A new energy vehicle thermal management system and a thermal management method thereof

Abstract

The application relates to the technical field of new energy vehicles, and discloses a new energy vehicle thermal management system and a thermal management method thereof, which comprise a multidimensional data acquisition module, a multidimensional data analysis module, a thermal evaluation module, a thermal treatment module and a thermal management module; the multidimensional data acquisition module is responsible for collecting thermal data, natural environment influence data and equipment parameters; the multidimensional data analysis module calculates a thermal energy efficiency optimization coefficient, a temperature control demand coefficient Ez and an integrated optimization coefficient Yv according to the collected data; the thermal evaluation module evaluates and diagnoses problems of unexpected energy efficiency, inaccurate temperature control and unreasonable integration occurring in the execution process of the thermal management system according to the calculation results of the multidimensional data analysis module; the thermal treatment module takes measures for the discovered problems according to the evaluation results of the thermal evaluation module; the thermal management module updates system parameters and operation strategies by incorporating new measures into the system according to the optimization measures taken by the thermal treatment module, and forms a new thermal management system composition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicles, in particular to a new energy vehicle thermal management system and a thermal management method thereof. BACKGROUND

[0002] The traditional fuel vehicle thermal management system mainly relies on engine waste heat heating and mechanical compression refrigeration. This design has inherent defects such as single energy efficiency structure and limited regulation range. The refrigeration process is driven by a mechanical compressor, which has high energy consumption. The heating can only use engine waste heat and cannot adapt to the scenario of new energy vehicles without continuous engine waste heat. In new energy vehicles, the traditional thermal management system needs to completely rely on electrically driven compressors, resulting in a sharp increase in heating energy consumption in low-temperature environments, a significant decrease in endurance mileage, and a particularly prominent energy efficiency problem.

[0003] In addition, the traditional thermal management system mainly uses single-temperature-zone extensive control and can only adjust the average temperature of the cabin, which is difficult to meet the differentiated temperature control requirements of key components such as batteries and motors. For example, the performance of the battery decays quickly in low-temperature or high-temperature environments, but the traditional system lacks precise control capability, resulting in a shortened battery life and increased safety hazards.

[0004] On the other hand, the traditional thermal management subsystems (air conditioner, cooling, battery cooling) are independently designed, the pipeline is complex, and the function redundancy problem is obvious. For example, the layout of the engine and the air conditioner condenser sharing the cooling circuit results in space waste and low efficiency in new energy vehicles due to the lack of engines, which is difficult to adapt to the compact integration requirements of the electrification platform.

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

[0006] (I) Technical problems solved

[0007] In view of the deficiencies of the prior art, the present application provides a new energy vehicle thermal management system and a thermal management method thereof, which has the advantages of energy efficiency improvement, precise temperature control, and compact integration, and solves the problems of high energy consumption, inaccurate temperature control, complex system, and difficulty in integration of the traditional thermal management system in new energy vehicles.

[0008] (II) Technical solutions

[0009] To achieve the above-mentioned purpose, the present application provides the following technical solutions: a new energy vehicle thermal management system, comprising 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.

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

[0011] The multi-dimensional data analysis module calculates the thermal energy efficiency optimization coefficient according to the collected data The temperature control demand coefficient Ez and the integrated optimization coefficient Yv.

[0012] The thermal evaluation module evaluates and diagnoses the problems of unanticipated energy efficiency, inaccurate temperature control and unreasonable integration that occur in the process of the thermal management system according to the calculation results of the multi-dimensional data analysis module;

[0013] The thermal treatment module takes measures for the problems found according to the evaluation results of the thermal evaluation module;

[0014] The thermal management module updates the system parameters and operation strategy by incorporating the new measures into the system according to the optimization measures taken by the thermal treatment module, 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 influence 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 batteries, motors and cabin parts, and heat exchange data during air conditioning refrigeration and heating processes.

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

[0018] Preferably, the equipment parameter acquisition unit obtains equipment parameters through a vehicle management data center and a sensor network, including equipment inherent 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, represents the thermal energy efficiency optimization coefficient, Q y represents the effective use of heat, W z represents the thermal power consumption of all components in the new energy vehicle, T1 represents the environmental temperature, T0 represents the standard temperature, and g represents the waste heat recovery efficiency coefficient.

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

[0024]

[0025] In the formula, Ez represents the temperature control demand coefficient, T l represents the ideal working temperature of the vehicle interior or the battery pack, T w represents the external environment temperature.

