Multi-dimensional comprehensive evaluation method and device for cooling efficiency of electric vehicle battery thermal management

Abstract

The application relates to the field of new energy technology, in particular to a multi-dimensional comprehensive evaluation method and equipment for battery thermal management cooling efficiency of an electric vehicle. The method comprises the following steps: placing a measured battery pack in an environment bin, and collecting battery parameters through sensors arranged on the battery pack; calculating multi-dimensional indexes according to the battery parameters; determining the first-level weight of each index by using a principal component analysis method; determining the second-level weight of each index under different scene working conditions; calculating an evaluation score according to the first-level weight, the second-level weight and the multi-dimensional indexes, and determining the grade of the battery thermal management cooling efficiency according to the evaluation score. The application constructs a BTMS cooling efficiency comprehensive evaluation system covering full dimensions and being standardized.

Description

Technical Field

[0001] This application relates to the field of new energy technology, and more specifically, to a multi-dimensional comprehensive evaluation method and device for the cooling efficiency of electric vehicle battery thermal management. Background Technology

[0002] With the large-scale development of the global new energy vehicle industry, the fast charging capability, driving range and safety performance of electric vehicles are highly dependent on the cooling efficiency of the battery thermal management system (BTMS), but the industry currently lacks a unified and accurate evaluation method.

[0003] From a technical perspective, the increase in battery energy density to over 150Wh / kg, the widespread adoption of 800V high-voltage platforms and fast charging technology have led to a 2-3 fold increase in BTMS cooling load. Furthermore, the coexistence of multiple technologies such as liquid cooling and air cooling means that traditional single-dimensional evaluation methods cannot meet the needs of comparing multi-dimensional performance (such as temperature uniformity and dynamic stability). From a market perspective, automakers need a fair benchmarking system, and consumers are concerned about thermal safety. However, differences in testing standards among various institutions lead to significant discrepancies in results, necessitating a unified third-party standard to improve the comparability of results and market transparency.

[0004] The existing testing system has the disadvantage of being fragmented and has difficulty in solving the problems of multi-objective optimization and comprehensive quantitative evaluation. Constructing a standardized comprehensive evaluation method for BTMS cooling performance that covers all dimensions has become the key to promoting the high-quality development of the industry. Summary of the Invention

[0005] The purpose of this application is to provide a multi-dimensional comprehensive evaluation method and device for the cooling performance of electric vehicle battery thermal management, and to construct a standardized comprehensive evaluation system for BTMS cooling performance that covers all dimensions.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides a multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management, including:

[0008] The battery pack to be tested is placed in an environmental chamber, and battery parameters are collected by sensors configured on the battery pack; the sensors include: a battery cell temperature sensor, a coolant temperature sensor, a DC power consumption sensor, an electromagnetic flow sensor, and a voltage sensor;

[0009] Based on the battery parameters, multi-dimensional indicators are calculated, including: dynamic temperature drop rate, extreme temperature control time, cooling system energy efficiency ratio, unit cooling energy consumption, cell temperature difference uniformity coefficient, inter-cell temperature difference, temperature fluctuation amplitude, and cooling system efficiency degradation rate. Specifically, the dynamic temperature drop rate is the rate of decrease in the battery's highest temperature from its initial value to the target temperature per unit time; the extreme temperature control time is the time it takes for the cooling system to bring a cell's temperature back to a safe range after it exceeds a safety threshold; the cooling system energy efficiency ratio is the ratio of cooling power to the total power consumption of the battery thermal management system; the unit cooling energy consumption is the total energy consumption of the battery thermal management system corresponding to a unit temperature drop; the cell temperature difference uniformity coefficient is the ratio of the maximum temperature difference to the average temperature difference among all cells in the battery pack; the inter-cell temperature difference is the maximum difference in average temperature between modules in the battery pack; the temperature fluctuation amplitude is the range of fluctuation in the battery's highest temperature during the stable testing phase; and the cooling system efficiency degradation rate is the degree of degradation in the cooling system's efficiency during the testing period.

