dissipation performance of power battery of new energy automobile

Abstract

The invention provides a new energy automobile power battery heat dissipation performance test system and method. Firstly, temperature data are collected through a temperature sensor; determining a thermal inertia factor used for representing the response speed of the heat dissipation system based on the temperature data collected during the dynamic thermal load step change; when the target power battery is in a steady-state high-load operation condition, determining a heat dissipation efficiency ratio representing the heat dissipation capability and energy efficiency of the heat dissipation system in a steady state based on the maximum temperature difference of the temperature distribution data, the average temperature and the power consumption data of the cooling system; and constructing a thermal response vector by fusing the thermal inertia factor and the heat dissipation efficiency ratio, further evaluating the heat dissipation performance of the target power battery according to the thermal response vector to obtain a heat dissipation performance index, and judging the test grade of the heat dissipation performance of the target power battery according to the heat dissipation performance index. By adopting the scheme of the application, the associated test of the dynamic and steady-state performance of the power battery heat dissipation system can be realized, so that the reliability of the evaluation result of the heat dissipation performance of the power battery is improved.

Description

Technical Field

[0001] This application relates to the field of heat dissipation performance testing technology, and more specifically, to a heat dissipation performance testing system and method for power batteries of new energy vehicles. Background Technology

[0002] With the continuous improvement of energy density and power density of power batteries for new energy vehicles, the heat generated by batteries under high-rate charge and discharge conditions has increased significantly. Heat dissipation performance directly affects battery life, safety and overall energy efficiency. Traditional battery heat dissipation performance testing methods mainly rely on steady-state temperature or single-point thermal response measurement. They assess heat dissipation capacity by recording temperature under static conditions or by simple power consumption analysis. Although these methods can provide some thermal management reference, they are difficult to fully reflect the performance characteristics of the heat dissipation system under actual operating conditions when faced with complex dynamic loads and rapid thermal responses, resulting in deviations between test results and real operating conditions.

[0003] However, existing technologies for evaluating heat dissipation performance suffer from limitations due to the reliance on a single metric. Most methods focus only on either steady-state temperature distribution or transient response speed, neglecting the intrinsic correlation between dynamic response characteristics and steady-state heat dissipation capacity. This leads to significant uncertainty in heat dissipation performance assessments under high loads or complex thermal cycling conditions. Particularly during dynamic load switching, uneven temperature gradient distribution, or fluctuations in cooling system power consumption, a single metric cannot comprehensively reflect the overall thermal management effect of the system, making test results susceptible to transient disturbances and lacking reliability and robustness. Therefore, how to achieve correlation testing of the dynamic and steady-state performance of power battery cooling systems, thereby improving the reliability of power battery heat dissipation performance evaluation results, has become a challenge for the industry. Summary of the Invention

[0004] This application provides a testing system and method for the heat dissipation performance of power batteries in new energy vehicles, which can realize the correlation testing of the dynamic and steady-state performance of the power battery heat dissipation system, thereby improving the reliability of the power battery heat dissipation performance evaluation results.

[0005] In a first aspect, this application provides a method for testing the heat dissipation performance of a power battery for new energy vehicles, applied to the heat dissipation performance testing of the heat dissipation system in a power battery for new energy vehicles, the method comprising the following steps: When dynamic thermal load excitation is applied to the target power battery, temperature data is collected by temperature sensors distributed at temperature measurement points in key areas of the target power battery and in the cooling circuit. Based on temperature data collected by temperature sensors during dynamic thermal load step changes, a thermal inertia factor is determined to characterize the response speed of the heat dissipation system. When the target power battery is in a steady-state high-load operating condition, the temperature distribution data and the power consumption data of the cooling system are collected by the sensor. Based on the maximum temperature difference, average temperature and power consumption data of the temperature distribution data, the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the cooling system under steady state, is determined. By combining the thermal inertia factor and the heat dissipation efficiency ratio, a thermal response vector is constructed to comprehensively evaluate the dynamic and steady-state performance of the heat dissipation system. The heat dissipation performance of the target power battery is evaluated based on the thermal response vector to obtain a heat dissipation performance index, and then the test level of the heat dissipation performance of the target power battery is determined based on the heat dissipation performance index.

[0006] Preferably, the thermal inertia factor used to characterize the response speed of the heat dissipation system, determined based on temperature data collected by a temperature sensor during a dynamic step change in thermal load, specifically includes: Record the initial temperature of the target power battery temperature measurement point at the start of the dynamic thermal load step change; Monitor temperature data until the peak temperature is reached, and record the response time from the start of the step change to the peak temperature. The ratio of the difference between the peak temperature and the initial temperature to the response time is used as the thermal inertia factor to characterize the response speed of the heat dissipation system.

[0007] Preferably, the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, is determined based on the maximum temperature difference, average temperature, and cooling system power consumption data of the temperature distribution data. Specifically, this includes: The average temperature and maximum temperature difference under steady-state operating conditions are determined based on the temperature distribution data. Monitor the power consumption of the cooling system during steady-state operation and obtain the steady-state average power consumption; The average temperature, the maximum temperature difference, and the steady-state average power consumption are processed to have unified dimensions. The heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, is generated based on the proportional relationship between the average temperature, maximum temperature difference, and steady-state average power consumption after processing with unified dimensions.

