New energy automobile thermal management system and thermal management method thereof

Abstract

The invention relates to the technical field of new energy automobiles, and discloses a new energy automobile thermal management system and a thermal management method thereof.The new energy automobile thermal management system comprises a multi-dimensional data acquisition module, a multi-dimensional data analysis module, a thermal evaluation module, a thermal treatment module and a thermal management module, and the multi-dimensional data acquisition module is responsible for acquiring thermal data, natural environment influence data and equipment parameters; the multi-dimensional data analysis module calculates a thermal energy efficiency optimization coefficient # imgabs0 # temperature control demand coefficient Ez and an integration optimization coefficient Yv according to the collected data, and the thermal evaluation module evaluates and diagnoses the problems that the energy efficiency does not reach the expectation, the temperature control is inaccurate and the integration is unreasonable in the execution process of the thermal management system according to the calculation result of the multi-dimensional data analysis module. The heat treatment module takes measures for found problems according to the evaluation result of the heat evaluation module, the heat management module brings new measures into the system according to optimization measures taken by the heat treatment module, system parameters and operation strategies are updated, and a new heat management system is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a thermal management system and a thermal management method for new energy vehicles. Background Art

[0002] Traditional thermal management systems for fuel-powered vehicles primarily rely on engine waste heat for heating and mechanical compression for cooling. This design suffers from inherent drawbacks: a single energy efficiency structure and a limited control range. The cooling process is driven by a mechanical compressor, which consumes a lot of energy; heating, on the other hand, can only utilize engine waste heat, making it unsuitable for new energy vehicles, which lack continuous engine waste heat. In new energy vehicles, traditional thermal management systems rely entirely on electricity to drive the compressor, resulting in a surge in heating energy consumption in low-temperature environments, a significant reduction in driving range, and particularly significant energy efficiency issues.

[0003] Furthermore, traditional thermal management systems primarily rely on single-zone, crude control, which can only regulate the average cabin temperature and is unable to meet the differentiated temperature control requirements of key components such as batteries and motors. For example, batteries experience rapid performance degradation in low or high temperature environments, but traditional systems lack the ability to precisely control them, resulting in shortened battery life and increased safety risks.

[0004] On the other hand, traditional thermal management subsystems (air conditioning, cooling, and battery cooling) are designed independently, resulting in complex piping and significant functional redundancy. For example, a layout where the engine and air conditioning condenser share a cooling circuit wastes space and is inefficient due to the lack of an engine in new energy vehicles, making it difficult to adapt to the compact integration requirements of electric platforms.

[0005] Therefore, in order to solve the above-mentioned defects of traditional thermal management systems, it is urgent to develop a new energy vehicle thermal management system and its thermal management method to achieve energy efficiency improvement, precise temperature control and compact integration to meet the development needs of new energy vehicles. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In response to the shortcomings of the existing technology, the present invention provides a new energy vehicle thermal management system and a thermal management method thereof, which have the advantages of improved energy efficiency, precise temperature control and compact integration, and solve the problems of high energy consumption, inaccurate temperature control, complex system and difficult integration of traditional thermal management systems in new energy vehicles.

[0008] (2) Technical solution

[0009] To achieve the above-mentioned object, the present invention provides the following technical solutions: a new energy vehicle thermal management system, comprising a multidimensional data acquisition module, a multidimensional data analysis module, a thermal evaluation module, a thermal treatment module and a thermal management module;

[0010] The multi-dimensional data acquisition module is responsible for collecting thermal data, natural environment impact data and equipment parameters;

[0011] The multi-dimensional data analysis module calculates the thermal energy efficiency optimization coefficient based on the collected data Temperature control demand coefficient Ez and integrated optimization coefficient Yv;

[0012] The thermal assessment module evaluates and diagnoses issues such as suboptimal energy efficiency, inaccurate temperature control, and unreasonable integration that arise during the thermal management system's execution, based on the calculation results of the multi-dimensional data analysis module.

[0013] The heat treatment module takes measures to address the problems found based on the evaluation results of the thermal assessment module;

[0014] Based on the optimization measures taken by the heat treatment module, the thermal management module incorporates new measures into the system, updates system parameters and operation strategies, and forms a new thermal management system composition.

[0015] Preferably, the multi-dimensional data acquisition module includes a thermal data acquisition unit, a natural environment impact data acquisition unit and an equipment parameter acquisition unit.

[0016] Preferably, the thermal data acquisition unit collects thermal data through temperature sensors, heat flow sensors and air-conditioning system monitoring equipment, including real-time temperature and heat generation rate data of the battery, motor and cabin parts, as well as heat exchange data during the air-conditioning cooling and heating process.

[0017] Preferably, the natural environment impact data acquisition unit collects natural environment impact data through environmental sensors, including environmental temperature, humidity and light intensity data.

[0018] Preferably, the device parameter acquisition unit obtains device parameters through the vehicle management data center and the sensor network, including device-specific parameters such as battery capacity, motor power, and compressor performance parameters.

[0019] Preferably, the multi-dimensional data analysis module includes an energy efficiency analysis unit, a multi-temperature zone analysis unit and an integrated scheduling unit.

[0020] Preferably, the energy efficiency analysis unit calculates the thermal energy efficiency optimization coefficient The calculation formula is:

[0021]

[0022] In the formula, Indicates the thermal energy efficiency optimization coefficient, Q y Indicates effective use of heat, W z It represents the thermal power consumption of all components in new energy vehicles, T1 represents the ambient temperature, T0 represents the standard temperature, and g represents the waste heat recovery efficiency coefficient.