[0010] Principal component analysis was used to determine the first-level weight of each indicator;

[0011] Determine the secondary weights of each indicator under different scenario conditions;

[0012] The evaluation score is calculated based on the primary weight, secondary weight, and multidimensional indicators, and the battery thermal management cooling efficiency is graded based on the evaluation score.

[0013] Secondly, this application provides an electronic device, comprising:

[0014] At least one processor, and a memory communicatively connected to at least one of the processors;

[0015] The memory stores instructions that can be executed by at least one of the processors, which are executed by at least one of the processors to enable the at least one of the processors to execute any of the multi-dimensional comprehensive evaluation methods for the thermal management cooling performance of electric vehicle batteries.

[0016] This application has the following technical effects:

[0017] This application constructs a four-dimensional quantitative index system of "cooling capacity, energy efficiency level, temperature uniformity, and dynamic stability," which breaks through the limitations of traditional single temperature index evaluation, comprehensively covers the core performance dimensions of BTMS, clarifies the quantitative calculation method of each index, and solves the problem that the evaluation of existing technologies is one-sided and cannot reflect actual performance, making the evaluation results more complete and scientific.

[0018] This application clarifies all sensor types, accuracies, installation specifications, and data acquisition methods required for testing, distinguishes between the applicable scenarios of sensor field testing and vehicle CAN bus data reading, constructs a standardized sensing and acquisition system, solves the problems of missing sensing specifications and poor data traceability in existing technologies, and ensures the accuracy and verifiability of test data.

[0019] This application establishes a two-level objective weight model based on principal component analysis, which makes the comprehensive evaluation score more accurate and more in line with the needs of practical applications, effectively improving the evaluation accuracy.

[0020] This application establishes a standardized testing specification covering the entire process of environmental control, sensor calibration, test object preprocessing, operating condition setting, and data preprocessing. It clarifies key control requirements such as environmental chamber accuracy, sensor parameters, and installation deviations, ensuring that test results from different vehicle models and testing institutions are comparable across different industries. This effectively solves the industry's pain point of "different scores for the same vehicle" and guarantees the fairness of the evaluation.

[0021] This application can be implemented based on existing environmental chambers, conventional sensing equipment, and vehicle CAN bus data, without the need for additional dedicated equipment. It also provides a clear and practical calculation process and performance level judgment standards, which lowers the application threshold for testing institutions. It can be directly applied to third-party testing, automotive R&D, and quality control, and has extremely high industrial application value. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management provided in an embodiment of this application.

[0024] Figure 2 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0025] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0026] Figure 1 This is a flowchart illustrating a multi-dimensional comprehensive evaluation method for the cooling performance of electric vehicle battery thermal management, provided in an embodiment of this application. This embodiment is applicable to scenarios where the cooling performance of electric vehicle BTMS is evaluated. Figure 1 The method shown consists of software and / or hardware and is integrated into an electronic device.

[0027] See Figure 1 The methods provided in this application include:

[0028] S110. The battery pack to be tested is placed in an environmental chamber, and battery parameters are collected by sensors configured on the battery pack; the sensors include: a battery cell temperature sensor, a coolant temperature sensor, a DC power consumption sensor, an electromagnetic flow sensor, and a voltage sensor.

[0029] The test must be conducted in an environmental chamber with a temperature control range of -10℃ to 55℃, an accuracy of ±0.5℃, a humidity control range of 30% to 80%, an accuracy of ±5%, and equipped with a controllable wind speed module.

[0030] The sensor and parameter requirements for this test are as follows: all sensors must support synchronous acquisition at a high frequency of 10Hz with the data acquisition instrument.

[0031]

[0032] Specifically, the sensor must be installed according to the following requirements, and the data acquisition frequency and timestamp must be synchronized with the actual sensor readings:

[0033] 1. Battery cell temperature sensor: If the actual measurement method is adopted, it should be pasted on the surface center of the four corner cells and the center cell of each battery module in the battery pack, and fixed with high temperature tape to ensure that the contact area between the sensor and the surface of the battery cell is not less than 1cm², and the installation position deviation is controlled within 5mm; the number of test cells in each battery pack should not be less than 20.