[0008] Preferably, the thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system is constructed by integrating the thermal inertia factor and the heat dissipation efficiency ratio, specifically including: The thermal inertia factor and the heat dissipation efficiency ratio are normalized, and the normalization reference value is set based on historical test data or standard threshold. The normalized thermal inertia factor and the heat dissipation efficiency ratio are combined into a two-dimensional vector to form a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system.

[0009] Preferably, the heat dissipation performance of the target power battery is evaluated based on the thermal response vector to obtain the heat dissipation performance index, specifically including: Obtain the thermal response vector constructed by fusing thermal inertia factor and heat dissipation efficiency ratio, and extract the vector components used to characterize dynamic response performance and steady-state heat dissipation performance. The vector components are input into a preset fuzzy inference model, and the fuzzy inference model generates corresponding fuzzy membership degrees based on multiple thermal response level rules that divide the dynamic response components and steady-state performance components. Based on the multi-dimensional fuzzy rule base constructed in the fuzzy inference model, the fuzzy membership degree is matched by rules to obtain the comprehensive fuzzy output after fuzzy inference processing; The comprehensive fuzzy output is defuzzified, and the defuzzified value is used as a heat dissipation performance index to characterize the heat dissipation performance of the target power battery.

[0010] Preferably, the test level for determining the heat dissipation performance of the target power battery based on the heat dissipation performance index specifically includes: Set heat dissipation performance level ranges; Match the heat dissipation performance index with the grade range; The test level information for the heat dissipation performance of the target power battery is determined based on the matching results.

[0011] Preferably, the dynamic thermal load excitation is a time-varying thermal power input achieved by adjusting the battery charging and discharging current.

[0012] Secondly, this application provides a new energy vehicle power battery heat dissipation performance testing system, comprising: The data acquisition module is used to acquire temperature data through temperature sensors distributed in key areas of the target power battery and in the cooling circuit when dynamic thermal load excitation is applied to the target power battery. The processing module is used to determine the thermal inertia factor, which characterizes the response speed of the heat dissipation system, based on the temperature data collected by the temperature sensor during a dynamic thermal load step change. The processing module is also used to collect temperature distribution data and cooling system power consumption data of the heat dissipation system by the sensor when the target power battery is in a steady-state high-load operation condition, and to determine the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, based on the maximum temperature difference, average temperature and cooling system power consumption data of the temperature distribution data. The processing module is also used to fuse the thermal inertia factor and the heat dissipation efficiency ratio to construct a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system. The execution module is used to evaluate the heat dissipation performance of the target power battery based on the thermal response vector, obtain a heat dissipation performance index, and then determine the test level of the heat dissipation performance of the target power battery based on the heat dissipation performance index.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described method for testing the heat dissipation performance of power batteries for new energy vehicles.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned method for testing the heat dissipation performance of a power battery for new energy vehicles.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In this embodiment, when a dynamic thermal load excitation is applied to the target power battery, temperature data is collected by temperature sensors distributed at temperature measurement points in key areas of the target power battery and in the cooling circuit. Based on the temperature data collected by the temperature sensors during the step change of the dynamic thermal load, a thermal inertia factor is determined to characterize the response speed of the heat dissipation system. When the target power battery is in a steady-state high-load operating condition, its temperature distribution data and the power consumption data of the cooling system are collected by the sensors. Based on the maximum temperature difference, average temperature of the temperature distribution data, and the power consumption data of the cooling system, a heat dissipation efficiency ratio characterizing the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state is determined. The thermal inertia factor and the heat dissipation efficiency ratio are fused to construct a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system. The heat dissipation performance of the target power battery is evaluated according to the thermal response vector to obtain a heat dissipation performance index, and then the test level of the heat dissipation performance of the target power battery is determined based on the heat dissipation performance index.