[0023] Preferably, the multi-temperature zone analysis unit calculates the temperature control demand coefficient Ez, and the calculation formula is:

[0024]

[0025] In the formula, Ez represents the temperature control demand coefficient, T l Indicates the ideal operating temperature in the vehicle or battery pack, T w Indicates the external ambient temperature.

[0026] Preferably, the integrated scheduling unit calculates the integrated optimization coefficient Yv, and the calculation formula is:

[0027]

[0028] In the formula, Yv represents the integrated optimization coefficient, i represents the subsystem type, and U i represents the volume of the ith subsystem, H i represents the power density of the ith subsystem, d represents the total length of the actual pipeline, and d min represents the theoretical minimum pipe length, P represents the actual system efficiency, and P max Represents the theoretical maximum system efficiency.

[0029] A new energy vehicle thermal management method includes the following steps:

[0030] Step 1: Establish a multidimensional data acquisition module, a multidimensional data analysis module, a thermal assessment module, a thermal treatment module, and a thermal management module;

[0031] Step 2: The multi-dimensional data acquisition module collects thermal data, natural environment impact data and equipment parameters through various sensors;

[0032] Step 3: Calculate the thermal energy efficiency optimization coefficient using the multi-dimensional data analysis module Temperature control demand coefficient Ez and integrated optimization coefficient Yv;

[0033] Step 4: The thermal assessment module evaluates and diagnoses issues such as suboptimal energy efficiency, inaccurate temperature control, and unreasonable integration that arise during the thermal management system's execution based on the calculation results.

[0034] Step 5: The heat treatment module takes corresponding measures for the problems found according to the evaluation results of the thermal evaluation module;

[0035] Step 6: The thermal management module incorporates the new measures into the system, updates the system parameters and operation strategies, and forms a new thermal management system.

[0036] Compared with the prior art, the present invention provides a new energy vehicle thermal management system and a thermal management method thereof, which have the following beneficial effects:

[0037] 1. The present invention calculates the thermal energy efficiency optimization coefficient As a standard for evaluating cooling and heating energy efficiency, the thermal energy efficiency optimization coefficient When the system is within the preset range, it indicates that the system energy efficiency meets the standard and maintains the current compressor frequency conversion strategy and waste heat recovery plan; when the thermal energy efficiency optimization coefficient When the pressure falls below the preset range, the system will trigger a series of optimization measures: the compressor will reduce the start-stop frequency or optimize the load distribution to reduce unnecessary energy consumption; at the same time, it will strengthen waste heat recovery and use the battery heat recovery for cabin heating to improve energy utilization. Through the above measures, not only can the system achieve energy efficiency improvement at different ambient temperatures, but also reduce the vehicle's endurance loss in low temperature environments, thereby optimizing overall performance.

[0038] 2. The present invention calculates the temperature control demand coefficient Ez and substitutes it into the PID control algorithm or fuzzy control algorithm as the basis for differentiated temperature control priority allocation. It dynamically adjusts the cooling / heating power of each area. When the battery temperature approaches the safety critical value, the battery temperature control weight is automatically increased to prioritize battery performance, improve the battery charging and discharging efficiency at extreme temperatures, and extend the battery life.

[0039] 3. The present invention evaluates the rationality of the system pipeline layout and functional integration by calculating the integrated optimization coefficient Yv. When the integrated optimization coefficient Yv is within the system preset range, it indicates that the system energy efficiency is in an ideal state and no adjustment is required, and the current pipeline design is maintained. When the integrated optimization coefficient Yv is not within the system preset range, the system automatically switches the multi-way valve to reduce the pipeline length or uses the motor waste heat to preheat the battery, ultimately achieving the beneficial effects of reducing the system volume, reducing pipeline pressure loss, and improving response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] See also Figure 1 , the new energy vehicle thermal management system includes a multi-dimensional data acquisition module, a multi-dimensional data analysis module, a thermal evaluation module, a thermal treatment module and a thermal management module;

[0043] The multi-dimensional data acquisition module is responsible for collecting thermal data, natural environment impact data and equipment parameters;

[0044] The multi-dimensional data analysis module calculates the thermal energy efficiency optimization coefficient based on the collected data The temperature control demand coefficient Ez and the integrated optimization coefficient Yv have the following functions:

[0045] Calculate thermal energy efficiency optimization coefficient Evaluate cooling and heating energy consumption, optimize energy efficiency structure, calculate temperature control demand coefficient Ez to analyze the temperature control requirements of key battery and motor components, implement differentiated temperature control, and calculate integration optimization coefficient Yv to analyze the integration issues of each subsystem and optimize pipeline design and functional layout;

[0046] Based on the calculation results of the multi-dimensional data analysis module, the thermal assessment module evaluates and diagnoses problems that occur during the thermal management system execution, such as energy efficiency that does not meet expectations, inaccurate temperature control, and unreasonable integration. It determines the type and severity of the problem and provides a basis for subsequent processing.

[0047] The thermal treatment module takes measures to address the problems found based on the evaluation results of the thermal assessment module, as follows:

[0048] When evaluating the thermal energy efficiency optimization coefficient If the frequency is not within the preset range, it indicates that the current energy efficiency is insufficient. In this case, the frequency conversion strategy of the compressor needs to be adjusted to reduce energy consumption and improve energy efficiency.

