An electric vehicle capacitor thermal management method and system based on intelligent detection

Abstract

The application relates to the technical field of electric vehicle thermal management, and particularly discloses an electric vehicle capacitor thermal management method and system based on intelligent detection, which utilizes the inherent electrical dynamic characteristics of a direct-current support capacitor in a super-fast charging scene, collects and analyzes the current flowing through the capacitor and the voltage across the capacitor, dynamically calculates the current equivalent dynamic resistance of the capacitor, directly reflects the most core heat generation capacity in the capacitor, and can be updated in real time along with the aging of the capacitor, overcomes the inaccuracy problem caused by the fact that the internal resistance changes due to capacitor aging and the thermal management system still uses fixed parameters for temperature estimation, and thus obtains the real heat generation characteristics of the direct-current support capacitor, thereby providing basic data for subsequent internal core temperature estimation and cooling strategy decision-making, and solving the problem that the heat generation of an aging capacitor during super-fast charging is difficult to accurately predict.

Description

Technical Field

[0001] This application relates to the field of electric vehicle thermal management technology, and more specifically, to an electric vehicle capacitor thermal management method and system based on intelligent detection. Background Technology

[0002] In the field of electric vehicles, especially during high-power super-fast charging, the DC support capacitor is a key component whose stability and lifespan are severely affected by internal heat generation. Due to the capacitor's internal resistance, it generates a large amount of heat during operation. If the temperature exceeds the safe range, it will not only accelerate capacitor aging and shorten its lifespan, but may also lead to malfunctions or even safety hazards.

[0003] Existing thermal management systems typically control the cooling system by installing temperature sensors near the capacitor casing to monitor the casing temperature in real time. This method provides some protection under normal charging conditions, but it has a significant delay. This is because the capacitor's core is the primary heat source, and heat conduction from the inside to the outer casing takes time. By the time the external sensor detects an excessive temperature, the actual core temperature inside the capacitor may have already far exceeded the safety limit.

[0004] To address the aforementioned delay issues, some improvements have introduced methods for calculating and estimating temperature. These methods combine information such as charging current, ambient temperature, and capacitor heat dissipation capacity to calculate the real-time core temperature inside the capacitor using pre-defined rules, enabling more proactive temperature management. However, this calculation method, which relies on fixed parameters, faces challenges throughout the vehicle's lifespan. When initially setting the calculation rules, key information about the capacitor's thermal characteristics (such as internal resistance) is determined based on the capacitor's condition at the time of manufacture. With prolonged vehicle use and repeated charging and discharging, the capacitor inevitably ages, its performance deteriorates, and its internal resistance gradually increases. An old capacitor that has been used for several years has an internal resistance several times higher than a new capacitor.

[0005] When a vehicle enters supercharging mode, the system calculates an estimated internal temperature based on the input current information using old rules, and adjusts the cooling system's operating intensity accordingly. However, in reality, due to capacitor aging, the heat generated inside the capacitor far exceeds the calculated result. The calculation rules may assume the temperature is still rising slowly within a safe range, so the cooling system may only operate at low power or even not start, while the actual core temperature of the capacitor is rising rapidly at a dangerous rate. This discrepancy between the calculation rules caused by component aging and the actual situation leads to incorrect judgments from the originally intended predictive control strategy.

[0006] There is currently no effective technical solution to the above problems. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for thermal management of electric vehicle capacitors based on intelligent detection, which solves the problem that the existing thermal management system for DC support capacitors in electric vehicles suffers from inaccurate temperature prediction due to increased internal resistance after capacitor aging, thus leading to serious thermal runaway safety risks.

[0008] To solve the above-mentioned technical problems, the solution proposed in this application is as follows: As one aspect of this application, a method for thermal management of electric vehicle capacitors based on intelligent detection is provided. This method is used to perform thermal management of the DC support capacitor of an electric vehicle undergoing super-fast charging. The method includes: Step S1: Real-time acquisition of the current signal flowing through the DC support capacitor and the voltage signal across the DC support capacitor; Step S2: Identify the response characteristics of the DC support capacitor in the current signal and the voltage signal, and determine the equivalent internal resistance of the DC support capacitor based on the response characteristics; Step S3: Calculate the real-time heating power of the DC support capacitor based on the equivalent internal resistance and the current charging current of the electric vehicle. Step S4: Input the calculated real-time heating power of the DC support capacitor into a preset heat conduction model to estimate the internal core temperature of the DC support capacitor; Step S5: Based on the estimated internal core temperature of the DC support capacitor, determine whether the cooling system needs to perform a cooling operation on the DC support capacitor.

[0009] Furthermore, step S2 includes: Step S21a: Input a preset transient electrical pulse into the electrical circuit of the DC support capacitor; Step S22a: Obtain the preset pulse current value when the transient electrical pulse passes through the DC support capacitor and the voltage response value between the two ends of the DC support capacitor when the pulse passes through the DC support capacitor from the collected current signal and voltage signal. Step S23a: Calculate the equivalent internal resistance of the DC support capacitor based on the ratio of the voltage response value to the pulse current value.

[0010] Furthermore, step S2 includes: Step S21b: Perform digital filtering on the current signal and voltage signal to separate the high-frequency ripple signal generated by the power converter switching action of the electric vehicle during super-fast charging from the current signal and voltage signal, wherein the high-frequency ripple signal includes high-frequency current ripple and high-frequency voltage ripple. Step S22b: Obtain the voltage amplitude, current amplitude, and phase difference between the high-frequency current ripple and the high-frequency voltage ripple at a set frequency; Step S23b: Calculate the equivalent internal resistance of the DC support capacitor based on the voltage amplitude, current amplitude, and the phase difference between the high-frequency current ripple and the high-frequency voltage ripple.