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

[0027]

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

[0029] The new energy vehicle thermal management method comprises the following steps:

[0030] Step one, establishing 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;

[0031] Step two, the multi-dimensional data acquisition module collects thermal data, natural environment influence data, and equipment parameters through various sensors;

[0032] Step three, the multi-dimensional data analysis module calculates a thermal energy efficiency optimization coefficient a temperature control demand coefficient Ez, and an integrated optimization coefficient Yv;

[0033] Step four, the thermal evaluation module evaluates and diagnoses problems such as unexpected energy efficiency, inaccurate temperature control, and unreasonable integration that occur during the execution of the thermal management system according to the calculation results;

[0034] Step five, the thermal treatment module takes corresponding measures for the problems found according to the evaluation results of the thermal evaluation module;

[0035] Step six, the thermal management module updates system parameters and operation strategies by incorporating new measures into the system, forming a new thermal management system composition.

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

[0037] 1、The present application calculates the thermal energy efficiency optimization coefficient As a standard for evaluating the energy consumption efficiency of refrigeration and heating, when the thermal energy efficiency optimization coefficient When the system is preset in the interval, it indicates that the system energy efficiency meets the standard, maintains the current compressor frequency conversion strategy and waste heat recovery scheme; when the thermal energy efficiency optimization coefficient Below the preset interval, the system will trigger a series of optimization measures: the compressor reduces the start-stop frequency or optimizes the load distribution to reduce unnecessary energy consumption; at the same time, the waste heat recovery is strengthened, and the battery heat recovery is used for cabin heating to improve energy utilization. Through the above measures, not only can the system realize energy efficiency improvement under different environmental temperatures, but also can reduce the endurance loss of the vehicle in low temperature environment, thereby optimizing the overall performance.

[0038] 2、The present application calculates the temperature control demand coefficient Ez, which is used as the basis for differentiated temperature control priority allocation and is substituted into the PID control algorithm or fuzzy control algorithm to dynamically adjust the cooling / heating power of each area. When the battery temperature approaches the safety threshold, the battery temperature control weight is automatically increased to prioritize battery performance, improve the charging and discharging efficiency of the battery under extreme temperature, and prolong the battery life.

[0039] 3、The present application calculates the integrated optimization coefficient Yv to evaluate the rationality of the system pipeline layout and function integration. When the integrated optimization coefficient Yv is in the system preset interval, it indicates that the system energy efficiency is in an ideal state and does not need to be adjusted, maintaining the current pipeline design. When the integrated optimization coefficient Yv is not in the system preset interval, the system automatically switches the multi-way valve to reduce the pipeline length or use the motor waste heat for battery preheating, ultimately achieving the beneficial effects of reducing the system volume, reducing the pipeline pressure loss and improving the response speed. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The present application is a system flowchart. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0042] Please refer to Figure 1 , a new energy vehicle thermal management system, comprising a multi-dimensional data acquisition module, a multi-dimensional data analysis module, a thermal evaluation module, a thermal processing module and a thermal management module;

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

[0044] The multi-dimensional data analysis module calculates the thermal energy efficiency optimization coefficient according to the collected data The temperature control demand coefficient Ez and the integration optimization coefficient Yv serve to:

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

[0046] The thermal evaluation module evaluates and diagnoses the problems of unexpected energy efficiency, inaccurate temperature control and unreasonable integration that occur in the process of the thermal management system according to the calculation results of the multi-dimensional data analysis module, determines the type and severity of the problems, and provides a basis for subsequent processing;

[0047] The thermal processing module takes measures according to the evaluation results of the thermal evaluation module for the problems found, as follows:

[0048] When the thermal energy efficiency optimization coefficient is not in the system preset interval, it means that the current energy efficiency is insufficient, at which time the frequency conversion strategy of the compressor needs to be adjusted to reduce energy consumption and improve energy efficiency;

[0049] When the temperature control is not accurate, the temperature control demand coefficient Ez is substituted into the temperature control adjustment algorithm, and the system automatically recalibrates the adjustment coefficient to realize accurate temperature control of the battery, motor and other key multi-zone parts;

[0050] When the integration optimization coefficient Yv is not in the system preset interval, it means that the current integration is unreasonable, at which time the pipeline layout and subsystem connection mode need to be optimized to reduce functional redundancy, improve the compactness and integration level of the system, and solve the problems existing in the system; The thermal management module incorporates the new measures taken by the thermal processing module into the system, updates the system parameters and operation strategy, forms a new thermal management system composition, and realizes the continuous optimization and improvement of the system.

