Cooperative control method, system and device of direct current liquid cooling charging pile and storage medium

Abstract

The invention relates to the technical field of direct current charging pile liquid cooling heat dissipation control. The invention provides a cooperative control method, system and device for a direct-current liquid cooling charging pile and a storage medium. The method comprises the following steps: collecting charging current, gun line temperature, power module temperature and environment temperature; respectively constructing improved prediction function control sub-models of the gun line temperature and the power module temperature, and generating corresponding liquid cooling oil flow velocity control signals; heat dissipation control is carried out on the gun line and the power module through the liquid cooling machine execution unit; actual temperature is collected in real time and compared with predicted temperature to obtain an error, a control signal is corrected according to the error, and an optimal control mode is selected by adopting a mode switching strategy; and according to the current thermal load state, dynamically distributing the weight output by each sub-model, and adjusting the control priorities of different heat dissipation objects to realize collaborative heat dissipation. The problems of unbalanced heat dissipation, response lag and low energy efficiency caused by lack of cooperative control of a plurality of heat dissipation objects in an existing direct current charging pile liquid cooling heat dissipation technology are solved.

Description

Technical Field

[0001] This invention relates to the field of liquid cooling heat dissipation control technology for DC charging piles, and more specifically, to a collaborative control method, system, device, and storage medium for DC liquid-cooled charging piles. Background Technology

[0002] With the rapid popularization of electric vehicles, the application of high-power DC charging piles is becoming increasingly widespread. To cope with the significant heat generated during high-power charging, liquid cooling technology has been widely introduced into charging pile systems, especially in the temperature control of key components such as charging gun cables and power modules.

[0003] In existing technologies, such as Chinese invention patent application CN120353274A, a multi-model adaptive control method for liquid cooler heat dissipation is proposed. This method divides the predictive function controller into multiple subspace controllers and optimizes the temperature control of the charging gun line based on a mode switching strategy, thereby improving heat dissipation efficiency and reducing energy consumption under different power requirements. However, this solution mainly focuses on the control optimization of the liquid cooler itself and does not address the coordinated heat dissipation problem among multiple heat dissipation objects (such as power modules and charging gun lines) in the charging pile system. In practical applications, there are multiple heat sources inside a DC charging pile. If only a single component is subjected to independent temperature control, it is easy to cause uneven heat dissipation, lag in response, or low energy efficiency, making it difficult to achieve the overall optimal heat dissipation performance of the entire machine. Therefore, there is an urgent need for a coordinated control method that can coordinate multiple heat dissipation units within the charging pile to improve the overall energy efficiency and intelligent level of thermal management of the system. Summary of the Invention

[0004] The purpose of this invention is to provide a collaborative control method, system, device and storage medium for DC liquid-cooled charging piles, aiming to solve the problems of uneven heat dissipation, slow response and low energy efficiency caused by the lack of collaborative control of multiple heat dissipation objects in the existing liquid cooling heat dissipation technology of DC charging piles.

[0005] This invention is achieved through the following technical solution: A collaborative control method for a DC liquid-cooled charging pile includes the following steps: Obtain current charging status data of the charging pile, including charging current, charging gun temperature, power module temperature, and ambient temperature; Based on charging condition data, a sub-model for predicting gun wire temperature and a sub-model for predicting power module temperature are constructed respectively. Each sub-model corresponds to an improved prediction function controller. The corresponding liquid cooling oil flow rate control signals are generated by the gun wire temperature prediction sub-model and the power module temperature prediction sub-model, respectively. The liquid cooling oil flow rate control signal is input to the liquid cooler execution unit to control the heat dissipation of the gun wire and power module respectively. The actual temperature of the gun wire and the actual temperature of the power module are collected in real time and compared with the predicted temperature of the corresponding sub-model to obtain the temperature error. Based on temperature error, the liquid cooling oil flow rate control signal is corrected, and a mode switching strategy is introduced to select the optimal control mode. The outputs of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model are dynamically weighted, and the control priority of each heat dissipation object is adjusted according to the current heat load status to achieve coordinated heat dissipation.

[0006] Optionally, the specific process of constructing the temperature prediction sub-model and the power module temperature prediction sub-model is as follows: Based on the charging condition data, dynamic mathematical models for the charging cable temperature and the power module temperature are established respectively; wherein, the dynamic mathematical model for the charging cable temperature is shown in the following equation (1):

[0007] The dynamic mathematical model of power module temperature is shown in equation (2) below:

[0008] in, and These represent the gun wire temperature and power module temperature at time k, respectively. and These represent the liquid cooling oil flow rate control signals at time k; This represents the change in ambient temperature at time k; This represents the charging current at time k; , , , , , These are model parameters; The dynamic mathematical model is discretized to obtain the state-space equation form; wherein, the state-space equation for the gun wire temperature is shown in equation (3) below:

[0009] The temperature state space equation of the power module is shown in equation (4) below:

[0010] in, and These represent the gun wire temperature and the state vector of the power module at time k, respectively. and These represent the gun wire temperature and the power module output temperature at time k, respectively. and These represent the disturbance vectors of the gun wire temperature and the power module, respectively; , , , , , , , For the system matrix; Based on the state-space equation, an improved prediction function controller is set, including defining the prediction time domain. Control Time Domain and a reference trajectory; wherein the reference trajectory is shown in equation (5):

[0011] in, express Reference temperature at any given time; Indicates the temperature setpoint; This is a smoothing factor.