[0011] Furthermore, in step S23b, the equivalent internal resistance of the DC support capacitor is calculated according to the following formula: R_a = (V_a / I_a) * cos(φ); Wherein, V_a is the voltage amplitude, I_a is the current amplitude, φ is the phase difference between the high-frequency current ripple and the high-frequency voltage ripple, and R_a is the equivalent internal resistance.

[0012] Furthermore, step S4 includes: Step S41: Obtain the current ambient temperature of the electric vehicle, the thermal capacity of the DC support capacitor, and the thermal resistance of the DC support capacitor. Step S42: Input the ambient temperature value, the thermal capacity of the DC support capacitor and the thermal resistance of the DC support capacitor, together with the real-time heating power, into the preset heat conduction model and calculate the internal core temperature change rate of the DC support capacitor. Step S43: Identify the initial temperature value of the DC support capacitor when the electric vehicle enters super-fast charging, and calculate the predicted value of the internal core temperature of the DC support capacitor based on the internal core temperature change rate of the DC support capacitor.

[0013] Furthermore, in step S42, the internal core temperature change rate of the DC support capacitor, obtained by inputting a preset heat conduction model, is expressed by the following formula: dT_core / dt=(P_heat-(T_core-T_amb) / R_th) / C_th; Where T_amb is the current ambient temperature of the electric vehicle, T_core is the real-time internal core temperature of the DC support capacitor, R_th is the thermal resistance of the DC support capacitor, C_th is the thermal capacitance of the DC support capacitor, and P_heat is the real-time heating power.

[0014] Furthermore, after step S43, the method further includes: Step S44: Read the surface temperature measured by the temperature sensor located outside the DC support capacitor; Step S45: Calculate the temperature difference between the surface temperature and the predicted internal core temperature of the DC support capacitor; Step S46: When the temperature difference is greater than the preset thermal conductivity difference range threshold, the predicted value of the internal core temperature of the DC support capacitor is corrected using a preset correction factor.

[0015] Furthermore, step S5 includes: Step S51: Compare the rate of change of the internal core temperature of the DC support capacitor with a preset heating rate threshold, and compare the predicted value of the internal core temperature of the DC support capacitor with a preset warning temperature threshold. Step S52: When the rate of change of the internal core temperature of the DC support capacitor exceeds the preset heating rate threshold and the predicted value of the internal core temperature of the DC support capacitor reaches the preset warning temperature threshold, it is determined that the cooling system needs to perform a cooling operation on the DC support capacitor.

[0016] Furthermore, after step S52, the method further includes: Step S53: Calculate the difference between the predicted value of the internal core temperature of the DC support capacitor and the set target value of the internal core temperature of the DC support capacitor, wherein the target value of the internal core temperature of the DC support capacitor is lower than the warning temperature threshold. Step S54: Based on the difference, a cooling power demand signal is generated using a preset proportional-integral-differential model; Step S55: Use the cooling power demand signal and convert it into a speed control command for the cooling fan or coolant pump in the cooling system.

[0017] As a second aspect of this application, an intelligent detection-based electric vehicle capacitor thermal management system is provided. This system is used for thermal management of the DC support capacitor of an electric vehicle undergoing super-fast charging. The system includes: A signal acquisition module is used to acquire in real time the current signal flowing through the DC support capacitor and the voltage signal across the DC support capacitor. An equivalent internal resistance determination module is used to identify the response characteristics of the DC support capacitor in the current signal and the voltage signal, and determine the equivalent internal resistance of the DC support capacitor based on the response characteristics. A real-time heat generation power calculation module is used to calculate the real-time heat generation power of the DC support capacitor based on the equivalent internal resistance and the current charging current of the electric vehicle. An internal core temperature estimation module is used to input the calculated real-time heating power of the DC support capacitor into a preset heat conduction model to estimate the internal core temperature of the DC support capacitor. A cooling operation judgment module is used to determine whether the cooling system needs to perform a cooling operation on the DC support capacitor based on the estimated internal core temperature of the DC support capacitor.

[0018] As can be seen from the above, this application provides a method and system for thermal management of electric vehicle capacitors based on intelligent detection. By utilizing the inherent electrical dynamic characteristics of DC-supported capacitors in super-fast charging scenarios, and by collecting and analyzing the current flowing through the capacitor and the voltage across its terminals, the current equivalent dynamic resistance of the capacitor is dynamically calculated. This directly reflects the core heat generation capacity of the capacitor and can be updated in real time as the capacitor ages. This overcomes the inaccuracy problem in the prior art caused by changes in internal resistance due to capacitor aging, while the thermal management system still uses fixed parameters for temperature estimation. In this way, the true heat generation characteristics of the DC-supported capacitor can be obtained, thus providing basic data for subsequent prediction of the internal core temperature and decision-making on cooling strategies. This solves the problem of difficulty in accurately predicting the heat generation of aging capacitors during super-fast charging. Attached Figure Description

[0019] Figure 1 A flowchart illustrating an electric vehicle capacitor thermal management method based on intelligent detection, provided as an embodiment of this application; Figure 2 A schematic diagram of the structure of an electric vehicle capacitor thermal management system based on intelligent detection is provided in an embodiment of this application; Figure reference numerals: 100, Electric vehicle capacitor thermal management system based on intelligent detection; 101, Signal acquisition module; 102, Equivalent internal resistance determination module; 103, Real-time heat generation power calculation module; 104, Internal core temperature estimation module; 105, Cooling operation judgment module. Detailed Implementation

[0020] To better illustrate the present invention, the invention will now be described in further detail with reference to the accompanying drawings.

