Method and System for Optimizing Component Parameters of In-vehicle Chargers Based on Mathematical Models

Abstract

The present invention relates to the technical field of electrical energy storage systems, and particularly to a method and system for optimizing the parameters of vehicle-mounted charger components based on a mathematical model. The method includes the following steps: performing a multi-physical field joint modeling according to the switching devices of the vehicle-mounted charger to obtain a simulation model of the vehicle-mounted charger components; performing a dynamic sensitivity analysis according to the simulation model of the vehicle-mounted charger components to obtain dynamic sensitivity matrix data; performing a hierarchical MINLP solution on the dynamic sensitivity matrix data to obtain battery impedance change curve data; and performing an online parameter reconstruction according to the battery impedance change curve data to obtain optimized data for the parameters of the vehicle-mounted charger components. The present invention improves the charging efficiency, enhances the adaptability of the system to different working conditions, and significantly improves the overall performance, stability and reliability of the vehicle-mounted charger.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage systems, and particularly to a method and system for optimizing the parameters of components of an on-vehicle charger based on a mathematical model. Background Art

[0002] With the rapid development of the electric vehicle (EV) and hybrid electric vehicle (HEV) markets, the on-vehicle charger, as a core component in the electric vehicle charging system, has gradually become the key to improving the energy efficiency and safety of electric vehicles. The main function of the on-vehicle charger is to convert the external alternating current (AC) power supply into the direct current (DC) required by the vehicle battery and charge the battery. In order to meet the high-efficiency charging requirements in different environments, the design of the on-vehicle charger must have high efficiency, good thermal management, and a small volume and weight.

[0003] Currently, the thermal management and electrical characteristics of the switching devices (such as MOSFETs, diodes, etc.) and PCBs (printed circuit boards) used in on-vehicle chargers still face challenges. Traditional design methods focus on the electrical performance of the devices and ignore their coupling effects with thermal behavior. This results in large thermal losses and poor heat dissipation in the on-vehicle charger system under high power density and high-temperature environments, thereby affecting the working stability and service life of the system. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for optimizing the parameters of components of an on-vehicle charger based on a mathematical model to solve at least one of the above technical problems.

[0005] The present application provides a method for optimizing the parameters of components of an on-vehicle charger based on a mathematical model, the method comprising:

[0006] Performing a multi-physical field joint modeling according to the switching devices of the on-vehicle charger to obtain a simulation model of the components of the on-vehicle charger;

[0007] Performing a dynamic sensitivity analysis according to the simulation model of the components of the on-vehicle charger to obtain dynamic sensitivity matrix data;

[0008] Performing a hierarchical MINLP solution on the dynamic sensitivity matrix data to obtain battery impedance change curve data;

[0009] Performing an online parameter reconstruction according to the battery impedance change curve data to obtain optimized data of the parameters of the components of the on-vehicle charger.

[0010] In the present invention, through multi-physics field coupled modeling, electrical, thermal, magnetic field and structural characteristics are highly integrated, enabling accurate description of the performance of in-vehicle charger switching devices in complex working environments. Through dynamic sensitivity analysis, the impacts of key device parameters of in-vehicle chargers (such as MOSFET on-resistance, switching frequency, inductance value, etc.) on system performance (such as charging efficiency, thermal loss, battery health status, etc.) can be quantitatively evaluated. The mixed-integer nonlinear programming (MINLP) method is used to optimize and solve the dynamic sensitivity matrix, achieving the collaborative optimization of discrete variables (such as the selection of switching device types) and continuous variables (such as inductance value, switching frequency). Through a hierarchical solution strategy, while ensuring the calculation efficiency, the global optimality of the optimization solution can be ensured. Different from traditional single-objective optimization, this method combines the battery impedance change curve to achieve refined control of the battery charging process, reducing overshoot and loss during charging, improving charging efficiency and extending battery life. Through online parameter reconstruction, the working parameters of the charger (such as PWM control strategy, switching frequency, inductance value, etc.) can be dynamically adjusted according to the real-time change of battery impedance, ensuring that the system can adapt to different battery health statuses, temperature environments and load conditions.

[0011] Optionally, the multi-physics field coupled modeling based on the in-vehicle charger switching device to obtain the in-vehicle charger component simulation model includes:

[0012] Collect the basic data of the in-vehicle charger for the in-vehicle charger switching device to obtain the basic data of the in-vehicle charger, where the basic data of the in-vehicle charger includes the electrical data of the in-vehicle charger and the thermal data of the in-vehicle charger;

[0013] Construct a device electro-thermal model based on the electrical data of the in-vehicle charger and the thermal data of the in-vehicle charger to obtain the device electro-thermal model;

[0014] Obtain the physical parameter data of the in-vehicle charger PCB and construct a multi-physics field model based on the physical parameter data;

[0015] Perform simulation calculations based on the multi-physics field model and the device electro-thermal model to obtain the overall simulation model of the in-vehicle charger.

[0016] In the present invention, electrical and thermal data of in-vehicle charger switching devices are collected, and through the combination of multi-physics field modeling and simulation calculations of electrical-thermal models, accurate prediction of the overall performance of in-vehicle chargers is effectively achieved. By constructing a highly integrated electrical-thermal simulation model, it is possible to identify and optimize electrical losses, thermal management problems, and component coupling effects in advance during the design phase, thereby significantly improving the energy efficiency and reliability of the charger. Based on comprehensive analysis methods of dynamic simulation and multi-physics field interaction, it is possible to quickly evaluate the thermal stability and electrical performance of the charger under different operating conditions, providing strong data support for the design of high-efficiency and low-loss in-vehicle chargers.

[0017] Optionally, performing dynamic sensitivity analysis based on the in-vehicle charger component simulation model to obtain dynamic sensitivity matrix data, including:

[0018] Performing sensitivity analysis based on the in-vehicle charger component simulation model to obtain dynamic sensitivity data;

[0019] Performing thermal network modeling based on the in-vehicle charger component simulation model to obtain dynamic temperature response data;

[0020] Constructing a constraint matrix based on the dynamic sensitivity data and the dynamic temperature response data to obtain complete dynamic constraint data.

[0021] In the present invention, based on the simulation model, through sensitivity analysis, the influence degree of input parameters (such as electrical characteristics, thermal characteristics, working environment, etc. of components) on system outputs (such as power loss, heat change, performance indicators, etc.) can be evaluated. Using the thermal network modeling method, a temperature response model of the charger under different operating conditions can be established. The thermal network will consider all heat sources (such as power loss, switching loss, etc.) and heat conduction paths (such as PCB, package, etc.). By modeling the thermal behavior of the in-vehicle charger, temperature distribution and thermal response data under different working conditions are obtained, which helps to analyze the thermal stability of the charger. The dynamic sensitivity data can help quantify the influence of different parameters on the charger performance, while the dynamic temperature response data characterizes the influence of heat transfer on the component performance. By combining these two types of data, a matrix containing electrical and thermal characteristic constraints can be established to describe the performance boundaries and feasible solution ranges of the system under different operating conditions.

[0022] Optionally, performing hierarchical MINLP solution on the dynamic sensitivity matrix data to obtain battery impedance change curve data, including:

[0023] Performing top-level MINLP solution on the dynamic sensitivity matrix data to obtain top-level device combination data;

[0024] Performing bottom-level MINLP solution on the dynamic sensitivity matrix data to obtain continuous optimization data;

[0025] Time-varying constraint generation is performed based on the dynamic sensitivity matrix data to obtain time-varying constraint data;

[0026] The top-level device combination data and the continuous optimization data are solved according to the time-varying constraint data to obtain the battery impedance change curve data.

[0027] In the present invention, by performing hierarchical MINLP (Mixed Integer Nonlinear Programming) solution on the dynamic sensitivity matrix data, the parameters of the in-vehicle charger components can be systematically optimized to meet the multiple requirements of battery performance and thermal management. The top-level MINLP solution can ensure the selection of the best device combination, thereby optimizing the performance of the entire system; the bottom-level MINLP solution further improves the accuracy and efficiency of the system through continuous optimization. The time-varying constraint generation and solution process provide an accurate model for the dynamic response of the battery, ensuring that the in-vehicle charger finely regulates the impedance change of the battery at different charging stages. The obtained battery impedance change curve data helps to improve the stability, efficiency, and battery life of the charging process, and enhances the reliability and performance of the charger in actual use.

[0028] Optionally, the online parameter reconstruction is performed according to the battery impedance change curve data to obtain the optimized data of the in-vehicle charger component parameters, including:

[0029] Obtain real-time electrical data, and perform optimal parameter matching according to the real-time electrical data and the battery impedance change curve data to obtain the primary optimized parameter data;

[0030] Perform real-time acceleration calculation and parameter adjustment according to the primary optimized parameter data to obtain the secondary optimized parameter data;

[0031] Perform continuous feedback adjustment according to the secondary optimized parameter data to obtain the optimized data of the in-vehicle charger component parameters.

[0032] In the present invention, by performing online parameter reconstruction based on the battery impedance change curve data, the working state of the in-vehicle charger components can be optimized in real time. The acquisition of real-time electrical data and the matching of the battery impedance change curve data ensure the best performance of the charger under various working conditions. The primary optimized parameter data is further adjusted through real-time acceleration calculation, improving the response speed and optimization accuracy of the system; the secondary optimization ensures the high stability and adaptability of the charger during actual operation through continuous feedback adjustment.

[0033] Optionally, the physical parameter data of the in-vehicle charger PCB is obtained, and a multi-physics field model is constructed according to the physical parameter data, including:

[0034] Obtain the physical parameter data of the in-vehicle charger PCB, where the physical parameter data includes IV characteristic curve data, on-resistance data, B-H curve data of magnetic components, and winding AC resistance;

[0035] Perform temperature piecewise fitting based on the physical parameter data to obtain electrical characteristic data;

[0036] Perform power consumption junction temperature mapping based on the physical parameter data to obtain thermal characteristic data;

[0037] Construct an electrical model of the MOSFET device based on the electrical characteristic data to obtain an electrical model:

[0038] Construct a thermal model based on the thermal characteristic data to obtain a thermal model:

[0039] Solve the thermal network model according to the power loss data in the thermal model and the electrical model to obtain junction temperature data;

[0040] Correct the electrical model according to the junction temperature data to obtain an electrically corrected model;

[0041] Perform proper orthogonal decomposition on the thermal model to obtain a reduced-order model;

[0042] Package the electrically corrected model and the reduced-order model to obtain a multi-physics field model.

