Heat dissipation method of charging device and computing device

Abstract

Embodiments of the present application provide a heat dissipation method of a charging device and a computing device. The method comprises: determining a temperature prediction sequence and a heating power curve based on a running parameter; determining a running reference interval of a water pump and a fan of the charging device in a full charging period based on the heating power curve; determining a control instruction in a target time period under a constraint condition that each predicted temperature value in the temperature prediction sequence does not exceed a preset upper temperature threshold based on the running reference interval and the temperature prediction sequence; and performing heat dissipation control on the charging device based on the control instruction. By using the above method, the overall heat dissipation demand is predicted in a long time domain to plan an energy efficiency optimal running reference, and the fan, the water pump and other heat dissipation parameters are finely adjusted in real time in a short time domain, so that the power consumption of the cooling distribution unit is significantly reduced while ensuring effective heat dissipation, and the dual consideration of heat dissipation performance and system energy efficiency is realized.

Description

Technical Field

[0001] This application relates to the field of charging equipment technology, and in particular to a heat dissipation method and computing device for a charging device. Background Technology

[0002] Liquid-cooled cooling distribution units (CDUs) are widely used in cooling systems for high-power-density electronic devices to provide efficient heat dissipation. Charging devices, as a typical example of high-power-density electronic devices, can benefit from CDUs that effectively manage their thermal load under continuous high-load conditions, ensuring stable system operation.

[0003] With the development of fast charging technology, the heat load generated by charging equipment during high-current charging has increased significantly, which places higher demands on the CDU's efficient heat dissipation capabilities to avoid performance degradation or safety risks due to excessive temperature rise.

[0004] To address this, existing technologies typically employ a combined liquid cooling and air cooling approach. Heat dissipation control strategies often determine fan and circulation pump speeds based on the difference or absolute value between the return liquid temperature and the outlet temperature, aiming to maintain the charging equipment's temperature within a safe range. However, such strategies struggle to reduce the CDU's own power consumption while ensuring effective heat dissipation, failing to balance heat dissipation performance with system energy efficiency requirements. Summary of the Invention

[0005] This application provides a heat dissipation method and computing device for a charging device. By predicting the overall heat dissipation demand in the long-term domain to plan the optimal operating benchmark for energy efficiency, and then combining it with short-term domain prediction to make real-time fine adjustment of heat dissipation parameters such as fans and water pumps, the power consumption of the cooling distribution unit itself is significantly reduced while ensuring effective heat dissipation, thus achieving a balance between heat dissipation performance and system energy efficiency.

[0006] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: In a first aspect, embodiments of this application provide a heat dissipation method for a charging device, comprising: acquiring operating parameters of the charging device; determining a temperature prediction sequence and a heat dissipation power curve based on the operating parameters; wherein the temperature prediction sequence is used to characterize multiple predicted temperature values ​​within a preset short time domain after the current control moment; the heat dissipation power curve is used to characterize the heat dissipation trend of the charging device during the entire charging cycle; determining an operating reference range for the water pump and fan of the charging device during the entire charging cycle based on the heat dissipation power curve; wherein the operating reference range includes the speed range of the water pump and the speed range of the fan; determining a control command based on the operating reference range and the temperature prediction sequence, provided that each predicted temperature value in the temperature prediction sequence does not exceed a preset upper temperature threshold; and adjusting the speed of the water pump and fan in the charging device based on the control command to perform heat dissipation control.

[0007] Based on this scheme, by acquiring the operating parameters of the charging equipment and determining the temperature prediction sequence and heat generation power curve accordingly, the temperature change trend in the short time domain and the heat generation pattern throughout the entire charging cycle can be predicted in advance. This helps to predict the heat dissipation system of the charging equipment in advance. Furthermore, based on the heat generation power curve, the operating reference range within the entire charging cycle is determined, providing a reasonable speed constraint range for the operation of the water pump and fan. This avoids over-operation of the heat dissipation components of the charging equipment and ensures that the charging equipment operates within a safe temperature range. Under the hard constraint that each predicted temperature value in the temperature prediction sequence does not exceed the preset upper temperature threshold, control commands are dynamically generated within the target time period to finely adjust the speed of the water pump and fan within the operating reference range, thus achieving precise heat dissipation control. In this way, by predicting the overall heat dissipation demand in the long time domain to plan the operating reference with optimal energy efficiency, and then combining this with real-time fine adjustment of the heat dissipation parameters of the fan, water pump, etc., effective heat dissipation is ensured while significantly reducing the power consumption of the cooling distribution unit itself, achieving a dual balance between heat dissipation performance and system energy efficiency.

[0008] In one possible implementation, based on the operating reference range and the temperature prediction sequence, and under the constraint that each predicted temperature value in the temperature prediction sequence does not exceed a preset upper temperature threshold, a control command is determined, including: based on the temperature prediction sequence and with the preset upper temperature threshold as a constraint, within the speed range defined by the operating reference range, determining the target operating parameters of the water pump and the fan; the target operating parameters are used to characterize the energy consumption operating parameters that minimize the sum of the power consumption of the water pump and the power consumption of the fan under the constraint conditions; and generating a control command based on the water pump operating parameters and the fan operating parameters.

[0009] Based on this scheme, when determining control commands, a temperature prediction sequence is used as the foundation, and a preset upper temperature threshold is used as a constraint. Within the speed range defined by the operating baseline interval, the energy consumption operating parameters corresponding to the minimum sum of energy consumption of the water pump and fan under this constraint are solved. This not only ensures that all predicted temperatures in the short-term future will not exceed the safety threshold, but also achieves optimal allocation of heat dissipation resources from an energy consumption perspective. Furthermore, by determining the water pump operating parameters and fan operating parameters separately, and generating control commands accordingly, effective heat dissipation is ensured while significantly reducing the power consumption of the cooling distribution unit itself, achieving a dual balance between heat dissipation performance and system energy efficiency.

[0010] In another possible implementation, the method further includes: updating the water pump operating parameters and fan operating parameters on a rolling basis according to a preset cycle to generate update control commands; wherein the water pump operating parameters are updated on a rolling basis according to a first update cycle, and the fan operating parameters are updated on a rolling basis according to a second update cycle, and the first update cycle is shorter than the second update cycle; wherein the update control command corresponds to a target gear that exceeds the operating reference range. The target gear includes the target gear of the water pump and the target gear of the fan.

[0011] Based on this scheme, when the temperature prediction sequence meets the preset over-temperature condition (predicted temperature approaching or potentially exceeding the safety threshold), the water pump operating parameters are adjusted frequently according to a shorter first update cycle, while the fan operating parameters are adjusted according to a relatively longer second update cycle, with the first update cycle being shorter than the second update cycle. This differentiated update mechanism reflects a layered strategy for heat dissipation control: when the over-temperature risk first appears, rapid cooling of the core heat source is prioritized by quickly adjusting the water pump speed, effectively suppressing the temperature rise trend; subsequently, if the over-temperature risk persists for a long time, the fan speed is adjusted in a timely manner, forming a linkage control mechanism with fast response in the inner loop and stable coordination in the outer loop. In this way, when the temperature prediction sequence meets the preset over-temperature condition (predicted temperature approaching or potentially exceeding the safety threshold), the original operating baseline range will be broken, and the operating parameters of the water pump and fan will be updated in a rolling manner to generate targeted update control commands, promptly responding to sudden or intensified heat loads to ensure safety.

[0012] In another possible implementation, the operating parameters include the target current of the charging device, the rate of change of current, the total equivalent resistance of the charging connection components of the charging device, and the real-time heat generation power of the charging device. Based on the operating parameters, a temperature prediction sequence and a heat generation power curve are determined, including: generating a heat generation power sequence within a preset short time domain after the current control moment based on the target current, the rate of change of current, multiple time points within a preset short time domain, the total equivalent resistance, and the real-time heat generation power of the charging device; using the heat generation power sequence as input to a dynamic thermal balance model, and using the dynamic thermal balance model to obtain a temperature prediction sequence within the preset short time domain; and generating a heat generation power curve for the entire charging cycle based on the historical charging data parameters of the charging device.