[0021] It should be understood that, in order to make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0022] The following description uses at least one specific embodiment as an example. In this embodiment: Firstly, such as Figure 1 As shown, a method for thermal management of electric vehicle capacitors based on intelligent detection is provided. This method is used to perform thermal management of the DC support capacitor of an electric vehicle undergoing super-fast charging. The method includes: Step S1: Real-time acquisition of the current signal flowing through the DC support capacitor and the voltage signal across the DC support capacitor; Step S2: Identify the response characteristics of the DC support capacitor in the current signal and the voltage signal, and determine the equivalent internal resistance of the DC support capacitor based on the response characteristics; Step S3: Calculate the real-time heating power of the DC support capacitor based on the equivalent internal resistance and the current charging current of the electric vehicle. Step S4: Input the calculated real-time heating power of the DC support capacitor into a preset heat conduction model to estimate the internal core temperature of the DC support capacitor; Step S5: Based on the estimated internal core temperature of the DC support capacitor, determine whether the cooling system needs to perform a cooling operation on the DC support capacitor.

[0023] In this embodiment, the equivalent internal resistance of the DC support capacitor is dynamically updated by detecting its response characteristics in real time, thereby accurately calculating the real-time heat generation power and predicting the internal core temperature. This solves the problem of inaccurate thermal management caused by capacitor aging in traditional methods and improves the safety of electric vehicles during super-fast charging.

[0024] Specifically, in step S1, it is necessary to acquire the current signal flowing through the DC support capacitor and the voltage signal across the DC support capacitor in real time. This can be achieved by connecting a current sensor (e.g., a Hall effect sensor) in series in the electrical circuit of the DC support capacitor to acquire the current signal, and by connecting a voltage sensor (e.g., a high-precision voltage divider or an isolated voltage sensor) in parallel to acquire the voltage signal.

[0025] In step S2, the response characteristics of the DC support capacitor are identified from the acquired current and voltage signals, and the equivalent internal resistance of the DC support capacitor is determined based on the response characteristics. By analyzing the current and voltage waveforms of the DC support capacitor under normal operating conditions, its inherent response characteristics are extracted.

[0026] In step S3, the real-time heating power of the DC support capacitor is calculated based on the equivalent internal resistance determined in step S2 and the current charging current of the electric vehicle. The heating power of the DC support capacitor is mainly determined by the Joule heat loss on its equivalent internal resistance. The calculation formula is usually P_heat = I^2 * R_eq, where P_heat is the real-time heating power, I is the charging current flowing through the DC support capacitor, and R_eq is the equivalent internal resistance.

[0027] In step S4, the calculated real-time heat generation power of the DC support capacitor is input into a preset heat conduction model to predict the internal core temperature of the DC support capacitor. The heat conduction model can be a lumped parameter model, treating the DC support capacitor as an object with specific heat capacity and thermal resistance. This model can calculate the rate of change of the internal core temperature of the DC support capacitor based on the input heat generation power, ambient temperature, and parameters such as the capacitor's heat capacity and thermal resistance, thereby predicting its internal core temperature.

[0028] In step S5, based on the estimated internal core temperature of the DC support capacitor, it is determined whether the cooling system needs to perform cooling operations on the DC support capacitor. This step aims to proactively control the operation of the cooling system based on the predicted internal core temperature to prevent overheating. For example, a warning temperature threshold can be set; when the estimated internal core temperature reaches or exceeds this threshold, the cooling system is instructed to start or increase its cooling intensity. Furthermore, the rate of change of the internal core temperature can also be considered. If the temperature rises too rapidly, cooling can be initiated in advance, even before the warning temperature threshold is reached, to achieve preventative thermal management.

[0029] By acquiring current and voltage signals in real time and identifying the response characteristics of the DC-supported capacitor to determine its equivalent internal resistance, this approach accurately reflects the true heating characteristics of the capacitor under different aging conditions, making subsequent real-time heating power calculations and internal core temperature predictions more precise. For example, when capacitor aging leads to an increase in equivalent internal resistance, this application can promptly detect the change by acquiring current and voltage signals in real time and adjust the calculated heating power accordingly, thus making the internal core temperature predicted by the heat conduction model closer to the actual value. This real-time adaptive parameter update mechanism solves the problem of inaccurate thermal management caused by capacitor aging in traditional methods. Furthermore, by predicting the internal core temperature, the cooling system can respond more promptly, avoiding insufficient or excessive cooling, thereby extending the service life of the DC-supported capacitor.

[0030] In some of the above implementations, how to obtain the response characteristics of the DC support capacitor in real time and accurately calculate its equivalent internal resistance is of great importance to the accuracy and response speed of thermal management. If only conventional signal analysis is relied upon, it may be difficult to effectively cope with the complex electrical environment and rapidly changing capacitor state during super-fast charging.