[0051] The multi-dimensional data acquisition module includes a thermal data acquisition unit, a natural environment influence data acquisition unit, and a device parameter acquisition unit. The thermal data acquisition unit acquires thermal data through temperature sensors, heat flow sensors, and air conditioning system monitoring devices (the temperature sensors are installed in the battery pack, the motor housing, and the cabin to monitor the temperature of each key part in real time; the heat flow sensors are used to measure the heat generated by the battery and the motor during operation and the heat transfer rate; the air conditioning system monitoring device is integrated in the air conditioning system to monitor the heat exchange during air conditioning cooling and heating, including the heat exchange data of the evaporator and the condenser), including real-time temperature and heat generation rate data of the battery, the motor, and the cabin, and heat exchange data during air conditioning cooling and heating.

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

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

[0054] The advantages are that through the above-mentioned acquisition mode of the multi-dimensional data acquisition module, the new energy vehicle thermal management system can comprehensively and accurately acquire various data required for thermal management, providing a solid foundation for the analysis and regulation of 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] The energy efficiency analysis unit calculates the thermal energy efficiency optimization coefficient The calculation formula is:

[0056]

[0057] In the formula, represents the thermal energy efficiency optimization coefficient (dimensionless), which is used to quantify the closeness of the actual energy efficiency of the system to the theoretical optimal energy efficiency, Q yrepresents the effective utilization of heat (kJ), W z represents the heat dissipation of all components in the new energy vehicle (kW), T1 represents the ambient temperature, T0 represents the standard temperature (℃), g represents the waste heat recovery efficiency coefficient (dimensionless), the value range is 0-1, reflecting the effective utilization rate of the waste heat recovery system.

[0058] The advantage is: by calculating the thermal energy efficiency optimization coefficient As a standard for evaluating the energy consumption efficiency of refrigeration and heating, when the thermal energy efficiency optimization coefficient When the system preset interval (such as 0.75-0.9), it indicates that the system energy efficiency meets the standard, maintains the current compressor frequency conversion strategy and waste heat recovery scheme; when the thermal energy efficiency optimization coefficient Lower than the preset interval, the system will trigger a series of optimization measures: the compressor reduces the start-stop frequency or optimizes the load distribution to reduce unnecessary energy consumption; at the same time, the waste heat recovery is strengthened, the battery heat recovery is used for cabin heating, and the energy utilization rate is improved. Through the above measures, not only can the system realize energy efficiency improvement under different environmental temperatures, but also can reduce the endurance loss of the vehicle in low temperature environment, so as to optimize the 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 demand intensity of the system for temperature regulation under different environmental temperatures, T l represents the ideal working temperature of the vehicle or the battery pack, T w represents the external environmental temperature.

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

[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 optimization degree of the 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 i-th subsystem (L), Hi represents the power density (kW / L) of the i-th subsystem, reflecting the heat dissipation capacity per unit volume, d represents the actual total length of the pipeline (m), d min represents the theoretical minimum pipeline length (m), determined by the system topology, P represents the actual system efficiency (%), P max represents the theoretical maximum system efficiency (%), such as the efficiency corresponding to the theoretical COP of the heat pump.

[0066] The advantage is that by calculating the integration optimization coefficient Yv, the rationality of the system pipeline layout and functional integration is evaluated. When the integration optimization coefficient Yv is in the system preset interval (such as 0.8-1.0), it indicates that the system energy efficiency is in an ideal state and does not need to be adjusted, maintaining the current pipeline design. When the integration optimization coefficient Yv is not in the system preset interval, the system automatically switches the multi-way valve to reduce the pipeline length, or uses the motor waste heat for battery preheating, ultimately achieving the beneficial effects of reducing the volume, reducing the pipeline pressure loss, and improving the response speed.

[0067] The new energy vehicle thermal management method comprises the following steps:

[0068] Step one, establish 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;

[0069] Step two, the multi-dimensional data acquisition module collects thermal data, natural environment influence data, and equipment parameters through various sensors; in specific implementation, through a multi-sensor network layout scheme, temperature monitoring data, environmental humidity data, and equipment load data are obtained, a data preprocessing module is used to standardize and noise-filter the original multi-dimensional data to obtain a standardized multi-dimensional data set, and if the data quality evaluation index is lower than the preset threshold 0.85, a data reacquisition mechanism is triggered to reacquire a more accurate data set.