[0043] In the present invention, by obtaining the physical parameter data of the in-vehicle charger PCB and constructing a multi-physics field model, the performance of the charger under different working environments can be accurately simulated and optimized. Combining temperature piecewise fitting and power consumption junction temperature mapping, the electrical and thermal characteristics are effectively coupled, providing a more accurate basis for the construction of the electrical model and the thermal model. Through the correction and reduction of the junction temperature data, the efficiency and accuracy of the model are ensured, making the thermal management and electrical performance of the charger more stable and reliable during long-term operation.

[0044] Optionally, the electrical characteristic data includes first electrical characteristic data, second electrical characteristic data, third electrical characteristic data, fourth electrical characteristic data, and fifth electrical characteristic data. The step of performing temperature piecewise fitting based on the physical parameter data to obtain electrical characteristic data includes:

[0045] Extract thermal characteristics from the thermal data of the in-vehicle charger to obtain thermal characteristic data;

[0046] Extract the conduction voltage, saturation current, and switching time from the IV characteristic curve data to obtain conduction voltage data, saturation current data, and switching time data respectively;

[0047] Perform characteristic curve fitting on the conduction voltage data, saturation current data, and switching time data according to the thermal characteristic data to obtain the first electrical characteristic data;

[0048] Perform temperature fitting on the on-resistance data according to the thermal characteristic data to obtain the second electrical characteristic data;

[0049] Calculate the power loss according to the conduction voltage data, saturation current data, and switching time data to obtain the power loss data;

[0050] Perform switching loss fitting according to the thermal characteristic data and the power loss data to obtain the third electrical characteristic data;

[0051] Perform piecewise exponential fitting on the B-H curve data of the magnetic component according to the thermal characteristic data to obtain the fourth electrical characteristic data;

[0052] Perform temperature-frequency change fitting according to the thermal characteristic data and the winding AC resistance data to obtain the fifth electrical characteristic data.

[0053] In the present invention, by performing multi-dimensional fitting and optimization on the electrical characteristic data of the on-vehicle charger, the performance changes of the device under different operating temperatures and environmental conditions can be accurately grasped. Based on the extraction and fitting of data such as the IV characteristic curve, conduction voltage, saturation current, and switching time, the electrical performance of the switching device can be reflected, especially providing accurate simulation and prediction for key indicators such as power loss and on-resistance. The introduction of thermal characteristics enables a more detailed description of the changes in power loss and switching loss, ensuring the rationality of the thermal management system. By further fitting the B-H curve of the magnetic component and the winding AC resistance data, the overall electrical characteristic data is optimized, improving the stability and reliability of the charger in complex environments.

[0054] Optionally, the obtaining of the thermal characteristic data by performing power consumption-junction temperature mapping according to the physical parameter data includes:

[0055] Perform transient power consumption decomposition according to the physical parameter data and the electrical characteristic data to obtain the time-varying power consumption distribution data;

[0056] Construct a three-dimensional thermal impedance network model according to the physical parameter data and the time-varying power consumption distribution data to obtain the three-dimensional thermal impedance network model;

[0057] Perform transient thermal field simulation according to the three-dimensional thermal impedance network model to obtain the transient thermal field data;

[0058] Perform transfer learning calibration according to the transient thermal field data to obtain the thermal characteristic data.

[0059] In the present invention, by introducing the transient power consumption decomposition and the construction of a three-dimensional thermal impedance network model, the thermal dynamic behavior of the in-vehicle charger under different working conditions is accurately described. The consideration of the time-varying power consumption distribution makes the thermal management more precise, avoids the simplified assumptions in the traditional thermal model, and improves the accuracy of thermal prediction. Through transient thermal field simulation, real-time thermal response data can be obtained to accurately evaluate the thermal behavior of the device under instantaneous load changes, ensuring effective heat management under high loads. Further, transfer learning calibration is adopted to correct the thermal model combined with actual data, further improving the reliability and accuracy of the thermal characteristics.

[0060] Optionally, constructing a three-dimensional thermal impedance network model according to the physical parameter data and the time-varying power consumption distribution data to obtain a three-dimensional thermal impedance network model, including:

[0061] Modeling the thermal behavior of transistors according to the physical parameter data and the time-varying power consumption distribution data to obtain a transistor thermal behavior model;

[0062] Modeling the thermal behavior of the PCB layer according to the physical parameter data and the time-varying power consumption distribution data to obtain a PCB layer thermal behavior model;

[0063] Modeling the thermal behavior of the structure according to the physical parameter data and the time-varying power consumption distribution data to obtain a structure thermal behavior model;

[0064] Performing a heat flow coupling simulation according to the transistor thermal behavior model, the PCB layer thermal behavior model, and the structure thermal behavior model to obtain a three-dimensional thermal impedance network model.

[0065] In the present invention, by separately modeling the thermal behaviors of transistors, the PCB layer, and the structure, and performing a heat flow coupling simulation, it is possible to achieve a detailed description of the thermal behaviors at each level in the in-vehicle charger, accurately describe the heat transfer path and the thermal coupling effect. Different from the traditional method that only relies on a simplified thermal network model, the multi-scale thermal modeling method can more realistically simulate the thermal coupling effect between different components, improving the accuracy and reliability of thermal management. Based on the time-varying power consumption distribution data, the model can dynamically reflect the thermal response of the device under different working conditions, avoiding the limitations of the static model.

[0066] Optionally, the present application also provides a system for optimizing the parameters of in-vehicle charger components based on a mathematical model, which is used to execute the method for optimizing the parameters of in-vehicle charger components based on a mathematical model as described above. The system for optimizing the parameters of in-vehicle charger components based on a mathematical model includes:

[0067] A multi-physical field joint modeling module, which is used to perform multi-physical field joint modeling according to the switching devices of the in-vehicle charger to obtain an in-vehicle charger component simulation model;

[0068] A dynamic sensitivity analysis module for performing dynamic sensitivity analysis based on the simulation model of in-vehicle charger components to obtain dynamic sensitivity matrix data;

[0069] A hierarchical MINLP solving module for performing hierarchical MINLP solving on the dynamic sensitivity matrix data to obtain battery impedance change curve data;

[0070] An online parameter reconstruction module for performing online parameter reconstruction based on the battery impedance change curve data to obtain optimized data for in-vehicle charger component parameters.

[0071] The purpose of the present invention is to integrate electrical, thermal, and structural characteristics into a unified simulation model through multi-physics field joint modeling, which can comprehensively and accurately describe the working behavior of in-vehicle charger components. By performing dynamic sensitivity analysis on the simulation model of in-vehicle charger components, the present invention can accurately identify the influence degree of each parameter on the system performance and generate a dynamic sensitivity matrix, providing scientific and reliable basic data for the optimization step. When performing hierarchical MINLP solving, the problem of excessively high computational complexity in traditional single optimization algorithms is effectively avoided through the hierarchical solving strategy. This optimization process can not only dynamically adjust the battery impedance change but also real-time feedback the change of system performance. Through hierarchical optimization, the method can better adapt to complex scenarios with multiple objectives and constraints, improving the solving efficiency and the stability of the system. By combining the battery impedance change curve data for online parameter reconstruction, this method realizes the real-time dynamic optimization of in-vehicle charger components. The optimization process can adjust the device parameters in real-time with the changes of the environment and load to ensure the optimal performance of the charger under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, purposes, and advantages of the present application will become more obvious:

[0073] Figure 1 The flowchart showing the steps of a method for optimizing the parameters of in-vehicle charger components based on a mathematical model in an embodiment;

[0074] Figure 2 The flowchart showing the steps of a multi-physics field joint modeling method in an embodiment;

[0075] Figure 3 The flowchart showing the steps of a dynamic sensitivity analysis method in an embodiment;

[0076] Figure 4 The flowchart showing the steps of a hierarchical MINLP solving method in an embodiment;

[0077] Figure 5The flowchart of the steps of an online parameter reconstruction method according to an embodiment is shown;

[0078] The realization of the object of the present invention, functional features and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0079] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0082] Please refer to Figures 1 to 5 , this application provides a method for optimizing the parameters of components of a vehicle-mounted charger based on a mathematical model. The method includes:

[0083] S1. Perform multi-physical field joint modeling according to the switching devices of the vehicle-mounted charger to obtain a simulation model of the components of the vehicle-mounted charger;

[0084] In one embodiment, simulation software such as COMSOL Multiphysics, ANSYS, or other software supporting multi-physical field coupling is used. Key parameters such as the material properties, current-carrying capacity, and operating frequency of switching components such as MOSFETs and diodes are clarified. The electric field, thermal field, magnetic field, etc. are to be coupled for modeling. According to the structure of the charger, its physical geometry model is established, including the power input terminal, output terminal, heat dissipation structure, etc. Electrical boundary conditions (such as voltage and current) and thermal boundary conditions (such as the heat dissipation effect of the cooling system) are set according to the actual working environment.

[0085] S2. Conduct dynamic sensitivity analysis based on the in-vehicle charger component simulation model to obtain dynamic sensitivity matrix data;

[0086] In one embodiment, key performance indicators (KPIs) of the in-vehicle charger are selected, such as conversion efficiency, voltage fluctuation, thermal runaway, etc. Through dynamic simulation of the simulation model under different working conditions (such as load change, ambient temperature fluctuation, etc.), response data of the charger during operation is obtained. Calculate the influence degree of each component (such as power switch, filter capacitor, etc.) on the target performance indicator. Use methods based on partial derivatives or methods for constructing sensitivity matrices (such as global sensitivity analysis). Generate a dynamic sensitivity matrix, which reflects the influence of changes in different component parameters on the performance of the in-vehicle charger.