[0031] In this regard, as one method for calculating the equivalent internal resistance, step S2 includes: Step S21a: Input a preset transient electrical pulse into the electrical circuit of the DC support capacitor; Step S22a: Obtain the preset pulse current value when the transient electrical pulse passes through the DC support capacitor and the voltage response value between the two ends of the DC support capacitor when the pulse passes through the DC support capacitor from the collected current signal and voltage signal. Step S23a: Calculate the equivalent internal resistance of the DC support capacitor based on the ratio of the voltage response value to the pulse current value.

[0032] Specifically, in step S21a, inputting a preset transient electrical pulse into the electrical circuit of the DC support capacitor refers to applying a brief electrical pulse signal with a specific waveform and amplitude to the DC support capacitor through an external excitation source or an internal control unit, without affecting or minimizing the impact on the normal charging process. This transient electrical pulse can be a square wave, a narrow pulse, or other transient signals, and its purpose is to excite the transient response of the DC support capacitor within a short time to facilitate subsequent measurement of its electrical characteristics.

[0033] In step S22a, the preset pulse current value when the transient electrical pulse passes through the DC support capacitor and the voltage response value between the two ends of the DC support capacitor are obtained from the collected current and voltage signals. This can be understood as real-time monitoring of the current flowing through the DC support capacitor and the voltage across it using a high-precision sensor during the application of the transient electrical pulse. The pulse current value refers to the peak current flowing through the capacitor under the action of the pulse or the current value at a specific moment, while the voltage response value refers to the voltage change across the capacitor caused by the pulse or the voltage value at a specific moment.

[0034] In practical applications, in step S23a, the equivalent internal resistance of the DC supporting capacitor is calculated based on the ratio of the voltage response value to the pulse current value. Under the action of a transient electrical pulse, the equivalent internal resistance of the DC supporting capacitor can be approximately obtained by measuring the ratio of its transient voltage response to its transient current response. For example, when a current pulse is applied, the equivalent internal resistance can be obtained by measuring the instantaneous voltage drop across the capacitor and dividing it by the pulse current value.

[0035] By actively inputting preset transient electrical pulses into the electrical circuit of the DC-DC supporting capacitor and accurately capturing its transient current and voltage response values ​​under the pulse, the equivalent internal resistance of the DC-DC supporting capacitor can be measured directly and quickly. This method avoids the limitations of relying solely on passive signal analysis under complex operating conditions. Especially in dynamic and high-power environments such as super-fast charging, active excitation can obtain clearer and more representative capacitor response characteristics. Through transient electrical pulse excitation, the internal loss characteristics of the DC-DC supporting capacitor are directly exposed, making the calculation of the equivalent internal resistance more direct and accurate, thus providing reliable basic data for subsequent heat generation power calculation and thermal management decisions.

[0036] In some preferred embodiments, when the electric vehicle enters super-fast charging mode, the thermal management system periodically injects a square-wave current pulse with a duration on the order of microseconds into the electrical circuit of the DC-DC supporting capacitor. Simultaneously with the pulse injection, high-precision current and voltage sensors synchronously acquire the current waveform flowing through the capacitor and the voltage waveform across the capacitor. The system extracts the peak value of the pulse current and the corresponding voltage response value from these waveforms. Then, by dividing the voltage response value by the pulse current value, the equivalent internal resistance of the DC-DC supporting capacitor at that moment can be calculated. This equivalent internal resistance value is immediately used to calculate the real-time heat dissipation power, thereby achieving accurate prediction and timely cooling control of the capacitor's internal core temperature.

[0037] As a second method for calculating the equivalent internal resistance, step S2 includes: Step S21b: Perform digital filtering on the current signal and voltage signal to separate the high-frequency ripple signal generated by the power converter switching action of the electric vehicle during super-fast charging from the current signal and voltage signal, wherein the high-frequency ripple signal includes high-frequency current ripple and high-frequency voltage ripple. Step S22b: Obtain the voltage amplitude, current amplitude, and phase difference between the high-frequency current ripple and the high-frequency voltage ripple at a set frequency; Step S23b: Calculate the equivalent internal resistance of the DC support capacitor based on the voltage amplitude, current amplitude, and the phase difference between the high-frequency current ripple and the high-frequency voltage ripple.

[0038] Specifically, digital filtering is performed on the current and voltage signals to remove unwanted noise components and extract the target signal. The aim is to separate the high-frequency ripple signal generated by the switching action of the electric vehicle power converter from the electrical environment. The high-frequency ripple signal is the response of the DC support capacitor to the switching action of the power converter under actual operating conditions, containing information about the capacitor's equivalent internal resistance. The digital filter can be a bandpass filter, with its center frequency set near the switching frequency of the power converter to effectively filter out DC components and noise of other frequencies, retaining only the target high-frequency ripple signal. The high-frequency ripple signal specifically includes high-frequency current ripple and high-frequency voltage ripple, representing the high-frequency current component flowing through the DC support capacitor and the high-frequency voltage component across the DC support capacitor, respectively.

[0039] Furthermore, obtaining the voltage and current amplitudes of the high-frequency ripple signal at a set frequency, as well as the phase difference between the high-frequency current ripple and the high-frequency voltage ripple, refers to extracting key parameters at a specific frequency from the high-frequency ripple signal obtained after digital filtering using signal processing algorithms (such as Fourier transform or phase-locked loop technology). The set frequency usually corresponds to the switching frequency of the power converter, at which the ripple signal is most significant and stable. The voltage and current amplitudes represent the peak or effective values ​​of the high-frequency voltage and current ripple, respectively, while the phase difference reflects the lag or lead relationship between the high-frequency voltage ripple and the high-frequency current ripple.