[0049] When temperature control is inaccurate, the temperature control demand coefficient Ez is substituted into the temperature control adjustment algorithm, and the system automatically recalibrates the adjustment coefficient to achieve accurate temperature control of the battery, motor and other key multi-zone parts;

[0050] When the integration optimization coefficient Yv is not within the system preset range, it means that the current integration is unreasonable. At this time, the pipeline layout and subsystem connection method are optimized to reduce functional redundancy, improve the compactness and integration of the system, and solve the problems existing in the system; the thermal management module incorporates the new measures into the system based on the optimization measures taken by the heat treatment module, updates the system parameters and operation strategies, forms a new thermal management system composition, and realizes continuous optimization and improvement of the system.

[0051] The multi-dimensional data acquisition module includes a thermal data acquisition unit, a natural environment impact data acquisition unit, and an equipment parameter acquisition unit. The thermal data acquisition unit collects thermal data, including real-time temperature and heat generation rate data for the battery, motor, and cabin, as well as heat exchange data during the cooling and heating processes, through temperature sensors, heat flow sensors, and air conditioning system monitoring equipment (temperature sensors are installed in the battery pack, motor housing, and cabin to monitor the temperature of key components in real time; heat flow sensors measure the heat generated by the battery and motor during operation and the rate of heat transfer; air conditioning system monitoring equipment is integrated into the air conditioning system to monitor heat exchange during cooling and heating, including heat exchange data between the evaporator and condenser).

[0052] The natural environment impact data acquisition unit collects natural environment impact data, including ambient temperature, humidity and light intensity data, through environmental sensors (ambient temperature sensors are installed on the outside of the vehicle to monitor the ambient temperature in real time; humidity sensors are used to measure ambient humidity to help the system evaluate the impact of ambient humidity on thermal management; light intensity sensors are used to measure the light intensity of the vehicle's environment and analyze the impact of direct sunlight on the vehicle's internal temperature) for analyzing the impact of the environment on the thermal management system.

[0053] The equipment parameter acquisition unit obtains equipment parameters, including inherent parameters of battery capacity, motor power and compressor performance parameters, through the vehicle management data center and sensor network (the vehicle management data center is used to store and obtain the overall vehicle operation status data, including parameters such as battery capacity, charge and discharge status provided by the battery management system (BMS), and motor power data provided by the motor controller; the sensor network obtains equipment operation parameters in real time through sensors distributed in various key components of the vehicle, such as the operating power and speed performance parameters of the compressor), providing basic data for system control.

[0054] The advantage is that through the multi-dimensional data acquisition module, the new energy vehicle thermal management system can comprehensively and accurately obtain all the data required for thermal management, providing a solid foundation for analysis and control in the subsequent multi-dimensional data analysis module. The multi-dimensional data analysis module includes an energy efficiency analysis unit, a multi-temperature zone analysis unit, and an integrated scheduling unit.

[0055] Energy efficiency analysis unit calculates thermal energy efficiency optimization coefficient The calculation formula is:

[0056]

[0057] In the formula, It represents the thermal energy efficiency optimization coefficient (dimensionless), which is used to quantify the degree of closeness between the actual energy efficiency of the system and the theoretical optimal energy efficiency. yIndicates effective utilization of heat (kJ), W z It represents the heat power consumption (kW) of all components in new energy vehicles, T1 represents the ambient temperature, T0 represents the standard temperature (°C), g represents the waste heat recovery efficiency coefficient (dimensionless), and the value range is 0-1, reflecting the effective utilization rate of the waste heat recovery system.

[0058] Advantages: By calculating the thermal energy efficiency optimization coefficient As a standard for evaluating cooling and heating energy efficiency, the thermal energy efficiency optimization coefficient When the system is within the preset range (such as 0.75-0.9), it indicates that the system energy efficiency meets the standard and the current compressor frequency conversion strategy and waste heat recovery plan are maintained; when the thermal energy efficiency optimization coefficient When the pressure falls below the preset range, the system will trigger a series of optimization measures: the compressor will reduce the start-stop frequency or optimize the load distribution to reduce unnecessary energy consumption; at the same time, it will strengthen waste heat recovery and use the battery heat recovery for cabin heating to improve energy utilization. Through the above measures, not only can the system achieve energy efficiency improvement at different ambient temperatures, but also reduce the vehicle's endurance loss in low temperature environments, thereby optimizing overall performance.

[0059] The multi-temperature zone analysis unit calculates the temperature control demand coefficient Ez, and its calculation formula is:

[0060]

[0061] In the formula, Ez represents the temperature control demand coefficient, which is used to measure the system's demand for temperature regulation at different ambient temperatures. l Indicates the ideal operating temperature in the vehicle or battery pack, T w Indicates the external ambient temperature.

[0062] The advantages are: by calculating the temperature control demand coefficient Ez, it is substituted into the PID control algorithm or fuzzy control algorithm as the basis for differentiated temperature control priority allocation, and the cooling / heating power of each area is dynamically adjusted. When the battery temperature approaches the safety critical value (such as low temperature <5°C or high temperature >40°C), the battery temperature control weight is automatically increased to prioritize battery performance, improve the battery charging and discharging efficiency at extreme temperatures, and extend battery life.

[0063] The integrated scheduling unit calculates the integrated optimization coefficient Yv, and its calculation formula is:

[0064]

[0065] In the formula, Yv represents the integrated optimization coefficient, which is used to evaluate the degree of optimization of system pipeline layout, power density and efficiency, i represents the subsystem type (1 = battery thermal management, 2 = air conditioning system, 3 = motor cooling), U i represents the volume of the ith subsystem (L), Hi represents the power density of the ith subsystem (kW / L), reflecting the heat dissipation capacity per unit volume, d represents the total length of the actual pipeline (m), and d min It represents the theoretical minimum pipe length (m), which is determined by the system topology. P represents the actual system efficiency (%). max Indicates the theoretical maximum system efficiency (in %), such as the efficiency corresponding to the theoretical COP of a heat pump.