[0013] Based on this scheme, by fusing and modeling operating parameters such as target current, rate of change of current, equivalent resistance, and real-time heat generation power, a heat generation power sequence in the short time domain can be accurately generated. Combined with a dynamic thermal balance model integrating a Smith predictor, the temperature response lag caused by heat conduction delay is effectively compensated, significantly improving the real-time performance and accuracy of temperature prediction. Simultaneously, constructing a full-cycle heat generation power curve using historical charging data helps to identify high-heat-risk stages in advance, enabling proactive scheduling of heat dissipation strategies. Thus, by constructing a dual-prediction architecture of short-term real-time tracking and long-term full-cycle planning, embedding a Smith predictor in the short time domain to dynamically compensate for heat conduction lag effects significantly improves the response speed and accuracy of temperature prediction. Furthermore, combining the full-cycle heat generation power curve enables global prediction of heat load trends, allowing heat dissipation control to both quickly suppress sudden temperature rises and collaboratively optimize overall energy efficiency, effectively solving the response lag and control lag problems caused by thermal lag in traditional thermal management.

[0014] In another possible implementation, based on the target current of the charging device, the rate of change of current, multiple time points within a preset short time domain, the total equivalent resistance, and the real-time heating power, a heating power sequence within a preset short time domain is generated after the current control moment. This includes: fitting the current within the preset short time domain to obtain a transient current sequence within the preset short time domain based on the target current of the charging device, the rate of change of current, and multiple time points within the preset short time domain; and generating a heating power sequence within the preset short time domain based on the transient current sequence, the total equivalent resistance, and the real-time heating power.

[0015] Based on this scheme, by performing time series fitting on the target current and its rate of change in the short time domain, the transient current waveform in the next few seconds can be accurately reconstructed. Combined with the total equivalent resistance and real-time heating power, a high-fidelity short-time heating power sequence can be efficiently generated. This can improve the dynamic response capability of heating prediction and provide a more accurate input basis for subsequent temperature prediction and thermal management control.

[0016] In another possible implementation, the operating parameters also include the battery's state of charge, the constant current to constant voltage switching point, the charging termination point, the upper limit charging current, and the ambient temperature. Based on the historical charging data parameters of the charging device, a heat generation power curve for the entire charging cycle is generated, including: fitting the current planning curve for the entire charging cycle based on the battery's state of charge, the constant current to constant voltage switching point, the charging termination point, and the upper limit charging current in the operating parameters; and generating the heat generation power curve for the entire charging cycle based on the current planning curve and the ambient temperature.

[0017] Based on this scheme, by utilizing key parameters in the operating parameters such as battery state of charge, constant current to constant voltage switching point, charging termination point, upper limit charging current, and ambient temperature, a current planning curve covering the entire charging cycle is first fitted. Then, by combining the influence of ambient temperature on heat loss, a high-precision full-cycle heat generation power curve is generated. In this way, the coupling law between current and thermal behavior in the actual charging process is effectively restored, providing a predictive basis for long-term thermal management.

[0018] In another possible implementation, based on the heat dissipation power curve, the operating reference interval of the charging device within the entire charging cycle is determined, including: dividing the entire charging cycle into multiple time nodes; wherein each time node corresponds to a water pump operating parameter and a fan operating parameter; inputting the heat dissipation power curve into a long-term predictive control model to obtain the control sequence of the water pump operating parameter and fan operating parameter corresponding to each time node within the entire charging cycle; wherein the long-term predictive control model adopts an optimization strategy with the goal of minimizing the total heat dissipation energy consumption of the charging device, and is constrained by the temperature of the charging connection component of the charging device being less than a preset upper temperature threshold; based on the control sequence, the operating reference interval of the water pump and the operating reference interval of the fan within the entire charging cycle are generated.

[0019] Based on this scheme, the entire charging cycle is divided into multiple time nodes, and the heat dissipation power curve generated from historical data is input into a long-term model predictive control (MPC) model. Under the constraint of minimizing the total energy consumption of the charging equipment for heat dissipation while ensuring that the temperature of the charging connection components remains below a preset upper temperature threshold, the control sequences of the water pump and fan corresponding to each time node are solved. This generates a reference operating range for the water pump and fan covering the entire cycle. In this way, optimal energy efficiency and safety are simultaneously guaranteed on a global time scale, effectively avoiding the problems of overcooling or excessive temperature rise caused by local responses in traditional control.

[0020] In another possible implementation, based on a temperature prediction sequence and constrained by a preset upper temperature threshold, target operating parameters for the water pump and fan are determined within a speed range defined by the operating reference interval. This includes: dividing the preset short-time domain into multiple consecutive control nodes; for each control node, obtaining the instantaneous power consumption of the water pump based on a preset power consumption ratio coefficient and the coolant volume flow rate of the water pump, and obtaining the instantaneous power consumption of the fan based on a preset power consumption ratio coefficient and the candidate speed of the fan; and determining the water pump operating parameters and the fan operating parameters from candidate speed combinations that meet preset candidate conditions based on the instantaneous power consumption of the water pump and the instantaneous power consumption of the fan; wherein the preset candidate conditions include the candidate speed of the water pump being within the water pump operating reference interval, the candidate speed of the fan being within the fan operating reference interval, and the temperature of the charging connection component of the charging device being less than the preset upper temperature threshold within the preset short-time domain.

[0021] Based on this scheme, by refining the control process into multiple consecutive control cycles within a preset short time domain and combining it with a real-time temperature prediction sequence, under the hard constraint of ensuring that the temperature of the charging connection components is always below the preset upper temperature threshold, the candidate speed combinations of the water pump and fan are finely screened: only feasible combinations that simultaneously satisfy the conditions that the water pump speed is within its operating reference range, the fan speed is within its operating reference range, and the temperature rise in the future short time domain does not exceed the limit are retained; on this basis, the operating parameter combination with the lowest overall energy consumption or the best response performance is further selected based on the instantaneous power consumption of the water pump and fan at each candidate speed. In this way, energy waste caused by conservative control is avoided, and the risk of temperature rise caused by aggressive control is prevented, which can effectively improve the thermal safety margin and operating efficiency of the charging equipment.

[0022] In another possible implementation, it also includes: under the hard constraint that any predicted temperature value in the temperature prediction sequence exceeds the preset upper temperature threshold, determining that the charging device is in an over-temperature warning state, and updating the target gear of the water pump to the upper limit of the water pump operating reference range based on the water pump operating reference range, and updating the target gear of the fan to the upper limit of the fan operating reference range.

[0023] Based on this scheme, when all predicted temperature values ​​in the temperature prediction sequence exceed the preset upper temperature threshold, the charging equipment is determined to enter an over-temperature warning state and immediately triggers a safety protection mechanism: the target speed of the water pump and fan is forcibly increased to the upper limit of their respective operating reference ranges to maximize heat dissipation and curb the temperature rise trend. In this way, while retaining the constraints of the reference range, rapid response and proactive intervention to extreme thermal risks are achieved. This ensures that thermal management actions are always within the preset safety control boundaries and effectively prevents overheating or even failure of connecting components due to temperature control lag, significantly enhancing the fault tolerance capability of the charging equipment.

