A method for determining the heat generation law of a liquid-cooled supercharging system for a DC high-power charging pile

Abstract

The present invention provides a method for determining the heat generation law of a liquid-cooled supercharging system of a DC high-power charging pile, which belongs to the technical field of charging piles, and includes: installing temperature sensors at multiple key hot spots to obtain charging parameters, liquid cooling parameters and temperature data in real time. A temperature rise equation and a liquid cooling equation considering the influence of charging and liquid cooling parameters are established, and a temperature change model of each key hot spot is fitted using the least squares method. The real-time temperature prediction value is compared with the measured temperature, and the model is refitted if the error rate exceeds the standard. Finally, based on the temperature change model of each key hot spot, a thermodynamic model of the entire liquid-cooled supercharging system is established, which can be used to predict the temperature distribution under different working conditions and provide a basis for optimizing system design. The method effectively predicts the temperature changes of key components through real-time monitoring and model fitting, and solves the technical problem that existing methods are difficult to accurately predict the temperature changes of the liquid cooling system of a DC high-power charging pile.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging piles, and in particular, relates to a method for determining a heat generation law of a liquid-cooled supercharging system of a DC high-power charging pile. Background Art

[0002] In recent years, with the rapid development of the new energy vehicle industry, the demand for charging infrastructure has also increased. Among them, DC high-power charging piles, as key equipment for fast charging of new energy vehicles, play an important role in the urban charging network. Compared with the traditional AC slow charging mode, DC high-power charging can significantly shorten the charging time, greatly improve the charging efficiency, and meet the rapid charging needs of electric vehicle users.

[0003] At present, there are many types of DC high-power charging pile products on the market. These charging piles usually use liquid cooling to cool the power devices to ensure safe and reliable operation under high-power charging conditions. The liquid cooling method absorbs the large amount of heat generated by the power devices through circulating coolant, which can effectively control the system temperature rise and prevent key components from overheating and damage.

[0004] However, there are still some technical problems that need to be solved in the practical application of DC high-power charging piles. First, during the high-power charging process, a large amount of heat will be generated inside the system. How to accurately predict the temperature change law of each key component is a challenge. Existing temperature prediction models are usually too simplistic and it is difficult to accurately describe the complex heat transfer mechanism of the charging pile liquid cooling system, resulting in a large deviation between the prediction results and the actual measurement. Secondly, the existing temperature monitoring methods are too limited. Relying on only a few temperature sensors cannot fully reflect the overall temperature distribution of the system, and it is difficult to detect potential hot spots. Finally, how to dynamically adjust the parameters of the liquid cooling system to achieve optimal temperature control for different charging powers and environmental conditions is also a problem that needs to be solved.

[0005] Therefore, there is an urgent need to develop a new temperature determination method that can accurately predict the temperature changes of the liquid cooling system of a DC high-power charging pile and provide strong support for the safe and reliable operation of the charging pile. Summary of the invention

[0006] In view of this, the present invention provides a method for determining the heat generation law of a liquid-cooled supercharging system of a DC high-power charging pile, which can solve the technical problem that the existing method is difficult to accurately predict the temperature change of the liquid-cooling system of a DC high-power charging pile.

[0007] The present invention is achieved in that:

[0008] The present invention provides a method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system, which comprises the following steps:

[0009] S10. Install temperature sensors at multiple key hot spots of the DC high-power charging pile liquid-cooled supercharging system;

[0010] S20, acquiring in real time charging parameters, liquid cooling parameters and temperature data of each temperature sensor during the charging process, wherein the charging parameters include charging current, charging voltage, charging power, charging time and charging efficiency, and the liquid cooling parameters include coolant flow rate, coolant inlet temperature, coolant outlet temperature, coolant pressure and coolant specific heat capacity;

[0011] S30, establishing a temperature rise equation considering the influence of charging parameters, and a liquid cooling equation considering the influence of liquid cooling parameters;

[0012] S40, fitting the temperature rise equation and liquid cooling equation of each key hotspot using the least squares method according to the real-time acquired charging parameters, liquid cooling parameters and temperature data of each key hotspot, and combining the fitted temperature rise equation and liquid cooling equation into a temperature change model corresponding to the key hotspot;

[0013] S50, using the temperature change model of each key hot spot, combined with the charging parameters and liquid cooling parameters obtained in real time, calculate the real-time temperature prediction value of each key hot spot;

[0014] S60, the real-time temperature prediction value is compared with the temperature actually measured by the temperature sensor, and the error rate is calculated. If the error rate exceeds the preset threshold, the process returns to step S40 and the model is refitted; otherwise, the process proceeds to the next step;

[0015] S70. Based on the temperature change model of each key hotspot, a thermodynamic model of the entire DC high-power charging pile liquid-cooled supercharging system is established to predict the temperature distribution of the DC high-power charging pile liquid-cooled supercharging system under different working conditions.

[0016] The preset threshold is 5% or 10%.

[0017] Among them, the multiple key hotspots of the DC high-power charging pile liquid-cooled supercharging system specifically include: power electronic devices, high-power charging connection terminals, liquid-cooled radiators, power filter capacitors, and wire and cable interfaces.

[0018] The temperature rise equation is specifically expressed as follows:

[0019] ;

[0020] in, is the spatial dimension, For temperature, For time, is the thermal diffusion coefficient, is the Laplace operator, is the material density, is the specific heat capacity of the material, is the input power, is the output power, is the thermal conductivity coefficient of each dimension, is the spatial coordinate, is the temperature fluctuation coefficient, is an imaginary unit, is the temperature fluctuation frequency, is the temperature power.

[0021] The liquid cooling equation is specifically expressed as follows:

[0022] ;

[0023] in, is the coolant temperature, is the coolant velocity vector, is the thermal conductivity of the coolant, is the coolant density, is the specific heat capacity of the coolant, is the gradient operator, is the Laplace operator, is the heat generation rate per unit volume, is the convective heat transfer coefficient, is the heat transfer surface area, is the coolant volume, is the solid surface temperature, is the temperature fluctuation amplitude coefficient, is the temperature fluctuation phase, is the temperature power.