[0066] The advantages are: by calculating the integrated optimization coefficient Yv, the rationality of the system pipeline layout and functional integration is evaluated. When the integrated optimization coefficient Yv is in the system preset range (such as 0.8-1.0), it means that the system energy efficiency is in an ideal state and no adjustment is required, and the current pipeline design is maintained. When the integrated optimization coefficient Yv is not in the system preset range, the system automatically switches the multi-way valve to reduce the pipeline length, or uses the motor waste heat for battery preheating, ultimately enabling the system to achieve the beneficial effects of reduced volume, reduced pipeline pressure loss and improved response speed.

[0067] A new energy vehicle thermal management method includes the following steps:

[0068] Step 1: Establish a multidimensional data acquisition module, a multidimensional data analysis module, a thermal assessment module, a thermal treatment module, and a thermal management module;

[0069] Step 2: The multidimensional data acquisition module collects thermal data, natural environment impact data, and equipment parameters through various sensors. In specific implementation, a multi-sensor network layout is used to obtain temperature monitoring data, environmental humidity data, and equipment load data. The data preprocessing module is used to standardize and filter noise from the original multidimensional data to obtain a standardized multidimensional data set. If the data quality assessment index is lower than the preset threshold of 0.85, the data re-collection mechanism is triggered to re-acquire a more accurate data set.

[0070] Step 3: Calculate the thermal energy efficiency optimization coefficient using the multi-dimensional data analysis module Temperature control demand coefficient Ez and integrated optimization coefficient Yv; energy efficiency analysis unit calculates thermal energy efficiency optimization coefficient The calculation formula is:

[0071]

[0072] In the formula, Indicates the thermal energy efficiency optimization coefficient, Q y Indicates effective use of heat, W z It represents the heat power consumption of all components in new energy vehicles, T1 represents the ambient temperature, T0 represents the standard temperature, and g represents the waste heat recovery efficiency coefficient;

[0073] The multi-temperature zone analysis unit calculates the temperature control demand coefficient Ez, and its calculation formula is:

[0074]

[0075] In the formula, Ez represents the temperature control demand coefficient, T l Indicates the ideal operating temperature in the vehicle or battery pack, T w Indicates the external ambient temperature;

[0076] The integrated scheduling unit calculates the integrated optimization coefficient Yv, and its calculation formula is:

[0077]

[0078] In the formula, Yv represents the integrated optimization coefficient, i represents the subsystem type, and U i represents the volume of the ith subsystem, H i represents the power density of the ith subsystem, d represents the total length of the actual pipeline, and d min represents the theoretical minimum pipe length, P represents the actual system efficiency, and P max represents the theoretical maximum system efficiency;

[0079] Step 4: The thermal assessment module evaluates and diagnoses issues such as suboptimal energy efficiency, inaccurate temperature control, and unreasonable integration that arise during the thermal management system's execution based on the calculation results.

[0080] Step 5: The heat treatment module takes corresponding measures for the problems found according to the evaluation results of the thermal evaluation module;

[0081] Specifically: When evaluating the thermal energy efficiency optimization coefficient If the frequency is not within the preset range, it indicates that the current energy efficiency is insufficient. In this case, the frequency conversion strategy of the compressor needs to be adjusted to reduce energy consumption and improve energy efficiency.

[0082] When temperature control is inaccurate, the temperature control demand coefficient Ez is substituted into the temperature control adjustment algorithm, and the system automatically recalibrates the adjustment coefficient to achieve accurate temperature control of the battery, motor and other key multi-zone parts;

[0083] When the integration optimization coefficient Yv is not within the system preset range, it means that the current integration is unreasonable. At this time, the pipeline layout and subsystem connection method are optimized to reduce functional redundancy, improve the compactness and integration of the system, and solve the problems existing in the system; Step 6, the thermal management module updates the system parameters and operation strategies based on the incorporation of new measures into the system to form a new thermal management system composition.

[0084] In addition, during specific implementation, the multidimensional data analysis module and the thermal evaluation module can also use the following method to perform performance evaluation: based on the standardized multidimensional data set, a weighted data fusion algorithm is used to dynamically allocate the temperature data weights, the ambient humidity data weights and the equipment load data weights, and a comprehensive thermal status index is obtained through a fusion calculation formula. The comprehensive thermal status index is used for subsequent thermal energy efficiency analysis, and the comprehensive thermal status index is determined as the input basis for thermal energy efficiency evaluation; specifically including: obtaining temperature data, humidity data and load data from the multidimensional data set, and using a preset standardization processing method to normalize each type of data to obtain a standardized first data set.

[0085] Based on the standardized first dataset, a weighted data fusion method is used to assign weights to temperature, humidity, and load data. Initial weights for each data type are calculated, resulting in a weighted second dataset. The weights in the second dataset are dynamically adjusted. If the weight of a particular data type deviates from a preset threshold, it is corrected to produce an adjusted third dataset.

[0086] According to the adjusted weight value in the third data set, a fusion calculation formula is used to perform weighted summation on the temperature data, humidity data and load data to calculate a preliminary thermal state value and obtain a first thermal state result.

[0087] The fusion calculation formula is: S = w1 T + w2H + w3L, where S represents the thermal state value, w1, w2, and w3 represent the weights of temperature data, humidity data, and load data, respectively, and T, H, and L represent the standardized values of temperature data, humidity data, and load data, respectively.

[0088] The first thermal state result is smoothed and the time series data of the thermal state value is filtered using a moving average method to obtain a smoothed second thermal state result.