[0024] Secondly, embodiments of this application also provide a heat dissipation device for a charging device, comprising: an acquisition module for acquiring operating parameters of the charging device; a determination module for determining a temperature prediction sequence and a heat dissipation power curve based on the operating parameters; wherein the temperature prediction sequence is used to characterize multiple predicted temperature values ​​within a preset short time domain after the current control moment; the heat dissipation power curve is used to characterize the heat dissipation trend of the charging device during the entire charging cycle; the determination module is further used to determine the operating reference range of the water pump and fan of the charging device during the entire charging cycle based on the heat dissipation power curve; wherein the operating reference range includes the speed range of the water pump and the speed range of the fan; the determination module is further used to determine a control command based on the operating reference range and the temperature prediction sequence, under the constraint that each predicted temperature value in the temperature prediction sequence does not exceed a preset upper temperature threshold; and an execution module for adjusting the speed of the water pump and fan in the charging device based on the control command to perform heat dissipation control.

[0025] In one possible implementation, the determining module is further configured to determine the target operating parameters of the water pump and the fan within the speed range defined by the operating reference interval, based on the temperature prediction sequence and with a preset upper temperature threshold as a constraint; the target operating parameters are used to characterize the energy consumption operating parameters that minimize the sum of the power consumption of the water pump and the power consumption of the fan under the condition of satisfying the constraint; and to generate control commands based on the water pump operating parameters and the fan operating parameters.

[0026] In another possible implementation, an update module is used to perform rolling updates on the water pump operating parameters and fan operating parameters based on a preset period to generate update control commands. The water pump operating parameters are updated according to a first update period, and the fan operating parameters are updated according to a second update period, with the first update period being shorter than the second update period. The update control command corresponds to a target gear exceeding the operating baseline range. The target gear includes the target gear for both the water pump and the fan.

[0027] In another possible implementation, the determining module is also used to generate a heat generation power sequence within a preset short time domain after the current control moment, based on the target current of the charging device, the rate of change of current, multiple time points within a preset short time domain, the total equivalent resistance, and the real-time heat generation power; using the heat generation power sequence as input to the dynamic thermal balance model, the temperature prediction sequence within the preset short time domain is obtained using the dynamic thermal balance model; and generating a heat generation power curve within the entire charging cycle based on the historical charging data parameters of the charging device.

[0028] In another possible implementation, the determining module is also used to fit the current in the preset short time domain to the target current, current change rate, and multiple time points in the preset short time domain of the charging device, thereby obtaining a transient current sequence in the preset short time domain; and to generate a heating power sequence in the preset short time domain based on the transient current sequence, the total equivalent resistance, and the real-time heating power.

[0029] In another possible implementation, the determination module is also used to fit the current planning curve for the entire charging cycle based on the battery's state of charge, constant current to constant voltage switching point, charging termination point, and upper limit charging current parameters in the historical charging data parameters; and to generate the heat generation power curve for the entire charging cycle based on the current planning curve and the ambient temperature.

[0030] In another possible implementation, the determining module is also used to divide the entire charging cycle into multiple time nodes; each time node corresponds to a water pump operating parameter and a fan operating parameter; the heat dissipation power curve is input into the long-term domain model predictive control model to obtain the control sequence of the water pump operating parameters and fan operating parameters corresponding to each time node in the entire charging cycle; wherein the long-term domain model predictive control model adopts an optimization strategy with the goal of minimizing the total energy consumption of heat dissipation of the charging equipment, and the model is constrained by the temperature of the charging connection component of the charging equipment being less than a preset upper temperature threshold; based on the control sequence, the water pump operating reference interval and the fan operating reference interval in the entire charging cycle are generated.

[0031] In another possible implementation, the determining module is further configured to divide a preset short-time domain into multiple consecutive control nodes; for each control node, based on a preset power consumption ratio coefficient and the coolant volume flow rate of the water pump, the instantaneous power consumption of the water pump is obtained, and based on a preset power consumption ratio coefficient and the candidate speed of the fan, the instantaneous power consumption of the fan is obtained; based on the instantaneous power consumption of the water pump and the instantaneous power consumption of the fan, the water pump operating parameters and the fan operating parameters are determined from the candidate speed combinations that meet the preset candidate conditions; wherein, the preset candidate conditions include the candidate speed of the water pump being within the water pump operating reference range, the candidate speed of the fan being within the fan operating reference range, and the temperature of the charging connection component of the charging device being less than the preset upper temperature threshold within the preset short-time domain.

[0032] In another possible implementation, the determining module is further configured to determine that the charging device is in an over-temperature warning state under the hard constraint that any predicted temperature value in the temperature prediction sequence exceeds the preset upper temperature threshold, and update the target gear of the water pump to the upper limit value of the water pump operating reference range, and update the target gear of the fan to the upper limit value of the fan operating reference range, based on the water pump operating reference range.

[0033] Thirdly, embodiments of this application also provide a charging device, including: a processor and a memory; the processor and the memory are coupled; the memory is used to store program instructions; the processor is used to execute the program instructions to perform the method as described in any of the first aspects above.

[0034] Fourthly, embodiments of this application provide a chip for performing the methods described in any of the first aspects above.

[0035] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a computer, implement the method as described in any of the first aspects.

[0036] In a sixth aspect, embodiments of this application provide a program product including a computer program that, when executed by a processor, implements the method as described in any of the first aspects. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the working principle of a CDU provided in an embodiment of this application; Figure 2 This is a schematic diagram of an intelligent heat dissipation system for a charging device provided in an embodiment of this application; Figure 3 This is a schematic diagram of an intelligent heat dissipation system for another charging device provided in an embodiment of this application; Figure 4 This is a first flowchart illustrating a heat dissipation method for a charging device provided in an embodiment of this application; Figure 5 This is a signaling interaction diagram of a heat dissipation method for a charging device provided in an embodiment of this application; Figure 6 This is a schematic diagram of a current planning curve for a full charging cycle provided in an embodiment of this application; Figure 7 This is a schematic flowchart of a method for determining target operating parameters of a water pump and a fan, provided in an embodiment of this application. Figure 8 This is a second flowchart illustrating a heat dissipation method for a charging device provided in an embodiment of this application; Figure 9 This is a schematic diagram of a complete heat dissipation method for a charging device provided in an embodiment of this application; Figure 10 This is a schematic diagram of a heat dissipation device for a charging device provided in an embodiment of this application; Figure 11 This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. To facilitate a clear description of the technical solutions of the embodiments of this application, the use of terms such as "first," "second," etc., in the embodiments of this application is for illustrative purposes and to distinguish the objects being described. There is no particular order between them, nor does it indicate a specific limitation on the number of devices in the embodiments of this application, and they do not constitute any limitation on the embodiments of this application.

[0039] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of this application.

[0040] It should be noted that many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below.

[0041] The following explanations of the technical terms mentioned in the embodiments of this application are provided to facilitate understanding by those skilled in the art.

[0042] A charging pile is a dedicated power supply device that provides electrical energy to electric vehicles, new energy construction machinery, and other electrified equipment. It converts grid power into charging power adapted to the electric equipment via a power interface, thereby achieving constant voltage and constant current charging of the equipment's internal battery. Charging piles are widely used in residential communities, commercial areas, highway service areas, industrial parks, and other scenarios. The charging pile integrates an embedded system, which includes a billing control unit (TCU), a power distribution unit (PDU), a human-machine interface (HMI), a coolant distribution unit (CDU), and multiple power modules, among other hardware units.

[0043] The liquid-cooled cooling distribution unit is a core functional module in the liquid-cooled thermal management system of charging piles. It is mainly used for efficient and controllable heat dissipation management of the heat generated during high-power charging. Through a closed-loop circulation system, coolant is delivered to the heat-generating components inside the charging pile to absorb the heat generated during operation. The high-temperature coolant is then guided to a heat exchanger, where it exchanges heat with the ambient air under the forced convection of a fan, thus cooling the coolant. The cooled coolant then flows back to the heat-generating area, forming a continuous and stable heat dissipation cycle.