[0024] Wherein, the calculation formula of the thermal diffusion coefficient is:

[0025] ; In the formula, is the thermal conductivity of the material.

[0026] Wherein, the calculation formula of the coolant flow rate is:

[0027] ; In the formula, is the coolant volume flow rate, is the cross-sectional area of ​​the pipe.

[0028] Wherein, the input power calculation formula is:

[0029] ; In the formula, is the charging current, is the charging voltage;

[0030] The output power calculation formula is:

[0031] ; In the formula, For charging efficiency.

[0032] The calculation formula of the heat generation rate per unit volume is:

[0033] ; In the formula, is the total volume of the liquid-cooled supercharge system.

[0034] Wherein, the calculation step of the convective heat transfer coefficient includes:

[0035] First calculate the Reynolds number: ;

[0036] in, is the hydraulic diameter of the pipe, is the coolant dynamic viscosity;

[0037] Then calculate the Prandtl number: ;

[0038] For laminar flow ( ), using the Sylt–Tate correlation:

[0039] ;

[0040] For turbulent flow ( ), using the Diets-Bolt correlation:

[0041] ;

[0042] Finally, calculate the convection heat transfer coefficient: .

[0043] Further, the error rate is defined as: Error rate = |measured temperature - predicted temperature| / measured temperature × 100%.

[0044] Specifically, step S10 includes: installing high-precision temperature sensors at multiple key hotspots of the DC high-power charging pile liquid-cooled supercharging system. Key hotspots include power electronic devices, high-power charging connection terminals, liquid-cooled radiators, power filter capacitors, and wire and cable interfaces. The selection of temperature sensors needs to consider indicators such as measurement range, response speed, and accuracy, and connect them to the data acquisition system to collect temperature data of each key hotspot in real time.

[0045] Among them, the step S20 specifically includes: real-time collection of charging parameter data such as charging current, charging voltage, charging power, charging time and charging efficiency during the charging process; real-time collection of liquid cooling system parameter data such as coolant flow, coolant inlet temperature, coolant outlet temperature, coolant pressure and coolant specific heat capacity; real-time collection of temperature data measured by each temperature sensor installed in step S10; time synchronization and storage of the above-mentioned various parameter data.

[0046] Among them, the step S30 specifically includes: establishing a temperature rise equation that describes the temperature rise law, and the equation includes terms related to parameters such as charging power and charging efficiency; establishing a liquid cooling equation that describes the liquid cooling law, and the equation includes terms related to parameters such as coolant flow rate and coolant temperature.

[0047] Among them, the step S40 specifically includes: collecting the charging parameters, liquid cooling parameters and temperature measurement data of each key hotspot obtained in step S20; using the least squares method to fit and optimize the temperature rise equation and liquid cooling equation established in step S30; combining the optimized temperature rise equation and liquid cooling equation into a temperature change model that describes the temperature change law of each key hotspot.

[0048] Among them, the step S50 specifically includes: real-time collection of charging parameter and liquid cooling parameter data obtained in step S20; substituting these real-time parameters into the temperature change model of each key hotspot established in step S40, and calculating the real-time temperature prediction value of each key hotspot.

[0049] Among them, the step S60 specifically includes: comparing the real-time temperature prediction value of each key hotspot calculated in step S50 with the temperature value actually measured by the temperature sensor in step S10, and calculating the error rate between the two; if the error rate exceeds the preset threshold, return to step S40, and re-use the measured data to fit and optimize the temperature rise equation and liquid cooling equation; otherwise, proceed to the next step.

[0050] The preset threshold is 5% or 10%. The threshold can be determined according to specific application requirements and is used to evaluate whether the prediction accuracy of the temperature change model meets the requirements.

[0051] Among them, the step S70 specifically includes: integrating the temperature change models of each key hot spot established in step S40 into an overall thermodynamic model, considering factors such as the system's geometric structure, material properties, and internal heat source distribution, and establishing a complete heat conduction, convection heat transfer, and radiation heat transfer mechanism; using the thermodynamic model to predict the overall temperature distribution of the system under different charging power levels and different coolant parameters; if there is a large deviation between the model prediction result and the actual measurement, it is necessary to return to the previous steps to further optimize and calibrate the temperature change model.

[0052] Compared with the prior art, the method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system provided by the present invention has the beneficial effect of being able to accurately predict the overall temperature distribution of the system under different working conditions, providing a strong guarantee for the safe operation of the charging pile, and having the following technical advantages:

[0053] 1. The use of multi-point temperature sensors can comprehensively monitor the temperature changes of key hot spots in the system, which is different from the existing limited temperature monitoring method that relies on only a few sensors. By obtaining the temperature data of each hot spot in real time, it lays the foundation for establishing an accurate temperature prediction model.

[0054] 2. The temperature rise equation and liquid cooling equation considering the influence of charging parameters and liquid cooling parameters are established, which can more accurately describe the complex heat transfer mechanism inside the system. Compared with the existing simple temperature prediction model, the temperature change model of this method has stronger adaptability and prediction accuracy.

[0055] 3. The least squares method is used to fit and optimize the temperature rise equation and the liquid cooling equation so that the model prediction results can better fit the measured data. Through continuous iterative optimization, the accuracy of the temperature change model can be further improved.

[0056] 4. Based on the temperature change model of each key hot spot, a thermodynamic model of the entire charging pile system was established. This model can predict the overall temperature distribution of the system under different working conditions, providing effective support for optimizing system design and realizing dynamic temperature control.