[0089] According to the second thermal state result, combined with the preset energy efficiency evaluation model, the comprehensive thermal state index is calculated to obtain the final thermal energy efficiency evaluation input data.

[0090] By storing and formatting the final thermal energy efficiency assessment input data, structured data records are generated to serve as the basis for subsequent analysis.

[0091] The thermal efficiency optimization coefficient calculation module is input with comprehensive thermal status indicators. The thermal efficiency optimization coefficient, temperature control demand coefficient, and integrated optimization coefficient are calculated using the heat conduction equation solving algorithm to obtain a three-dimensional optimization coefficient matrix. The three-dimensional optimization coefficient matrix is used to evaluate the system status and determine the potential problem types of system operation. Specifically, it includes:

[0092] Thermal state indicator data are obtained through the sensor network, and the data are preprocessed using a standardized protocol to obtain a normalized thermal state dataset.

[0093] The heat conduction equation solving algorithm is used to calculate the thermal energy efficiency optimization coefficient, temperature control demand coefficient and integrated optimization coefficient for the normalized thermal state data set to obtain a three-dimensional coefficient set.

[0094] Using a matrix construction algorithm, the three-dimensional coefficient set is converted into a three-dimensional optimized coefficient matrix, generating structured matrix data. If any coefficient in the structured matrix data exceeds a preset threshold, a support vector machine algorithm is used to classify the matrix data, determine the type of system state anomaly, and obtain the state classification result.

[0095] According to the status classification results, the cluster analysis algorithm is used to group the abnormal types, determine the distribution characteristics of potential problem types, and obtain the problem distribution data set.

[0096] Through the problem distribution data set, the time series analysis method is used to detect the changing trend of problem types and obtain trend feature data.

[0097] If the change in the trend feature data exceeds a preset threshold, the corresponding problem type is matched through the pre-established rule library, and the optimization adjustment parameters are generated to obtain the system optimization instruction set.

[0098] According to the three-dimensional optimization coefficient matrix, if the thermal energy efficiency optimization coefficient is lower than the preset energy efficiency threshold of 0.8, the system is judged to have insufficient energy efficiency. If the deviation of the temperature control demand coefficient exceeds the preset temperature control deviation range of 0.15, the system is judged to have a temperature control deviation. If the integration optimization coefficient is lower than the preset integration threshold standard of 0.75, the system is judged to have improper integration. The corresponding problem type identification and severity rating of insufficient energy efficiency, temperature control deviation or improper integration are generated, and the problem classification results are obtained for subsequent parameter adjustment; specifically, the following are included:

[0099] The thermal energy efficiency optimization coefficient, temperature control demand coefficient and integrated optimization coefficient are obtained from the system operation data, and the difference between each coefficient and the corresponding preset threshold is calculated to obtain the initial deviation data.

[0100] If the difference between the thermal energy efficiency optimization coefficient and the preset energy efficiency threshold is greater than zero, it is determined to be an energy efficiency deficiency problem and an energy efficiency deficiency mark is generated;

[0101] If the difference between the temperature control demand coefficient and the preset temperature control deviation is greater than zero, it is determined to be a temperature control deviation problem and a temperature control deviation flag is generated;

[0102] If the difference between the integrated optimization coefficient and the preset integration threshold is greater than zero, it is determined to be an improper integration problem, an improper integration identifier is generated, and a problem type identifier set is obtained.

[0103] According to the problem type identification set, the K-means clustering algorithm is used to classify the deviation data, determine the severity rating of each problem, and obtain the rating results.

[0104] Based on the rating results, the parameter adjustment rules associated with each problem type identifier are obtained, the corresponding adjustment strategies are extracted from the pre-established rule library, and the parameter adjustment plan is generated.

[0105] According to the parameter adjustment plan, the predicted values of the adjusted thermal energy efficiency optimization coefficient, temperature control demand coefficient and integrated optimization coefficient are calculated to obtain the optimization coefficient prediction results.

[0106] If any coefficient in the optimized coefficient prediction result is still lower than the corresponding preset threshold, the parameter scheme is iteratively adjusted and the prediction value is repeatedly calculated until all coefficients meet the threshold requirements to obtain the final parameter adjustment result.

[0107] Based on the final parameter adjustment results, the system operation configuration is updated to generate optimized system operation data.

[0108] Based on the problem classification results and severity ratings, the fuzzy logic control algorithm is used to adaptively adjust the system parameters. For energy efficiency issues, the heat dissipation power parameters are updated through the heat dissipation power adjustment formula. For temperature control deviation issues, the control gain parameters are updated through the control gain correction formula. For improper integration issues, the coordination coefficient parameters are updated through the coordination coefficient update formula. The adjusted parameter combination is used for subsequent verification. Specifically, the following are included:

[0109] Obtain the problem classification results and severity ratings, use the preset fuzzy logic rule base to generate the initial system parameter adjustment plan, and obtain the parameter adjustment vector.

[0110] Extract the heat dissipation power-related components from the parameter adjustment vector, and use the heat dissipation power adjustment formula P_new = P_old + k1(E_target - E_current) to update the heat dissipation power parameters, where P_new represents the updated heat dissipation power, P_old represents the current heat dissipation power, E_target represents the target energy efficiency, E_current represents the current energy efficiency, and k1 represents the heat dissipation adjustment coefficient to obtain the updated heat dissipation power parameters.

[0111] The temperature control related data is obtained from the updated heat dissipation power parameter. If the temperature control deviation |T_target-T_current| is greater than the preset threshold, the control gain parameter is updated through the control gain correction formula G_new=G_old(1+k2|T_target-T_current|), where G_new represents the updated control gain, G_old represents the current control gain, T_target represents the target temperature, T_current represents the current temperature, and k2 represents the gain adjustment coefficient, to obtain the updated control gain parameter.