[0044] A pre-calibrated multidimensional data table (look-up table or calibration map, MAP table) refers to a multidimensional parameter mapping table pre-stored in the controller. It uses input variables as indexes to look up the corresponding control parameters or physical quantities.

[0045] The embodiments of this application will now be described with reference to the accompanying drawings.

[0046] Liquid-cooled cooling distribution units (CDUs) are widely used in cooling systems for high-power-density electronic devices, providing efficient heat dissipation. Charging equipment, as a typical example of high-power-density electronic equipment, can effectively manage its thermal load under continuous high-load conditions through a CDU, ensuring stable system operation. Charging equipment (such as charging piles) is another example of high-power-density electronic equipment that can effectively manage its thermal load under continuous high-load conditions through a CDU, ensuring stable system operation.

[0047] The following is combined Figure 1 Taking a charging station as an example, the working principle of the CDU in the embodiments of this application will be explained in detail.

[0048] Figure 1 This is a schematic diagram of the working principle of a CDU provided in an embodiment of this application.

[0049] like Figure 1 As shown, the CDU system 1 can include multiple core components: a controller 11, a water pump 12, a coolant tank 13, a heat exchanger 14, a fan 15, and the internal heat-generating components 16 of the charging pile. The controller 11 monitors the temperature signal in real time and intelligently adjusts the operating parameters of the water pump 12 and the fan 15 to achieve efficient and safe thermal management. The water pump 12 drives the coolant to circulate in the loop, carrying away the heat generated by the internal heat-generating components 16 of the charging pile. The coolant tank 13 stores and stabilizes the coolant, buffers the volume expansion caused by temperature changes, and assists in gas-liquid separation to ensure reliable system operation. The heat exchanger 14 serves as the core of heat exchange, enabling heat transfer between the high-temperature coolant and the ambient air. The fan 15 enhances the heat dissipation efficiency of the heat exchanger 14 through forced convection, accelerating the cooling of the coolant, thus forming a closed-loop, controllable, and efficient liquid cooling system. Figure 1 The solid line represents the physical flow path of the coolant, and the dashed line represents the transmission path of the control signal issued by the controller 11.

[0050] The specific workflow of CDU system 1 is as follows: During high-power charging at the charging station, when current is transmitted through the charging gun and the vehicle interface, significant Joule heating is generated due to contact resistance and conductor impedance, causing the temperature of the charging connection components to rise (marked as temperature signal T in the figure). To prevent local overheating from damaging the charging gun or vehicle interface and preventing normal charging, the CDU activates an active cooling mechanism: the controller 11 first collects temperature signals from key locations (such as the charging connector, coolant inlet and outlet, etc.) in real time, and dynamically adjusts the operating parameters of the water pump 12 and the fan 15 accordingly. Under the controller's command, the water pump 12 drives the coolant to flow out of the reservoir 13, pressurize it, and enter the liquid cooling channel inside the charging station, absorbing the heat generated by the heat-generating component 16 and raising its temperature; subsequently, the high-temperature coolant carrying heat flows into the heat exchanger 14, where it undergoes efficient convective heat exchange with the ambient air forcibly introduced by the fan 15, achieving cooling; the cooled low-temperature liquid returns to the reservoir 13 along the loop, completing one cycle.

[0051] To achieve more efficient and safe thermal management and intelligently control the operating parameters of the water pump 12 and fan 15, this application provides a heat dissipation method and computing device for a charging device. By predicting overall heat dissipation demand in the long-term domain to plan the optimal energy-efficient operating baseline, and then combining this with short-term domain prediction to finely adjust the heat dissipation parameters of the fan, water pump, etc., in real time, effective heat dissipation is ensured while significantly reducing the power consumption of the cooling distribution unit itself, achieving a balance between heat dissipation performance and system energy efficiency. Here, the long-term domain refers to the entire time range of the complete charging cycle, while the short-term domain refers to a pre-set shorter time window, such as 5 seconds, 10 seconds, or 15 seconds.

[0052] Figure 2 This is a schematic diagram of an intelligent heat dissipation system for a charging device provided in an embodiment of this application.

[0053] like Figure 2As shown, the intelligent heat dissipation system 2 of the charging equipment includes a BMS charging data interaction layer 21, a dual time-domain prediction layer 22, a dual closed-loop MPC control kernel layer 23, an actuator collaborative control layer 24, and a feedback correction and fault tolerance protection layer 25. The BMS charging data interaction layer 21 serves as an information bridge between the charging equipment's heat dissipation system and the battery management system (BMS). It acquires key operating parameters during the charging process in real time through communication protocols such as CAN and RS485, including the target current, current change rate, total equivalent resistance of the charging connection components, and real-time heat generation power. The dual time-domain prediction layer 22 constructs a dual-prediction architecture to predict the temperature rise trend of heat-generating components inside the charging pile at multiple scales. The short time domain refers to a preset short time period (e.g., 5 seconds), while the long time domain refers to the entire full charging cycle. Short-time domain prediction tracks the temperature in real time for short periods after the current moment to cope with transient high-power impacts, while long-time domain prediction globally plans the heat dissipation requirements throughout the entire charging cycle to avoid overheating risks. The dual-loop MPC control kernel layer 23 serves as the control core. This layer employs a Model Predictive Control (MPC) algorithm to construct a nested architecture of inner and outer dual loops: the outer loop operates at a low frequency throughout the entire charging cycle, dynamically optimizing the control objective based on long-term time-domain prediction results with a longer update cycle, generating the operating reference range for the water pump and fan; the inner loop operates at a high frequency in the short time domain, rapidly adjusting the coolant flow and airflow based on short-term temperature predictions with a shorter update cycle to track the set temperature. The actuator coordination control layer 24 coordinates the operating status of actuators such as the water pump 12 and fan 15. The feedback correction and fault tolerance protection layer 25 monitors the operating status in real time (such as actuator feedback and abnormal events) and performs online correction; simultaneously, it automatically triggers fault tolerance protection mechanisms such as power reduction, forced cooling, or shutdown when sensor failure, communication interruption, or temperature exceeding limits are detected; the intelligent heat dissipation system can run on the MCU (microcontroller) or DSP in the charging pile.

[0054] Furthermore, in Figure 2 Based on this, the software unit modules in the architecture will be further explained: Figure 3 This is a schematic diagram of an intelligent heat dissipation system for another charging device provided in an embodiment of this application.

[0055] like Figure 3As shown, the BMS charging data interaction layer 21 includes a data acquisition unit 211 for acquiring data from the charging device. Exemplarily, the data acquisition unit 211 includes a real-time data acquisition unit 2111 and a planning-type data acquisition unit 2112. The real-time data acquisition unit 2111 is used to acquire transient parameters during the charging process, including the current charging current, the rate of change of current (di / dt), and the total equivalent resistance of the charging connection components. The current charging current refers to the current value actually output by the charging pile and flowing through the charging circuit at the current moment; the rate of change of current refers to the rate of change of the charging current with respect to time, the derivative of the current with respect to time, di / dt; the total equivalent resistance of the charging connection components refers to the equivalent total equivalent resistance value of all physical components in the charging circuit related to the charging connection, including all physical components in the charging circuit used to realize the electrical connection between the charging pile and the vehicle; these components specifically include the charging gun plug, the vehicle charging socket, the charging cable, and the internal terminals of the charging gun. The planning data acquisition unit 2112 is used to acquire global information related to the charging task, such as the preset charging curve, target SOC, expected charging time, and maximum allowable temperature rise limit.