[0087] In one embodiment, key performance indicators (KPI) are selected: charging efficiency , junction temperature , output voltage fluctuation . Local sensitivity analysis: , where is the component parameter, such as MOSFET switching frequency, transformer turns ratio, is the KPI of the charger, such as charging efficiency, junction temperature, output voltage fluctuation, is the component parameter, such as MOSFET switching frequency, transformer turns ratio, filter capacitor value, etc. Global sensitivity analysis: , where is the global sensitivity index of parameter , is, is the variance of the KPI expected value caused by the change of , is the system response, such as charging efficiency, junction temperature, output voltage fluctuation, is an input parameter, such as MOSFET switching frequency, transformer turns ratio, filter capacitor value, etc., is the total variance of the KPI. Organize the sensitivity values of all parameters to form a dynamic sensitivity matrix.

[0088] S3. Conduct hierarchical MINLP solution on the dynamic sensitivity matrix data to obtain battery impedance change curve data;

[0089] In one embodiment, an optimization objective function is set, usually system efficiency, power loss, thermal stability, etc. Technical and physical constraints are set, such as the maximum current carrying capacity of components, operating temperature limits, cost limits, etc. The optimization variables are defined, including the switching frequency of the power switch, the capacitance of the filter capacitor, the structure of the radiator, etc. Layer 1: Wide-range parameter optimization. Coarse optimization is carried out to determine which component parameters have a greater impact on performance, and these components are preferentially optimized. Global optimization methods such as genetic algorithms and particle swarm optimization are used. Layer 2: Fine optimization. For key parameters, fine optimization is carried out, and local optimization algorithms such as the Newton-Raphson method are used to improve the solution accuracy. Solve the above mixed integer nonlinear programming problem to obtain the optimal parameter configuration of the components. This step can be performed using a MINLP solver (such as GAMS, XPRESS), and optimization algorithms (such as branch and bound method, interior point method) are used to handle it.

[0090] Model the battery using an equivalent circuit model or an electrochemical model. The battery model includes the internal resistance, capacitance, inductive reactance, etc. of the battery. Through simulation or experimental tests, the impedance change curve of the battery under different charging conditions is obtained. Analyze the relationship between the battery impedance and the output of the charger: Analyze the influence of the output current, voltage, etc. of the in-vehicle charger on the battery impedance. According to the impedance change data of the battery under different charging conditions, draw the battery impedance change curve and evaluate its correlation with the charger parameters.

[0091] In one embodiment, the sensitivity matrix data is optimized and solved to obtain the optimal battery impedance model. Set the objective function: such that where is minimized, is the total power loss of the system, including MOSFET loss, transformer loss, filter loss, etc. Constraint conditions: . Solve in layers. The first layer: Coarse-grained optimization (genetic algorithm), the second layer: Fine-grained optimization (gradient descent method). Through optimization, calculate the impedance curve of the battery equivalent circuit model: where is the impedance of the battery at different frequencies , is the static ohmic resistance (DC internal resistance) of the battery, is the dynamic resistance of the battery, used to describe the polarization effect, is the imaginary unit, is the angular frequency, is the time constant, describing the time scale of the battery polarization effect.

[0092] S4. According to the battery impedance change curve data, perform online parameter reconstruction to obtain the optimized data of the in-vehicle charger component parameters.

[0093] In one embodiment, during the actual operation of the in-vehicle charger, the battery impedance and the output characteristics of the charger are measured in real time. Based on the battery impedance change curve and the sensitivity analysis results, the control parameters of the in-vehicle charger are adjusted in real time (such as adjusting the switching frequency, output voltage, etc.). By combining the real-time data with the optimization model, closed-loop control of the parameters is achieved to ensure that the charger is always in the optimal working state.

[0094] In one embodiment, high-precision ADC is used for real-time measurement, including the input voltage , the charging current , and the battery terminal voltage . Parameter estimation is carried out as follows: , where is the state variable at the k+1 moment (parameters to be estimated, such as the state of charge SOC of the battery, the internal resistance of the battery, etc.), is the state transition matrix, which describes the state change of the system from the k moment to the k+1 moment, is the state variable at the k moment (the battery state at the current moment), is the control matrix, indicating the influence of the control input on the system state, is the input variable at the k moment, such as the charging current and voltage, is the process noise (model error or measurement noise). The optimal switching frequency, PWM duty cycle, etc. are matched. The optimal value of the MOSFET switching frequency is calculated as follows: , where is the optimal switching frequency of the MOSFET, is the constant term of pi, is the inductance value, is the value of the filter capacitor. By combining the fuzzy control algorithm, the charging mode is automatically adjusted, such as the constant current mode (CC), constant voltage mode (CV), and trickle charge mode, so as to adjust and optimize the parameters in real time.

[0095] Optionally, multi-physics field joint modeling is carried out according to the switching device of the in-vehicle charger to obtain the simulation model of the in-vehicle charger components, including:

[0096] S11. Collect the basic data of the in-vehicle charger for the switching device of the in-vehicle charger to obtain the basic data of the in-vehicle charger, where the basic data of the in-vehicle charger includes the electrical data and thermal data of the in-vehicle charger;

[0097] In one embodiment, a standard test device (such as a power analyzer, a digital oscilloscope) is used to measure the input voltage and output current of the charger. The main parameters of the electrical data include the input voltage range (such as DC 12V–36V), the output voltage (such as 12V, 24V), the output current (such as 5A–20A), etc. The switching frequency of the switching element (usually from dozens of kHz to hundreds of kHz) is measured, and the switching loss is evaluated. The oscilloscope and high-frequency probe are used to collect the switching waveforms, and the switching loss, conduction loss, reverse recovery loss, etc. are analyzed. The parameters of key electrical components are measured, such as the capacitance, inductance, internal resistance of the switching transistor, quiescent current, etc.

[0098] The heat sources in the charger are located by a thermal imager or a temperature sensor to obtain the temperature distribution data inside and outside the charger. The heat sources include power switch devices, filter capacitors, transformers, etc. The thermal resistance of the radiator is measured to evaluate the heat dissipation efficiency. Through the temperature sensor and the fluid temperature probe, the temperature change on the surface of the radiator and the cooling efficiency of the cooling system are tested. The heat conduction data of the charger is obtained to establish a heat conduction model. The internal temperature gradient of the charger is obtained through experiments, or the heat convection characteristics are simulated by numerical simulation.

[0099] In one embodiment, a power analyzer is used to calculate the conversion efficiency: , where is the conversion efficiency, is the output power, is the input power. An infrared thermal imager is used to measure the operating temperatures of key components such as MOSFETs, diodes, transformers, etc.: , where is the thermal resistance, which measures the heat conduction ability of the component, is the junction temperature, the maximum temperature (°C) of the MOSFET or other components, is the ambient temperature, is the power loss.

[0100] S12. Construct a device electro-thermal model based on the electrical data and thermal data of the in-vehicle charger to obtain the device electro-thermal model;

[0101] In one embodiment, the electrical model includes an equivalent circuit model, an inductor and capacitor model, and a switching loss model. Equivalent circuit model: Based on the electrical data of the switching device, its equivalent circuit model is constructed. For switching devices such as MOSFETs or IGBTs, parameters such as their on-resistance, switching frequency, and current-carrying capacity need to be included. Inductor and capacitor model: An inductor-capacitor network model of the charger power supply part is constructed, mainly considering the inductor and capacitor parameters of the power supply filtering part. Switching loss model: Considering the switching loss, reverse recovery loss, conduction loss, etc. of the switching transistor, the corresponding loss model is constructed through the collected waveform data. The loss function of the switching transistor is obtained through experiments and introduced into the circuit simulation.

[0102] The thermal model includes a heat convection and radiation model and a component thermal model. Based on the thermal data, a heat conduction model inside the charger is established. Considering the heat transfer process between components (such as switching devices, capacitors, inductors, etc.), the Fourier heat conduction equation or a more complex unsteady heat conduction model is adopted. The heat convection and radiation model obtains the convective thermal resistance between the charger surface and the environment and the radiative heat loss through experiments. For natural convection and forced convection (such as fan cooling), separate models need to be established. An independent thermal model is constructed for each component (such as MOSFET, diode, transformer, etc.) to obtain the component thermal model, considering its heat generation, heat transfer, and heat diffusion characteristics.

[0103] Based on the electrical model, considering the influence of temperature on electrical performance, a thermal-electrical coupling effect is constructed through a mathematical model. For example, the on-resistance of a switching device usually increases with the increase of temperature, thus affecting the switching efficiency. A non-linear model is used to describe the relationship between temperature and electrical parameters. Combining the electrical-thermal coupling model, a thermal management strategy for the charger is designed, such as selecting appropriate heat sinks, fans, coolants, etc., to ensure the stability of the charger in a high-temperature environment.

[0104] In one embodiment, the MOSFET electrical-thermal modeling includes an on-resistance variation with temperature model: , where is the temperature-dependent on-resistance, is the on-resistance at the reference temperature, is the temperature coefficient, the temperature change rate of the MOSFET on-resistance (1 / °C), is the current operating temperature of the MOSFET, is the reference temperature; switching loss model: , where is the switching loss, the switching loss of the MOSFET during operation (W), is the drain-source voltage, the voltage between the drain and source of the MOSFET (V), is the drain current, the drain current of the MOSFET (A), is the turn-on time, the time for the MOSFET to turn from off to fully on (s), is the turn-off time, the time for the MOSFET to turn from on to fully off (s), is the switching frequency, the switching frequency of the MOSFET (Hz). The electrical-thermal modeling of the transformer includes B-H curve measurement: , where is the core loss of the transformer, the core loss of the transformer (W), is the loss coefficient, a constant related to the transformer material and design, is the switching frequency, is the frequency exponent, an exponent reflecting the change of loss with frequency ( ), is the maximum magnetic flux density, the maximum magnetic flux density of the transformer core (T), is the magnetic flux density exponent, an exponent reflecting the change of loss with magnetic flux density ( ); Calculation of winding loss: , where is the AC winding resistance, the AC resistance of the transformer winding (Ω), is the DC winding resistance, the DC resistance of the transformer winding (Ω), is the frequency-dependent coefficient, the frequency influence coefficient, related to the skin effect, is the switching frequency, the operating switching frequency of the transformer (Hz).