[0040] Therefore, based on the voltage amplitude, current amplitude, and the phase difference between the high-frequency current ripple and the high-frequency voltage ripple, the equivalent internal resistance of the DC support capacitor is calculated.

[0041] The equivalent internal resistance of the DC support capacitor is calculated according to the following formula: R_a = (V_a / I_a) * cos(φ); Wherein, V_a is the voltage amplitude, I_a is the current amplitude, φ is the phase difference between the high-frequency current ripple and the high-frequency voltage ripple, and R_a is the equivalent internal resistance.

[0042] In some of the above embodiments, for the process of estimating the internal core temperature of the DC support capacitor, step S4 can be further refined to improve the accuracy of the estimation.

[0043] Furthermore, step S4 includes: Step S41: Obtain the current ambient temperature of the electric vehicle, the thermal capacity of the DC support capacitor, and the thermal resistance of the DC support capacitor. Step S42: Input the ambient temperature value, the thermal capacity of the DC support capacitor and the thermal resistance of the DC support capacitor, together with the real-time heating power, into the preset heat conduction model and calculate the internal core temperature change rate of the DC support capacitor. Step S43: Identify the initial temperature value of the DC support capacitor when the electric vehicle enters super-fast charging, and calculate the predicted value of the internal core temperature of the DC support capacitor based on the internal core temperature change rate of the DC support capacitor.

[0044] In step S41, the ambient temperature value refers to the current external ambient temperature of the electric vehicle, which can be obtained in real time by a temperature sensor installed on the outside of the electric vehicle. The thermal capacitance and thermal resistance of the DC support capacitor are physical parameters characterizing its thermal properties. Thermal capacitance reflects the capacitor's ability to absorb or release heat, while thermal resistance reflects the degree to which heat is impeded from the capacitor's interior to the external environment. These parameters can be obtained through experimental testing or by consulting the capacitor's datasheet and are preset during system design.

[0045] Furthermore, in step S42, the preset heat conduction model is a mathematical model describing the heat transfer within the DC support capacitor and between it and the external environment. This model comprehensively considers factors such as the capacitor's real-time heating power, ambient temperature, and the capacitor's own thermal capacity and thermal resistance to calculate the rate of change of the core temperature inside the DC support capacitor over time. By inputting these parameters into the model, the upward or downward trend of the capacitor's internal temperature can be evaluated.

[0046] In step S43, identifying the initial temperature value of the DC support capacitor when the electric vehicle enters super-fast charging refers to obtaining the capacitor's initial temperature by measuring it with a temperature sensor or recording it through the system before charging begins or at the initial stage of charging. Subsequently, based on the internal core temperature change rate calculated in step S42, and combined with this initial temperature value, the predicted internal core temperature value of the DC support capacitor at subsequent time points can be obtained through integration or iterative calculation. For example, the Euler method or other numerical integration methods can be used to predict the temperature at the next moment based on the current temperature and the temperature change rate.

[0047] By refining the process of predicting the internal core temperature of a DC-DC supporting capacitor into steps such as acquiring key thermal parameters, calculating the rate of temperature change, and making predictions based on the initial temperature, accurate dynamic tracking of the capacitor's internal core temperature is achieved. Specifically, by acquiring the ambient temperature, the capacitor's thermal capacity, and thermal resistance, the necessary boundary conditions and physical properties are provided for the heat conduction model. Combined with real-time heat generation power, the heat conduction model can accurately calculate the trend of the capacitor's internal core temperature change. Based on this, by identifying the initial temperature at the start of charging and combining it with the continuous rate of temperature change, the internal core temperature of the capacitor during super-fast charging can be effectively predicted, thus avoiding the errors caused by relying solely on external surface temperature measurements or simple empirical models.

[0048] Furthermore, the internal core temperature change rate of the DC support capacitor, calculated using the preset heat conduction model, is expressed by the following formula: dT_core / dt=(P_heat-(T_core-T_amb) / R_th) / C_th; Where T_amb is the current ambient temperature of the electric vehicle, T_core is the real-time internal core temperature of the DC support capacitor, R_th is the thermal resistance of the DC support capacitor, C_th is the thermal capacitance of the DC support capacitor, and P_heat is the real-time heating power.

[0049] In some of the above embodiments, the predicted internal core temperature of the DC-supported capacitor is calculated using a preset heat conduction model and the rate of change of the internal core temperature. However, in practical applications, due to the simplification of the heat conduction model, the complexity of environmental factors, and changes in the capacitor's own characteristics, relying solely on model predictions may lead to deviations between the predicted internal core temperature and the actual situation, thus affecting the accuracy of thermal management decisions. Therefore, this application further proposes a method for correcting the predicted internal core temperature of the DC-supported capacitor to improve the accuracy of temperature estimation.

[0050] Furthermore, after step S43, the method further includes: Step S44: Read the surface temperature measured by the temperature sensor located outside the DC support capacitor; Step S45: Calculate the temperature difference between the surface temperature and the predicted internal core temperature of the DC support capacitor; Step S46: When the temperature difference is greater than the preset thermal conductivity difference range threshold, the predicted value of the internal core temperature of the DC support capacitor is corrected using a preset correction factor.