[0112] Integration-related data is extracted from the updated control gain parameters, and the coordination coefficient parameters are updated using the coordination coefficient update formula C_new=C_old+k3(I_target-I_current), where C_new represents the updated coordination coefficient, C_old represents the current coordination coefficient, I_target represents the target integration degree, I_current represents the current integration degree, and k3 represents the coordination adjustment coefficient, to obtain the updated coordination coefficient parameters.

[0113] The system parameter combination is obtained from the updated coordination coefficient parameters, and the parameter combination is iteratively optimized using the fuzzy logic control algorithm to generate the optimized parameter configuration.

[0114] Verification data is extracted from the optimized parameter configuration, and the parameter configuration is simulated and tested using a preset verification model to obtain a verified parameter combination.

[0115] Extract the performance index from the verified parameter combination. If the performance index meets the preset threshold, output the final parameter combination. Otherwise, return to the first step to regenerate the parameter adjustment vector.

[0116] By using the adjusted parameter combination, a simulation verification environment is built on the virtual simulation platform to test the heat dissipation power parameters, control gain parameters, and coordination coefficient parameters. At the same time, parameter operation data is collected at a real-time monitoring frequency at fixed time intervals to obtain parameter operation performance data for performance evaluation. Specifically, the following are included:

[0117] Initialize the test environment through the virtual simulation platform, configure the initial values of the heat dissipation power parameters, control gain parameters and coordination coefficient parameters, and determine the operating status of the test environment.

[0118] According to the initialized test environment, a real-time monitoring frequency with a fixed time interval is adopted to obtain the operation data and obtain the dynamic performance data of the parameters during operation.

[0119] By analyzing the dynamic performance data, the support vector machine algorithm is used to classify the heat dissipation power parameters and control gain parameters to determine the performance of the parameter combination.

[0120] If the classification result shows that the performance is lower than the preset threshold, the heat dissipation power parameter and the control gain parameter are optimized by the gradient descent algorithm to obtain the adjusted parameter combination.

[0121] According to the adjusted parameter combination, the test environment is reconfigured, new operating data is obtained, and updated parameter performance data is obtained.

[0122] By comparing the updated parameter performance data with the initial parameter performance data, a linear regression algorithm is used to analyze the impact of parameter adjustment on performance and determine the performance improvement trend.

[0123] Based on the performance improvement trend, iteratively optimize the coordination coefficient parameters, obtain the final parameter performance data, and determine the optimal parameter configuration for the test environment.

[0124] Based on the parameter performance data, the pre-adjustment data is used as the performance comparison benchmark. The expected performance improvement rate is calculated using the improvement rate calculation logic. The improvement rate is quantified based on the relative change of the optimization coefficient. If the improvement rate is greater than the preset performance improvement threshold of 5%, the parameter update solution is determined to be effective. At the same time, the parameter stability test verifies the operating performance of the parameters under different loads and obtains the verification result data. Specifically, it includes:

[0125] By obtaining pre-adjustment data from historical records, building a benchmark data set, and using statistical analysis methods to calculate basic performance indicators, the initial performance benchmark value is obtained.

[0126] Based on the initial performance benchmark value, the relationship between the optimization coefficient and the relative change is calculated, and the linear regression model is used to analyze the correlation between the two to determine the adjustment direction of the optimization coefficient.

[0127] Based on the adjustment direction of the optimization coefficient, simulate the change trend of the expected performance, obtain the predicted data of the performance improvement rate, and determine whether the predicted result meets the preset improvement threshold.

[0128] If the predicted data exceeds the preset improvement threshold, the parameter update plan is preliminarily confirmed, and test scenarios under different loads are extracted to obtain the load distribution data set.

[0129] Through the load distribution data set, stability testing is performed to analyze the operating performance of parameters under various load conditions and obtain the fluctuation data of the operating performance.

[0130] Based on the fluctuation data of the operating performance, the adaptability of the parameter update scheme under different loads is verified, and the standard deviation calculation method is used to analyze the fluctuation range to determine the final verification data.

[0131] Through the final verification data, comprehensively evaluate the performance improvement rate and stability test results, determine whether the parameter update plan meets all the preset conditions, and obtain the feasibility conclusion of the plan.

[0132] Based on the verification result data and the data recording accuracy requirements, the verification results are transmitted to the strategy optimization module in a structured feedback data format. The optimization strategy is implemented in stages according to the strategy execution cycle of the preset time period to obtain the initial operating status data after the strategy is executed. Specifically, it includes:

[0133] Obtain the original data set that meets the data accuracy requirements from the verification result data, organize the data using a preset structured format, and obtain standardized feedback data.

[0134] If the feedback data meets the transmission conditions, it is transmitted to the strategy optimization module through the data interface to determine the transmission completion status. According to the preset time period, the configuration parameters of the current strategy execution cycle are obtained from the strategy optimization module to determine the optimization strategy to be implemented in stages.

[0135] For the optimization strategy, a linear regression algorithm is used to predict the strategy execution effect and obtain the parameter set after the strategy adjustment.

[0136] Through the adjusted parameter set, the optimization strategy is implemented in stages and the operating data after the strategy is executed is obtained.

[0137] If there is a deviation between the operating data and the initial operating state, the operating data is verified twice to obtain the final initial operating state data.

[0138] According to the final initial operating status data, the business logic association of the strategy optimization module is updated to obtain an optimized data processing flow.