[0105] S13. Obtain the physical parameter data of the in-vehicle charger PCB and construct a multi-physics field model based on the physical parameter data;

[0106] In one embodiment, obtain the PCB physical parameter data, measure and obtain the material properties of the PCB board, such as the resistivity of the conductive material (copper), the dielectric constant of the dielectric material, etc. Collect the layout information of each component on the PCB, including geometric dimension data such as layer thickness, line width, and spacing. These data can be exported through CAD software. Analyze the heat conduction characteristics of the PCB and draw the path of the current flowing through the PCB, considering the current-carrying capacity and heat dissipation capacity of the wires. Based on the physical parameters of the PCB, establish an electrical-thermal coupling model. Use simulation software (such as ANSYS Maxwell, COMSOL, etc.) to handle problems such as current density distribution, temperature distribution, and heat source generation. For the high-frequency current transmission part, the coupling between the electromagnetic field and the thermal field cannot be ignored. Through multi-physics field modeling, consider the electromagnetic effects caused by the current and the thermal effects caused by the current loss.

[0107] In one embodiment, physical parameters of the PCB material are obtained, including the copper layer thickness (such as 35 µm, 70 µm, etc.), the thermal conductivity of the FR4 dielectric layer (k = 0.3 W / m·K), and the calculation of the resistance of the PCB copper wire: , where is the resistance of the PCB copper wire, the resistance of the PCB copper wire (Ω), is the resistivity of copper, the resistivity of copper ( ), , is the length of the copper wire, the length of the PCB copper wire (m), is the cross-sectional area of the copper wire, the cross-sectional area of the copper wire ( ), and the calculation method is: , is the width of the copper wire (m), is the copper layer thickness (m). A coupled electric field-thermal field-mechanical field model is established: , where is the current density, is the current density passing through the copper wire ( ), is the cross-sectional area of the copper wire, the cross-sectional area of the copper wire ( ). Heat conduction calculation: , where is the heat conduction power, the heat transfer rate through the PCB material (W), is the thermal conductivity, the thermal conductivity of the material , is the temperature gradient, the spatial change rate of temperature (K / m).

[0108] S14. According to the multi-physical field model and the device electrical-thermal model, perform simulation calculations to obtain the overall simulation model of the vehicle charger.

[0109] In one embodiment, a simulation tool that supports multi-physical field coupling is used, such as COMSOL Multiphysics, ANSYS, Simulink (for dynamic electrical analysis), etc. Based on the electrical model, perform electrical simulation to calculate parameters such as current and voltage when the charger is working. Use the thermal model to perform thermal simulation to calculate the temperature distribution and thermal effects of the charger. Perform electro-thermal coupling simulation to evaluate the impact of temperature changes on electrical performance and the feedback effect of electrical losses on the temperature field. Analyze the simulation results to evaluate the working efficiency of the charger, the thermal management effect, the temperature distribution of components, switching losses, etc. According to the simulation results, optimize the selection, layout, and heat dissipation scheme of components to improve the overall performance and stability of the charger.

[0110] In one embodiment, the boundary condition is set to room temperature setting . The load is set Run the simulation and perform the following calculation steps: calculate the output voltage and current of the charger, calculate the power losses of the MOSFET, diode, and transformer, and calculate the change in the MOSFET junction temperature. , is the change in the MOSFET junction temperature, and the junction temperature (°C) of the MOSFET at time . is the power loss, and the power loss (W) of the MOSFET. is the thermal resistance. is the base of the natural logarithm. is the time. is the thermal time constant. Calculate the electromagnetic field distribution, analyze the high-frequency current effect, calculate the heat transfer path, optimize the heat dissipation design, and based on the above calculations, obtain the overall simulation model of the in-vehicle charger.

[0111] Optionally, perform dynamic sensitivity analysis based on the in-vehicle charger component simulation model to obtain dynamic sensitivity matrix data, including:

[0112] S21. Perform sensitivity analysis based on the in-vehicle charger component simulation model to obtain dynamic sensitivity data.

[0113] In one embodiment, the purpose of sensitivity analysis is to evaluate the impact of different component parameters or operating conditions on the overall performance of the in-vehicle charger. Dynamic sensitivity refers to how the performance indicators of the in-vehicle charger (such as efficiency, output fluctuation, thermal response, etc.) change over time when component parameters change. Select performance indicators to be optimized or controlled, such as output voltage fluctuation, conversion efficiency, switching loss, current ripple, etc. According to the objectives, sensitivity analysis will evaluate the impact of component changes on these indicators. Define the parameters of the analysis object (components), including switching frequency, on-resistance, switching loss of power transistors, value of filter capacitors, magnetic flux density of transformers, etc. Based on the simulation model of the in-vehicle charger (including electrical and thermal models), set to simulate the dynamic behavior of the charger under actual operating conditions. Use simulation tools (such as Simulink, ANSYS Maxwell, COMSOL, etc.) to perform time-domain simulation and simulate the response of the charger under dynamic conditions such as load change and temperature fluctuation. Calculate the impact of the change in each component parameter (such as the on-resistance of the power switch) on the charger performance through the partial derivative method. That is, for the target performance indicator f(x), solve the sensitivity of each component parameter. Use the Monte Carlo method or Sobol index method for global sensitivity analysis to evaluate the overall impact of all components and operating parameters on the system performance. Summarize the impact of all parameters on the performance indicators into a matrix form to obtain the dynamic sensitivity matrix. Through the above steps, the obtained dynamic sensitivity data contains the immediate response of the change in each component parameter to the system performance, and the result is a sensitivity matrix (such as a multi-dimensional matrix), where the rows represent component parameters and the columns represent performance indicators.

[0114] In one embodiment, local sensitivity analysis: , where is the component parameter, such as the MOSFET switching frequency, the number of turns of the transformer, is the system performance indicator, such as conversion efficiency, output voltage fluctuation, MOSFET junction temperature, is the component parameter, the design parameter that affects the charger performance, such as switching frequency, MOSFET on-resistance, transformer turns ratio. Global sensitivity analysis: , where is the global sensitivity index, indicating the global impact of parameter on the output variable , is the variance of the conditional expectation, is the conditional expectation, is the system output, is the component parameter, is the total variance of the system output. Organize the sensitivity values of all parameters to form a dynamic sensitivity matrix:

[0115] ;

[0116] wherein is the sensitivity matrix, is the sensitivity of the charging efficiency to the switching frequency, is the sensitivity of the charging efficiency to the MOSFET on-resistance, is the sensitivity of the charging efficiency to the transformer turns ratio, is the sensitivity of the output voltage ripple to the switching frequency, is the sensitivity of the output voltage ripple to the MOSFET on-resistance, is the sensitivity of the output voltage ripple to the transformer turns ratio, is the sensitivity of the MOSFET junction temperature to the switching frequency, is the sensitivity of the MOSFET junction temperature to the MOSFET on-resistance, is the sensitivity of the MOSFET junction temperature to the transformer turns ratio.

[0117] S22. Perform thermal network modeling based on the in-vehicle charger component simulation model to obtain dynamic temperature response data;

[0118] In one embodiment, during the operation of the in-vehicle charger, heat is generated, which affects the temperature distribution of components and thus changes their electrical performance. Thermal models are established for each key component (such as MOSFET, filter capacitor, transformer, etc.). These components can generally be regarded as heat sources, and the heat they generate (for example, the heat generated through switching losses) is defined. For each component, its thermal resistance (representing the heat transfer conduction ability) and heat capacity (representing its heat storage ability) are defined. For example, for metal materials, its thermal conductivity is used to calculate the thermal resistance. Build the thermal network model of the in-vehicle charger, considering the heat conduction paths between components. The thermal network can be modeled through electro-thermal coupling simulation tools (such as the COMSOL Heat Transfer module). According to the power loss data in the electrical model, the heat source intensity of each component is defined. Considering the thermal coupling characteristics of the charger and the air convection cooling effect, the thermal resistance of the cooling system (such as radiator, fan, etc.) is defined. Use the thermal network model to perform time-domain thermal simulation to simulate the dynamic response of the temperature of the charger under different working conditions (such as switching frequency change, load fluctuation, etc.). Extract the dynamic temperature data from the simulation results, observe the temperature changes of each component at different time periods, and how the temperature fluctuation affects the performance of the charger. Through simulation, the dynamic temperature response data of each component are obtained, usually output in the form of a time series, such as the temperature values of each component at different time points. These data are combined with the sensitivity data to establish a complete constraint matrix.

[0119] In one embodiment, establish a thermal resistance-capacitance network: , is the MOSFET junction temperature varying with time, is the power loss, is the thermal resistance, is the base of the natural logarithm, is the time, is the thermal time constant. The finite element method (FEM) is used to calculate the heat flow path: MOSFET junction temperature → PCB → heat sink, transformer core loss → insulation layer, inductor copper loss → air convection. Calculate the heat conduction: , where is the heat conduction power, is the thermal conductivity, is the temperature gradient. The finite difference method (FDM) or thermal network simulation (ANSYS Icepak) is used. Calculate the steady-state temperature and transient response curves, specifically including the MOSFET temperature rise curve, PCB copper layer temperature distribution, and transformer thermal response time.

[0120] S23. Construct the constraint matrix based on the dynamic sensitivity data and dynamic temperature response data to obtain the complete dynamic constraint data.