[0034] 2. Coolant temperature sensor: It consists of an inlet sensor and an outlet sensor, both of which are installed 10cm from the inlet and outlet of the battery pack liquid cooling water circuit. The sensor sensing surface is perpendicular to the direction of coolant flow to ensure full contact with the coolant.

[0035] 3. DC power consumption sensors: ① One BTMS main power supply circuit is connected in series to collect the total power consumption of the BTMS; ② One independent power supply circuit for the compressor, cooling pump and fan is connected in series to collect the individual power consumption of each component; the installation position of all power consumption sensors shall not be more than 50cm away from the corresponding terminal.

[0036] 4. Electromagnetic flow sensor: Connected in series in the main coolant circuit of the battery pack, installed 10cm from the outlet of the coolant pump to ensure full flow of liquid in the circuit.

[0037] 5. Voltage sensor: Connected in parallel across the positive and negative terminals of the BTMS main power supply circuit to collect the BTMS power supply voltage.

[0038] Within 24 hours before the test, all temperature sensors were calibrated using a standard thermometer with an accuracy of ±0.01℃. If the deviation exceeded ±0.2℃, the sensors needed to be replaced. All power sensors were calibrated using a standard power source to ensure that the measurement error was no greater than 0.5%. Electromagnetic flow sensors were calibrated using a standard flow calibrator, and voltage sensors were calibrated using a standard voltage source to ensure that the measurement accuracy of all types of sensors met the requirements, thus ensuring the accuracy and reliability of data acquisition from the source.

[0039] The tested object is an electric vehicle equipped with a battery pack. The pretreatment consists of two steps: adjusting the battery pack's SOC and unifying the temperature of the vehicle and the battery pack to ensure that the initial boundary conditions for thermal management are consistent.

[0040] The battery pack SOC adjustment includes: charging the electric vehicle to 100% using the standard charging process, then letting the vehicle stand still for 2 hours; then discharging the vehicle to the target SOC set in the test through the constant power discharge mode, and letting it stand still for 30 minutes after the discharge is completed.

[0041] The temperature uniformity of the whole vehicle and battery pack includes: placing the vehicle under test in an environmental chamber, turning off all electrical equipment in the vehicle (except for BMS basic monitoring), and keeping it at a constant temperature and humidity for at least 4 hours until the temperature of all cells in the battery pack is within ±1℃ of the set temperature in the environmental chamber, and the temperature of key parts such as the power compartment and chassis of the whole vehicle is ≤2℃ of the ambient temperature.

[0042] Battery parameters are collected using the aforementioned sensors under different operating conditions, as detailed in the table below:

[0043]

[0044] S120. Calculate multi-dimensional indicators based on battery parameters. The multi-dimensional indicators include: dynamic temperature drop rate, extreme temperature control time, cooling system energy efficiency ratio, unit cooling energy consumption, cell temperature difference uniformity coefficient, temperature difference between battery modules, temperature fluctuation amplitude, and cooling system efficiency decay rate.

[0045] Before calculating multidimensional indicators based on battery parameters, the battery parameters need to be preprocessed: outlier removal, missing value interpolation, and trend smoothing.

[0046] Specifically, outlier removal includes: calculating the mean μ' and standard deviation σ for each battery parameter; removing values ​​exceeding [μ'-3σ, μ'+3σ]; for ≥3 consecutive outliers, checking the sensor status; and if the sensor is normal, using linear interpolation to supplement the outliers.

[0047] Missing value interpolation includes: when the amount of missing data for battery parameters collected under a certain scenario is ≤5%, linear interpolation is used; when the amount of missing data for battery parameters collected under a certain scenario is >5%, the scenario is retested.

[0048] Trend smoothing includes: using a 5-point moving average to eliminate high-frequency noise (the first and last data are averaged using a 3-point moving average).