[0139] Based on the initial operating status data after policy execution, performance change tracking technology is used to record the system operating status after parameter adjustment in real time. Abnormal fluctuation detection is performed on abnormal data that occurs during operation. At the same time, the specific parameters and time points of each policy change are recorded in the policy adjustment log to obtain system operating status tracking data. Specifically, it includes:

[0140] Initial operating status data is obtained from the system operating environment through sensors and monitoring modules, stored in a preset database, and an initial operating status data set is obtained.

[0141] Based on the initial operating status data set, the time series analysis method is used to calculate the performance change trend and generate performance change tracking data.

[0142] If the performance change tracking data exceeds the preset threshold, the abnormal data points are identified through the anomaly detection algorithm to obtain the abnormal data set.

[0143] For abnormal data sets, the isolation forest algorithm is used to analyze the fluctuation characteristics, determine the fluctuation type and amplitude, and generate fluctuation detection results.

[0144] Through the fluctuation detection results, the policy change parameters and corresponding time points that triggered the anomaly are obtained, recorded in the adjustment log database, and the policy adjustment log is obtained.

[0145] According to the strategy adjustment log, the association rule mining algorithm is used to analyze the relationship between parameter adjustment and abnormal fluctuations, and generate parameter impact analysis results.

[0146] Based on the parameter impact analysis results, the system operation status tracking data is updated and stored in the status tracking database to obtain the optimized status tracking data set.

[0147] Based on the system operation status tracking data and stability assessment criteria, the system operation stability is judged through continuous periodic data. At the same time, monitoring data dimensions covering multiple dimensions such as heat dissipation power and control gain are collected to obtain the system stability assessment results. Specifically, the results include:

[0148] By obtaining status tracking data from system operation, using preset collection tools, and storing the operation records within a continuous period, a preliminary status data set is obtained.

[0149] Based on the preliminary state data set, multidimensional data such as heat dissipation power and control gain are classified and processed, and the indicators of different dimensions are uniformly formatted using a standardized method to determine the classified multidimensional data set.

[0150] Through the classified multidimensional data set, feature extraction is performed on the monitoring indicators. If the fluctuation range of a certain indicator exceeds the preset threshold, it is marked to obtain the marked feature data set.

[0151] According to the labeled feature data set, the support vector machine algorithm is used to classify the relevant features of stability evaluation, judge the stability tendency of system operation, and obtain the stability classification result.

[0152] The stationary classification results are combined with the evaluation criteria to compare the data analysis within consecutive periods. If the stationary classification results have a large deviation from the standard, a secondary analysis is performed on the outliers to determine the distribution of the abnormal data.

[0153] According to the distribution of abnormal data, the weights of potential unstable factors in system operation are calculated to obtain the priority ranking of unstable factors and the final result of system stability assessment.

[0154] Based on the final results, visual charts are generated for key indicators in the evaluation results. Automated tools are used to dynamically update the trends of heat dissipation power and control gain to determine the long-term stability of system operation.

[0155] Based on the system stability evaluation results, if the system performance indicators remain stable and meet the preset requirements during the continuous monitoring period, the optimization strategy is considered to be effective. At the same time, through the feedback closed loop path, the optimization results are transmitted to the data fusion module using the standardized protocol result transmission protocol to update the weight distribution and obtain the updated weight distribution data; specifically, it includes:

[0156] The system performance indicators are obtained through continuous monitoring cycles, and statistical analysis is used to determine the performance stability to obtain the stability evaluation results.

[0157] If the stability assessment results meet the preset threshold, a logical judgment is used to confirm that the optimization strategy has taken effect, generating validation data. Based on this validation data, the optimization results are transmitted to the data fusion module using a standardized protocol, generating a transmission completion status. The data fusion module processes this transmission completion status, updates the weight distribution, and generates preliminary weight adjustment data.

[0158] The gradient descent method in the machine learning algorithm is used to optimize the initial weight adjustment data to obtain the optimized weight data. The optimized weight data is used to adjust the system performance indicators, judge the system operation status, and obtain the updated operation parameters.

[0159] According to the updated operating parameters, a loop feedback mechanism is used to adjust the continuous monitoring period to obtain an adjusted monitoring plan.

[0160] Based on the updated weight distribution data, the operating status of the micro thermal management unit is continuously monitored through a closed-loop control mechanism. If new changes in ambient humidity or fluctuations in equipment load are detected, the multi-dimensional data collection process is restarted according to the adjustment priority, and new temperature monitoring data, ambient humidity data, and equipment load data are obtained to obtain a new round of multi-dimensional data sets for cyclic optimization. Specifically, it includes:

[0161] Real-time monitoring captures the operating status of the micro-thermal management unit, forming an initial status dataset for subsequent analysis. If changes in ambient humidity or device load are detected in this initial status dataset, a closed-loop control mechanism is triggered. Based on the adjustment priority, a multi-dimensional data collection process is initiated, resulting in a comprehensive dataset containing temperature, humidity, and load data.

[0162] Based on the humidity data and load data in the comprehensive data set, preset thresholds are used for comparison. If any data exceeds the threshold range, it is determined to be an abnormal state and a corresponding adjustment instruction set is generated.

[0163] By adjusting the instruction set and combining it with the closed-loop control mechanism, the operating parameters of the micro thermal management unit are dynamically adjusted, the adjusted operating status data is obtained, and an updated status data set is formed.

[0164] For the updated status data set, the support vector machine algorithm is used to classify the temperature data, humidity data and load data to determine the change trend of the data in each dimension.