[0121] In one embodiment, the dynamic constraint matrix is a matrix that describes how various limiting conditions (such as temperature limits, power losses, operating efficiencies, etc.) interact during the dynamic change process of the system. By combining the sensitivity data with the temperature response data, the constraint matrix can be constructed and adjusted according to the performance requirements of the on-vehicle charger. Based on the thermal response data of the components, define the operating temperature range of each component (for example, the maximum operating temperature of the MOSFET is 150°C), and constrain the situations beyond the temperature range. According to the results of the dynamic sensitivity analysis, set the maximum tolerance value of the power loss. For example, under high load conditions, the switching loss cannot exceed a certain value to avoid overheating. Set that the conversion efficiency of the charger should be maintained within a certain range. For example, it is required that the system efficiency is not less than 90%. Construct the constraint conditions such as temperature, power loss, and efficiency into matrix form. The constraint matrix is usually a multi-dimensional matrix, where the rows represent different constraint conditions (such as temperature constraints, power constraints), and the columns represent component parameters or performance indicators. List the temperature limits of each component and combine them with the temperature response data. For example, when the temperature of a certain component exceeds 150°C, this constraint condition is not met. List the maximum power loss limits of each component and combine them with the sensitivity data. For example, when the power loss of a certain switching device is too high, its operating frequency needs to be adjusted. The constraint matrix will include all dynamic constraint conditions (such as temperature, power loss, efficiency, etc.) and the relationships of component parameters or performance indicators, forming the complete dynamic constraint data. This data can be used to further optimize or adjust the design of the on-vehicle charger.

[0122] In one embodiment, set the objective function: , such that , where is minimized, is the power loss. Constraint: . Construct the optimization constraint matrix: , where is the optimization constraint matrix, is the optimization variable, is the constraint vector, forming the matrix:

[0123]

[0124] where is the switching frequency, is the on-resistance of the MOSFET, is the number of turns of the primary winding of the transformer, is the number of turns of the secondary winding of the transformer. Use the Lagrange multiplier method to solve for the optimal parameters , where is the gradient change of the Lagrangian function, is the gradient of the objective function, is the Lagrange multiplier, is the constraint gradient. Use the genetic algorithm for global search to find the optimal switching frequency, MOSFET parameters, and transformer parameters.

[0125] Optionally, perform hierarchical MINLP solution on the dynamic sensitivity matrix data to obtain battery impedance change curve data, including:

[0126] S31. Perform top-level MINLP solution on the dynamic sensitivity matrix data to obtain top-level device combination data;

[0127] In one embodiment, maximize the charger conversion efficiency: , where is maximized, is the conversion efficiency, is the output power, is the input power. Minimize the power losses of components such as MOSFET, transformer, and filter capacitor: , where is minimized, is the total power loss, is the on-loss of the MOSFET, is the switching loss of the MOSFET, is the core loss of the transformer, is the AC loss of the filter capacitor. The MOSFET junction temperature does not exceed 150 °C: , and the transformer core temperature does not exceed 120 °C: . Select different specifications of available MOSFETs, diodes, transformers, and filter capacitors to form a set of discrete variables: , , where is the variable for MOSFET selection, is a silicon-based MOSFET, is a silicon carbide MOSFET, is a gallium nitride MOSFET, is the variable for transformer turns selection, is winding scheme 1, is winding scheme 2, is winding scheme 3, and the scheme is preset different winding parameter data, which can be obtained by querying existing materials. Solve using the MINLP method, , where is minimization, are the variables to be optimized, including MOSFETs, transformers, filter capacitors, etc., is the objective function, the power loss model of the charger, are the inequality constraints, are the equality constraints.

[0128] S32. Perform bottom-layer MINLP solution on the dynamic sensitivity matrix data to obtain continuous optimization data;

[0129] In one embodiment, perform continuous parameter optimization on the selected components (such as switching frequency, PWM duty cycle, inductance value, etc.). Define the optimization variables, such as the switching frequency , optimize between 50 kHz and 500 kHz: ; the PWM duty cycle D, adjust between 0.2 and 0.8: ; the inductance value L satisfies the CCM (continuous conduction mode) condition: , where is the inductance value, is the output voltage, is the PWM duty cycle, is the switching frequency, is the current ripple. Establish a continuous optimization mathematical model: , where is minimization, is the power loss, is the switching frequency, is the PWM duty cycle, is the inductance value; define the constraints, such as the voltage ripple constraint: , where is the output voltage ripple; the maximum current constraint at the battery end: the battery charging current maximum allowable current Solve using the MINLP method, , where, is the iterative value of the optimization variable, is the current value of the optimization variable, is the learning rate, is the gradient of the objective function.

[0130] S33. Generate time-varying constraints based on the dynamic sensitivity matrix data to obtain time-varying constraint data;

[0131] In one embodiment, time-varying constraints are imposed on the optimization variables based on the change in battery SOC (state of charge). SOC-related battery impedance: , where is the battery impedance, is the DC equivalent resistance, is the AC equivalent resistance, is the imaginary unit, is the angular frequency, is the battery equivalent capacitance; , where is the SOC-related internal resistance of the battery, is the DC equivalent resistance, is the exponential decay coefficient, is the natural exponent, is the SOC influence coefficient, is the state of charge of the battery. Dynamically adjust the constraints. For example, during the charging process, the battery voltage is maintained within the allowable range: , where is the battery terminal voltage, is the minimum voltage, the lowest allowable charging voltage (V), is the maximum voltage, the highest allowable charging voltage (V); Dynamically adjust the charging current: , where is the charging current, the current provided by the charger to the battery (A), is the SOC-related charging current function, the dynamic regulation strategy of the charging current.

[0132] S34. Solve the top-level device combination data and continuous optimization data according to the time-varying constraint data to obtain the battery impedance change curve data.

[0133] In one embodiment, use the optimized device combination and continuous optimization parameters to calculate the battery impedance change curve. Combine all optimization parameters to construct an optimization problem: , constraints: , where is the battery terminal voltage, is the minimum voltage, the lowest allowable charging voltage (V), is the maximum voltage, the highest allowable charging voltage (V). Using MATLAB Simulink / EIS (Electrochemical Impedance Spectroscopy) test, plot the SOC change vs. impedance curve, calculate the impedance change under different charging parameters, and obtain the battery impedance change curve data.

[0134] Optionally, perform online parameter reconstruction based on the battery impedance change curve data to obtain optimized data for in-vehicle charger component parameters, including:

[0135] S41. Obtain real-time electrical data, and perform optimal parameter matching based on the real-time electrical data and the battery impedance change curve data to obtain primary optimized parameter data;

[0136] In one embodiment, the voltage and current data at the battery terminal during the charging process of the in-vehicle charger are collected in real time through high-precision voltage and current sensors. The high-frequency sampling module (such as the ADC module) is used to sample at a rate of more than a thousand times per second to ensure that the rapidly changing dynamic signals during the charging process can be captured. According to the internal resistance change characteristics of the battery, a known battery electrochemical model (such as the Randles circuit model) is used to describe the change of the battery impedance. The internal resistance of the battery usually changes with different temperatures, currents, and states of charge (SOC). By monitoring the charging and discharging processes of the battery, real-time impedance change data are obtained. The impedance of the battery is measured in real time using a pulse signal or frequency response analysis method (such as EIS, Electrochemical Impedance Spectroscopy) to obtain the change curve of the battery impedance over time. Combine the real-time battery impedance change curve with the voltage and current data at the battery terminal. According to the change law of the battery impedance, use it as a constraint condition to match with the electrical data of the in-vehicle charger, and calculate the charger parameters suitable for the current working state. For example, according to the change of the internal resistance of the battery, adjust the output voltage and current of the charger to improve the charging efficiency and avoid overcharging. Use an optimization algorithm (such as genetic algorithm, particle swarm optimization, least squares method, etc.) to match the parameters. Input variables such as the impedance change, output current, and voltage of the battery into the optimization algorithm to obtain the preliminary optimized parameters of the charger components (such as switching frequency, capacitance, inductance, etc.). Through optimal parameter matching, primary optimized parameter data are obtained. For example, set the charging current, charging voltage, and parameters of other components (such as the switching frequency of the MOSFET) suitable for the current battery state. These primary parameters can be used as the basis for preliminary adjustment during the real-time charging process.

[0137] S42. Perform real-time acceleration calculation and parameter adjustment based on the primary optimized parameter data to obtain secondary optimized parameter data;

[0138] In one embodiment, the primary optimization parameter data is calculated in real time through a simulation model of the in-vehicle charger (including electrical and thermal models), simulating various dynamic situations that may occur during the actual operation of the charger, such as battery voltage fluctuations, current peaks, etc. A real-time computing framework (such as the real-time simulation module of MATLAB Simulink) is used to accelerate the simulation to ensure accurate results can be obtained in a relatively short time. The optimization parameters of the charger components are quickly calculated through parallel computing and GPU acceleration technologies. For example, GPU parallel computing is performed using frameworks such as CUDA and OpenCL, thus greatly improving the computing speed. According to the results of the accelerated calculation, the key parameters of the in-vehicle charger (such as switching frequency, current peak, filter capacitor, etc.) are adjusted to ensure that the operating states of the charger components are within the optimized range during the charging process. For example, if the simulation results show that a certain parameter will cause excessive heating, its value is adjusted in a timely manner. The charging strategy is adjusted in real time according to the calculation results. For example, during the charging process, as the battery impedance changes, the charging current or voltage can be automatically adjusted, thereby extending the battery life and improving the charging efficiency. Based on the results of real-time acceleration calculation and adjustment, more accurate secondary optimization parameters are obtained. For example, it includes further refined battery charging curves, current waveforms, charging times, etc.

[0139] S43. Continuously feedback and adjust according to the secondary optimization parameter data to obtain the optimized data of the in-vehicle charger component parameters.