[0051] Specifically, in step S44, the temperature sensor located outside the DC support capacitor can be a thermistor, thermocouple, or infrared temperature sensor, etc., to acquire the external surface temperature of the DC support capacitor in real time. This surface temperature, as an actual measurement value, provides a reference for subsequent prediction correction. In step S45, calculating the temperature difference between the surface temperature and the predicted internal core temperature of the DC support capacitor involves comparing the predicted internal core temperature obtained through the model with the actual measured surface temperature to quantify the deviation between the two. This difference reflects the accuracy of the model prediction and the degree of deviation between the actual thermal state and the model assumptions. In practical applications, in step S46, when the temperature difference exceeds a preset thermal conductivity difference range threshold, it indicates a significant deviation between the model prediction and the actual measurement value, requiring adjustment of the prediction value. The preset thermal conductivity difference range threshold is a configurable parameter used to define the acceptable prediction error range. When this range is exceeded, the predicted internal core temperature of the DC support capacitor is corrected using a preset correction factor. The correction factor can be a fixed value, its purpose being to make the predicted value closer to the actual situation, improving the accuracy and reliability of temperature estimation.

[0052] By incorporating measurements from an external temperature sensor of the DC-supported capacitor and comparing them with the model's predicted internal core temperature, a feedback correction mechanism is established. Because heat conduction models may have limitations under complex operating conditions, leading to potentially inaccurate model predictions, it is necessary to incorporate actual measurement data for correction. By calculating the difference between the predicted and actual measurements and setting a reasonable threshold for this difference, deviations in the model prediction can be detected promptly. Once the deviation exceeds an acceptable range, a preset correction factor is used to adjust the predicted value, effectively compensating for the shortcomings of pure model predictions and making the estimated internal core temperature more closely reflect the actual thermal state of the DC-supported capacitor.

[0053] In some preferred embodiments, a specific example is given below. Assume that during super-fast charging of an electric vehicle, the predicted internal core temperature of the DC support capacitor is calculated to be 65°C using step S43. Simultaneously, a temperature sensor located outside the DC support capacitor measures its surface temperature to be 55°C in real time. At this point, the system executes step S45 to calculate the temperature difference between the surface temperature and the predicted internal core temperature, i.e., 65°C - 55°C = 10°C. A preset threshold for the thermal conductivity difference range is set to 5°C. Since the calculated 10°C is greater than the preset threshold of 5°C, it indicates a significant deviation between the model prediction and the actual measurement. Therefore, the system executes step S46 to correct the predicted internal core temperature using a preset correction factor. For example, the correction factor can be set to adjust the predicted value towards the surface temperature, or it can be corrected according to an empirical formula. Assuming the correction factor adjusts the predicted value downwards by 8°C, the corrected predicted internal core temperature is 65°C - 8°C = 57°C. In this way, even if there are deviations in the model predictions, the predicted values ​​can be effectively corrected using actual measurement data, ensuring the accuracy of thermal management decisions.

[0054] In some of the above embodiments, the determination of whether the cooling system needs to perform cooling operations on the DC support capacitor is mainly based on the estimated internal core temperature of the DC support capacitor. However, relying solely on a single temperature prediction value may fail to respond promptly to rapid increases in the capacitor's internal temperature, resulting in a cooling response lag. Therefore, this application further proposes a more refined determination mechanism.

[0055] Specifically, step S5 includes: Step S51: Compare the rate of change of the internal core temperature of the DC support capacitor with a preset heating rate threshold, and compare the predicted value of the internal core temperature of the DC support capacitor with a preset warning temperature threshold. Step S52: When the rate of change of the internal core temperature of the DC support capacitor exceeds the preset heating rate threshold and the predicted value of the internal core temperature of the DC support capacitor reaches the preset warning temperature threshold, it is determined that the cooling system needs to perform a cooling operation on the DC support capacitor.

[0056] Specifically, in step S51, the rate of change of the internal core temperature of the DC support capacitor refers to the rate at which the internal core temperature of the DC support capacitor increases or decreases per unit time, reflecting the current heating trend of the capacitor. The preset temperature rise rate threshold is a critical value set according to the capacitor's safe operation requirements and thermal management strategy, used to determine whether the temperature rise is too rapid. The predicted value of the internal core temperature of the DC support capacitor is the current value or a predicted value for the capacitor's internal core temperature in the near future, estimated based on a heat conduction model. The preset warning temperature threshold is the highest safe temperature the capacitor is allowed to reach or a warning value approaching that temperature, used to indicate whether the capacitor is about to reach a dangerous temperature. By comparing these real-time or predicted parameters with the corresponding thresholds, the thermal state of the capacitor can be preliminarily assessed.

[0057] In step S52, the determination of whether the cooling system needs to perform a cooling operation is based on the combined satisfaction of two conditions. Specifically, when the rate of change of the internal core temperature of the DC support capacitor exceeds a preset heating rate threshold, it indicates that the capacitor is heating up rapidly and there is a risk of overheating; simultaneously, when the predicted value of the internal core temperature of the DC support capacitor reaches a preset warning temperature threshold, it indicates that the actual temperature of the capacitor is approaching or has reached a dangerous level. When both conditions are met simultaneously, it is determined that the cooling system needs to perform a cooling operation on the DC support capacitor, ensuring that the triggering of the cooling operation is based on a comprehensive assessment of the capacitor's actual heat load and potential risks, avoiding unnecessary cooling and preventing insufficient cooling.