[0070] Step three, the multi-dimensional data analysis module calculates the thermal energy efficiency optimization coefficient the temperature control demand coefficient Ez and the integration optimization coefficient Yv; the energy efficiency analysis unit calculates the thermal energy efficiency optimization coefficient The calculation formula is:

[0071]

[0072] In the formula, represents the thermal energy efficiency optimization coefficient, Q y represents the effective use of heat, W z represents the thermal power consumption of all components in the new energy vehicle, T1 represents the environmental 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 the calculation formula is:

[0074]

[0075] In the formula, Ez represents the temperature control demand coefficient, T l represents the ideal working temperature of the vehicle interior or the battery pack, T w represents the external environment temperature;

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

[0077]

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

[0079] Step four, the thermal evaluation module evaluates and diagnoses the problems of unanticipated energy efficiency, inaccurate temperature control, and unreasonable integration that occur in the execution of the thermal management system according to the calculation results;

[0080] Step five, the thermal treatment module takes corresponding measures for the problems found according to the evaluation results of the thermal evaluation module;

[0081] Specifically, when the thermal energy efficiency optimization coefficient is not in the system preset interval, it indicates that the current energy efficiency is insufficient, and at this time, the frequency conversion strategy of the compressor needs to be adjusted to reduce energy consumption and improve energy efficiency;

[0082] When the temperature control is not accurate, the temperature control demand coefficient Ez is substituted into the temperature control adjustment algorithm, and the system automatically recalibrates the adjustment coefficient to realize accurate temperature control of the battery, motor, and other key multi-zone parts;

[0083] When the integrated optimization coefficient Yv is not in the system preset interval, it indicates that the current integration is unreasonable, at this time, the pipeline layout and subsystem connection mode are optimized to reduce functional redundancy, improve the compactness and integration level of the system, and solve the problems existing in the system; Step six, the thermal management module updates the system parameters and operation strategy by incorporating new measures into the system, forming a new thermal management system composition.

[0084] In addition, in specific implementation, the multi-dimensional data analysis module and the thermal evaluation module can also perform performance evaluation by using the following method: according to the standardized multi-dimensional data set, a weighted data fusion algorithm is used to dynamically allocate the weight of temperature data, the weight of environmental humidity data and the weight of equipment load data, and a comprehensive thermal state index is obtained through a fusion calculation formula, the comprehensive thermal state index is used for subsequent thermal energy efficiency analysis, and the comprehensive thermal state index is determined as the input basis for thermal energy efficiency evaluation; specifically including: obtaining temperature data, humidity data and load data from the multi-dimensional data set, and normalizing each type of data by using a preset standardization processing method to obtain a first data set after standardization.

[0085] According to the first data set after standardization, the weight distribution is performed on the temperature data, humidity data and load data by using a weighted data fusion method, the initial weight value of each type of data is calculated, and a second data set after weight distribution is obtained. By dynamically adjusting the weight value in the second data set, if the weight value of a certain type of data deviates from the preset threshold range, the correction processing is performed on it, and a third data set after adjustment is obtained.

[0086] According to the adjusted weight value in the third data set, the temperature data, humidity data and load data are weighted and summed by using a fusion calculation formula, and a preliminary thermal state value is calculated, and a first thermal state result is obtained.

[0087] The fusion calculation formula is: S = w1T + w2H + w3L, 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, the time series data of the thermal state value is filtered by using a moving average method, and a second thermal state result after smoothing is obtained.

[0089] According to the second thermal state result, a comprehensive thermal state index is calculated by combining a preset energy efficiency evaluation model, and final thermal energy efficiency evaluation input data is obtained.

[0090] The final thermal energy efficiency evaluation input data is stored and formatted, a structured data record is generated, and the record is determined as the basis for subsequent analysis.

[0091] The comprehensive thermal state index is input into a thermal energy efficiency optimization coefficient calculation module, a heat conduction equation solving algorithm is used to calculate thermal energy efficiency optimization coefficients, temperature control demand coefficients and integrated optimization coefficients, a three-dimensional optimization coefficient matrix is obtained, and the three-dimensional optimization coefficient matrix is used for system state evaluation to determine the potential problem type of system operation; specifically including:

[0092] The thermal state index data is acquired through a sensor network, and the data is preprocessed using a standardized protocol to obtain a normalized thermal state data set.

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

[0094] The three-dimensional coefficient set is converted into a three-dimensional optimization coefficient matrix through a matrix construction algorithm, to generate 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, to determine a system state abnormality type, and obtain a state classification result.