[0057] In summary, the present invention solves the technical problem that the existing methods are difficult to accurately predict the temperature change of the liquid cooling system of a DC high-power charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0060] like Figure 1 As shown, it is a flow chart of a method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system provided by the present invention. The method comprises the following steps:

[0061] S10. Install temperature sensors at multiple key hot spots of the DC high-power charging pile liquid-cooled supercharging system;

[0062] S20, acquiring charging parameters, liquid cooling parameters and temperature data of each temperature sensor during the charging process in real time, wherein the charging parameters include charging current, charging voltage, charging power, charging time and charging efficiency, and the liquid cooling parameters include coolant flow rate, coolant inlet temperature, coolant outlet temperature, coolant pressure and coolant specific heat capacity;

[0063] S30, establishing a temperature rise equation considering the influence of charging parameters, and a liquid cooling equation considering the influence of liquid cooling parameters;

[0064] S40, fitting the temperature rise equation and liquid cooling equation of each key hotspot using the least squares method according to the real-time acquired charging parameters, liquid cooling parameters and temperature data of each key hotspot, and combining the fitted temperature rise equation and liquid cooling equation into a temperature change model corresponding to the key hotspot;

[0065] S50, using the temperature change model of each key hot spot, combined with the charging parameters and liquid cooling parameters obtained in real time, calculate the real-time temperature prediction value of each key hot spot;

[0066] S60, the real-time temperature prediction value is compared with the temperature actually measured by the temperature sensor, and the error rate is calculated. If the error rate exceeds the preset threshold, the process returns to step S40 and the model is refitted; otherwise, the process proceeds to the next step;

[0067] S70. Based on the temperature change model of each key hotspot, a thermodynamic model of the entire DC high-power charging pile liquid-cooled supercharging system is established to predict the temperature distribution of the DC high-power charging pile liquid-cooled supercharging system under different working conditions.

[0068] The specific implementation methods of the above steps are described in detail below:

[0069] Step S10: Install temperature sensors at multiple key hotspots of the DC high-power charging pile liquid-cooled supercharging system.

[0070] The purpose of this step is to obtain real-time temperature data of key hot spots during the charging process, and provide basic data for subsequent temperature analysis and modeling. First, it is necessary to identify the key hot spots in the system. Based on experience and engineering analysis, the key hot spots of the DC high-power charging pile liquid-cooled supercharging system usually include: power electronic devices, high-power charging connection terminals, liquid-cooled radiators, power filter capacitors, and wire and cable interfaces. These locations are where heat concentration may occur in the system and need to be monitored.

[0071] Secondly, install high-precision temperature sensors at each key hotspot. The selection of sensors needs to consider indicators such as measurement range, response speed and accuracy to ensure that real-time temperature data of key hotspots can be accurately obtained. Sensors can be thermocouples, thermal resistors or semiconductor temperature sensors. In order to obtain more accurate temperature measurement data, you can consider using multiple temperature sensors to measure and take the average value.

[0072] Finally, the temperature sensor needs to be connected to the data acquisition system to collect and record the temperature data of each key hot spot in real time. The data acquisition frequency should be set according to actual needs. Usually, a sampling frequency of 1-10 times per second can meet the needs of subsequent analysis.

[0073] Step S20: acquiring charging parameters, liquid cooling parameters and temperature data of each temperature sensor during the charging process in real time.

[0074] The purpose of this step is to collect various relevant parameter data during the charging process to provide a basis for subsequent temperature analysis and modeling. It includes the following sub-steps:

[0075] 1) Real-time collection of key parameter data of the charging process, including charging current, charging voltage, charging power, charging time and charging efficiency, etc. These parameter data reflect the power input of the charging process and are important factors affecting the temperature rise of the system.

[0076] 2) Real-time collection of key parameter data of the liquid cooling system, including coolant flow, coolant inlet temperature, coolant outlet temperature, coolant pressure and coolant specific heat capacity, etc. These parameter data reflect the heat exchange performance of the liquid cooling system and are another important factor affecting the temperature rise of the system.

[0077] 3) Real-time acquisition of temperature data measured by each temperature sensor installed in step S10. These data reflect the actual temperature rise of each key hot spot in the system and are the basis for establishing a temperature rise model.

[0078] 4) Time-synchronize and store the above-mentioned parameter data to provide necessary data support for subsequent temperature analysis and modeling.

[0079] Step S30: establishing a temperature rise equation that takes into account the influence of charging parameters, and a liquid cooling equation that takes into account the influence of liquid cooling parameters.

[0080] The purpose of this step is to establish a mathematical model that describes the temperature variation of each key hot spot. It includes the following sub-steps:

[0081] 1) Considering the influence of charging parameters, establish a temperature rise equation to describe the temperature change of each key hot spot. The temperature rise equation should include terms related to parameters such as charging power and charging efficiency. The temperature rise equation can be expressed as:

[0082]

[0083] in, is the spatial dimension, For temperature, For time, is the thermal diffusion coefficient, is the Laplace operator, is the material density, is the specific heat capacity of the material, is the input power, is the output power, is the thermal conductivity coefficient of each dimension, is the spatial coordinate, is the temperature fluctuation coefficient, is an imaginary unit, is the temperature fluctuation frequency, is the temperature power.

[0084] 2) Consider the influence of liquid cooling system parameters and establish a liquid cooling equation to describe the temperature change of each key hot spot. The liquid cooling equation should include terms related to parameters such as coolant flow rate and coolant temperature. The liquid cooling equation can be expressed as:

[0085]

[0086] in, is the coolant temperature, is the coolant velocity vector, is the thermal conductivity of the coolant, is the coolant density, is the specific heat capacity of the coolant, is the gradient operator, is the Laplace operator, is the heat generation rate per unit volume, is the convective heat transfer coefficient, is the heat transfer surface area, is the coolant volume, is the solid surface temperature, is the temperature fluctuation amplitude coefficient, is the temperature fluctuation phase, is the temperature power.

[0087] These two equations are the basis for establishing a temperature prediction model, which can describe the temperature change patterns of key hotspots during the charging process.

[0088] Step S40: According to the real-time acquired charging parameters, liquid cooling parameters and temperature data of each key hotspot, the temperature rise equation and liquid cooling equation of each key hotspot are fitted using the least squares method, and the fitted temperature rise equation and liquid cooling equation are combined into a temperature change model corresponding to the key hotspot.

[0089] The purpose of this step is to use the measured data to fit and optimize the temperature rise equation and liquid cooling equation established in step S30 to obtain a more accurate temperature change model. Specifically, it includes the following sub-steps:

[0090] 1) Collect the charging parameters, liquid cooling parameters and temperature measurement data of each key hot spot obtained in step S20. These data will serve as the basis for fitting the temperature change model.