[0012] Optionally, the parameters of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model are updated using an online recursive least squares method to adapt to dynamic changes in the system; wherein, the parameter update formula is shown in the following equation (6):

[0013] in, Represents the parameter estimation vector. Represents the regression vector. Represents the gain matrix. Let represent the covariance matrix.

[0014] Optionally, the specific process of generating corresponding liquid cooling oil flow rate control signals through the gun wire temperature prediction sub-model and the power module temperature prediction sub-model is as follows: Based on the current state vector and and perturbation vector and The state-space equations (3) and (4) are used to predict the future prediction time domain. Internal gun wire temperature and power module temperature ； in, ; Based on the reference trajectory formula (5), calculate the future prediction time domain. Internal gun wire reference temperature and power module reference temperature ; Define the optimization objective function. For gun wire temperature control, the objective function is as shown in equation (7):

[0015] For the temperature control of the power module, the objective function is shown in equation (8) below:

[0016] in, and These represent the increments of the gun wire temperature control signal and the power module temperature control signal, respectively. and These are the weighting coefficients; In the control time domain Within this process, the optimal control sequence is obtained by solving the optimization objective functions (7) and (8). and ;in, ; The first step control signal of each control sequence and These are output as the liquid cooling oil flow rate control signals at the current moment.

[0017] Optionally, the specific process of correcting the liquid cooling oil flow rate control signal based on temperature error and introducing a mode switching strategy to select the optimal control mode is as follows: Calculate the temperature error of the gun wire Temperature error of power module As shown in equation (9):

[0018] in, and These represent the gun wire temperature and power module temperature at time k, respectively. and These represent the gun wire reference temperature and the power module reference temperature at time k, respectively. Based on the temperature error, mode switching conditions are defined; wherein, the mode switching conditions include: when and When selecting the gun wire temperature priority control mode; when and When selecting the power module temperature priority control mode; when and When selecting a cooperative control mode; Otherwise, select the default control mode; in, and These are the error thresholds for the gun wire temperature and the power module temperature, respectively. Under each control mode, a rolling optimization algorithm is used to correct the liquid cooling oil flow rate control signal.

[0019] Optionally, the rolling optimization algorithm is based on the optimization objective functions (7) and (8), and adjusts the weight coefficients according to the selected control mode. and Or constraints; For the gun wire temperature priority control mode, increase the weighting coefficient of the gun wire temperature control objective function. Alternatively, reduce the weighting coefficients of the objective function for power module temperature control. ; For the power module temperature-priority control mode, increase the weighting coefficients of the power module temperature control objective function. Alternatively, reduce the weighting coefficients of the objective function for gun wire temperature control. ; For the cooperative control mode, default weight coefficients are used, and both objective functions are optimized simultaneously. For the default control mode, maintain the current control signal or use the energy-saving mode; In each control cycle, the control mode is updated according to the mode switching conditions, and the liquid cooling oil flow rate control signal is recalculated. and .

[0020] Optionally, under the cooperative control mode, a system-level cooperative optimization objective function is constructed, which simultaneously minimizes the gun wire temperature error, the power module temperature error, and the system energy consumption. The objective function is solved by a rolling optimization algorithm to generate a cooperative liquid cooling oil flow rate control signal. The system-level cooperative optimization objective function is shown in the following equation (10):

[0021] in, This represents the system-level collaborative optimization objective function; This represents the system energy consumption weighting coefficient; and These represent the increments of the gun wire temperature control signal and the power module temperature control signal, respectively. Based on the aforementioned system-level collaborative optimization objective function, a rolling optimization algorithm is applied in the control time domain. The optimal cooperative control sequence is solved internally, and the control signal of the first step of the cooperative control sequence is output as the cooperative liquid cooling oil flow rate control signal at the current moment, so as to realize the balanced control of the gun wire temperature and the power module temperature and the optimization of system energy efficiency.