[0095] According to the state classification result, a clustering analysis algorithm is used to group the abnormality types, to determine the distribution characteristics of potential problem types, and obtain a problem distribution data set.

[0096] Through the problem distribution data set, a time series analysis method is used to detect the change trend of the problem types, to obtain trend characteristic data.

[0097] If the change amplitude in the trend characteristic data exceeds a preset threshold, an optimization adjustment parameter is generated by matching the corresponding problem type through a pre-established rule base, to obtain a system optimization instruction set.

[0098] According to the three-dimensional optimization coefficient matrix, if the thermal energy efficiency optimization coefficient is lower than a preset energy efficiency threshold of 0.8, it is determined that the system has an energy efficiency deficiency problem, if the deviation of the temperature control demand coefficient exceeds a preset temperature control deviation range of 0.15, it is determined that the system has a temperature control deviation problem, and if the integrated optimization coefficient is lower than a preset integrated threshold of 0.75, it is determined that the system has an improper integration problem, to generate a corresponding energy efficiency deficiency, temperature control deviation, or improper integration problem type identifier and severity rating, to obtain a problem classification result for subsequent parameter adjustment; specifically including:

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

[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 identifier 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 identifier is generated;

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

[0103] According to the problem type identification set, the deviation data is classified by using a K-means clustering algorithm, the severity rating of each problem is determined, and a rating result is obtained.

[0104] Through the rating result, the parameter adjustment rule associated with each problem type identifier is obtained, the corresponding adjustment strategy is extracted from the pre-established rule library, and a parameter adjustment scheme is generated.

[0105] According to the parameter adjustment scheme, the predicted values of the adjusted thermal energy efficiency optimization coefficient, the temperature control demand coefficient, and the integrated optimization coefficient are calculated, and an optimization coefficient prediction result is obtained.

[0106] If any coefficient in the optimization coefficient prediction result is still lower than the corresponding preset threshold, the parameter adjustment scheme is iteratively adjusted, the predicted values are repeatedly calculated, until all coefficients meet the threshold requirement, and a final parameter adjustment result is obtained.

[0107] Through the final parameter adjustment result, the system operation configuration is updated, and the optimized system operation data is generated.

[0108] According to the problem classification result and the severity rating, a fuzzy logic control algorithm is used to adaptively adjust the system parameters, the heat dissipation power parameter is updated through a heat dissipation power adjustment formula for the energy efficiency deficiency problem, the control gain parameter is updated through a control gain correction formula for the temperature control deviation problem, and the coordination coefficient parameter is updated through a coordination coefficient update formula for the improper integration problem, to obtain an adjusted parameter combination for subsequent verification; specifically including:

[0109] The problem classification result and the severity rating are obtained, a preset fuzzy logic rule library is used to generate an initial system parameter adjustment scheme, and a parameter adjustment vector is obtained.

[0110] The heat dissipation power related components are extracted from the parameter adjustment vector, and the heat dissipation power parameter is updated using the heat dissipation power adjustment formula P_new = P_old + k1(E_target - E_current), 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 parameter.

[0111] Obtain temperature control related data from the updated heat dissipation power parameter, and determine whether the temperature control deviation |T_target-T_current| is greater than a preset threshold value, and if so, update the control gain parameter by a control gain correction formula G_new=G_old(1+k2|T_target-T_current|), wherein 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, k2 represents the gain adjustment coefficient, and obtain the updated control gain parameter.

[0112] Extract integration related data from the updated control gain parameter, and update the coordination coefficient parameter by a coordination coefficient update formula C_new=C_old+k3(I_target-I_current), wherein C_new represents the updated coordination coefficient, C_old represents the current coordination coefficient, I_target represents the target integration, I_current represents the current integration, k3 represents the coordination adjustment coefficient, and obtain the updated coordination coefficient parameter.

[0113] Obtain system parameter combinations from the updated coordination coefficient parameter, and generate optimized parameter configurations by iteratively optimizing the parameter combinations using a fuzzy logic control algorithm.

[0114] Extract verification data from the optimized parameter configurations, and perform simulation testing on the parameter configurations by a preset verification model to obtain verified parameter combinations.

[0115] Extract performance indicators from the verified parameter combinations, and determine whether the performance indicators meet a preset threshold value, and if so, output the final parameter combinations, otherwise return to the first step to generate a parameter adjustment vector again.