[0056] The dual-time-domain prediction layer 22 includes a short-time-domain prediction unit 221 and a long-time-domain prediction unit 222. The short-time-domain prediction unit 221 continuously updates the temperature prediction results within a preset short time domain (a preset short time period, such as 5 seconds). It embeds a dynamic thermal balance model to reflect the transient relationship between changes in heat generation power and temperature changes caused by the cooling fluid's absorption of heat and dissipation through the heat exchanger. It also compensates for the predicted value online based on the current temperature rise deviation, thereby improving the accuracy and robustness of short-term temperature prediction. The long-time-domain prediction unit 222 (within the entire full charging cycle, such as 15 minutes to 30 minutes) updates the heat generation and temperature rise curves throughout the full charging cycle. By fitting historical data, it generates the overall heat load evolution trend and the heat generation power curve for the entire charging cycle.

[0057] The dual-closed-loop MPC control kernel layer 23 includes a long-time domain MPC outer loop unit 231 and a short-time domain MPC inner loop unit 232. The long-time domain MPC outer loop unit 231 receives the full-charge-cycle heat generation power curve output by the long-time domain prediction unit 222, and calculates and updates the water pump and fan operating reference ranges at preset time intervals (e.g., 30 seconds) with the goal of minimizing the total energy consumption of the charging equipment. The short-time domain MPC inner loop unit 232 receives the temperature prediction sequence output by the short-time domain prediction unit 221 and the real-time temperature feedback signal, and dynamically solves for water pump and fan operating parameters that satisfy temperature constraints and minimize energy consumption within the reference range set by the outer loop at shorter time intervals (e.g., per second).

[0058] The actuator coordination control layer 24 includes a water pump control unit 241 and a fan control unit 242. The water pump control unit 241 is used to adjust the speed of the water pump 12 and control the coolant circulation rate according to the flow command output by the MPC; the fan control unit 242 is used to adjust the speed of the fan 15.

[0059] The feedback correction and fault-tolerant protection layer 25 includes a parameter identification unit 251, an execution deviation closed-loop fine-tuning unit 252, and a fault-tolerant protection unit 253. The parameter identification unit 251 identifies key parameters such as system thermal resistance, thermal capacity, and pump / valve characteristics in real time, and corrects the prediction model (dynamic thermal balance model) online to improve control accuracy. The execution deviation closed-loop fine-tuning unit 252 monitors the actual execution effect (such as temperature deviation and flow error), fine-tunes and compensates the control commands, and suppresses performance drift caused by actuator nonlinearity or aging. When an anomaly is detected (such as temperature exceeding limits, communication interruption, sensor failure, or actuator jamming), the fault-tolerant protection unit 253 automatically activates safety mechanisms, including power-reduced charging, forced full-speed cooling, switching to a backup control mode, or emergency shutdown, to ensure safe and reliable system operation.

[0060] Continue to combine Figure 3 As shown, the specific execution logic is as follows: First, the transient operating parameters and full-cycle task information of the charging equipment are acquired through the real-time data acquisition unit 2111 and the planning data acquisition unit 2112 in the BMS charging data interaction layer 21. Then, the dual-time domain prediction layer 22 uses the dynamic thermal balance model embedded in the short-time domain prediction unit 221 to generate a preset temperature prediction sequence in the short time domain. Based on the current, power and temperature samples in the historical charging data, the long-time domain prediction unit 222 uses polynomial fitting or moving average methods to fit the heat generation power curve of the entire charging cycle. Based on this curve, the long-time domain MPC outer loop unit 231 determines the operating reference range of the water pump and fan in the entire charging process. Under this constraint, the short-time domain MPC inner loop unit 232 solves for the optimal water pump and fan speed control command in the target time period, with all predicted temperatures not exceeding the upper temperature limit as the constraint condition. This command is executed by the water pump control unit 241 and the fan control unit 242 in the actuator collaborative control layer 24 to control the heat dissipation of the charging equipment. During execution, the real-time data acquisition unit 2111 continuously collects the actual operating status of the charging equipment after execution and feeds back the actual operating status to three units in the feedback correction and fault tolerance protection layer 25: the parameter identification unit 251 updates the dynamic thermal balance model online based on the real-time collected operating data, and the updated model parameters are fed back to the short-time domain prediction unit 221, so that the next round of temperature prediction sequence is generated based on the updated model parameters, thereby forming a prediction model self-correction loop; the execution deviation closed-loop fine-tuning unit 252 fine-tunes the control command based on the deviation between the actual temperature and the predicted temperature, and adds the deviation as a compensation amount to the control command output by the short-time domain MPC inner loop unit 232 to obtain the deviation-compensated water pump and fan speed control commands and sends them to the water pump control unit 241 and the fan control unit 242. This forms a control command fine-tuning loop; the fault-tolerant protection unit 253 performs anomaly detection on the real-time acquired operating status. When a sensor failure, communication interruption, or temperature over-limit is detected, the bypass short-time domain MPC inner loop unit 232 directly issues a preset safety protection command to the water pump control unit 241 and the fan control unit 242, thereby forming a safety fault-tolerant protection loop; after completing the current control cycle, the real-time data acquisition unit 2111 continues to acquire the operating status of the charging equipment after the execution of the water pump control unit 241 and the fan control unit 242, and inputs the updated operating status back into the dual-time domain prediction layer 22 and the feedback correction and fault-tolerant protection layer 25, and enters the next control cycle, re-executes the prediction, optimization, control, feedback and correction process, thereby forming a rolling optimization closed-loop heat dissipation control mechanism to realize closed-loop, intelligent and reliable heat dissipation control of the charging equipment.

[0061] Optionally, the intelligent heat dissipation system 2 can be selected in different hardware application modes according to actual needs. The intelligent heat dissipation system 2 can be flexibly deployed in any or a combination of the following hardware: integrated on a single main control chip of the charging pile controller, with each module running inside the same chip; or distributed on multiple chips of the charging pile controller, with each module running on a different chip, working together through a board-level communication bus to form a complete logic processing link.

[0062] The following section, with reference to the accompanying diagrams, details the specific implementation process of the charging pile upgrade method.

[0063] Figure 4 This is a first flowchart illustrating a heat dissipation method for a charging device provided in an embodiment of this application.

[0064] like Figure 4 As shown, the heat dissipation method for the charging device includes the following steps: S1: Obtain the operating parameters of the charging device.

[0065] The operating parameters of the charging equipment include charging planning parameters and real-time operating parameters.

[0066] Real-time operating parameters refer to key electrical and thermal indicators that reflect the charging status and performance characteristics of the charging equipment during the charging process. These parameters include the target current, current change rate, total equivalent resistance of the charging connection components, real-time heat generation power, battery state of charge (SOC), and ambient temperature. The target current refers to the expected charging current value determined by the vehicle battery management system (BMS) and the charging pile control system during the current charging phase, and planned for output by the charging pile. The current change rate refers to the rate at which the charging current changes over time during charging, typically measured in amperes per second (A / s), reflecting how quickly the charging current adjusts from its current value towards the target current. The total equivalent resistance of the charging connection components refers to the total equivalent resistance value of all physical components related to the charging connection in the charging circuit, reflecting the conductivity, contact reliability, and potential heat generation and energy loss of the charging connection system. The charging connection components can include at least two of the following: a charging gun plug, a vehicle charging socket, a charging cable, and internal terminals of the charging gun. The real-time heat generation power of a charging device refers to the heat generation power of the power module calculated by the difference between the input power and the output power within each control sampling cycle (e.g., 100ms). The state of charge (SOC) of a battery represents the percentage of its total capacity remaining, a core indicator reflecting the battery's energy level, with a value ranging from 0% to 100%. For example, SOC=60% means the battery is 60% charged. Ambient temperature refers to the air temperature of the external environment where the charging device is located, collected in real-time by the charging device's built-in temperature sensor, and measured in °C.