[0140] In one embodiment, by real-time monitoring the operating states of the battery and the charger, combined with the aforementioned optimization parameters, various parameters during the charging process are continuously feedback and adjusted. It is implemented through an embedded control system that real-time feedbacks the state information of the battery (such as SOC, impedance, etc.) in each charging cycle and automatically adjusts the output of the charger. Through predefined control algorithms, the charger can automatically adjust its working parameters according to the real-time state and charging requirements of the battery. For example, algorithms such as fuzzy control and PID control are used to adjust the charging current and voltage according to the state of the battery. According to the performance of the battery during multiple charging processes, the working parameters of the charger are gradually adjusted. For example, as the number of charging times increases, the internal resistance of the battery changes, and the charger dynamically adjusts the working voltage, current, etc. to ensure the best charging effect. Analyze the stability of the system to ensure that the working state of the charger after each adjustment can operate stably without imposing excessive load or damage on the battery or charger components. Through continuous feedback adjustment, the optimal in-vehicle charger component parameter data is finally obtained. It includes parameters such as the final charging current, voltage, switching frequency, filter capacitor, battery terminal voltage, etc., improving the charging efficiency, extending the battery life, and ensuring the safety and stability of the charging process.

[0141] Optionally, obtaining the physical parameter data of the in-vehicle charger PCB and constructing a multi-physics field model based on the physical parameter data includes:

[0142] Obtaining the physical parameter data of the in-vehicle charger PCB, where the physical parameter data includes IV characteristic curve data, on-resistance data, B-H curve data of magnetic components, and winding AC resistance;

[0143] In one embodiment, a source measurement unit (SMU) or a power analyzer is used to perform current-voltage (IV) characteristic tests on the power semiconductor devices (MOSFET, diode) of the in-vehicle charger. Record the conduction characteristics, current leakage characteristics, avalanche voltage characteristics, etc. of the MOSFET or diode at different temperatures. Apply a certain gate drive voltage under different temperature conditions and measure the source-drain on-resistance of the MOSFET. Use a temperature control platform (such as a hot stage) to measure at different temperature points, usually in the range of -40°C to 150°C, for electrical model construction to determine the relationship between temperature and conduction loss. Use a magnetic analyzer (such as an LCR meter, vector network analyzer) to measure the hysteresis loop (B-H curve) of the magnetic core material. To describe the non-linear permeability characteristics of transformers and inductors, so as to accurately simulate inductance, current ripple, and core loss. Use a spectrum analyzer or vector network analyzer to measure the AC resistance (R_AC) of the transformer and inductor windings at different frequencies. To analyze the skin effect and proximity effect under high-frequency currents and optimize the winding structure to reduce AC loss.

[0144] Perform temperature piecewise fitting according to the physical parameter data to obtain electrical characteristic data;

[0145] In one embodiment, since the electrical characteristics of devices such as MOSFET and inductor change with temperature, piecewise fitting is required to describe their temperature dependence. Use polynomial fitting or exponential fitting to fit the curve of on-resistance changing with temperature, fit the B-H curve data, and extract the permeability (μ) at different temperatures. Fit the winding AC resistance data to construct a frequency-temperature dependence relationship. Obtain a set of temperature-dependent electrical characteristic data for electrical simulation.

[0146] Perform power consumption junction temperature mapping according to the physical parameter data to obtain thermal characteristic data;

[0147] In one embodiment, establish the relationship between power consumption and junction temperature for thermal modeling. Use a finite element analysis (FEM) tool (such as ANSYS Icepak) to calculate the influence of the power consumption of each component on the junction temperature, and obtain the power consumption-junction temperature mapping relationship data for subsequent thermal model construction.

[0148] Construct the electrical model of the MOSFET device based on the electrical characteristic data to obtain the electrical model:

[0149] In one embodiment, an electrical model of the MOSFET is established for electromagnetic simulation. Based on the data of piecewise fitting of temperature, a SPICE equivalent model of the MOSFET is created, and the parameters are adjusted to match the measured IV characteristics. The equivalent capacitance and equivalent inductance are established using the Cauer model or the Foster model. Calculate the parasitic parameters of the MOSFET (such as gate charge, gate-drain capacitance, etc.).

[0150] Construct the thermal model according to the thermal characteristic data to obtain the thermal model:

[0151] In one embodiment, a thermal network model is established to describe the heat conduction and heat radiation characteristics of components such as MOSFETs. The finite difference method (FDM) or the finite element method (FEM) is used to calculate the temperature distribution. Construct the thermal network model , where is the MOSFET junction temperature, is the total power loss, is the thermal resistance, is the base of the natural logarithm, is the time, is the thermal time constant, the heat transfer path of junction temperature - PCB - heat sink - external air.

[0152] Solve the thermal network model according to the thermal model and the power loss data in the electrical model to obtain the junction temperature data;

[0153] In one embodiment, calculate the junction temperatures of different components during the operation of the in-vehicle charger. Combine the thermal model and the electrical model, and use software such as COMSOL and ANSYS for thermal simulation to solve the temperature distribution. Output the junction temperature data for optimizing the electrical model.

[0154] Modify the electrical model according to the junction temperature data to obtain the electrically corrected model;

[0155] In one embodiment, according to the calculated junction temperature data, perform weighted calculation , where is the temperature-dependent on-resistance, is the on-resistance at the reference temperature, is the temperature coefficient, is the MOSFET junction temperature, and adjust the IV characteristic parameters of the MOSFET.

[0156] Perform proper orthogonal decomposition on the thermal model to obtain the reduced-order model;

[0157] In one embodiment, use POD decomposition: , where is the distribution of the thermal field over time and space, is the mode index, representing the index of the eigenmode, is the number of retained modes, the selected number of reduced-order eigenmodes, is the time-dependent coefficient, reflecting the weight coefficient of the mode over time, is the spatial eigenfunction, representing the spatial distribution of the main heat transfer mode. Perform dimensionality reduction on the high-dimensional thermal model. Calculate the main eigenmodes: , where is the left singular matrix, representing the main spatial modes of the system, is the diagonal matrix of singular values, representing the energy distribution. The larger the singular value, the more important the mode, is the right singular matrix, representing the time-related information, is the input matrix, representing the temperature field data matrix of the thermal model, is the singular value decomposition, calculating the singular value decomposition of the matrix to extract the most important modes. Establish the reduced-order state-space model: , where is the derivative of the state variable, is the reduced-order system matrix, the system dynamics matrix after reduction, is the state variable, the vector of state variables after reduction, is the input matrix, the input matrix after reduction, is the input variable of the system, such as the heat source power, is the output variable of the system, such as the temperature measurement value, is the output matrix after reduction.

[0158] Package the electrical correction model and the reduced-order model to obtain the multi-physics field model.

[0159] In one embodiment, use the ROM framework to integrate different sub-models into a simulation framework, integrating electrical and thermal models to form a complete multi-physics field simulation framework. Use Matlab Simulink + Simscape to integrate multi-physics field models, such as electrical models (SPICE / Simulink), thermal models (COMSOL / Icepak), and magnetic models (JMAG / Maxwell). Establish a simulation process, such as input power, calculate electrical losses, calculate heat flux distribution, update electrical parameters, and output system simulation results. Build a data transmission interface, such as electro-thermal coupling: , thermal-electric feedback: , where is the power loss, such as the total power loss (W) of MOSFET, inductor, and transformer, is the MOSFET junction temperature, the MOSFET junction temperature (°C) varying with time, is the on-resistance, the temperature-dependent on-resistance of the MOSFET (Ω), is the saturation current, the saturation current of the MOSFET (A).

[0160] Optionally, the electrical characteristic data includes first electrical characteristic data, second electrical characteristic data, third electrical characteristic data, fourth electrical characteristic data, and fifth electrical characteristic data. The temperature segmentation fitting based on the physical parameter data to obtain the electrical characteristic data includes:

[0161] Performing thermal characteristic extraction based on the in-vehicle charger thermal data to obtain thermal characteristic data;

[0162] In one embodiment, an infrared thermal imager or a thermocouple is used to measure the temperature fields of the main components (MOSFET, inductor, transformer, etc.) of the in-vehicle charger in different operating states. The measurement points include the MOSFET junction temperature, the PCB copper wire temperature, the magnetic component temperature, and the air convection temperature. The thermal resistance is calculated using the power consumption-temperature relationship ( ): , where is the thermal resistance, is the MOSFET junction temperature, is the ambient temperature, the air temperature around the in-vehicle charger (°C), is the total power loss, the total power loss generated by the MOSFET (W).

[0163] Performing on-state voltage extraction, saturation current extraction, and switching time extraction based on the IV characteristic curve data to obtain on-state voltage data, saturation current data, and switching time data respectively;

[0164] In one embodiment, key electrical characteristic data including on-state voltage, saturation current, and switching time are extracted from the IV curve. The MOSFET is subjected to an IV test using a source measurement unit (SMU): extracting the on-state voltage ( ), extracting the saturation current ( ). Measuring the switching time, and using an oscilloscope to capture the MOSFET turn-on / turn-off waveforms: obtaining the rise time ( ), obtaining the fall time ( ).

[0165] Performing characteristic curve fitting on the on-state voltage data, saturation current data, and switching time data according to the thermal characteristic data to obtain the first electrical characteristic data;

[0166] In one embodiment, using the linear regression method to fit the relationship between the on-state voltage and temperature: , where is the temperature-related threshold voltage, the threshold voltage (V) of the MOSFET at temperature ; is the threshold voltage at the reference temperature, the threshold voltage (V) at the reference temperature such as 25 °C; is the temperature coefficient, a coefficient reflecting the influence of temperature on the threshold voltage, is the device temperature, is the reference temperature, and an exponential decay model is used for the temperature fitting of the saturation current: where is the temperature-related saturation current, is the saturation current at the reference temperature, is the natural exponential, is the exponential decay coefficient, is the device temperature, is the reference temperature, and a second-order polynomial fitting is used for the temperature fitting of the switching time, where is the temperature-related switching time, is the second-order coefficient, the coefficient controlling the temperature square term, is the device temperature, is the first-order coefficient, the coefficient controlling the linear temperature term, is the constant term, controlling the temperature-independent offset.