[0016] Therefore, this application evaluates the heat dissipation performance of the target power battery based on the thermal response vector to obtain a heat dissipation performance index, and then determines the test level of the target power battery's heat dissipation performance based on the heat dissipation performance index. Specifically, firstly, based on temperature data collected by temperature sensors during dynamic thermal load step changes, a thermal inertia factor is determined to characterize the response speed of the heat dissipation system. This thermal inertia factor accurately quantifies the rate of temperature change of the power battery under transient thermal excitation and the system's response capability to rapid thermal disturbances, thus enabling an objective evaluation of the heat dissipation system's adaptability and transient thermal control performance under dynamic operating conditions. Secondly, based on the maximum temperature difference, average temperature, and cooling system power consumption data from the temperature distribution data, a heat dissipation efficiency ratio is determined to characterize the heat dissipation capacity and energy efficiency of the heat dissipation system under steady-state conditions. This heat dissipation efficiency ratio quantifies the overall thermal balance capability of the power battery under continuous high-load operation and the temperature control effect per unit cooling power consumption. This paper analyzes and evaluates the energy efficiency and steady-state temperature balance performance of the cooling system, providing a reliable basis for optimizing thermal management strategies. Then, by integrating the thermal inertia factor and the heat dissipation efficiency ratio, a thermal response vector is constructed to comprehensively evaluate the dynamic and steady-state performance of the cooling system. The heat dissipation performance of the target power battery is evaluated based on this thermal response vector, resulting in a heat dissipation performance index. Evaluating the heat dissipation performance of the target power battery through the thermal response vector enables a correlation analysis between dynamic response and steady-state thermal management capabilities, overcoming the problem that traditional single indicators cannot reflect the overall system performance. It also enhances the sensitivity to changes in heat dissipation performance under complex operating conditions, ensuring that the evaluation results reflect both transient thermal characteristics and steady-state thermal balance effects. Finally, the test level of the target power battery's heat dissipation performance is determined based on the heat dissipation performance index, enabling a quantitative classification of the power battery's heat dissipation performance and providing a basis for battery system design optimization and thermal management strategy adjustment. In conclusion, the solution proposed in this application can achieve a correlation test of the dynamic and steady-state performance of the power battery cooling system, thereby improving the reliability of the power battery heat dissipation performance evaluation results under complex operating conditions. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a method for testing the heat dissipation performance of a power battery for new energy vehicles, according to some embodiments of this application. Figure 2 This is a schematic diagram illustrating the application scenario of a new energy vehicle power battery heat dissipation performance testing system according to some embodiments of this application; Figure 3 This is a schematic flowchart illustrating the process of determining the heat dissipation performance index according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a new energy vehicle power battery heat dissipation performance testing system according to some embodiments of this application; Figure 5This is a schematic diagram of the structure of a computer device for implementing a method for testing the heat dissipation performance of a power battery for new energy vehicles, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a method for testing the heat dissipation performance of a power battery for new energy vehicles, according to some embodiments of this application. This method is applied to the heat dissipation performance testing of the heat dissipation system in a power battery for new energy vehicles, and includes the following steps: In step 101, when applying dynamic thermal load excitation to the target power battery, temperature data is collected by temperature sensors distributed at temperature measurement points in key areas of the target power battery and in the cooling circuit.

[0020] In some embodiments, reference Figure 2 As shown in the figure, this is a schematic diagram of the application scenario of the new energy vehicle power battery heat dissipation performance testing system according to some embodiments of the present invention. It mainly includes a data acquisition end and a server. The data acquisition end includes sensors, including temperature sensors and power sensors. The sensors can collect temperature data of the power battery and power consumption data of the cooling system. Furthermore, the data collected by the sensors can be uploaded to the server through a communication network. The server executes the execution code of the new energy vehicle power battery heat dissipation performance testing method to complete the evaluation of the power battery heat dissipation performance test level.

[0021] It should be noted that the dynamic thermal load excitation in this application is a test input used to simulate the drastic fluctuations in the thermal load power of the power battery under complex operating conditions such as acceleration, hill climbing, high-speed cruising, and regenerative braking experienced by new energy vehicles during actual driving; the key area temperature measurement point is used to refer to the external surface temperature measurement position determined by the thermal simulation analysis results based on the computer-aided engineering model of the target power battery and combined with its external geometric features, which can indirectly reflect the thermal state of the internal core area; the cooling circuit is used to form the circulation path of the external thermal management system of the target power battery, including the assembly of coolant pipes, interfaces, and heat exchangers.

[0022] In some embodiments, when applying dynamic thermal load excitation to the target power battery, temperature data can be collected by temperature sensors distributed at key temperature measurement points and cooling circuits in the target power battery, as follows: First, based on the voltage, current, and temperature safe operating range specified in the target power battery product specifications, a dynamic thermal load excitation profile completely within this boundary range is designed. This profile is typically composed of a standardized light vehicle test cycle power spectrum superimposed with a short-duration peak power pulse with strictly limited amplitude, ensuring that the target power battery will not suffer any risks that may damage its health or safety, such as overcharging, over-discharging, or exceeding temperature limits, during the entire test process. Simultaneously with applying this safe dynamic thermal load excitation, a synchronous temperature data acquisition process is initiated. Temperature sensors, based on a distributed deployment scheme determined in advance through thermal simulation, are installed at key temperature measurement points on the target power battery's casing corresponding to internal high-heat-generating components (such as the middle of the cell) and temperature-sensitive components (such as the edge of the cell), and thermally conductive... To ensure measurement accuracy, a paste is used. Simultaneously, identical temperature sensors are securely installed on the outer surface of the metal pipe walls at the inlet and outlet of the cooling circuit. All temperature sensors are connected to a high-precision data acquisition instrument via data cables. This instrument continuously and synchronously reads the measured values ​​of all channels at a set sampling frequency, such as 10 samples per second. This complete temperature data stream with precise timestamps is stored in real-time on the hard drive of the host computer. This complete, synchronous temperature data stream, acquired without damaging the target power battery, is then used as input for subsequent non-destructive performance evaluation.

[0023] In step 102, a thermal inertia factor is determined based on temperature data collected by a temperature sensor during a dynamic thermal load step change. This factor characterizes the response speed of the heat dissipation system.