[0022] Based on the same inventive concept, the present invention also provides a collaborative control system for a DC liquid-cooled charging pile, used to implement the collaborative control method for the DC liquid-cooled charging pile, comprising: The data acquisition module is used to collect charging condition data in real time, including the temperature of the charging gun wire, the temperature of the power module, the change in ambient temperature, and the charging current. The model building module, connected to the data acquisition module, is used to build a sub-model for predicting the charging cable temperature and a sub-model for predicting the power module temperature based on the charging condition data. The model building module establishes the sub-models through dynamic mathematical models and state-space equations, and sets an improved prediction function controller, including defining the prediction time domain, control time domain, and reference trajectory. The parameter update module, connected to the model building module, is used to update the parameters of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model through online recursive least squares method to adapt to dynamic changes in the system. The control signal generation module is connected to the model building module and the parameter update module. It is used to predict the future temperature through the gun wire temperature prediction sub-model and the power module temperature prediction sub-model, and generate the corresponding liquid cooling oil flow rate control signal based on the optimization objective function. An error calculation module, connected to the data acquisition module and the control signal generation module, is used to calculate the gun wire temperature error and the power module temperature error. The mode switching module is connected to the error calculation module and the control signal generation module. It is used to correct the liquid cooling oil flow rate control signal based on the temperature error and introduce a mode switching strategy to select the optimal control mode according to the error threshold, including the gun wire temperature priority control mode, the power module temperature priority control mode, the cooperative control mode, or the default control mode. An optimization execution module, connected to the mode switching module and the control signal generation module, is used to correct the liquid cooling oil flow rate control signal using a rolling optimization algorithm under each control mode, wherein the rolling optimization algorithm adjusts the weight coefficients or constraints according to the selected control mode. The collaborative optimization module, connected to the optimization execution module and the mode switching module, is used to construct a system-level collaborative optimization objective function under the collaborative control mode, and solve the objective function through a rolling optimization algorithm to generate a collaborative liquid cooling oil flow rate control signal, thereby achieving balanced control of the gun wire temperature and the power module temperature and optimizing system energy efficiency.

[0023] Based on the same inventive concept, the present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described collaborative control method for DC liquid-cooled charging piles.

[0024] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described collaborative control method for DC liquid-cooled charging piles.

[0025] The technical solution of the present invention has at least the following advantages and beneficial effects: By constructing a dual-prediction sub-model for gun wire temperature and power module temperature, and introducing a dynamic weight allocation mechanism, the control priority of each heat dissipation object can be intelligently adjusted according to the real-time heat load status. This enables dynamic allocation of heat dissipation resources among multiple heat sources, overcoming the problem of uneven heat dissipation caused by traditional single-object temperature control, effectively improving the overall heat dissipation efficiency and reducing the overall system energy consumption.

[0026] An improved predictive function controller is used to independently model and control the charging line and power module. Combined with real-time temperature error feedback and mode switching strategies, the system can quickly adapt to the heat dissipation requirements under different charging power and environmental conditions. This effectively reduces temperature fluctuations caused by response lag in traditional control and enhances the system's stability and adaptability.

[0027] By integrating multi-model prediction, error correction, and mode switching strategies, the system can autonomously select the optimal control mode based on the actual operating conditions and dynamically adjust the coolant flow rate, thus achieving intelligent thermal management under different operating conditions. This not only improves the flexibility of heat dissipation control but also ensures the reliable operation of charging piles in complex application scenarios. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the collaborative control method for a DC liquid-cooled charging pile according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the collaborative control system of a DC liquid-cooled charging pile according to an embodiment of the present invention. Detailed Implementation

[0029] The following is a detailed description of the embodiments, in conjunction with the accompanying drawings.

[0030] Reference Figure 1 A collaborative control method for a DC liquid-cooled charging pile includes the following steps: Step 1: Obtain the current charging status data of the charging pile, including charging current, charging gun temperature, power module temperature, and ambient temperature.

[0031] In some embodiments, the charging current acquisition can employ a DC Hall current sensor (model: ACS758-500B-PFF-T), with a range of 0–500A (covering high-power charging scenarios of 150–480kW), an accuracy of ±1.5%, an output signal of 0–5V (analog), and an isolation withstand voltage of 2.5kVrms (to resist high-voltage interference from charging piles). The sensor is connected in series with the positive busbar of the charging pile's DC output circuit (near the charging gun interface). The sensor body is fixed in the cabinet by a metal bracket, with a distance of ≥50mm from high-voltage components to avoid electromagnetic coupling interference. The sensor detects the busbar current in real time, converts the current signal into a linear voltage signal (e.g., 1V corresponds to 100A), and outputs it to the signal conditioning circuit.

[0032] The charging cable temperature can be acquired using a flexible-encapsulated PT1000 platinum resistance thermometer (model: PT1000-FF-100), with a temperature range of -50℃ to 200℃ (covering the extreme conditions of liquid cooling for the charging cable), an accuracy of ±0.1℃ (at 25℃), and a response time ≤1s (meeting the requirements for dynamic temperature change monitoring). Two PT1000 sensors are evenly embedded along the length of the charging cable (15cm and 30cm from the charging head), and fixed to the inner wall where the liquid cooling pipe contacts the conductor (directly monitoring the core heat-generating area). A surface-mount PT1000 sensor is attached next to the metal contact between the charging head and the vehicle interface (monitoring insertion / removal friction and contact resistance heating). The sensor leads are made of oil-resistant shielded wire, isolated from the liquid cooling pipe to avoid oil corrosion.

[0033] Temperature acquisition for the power module can utilize surface-mount NTC thermistors (model: NCP18XH103F03RC), with a temperature range of -40℃ to 125℃ (matching the normal operating temperature range of the IGBT power module), an accuracy of ±2% (-10℃ to 85℃), and a package size of 0603 (suitable for power module installation in confined spaces). Two NTCs are mounted on the heat sink of each IGBT chip (symmetrically distributed on both sides of the chip's heat dissipation center); one NTC is mounted on the top of the module housing. Thermal grease (thermal conductivity ≥ 3.0 W / (m²)) is applied between the sensor and the substrate. K)) to ensure heat transfer efficiency and avoid temperature measurement lag caused by air gaps.