[0049] The calculation process for each indicator is explained in detail below:

[0050] Cooling capability dimensions include dynamic temperature drop rate and extreme temperature control time. Dynamic temperature drop rate is the rate at which the battery's maximum temperature drops from its initial value to the target temperature per unit time, reflecting the response speed of the cooling system; the battery's maximum temperature is the real-time maximum value among all individual cell temperatures within the battery pack, acquired or read by individual cell temperature sensors.

[0051] ;

[0052] in, The dynamic temperature drop rate (°C / min); The highest battery temperature (°C) at the initial moment of the test. The highest battery temperature (°C) at time t is the target temperature. The test cycle (min) is specified. The acceptable threshold for dynamic temperature drop rate is ≥0.3℃ / min.

[0053] The extreme temperature control time is the time it takes for the cooling system to bring the temperature of a single battery cell back to a safe range after the cell temperature exceeds the safety threshold.

[0054] ;

[0055] The extreme temperature control time (s); The moment when the temperature of any single battery cell first exceeds the safety threshold; This refers to the time it takes for the temperature of a single battery cell to drop below a safe threshold and remain stable. The acceptable threshold for extreme temperature control time is ≤60 seconds.

[0056] Energy efficiency levels are measured in two dimensions: cooling system energy efficiency ratio and unit cooling energy consumption. The cooling system energy efficiency ratio is the ratio of cooling power to the total power consumption of the BTMS, reflecting energy efficiency and economy.

[0057] Cooling power is calculated using the following formula:

[0058] ;

[0059] In the formula: Cooling power (kW); The specific heat capacity of the coolant is taken as 4.2 kJ / (kg·℃); The density of the coolant is taken as 1000 kg / m³; The coolant flow rate (m³ / s) is collected by an electromagnetic flow sensor. , The coolant outlet temperature and inlet temperature (°C) are collected by a coolant temperature sensor.

[0060] The total power consumption of BTMS is calculated using the following formula:

[0061] ;

[0062] Total power consumption of BTMS (kW); , , The real-time power consumption (kW) of the compressor, cooling pump, and fan are respectively acquired by CAN bus signals.

[0063] Cooling system coefficient of performance (COP) calculation formula:

[0064] ;

[0065] The acceptable threshold for the energy efficiency ratio of the cooling system is ≥3.5.

[0066] Unit cooling energy consumption is the total BTMS energy consumption corresponding to a unit temperature drop, reflecting the cost of cooling.

[0067] The formula for calculating the total energy consumption of BTMS is as follows:

[0068] ;

[0069] In the formula: The total energy consumption of BTMS during the test period (kWh); Total power consumption of BTMS (kW); The test period (min) is the test cycle.

[0070] The formula for calculating unit cooling energy consumption is as follows:

[0071] ;

[0072] Unit cooling energy consumption (kWh / ℃); The total energy consumption of BTMS during the test period (kWh); The highest battery temperature (°C) at the initial moment of the test. The highest battery temperature (°C) at the end of the test was collected by the individual battery cell temperature sensors. The acceptable threshold for unit cooling energy consumption is ≤0.8 kWh / °C.

[0073] Temperature uniformity dimensions include: the temperature difference uniformity coefficient between individual cells and the temperature difference between battery modules. The temperature difference uniformity coefficient between individual cells is the ratio of the maximum temperature difference to the average temperature difference among all cells in the battery pack, reflecting the consistency of temperature distribution among individual cells.

[0074] ;

[0075] In the formula: The coefficient for uniformity of temperature difference between individual units; The maximum temperature difference (°C) between individual cells in the battery pack during the test cycle is the maximum value of the difference between the highest and lowest temperatures of all cells at the same time. The average temperature difference (°C) of each cell in the battery pack during the test cycle is the arithmetic mean of the temperature differences of each cell at all times; all temperatures are collected by cell temperature sensors. The acceptable threshold for the cell temperature difference uniformity coefficient is ≤1.5.

[0076] The temperature difference between battery modules is the maximum difference in average temperature among all battery modules within the battery pack, reflecting the consistency of heat dissipation between modules. Average temperature of a single battery module:

[0077] ;

[0078] In the formula: The average temperature (°C) of a single battery module. This refers to the number of individual cells tested within the single battery module. The real-time temperature (°C) of each cell within a single battery module is collected by the cell temperature sensor.