[0024] In some embodiments, the thermal inertia factor characterizing the response speed of the heat dissipation system can be determined based on temperature data collected by a temperature sensor during a dynamic step change in thermal load by the following steps: Record the initial temperature of the target power battery temperature measurement point at the start of the dynamic thermal load step change; Monitor temperature data until the peak temperature is reached, and record the response time from the start of the step change to the peak temperature. The ratio of the difference between the peak temperature and the initial temperature to the response time is used as the thermal inertia factor to characterize the response speed of the heat dissipation system.

[0025] It should be noted that the initial temperature in this application is the temperature value at the start of the step for calculating the temperature rise reference; the peak temperature is the highest temperature value that the temperature measurement point can reach under thermal load excitation; the response time is the time interval from the start of the step to the temperature reaching the peak value, used to quantify the speed of the heat dissipation system's response; and the thermal inertia factor is a dynamic response index that measures the ability of the heat dissipation system to suppress the rapid rise of battery cell temperature when subjected to dynamic thermal load shock.

[0026] In specific implementation, firstly, recording the initial temperature of the target power battery temperature measurement point at the start of a dynamic thermal load step change can be achieved as follows: The host computer software controlling the power battery testing system, at the same moment it issues the command to the testing system to execute a dynamic thermal load step change, records this moment as the start time of the step change, and synchronously reads the temperature readings of all temperature measurement points at this time from the data acquisition system, using these readings as the initial temperature of each temperature measurement point. Secondly, monitoring the temperature data until the peak temperature is reached, and recording the response time from the start of the step change to reaching the peak temperature, can be achieved as follows: The system enters a monitoring state, continuously receiving and recording temperature data from all temperature measurement points. The data acquisition system compares the current temperature reading with previously recorded historical readings in real time. When the system detects that the temperature reading of a certain temperature measurement point has stopped rising for several consecutive sampling periods and begins to show a decrease in temperature, the system will continue monitoring until the peak temperature is reached. When the temperature reading is decreasing or remains stable, the last highest temperature value before the inflection point of this temperature reading change trend is determined as the peak temperature of the temperature measurement point. At the same time, the time elapsed from the start of the step change to the time when this peak temperature is reached is calculated by the timestamp of the data acquisition system, and this time is recorded as the response time of the temperature measurement point. Then, the ratio of the difference between the peak temperature and the initial temperature to the response time can be used as a thermal inertia factor to characterize the response speed of the heat dissipation system. This can be achieved in the following way: The data processing unit calculates the difference between the peak temperature and the initial temperature of each temperature measurement point according to the recorded initial temperature, peak temperature and response time, according to a pre-set calculation formula, and divides the difference by the corresponding response time. The result is used as a thermal inertia factor to characterize the thermal response speed of the heat dissipation system in the area where the temperature measurement point is located.

[0027] It should also be noted that the scheme in this embodiment constructs a thermal inertia factor index for quantifying the dynamic response performance of the heat dissipation system. By measuring three characteristic parameters—initial temperature, peak temperature, and response time—during a step change in dynamic thermal load, a quantitative model characterizing the response speed of the heat dissipation system is established. Its beneficial effects are reflected in three aspects: First, it establishes a quantitative characterization system for the dynamic performance of the heat dissipation system, realizing the expansion of the evaluation dimension from static heat dissipation capacity to dynamic response characteristics; second, it uses directly measurable temperature and time parameters for calculation, avoiding complex modeling processes and improving the feasibility of engineering applications; third, it provides clear design guidance for the optimization of the heat dissipation system. By comparing the thermal inertia factors of different schemes, the heat dissipation structure design can be improved in a targeted manner, effectively enhancing the thermal management capability of the battery system in response to power fluctuations.

[0028] In step 103, when the target power battery is in a steady-state high-load operating condition, the sensor collects its temperature distribution data and the cooling system power consumption data of the heat dissipation system. Based on the maximum temperature difference, average temperature and cooling system power consumption data of the temperature distribution data, the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, is determined.

[0029] It should be noted that the steady-state high-load operating condition is used to characterize the thermal stability state of the target power battery under continuous high-power operation. Specifically, after applying a constant high-rate discharge current or a constant high-power charging current to the target power battery, the temperature changes collected by temperature sensors deployed in key areas of the power battery are continuously monitored, and the power battery terminal voltage and the power consumption changes of the cooling system under the corresponding operating conditions are also monitored. During the continuous monitoring period, when the temperature change rate is lower than the preset steady-state threshold, the voltage change rate is within the steady-state allowable range, and the fluctuation of the cooling system power consumption does not exceed the preset range, the target power battery is determined to have entered the steady-state high-load operating condition. During this condition, the rate of heat generation inside the power battery and the rate of heat removal by the heat dissipation system reach a dynamic balance. At this time, the collected temperature distribution data and cooling system power consumption data can reflect the steady-state heat dissipation capacity and energy efficiency level of the heat dissipation system under high heat load conditions.

[0030] In some embodiments, the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, can be determined based on the maximum temperature difference, average temperature, and cooling system power consumption data of the temperature distribution data, using the following steps: The average temperature and maximum temperature difference under steady-state operating conditions are determined based on the temperature distribution data. Monitor the power consumption of the cooling system during steady-state operation and obtain the steady-state average power consumption; The average temperature, the maximum temperature difference, and the steady-state average power consumption are processed to have unified dimensions. The heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, is generated based on the proportional relationship between the average temperature, maximum temperature difference, and steady-state average power consumption after processing with unified dimensions.