[0034] Ambient temperature data can be acquired using a digital temperature and humidity sensor (model: SHT30), with a temperature range of -40℃ to 125℃ and an accuracy of ±0.2℃ (at 25℃). It outputs an I²C digital signal (requiring no analog-to-analog conversion and offering strong anti-interference capabilities) and can also collect humidity data (to aid in assessing the environment's heat dissipation capacity). The sensor is fixed to the ventilation opening on the outside of the charging pile cabinet (≥10cm from the cabinet body to avoid interference from the cabinet's own heat dissipation), using a plastic bracket. The sensor should face away from direct sunlight and rain splashes, and ventilation holes in the casing ensure airflow with the environment.

[0035] Step 2: Based on the charging condition data, construct a sub-model for predicting the temperature of the charging gun wire and a sub-model for predicting the temperature of the power module. Each sub-model corresponds to an improved prediction function controller.

[0036] In some embodiments, the specific process of constructing the temperature prediction sub-model and the power module temperature prediction sub-model is as follows: Based on the charging condition data, dynamic mathematical models for the charging cable temperature and the power module temperature are established respectively; wherein, the dynamic mathematical model for the charging cable temperature is shown in the following equation (1):

[0037] The dynamic mathematical model of power module temperature is shown in equation (2) below:

[0038] in, and These represent the gun wire temperature and power module temperature at time k, respectively. and These represent the liquid cooling oil flow rate control signals at time k; This represents the change in ambient temperature at time k; This represents the charging current at time k; , , , , , These are model parameters; The dynamic mathematical model is discretized to obtain the state-space equation form; wherein, the state-space equation for the gun wire temperature is shown in equation (3) below:

[0039] The temperature state space equation of the power module is shown in equation (4) below:

[0040] in, and These represent the gun wire temperature and the state vector of the power module at time k, respectively. and These represent the gun wire temperature and the power module output temperature at time k, respectively. and These represent the disturbance vectors of the gun wire temperature and the power module, respectively; , , , , , , , For the system matrix; Based on the state-space equation, an improved prediction function controller is set, including defining the prediction time domain. Control Time Domain and a reference trajectory; wherein the reference trajectory is shown in equation (5):

[0041] in, express Reference temperature at any given time; Indicates the temperature setpoint; This is a smoothing factor.

[0042] In some embodiments, the parameters of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model are updated using an online recursive least squares method to adapt to dynamic changes in the system; wherein, the parameter update formula is shown in the following equation (6):

[0043] in, Represents the parameter estimation vector. Represents the regression vector. Represents the gain matrix. Let represent the covariance matrix.

[0044] Step 3: Generate corresponding liquid cooling oil flow rate control signals using the gun wire temperature prediction sub-model and the power module temperature prediction sub-model, respectively.

[0045] In some embodiments, the specific process of generating corresponding liquid cooling oil flow rate control signals through the gun wire temperature prediction sub-model and the power module temperature prediction sub-model is as follows: Based on the current state vector and and perturbation vector and The state-space equations (3) and (4) are used to predict the future prediction time domain. Internal gun wire temperature and power module temperature ;in, ; Based on the reference trajectory formula (5), calculate the future prediction time domain. Internal gun wire reference temperature and power module reference temperature ; Define the optimization objective function. For gun wire temperature control, the objective function is as shown in equation (7):

[0046] For the temperature control of the power module, the objective function is shown in equation (8) below:

[0047] in, and These represent the increments of the gun wire temperature control signal and the power module temperature control signal, respectively. and These are the weighting coefficients; In the control time domain Within this process, the optimal control sequence is obtained by solving the optimization objective functions (7) and (8). and ;in, ; The first step control signal of each control sequence and These are output as the liquid cooling oil flow rate control signals at the current moment.

[0048] Step 4: Input the liquid cooling oil flow rate control signal to the liquid cooler execution unit to control the heat dissipation of the gun wire and power module respectively.

[0049] In some implementations, the local controller of the liquid chiller (such as a PLC or dedicated control board) receives data calculated by the upper-level collaborative control algorithm through its digital communication interface (such as EtherCAT or CAN bus) or analog input module. and The controller internally converts these two control signal values ​​into instructions for the specific actuators based on a preset mapping relationship. For example: Mapped to the target opening degree (0-100%) of the three-way regulating valve 1 (gun line); Mapped to the target opening degree (0-100%) of the three-way control valve 2 (power module); At the same time, the system according to and Based on the combined requirements (e.g., taking the larger of the two, or calculated based on the total heat load), a target speed command for the main circulation pump is generated.

[0050] The PLC's analog output module converts the above instructions into corresponding physical signals: Output a 0-10VDC or 4-20mA analog signal to the frequency converter of the main circulating pump to control its speed; The electric actuators of the two three-way proportional control valves output 0-10VDC analog signals respectively to control the valve core position, thereby accurately distributing the flow.