[0116] Build a simulation verification environment on a virtual simulation platform by adjusting the parameter combinations, test the heat dissipation power parameter, the control gain parameter and the coordination coefficient parameter, and collect parameter running data at a fixed time interval real-time monitoring frequency to obtain parameter running performance data for performance evaluation; specifically including:

[0117] Initialize the test environment by the virtual simulation platform, configure the initial values of the heat dissipation power parameter, the control gain parameter and the coordination coefficient parameter, and determine the running state of the test environment.

[0118] According to the initialized test environment, obtain running data at a fixed time interval real-time monitoring frequency to obtain dynamic performance data of the parameters during running.

[0119] By analyzing the dynamic performance data, classify the heat dissipation power parameter and the control gain parameter using a support vector machine algorithm to determine the performance of the parameter combinations.

[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 a gradient descent algorithm to obtain an adjusted parameter combination.

[0121] According to the adjusted parameter combination, the test environment is reconfigured, new running 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 influence of parameter adjustment on performance to determine the performance improvement trend.

[0123] According to the performance improvement trend, the coordination coefficient parameter is iteratively optimized to obtain final parameter performance data, and the best parameter configuration of the test environment is determined.

[0124] According to the parameter running performance data, the data before adjustment is taken as the performance comparison benchmark, and an improvement rate calculation logic is used to calculate the expected performance improvement rate. 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%, it is determined that the parameter update scheme is effective. At the same time, the parameter stability test is used to verify the running performance of the parameter under different loads to obtain verification result data. Specifically, it includes:

[0125] The data before adjustment is obtained from the historical records to construct a benchmark data set, and a statistical analysis method is used to calculate the basic performance index to obtain the initial performance benchmark value.

[0126] According to the initial performance benchmark value, the relationship between the optimization coefficient and the relative change is calculated, a linear regression model is applied to analyze the correlation between the two, and the adjustment direction of the optimization coefficient is determined.

[0127] According to the adjustment direction of the optimization coefficient, the expected performance change trend is simulated to obtain the prediction data of the performance improvement rate, and it is judged whether the prediction result meets the preset improvement threshold.

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

[0129] Through the load distribution data set, a stability test is performed to analyze the running performance of the parameter under various load conditions to obtain fluctuation data of the running performance.

[0130] According to the fluctuation data of the running performance, the adaptability of the parameter update scheme under different loads is verified, a standard deviation calculation method is used to analyze the fluctuation range, and the final verification data is determined.

[0131] Through the final verification data, the performance improvement rate and the results of the stability test are comprehensively evaluated to determine whether the parameter updating scheme meets all the preset conditions and obtain the feasibility conclusion of the scheme.

[0132] According to the verification result data, the verification result is transmitted to the strategy optimization module in a structured feedback data format according to the data recording accuracy requirement, and the optimization strategy is implemented in stages according to the preset strategy execution period, to obtain the initial running state data after the strategy execution; specifically including:

[0133] From the verification result data, the original data set meeting the data accuracy requirement is obtained, and the data is arranged in a preset structured format to obtain standardized feedback data.

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

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

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

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

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

[0139] Through the initial running state data after the strategy execution, the performance change tracking technology is used to record the system running state after the parameter adjustment in real time, the abnormal data appearing in the running is detected for abnormal fluctuation, and the specific parameters and time points of each strategy change are recorded in the strategy adjustment log, to obtain the system running state tracking data; specifically including:

[0140] Through the sensor and the monitoring module, the initial running state data is obtained from the system running environment and stored in the preset database to obtain the initial running state data set.

[0141] According to the initial running state data set, the time series analysis method is used to calculate the performance change trend to generate performance change tracking data.

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

[0143] For the abnormal data set, the isolated forest algorithm is used to analyze the fluctuation characteristics, determine the fluctuation type and amplitude, and generate the fluctuation detection result.

[0144] Through the fluctuation detection result, the strategy change parameter triggering the abnormality and the corresponding time point are obtained, recorded into the adjustment log database, and the strategy 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 fluctuation, and the parameter influence analysis result is generated.

[0146] Through the parameter influence analysis result, the system running state tracking data is updated and stored in the state tracking database, and the optimized state tracking data set is obtained.

[0147] According to the system running state tracking data, based on the stability evaluation standard, the system running stability is judged through the continuous period data, and the monitoring data dimension covering the heat dissipation power, control gain and other multi-dimensional indexes is collected, and the system stability evaluation result is obtained; Specifically including:

[0148] Through the state tracking data obtained from the system running, the preset collection tool is used to store the running record in the continuous period, and the preliminary state data set is obtained.