[0079] ;

[0080] In the formula: Temperature difference (°C) between battery modules; , These represent the maximum and minimum average temperatures (°C) of all battery modules in the battery pack during the test period. The acceptable threshold for temperature difference between battery modules is ≤3°C.

[0081] Dynamic stability dimensions include: temperature fluctuation amplitude and performance degradation rate. Temperature fluctuation amplitude refers to the range of the battery's highest temperature fluctuation during the stable testing phase, reflecting the temperature control accuracy of the BTMS. The stable testing phase refers to the period from the start of the test (10 minutes) to the end of the test, during which the battery's highest temperature is collected by the individual cell temperature sensors. Calculation formula:

[0082] ;

[0083] In the formula: The temperature fluctuation range (°C); , These are the peak and trough values ​​(°C) of the battery's highest temperature during the stable testing phase. The acceptable threshold for temperature fluctuation is ≤1°C.

[0084] The efficiency degradation rate is the degree of energy efficiency decline of the cooling system during the test cycle, reflecting the long-term reliability of BTMS. Calculation formula:

[0085] ;

[0086] In the formula: Performance degradation rate (%) To test the cooling system's energy efficiency ratio at the initial moment (i.e., the energy efficiency ratio in the first 5 minutes of the test). (arithmetic mean) The cooling system energy efficiency ratio at the end of the test (i.e., 5 minutes after the test). The arithmetic mean (the ratio of the two values) is calculated using the aforementioned formula for the energy efficiency ratio of the cooling system. The acceptable threshold for efficiency degradation rate is ≤8%.

[0087] Because the numerical ranges of various indicators differ, range standardization, also known as min-max normalization, is performed on each indicator. This scales the original indicator to a specific range, such as (0,1). For indicators where larger values ​​generally indicate better performance, such as the cooling system's energy efficiency ratio, range standardization is performed using the following formula:

[0088] ;

[0089] For metrics where smaller values ​​generally indicate better performance, such as unit cooling energy consumption, the following formula is used for range standardization:

[0090] ;

[0091] In the formula: For the original indicators, , These are the minimum and maximum values ​​of this indicator within the same batch of tests; This is the index after range standardization (range [0,1]).

[0092] S130. Principal component analysis is used to determine the first-level weight of each indicator.

[0093] Obtain multi-dimensional indicators for m vehicle models (in this application, the indicators are 8-dimensional), and construct a sample matrix (m×8) from the obtained multi-dimensional indicators; m is a natural number greater than 3.

[0094] Then, the covariance (or correlation coefficient matrix) of the sample matrix is ​​calculated, and eigenvalue decomposition is performed on the covariance to obtain eigenvalues ​​and eigenvectors. Based on the contribution rates of the first k principal components, the first-level weight of each indicator is obtained; where k is a natural number greater than 3.

[0095] Among them, the cumulative contribution rate of the first k principal components is greater than or equal to 85%, and the formula for calculating the first-level weight is as follows:

[0096] ( );

[0097] in, is the i-th eigenvalue, and k is the number of principal components. For ease of subsequent calculation, k is the same as the number of indicators, which is 8. It is the first-level weight of the i-th indicator.

[0098] S140. Determine the secondary weight of each indicator under different scenario conditions.

[0099] The operating conditions include: high-temperature charging, high-speed driving, and static heat dissipation.

[0100] The determination of secondary weights is based on the fundamental principles of aligning with the actual operational needs of the Battery Thermal Management System (BTMS), matching safety risk priorities, and conforming to core user perceptions. To ensure the objectivity and quantifiability of weight allocation, this embodiment designs three dimensions (heat load, industry risk priority, and user usage frequency) to reflect the technical constraints, safety operating baseline, and actual user experience of the BTMS. Specifically, heat load is quantified using a cooling load coefficient, the industry risk priority dimension uses the risk occurrence rate as a metric, and user usage frequency is characterized by the proportion of usage in different scenarios.