[0051] The main circulation pump operates at the commanded speed, generating the required total system flow and pressure. After being pumped out, the coolant splits into two branches in the pipeline. Three-way regulating valve 1, depending on its opening, allows a portion of the flow to enter the liquid-cooled pipe of the charging gun cable. As the coolant flows through the cable, it absorbs Joule heat generated by the high current transmission through close contact with the conductor. Three-way regulating valve 2, depending on its opening, allows the remaining flow to enter the cold plate of the power module. The coolant flows through the precision flow channels beneath power devices such as IGBTs, efficiently carrying away the heat generated by the semiconductor switches. The two heated coolant streams converge in the return pipeline and flow back to the heat exchanger. Here, heat is transferred to the external cooling system, the coolant temperature decreases, and one cycle is completed.

[0052] Step 5: Collect the actual temperature of the gun wire and the actual temperature of the power module in real time, and compare them with the predicted temperature of the corresponding sub-model to obtain the temperature error.

[0053] In some embodiments, the actual temperature of the charging cable can be acquired in real time using a flexible-encapsulated PT1000 platinum resistance sensor (model: PT1000-FF-100) pre-embedded within the charging cable. The sensor output signal is transmitted to a signal conditioning circuit via an oil-resistant shielded wire for filtering and amplification to eliminate noise interference. The processed analog signal is converted into a digital signal by an ADC (analog-to-digital converter) and read by the main controller (such as a PLC or embedded processor). The acquisition frequency is set to 10Hz (i.e., once every 100ms) to ensure real-time performance. For multiple sensor data, the average value is taken as the actual temperature of the charging cable at the current moment. .

[0054] The actual temperature of the power module can be acquired in real time using surface-mount NTC thermistors (model: NCP18XH103F03RC). The sensors are mounted symmetrically on the heat sink of the IGBT chips and on the top of the module housing, with thermal grease ensuring efficient heat transfer. The sensor output signals are read through an analog input module and linearized (converting resistance values ​​to temperature values) before being acquired by the main controller. The acquisition frequency is also 10Hz. For multiple sensor data points, the maximum value is taken as the actual temperature of the power module at the current moment. This reflects the temperature of the hottest spot. The collected temperature data is compared with the current time. The timestamps are synchronized to ensure that the gun wire temperature and power module temperature are processed within the same control cycle. If the sensor data exceeds a reasonable range (e.g., the gun wire temperature exceeds 200°C or the power module temperature exceeds 125°C), an alarm is triggered and backup data from the previous cycle is used.

[0055] Obtain the current time value from the gun wire temperature prediction sub-model and the power module temperature prediction sub-model constructed in step two. The predicted temperature is calculated by comparing the collected actual temperature with the predicted temperature. The temperature error is then transmitted to the mode switching logic in step six via an internal communication bus (such as CAN or EtherCAT). Simultaneously, the temperature error is used to update the parameters of the prediction sub-model online to adapt to dynamic changes in the system.

[0056] Step 6: Based on the temperature error, correct the liquid cooling oil flow rate control signal and introduce a mode switching strategy to select the optimal control mode.

[0057] In some embodiments, the specific process of correcting the liquid cooling oil flow rate control signal based on temperature error and introducing a mode switching strategy to select the optimal control mode is as follows: Calculate the temperature error of the gun wire Temperature error of power module As shown in equation (9):

[0058] in, and These represent the gun wire temperature and power module temperature at time k, respectively. and These represent the gun wire reference temperature and the power module reference temperature at time k, respectively. Based on the temperature error, mode switching conditions are defined; wherein, the mode switching conditions include: when and When selecting the gun wire temperature priority control mode; when and When selecting the power module temperature priority control mode; when and When selecting a cooperative control mode; Otherwise, select the default control mode; in, and These are the error thresholds for the gun wire temperature and the power module temperature, respectively. Under each control mode, a rolling optimization algorithm is used to correct the liquid cooling oil flow rate control signal.

[0059] In some embodiments, the rolling optimization algorithm is based on the optimization objective functions (7) and (8), and adjusts the weight coefficients according to the selected control mode. and Or constraints; For the gun wire temperature priority control mode, increase the weighting coefficient of the gun wire temperature control objective function. Alternatively, reduce the weighting coefficients of the objective function for power module temperature control. ; For the power module temperature-priority control mode, increase the weighting coefficients of the power module temperature control objective function. Alternatively, reduce the weighting coefficients of the objective function for gun wire temperature control. ; For the cooperative control mode, default weight coefficients are used, and both objective functions are optimized simultaneously. For the default control mode, maintain the current control signal or use the energy-saving mode; In each control cycle, the control mode is updated according to the mode switching conditions, and the liquid cooling oil flow rate control signal is recalculated. and .