[0031] It should be noted that the steady-state average power consumption in this application refers to the average energy consumption per unit time during the steady-state high-load operation of the cooling system; the heat dissipation efficiency ratio is an indicator that measures the temperature control capability that the cooling system can achieve per unit energy consumption under steady-state high-load conditions.

[0032] In specific implementation, firstly, determining the average temperature and maximum temperature difference under steady-state operating conditions based on the temperature distribution data can be achieved as follows: The arithmetic mean of all measuring points during the steady-state period is taken using numerical statistical methods, and the result is used as the average temperature under steady-state operating conditions. The range of steady-state temperatures at all measuring points is taken as the maximum temperature difference under steady-state operating conditions. Secondly, monitoring the power consumption of the cooling system during steady-state operation and obtaining the steady-state average power consumption can be achieved as follows: A power measuring device pre-installed on the cooling circuit records the instantaneous power consumption of the cooling system during the same steady-state period, and then all instantaneous power consumption is recorded. The average power consumption is taken as the steady-state average power consumption. Then, the average temperature, the maximum temperature difference, and the steady-state average power consumption are processed to unify their dimensions. This can be achieved by mapping the three indicators to the same dimensionless scale using a minimum-maximum normalization method based on historical test samples to eliminate differences in physical dimensions. Finally, the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady-state conditions, is generated based on the proportional relationship between the average temperature, maximum temperature difference, and steady-state average power consumption after unification. This can be achieved by using the designed engineering weights or weighting coefficients determined based on orthogonal experiments / expert experience. The normalized average temperature and maximum temperature difference are linearly weighted or combined using the L2 norm to form a steady-state temperature control characteristic. The heat dissipation efficiency ratio is then established by comparing this characteristic with the normalized steady-state average power consumption, thus reflecting the temperature control capability per unit of cooling power consumption. It should be further explained that the principle lies in placing the key temperature indicator reflecting the difficulty of temperature control during steady-state operation and the cooling power consumption indicator reflecting energy consumption level on the same dimensionless scale, constructing a comprehensive quantitative indicator that reflects the balance between heat dissipation capability and energy efficiency through a proportional relationship. The average temperature reflects the overall thermal level of the battery, and the maximum temperature difference reflects the temperature... The uniformity of temperature distribution is determined by normalizing both the temperature control load and the average steady-state power consumption. The two are then linearly combined according to preset weights or calculated using the L2 norm to obtain a steady-state temperature control characteristic quantity. This characteristic quantity comprehensively reflects the temperature control load that the thermal management system needs to overcome under steady-state high load conditions. Then, the ratio of this temperature control characteristic quantity to the normalized steady-state average power consumption is calculated. By comparing the temperature control effect corresponding to unit energy consumption, a unified evaluation of the steady-state heat dissipation capacity and energy efficiency of the heat dissipation system is achieved. The fundamental logic is: the higher the temperature control load and the lower the required cooling power consumption, the higher the heat dissipation efficiency. This proportional model allows for a direct quantification of steady-state heat dissipation performance.

[0033] In step 104, the thermal inertia factor and the heat dissipation efficiency ratio are combined to construct a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system.

[0034] In some embodiments, the thermal response vector for comprehensively evaluating the dynamic and steady-state performance of a heat dissipation system can be constructed by fusing the thermal inertia factor and the heat dissipation efficiency ratio using the following steps: The thermal inertia factor and the heat dissipation efficiency ratio are normalized, and the normalization reference value is set based on historical test data or standard threshold. The normalized thermal inertia factor and the heat dissipation efficiency ratio are combined into a two-dimensional vector to form a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system.

[0035] It should be noted that the normalized reference value in this application refers to the threshold set based on historical test data or design standards to provide a benchmark for normalization processing; the thermal response vector refers to a composite index used to comprehensively evaluate the dynamic and steady-state performance of the heat dissipation system.

[0036] In specific implementation, firstly, the thermal inertia factor and the heat dissipation efficiency ratio are normalized. The normalization reference value is set based on historical test data or a standard threshold, which can be achieved in the following way: The data processing unit normalizes the calculated thermal inertia factor and heat dissipation efficiency ratio, selects typical values ​​from a historical test database containing test data from each test, or determines a standard threshold as the normalization reference value according to the design specifications, divides the thermal inertia factor by its corresponding normalization reference value to obtain the normalized thermal inertia factor, and similarly divides the heat dissipation efficiency ratio by its corresponding normalization reference value. The normalized heat dissipation efficiency ratio is obtained, converting both indicators into dimensionless scalars. Then, the normalized thermal inertia factor and the heat dissipation efficiency ratio are combined into a two-dimensional vector to form a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system. This can be achieved in the following way: the data processing unit takes the normalized thermal inertia factor as the first component and the normalized heat dissipation efficiency ratio as the second component, and combines them in sequence into a two-dimensional vector with two components. This two-dimensional vector is used as the thermal response vector that can comprehensively characterize the dynamic response characteristics and steady-state heat dissipation performance of the heat dissipation system.