[0149] According to the preliminary state data set, the multi-dimensional data such as heat dissipation power and control gain are classified and processed, and the standardized method is used to unify the format of different dimensional indexes, and the classified multi-dimensional data set is determined.

[0150] Through the classified multi-dimensional data set, the feature extraction is carried out for the monitoring indexes, if the fluctuation amplitude of an index exceeds the preset threshold, it is marked, and the marked feature data set is obtained.

[0151] According to the marked feature data set, the support vector machine algorithm is used to classify and process the related features of stability evaluation, to judge the stability tendency of system running, and the stability classification result is obtained.

[0152] Through the stability classification result, combined with the evaluation standard, the data analysis in the continuous period is compared, if the stability classification result and the standard deviation are large, the abnormal point is analyzed again, and the abnormal data distribution is determined.

[0153] According to the abnormal data distribution, the weight calculation is carried out for the potential unstable factors of system running, the priority order of unstable factors is obtained, and the final result of system stability evaluation is obtained.

[0154] Through the final result, a visual chart is generated for the key indicators in the evaluation result, and an automatic tool is used to dynamically update the trends of heat dissipation power and control gain to determine the long-term stability of system operation.

[0155] Through the system stability evaluation result, if the system performance indicators remain stable and meet the preset requirements within the continuous monitoring period, it is determined that the optimization strategy takes effect, and at the same time, through the feedback closed-loop path, the optimization result is transmitted to the data fusion module to update the weight distribution using the result transmission protocol of the standardized protocol, to obtain updated weight distribution data; specifically including:

[0156] Through the continuous monitoring period, the system performance indicators are obtained, and statistical analysis is used to determine the performance stability to obtain the stability evaluation result.

[0157] If the stability evaluation result meets the preset threshold, the optimization strategy is determined to take effect using logical judgment to obtain effectiveness confirmation data. According to the effectiveness confirmation data, the optimization result is transmitted to the data fusion module using the standardized protocol to obtain a transmission completion state. The data fusion module processes the transmission completion state to update the weight distribution to obtain preliminary weight adjustment data.

[0158] The gradient descent method in machine learning algorithm is used to optimize the preliminary weight adjustment data to obtain optimized weight data. The system performance indicators are adjusted through the optimized weight data to determine the system operation state to obtain updated operation parameters.

[0159] According to the updated operation parameters, the continuous monitoring period is adjusted using a cyclic feedback mechanism to obtain an adjusted monitoring scheme.

[0160] According to the updated weight distribution data, the running state of the micro thermal management unit is continuously monitored through the closed-loop control mechanism. If a new environmental humidity change or equipment load fluctuation is detected, the multi-dimensional data acquisition process is restarted according to the adjustment priority ranking to obtain new temperature monitoring data, environmental humidity data and equipment load data to obtain a new round of multi-dimensional data set for cyclic optimization. Specifically including:

[0161] Through real-time monitoring, the running state data of the micro thermal management unit is obtained to form an initial state data set for subsequent analysis. If changes in environmental humidity or equipment load are detected in the initial state data set, the closed-loop control mechanism is triggered, and the multi-dimensional data acquisition process is started according to the adjustment priority ranking to obtain a comprehensive data set containing temperature data, humidity data and load data.

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

[0163] By adjusting the instruction set, combining the closed-loop control mechanism, the operation parameters of the micro thermal management unit are dynamically adjusted, the adjusted operation state data is acquired, and the updated state data set is formed.

[0164] According to the updated state data set, support vector machine algorithm is used for classification processing of temperature data, humidity data and load data, and the change trend of each dimension data is determined.

[0165] According to the change trend, the adjustment priority is re-evaluated, and the optimized adjustment strategy data set is generated to guide the next round of multi-dimensional data acquisition and closed-loop control adjustment.

[0166] The optimized adjustment strategy data set is acquired, and whether there is a continuous anomaly is judged by comparison with historical data, if there is, the data acquisition process is triggered in a cycle, the comprehensive data set is updated, and a new round of optimization adjustment is entered.

[0167] The advantages of the present application are: by establishing a multi-dimensional data acquisition module, a multi-dimensional data analysis module, a thermal evaluation module, a thermal processing module and a thermal management module, all modules cooperate with each other, wherein the multi-dimensional data acquisition module acquires thermal data, environmental data and device parameters in real time to provide accurate basis for system decision, the multi-dimensional data analysis module quantifies energy efficiency, temperature control and integration problem through three core systems to realize accurate positioning of the problem, the thermal evaluation-processing-management module forms a closed-loop optimization mechanism to automatically adjust system parameters (such as compressor strategy, pipeline layout), finally the system comprehensive energy efficiency is improved, the service life is prolonged, the system volume is reduced and the weight is reduced, and it is more suitable for more compact electric platform.