[0060] In some embodiments, under the cooperative control mode, a system-level cooperative optimization objective function is constructed, which simultaneously minimizes the gun wire temperature error, the power module temperature error, and the system energy consumption. The objective function is solved by a rolling optimization algorithm to generate a cooperative liquid cooling oil flow rate control signal. The system-level cooperative optimization objective function is shown in the following equation (10):

[0061] in, This represents the system-level collaborative optimization objective function; This represents the system energy consumption weighting coefficient; and These represent the increments of the gun wire temperature control signal and the power module temperature control signal, respectively. Based on the aforementioned system-level collaborative optimization objective function, a rolling optimization algorithm is applied in the control time domain. The optimal cooperative control sequence is solved internally, and the control signal of the first step of the cooperative control sequence is output as the cooperative liquid cooling oil flow rate control signal at the current moment, so as to realize the balanced control of the gun wire temperature and the power module temperature and the optimization of system energy efficiency.

[0062] Step 7: Dynamically assign weights to the outputs of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model, and adjust the control priority of each heat dissipation object according to the current heat load status to achieve coordinated heat dissipation.

[0063] In some embodiments, based on the current charging status data of the charging pile (including charging current) Gun wire temperature Power module temperature Ambient temperature Real-time estimation of gun wire heat load and power module thermal load .

[0064] Gun wire heat load estimate The calculation is mainly based on the charging current and the gun wire resistance, as shown in the following formula (11):

[0065] in, The resistance per unit length of the gun wire is pre-calibrated based on the gun wire material and size.

[0066] Estimated thermal load of power module The calculation is mainly based on the charging current and the power module loss characteristics, as shown in the following formula (12):

[0067] in, This is the dynamic loss factor of the power module, which is related to the IGBT switching frequency and on-state voltage drop. This refers to static losses, including power consumption of the drive circuit, etc.

[0068] Calculate the total heat load And further calculate the heat load ratio of the gun wire. and power module heat load ratio As shown in equation (13):

[0069] Introducing a control priority factor and This is used to dynamically adjust the weights of the gun wire and power module in coordinated control. The priority factor is based on heat load ratio and temperature error. and (From step five), and combined with the calculation of the rate of temperature change: The rate of temperature change is calculated as shown in equation (14):

[0070] The priority factor formula is shown in equation (15) below:

[0071] in, and For the maximum permissible temperature error (e.g., , ), and For the maximum permissible rate of temperature change (e.g., , Weighting coefficients and For adjustable parameters, satisfying and The default value can be set to and This is to balance the effects of heat load, error, and rate of change.

[0072] The priority factor is normalized to ensure coordinated weight allocation, as shown in equation (16) below:

[0073] in, and These are dynamic weights used to adjust the relative importance of gun wire temperature and power module temperature in the control objective function.

[0074] Dynamic weights are introduced into the system-level collaborative optimization objective function (10) to form an improved objective function, as shown in equation (16) below:

[0075] Dynamic weights and Real-time updates ensure higher priority is assigned to objects with high heat loads or large temperature errors, thereby achieving coordinated heat dissipation. For example: When the heat load ratio of the gun wire is high and the temperature error is large... With increased power, the controller focuses more on tracking the gun wire temperature. When the power module has a high heat load ratio and a large temperature change rate With increased power, the controller pays more attention to the temperature stability of the power module.

[0076] The dynamic weight allocation process is repeated in each control cycle (e.g., a cycle of 100ms) to ensure that control priority is adjusted according to real-time heat load conditions. Simultaneously, combined with the mode switching strategy in step six, if the system still exhibits a significant temperature error after dynamic weight allocation, mode switching (such as line-priority or power module-priority mode) can be triggered as a backup mechanism. The accuracy of the prediction sub-model is continuously optimized through online parameter updates (such as recursive least squares), improving the effectiveness of dynamic weight allocation.

[0077] Based on the same inventive concept, and corresponding to any of the above embodiments, refer to... Figure 2 This invention provides a collaborative control system for a DC liquid-cooled charging pile, used to implement the aforementioned collaborative control method for a DC liquid-cooled charging pile, comprising: The data acquisition module is used to collect charging condition data in real time, including the temperature of the charging gun wire, the temperature of the power module, the change in ambient temperature, and the charging current. The model building module, connected to the data acquisition module, is used to build a sub-model for predicting the charging cable temperature and a sub-model for predicting the power module temperature based on the charging condition data. The model building module establishes the sub-models through dynamic mathematical models and state-space equations, and sets an improved prediction function controller, including defining the prediction time domain, control time domain, and reference trajectory. The parameter update module, connected to the model building module, is used to update the parameters of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model through online recursive least squares method to adapt to dynamic changes in the system. The control signal generation module is connected to the model building module and the parameter update module. It is used to predict the future temperature through the gun wire temperature prediction sub-model and the power module temperature prediction sub-model, and generate the corresponding liquid cooling oil flow rate control signal based on the optimization objective function. An error calculation module, connected to the data acquisition module and the control signal generation module, is used to calculate the gun wire temperature error and the power module temperature error. The mode switching module is connected to the error calculation module and the control signal generation module. It is used to correct the liquid cooling oil flow rate control signal based on the temperature error and introduce a mode switching strategy to select the optimal control mode according to the error threshold, including the gun wire temperature priority control mode, the power module temperature priority control mode, the cooperative control mode, or the default control mode. An optimization execution module, connected to the mode switching module and the control signal generation module, is used to correct the liquid cooling oil flow rate control signal using a rolling optimization algorithm under each control mode, wherein the rolling optimization algorithm adjusts the weight coefficients or constraints according to the selected control mode. The collaborative optimization module, connected to the optimization execution module and the mode switching module, is used to construct a system-level collaborative optimization objective function under the collaborative control mode, and solve the objective function through a rolling optimization algorithm to generate a collaborative liquid cooling oil flow rate control signal, thereby achieving balanced control of the gun wire temperature and the power module temperature and optimizing system energy efficiency.