[0091] 2) The temperature rise equation and the liquid cooling equation established in step S30 are fitted using the least squares method. The least squares method can automatically adjust the coefficients of various parameters in the equation according to the measured data to minimize the deviation between the model prediction result and the measured data.

[0092] 3) The temperature rise equation and liquid cooling equation after fitting optimization are combined into a temperature variation model that describes the temperature variation law of each key hot spot. This temperature variation model can better reflect the temperature variation characteristics of the actual system.

[0093] 4) Such a temperature change model is established for each key hotspot. Different key hotspots may have different parameter characteristics, so corresponding temperature change models need to be established respectively.

[0094] Step S50: using the temperature change model of each key hotspot, combined with the charging parameters and liquid cooling parameters acquired in real time, calculate the real-time temperature prediction value of each key hotspot.

[0095] The purpose of this step is to use the previously established temperature change model and real-time operating parameters to predict the real-time temperature of each key hot spot. It includes the following sub-steps:

[0096] 1) Collect the charging parameters and liquid cooling parameter data obtained in step S20 in real time. These real-time parameters will be used as input conditions to be brought into the temperature change model for calculation.

[0097] 2) Substitute the real-time charging parameters and liquid cooling parameters into the temperature variation model of each key hot spot established in step S40. The temperature variation model can calculate the real-time temperature prediction value of each key hot spot based on these input parameters.

[0098] 3) After obtaining the real-time temperature prediction value of each key hot spot, compare and analyze it with the actual temperature measured by the temperature sensor in step S10. This provides a basis for subsequent model optimization.

[0099] Through this step, the temperature changes of key hot spots in the liquid-cooled supercharging system of a DC high-power charging pile can be predicted in real time, providing support for temperature monitoring and optimization of the system.

[0100] Step S60: Compare the real-time temperature prediction value with the temperature actually measured by the temperature sensor, and calculate the error rate. If the error rate exceeds a preset threshold, return to step S40 and re-fit the model; otherwise, proceed to the next step.

[0101] The purpose of this step is to evaluate the prediction accuracy of the temperature change model and optimize the model as needed. It includes the following sub-steps:

[0102] 1) Compare the real-time temperature prediction value of each key hot spot calculated in step S50 with the temperature value actually measured by the temperature sensor in step S10.

[0103] 2) Calculate the error rate between the two. The error rate can be defined as:

[0104] Error rate = |measured temperature - predicted temperature| / measured temperature × 100%

[0105] 3) Compare the calculated error rate with a preset threshold value, which can be set according to specific application requirements and is usually set to 5% or 10%.

[0106] 4) If the error rate exceeds the preset threshold, it means that the prediction accuracy of the temperature change model needs to be further improved. At this time, it is necessary to return to step S40 and re-use the measured data to fit and optimize the temperature rise equation and the liquid cooling equation to obtain a more accurate temperature change model.

[0107] 5) If the error rate is within the preset threshold, it can be considered that the temperature change model has achieved sufficient prediction accuracy and can proceed to the next step.

[0108] Through the iterative optimization of this step, the prediction accuracy of the temperature change model can be continuously improved, providing reliable basic data for subsequent system temperature analysis and optimization.

[0109] Step S70: Based on the temperature change model of each key hot spot, a thermodynamic model of the entire DC high-power charging pile liquid-cooled supercharging system is established to predict the temperature distribution of the DC high-power charging pile liquid-cooled supercharging system under different working conditions.

[0110] The purpose of this step is to use the temperature change models of the key hot spots established previously to construct a thermodynamic model of the entire DC high-power charging pile liquid-cooled supercharging system to predict the overall temperature distribution of the system under different working conditions. It specifically includes the following sub-steps:

[0111] 1) Integrate the temperature change models of each key hot spot established in step S40 into an overall thermodynamic model. This thermodynamic model should be able to describe the coupling relationship and heat flow process between the hot spots in the system.

[0112] 2) Consider the geometric structure, material properties, internal heat source distribution and other factors of the system in the thermodynamic model, and establish a complete heat conduction, convection and radiation heat transfer mechanism.

[0113] 3) Use thermodynamic models to predict the overall temperature distribution of the system at different charging power levels and different coolant parameters. This provides a basis for the optimal design and thermal management of the system.

[0114] 4) If there is a large deviation between the model prediction results and the actual measurements, it is necessary to return to the previous steps to further optimize and calibrate the temperature change model until the prediction accuracy of the thermodynamic model meets the requirements.

[0115] Through this step, the overall temperature distribution prediction of the DC high-power charging pile liquid-cooled supercharging system under various working conditions can be obtained, providing support for the safe and reliable operation of the system.

[0116] In summary, the method for determining the temperature law of the liquid-cooled supercharging system of this DC high-power charging pile includes several main steps, involving the installation of temperature sensors, the collection of charging and discharging parameters and temperature data, the establishment of temperature rise equations and liquid cooling equations, the fitting optimization of temperature change models, real-time temperature prediction, and the construction of an overall thermodynamic model. Each step has multiple specific sub-steps, involving mathematical modeling, parameter calculation, data analysis, etc. The purpose is to ultimately establish a thermodynamic model that can accurately predict the temperature distribution of the system. This method can provide strong support for the safe and reliable operation of DC high-power charging piles. The relevant variable explanations are shown in Table 1:

[0117] Table 1 Variable explanation table

[0118]

[0119] Specifically, the principle of the present invention is:

[0120] 1. Install multiple temperature sensors at key hotspots. Real-time monitoring of the temperature changes of these hotspots provides basic data for subsequent temperature analysis and modeling. The determination of key hotspots needs to be combined with the actual structure and working characteristics of the charging pile system, which usually includes power electronic devices, high-power charging terminals, liquid cooling radiators and other parts prone to heat concentration.

[0121] 2. Synchronously collect various parameter data of the charging process, including charging parameters such as charging current, charging voltage, charging power, charging time, charging efficiency, etc. that reflect the power input, as well as liquid cooling parameters such as coolant flow, coolant temperature, and coolant pressure that characterize the liquid cooling performance. These parameter data are the key inputs for establishing the temperature prediction model.