[0168] Summary: the present application scheme solves the problems of low energy efficiency, extensive temperature control and structural redundancy of traditional thermal management system through data-driven intelligent algorithm and integrated optimization design.

[0169] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A new energy vehicle thermal management system, characterized in that, The 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. The multi-dimensional data acquisition module is responsible for collecting thermal data, natural environment influence data and equipment parameters. The multi-dimensional data analysis module calculates a thermal energy efficiency optimization coefficient according to the collected data , a temperature control demand coefficient , and an integrated optimization coefficient ; The thermal evaluation module evaluates and diagnoses problems of unanticipated energy efficiency, inaccurate temperature control and unreasonable integration that occur in the process of thermal management system execution according to the calculation results of the multi-dimensional 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 updates system parameters and operation strategies by incorporating new measures into the system according to the optimization measures taken by the thermal treatment module, and forms a new thermal management system composition. The multi-dimensional data analysis module comprises an energy efficiency analysis unit, a multi-temperature zone analysis unit and an integrated scheduling unit. The energy efficiency analysis unit calculates a thermal energy efficiency optimization coefficient The calculation formula is: ； In the formula, represents the thermal energy efficiency optimization coefficient, represents the effective use of heat, represents the thermal power consumption of all components in the new energy vehicle, represents the ambient temperature, represents the standard temperature, and g represents the waste heat recovery efficiency coefficient.

2. The new energy vehicle thermal management system according to claim 1, characterized in that: The multi-dimensional data acquisition module comprises a thermal data acquisition unit, a natural environment influence 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 batteries, motors and cabin parts, and heat exchange data in the air conditioning refrigeration and heating process.

4. The new energy vehicle thermal management system according to claim 2, characterized in that: The natural environment influence data acquisition unit collects natural environment influence data through environmental sensors, including environmental temperature, humidity and light intensity data. 5.The new energy vehicle thermal management system of claim 2, wherein: The equipment parameter acquisition unit obtains equipment parameters through a vehicle management data center and a sensor network, including equipment inherent parameters such as battery capacity, motor power and compressor performance parameters. 6.The new energy vehicle thermal management system of claim 1, wherein: The multi-temperature-zone analysis unit calculates a temperature control demand coefficient The calculation formula is: ； In the formula, represents the temperature control demand coefficient, represents the ideal working temperature of the vehicle interior or the battery pack, represents the external environment temperature. 7.The new energy vehicle thermal management system of claim 1, wherein: The integrated scheduling unit calculates an integrated optimization coefficient The calculation formula is: ；； In the formula, represents an integrated optimization coefficient, represents a subsystem type, represents a volume of the subsystem, represents a power density of the subsystem, represents an actual total length of a pipeline, represents a theoretical minimum pipeline length, represents an actual system efficiency, represents a theoretical maximum system efficiency.

8. A new energy vehicle thermal management method, characterized in that, The system comprises the following steps: Step one, establishing 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. Step two, the multi-dimensional data acquisition module collects thermal data, natural environment influence data and equipment parameters through various sensors. Step three, the multi-dimensional data analysis module calculates a thermal energy efficiency optimization coefficient , a temperature control demand coefficient , and an integrated optimization coefficient ; The multi-dimensional data analysis module comprises an energy efficiency analysis unit, a multi-temperature zone analysis unit and an integrated scheduling unit. The energy efficiency analysis unit calculates a thermal energy efficiency optimization coefficient The calculation formula is: ； In the formula, represents the thermal energy efficiency optimization coefficient, represents the effective use of heat, represents the thermal power consumption of all components in the new energy vehicle, represents the ambient temperature, represents the standard temperature, and g represents the waste heat recovery efficiency coefficient; Step four, the thermal evaluation module evaluates and diagnoses problems of unanticipated energy efficiency, inaccurate temperature control and unreasonable integration that occur in the process of thermal management system execution according to the calculation results. Step five, the thermal treatment module takes corresponding measures for the problems found according to the evaluation results of the thermal evaluation module. Step six, the thermal management module updates system parameters and operation strategies by incorporating new measures into the system, and forms a new thermal management system composition.

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