[0078] Based on the same inventive concept, corresponding to any of the above embodiments, the present invention provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the collaborative control method of the DC liquid-cooled charging pile of the embodiment.

[0079] Alternatively, the aforementioned electronic device may be a server.

[0080] In addition, this embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the collaborative control method of the DC liquid-cooled charging pile of the embodiment.

[0081] It is understood that the processor in the embodiments of the present invention may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0082] The method steps in the embodiments of the present invention can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0083] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a storage medium or transmitted through a storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive (SSD)).

Claims

1. A collaborative control method for a DC liquid-cooled charging pile, characterized in that, Includes the following steps: Obtain current charging status data of the charging pile, including charging current, charging gun temperature, power module temperature, and ambient temperature; Based on charging condition data, a sub-model for predicting gun wire temperature and a sub-model for predicting power module temperature are constructed respectively. Each sub-model corresponds to an improved prediction function controller. The corresponding liquid cooling oil flow rate control signals are generated by the gun wire temperature prediction sub-model and the power module temperature prediction sub-model, respectively. The liquid cooling oil flow rate control signal is input to the liquid cooler execution unit to control the heat dissipation of the gun wire and power module respectively. The actual temperature of the gun wire and the actual temperature of the power module are collected in real time and compared with the predicted temperature of the corresponding sub-model to obtain the temperature error. Based on temperature error, the liquid cooling oil flow rate control signal is corrected, and a mode switching strategy is introduced to select the optimal control mode. The outputs of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model are dynamically weighted, and the control priority of each heat dissipation object is adjusted according to the current heat load status to achieve coordinated heat dissipation.

2. The collaborative control method for DC liquid-cooled charging piles as described in claim 1, characterized in that, The specific process for constructing the temperature prediction sub-model and the power module temperature prediction sub-model is as follows: Based on the charging condition data, dynamic mathematical models for the charging cable temperature and the power module temperature are established respectively; wherein, the dynamic mathematical model for the charging cable temperature is shown in the following equation (1): The dynamic mathematical model of power module temperature is shown in equation (2) below: in, and These represent the gun wire temperature and power module temperature at time k, respectively. and These represent the liquid cooling oil flow rate control signals at time k; This represents the change in ambient temperature at time k; This represents the charging current at time k; , , , , , These are model parameters; The dynamic mathematical model is discretized to obtain the state-space equation form; wherein, the state-space equation for the gun wire temperature is shown in equation (3) below: The temperature state space equation of the power module is shown in equation (4) below: in, and These represent the gun wire temperature and the state vector of the power module at time k, respectively. and These represent the gun wire temperature and the power module output temperature at time k, respectively. and These represent the disturbance vectors of the gun wire temperature and the power module, respectively; , , , , , , , For the system matrix; Based on the state-space equation, an improved prediction function controller is set, including defining the prediction time domain. Control Time Domain and a reference trajectory; wherein the reference trajectory is shown in equation (5): in, express Reference temperature at any given time; Indicates the temperature setpoint; This is a smoothing factor.

3. The collaborative control method for DC liquid-cooled charging piles as described in claim 2, characterized in that, The parameters of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model are updated using the online recursive least squares method to adapt to dynamic changes in the system; wherein, the parameter update formula is shown in the following formula (6): in, Represents the parameter estimation vector. Represents the regression vector. Represents the gain matrix. Let represent the covariance matrix.

4. The collaborative control method for DC liquid-cooled charging piles as described in claim 2, characterized in that, The specific process of generating corresponding liquid cooling oil flow rate control signals through the gun wire temperature prediction sub-model and the power module temperature prediction sub-model is as follows: Based on the current state vector and and perturbation vector and The state-space equations (3) and (4) are used to predict the future prediction time domain. Internal gun wire temperature and power module temperature ;in, ; Based on the reference trajectory formula (5), calculate the future prediction time domain. Internal gun wire reference temperature and power module reference temperature ; Define the optimization objective function. For gun wire temperature control, the objective function is as shown in equation (7): For the temperature control of the power module, the objective function is shown in equation (8) below: in, and These represent the increments of the gun wire temperature control signal and the power module temperature control signal, respectively. and These are the weighting coefficients; In the control time domain Within this process, the optimal control sequence is obtained by solving the optimization objective functions (7) and (8). and ;in, ; The first step control signal of each control sequence and These are output as the liquid cooling oil flow rate control signals at the current moment.