[0101] Based on the above analysis, firstly, the cooling load coefficient, risk occurrence rate, and usage ratio of different scenarios are determined;

[0102] Among them, the cooling load coefficient is a core parameter that quantifies the intensity of the thermal load that the battery thermal management system needs to bear under different operating conditions. It is used to characterize the rate of heat generation and heat dissipation demand of the battery under the operating conditions. The higher the coefficient, the greater the cooling pressure that the cooling system needs to cope with. The risk occurrence rate is a parameter that quantifies the probability of battery thermal safety risks occurring under different operating conditions by combining industry power battery thermal failure statistics. It is used to characterize the frequency of safety problems such as temperature runaway and excessive temperature difference under the operating conditions. The higher the occurrence rate, the higher the safety control priority of the corresponding operating condition. The scenario usage ratio is a parameter that quantifies the frequency of occurrence and usage time of different operating conditions in the entire life cycle of users' vehicle use based on big data statistics of actual vehicle use behavior of new energy vehicle users. It is used to characterize the actual user coverage of the operating condition. The higher the ratio, the stronger the fit between the operating condition and the user's daily vehicle use scenario.

[0103] Then, based on the cooling load coefficient, risk occurrence rate, and usage ratio of different scenarios, the secondary weight of each indicator under different scenario conditions is determined.

[0104] Scientific adaptation of evaluation systems to different scenarios.

[0105] For example, the secondary weights for each dimension are as follows:

[0106]

[0107] The table above contains 8 specific indicators across 4 dimensions. The indicators within each dimension are equally weighted in the secondary weighting of that dimension. For example, the secondary weighting of the cooling capacity dimension under high-temperature charging conditions is 45%. The cooling capacity dimension includes dynamic temperature drop rate and extreme temperature control time. Therefore, the secondary weighting of dynamic temperature drop rate under high-temperature charging conditions is 45 / 2%, or 22.5%; the secondary weighting of extreme temperature control time under high-temperature charging conditions is also 22.5%.

[0108] S150. Calculate the evaluation score based on the primary weight, secondary weight, and multi-dimensional indicators, and determine the level of battery thermal management cooling efficiency based on the evaluation score.

[0109] First, multiply the primary and secondary weights of the same indicator to obtain the overall weight of the indicator:

[0110] ,and ;

[0111] In the formula, The comprehensive weight of the i-th indicator; Let be the first-level weight of the i-th indicator; Let be the secondary weight of the i-th indicator.

[0112] Then, based on the multidimensional index and eigenvector after range standardization, the principal component scores are obtained:

[0113] ( );

[0114] In the formula, Let be the score of the i-th principal component; aij is the component corresponding to the j-th index in the feature vector corresponding to the i-th principal component. is the j-th index after range standardization.

[0115] Finally, based on the principal component scores and the overall weights, the evaluation score is obtained:

[0116] ;

[0117] The evaluation score (out of 100) for battery thermal management cooling efficiency; The score for the i-th principal component; is the comprehensive weight of the i-th indicator.

[0118] Grading: Excellent (S≥80 points), Pass (60≤S<80 points), Fail (S<60 points).

[0119] This application has the following technical effects:

[0120] This application constructs a four-dimensional quantitative index system of "cooling capacity, energy efficiency level, temperature uniformity, and dynamic stability," which breaks through the limitations of traditional single temperature index evaluation, comprehensively covers the core performance dimensions of BTMS, clarifies the quantitative calculation method of each index, and solves the problem that the evaluation of existing technologies is one-sided and cannot reflect actual performance, making the evaluation results more complete and scientific.

[0121] This application clarifies all sensor types, accuracies, installation specifications, and data acquisition methods required for testing, distinguishes between the applicable scenarios of sensor field testing and vehicle CAN bus data reading, constructs a standardized sensing and acquisition system, solves the problems of missing sensing specifications and poor data traceability in existing technologies, and ensures the accuracy and verifiability of test data.

[0122] This application establishes a two-level objective weight model based on principal component analysis, which makes the comprehensive evaluation score more accurate and more in line with the needs of practical applications, effectively improving the evaluation accuracy.