[0067] Charging planning parameters refer to key operational and state variables recorded during past charging processes that can be used to model, analyze, or optimize current and future charging behavior. These parameters include the constant current to constant voltage switching point, the charging termination point, the upper limit charging current, and ambient temperature. The constant current to constant voltage switching point represents the battery voltage or SOC value corresponding to the switch from the constant current (CC) stage to the constant voltage (CV) stage during charging. This point is typically affected by battery chemistry, temperature, and aging status. The charging termination point characterizes the conditions for ending the current charging session. These conditions can include at least reaching a preset SOC threshold or the charging current dropping to a lower limit. In other words, if the termination condition is SOC ≥ 95%, the termination point corresponds to the SOC threshold; if the termination condition is current ≤ 0.05C, the termination point corresponds to the current threshold, such as 0.05C. The upper limit charging current represents the maximum charging current that the battery management system (BMS) or charging equipment is allowed to apply under specific environmental conditions or battery states (such as different SOC or temperature ranges).

[0068] It should be noted that in a charging station, multiple charging devices can perform charging tasks simultaneously, and each charging device can independently execute the method of the embodiments of this application. For ease of explanation, the following description uses any target charging device currently performing a charging task within the charging station as an example. The target charging device can establish a current charging session with one vehicle at a time, and subsequent steps will be described with the target charging device as the control object.

[0069] Figure 5 This is a signaling interaction diagram of a heat dissipation method for a charging device provided in an embodiment of this application.

[0070] like Figure 5 As shown, step S1 includes step S1001.

[0071] S1001: The data acquisition unit acquires the operating parameters of the target charging device.

[0072] The operating parameters may include real-time operating parameters and charging planning parameters. The target charging device mentioned above is a single charging device within the charging station that is currently performing a charging task.

[0073] For example, the data acquisition unit acquires the real-time operating parameters A and charging planning parameters B of the target charging device. The real-time operating parameter A includes the target current I, which is sent in real-time to the target charging device by the vehicle's battery management system corresponding to the current charging task. target , current change rate ki, and total equivalent resistance R corresponding to the charging connection components of the target charging device. total(Treal) Real-time heat generation power P of the target charging device heat_module_real； The charging planning parameters B include: the battery's state of charge provided by the vehicle's BMS (Battery Management System) during the current charging task, and the ambient temperature collected by the temperature sensor of the current charging equipment. The charging planning parameters B also include: the constant current to constant voltage switching point, the charging termination point, and the upper limit charging current.

[0074] S1002: The data acquisition unit sends real-time operating parameters to the short-time domain prediction unit and charging planning parameters and real-time operating parameters to the long-time domain prediction unit.

[0075] Continuing with the example above, the data acquisition unit sends real-time operating parameters A to the short-term prediction unit, and sends charging planning parameters B, along with the battery's state of charge and ambient temperature from the real-time operating parameters, to the long-term prediction unit.

[0076] S2: Based on operating parameters, determine the temperature prediction sequence and heating power curve.

[0077] The temperature prediction sequence is used to characterize multiple predicted temperature values ​​within a preset short time domain after the current control moment. The preset short time domain refers to a relatively short future time window after the current control moment used for temperature prediction; its length is set according to the requirements and response characteristics of the actual control system. Optionally, the preset short time domain can be set to 5 seconds or 10 seconds; no specific limitation is made here.

[0078] In one example, within the aforementioned time window, a temperature prediction sequence can be generated based on operating parameters and a dynamic thermal balance model. This sequence consists of a series of future temperature estimates arranged by time steps (e.g., one prediction point per second, with a total of 5 points corresponding to 5 seconds). The dynamic thermal balance model integrates a Smith predictor to dynamically correct for the pure hysteresis effect caused by the heat conduction delay in the charging device.

[0079] The heat generation power curve is used to characterize the heat generation trend of the charging device during the entire charging cycle. The entire charging cycle refers to the entire time process from the start to the end of charging; it is the complete time period experienced by the electric vehicle or battery system from the start of charging until the preset termination condition is reached. Optionally, the preset termination condition can be full charge, user interruption, or charging failure. For example, the entire charging cycle can be 15 minutes, 30 minutes, etc., without specific limitations.

[0080] It should be noted that the following explanation will take a preset short time range of 5 seconds as an example and a full charging cycle of 15 minutes as an example.

[0081] The following will continue to combine Figure 5 As shown, the specific explanation of step S2, which determines the temperature prediction sequence and the heating power curve, is provided. Step S2 includes steps S1003-S1008.

[0082] S1003: The short-time domain prediction unit generates a sequence of heating power in the preset short-time domain after the current control moment based on the target current of the charging device, the rate of change of current, multiple time points in the preset short-time domain, the total equivalent resistance, and the real-time heating power.

[0083] In one implementation, before determining the heat generation power sequence, it is necessary to fit the current in the preset short time domain based on the target current of the charging device, the rate of change of current, and multiple time points in the preset short time domain to obtain the transient current sequence in the preset short time domain.

[0084] The transient current sequence refers to the dynamic prediction sequence of the charging current over a preset short time domain after the current control moment. It can reflect the unsteady ("transient") evolution of the current in the future short period of time under the constraints of the target current and the rate of change of current.

[0085] Optionally, a linear fitting model can be used to predict the transient current sequence. For example, a linear model based on equation (1) can be built in: Formula (1) Among them, transient current sequence This represents the predicted transient current value within a preset short time domain after the current control moment. It is a function of time t, and within the preset short time domain, it provides the expected current value corresponding to each predicted time point t. This represents the target current (in A) issued at the current control moment. Target current I target The charging current value sent from the vehicle BMS to the current charging pile at the known current control moment is the target current in the operating parameter A already obtained by the data acquisition unit in step S1. The current change rate (unit: A / s) can be positive or negative, representing the current's upward or downward trend over time. t represents multiple prediction time points set within a preset short time domain, ranging from t∈[0,Thorizon]. For example, if the preset short time domain is Thorizon=5s, then t can take discrete values ​​such as 0, 1, 2, 3, 4, 5s. Higher time resolution (such as every 0.5 seconds or smaller steps) can also be used to obtain a more refined transient current sequence.

[0086] Following the example above, the short-time domain prediction unit determines the transient current sequence within 0-5s based on formula (1). , , , , ].

[0087] Following the above implementation method, after determining the transient current sequence, a heat generation power sequence within a preset short time domain is further determined. Specifically, based on the transient current sequence, the total equivalent resistance, and the real-time heat generation power, a heat generation power sequence within a preset short time domain is generated.

[0088] Optionally, a temperature-adaptive resistance model can be used to calculate the heating power sequence. For example, a temperature-adaptive resistance model based on formula (2) can be built in: Formula (2) in, To preset the heating power sequence in the short time domain, The total equivalent resistance of the charging connection components is a function of the real-time temperature Treal. At the current measured temperature Treal, the total equivalent resistance of the charging connection components (including the charging gun plug, vehicle charging socket, charging cable, and internal terminals of the charging gun) is given. This represents the real-time heat output of the current charging pile power module.

[0089] Following the example above, the heating power sequence within 0-5s is determined based on formula (2): [ , , , , , ].

[0090] S1004: Using the heating power sequence as the input to the dynamic thermal balance model, the short-time domain prediction unit uses the dynamic thermal balance model to obtain a temperature prediction sequence within a predetermined short time domain.

[0091] Traditional thermal management strategies typically use the coolant outlet temperature of the heat-generating components as the feedback basis for the system's thermal state. However, due to inherent delays in heat transfer and fluid flow, the coolant outlet temperature often fails to reflect the actual temperature of the heat source in real time—a thermal hysteresis effect exists. This can lead to problems such as: when the heat-generating components have already experienced localized overheating, the coolant outlet temperature has not yet risen significantly, causing the charging equipment to misjudge as "not overheating," thus failing to promptly enhance heat dissipation measures, resulting in the risk of thermal runaway or inaccurate heat dissipation response. To address this issue, this solution introduces thermal hysteresis compensation, employing a dynamic thermal balance model to compensate for the hysteresis effect during the thermal response process and predict the temperature evolution trend of key components in the short-term time domain, thereby achieving more precise and forward-looking thermal safety control.