5. The collaborative control method for DC liquid-cooled charging piles as described in claim 4, characterized in that, The specific process of correcting the liquid cooling oil flow rate control signal based on temperature error and introducing a mode switching strategy to select the optimal control mode is as follows: Calculate the temperature error of the gun wire Temperature error of power module As shown in equation (9): in, and These represent the gun wire temperature and power module temperature at time k, respectively. and These represent the gun wire reference temperature and the power module reference temperature at time k, respectively. Based on the temperature error, mode switching conditions are defined; wherein, the mode switching conditions include: when and When selecting the gun wire temperature priority control mode; when and When selecting the power module temperature priority control mode; when and When selecting a cooperative control mode; Otherwise, select the default control mode; in, and These are the error thresholds for the gun wire temperature and the power module temperature, respectively. Under each control mode, a rolling optimization algorithm is used to correct the liquid cooling oil flow rate control signal.

6. The collaborative control method for DC liquid-cooled charging piles as described in claim 5, characterized in that, The rolling optimization algorithm is based on the optimization objective functions (7) and (8), and adjusts the weight coefficients according to the selected control mode. and Or constraints; For the gun wire temperature priority control mode, increase the weighting coefficient of the gun wire temperature control objective function. Alternatively, reduce the weighting coefficients of the objective function for power module temperature control. ; For the power module temperature-priority control mode, increase the weighting coefficients of the power module temperature control objective function. Alternatively, reduce the weighting coefficients of the objective function for gun wire temperature control. ; For the cooperative control mode, default weight coefficients are used, and both objective functions are optimized simultaneously. For the default control mode, maintain the current control signal or use the energy-saving mode; In each control cycle, the control mode is updated according to the mode switching conditions, and the liquid cooling oil flow rate control signal is recalculated. and .

7. The collaborative control method for DC liquid-cooled charging piles as described in claim 1, characterized in that, Under the cooperative control mode, a system-level cooperative optimization objective function is constructed. The system-level cooperative optimization objective function simultaneously minimizes the gun wire temperature error, the power module temperature error, and the system energy consumption. The objective function is solved by a rolling optimization algorithm to generate a cooperative liquid cooling oil flow rate control signal. The system-level cooperative optimization objective function is shown in the following equation (10): in, This represents the system-level collaborative optimization objective function; This represents the system energy consumption weighting coefficient; and These represent the increments of the gun wire temperature control signal and the power module temperature control signal, respectively. Based on the aforementioned system-level collaborative optimization objective function, a rolling optimization algorithm is applied in the control time domain. The optimal cooperative control sequence is solved internally, and the control signal of the first step of the cooperative control sequence is output as the cooperative liquid cooling oil flow rate control signal at the current moment, so as to realize the balanced control of the gun wire temperature and the power module temperature and the optimization of system energy efficiency.

8. A collaborative control system for a DC liquid-cooled charging pile, used to implement the collaborative control method for the DC liquid-cooled charging pile according to any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect charging condition data in real time, including the temperature of the charging gun wire, the temperature of the power module, the change in ambient temperature, and the charging current. The model building module, connected to the data acquisition module, is used to build a sub-model for predicting the charging cable temperature and a sub-model for predicting the power module temperature based on the charging condition data. The model building module establishes the sub-models through dynamic mathematical models and state-space equations, and sets an improved prediction function controller, including defining the prediction time domain, control time domain, and reference trajectory. The parameter update module, connected to the model building module, is used to update the parameters of the gun wire temperature prediction sub-model and the power module temperature prediction sub-model through online recursive least squares method to adapt to dynamic changes in the system. The control signal generation module is connected to the model building module and the parameter update module. It is used to predict the future temperature through the gun wire temperature prediction sub-model and the power module temperature prediction sub-model, and generate the corresponding liquid cooling oil flow rate control signal based on the optimization objective function. An error calculation module, connected to the data acquisition module and the control signal generation module, is used to calculate the gun wire temperature error and the power module temperature error. The mode switching module is connected to the error calculation module and the control signal generation module. It is used to correct the liquid cooling oil flow rate control signal based on the temperature error and introduce a mode switching strategy to select the optimal control mode according to the error threshold, including the gun wire temperature priority control mode, the power module temperature priority control mode, the cooperative control mode, or the default control mode. An optimization execution module, connected to the mode switching module and the control signal generation module, is used to correct the liquid cooling oil flow rate control signal using a rolling optimization algorithm under each control mode, wherein the rolling optimization algorithm adjusts the weight coefficients or constraints according to the selected control mode. The collaborative optimization module, connected to the optimization execution module and the mode switching module, is used to construct a system-level collaborative optimization objective function under the collaborative control mode, and solve the objective function through a rolling optimization algorithm to generate a collaborative liquid cooling oil flow rate control signal, thereby achieving balanced control of the gun wire temperature and the power module temperature and optimizing system energy efficiency.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the collaborative control method of the DC liquid-cooled charging pile according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the collaborative control method of the DC liquid-cooled charging pile as described in any one of claims 1-7.

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