[0122] 3. Establish a mathematical model to describe the law of temperature change. Specifically include:

[0123] (1) Temperature rise equation, which takes into account the impact of factors such as charging power and charging efficiency on temperature change;

[0124] (2) Liquid cooling equation, which considers the influence of factors such as coolant flow rate and coolant temperature on temperature change;

[0125] 4. The least squares method is used to fit and optimize the temperature rise equation and the liquid cooling equation to obtain the temperature change model of each key hot spot. This process can make the model prediction results better match the measured data and improve the accuracy and adaptability of the model.

[0126] 5. Based on the optimized temperature change model, combined with the real-time charging parameters and liquid cooling parameters, the real-time temperature prediction values ​​of each key hotspot are calculated.

[0127] 6. Compare the predicted temperature with the measured temperature and calculate the error rate. If the error rate exceeds the preset threshold, return to the fitting optimization step until the model accuracy meets the requirements.

[0128] 7. Finally, a thermodynamic model of the entire charging pile system is established, which can predict the overall temperature distribution of the system under different working conditions. This provides a basis for the optimal design and dynamic temperature control of the system.

[0129] The key to the present invention is to establish a temperature prediction model that takes into account multiple influencing factors and continuously optimize the model parameters so that it can more accurately describe the temperature change law of the system. Compared with the existing simple temperature prediction method, the method of the present invention can better reflect the actual heat conduction, convection heat transfer and temperature fluctuation characteristics of the system, thereby improving the accuracy and applicability of temperature prediction.

[0130] In order to better understand and implement the present invention, a specific embodiment 1 of the present invention is provided below: The specific implementation steps of this embodiment 1 include:

[0131] Step S10: Install temperature sensors at multiple key hotspots of the DC high-power charging pile liquid-cooled supercharging system.

[0132] The purpose of this step is to obtain real-time temperature data of key hot spots during the charging process. First, the key hot spots in the system need to be determined. According to empirical analysis, the key hot spots of the DC high-power charging pile liquid-cooled supercharging system usually include: power electronic devices, high-power charging connection terminals, liquid-cooled radiators, power filter capacitors, and wire and cable interfaces. These locations are where heat concentration may occur in the system and need to be monitored.

[0133] It is critical to install high-precision temperature sensors at each key hotspot. The selection of temperature sensors needs to consider indicators such as measurement range, response speed and accuracy to ensure that real-time temperature data of key hotspots can be accurately obtained. Commonly used temperature sensors include thermocouples, thermal resistors and semiconductor temperature sensors. In order to obtain more accurate temperature measurement data, you can consider using multiple temperature sensors to measure and take the average value.

[0134] Assume that in the system Temperature sensors were installed at key hotspots Each temperature sensor can measure the temperature value of the corresponding position in real time and transmit the data to the data acquisition system. The data acquisition frequency is usually set to 1-10 times per second to meet the needs of subsequent analysis.

[0135] Step S20: acquiring charging parameters, liquid cooling parameters and temperature data of each temperature sensor during the charging process in real time.

[0136] The purpose of this step is to collect various relevant parameter data during the charging process to provide a basis for subsequent temperature analysis and modeling. Specifically, it includes the following:

[0137] 1. Real-time collection of key parameter data of the charging process:

[0138] Charging current:

[0139] Charging voltage:

[0140] Charging power:

[0141] Charging time:

[0142] Charging efficiency:

[0143] These parameter data reflect the power input during the charging process and are important factors affecting the temperature rise of the system.

[0144] 2. Real-time collection of key parameter data of liquid cooling system:

[0145] Coolant flow:

[0146] Coolant inlet temperature:

[0147] Coolant outlet temperature:

[0148] Coolant pressure:

[0149] Coolant specific heat capacity:

[0150] These parameter data reflect the heat exchange performance of the liquid cooling system and are another important factor affecting the temperature rise of the system.

[0151] 3. Real-time collection of measurement data of each temperature sensor installed in step S10:

[0152]

[0153] These data reflect the actual temperature rise of each key hotspot in the system and are the basis for establishing a temperature rise model.

[0154] The above-mentioned parameter data need to be synchronized and stored in time to provide necessary data support for subsequent temperature analysis and modeling.

[0155] Step S30: establishing a temperature rise equation that takes into account the influence of charging parameters, and a liquid cooling equation that takes into account the influence of liquid cooling parameters.

[0156] The purpose of this step is to establish a mathematical model that describes the temperature variation of each key hot spot. It is divided into two parts:

[0157] 1. Establish the temperature rise equation

[0158] The temperature rise equation should take into account the power input during the charging process. According to the heat conduction theory, the temperature change can be described by the following partial differential equation:

[0159]

[0160] in:

[0161] is the temperature (K)

[0162] Time(s)

[0163] is the thermal diffusivity (m² / s), is the thermal conductivity (W / (m·K)), is the density (kg / m³), is the specific heat capacity (J / (kg·K))

[0164] is the Laplace operator

[0165] is the input power (W),

[0166] is the output power (W),

[0167] is the thermal conductivity of each dimension (W / (m·K))

[0168] is the spatial coordinate (m)

[0169] is the temperature fluctuation coefficient

[0170] Imaginary unit

[0171] is the temperature fluctuation frequency (rad / s)

[0172] is the temperature power, usually 1.5~2.5

[0173] This temperature rise equation describes how temperature changes are affected by factors such as charging power and charging efficiency.

[0174] 2. Establish the liquid cooling equation

[0175] The liquid cooling equation should take into account the heat transfer performance of the liquid cooling system. According to the convection heat transfer theory, the liquid cooling process can be described by the following partial differential equation:

[0176]

[0177] in:

[0178] is the coolant temperature (K)

[0179] is the coolant velocity vector (m / s), is the cross-sectional area of ​​the pipe (m²)

[0180] is the thermal conductivity of the coolant (W / (m·K))

[0181] is the coolant density (kg / m³)

[0182] is the specific heat capacity of the coolant (J / (kg·K))

[0183] is the gradient operator

[0184] is the Laplace operator

[0185] is the heat generation rate per unit volume (W / m³), is the system volume (m³)

[0186] is the convection heat transfer coefficient (W / (m²·K)), is the Nusselt number, is the hydraulic diameter of the pipe (m)

[0187] is the heat exchange surface area (m²)

[0188] is the volume of coolant (m³)

[0189] is the solid surface temperature (K)

[0190] is the temperature fluctuation coefficient

[0191] is the temperature fluctuation phase (rad / s)

[0192] is the temperature power, usually 1~2

[0193] This liquid cooling equation describes how temperature changes are affected by factors such as coolant flow rate and coolant temperature.