[0092] Optionally, a dynamic thermal balance model can be used to predict the temperature prediction sequence. For example, a dynamic thermal balance model based on formula (3) can be built in: Formula (3) in, For temperature prediction sequences, The thermal hysteresis time is the delay between the change in heating power and the temperature response. It is obtained through thermal characteristic testing before leaving the factory and is corrected in real time through online identification during operation to achieve complete compensation for the thermal hysteresis effect. The current measured or estimated core temperature (the maximum value among the sampled values ​​of the positive and negative temperature sensors of the charging gun plug, used as the initial value for prediction). The total heat capacity of the equipment (J / K) is obtained through bench calibration. For the specific heat capacity of the coolant at constant pressure, a fixed calibration value is used. Coolant density, fixed calibration value; The volumetric flow rate of the coolant (m³ / s) is linearly related to the pump speed. This refers to the temperature difference of the coolant as it rises. This is the natural heat dissipation power, which is temperature-dependent and is the product of temperature and a prediction coefficient.

[0093] Following the example above, the short-time domain prediction unit can determine the temperature prediction sequence based on formula (3): [ , , , , , ].

[0094] It should be noted that in the process of determining the temperature prediction sequence, the short-time domain prediction unit satisfies a constraint: the predicted temperature value of the charging connection component (such as the charging gun) at each moment in the temperature prediction sequence within the preset short-time domain shall not exceed the preset upper temperature threshold Twarn(T).

[0095] Optionally, the preset upper temperature threshold Twarn(T) is not a fixed constant, but a time-varying constraint boundary dynamically determined based on multiple safety-related requirements, and can be determined based on the following formulas (4) and (5): Alternatively, formula (4): Formula (4) in, This refers to the maximum permissible temperature of the charging gun. This is a real-time ambient temperature sample value; The maximum allowable temperature rise is set to prevent sealing failure and component aging problems caused by excessive temperature rise even when the absolute temperature is within the limit in low-temperature environments. For example, ΔTrise_limit=25K.

[0096] In order to trigger the improvement of heat dissipation capacity in advance, a 10% thermal safety margin is introduced to avoid the risk of overheating from the source, as shown in formula (5): Formula (5) in, It is a preset upper temperature threshold that is dynamically determined based on the absolute temperature limit and the allowable temperature rise.

[0097] At any given moment within the preset short time domain, Tcore_pre(t) must be guaranteed to be ≤ Twarn(t). If Tcore_pre(t) ≥ Twarn(t), an over-temperature warning state is immediately triggered, and the heat dissipation power is forcibly increased. This will be explained in detail in subsequent embodiments, but will not be specifically described in this embodiment. Here, Tcore_pre(t) is the predicted temperature of the core region inside the charging device at time t, and in this embodiment, it is the predicted temperature evolution of the charging device during a charging session.

[0098] S1005: The short-time domain prediction unit sends the temperature prediction sequence to the short-time domain MPC inner loop unit.

[0099] Continuing with the example above, the short-time domain prediction unit sends a temperature prediction sequence to the short-time domain MPC inner loop unit: [ , , , , , ].

[0100] S1006: The long-term prediction unit generates the heat generation power curve for the entire charging cycle based on the charging planning parameters and real-time operating parameters of the charging equipment.

[0101] The charging planning parameters in the operating parameters include the constant current to constant voltage switching point, the charging termination point, and the upper limit of the charging current. The real-time operating parameters include the battery's state of charge and the ambient temperature.

[0102] In one possible approach, a full-cycle current curve fitting can be performed first. Specifically, based on the battery's state of charge, the constant current to constant voltage switching point, the charging termination point, and the upper limit charging current, the current planning curve Iplan(T) for the entire charging cycle is fitted.

[0103] Optionally, a linear fitting model can be used to fit the current planning curve for the entire charging cycle. For example, the linear fitting model can incorporate a formula model based on the battery's state of charge, the constant current to constant voltage switching point, the charging termination point, and the upper limit of the charging current.

[0104] Figure 6 This is a schematic diagram of a current planning curve for a full charging cycle provided in an embodiment of this application.

[0105] Continuing with the example above, such as Figure 6 As shown, the long-term prediction unit determines the current planning curve Iplan(T) for the entire charging cycle based on the battery's state of charge, the constant current to constant voltage switching point, the charging termination point, and the upper limit charging current. The current planning curve Iplan(T) for the entire charging cycle is not a real-time measurement value, but is pre-calculated based on the following key parameters and constraints using offline or near-real-time optimization algorithms.

[0106] Following the above implementation method, after determining the current planning curve for the entire charging cycle, the heat power curve for the entire charging cycle is generated by combining the ambient temperature. Specifically, the current value at each time point in the current planning curve Iplan(T) is substituted into the calculation formula of the heat power sequence, as shown in formula (2). Combined with the total equivalent resistance and the ambient temperature, the heat power value at each time point in the entire charging cycle is obtained, thereby forming the heat power curve Pheat_plan(T).

[0107] S1007: The long-time domain prediction unit sends the heat generation power curve to the long-time domain MPC outer ring unit.

[0108] Continuing with the example above, the long-time domain prediction unit sends the heat generation power curve Pheat_plan(T) for the entire charging cycle to the long-time domain MPC outer loop unit.

[0109] It should be noted that the heat dissipation power prediction curve is not generated once and then fixed, but is dynamically revised periodically (e.g., every 30 seconds). Each revision is based on the following real-time information: measured current and voltage data during the current charging process, the battery's current state of charge, and the latest charging current command or power limit. Through the fusion and updating of the above multi-source information, the heat dissipation power prediction curve is ensured to maintain a high degree of consistency with the actual charging conditions, guaranteeing that the subsequently generated heat dissipation requirements are relatively accurate.

[0110] It should be noted that the order of steps S1004-S1005 and S1006-S1007 is not specifically limited.

[0111] After completing the full-cycle prediction of heat generation power, it is necessary to first define the basic operating range of the cooling system based on the prediction results, and then further determine the specific execution values ​​within this range. There is a strict sequential dependency between the two. The following example illustrates this with reference to step S3: S3: Based on the heat generation power curve, determine the operating reference range of the charging equipment during the entire charging cycle.

[0112] The operating reference range includes the range of pump speed settings and the range of fan speed settings.

[0113] Continue to combine Figure 5 As shown, step S3 includes steps S1008-S1010.

[0114] S1008: The long-time domain MPC outer loop unit divides the entire charging cycle into multiple time nodes.

[0115] Each time point corresponds to a water pump operating parameter and a fan operating parameter. The long-time domain MPC outer loop unit divides the entire charging cycle into a series of discrete time points with non-uniform time steps. For example, it uses small step sizes in the range where current / heating power changes drastically, and large step sizes in the steady-state or slowly changing range. Alternatively, it can divide the cycle into a series of discrete time points with uniform time steps.

[0116] Continuing with the example above, let's divide the time cycle into a series of discrete time nodes with a uniform time step: assuming the entire cycle is 30 seconds, we divide the entire charging cycle into 30 time nodes.

[0117] S1009: The long-time domain MPC outer loop unit inputs the heat generation power curve into the long-time domain model predictive control model to obtain the control sequence of water pump operating parameters and fan operating parameters corresponding to each time node in the full charging cycle.

[0118] Optionally, the heat dissipation power curve Pheat_plan(T) is used as a disturbance input and substituted into a long-time domain model predictive control (MPC) model to obtain the control sequence of water pump operating parameters and fan operating parameters at each time point during the entire charging cycle. The long-time domain MPC model employs the following optimization strategy and constraints: it adopts an optimization strategy aimed at minimizing the total energy consumption of the charging equipment's heat dissipation, and the constraint is that the temperature of the charging connection components of the charging equipment is less than a preset upper temperature threshold.