[0165] Based on the changing trends, the adjustment priority is re-evaluated and an optimized adjustment strategy data set is generated to guide the next round of multi-dimensional data collection and closed-loop control adjustments.

[0166] Obtain the optimized adjustment strategy data set and compare it with historical data to determine whether there are persistent anomalies. If so, trigger the data collection process cyclically, update the comprehensive data set, and enter a new round of optimization and adjustment.

[0167] The advantages of the present invention are: by establishing a multidimensional data acquisition module, a multidimensional data analysis module, a thermal assessment module, a heat treatment module and a thermal management module, all modules cooperate with each other, among which the multidimensional data acquisition module obtains thermal data, environmental data and equipment parameters in real time, providing an accurate basis for system decision-making. The multidimensional data analysis module quantifies energy efficiency, temperature control and integration problems through three core coefficients to achieve accurate positioning of problems. The thermal assessment-processing-management module forms a closed-loop optimization mechanism and automatically adjusts system parameters (such as compressor strategy, pipeline layout), ultimately improving the system's overall energy efficiency, extending its life, reducing the system volume and weight, and making it more suitable for a more compact electrification platform.

[0168] Summary: The solution of the present invention effectively solves the problems of low energy efficiency, extensive temperature control and structural redundancy in traditional thermal management systems through data-driven intelligent algorithms and integrated optimization design.

[0169] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. New energy vehicle thermal management system, characterized in that: It includes multi-dimensional data acquisition module, multi-dimensional data analysis module, thermal assessment module, thermal treatment module and thermal management module; The multi-dimensional data acquisition module is responsible for collecting thermal data, natural environment impact data and equipment parameters; The multidimensional data analysis module calculates the thermal energy efficiency optimization coefficient based on the collected data Temperature control demand coefficient Ez and integrated optimization coefficient Yv; The thermal assessment module evaluates and diagnoses problems such as unsatisfactory energy efficiency, inaccurate temperature control, and unreasonable integration that occur during the execution of the thermal management system based on the calculation results of the multidimensional data analysis module; The thermal treatment module takes measures for the problems found according to the evaluation results of the thermal evaluation module; The thermal management module incorporates new measures into the system based on the optimization measures taken by the thermal treatment module, updates system parameters and operation strategies, and forms a new thermal management system composition.

2. The new energy vehicle thermal management system according to claim 1, characterized in that: The multi-dimensional data acquisition module includes a thermal data acquisition unit, a natural environment impact data acquisition unit and an equipment parameter acquisition unit.

3. The new energy vehicle thermal management system according to claim 2, characterized in that: The thermal data acquisition unit collects thermal data through temperature sensors, heat flow sensors and air conditioning system monitoring equipment, including real-time temperature and heat generation rate data of the battery, motor and cabin parts, as well as heat exchange data during the air conditioning cooling and heating process.

4. The new energy vehicle thermal management system according to claim 2, characterized in that: The natural environment impact data acquisition unit collects natural environment impact data through environmental sensors, including environmental temperature, humidity and light intensity data.

5. The new energy vehicle thermal management system according to claim 2, characterized in that: The device parameter acquisition unit obtains device parameters through the vehicle management data center and the sensor network, including device-specific parameters such as battery capacity, motor power, and compressor performance parameters.

6. The new energy vehicle thermal management system according to claim 1, characterized in that: The multi-dimensional data analysis module includes an energy efficiency analysis unit, a multi-temperature zone analysis unit and an integrated scheduling unit.

7. The new energy vehicle thermal management system according to claim 6, characterized in that: The energy efficiency analysis unit calculates the thermal energy efficiency optimization coefficient The calculation formula is: In the formula, Indicates the thermal energy efficiency optimization coefficient, Q y Indicates effective use of heat, W z It represents the thermal power consumption of all components in new energy vehicles, T1 represents the ambient temperature, T0 represents the standard temperature, and g represents the waste heat recovery efficiency coefficient.

8. The new energy vehicle thermal management system according to claim 6, characterized in that: The multi-temperature zone analysis unit calculates the temperature control demand coefficient Ez, and its calculation formula is: In the formula, Ez represents the temperature control demand coefficient, T l Indicates the ideal operating temperature in the vehicle or battery pack, T w Indicates the external ambient temperature.

9. The new energy vehicle thermal management system according to claim 6, characterized in that: The integrated scheduling unit calculates the integrated optimization coefficient Yv, which is calculated as follows: In the formula, Yv represents the integrated optimization coefficient, i represents the subsystem type, and U i represents the volume of the ith subsystem, H i represents the power density of the ith subsystem, d represents the total length of the actual pipeline, and d min represents the theoretical minimum pipe length, P represents the actual system efficiency, and P max Represents the theoretical maximum system efficiency.

10. A thermal management method for new energy vehicles, characterized in that: The following steps are involved: Step 1: Establish a multidimensional data acquisition module, a multidimensional data analysis module, a thermal assessment module, a thermal treatment module, and a thermal management module; Step 2: The multi-dimensional data acquisition module collects thermal data, natural environment impact data and equipment parameters through various sensors; Step 3: Calculate the thermal energy efficiency optimization coefficient using the multi-dimensional data analysis module Temperature control demand coefficient Ez and integrated optimization coefficient Yv; Step 4: The thermal assessment module evaluates and diagnoses issues such as suboptimal energy efficiency, inaccurate temperature control, and unreasonable integration that arise during the thermal management system's execution based on the calculation results. Step 5: The heat treatment module takes corresponding measures for the problems found according to the evaluation results of the thermal evaluation module; Step 6: The thermal management module incorporates the new measures into the system, updates the system parameters and operation strategies, and forms a new thermal management system.

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