[0194] Through the above two equations, a mathematical model can be established to describe the temperature changes of each key hot spot.

[0195] Step S40: According to the real-time acquired charging parameters, liquid cooling parameters and temperature data of each key hotspot, the temperature rise equation and liquid cooling equation of each key hotspot are fitted using the least squares method, and the fitted temperature rise equation and liquid cooling equation are combined into a temperature change model corresponding to the key hotspot.

[0196] The purpose of this step is to use the measured data to fit and optimize the temperature rise equation and liquid cooling equation established in step S30 to obtain a more accurate temperature change model. Specifically, it includes the following contents:

[0197] 1. Collect the charging parameters, liquid cooling parameters and temperature measurement data of each key hot spot obtained in step S20. These data will serve as the basis for fitting the temperature change model.

[0198] 2. The temperature rise equation and the liquid cooling equation established in step S30 are fitted using the least squares method. The least squares method can automatically adjust the coefficients of various parameters in the equation according to the measured data to minimize the deviation between the model prediction result and the measured data.

[0199] For the temperature rise equation, the fitting process can be expressed as:

[0200]

[0201] in, is the number of measured data points, Indicates data points.

[0202] For the liquid cooling equation, the fitting process can be expressed as:

[0203]

[0204] 3. The temperature rise equation and liquid cooling equation after fitting optimization are combined into a temperature variation model that describes the temperature variation law of each key hot spot. This temperature variation model can better reflect the temperature variation characteristics of the actual system.

[0205] 4. Such a temperature change model is established for each key hotspot. Different key hotspots may have different parameter characteristics, so corresponding temperature change models need to be established respectively.

[0206] Through the fitting optimization in this step, a more accurate mathematical model describing the temperature change of the system can be obtained.

[0207] Step S50: using the temperature change model of each key hotspot, combined with the charging parameters and liquid cooling parameters acquired in real time, calculate the real-time temperature prediction value of each key hotspot.

[0208] The purpose of this step is to use the previously established temperature change model and real-time operating parameters to predict the real-time temperature of each key hot spot. Specifically, it includes the following:

[0209] 1. Collect the charging parameters and liquid cooling parameter data obtained in step S20 in real time. These real-time parameters will be used as input conditions to be brought into the temperature change model for calculation.

[0210] 2. Substitute the real-time charging parameters and liquid cooling parameters into the temperature variation model of each key hot spot established in step S40. The temperature variation model can calculate the real-time temperature prediction value of each key hot spot based on these input parameters.

[0211] Key hot spots For example, its temperature change model can be expressed as:

[0212]

[0213]

[0214] Substituting the real-time charging parameters and liquid cooling parameters into the above model, the key hot spots can be calculated. Real-time temperature prediction and .

[0215] 3. After obtaining the real-time temperature prediction value of each key hot spot, compare and analyze it with the actual temperature measured by the temperature sensor in step S10. This provides a basis for subsequent model optimization.

[0216] Through this step, the temperature changes of key hot spots in the liquid-cooled supercharging system of a DC high-power charging pile can be predicted in real time, providing support for temperature monitoring and optimization of the system.

[0217] Step S60: Compare the real-time temperature prediction value with the temperature actually measured by the temperature sensor, and calculate the error rate. If the error rate exceeds a preset threshold, return to step S40 and re-fit the model; otherwise, proceed to the next step.

[0218] 2. Calculate the relative error rate between the two, which can be defined as:

[0219]

[0220] in, Key hot spots The relative error rate.

[0221] 3. Compare the calculated error rate of each key hot spot with the pre-set threshold value. The threshold value can be determined according to the specific application requirements and can usually be set to 5% or 10%.

[0222] 4. If the error rate of any key hotspot If the value exceeds the preset threshold, it means that the prediction accuracy of the temperature change model needs to be further improved. At this time, it is necessary to return to step S40 and re-use the measured data to fit and optimize the temperature rise equation and the liquid cooling equation to obtain a more accurate temperature change model.

[0223] 5. If the error rates of all key hot spots are within the preset threshold range, it can be considered that the temperature change model has achieved sufficient prediction accuracy and can proceed to the next step.

[0224] Through the iterative optimization of this step, the prediction accuracy of the temperature change model can be continuously improved, providing reliable basic data for subsequent system temperature analysis and optimization.

[0225] Step S70: Based on the temperature change model of each key hot spot, a thermodynamic model of the entire DC high-power charging pile liquid-cooled supercharging system is established to predict the temperature distribution of the DC high-power charging pile liquid-cooled supercharging system under different working conditions.

[0226] The purpose of this step is to use the temperature change models of the key hot spots established previously to construct a thermodynamic model of the entire DC high-power charging pile liquid-cooled supercharging system to predict the overall temperature distribution of the system under different working conditions. Specifically, it includes the following:

[0227] 1. Integrate the temperature change models of each key hot spot established in step S40 into an overall thermodynamic model. This thermodynamic model should be able to describe the coupling relationship and heat flow process between the hot spots in the system.

[0228] Assume that the system has a total of If there are a number of key hot spots, the thermodynamic model can be expressed as:

[0229]

[0230]

[0231] Among them, the subscript Indicates A key hotspot.

[0232] 2. Consider the geometric structure, material properties, internal heat source distribution and other factors of the system in the thermodynamic model to establish a complete mechanism of heat conduction, convection and radiation.

[0233] 3. Use thermodynamic models to predict the overall temperature distribution of the system at different charging power levels and different coolant parameters. This provides a basis for the optimal design and thermal management of the system.

[0234] 4. If there is a large deviation between the model prediction results and the actual measurement, it is necessary to return to the previous steps to further optimize and calibrate the temperature change model until the prediction accuracy of the thermodynamic model meets the requirements.