[0037] In step 105, the heat dissipation performance of the target power battery is evaluated based on the thermal response vector to obtain a heat dissipation performance index, and then the test level of the heat dissipation performance of the target power battery is determined based on the heat dissipation performance index.

[0038] In some embodiments, reference Figure 3 As shown in the figure, this is a flowchart illustrating the process of determining the heat dissipation performance index in some embodiments of this application. In this embodiment, the heat dissipation performance of the target power battery is evaluated based on the thermal response vector, and the heat dissipation performance index is obtained by the following steps: In step 1051, the thermal response vector constructed by fusing thermal inertia factor and heat dissipation efficiency ratio is obtained, and the vector components used to characterize dynamic response performance and steady-state heat dissipation performance are extracted. In step 1052, the vector components are input into a preset fuzzy inference model, and the fuzzy inference model generates corresponding fuzzy membership degrees based on multiple thermal response level rules that divide the dynamic response components and steady-state performance components. In step 1053, the fuzzy membership degree is matched according to the multi-dimensional fuzzy rule base constructed in the fuzzy inference model to obtain the comprehensive fuzzy output after fuzzy inference processing; In step 1054, the comprehensive fuzzy output is defuzzified, and the defuzzified value is used as a heat dissipation performance index to characterize the heat dissipation performance of the target power battery.

[0039] It should be noted that the fuzzy inference model in this application is a computational model used to convert vector component inputs into fuzzy semantic representations; the thermal response level rule is a set of logical judgment conditions used to define the correspondence between different numerical ranges of vector components and the semantic description of heat dissipation performance; the fuzzy membership degree is a numerical value used to quantify the degree to which a vector component belongs to a fuzzy set defined by a certain thermal response level rule; the multidimensional fuzzy rule base is a set of rules used to store the mapping relationship between various fuzzy combinations of dynamic response components and steady-state performance components and the comprehensive output result, established based on expert experience; the comprehensive fuzzy output is used to reflect the comprehensive evaluation result of heat dissipation performance obtained after fuzzy inference but not yet converted into precise values; and the heat dissipation performance index is an indicator used to quantitatively characterize the heat dissipation performance of the target power battery.

[0040] In specific implementation, firstly, the thermal response vector constructed by fusing the thermal inertia factor and the heat dissipation efficiency ratio is obtained, and the vector components characterizing the dynamic response performance and steady-state heat dissipation performance are extracted. This can be achieved as follows: two vector components, namely the normalized thermal inertia factor and the normalized heat dissipation efficiency ratio, can be obtained from the constructed thermal response vector. Secondly, the vector components are input into a preset fuzzy inference model, and the fuzzy inference model generates corresponding fuzzy membership degrees based on multiple thermal response level rules divided by the dynamic response components and steady-state performance components. This can be achieved as follows: the normalized dynamic response components and steady-state performance components are used as precise input quantities and input into the preset fuzzy inference model. This system employs the widely used Mamdani-type fuzzy inference system. Its core consists of predefined fuzzy sets and membership functions. Each input component is divided into three fuzzy levels: "high," "medium," and "low." The most commonly used triangular membership function in engineering is employed to define the membership relationship for each level. For example, for a dynamic response component, the vertex of the triangular function corresponding to the "high" level is set to 0.8, the vertex to the "medium" level to 0.5, and the vertex to the "low" level to 0.2. When a specific component value is input, the system automatically generates the degree to which it belongs to the "high," "medium," or "low" fuzzy level by calculating the membership degree of the input component value to each triangular function. These calculated degree values ​​are the corresponding... The system first obtains the fuzzy membership degree; then, based on the multi-dimensional fuzzy rule base constructed in the fuzzy inference model, it performs rule matching on the fuzzy membership degree to obtain the comprehensive fuzzy output processed by fuzzy inference. This can be achieved in the following way: The system inputs the fuzzy membership degree obtained in the previous step into the preset multi-dimensional fuzzy rule base. This rule base adopts the general "IF-THEN" rule form, such as "IF dynamic response is high AND steady-state performance is high THEN comprehensive output is excellent". When performing rule matching, the standard minimum operation rule in fuzzy inference is adopted, that is, the minimum membership degree of multiple conditions in the premise part of each rule is taken as the activation strength of the rule. Then, the conclusion parts of all activated rules are synthesized by taking the maximum operation, that is, for each rule... The output takes the maximum membership degree, and finally obtains an output membership function curve representing the comprehensive evaluation result. This output curve obtained through the standard fuzzy inference process is the comprehensive fuzzy output. Finally, the comprehensive fuzzy output is defuzzified, and the value obtained after defuzzification is used as the heat dissipation performance index to characterize the heat dissipation performance of the target power battery. This can be achieved in the following way: the membership function curve of the comprehensive fuzzy output is discretized on the horizontal axis to obtain the membership degree values ​​of multiple discrete points. Then, the product of the horizontal coordinate value of each discrete point and the corresponding membership degree value is calculated, and all product results are summed. Furthermore, the sum is divided by the sum of the membership degree values ​​of all discrete points, and the quotient is the heat dissipation performance index.