[0167] Based on the thermal characteristic data, temperature fitting is performed on the on-resistance data to obtain the second electrical characteristic data;

[0168] In one embodiment, a model of the MOSFET on-resistance varying with temperature is established, and the MOSFET on-resistance at different temperatures is measured using the four-probe method where is the temperature-related on-resistance, the on-resistance (Ω) of the MOSFET at temperature T, is the drain-source voltage, the MOSFET drain-source voltage (V), is the drain current, the MOSFET drain current (A). A second-order fitting of the temperature dependence is used: ; is the temperature-related on-resistance, is the on-resistance at the reference temperature, is the first-order temperature coefficient, used for fitting in the medium and low temperature ranges, is the device temperature, is the second-order temperature coefficient, mainly affecting the resistance change in the high temperature region, and the coefficient is obtained by fitting empirical data or current data.

[0169] The power loss is calculated based on the turn-on voltage data, saturation current data, and switching time data to obtain the power loss data;

[0170] In one embodiment, the power loss is calculated according to the turn-on voltage, saturation current, and switching time. Calculate the loss of the MOSFET in the on state: , where is the conduction loss, the power loss (W) of the MOSFET in the on state, is the square of the drain current, the square of the MOSFET drain current ( ), is the on-resistance, the on-resistance of the MOSFET (Ω). The standard loss mathematical model is used to calculate the switching loss: , where is the switching loss, is the drain-source voltage, the MOSFET drain-source voltage (V), is the drain current, the drain current of the MOSFET (A), is the turn-on time of the MOSFET (s), is the turn-off time of the MOSFET (s), is the switching frequency of the MOSFET (Hz).

[0171] The switching loss is fitted based on the thermal characteristic data and the power loss data to obtain the third electrical characteristic data;

[0172] In one embodiment, a mathematical model of the MOSFET switching loss varying with temperature is established, and the exponential regression model is used to fit the switching loss-temperature relationship , where is the temperature-dependent switching loss, is the switching loss at the reference temperature, is the natural exponent, is the temperature gain coefficient, is the device temperature, is the reference temperature.

[0173] The B-H curve data of the magnetic component is segmented and exponentially fitted based on the thermal characteristic data to obtain the fourth electrical characteristic data;

[0174] In one embodiment, a temperature-dependent magnetic model of the transformer and inductor is established, and the B-H curve is segmented and exponentially fitted for the core loss, , where is the temperature-dependent magnetic induction intensity, is the magnetic induction intensity at the reference temperature, is the natural exponent, is the temperature attenuation coefficient, is the device temperature, is the reference temperature.

[0175] Based on the thermal characteristic data and the winding AC resistance data, perform temperature-frequency variation fitting to obtain the fifth electrical characteristic data.

[0176] In one embodiment, power-law regression fitting is used to fit the AC resistance temperature-frequency relationship, , where is the AC resistance related to temperature and frequency, is the AC resistance at the reference temperature and frequency, is the frequency gain factor, is the frequency power-law term, and the power-law relationship of frequency reflects the influence of the skin effect and the proximity effect, is the natural exponent, is the temperature exponential growth coefficient, is the device temperature, is the reference temperature.

[0177] Optionally, the mapping of power consumption to junction temperature based on the physical parameter data to obtain the thermal characteristic data includes:

[0178] Perform transient power consumption decomposition based on the physical parameter data and the electrical characteristic data to obtain the time-varying power consumption distribution data;

[0179] In one embodiment, extract the electrical loss data, such as the MOSFET conduction loss: , where is the MOSFET conduction loss, is the square of the MOSFET drain current, is the MOSFET on-resistance; the MOSFET switching loss: , where is the MOSFET switching loss, is the MOSFET drain-source voltage, is the MOSFET drain current, is the MOSFET turn-on time, is the MOSFET turn-off time, is the MOSFET switching frequency; the transformer core loss: , is the core loss, is the loss coefficient, is the switching frequency exponential term, reflecting the power-law relationship of the core loss varying with the switching frequency, is the exponential coefficient, the influence exponent of the frequency on the core loss, is the magnetic induction intensity exponential term, reflecting the change of the core loss with the magnetic induction intensity, is the exponential coefficient, the influence exponent of magnetic induction intensity on core loss. Decompose the high-frequency switching loss: , is the high-frequency switching loss, is the harmonic component index, is the harmonic order, is the harmonic amplitude, is the resonant frequency, is the time, is the phase angle (rad) of the th harmonic. Using time-varying signal analysis, record the power consumption fluctuations at different time points.

[0180] Construct a three-dimensional thermal impedance network model based on the physical parameter data and the time-varying power consumption distribution data to obtain a three-dimensional thermal impedance network model;

[0181] In one embodiment, transistor thermal network modeling: , where is the transient thermal impedance, is the thermal resistance component index, is the number of thermal network layers, is the thermal resistance of the th layer, is the natural exponent, is the time, is the thermal time constant of the th layer. Use the finite element method (FEM) to calculate the horizontal thermal diffusion of the PCB copper layer: , where is the heat conduction power, is the thermal conductivity, is the temperature gradient, calculate the heat flow path from PCB to heat sink, and analyze the air-cooling / liquid-cooling scheme.

[0182] Conduct transient thermal field simulation based on the three-dimensional thermal impedance network model to obtain transient thermal field data;

[0183] In one embodiment, based on the three-dimensional thermal impedance network model, conduct thermal field simulation and analyze the temperature change during the operation of the charger. Set the simulation boundary conditions, input the power consumption data (from transient power consumption decomposition), set the ambient temperature ( ), and the air convection heat dissipation parameters (wind speed, heat dissipation coefficient). Solve the steady-state thermal distribution: , where is the steady-state temperature distribution, is the thermal power input, is the thermal conductivity, is the heat dissipation area, is the ambient temperature. Solve the transient temperature change: , where is the material density ( ), is the specific heat capacity of the material ( ), is the thermal diffusion operator, i.e., the heat conduction term, which describes the spatial diffusion of heat flow, is the thermal conductivity of the material, is the spatial change rate of temperature, is the device power consumption, and transient simulation is performed using COMSOL Multiphysics / ANSYS Icepak. Extract the MOSFET junction temperature curve: , where is the MOSFET junction temperature, is the MOSFET junction temperature function, and the junction temperature is related to the power loss , thermal resistance relation function.

[0184] Perform transfer learning calibration based on the transient thermal field data to obtain thermal property data.

[0185] In one embodiment, the experimental measurement data is compared with the simulation data, and the transfer learning is used to optimize the thermal model. A transfer learning model is constructed using a neural network (DNN) + Gaussian process regression (GPR). The simulation temperature data is input, and the experimental temperature measurement data is output. Through the loss function , where is to minimize the loss function, is the thermal model parameter to be optimized, is the temperature data (°C) obtained through transient thermal field simulation, is the real temperature data (°C) measured by an infrared thermal imager or a thermocouple. After training, update the thermal resistance parameter: , where is the thermal resistance optimized by transfer learning, is the thermal resistance in the initial simulation model (°C / W), is the thermal resistance correction amount updated after transfer learning (°C / W).

[0186] Optionally, constructing a three-dimensional thermal impedance network model according to the physical parameter data and the time-varying power consumption distribution data to obtain a three-dimensional thermal impedance network model, including:

[0187] Model the thermal behavior of the transistor according to the physical parameter data and the time-varying power consumption distribution data to obtain a transistor thermal behavior model;

[0188] In one embodiment, based on physical parameter data and time-varying power consumption distribution, a thermal behavior model of power semiconductors such as MOSFETs and IGBTs is established to describe the relationship between their power loss and junction temperature change. The thermal conductivity of MOSFET chips (such as Si, SiC, GaN) is obtained using a material database. For example, = 148 W / (m·K). For GaN: = 230 W / (m·K), and for SiC: = 350 W / (m·K). Specific heat capacity ( ): Calculate the specific heat capacity of the chip material. For example, the specific heat capacity of Si: = 700 J / (kg·K). The junction-case thermal resistance is obtained through experiments or data sheets: , where is the junction-case thermal resistance, the thermal resistance from the transistor junction to the case, is the junction-case temperature difference, the temperature difference (°C) between the transistor junction temperature and the case temperature, is the device power loss.

[0189] Establish a transistor thermal network model: , where is the transient thermal impedance, the thermal impedance (°C / W) of the transistor at time , is the thermal resistance component index, is the number of layers in the thermal network, is the thermal resistance of the th layer, is the natural exponent, is the time, is the thermal time constant of the th layer. Steady-state thermal distribution: , is the steady-state junction temperature, the junction temperature (°C) of the MOSFET / IGBT under steady state, is the power loss, is the junction-case thermal resistance, is the case-air thermal resistance.

[0190] Perform PCB layer thermal behavior modeling based on physical parameter data and time-varying power consumption distribution data to obtain a PCB layer thermal behavior model;

[0191] In one embodiment, the thermal conductivity of the copper layer includes the copper layer: = 385 W / (m·K), and the FR4 insulation layer: = 0.3 W / (m·K).

[0192] Use the finite difference method (FDM) to calculate the internal temperature distribution of the PCB. The PCB can be decomposed into multiple layers, and the temperature of each layer can be calculated by the heat balance equation: , where is the thermal conductivity, and the thermal conductivity of the material ( ), determines the heat conduction ability, is the temperature at the th moment / sampling point, is the temperature at the th moment / sampling point, is the interlayer thickness, is the heat generation per unit volume. The heat flow path is calculated as the horizontal heat diffusion of the copper layer, the thermal resistance of the heat flow path from the pad to the MOSFET is calculated, and the conduction path from the PCB to the radiator is calculated.