[0123] This application establishes a standardized testing specification covering the entire process of environmental control, sensor calibration, test object preprocessing, operating condition setting, and data preprocessing. It clarifies key control requirements such as environmental chamber accuracy, sensor parameters, and installation deviations, ensuring that test results from different vehicle models and testing institutions are comparable across different industries. This effectively solves the industry's pain point of "different scores for the same vehicle" and guarantees the fairness of the evaluation.

[0124] This application can be implemented based on existing environmental chambers, conventional sensing equipment, and vehicle CAN bus data, without the need for additional dedicated equipment. It also provides a clear and practical calculation process and performance level judgment standards, which lowers the application threshold for testing institutions. It can be directly applied to third-party testing, automotive R&D, and quality control, and has extremely high industrial application value.

[0125] This embodiment provides an electronic device, see [link / reference] Figure 2 It includes at least one processor 301 and a memory 302 communicatively connected to at least one of the processors 301;

[0126] The memory 302 stores instructions that can be executed by at least one of the processors 301, which enable at least one of the processors 301 to execute the multi-dimensional comprehensive evaluation method for the thermal management cooling efficiency of electric vehicle batteries described above, and thus have at least the same advantages as the method described above.

[0127] Optionally, the electronic device also includes interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The components are interconnected using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor 301 can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors can be used with multiple memories, and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple electronic devices (e.g., as a server array, a group of blade servers, or a multiprocessor system) can be connected, each providing some of the necessary operations.

[0128] The memory 302, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the multi-dimensional comprehensive evaluation method for the thermal management cooling efficiency of electric vehicle batteries in this embodiment. The processor 301 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 302, thereby realizing the aforementioned multi-dimensional comprehensive evaluation method for the thermal management cooling efficiency of electric vehicle batteries.

[0129] The memory 302 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 302 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include memory remotely configured relative to the processor, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0130] The electronic device may also include an input device 303 and an output device 304. The processor 301, memory 302, input device 303, and output device 304 may be connected via a bus or other means.

[0131] Input device 303 can receive input digital or character information, and output device 304 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touchscreen.

[0132] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0133] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management, characterized in that, include: The battery pack to be tested is placed in an environmental chamber, and battery parameters are collected by sensors configured on the battery pack. The sensors include: a battery cell temperature sensor, a coolant temperature sensor, a DC power consumption sensor, an electromagnetic flow sensor, and a voltage sensor; Based on the battery parameters, multi-dimensional indicators are calculated, including: dynamic temperature drop rate, extreme temperature control time, cooling system energy efficiency ratio, unit cooling energy consumption, cell temperature difference uniformity coefficient, inter-cell temperature difference, temperature fluctuation amplitude, and cooling system efficiency degradation rate. Specifically, the dynamic temperature drop rate is the rate of decrease in the battery's highest temperature from its initial value to the target temperature per unit time; the extreme temperature control time is the time it takes for the cooling system to bring a cell's temperature back to a safe range after it exceeds a safety threshold; the cooling system energy efficiency ratio is the ratio of cooling power to the total power consumption of the battery thermal management system; the unit cooling energy consumption is the total energy consumption of the battery thermal management system corresponding to a unit temperature drop; the cell temperature difference uniformity coefficient is the ratio of the maximum temperature difference to the average temperature difference among all cells in the battery pack; the inter-cell temperature difference is the maximum difference in average temperature between modules in the battery pack; the temperature fluctuation amplitude is the range of fluctuation in the battery's highest temperature during the stable testing phase; and the cooling system efficiency degradation rate is the degree of degradation in the cooling system's efficiency during the testing period. Principal component analysis was used to determine the first-level weight of each indicator; Determine the secondary weights of each indicator under different scenario conditions; The evaluation score is calculated based on the primary weight, secondary weight, and multidimensional indicators, and the battery thermal management cooling efficiency is graded based on the evaluation score.