[0058] Furthermore, after step S52, the method further includes: Step S53: Calculate the difference between the predicted value of the internal core temperature of the DC support capacitor and the set target value of the internal core temperature of the DC support capacitor, wherein the target value of the internal core temperature of the DC support capacitor is lower than the warning temperature threshold. Step S54: Based on the difference, a cooling power demand signal is generated using a preset proportional-integral-differential model; Step S55: Use the cooling power demand signal and convert it into a speed control command for the cooling fan or coolant pump in the cooling system.

[0059] Specifically, in step S53, the predicted internal core temperature of the DC support capacitor refers to the internal core temperature of the capacitor estimated using the aforementioned heat conduction model. The set target internal core temperature of the DC support capacitor is a pre-defined value within the ideal temperature range that the DC support capacitor is expected to maintain during super-fast charging. This target value is typically lower than the warning temperature threshold to ensure that refined cooling control begins before the warning state is reached. By calculating the difference between these two values, the deviation between the current temperature and the desired temperature can be quantified, providing a basis for subsequent cooling control.

[0060] In step S54, the Proportional-Integral-Derivative (PID) model is a feedback control algorithm applied in industrial control. This model calculates the output signal, i.e., the cooling power demand signal, based on the proportional, integral, and derivative terms of the temperature difference (error). The proportional term reflects the magnitude of the current error, the integral term reflects the trend of error accumulation, and the derivative term reflects the rate of error change. Through the PID model, precise and dynamic adjustment of the cooling power can be achieved to quickly eliminate temperature deviations and maintain system stability.

[0061] In practical applications, in step S55, the cooling power demand signal is an abstract signal output by the PID model representing the required cooling intensity. This signal needs to be converted into speed control commands that can be recognized and executed by the specific actuators in the cooling system (such as cooling fans or coolant pumps). For example, the cooling power demand signal can be converted into a PWM (Pulse Width Modulation) signal to control the fan speed or coolant pump flow rate, thereby adjusting the cooling intensity.

[0062] By introducing a refined cooling control strategy, the limitations of simply determining whether cooling is needed are effectively addressed. Specifically, when the cooling operation judgment module determines that cooling is required, it first quantifies the deviation between the current temperature and the desired temperature by calculating the difference between the predicted internal core temperature of the DC support capacitor and the set target internal core temperature. This quantification allows for targeted cooling control. Based on this, a preset proportional-integral-differential model is used to dynamically generate a precise cooling power demand signal according to the magnitude, cumulative trend, and rate of change of the temperature deviation. Finally, by converting the cooling power demand signal into speed control commands for the cooling fan or coolant pump, the cooling intensity is controlled, thereby stabilizing the internal core temperature of the DC support capacitor near the target value and avoiding over-cooling or under-cooling.

[0063] Secondly, such as Figure 2 As shown, an intelligent detection-based electric vehicle capacitor thermal management system 100 is provided. This system is used for thermal management of the DC support capacitor of an electric vehicle undergoing super-fast charging. The system includes: The signal acquisition module 101 is used to acquire the current signal flowing through the DC support capacitor and the voltage signal across the DC support capacitor in real time. Equivalent internal resistance determination module 102 is used to identify the response characteristics of the DC support capacitor in the current signal and the voltage signal, and determine the equivalent internal resistance of the DC support capacitor based on the response characteristics. The real-time heating power calculation module 103 is used to calculate the real-time heating power of the DC support capacitor based on the equivalent internal resistance and the current charging current of the electric vehicle. An internal core temperature estimation module 104 is used to input the calculated real-time heating power of the DC support capacitor into a preset heat conduction model to estimate the internal core temperature of the DC support capacitor. Cooling operation judgment module 105 is used to determine whether the cooling system needs to perform a cooling operation on the DC support capacitor based on the estimated internal core temperature of the DC support capacitor.

[0064] As can be seen from the above, this application provides a method and system for thermal management of electric vehicle capacitors based on intelligent detection. By utilizing the inherent electrical dynamic characteristics of DC-supported capacitors in super-fast charging scenarios, and by collecting and analyzing the current flowing through the capacitor and the voltage across its terminals, the current equivalent dynamic resistance of the capacitor is dynamically calculated. This directly reflects the core heat generation capacity of the capacitor and can be updated in real time as the capacitor ages. This overcomes the inaccuracy problem in the prior art caused by changes in internal resistance due to capacitor aging, while the thermal management system still uses fixed parameters for temperature estimation. In this way, the true heat generation characteristics of the DC-supported capacitor can be obtained, thus providing basic data for subsequent prediction of the internal core temperature and decision-making on cooling strategies. This solves the problem of difficulty in accurately predicting the heat generation of aging capacitors during super-fast charging.

[0065] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit them. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure.

Claims

1. A method for thermal management of electric vehicle capacitors based on intelligent detection, wherein the method is used for thermal management of the DC support capacitor of an electric vehicle undergoing super-fast charging, characterized in that, The method includes: Step S1: Real-time acquisition of the current signal flowing through the DC support capacitor and the voltage signal across the DC support capacitor; Step S2: Identify the response characteristics of the DC support capacitor in the current signal and the voltage signal, and determine the equivalent internal resistance of the DC support capacitor based on the response characteristics; Step S3: Calculate the real-time heating power of the DC support capacitor based on the equivalent internal resistance and the current charging current of the electric vehicle. Step S4: Input the calculated real-time heating power of the DC support capacitor into a preset heat conduction model to estimate the internal core temperature of the DC support capacitor; Step S5: Based on the estimated internal core temperature of the DC support capacitor, determine whether the cooling system needs to perform a cooling operation on the DC support capacitor.

2. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 1, characterized in that, Step S2 includes: Step S21a: Input a preset transient electrical pulse into the electrical circuit of the DC support capacitor; Step S22a: Obtain the preset pulse current value when the transient electrical pulse passes through the DC support capacitor and the voltage response value between the two ends of the DC support capacitor when the pulse passes through the DC support capacitor from the collected current signal and voltage signal. Step S23a: Calculate the equivalent internal resistance of the DC support capacitor based on the ratio of the voltage response value to the pulse current value.

3. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 1, characterized in that, Step S2 includes: Step S21b: Perform digital filtering on the current signal and voltage signal to separate the high-frequency ripple signal generated by the power converter switching action of the electric vehicle during super-fast charging from the current signal and voltage signal, wherein the high-frequency ripple signal includes high-frequency current ripple and high-frequency voltage ripple. Step S22b: Obtain the voltage amplitude, current amplitude, and phase difference between the high-frequency current ripple and the high-frequency voltage ripple at a set frequency; Step S23b: Calculate the equivalent internal resistance of the DC support capacitor based on the voltage amplitude, current amplitude, and the phase difference between the high-frequency current ripple and the high-frequency voltage ripple.

4. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 3, characterized in that, In step S23b, the equivalent internal resistance of the DC support capacitor is calculated according to the following formula: R_a = (V_a / I_a) * cos(φ); Wherein, V_a is the voltage amplitude, I_a is the current amplitude, φ is the phase difference between the high-frequency current ripple and the high-frequency voltage ripple, and R_a is the equivalent internal resistance.

5. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 1, characterized in that, Step S4 includes: Step S41: Obtain the current ambient temperature of the electric vehicle, the thermal capacity of the DC support capacitor, and the thermal resistance of the DC support capacitor. Step S42: Input the ambient temperature value, the thermal capacity of the DC support capacitor and the thermal resistance of the DC support capacitor, together with the real-time heating power, into the preset heat conduction model and calculate the internal core temperature change rate of the DC support capacitor. Step S43: Identify the initial temperature value of the DC support capacitor when the electric vehicle enters super-fast charging, and calculate the predicted value of the internal core temperature of the DC support capacitor based on the internal core temperature change rate of the DC support capacitor.

6. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 5, characterized in that, In step S42, the internal core temperature change rate of the DC support capacitor, obtained by inputting a preset heat conduction model and calculating it, is expressed by the following formula: dT_core / dt=(P_heat-(T_core-T_amb) / R_th) / C_th; Where T_amb is the current ambient temperature of the electric vehicle, T_core is the real-time internal core temperature of the DC support capacitor, R_th is the thermal resistance of the DC support capacitor, C_th is the thermal capacitance of the DC support capacitor, and P_heat is the real-time heating power.

7. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 5, characterized in that, After step S43, the method further includes: Step S44: Read the surface temperature measured by the temperature sensor located outside the DC support capacitor; Step S45: Calculate the temperature difference between the surface temperature and the predicted internal core temperature of the DC support capacitor; Step S46: When the temperature difference is greater than the preset thermal conductivity difference range threshold, the predicted value of the internal core temperature of the DC support capacitor is corrected using a preset correction factor.

8. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 5, characterized in that, Step S5 includes: Step S51: Compare the rate of change of the internal core temperature of the DC support capacitor with a preset heating rate threshold, and compare the predicted value of the internal core temperature of the DC support capacitor with a preset warning temperature threshold. Step S52: When the rate of change of the internal core temperature of the DC support capacitor exceeds the preset heating rate threshold and the predicted value of the internal core temperature of the DC support capacitor reaches the preset warning temperature threshold, it is determined that the cooling system needs to perform a cooling operation on the DC support capacitor.

9. The electric vehicle capacitor thermal management method based on intelligent detection according to claim 8, characterized in that, After step S52, the method further includes: Step S53: Calculate the difference between the predicted value of the internal core temperature of the DC support capacitor and the set target value of the internal core temperature of the DC support capacitor, wherein the target value of the internal core temperature of the DC support capacitor is lower than the warning temperature threshold. Step S54: Based on the difference, a cooling power demand signal is generated using a preset proportional-integral-differential model; Step S55: Use the cooling power demand signal and convert it into a speed control command for the cooling fan or coolant pump in the cooling system.

10. A capacitor thermal management system for electric vehicles based on intelligent detection, the system being used for thermal management of the DC support capacitor of an electric vehicle undergoing super-fast charging, characterized in that, The system includes: A signal acquisition module is used to acquire in real time the current signal flowing through the DC support capacitor and the voltage signal across the DC support capacitor. An equivalent internal resistance determination module is used to identify the response characteristics of the DC support capacitor in the current signal and the voltage signal, and determine the equivalent internal resistance of the DC support capacitor based on the response characteristics. A real-time heat generation power calculation module is used to calculate the real-time heat generation power of the DC support capacitor based on the equivalent internal resistance and the current charging current of the electric vehicle. An internal core temperature estimation module is used to input the calculated real-time heating power of the DC support capacitor into a preset heat conduction model to estimate the internal core temperature of the DC support capacitor. A cooling operation judgment module is used to determine whether the cooling system needs to perform a cooling operation on the DC support capacitor based on the estimated internal core temperature of the DC support capacitor.

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