[0193] Based on the physical parameter data and the time-varying power consumption distribution data, a structural thermal behavior model is established;

[0194] In one embodiment, the thermal behaviors of the heat sink, air convection, and package housing are calculated. The thermal conductivity of the aluminum heat sink is obtained ( = 237 W / (m·K). The convective heat transfer coefficient (h) of the heat sink is calculated: , is the convective heat transfer coefficient, is the fluid thermal conductivity, is the characteristic length, is the Nusselt number (calculated based on the Reynolds number and Prandtl number). The heat dissipation capacity of the fan is calculated , is the fan heat dissipation power, is the air mass flow rate, is the specific heat capacity of air, is the air temperature rise. The thermal resistance of the housing is calculated: , is the housing thermal resistance, is the package thickness, is the thermal conductivity of the package material, is the package surface area.

[0195] Based on the transistor thermal behavior model, the PCB layer thermal behavior model, and the structural thermal behavior model, a heat flow coupling simulation is performed to obtain a three-dimensional thermal impedance network model.

[0196] In one embodiment, combining the thermal behavior models of the transistor, PCB, and structure, a three-dimensional thermal impedance network is constructed, a heat flow coupling simulation is performed, and a three-dimensional thermal network equation is established: , where is the thermal conductance matrix, is the temperature vector, is the heat source vector. Finite element analysis (FEM) or finite difference method (FDM) is used, and simulations are performed using COMSOL, ANSYS, MATLAB PDEToolbox

[0197] Optionally, the present application further provides a system for optimizing the parameters of in-vehicle charger components based on a mathematical model, which is used to execute the method for optimizing the parameters of in-vehicle charger components based on a mathematical model as described above. The system for optimizing the parameters of in-vehicle charger components based on a mathematical model includes:

[0198] A multi-physics field joint modeling module, which is used to perform multi-physics field joint modeling according to the in-vehicle charger switching device to obtain an in-vehicle charger component simulation model;

[0199] A dynamic sensitivity analysis module, which is used to perform dynamic sensitivity analysis according to the in-vehicle charger component simulation model to obtain dynamic sensitivity matrix data;

[0200] A hierarchical MINLP solving module, which is used to perform hierarchical MINLP solving on the dynamic sensitivity matrix data to obtain battery impedance change curve data;

[0201] An online parameter reconstruction module, which is used to perform online parameter reconstruction according to the battery impedance change curve data to obtain optimized data for the parameters of in-vehicle charger components.

[0202] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended application documents rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0203] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the parameters of vehicle charger components based on a mathematical model, characterized in that: The method comprises: Based on the on-board charger switch device, multi-physics field joint modeling is performed to obtain the on-board charger component simulation model; Perform dynamic sensitivity analysis based on the on-board charger component simulation model to obtain dynamic sensitivity matrix data; Perform hierarchical mixed integer nonlinear programming on the dynamic sensitivity matrix data to obtain the battery impedance change curve data; Online parameter reconstruction is performed based on the battery impedance change curve data to obtain the parameter optimization data of the on-board charger components.

2. The method according to claim 1, characterized in that: The multi-physics field joint modeling is performed according to the on-board charger switch device to obtain the on-board charger component simulation model, including: Collecting basic data of the on-board charger for the on-board charger switch device to obtain basic data of the on-board charger, wherein the basic data of the on-board charger includes electrical data of the on-board charger and thermal data of the on-board charger; The electrical thermal model of the device is constructed according to the electrical data and thermal data of the on-board charger to obtain the electrical thermal model of the device; Obtain the physical parameter data of the on-board charger PCB and build a multi-physics field model based on the physical parameter data; The overall simulation model of the on-board charger is obtained by performing simulation calculations based on the multi-physics field model and the device electrical and thermal model.

3. The method according to claim 1, characterized in that The dynamic sensitivity analysis is performed according to the vehicle charger component simulation model to obtain dynamic sensitivity matrix data, including: Perform sensitivity analysis based on the on-board charger component simulation model to obtain dynamic sensitivity data; Thermal network modeling is performed based on the vehicle charger component simulation model to obtain dynamic temperature response data; The constraint matrix is ​​constructed according to the dynamic sensitivity data and the dynamic temperature response data to obtain complete dynamic constraint data.

4. The method according to claim 1, characterized in that: The step of performing hierarchical mixed integer nonlinear programming on the dynamic sensitivity matrix data to obtain battery impedance change curve data includes: Perform top-level mixed integer nonlinear programming on the dynamic sensitivity matrix data to obtain top-level device combination data; Perform underlying mixed integer nonlinear programming on dynamic sensitivity matrix data to obtain continuous optimization data; Generate time-varying constraints according to dynamic sensitivity matrix data to obtain time-varying constraint data; The top-level device combination data and the continuous optimization data are solved according to the time-varying constraint data to obtain the battery impedance change curve data.

5. The method according to claim 1, characterized in that The online parameter reconstruction is performed according to the battery impedance change curve data to obtain the vehicle charger component parameter optimization data, including: Acquire real-time electrical data, and perform optimal parameter matching based on the real-time electrical data and battery impedance change curve data to obtain primary optimization parameter data; Perform real-time accelerated calculation and parameter adjustment based on primary optimization parameter data to obtain secondary optimization parameter data; Continuous feedback adjustment is performed based on the secondary optimization parameter data to obtain the vehicle charger component parameter optimization data.

6. The method according to claim 2, characterized in that The obtaining of physical parameter data of the on-board charger PCB and constructing a multi-physics field model according to the physical parameter data includes: Obtaining physical parameter data of the on-board charger PCB, wherein the physical parameter data includes IV characteristic curve data, on-resistance data, BH curve data of magnetic components, and winding AC resistance; Perform temperature segment fitting according to physical parameter data to obtain electrical characteristic data; Power consumption and junction temperature mapping are performed based on physical parameter data to obtain thermal characteristic data; The electrical property model of the MOSFET device is constructed according to the electrical characteristic data to obtain the electrical property model: The thermal model is constructed according to the thermal characteristic data to obtain the thermal model: Solve the thermal network model based on the power loss data in the thermal model and electrical model to obtain the junction temperature data; The electrical property model is modified according to the junction temperature data to obtain an electrical property modification model; Perform intrinsic orthogonal decomposition on the thermal model to obtain a reduced-order model; The electrical correction model and the reduced-order model are packaged to obtain a multi-physics field model.

7. The method according to claim 6, characterized in that The electrical characteristic data include first electrical characteristic data, second electrical characteristic data, third electrical characteristic data, fourth electrical characteristic data and fifth electrical characteristic data. The first electrical characteristic data is characteristic data of on-state voltage, saturation current and switching time varying with temperature. The second electrical characteristic data is characteristic data of on-state resistance varying with temperature. The third electrical characteristic data is characteristic data of switching loss varying with temperature. The fourth electrical characteristic data is characteristic data of magnetic induction intensity of magnetic element varying with temperature. The fifth electrical characteristic data is characteristic data of winding AC resistance varying with frequency and temperature. The electrical characteristic data are obtained by performing temperature segmented fitting according to the physical parameter data, including: Extract thermal characteristics based on the thermal data of the on-board charger to obtain thermal characteristic data; Perform on-state voltage extraction, saturation current extraction and switching time extraction according to IV characteristic curve data, and obtain on-state voltage data, saturation current data and switching time data respectively; Performing characteristic curve fitting on the on-voltage data, the saturation current data, and the switching time data according to the thermal characteristic data to obtain first electrical characteristic data; Performing temperature fitting on the on-resistance data according to the thermal characteristic data to obtain second electrical characteristic data; Calculate the power loss according to the on-voltage data, the saturation current data and the switching time data to obtain the power loss data; Perform switching loss fitting according to the thermal characteristic data and the power loss data to obtain third electrical characteristic data; Performing segmented exponential fitting on the BH curve data of the magnetic element according to the thermal characteristic data to obtain fourth electrical characteristic data; The fifth electrical characteristic data is obtained by fitting the temperature-frequency variation according to the thermal characteristic data and the winding AC resistance data.

8. The method according to claim 6, characterized in that The power consumption junction temperature mapping is performed according to the physical parameter data to obtain thermal characteristic data, including: Decompose transient power consumption based on physical parameter data and electrical characteristic data to obtain time-varying power consumption distribution data; A three-dimensional thermal impedance network model is constructed according to the physical parameter data and the time-varying power consumption distribution data to obtain a three-dimensional thermal impedance network model; Perform transient thermal field simulation based on the three-dimensional thermal impedance network model to obtain transient thermal field data; Transfer learning calibration is performed based on transient thermal field data to obtain thermal characteristic data.

9. The method according to claim 8, characterized in that The three-dimensional thermal impedance network model is constructed according to the physical parameter data and the time-varying power consumption distribution data to obtain the three-dimensional thermal impedance network model, including: Modeling the thermal behavior of transistors based on physical parameter data and time-varying power consumption distribution data to obtain a thermal behavior model of transistors; The thermal behavior model of the PCB layer is modeled according to the physical parameter data and the time-varying power consumption distribution data to obtain the thermal behavior model of the PCB layer; Structural thermal behavior modeling is performed according to physical parameter data and time-varying power consumption distribution data to obtain a structural thermal behavior model; According to the transistor thermal behavior model, PCB layer thermal behavior model and structural thermal behavior model, heat flow coupling simulation is performed to obtain a three-dimensional thermal impedance network model.

10. A vehicle charger component parameter optimization system based on mathematical model, characterized in that: Used to execute the vehicle charger component parameter optimization method based on mathematical model as claimed in claim 1, the vehicle charger component parameter optimization system based on mathematical model comprises: A multi-physics joint modeling module is used to perform multi-physics joint modeling based on the on-board charger switch device to obtain a simulation model of the on-board charger components; A dynamic sensitivity analysis module is used to perform dynamic sensitivity analysis based on the vehicle charger component simulation model to obtain dynamic sensitivity matrix data; A hierarchical mixed integer nonlinear programming solution module is used to perform hierarchical mixed integer nonlinear programming solution on dynamic sensitivity matrix data to obtain battery impedance change curve data; The online parameter reconstruction module is used to perform online parameter reconstruction according to the battery impedance change curve data to obtain the parameter optimization data of the vehicle charger components.

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