[0041] Furthermore, it should be noted that the beneficial effect of this application's solution in evaluating the heat dissipation performance of the target power battery by introducing a fuzzy inference mechanism is that it can provide continuous, quantifiable, and interpretable heat dissipation performance indicators even when facing uncertainties, nonlinearities, and measurement noise in the power battery thermal response data. By mapping the dynamic response component and steady-state performance component in the thermal response vector to multiple fuzzy levels and using a multi-dimensional fuzzy rule base for inference, the system can comprehensively consider the trade-off between transient thermal response speed and steady-state temperature control capability, achieving a global evaluation of the overall performance of the heat dissipation system. Compared with traditional single index or linear weighting methods, fuzzy inference can handle the fuzzy boundaries and uncertainties of the input data, reducing evaluation bias caused by extreme temperature points or transient disturbances. At the same time, by defuzzifying the fuzzy output, it transforms the fuzzy output into a comparable heat dissipation performance index, making the evaluation results both consistent with engineering experience and numerically comparable. This provides a more robust and accurate technical basis for the optimized design, operating condition determination, and level classification of the power battery heat dissipation system.

[0042] In some embodiments, determining the test level of the heat dissipation performance of the target power battery based on the heat dissipation performance index can be achieved by the following steps: Set heat dissipation performance level ranges; Match the heat dissipation performance index with the grade range; The test level information for the heat dissipation performance of the target power battery is determined based on the matching results.

[0043] It should be noted that the heat dissipation performance level range in this application refers to the numerical segment used to divide the corresponding level range of the heat dissipation performance index; the test level information refers to the classification result used to identify the level to which the target power battery belongs to the heat dissipation performance.

[0044] In specific implementation, firstly, setting the heat dissipation performance level intervals can be achieved in the following way: the interval boundaries can be determined based on commonly used level division methods in the existing testing system. For example, the temperature control capability evaluation thresholds published in existing power battery heat dissipation testing standards can be used, or several level intervals can be divided according to preset percentiles based on the statistical distribution of heat dissipation performance indices of historical samples. When determining the intervals, the rationality of the intervals can be checked by combining the number of samples, the coverage of operating conditions, and the calculated interval boundaries, thereby obtaining a level division table for subsequent comparison. Secondly, matching the heat dissipation performance index with the level intervals can be achieved in the following way: a level interval lookup module can be pre-installed in the test control system. The heat dissipation performance index is used as an input parameter. The range in which the value falls is determined sequentially through comparison. When the value approaches the boundary of an interval, the distance between adjacent intervals is recorded to characterize the stability of the matching result. The above matching operation can be implemented through commonly used software rule engines or database comparison commands, which are common knowledge and will not be elaborated here. Then, the heat dissipation performance test level information of the target power battery is determined based on the interval matching result. The test level, its corresponding reference interval, matching timestamp, and test batch number are written into the test record. At the same time, the test level can be used as the basis for subsequent release, optimization, or retesting decisions in conjunction with the existing quality management process to achieve closed-loop management of test data.

[0045] On the other hand, in some embodiments, this application provides a new energy vehicle power battery heat dissipation performance testing system, with reference to Figure 4 The figure is a schematic diagram of the structure of a new energy vehicle power battery heat dissipation performance testing system according to some embodiments of this application. The new energy vehicle power battery heat dissipation performance testing system 400 includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire temperature data by temperature sensors distributed in key areas of the target power battery and in the cooling circuit when dynamic thermal load excitation is applied to the target power battery. Processing module 402, in this application, is used to determine the thermal inertia factor characterizing the response speed of the heat dissipation system based on the temperature data collected by the temperature sensor during a dynamic thermal load step change. In this application, the processing module 402 is also used to collect temperature distribution data and cooling system power consumption data of the heat dissipation system by the sensor when the target power battery is in a steady-state high-load operation condition, and to determine the heat dissipation efficiency ratio that characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state based on the maximum temperature difference, average temperature and cooling system power consumption data of the temperature distribution data. In this application, the processing module 402 is also used to fuse the thermal inertia factor and the heat dissipation efficiency ratio to construct a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system; The execution module 403 in this application is mainly used to evaluate the heat dissipation performance of the target power battery based on the thermal response vector, obtain the heat dissipation performance index, and then determine the test level of the heat dissipation performance of the target power battery based on the heat dissipation performance index.

[0046] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described method for testing the heat dissipation performance of a new energy vehicle power battery.

[0047] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a method for testing the heat dissipation performance of a power battery in a new energy vehicle, according to some embodiments of this application. The heat dissipation performance testing method for a power battery in a new energy vehicle in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0048] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0049] The communication bus 502 can be used to transmit information between the aforementioned components.

[0050] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0051] The memory 503 stores program code for executing the solution of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. In the above embodiment, the method for testing the heat dissipation performance of a new energy vehicle power battery can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0052] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0053] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0054] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0055] In addition, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for testing the heat dissipation performance of a power battery for new energy vehicles.