[0235] Through this step, the overall temperature distribution prediction of the DC high-power charging pile liquid-cooled supercharging system under various working conditions can be obtained, providing support for the safe and reliable operation of the system.

[0236] In order to better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: This Example 2 takes a certain model of DC high-power charging pile as an example to introduce how to use the temperature determination method proposed in the present invention to analyze and predict the temperature change law of the system.

[0237] 1. System Overview

[0238] The rated power of this DC high-power charging pile is 120kW, which is suitable for fast charging of new energy vehicles. The system uses liquid cooling for heat management and mainly includes the following key components:

[0239] (1) Power electronic devices: high-power commutation circuit composed of IGBT modules, rectifier bridges, filter capacitors, etc.;

[0240] (2) High-power charging connection terminal: a high-current charging interface that connects the charging pile to the electric vehicle;

[0241] (3) Liquid cooling radiator: used to transfer the heat generated by the power device to the coolant for heat dissipation;

[0242] (4) Power filter capacitor: used for filtering and voltage stabilization, and is also one of the parts where heat generation is more concentrated;

[0243] (5) Wire and cable interface: During high-current charging, the cable connection is also a key monitoring object.

[0244] 2. Temperature sensor installation

[0245] According to the distribution of the above key hot spots, a total of 6 temperature sensors are installed in the charging pile system, located at:

[0246] (1) IGBT module surface

[0247] (2) Rectifier bridge surface

[0248] (3) Power filter capacitor surface

[0249] (4) Charging connection terminal surface

[0250] (5) Liquid cooling radiator import

[0251] (6) Liquid cooling radiator outlet

[0252] These six locations are considered to be the key parts in the system that are most prone to high temperatures and their temperature changes need to be monitored closely.

[0253] The temperature sensor used is a K-type thermocouple with a temperature measurement range of -50~400℃, a response time of less than 0.5 seconds, and a temperature measurement accuracy of ±0.5℃. These temperature sensors are connected to the data acquisition system to record the temperature data of each key hot spot in real time, and the sampling frequency is set to 2Hz.

[0254] 3. Charging parameters and liquid cooling parameters collection

[0255] In addition to temperature data, other key parameters during the charging process are also collected and recorded simultaneously, including:

[0256] Charging parameters:

[0257] Charging Current

[0258] Charging voltage

[0259] Charging power

[0260] Charging time

[0261] Charging efficiency

[0262] Liquid cooling parameters:

[0263] Coolant volume flow

[0264] Coolant inlet temperature

[0265] Coolant outlet temperature

[0266] Coolant pressure

[0267] Coolant specific heat capacity

[0268] These parameter data will serve as input for the subsequent establishment of temperature prediction model.

[0269] 4. Establishment of temperature rise equation and liquid cooling equation

[0270] According to the method of step S30, a temperature rise equation and a liquid cooling equation describing the temperature variation law of the system can be established.

[0271] Temperature rise equation:

[0272] The surface temperature of IGBT module For example, the temperature rise equation can be expressed as:

[0273]

[0274] in, is the thermal diffusion coefficient of the IGBT module, is the density of the IGBT module, is the specific heat capacity of the IGBT module, is the thermal conductivity of the IGBT module, is the temperature fluctuation coefficient, is the temperature fluctuation frequency, is the temperature power.

[0275] Liquid Cooling Equation:

[0276] Liquid cooling radiator outlet temperature For example, the liquid cooling equation can be expressed as:

[0277]

[0278] in, is the coolant flow rate, is the thermal conductivity of the coolant, is the coolant density, is the specific heat capacity of the coolant, is the heat generation rate per unit volume, is the convective heat transfer coefficient, is the heat transfer surface area, is the coolant volume, is the radiator surface temperature, is the temperature fluctuation amplitude coefficient, is the temperature fluctuation phase, is the temperature power.

[0279] The above temperature rise equation and liquid cooling equation describe the variation of IGBT module temperature and radiator outlet temperature respectively, taking into account the influence of various factors such as charging power, heat exchange efficiency and liquid cooling system parameters. Similar temperature equations need to be established for other key hot spots.

[0280] 5. Fitting optimization of temperature change model

[0281] According to step S40, the temperature rise equation and the liquid cooling equation are fitted and optimized using the least squares method to obtain the temperature change model of each key hot spot.

[0282] The surface temperature of IGBT module For example, the temperature rise equation after fitting optimization can be expressed as:

[0283]

[0284] Similarly, the outlet temperature of the liquid cooling radiator The fitting result of the liquid cooling equation is:

[0285]

[0286] Combining the above temperature rise equation with the liquid cooling equation, we can get the complete temperature variation model of the IGBT module and the liquid cooling radiator outlet. Similar temperature variation models are also established for the other four key hot spots.

[0287] 6. Temperature prediction and verification

[0288] By using the temperature change model established in step S50 and combining the charging parameters and liquid cooling parameters collected in real time, the real-time temperature prediction value of each key hot spot can be calculated.

[0289] Taking a 120kW fast charging process as an example, it can be seen that within the first 10 minutes after charging starts, the temperature of the IGBT module surface and the power filter capacitor rises rapidly, reaching about 100°C and 85°C; while the temperature of the charging connection terminal and the liquid cooling radiator outlet is relatively low, only about 60°C and 50°C. This shows that the IGBT module and capacitor are the key parts of the system that are most prone to heat accumulation and need to be paid special attention to.

[0290] To verify the prediction accuracy of the model, the predicted temperature values ​​were compared with the actual measured values. The calculated relative errors between the predicted temperature and the measured temperature of the IGBT module surface, power filter capacitor, charging connection terminal and liquid cooling radiator outlet were 4.8%, 6.2%, 3.9% and 5.1%, respectively, all within the preset error range of 5%±5%, indicating that the prediction results of the temperature change model are consistent with the actual measurement.

[0291] 7. Establishment of the overall thermodynamic model

[0292] Based on the temperature change models of the above key hot spots, a thermodynamic model of the entire charging pile system is further constructed to predict the overall temperature distribution under different working conditions. The thermodynamic model can be expressed as:

[0293] For IGBT modules:

[0294]

[0295] For rectifier bridge:

[0296]

[0297] For power supply filter capacitors:

[0298]

[0299] For charging connector:

[0300]

[0301] For liquid cooling radiators:

[0302]

[0303] The above thermodynamic model fully considers factors such as the system's geometric structure, material properties, and internal heat source distribution, and can more accurately describe the temperature field distribution of the entire charging pile under different working conditions.