2. The multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management according to claim 1, characterized in that, Calculating the dynamic temperature drop rate based on the battery parameters includes: ； in, For dynamic temperature drop rate; The highest battery temperature at the initial moment of the test; To measure the highest battery temperature at time t, i.e., the target temperature, For testing cycles; Calculating the extreme temperature control time based on the battery parameters includes: ； in, This refers to the time for controlling the extreme temperature. The moment when the temperature of any single battery cell first exceeds the safety threshold; The point at which the temperature of the battery cell drops below a safe threshold and remains stable.

3. The multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management according to claim 2, characterized in that, Calculating the cooling system's energy efficiency ratio based on the battery parameters includes: ； ； ； in, Cooling power; This refers to the specific heat capacity of the coolant. This refers to the density of the coolant. This refers to the coolant flow rate; , These are the coolant outlet temperature and the coolant inlet temperature, respectively. This represents the total power consumption of the battery thermal management system. , , These are the real-time power consumption of the compressor, cooling pump, and fan, respectively. The energy efficiency ratio of the cooling system; Calculate the unit cooling energy consumption based on the battery parameters, including: ； ； in, This represents the total energy consumption of the battery thermal management system during the test cycle. For testing cycles; The highest battery temperature at the initial moment of the test; The highest battery temperature at the end of the test. Unit cooling energy consumption.

4. The multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management according to claim 3, characterized in that, Calculate the cell temperature difference uniformity coefficient based on the battery parameters, including: ； in, The coefficient for uniformity of temperature difference between individual units; The maximum temperature difference between individual cells in the battery pack during the test cycle is the maximum value of the difference between the highest and lowest temperatures of all cells at the same time. The average temperature difference of a single cell in the battery pack during the test cycle is the arithmetic mean of the temperature differences of a single cell at all times. Calculating the temperature difference between battery modules based on the battery parameters includes: ； ； in, This represents the average temperature of a single battery module. The number of test cells within the battery module; This refers to the real-time temperature of each cell within the battery module. This refers to the temperature difference between battery modules; , These represent the maximum and minimum average temperatures of all battery modules within the battery pack during the test period.

5. The multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management according to claim 4, characterized in that, Calculate the temperature fluctuation range based on the battery parameters, including: ； in, This refers to the amplitude of temperature fluctuations. , These are the peak and valley values ​​of the battery's highest temperature during the stable testing phase; Calculate the cooling system efficiency degradation rate based on the battery parameters, including: ； in, This refers to the cooling system efficiency degradation rate. To test the initial energy efficiency ratio of the cooling system; The energy efficiency ratio of the cooling system at the end of the test.

6. The multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management according to claim 5, characterized in that, Principal component analysis was used to determine the first-level weights of each indicator, including: Obtain multidimensional indicators for m vehicle models and construct a sample matrix from the obtained multidimensional indicators; m is a natural number greater than 3. Calculate the covariance of the sample matrix, and perform eigenvalue decomposition on the covariance to obtain eigenvalues ​​and eigenvectors; Based on the contribution rates of the first k principal components, the first-level weight of each indicator is obtained; where k is a natural number greater than 3.

7. The multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management according to claim 6, characterized in that, The operating conditions include: high-temperature charging, high-speed driving, and static heat dissipation. Determine the secondary weights of each indicator under different scenario conditions, including: Determine the cooling load coefficient, risk incidence rate, and usage ratio of different scenarios under various operating conditions; Based on the cooling load coefficient, risk occurrence rate, and scenario usage ratio, the secondary weight of each indicator under different scenario conditions is determined.

8. The multi-dimensional comprehensive evaluation method for the cooling efficiency of electric vehicle battery thermal management according to claim 7, characterized in that, The evaluation score is calculated based on the primary weight, secondary weight, and multidimensional indicators, including: Multiply the primary and secondary weights of the same indicator to obtain the comprehensive weight of the indicator; Principal component scores are obtained based on the multidimensional indices and eigenvectors after range standardization. The evaluation score is obtained based on the principal component scores and the overall weights.

9. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, which are executed by at least one of the processors to enable the at least one of the processors to perform the multi-dimensional comprehensive evaluation method for the thermal management cooling efficiency of electric vehicle batteries according to any one of claims 1-8.

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