[0056] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0057] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for testing the heat dissipation performance of a power battery for new energy vehicles, applied to testing the heat dissipation performance of the heat dissipation system in a power battery for new energy vehicles, characterized in that, The method includes the following steps: When dynamic thermal load excitation is applied to the target power battery, temperature data is collected by temperature sensors distributed at temperature measurement points in key areas of the target power battery and in the cooling circuit. Based on temperature data collected by temperature sensors during dynamic thermal load step changes, a thermal inertia factor is determined to characterize the response speed of the heat dissipation system. When the target power battery is in a steady-state high-load operating condition, the temperature distribution data and the power consumption data of the cooling system are collected by the sensor. Based on the maximum temperature difference, average temperature and power consumption data of the temperature distribution data, the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the cooling system under steady state, is determined. By combining the thermal inertia factor and the heat dissipation efficiency ratio, a thermal response vector is constructed to comprehensively evaluate the dynamic and steady-state performance of the heat dissipation system. The heat dissipation performance of the target power battery is evaluated based on the thermal response vector to obtain a heat dissipation performance index, and then the test level of the heat dissipation performance of the target power battery is determined based on the heat dissipation performance index.

2. The method as described in claim 1, characterized in that, Based on temperature data collected by temperature sensors during dynamic thermal load step changes, the thermal inertia factor used to characterize the response speed of the heat dissipation system is determined, specifically including: Record the initial temperature of the target power battery temperature measurement point at the start of the dynamic thermal load step change; Monitor temperature data until the peak temperature is reached, and record the response time from the start of the step change to the peak temperature. The ratio of the difference between the peak temperature and the initial temperature to the response time is used as the thermal inertia factor to characterize the response speed of the heat dissipation system.

3. The method as described in claim 1, characterized in that, The heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady-state conditions, is determined based on the maximum temperature difference, average temperature, and cooling system power consumption data from temperature distribution data. Specifically, this includes: The average temperature and maximum temperature difference under steady-state operating conditions are determined based on the temperature distribution data. Monitor the power consumption of the cooling system during steady-state operation and obtain the steady-state average power consumption; The average temperature, the maximum temperature difference, and the steady-state average power consumption are processed to have unified dimensions. The heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, is generated based on the proportional relationship between the average temperature, maximum temperature difference, and steady-state average power consumption after processing with unified dimensions.

4. The method as described in claim 1, characterized in that, The thermal response vector, constructed by integrating the thermal inertia factor and the heat dissipation efficiency ratio, for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system, specifically includes: The thermal inertia factor and the heat dissipation efficiency ratio are normalized, and the normalization reference value is set based on historical test data or standard threshold. The normalized thermal inertia factor and the heat dissipation efficiency ratio are combined into a two-dimensional vector to form a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system.

5. The method as described in claim 1, characterized in that, The heat dissipation performance of the target power battery is evaluated based on the thermal response vector, and the heat dissipation performance index is obtained, specifically including: Obtain the thermal response vector constructed by fusing thermal inertia factor and heat dissipation efficiency ratio, and extract the vector components used to characterize dynamic response performance and steady-state heat dissipation performance. The vector components are input into a preset fuzzy inference model, and the fuzzy inference model generates corresponding fuzzy membership degrees based on multiple thermal response level rules that divide the dynamic response components and steady-state performance components. Based on the multi-dimensional fuzzy rule base constructed in the fuzzy inference model, the fuzzy membership degree is matched by rules to obtain the comprehensive fuzzy output after fuzzy inference processing; The comprehensive fuzzy output is defuzzified, and the defuzzified value is used as a heat dissipation performance index to characterize the heat dissipation performance of the target power battery.

6. The method as described in claim 1, characterized in that, The test level for determining the heat dissipation performance of the target power battery based on the aforementioned heat dissipation performance index specifically includes: Set heat dissipation performance level ranges; Match the heat dissipation performance index with the grade range; The test level information for the heat dissipation performance of the target power battery is determined based on the matching results.

7. The method as described in claim 1, characterized in that, The dynamic thermal load excitation is a time-varying thermal power input achieved by adjusting the battery charging and discharging current.

8. A testing system for the heat dissipation performance of a power battery in a new energy vehicle, characterized in that, include: The data acquisition module is used to collect temperature data through temperature sensors distributed in key areas of the target power battery and in the cooling circuit when dynamic thermal load excitation is applied to the target power battery. The processing module is used to determine the thermal inertia factor, which characterizes the response speed of the heat dissipation system, based on the temperature data collected by the temperature sensor during a dynamic thermal load step change. The processing module is also used to collect temperature distribution data and cooling system power consumption data of the heat dissipation system by the sensor when the target power battery is in a steady-state high-load operation condition, and to determine the heat dissipation efficiency ratio, which characterizes the heat dissipation capacity and energy efficiency of the heat dissipation system under steady state, based on the maximum temperature difference, average temperature and cooling system power consumption data of the temperature distribution data. The processing module is also used to fuse the thermal inertia factor and the heat dissipation efficiency ratio to construct a thermal response vector for comprehensively evaluating the dynamic and steady-state performance of the heat dissipation system. The execution module is used to evaluate the heat dissipation performance of the target power battery based on the thermal response vector, obtain a heat dissipation performance index, and then determine the test level of the heat dissipation performance of the target power battery based on the heat dissipation performance index.

9. A computer device comprising a memory and a processor, the memory storing code, characterized in that, The processor is configured to acquire the code and execute the new energy vehicle power battery heat dissipation performance test method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the heat dissipation performance test method for new energy vehicle power batteries as described in any one of claims 1 to 7.

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