[0304] Based on this thermodynamic model, parameter optimization research was further carried out. For example, the effect of adjusting the coolant flow rate on the system temperature distribution shows that, under the condition of constant charging power, appropriately increasing the coolant flow rate can effectively reduce the temperature rise of key components, and the maximum surface temperature of the IGBT module can be reduced from 100°C to 85°C. This provides a theoretical basis for optimizing system design and dynamic temperature control.

[0305] Specifically, this embodiment embodies the following technical innovations:

[0306] 1. The use of multi-point temperature sensors can comprehensively monitor the temperature changes of key hotspots in the system, which is more comprehensive and accurate than the existing limited temperature monitoring method that only relies on a few sensors.

[0307] 2. The temperature rise equation and liquid cooling equation that take into account the influence of charging parameters and liquid cooling parameters are established, which can more accurately describe the complex heat transfer mechanism inside the system rather than using a simple temperature prediction model.

[0308] 3. Use the least squares method to fit and optimize the temperature rise equation and liquid cooling equation so that the model prediction results can better fit the measured data and continuously improve the accuracy of the temperature change model.

[0309] 4. Based on the optimized temperature change model, a thermodynamic model of the entire charging pile system was constructed, which can predict the overall temperature distribution of the system under different working conditions, providing a basis for optimizing system design and realizing dynamic temperature control.

[0310] 5. By analyzing the impact of adjusting the coolant flow rate on temperature distribution, the practicality of the thermodynamic model was verified, providing a useful reference for system parameter optimization.

[0311] In summary, this embodiment 2 fully demonstrates the innovation and application value of the temperature determination method of the present invention. This method can provide strong support for the safe and reliable operation of DC high-power charging piles.

[0312] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system, characterized in that , including the following steps: S10. Install temperature sensors at multiple key hot spots of the DC high-power charging pile liquid-cooled supercharging system; S20, acquiring in real time charging parameters, liquid cooling parameters and temperature data of each temperature sensor during the charging process, wherein the charging parameters include charging current, charging voltage, charging power, charging time and charging efficiency, and the liquid cooling parameters include coolant flow rate, coolant inlet temperature, coolant outlet temperature, coolant pressure and coolant specific heat capacity; S30, establishing a temperature rise equation considering the influence of charging parameters, and a liquid cooling equation considering the influence of liquid cooling parameters; S40, fitting the temperature rise equation and liquid cooling equation of each key hotspot using the least squares method according to the real-time acquired charging parameters, liquid cooling parameters and temperature data of each key hotspot, and combining the fitted temperature rise equation and liquid cooling equation into a temperature change model corresponding to the key hotspot; S50, using the temperature change model of each key hot spot, combined with the real-time acquired charging parameters and liquid cooling parameters, calculate the real-time temperature prediction value of each key hot spot; S60, the real-time temperature prediction value is compared with the temperature actually measured by the temperature sensor, and the error rate is calculated. If the error rate exceeds the preset threshold, the process returns to step S40 and the model is refitted; otherwise, the process proceeds to the next step; S70. Based on the temperature variation model of each key hot spot, a thermodynamic model of the entire DC high-power charging pile liquid-cooled supercharging system is established to predict the temperature distribution of the DC high-power charging pile liquid-cooled supercharging system under different working conditions; The temperature rise equation is specifically expressed as follows: ； in, is the spatial dimension, For temperature, For time, is the thermal diffusion coefficient, is the Laplace operator, is the material density, is the specific heat capacity of the material, is the input power, is the output power, is the thermal conductivity coefficient of each dimension, is the spatial coordinate, is the temperature fluctuation coefficient, is an imaginary unit, is the temperature fluctuation frequency, is the temperature power; The liquid cooling equation is specifically expressed as follows: ； in, is the coolant temperature, is the coolant velocity vector, is the thermal conductivity of the coolant, is the coolant density, is the specific heat capacity of the coolant, is the gradient operator, is the Laplace operator, is the heat generation rate per unit volume, is the convective heat transfer coefficient, is the heat transfer surface area, is the coolant volume, is the solid surface temperature, is the temperature fluctuation amplitude coefficient, is the temperature fluctuation phase, is the temperature power.

2. The method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system according to claim 1 is characterized in that: The multiple key hotspots of the DC high-power charging pile liquid-cooled supercharging system specifically include: power electronic devices, high-power charging connection terminals, liquid-cooled radiators, power filter capacitors, and wire and cable interfaces.

3. The method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system according to claim 2 is characterized in that: The calculation formula of the thermal diffusion coefficient is: ; In the formula, is the thermal conductivity of the material.

4. The method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system according to claim 3 is characterized in that: The calculation formula of the coolant flow rate is: ; In the formula, is the coolant volume flow rate, is the cross-sectional area of ​​the pipe.

5. The method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system according to claim 4 is characterized in that: The input power calculation formula is: ; In the formula, is the charging current, is the charging voltage; The output power calculation formula is: ; In the formula, For charging efficiency.

6. The method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system according to claim 5 is characterized in that: The heat generation rate per unit volume is calculated as follows: ; In the formula, is the total volume of the liquid-cooled supercharge system.

7. The method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system according to claim 6 is characterized in that: The calculation steps of the convective heat transfer coefficient include: First calculate the Reynolds number: ; in, is the hydraulic diameter of the pipe, is the coolant dynamic viscosity; Then calculate the Prandtl number: ; For laminar flow ( ), using the Sylt–Tate correlation: ； in, is the pipe length, is the dynamic viscosity of the coolant at the wall; For turbulent flow ( ), using the Diets-Bolt correlation: ； Finally, calculate the convection heat transfer coefficient: .

8. The method for determining the heat generation law of a DC high-power charging pile liquid-cooled supercharging system according to claim 7 is characterized in that: The error rate is defined as: Error rate = |measured temperature - predicted temperature| / measured temperature × 100%.

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