[0119] Optionally, an optimization strategy aimed at minimizing the total energy consumption for heat dissipation of the charging device can be determined based on formula (6): Formula (6) in, This represents the total power consumption of the water pump; The total power consumption of the fan. Tend is the predicted charging end time, such as 30 seconds.

[0120] It should be noted that during the determination of the control sequence, the long-time domain MPC outer loop unit must strictly meet an absolutely insurmountable constraint: the predicted temperature value of the charging connection component (such as the charging gun) at each moment in the preset short-time domain temperature prediction sequence must not exceed the preset upper temperature threshold Twarn(T).

[0121] S1010: The long-time domain MPC outer loop unit generates the water pump operating reference range and the fan operating reference range within the full charging cycle based on the control sequence.

[0122] The reference interval, as a mid-level scheduling instruction, retains the necessary execution flexibility (to cope with disturbances and delays) while providing the system with a clear safe operating domain, which can effectively ensure the safety of heat dissipation of charging equipment.

[0123] For each time point, a range of allowable speed fluctuations is set around the optimal speed value at that moment, forming the operating reference interval for that moment. Specifically, the water pump operating reference interval is [Pmin, Pmax], where Pmin is the lowest allowable speed value for the water pump, and Pmax is the highest allowable speed value for the water pump; the fan operating reference interval is [Fmin, Fmax], where Fmin is the lowest allowable speed value for the fan, and Fmax is the highest allowable speed value for the fan.

[0124] Continuing with the example above, the long-time domain MPC outer loop unit generates the water pump operating reference range [Pmin, Pmax] and the fan operating reference range [Fmin, Fmax] within the full charging cycle based on the control sequence.

[0125] S1011: The long-time domain MPC outer loop unit sends the water pump operating reference range and the fan operating reference range within the full charging cycle to the short-time domain MPC inner loop unit.

[0126] Continuing with the example above, assume that both the water pump and the fan have 5 speed settings, each corresponding to a pre-set fixed RPM value. The water pump's RPM determines the coolant volumetric flow rate, while the fan's RPM determines the airflow. Higher speed settings correspond to higher RPMs, resulting in greater coolant volumetric flow rate or airflow, but also higher power consumption. For example, for the water pump, setting 1 corresponds to the lowest pump RPM, lowest coolant volumetric flow rate, and lowest power consumption, while setting 5 corresponds to the highest pump RPM, highest coolant volumetric flow rate, and highest power consumption. Similarly, for the fan, setting 1 corresponds to the lowest fan RPM, lowest airflow, and lowest power consumption, while setting 5 corresponds to the highest fan RPM, highest airflow, and highest power consumption. For clarity, the parameters for each speed setting are divided into two sub-tables, as shown in Table 1. Sub-table 1-a of Table 1 shows the RPM and coolant volumetric flow rate for each water pump speed setting, and sub-table 1-b shows the power consumption for each speed setting.

[0127] For example, as shown in Table 1-a, the long-time domain MPC outer loop unit sends the water pump operating reference range [Pmin, Pmax] and the fan operating reference range [Fmin, Fmax] within the entire charging cycle to the short-time domain MPC inner loop unit. Each gear corresponds to a fixed speed value; the higher the speed, the greater the coolant flow rate of the water pump or the greater the airflow of the fan. Taking the water pump as an example, gear 1 corresponds to the lowest water pump speed and lowest coolant flow rate, while gear 5 corresponds to the highest water pump speed and highest coolant flow rate. Similarly, for the fan, gear 1 corresponds to the lowest fan speed and lowest airflow, while gear 5 corresponds to the highest fan speed and highest airflow. Each gear corresponds to a different power consumption.

[0128] For example, assuming the water pump is at speed 4 or higher, the water pump operating reference range is: Pmin=4, Pmax=5; assuming the fan operating reference range is at speed 3 or higher, the fan operating reference range is: Fmin=3, Fmax=5.

[0129] Table 1-a

[0130] S4: Based on the operating reference interval and temperature prediction sequence, under the constraint that each predicted temperature value in the temperature prediction sequence does not exceed the preset upper temperature threshold, determine the control command.

[0131] Among them, the control commands are used to dynamically adjust the speed of the water pump and the fan within the speed range defined by the operating reference range.

[0132] Continue as Figure 5 As shown, step S4 includes steps S1012-S1013.

[0133] S1012: The short-time domain MPC inner loop unit determines the target operating parameters of the water pump and fan within the range of gears defined by the operating reference interval, based on the temperature prediction sequence and with the preset upper temperature threshold as a constraint.

[0134] Among them, the range of gears defined by the operating reference range is used to characterize the operating reference range of the water pump and the operating reference range of the fan. For example, the operating reference range of the fan is: [Fmin=3, Fmax=5] and the operating reference range of the water pump is: [Pmin=4, Pmax=5].

[0135] Figure 7 This is a schematic flowchart of a method for determining target operating parameters of a water pump and a fan, provided in an embodiment of this application.

[0136] like Figure 7 As shown, step S1012 may include steps S7001-S7003.

[0137] S7001: Divide the preset short time domain into multiple continuous control nodes.

[0138] Among them, the preset short time domain can be divided into a series of discrete time nodes according to a uniform time step.

[0139] Continuing with the example above, assuming a preset short time domain of 5 seconds, and using 1 second as the time step, divide the time domain into 5 consecutive control nodes.

[0140] S7002: For each control node, the instantaneous power consumption of the water pump is obtained based on the preset power consumption ratio coefficient and the coolant volume flow rate of the water pump, and the instantaneous power consumption of the fan is obtained based on the preset power consumption ratio coefficient and the candidate speed of the fan.

[0141] The preset power consumption ratio coefficients include the calibration ratio coefficients for the water pump and the fan.

[0142] Alternatively, the instantaneous power consumption of the water pump can be determined using formula (7): Formula (7) in, Let T be the instantaneous power consumption of the water pump at time T. η1 is the calibration proportional coefficient of the water pump, which physically represents the proportionality factor that converts the cubic term of the rotational speed into the actual electrical power. η1 can be obtained through pre-shipment thermal characteristic testing and stored in the controller as an efficiency MAP table. It is dynamically lookup-based based on the current operating point, thus improving the accuracy of power consumption estimation. Q is the coolant volumetric flow rate.

[0143] Alternatively, the instantaneous power consumption of the fan can be determined using formula (8): (8) Let be the instantaneous power consumption of the fan at time T. This is the fan's calibration proportionality factor, which physically represents the proportionality factor that converts the cubic term of the rotational speed into the actual electrical power. The power consumption is obtained through thermal characteristic testing before leaving the factory and stored in the controller in the form of an efficiency MAP table. The value is obtained by dynamically looking up the table and interpolating based on the current operating point, thereby improving the accuracy of power consumption estimation. This refers to the fan speed.

[0144] Following the example above, based on the water pump coolant volume flow rate and fan speed corresponding to each gear, and combined with formulas (7) and (8), the water pump power consumption and fan power consumption corresponding to each gear are calculated, and the correspondence between gear and power consumption is established, as shown in Table 1-b.

[0145] Table 1-b

[0146] It should be noted that Table 1-b is actually based on 1-a and is pre-established based on formulas (7) and (8).

[0147] For example, in S7002, for each candidate gear combination, the power consumption of the water pump and the power consumption of the fan corresponding to that gear combination are obtained according to Table 1-b. For example, when the candidate fan gear is 3, the corresponding fan power consumption is 80W; when the candidate water pump gear is 4, the corresponding water pump power consumption is 800W.

[0148] S7003: Based on the instantaneous power consumption of the water pump and the instantaneous power consumption of the fan, determine the water pump operating parameters and the fan operating parameters from the candidate speed combinations that meet the preset candidate conditions.

[0149] Candidate gear combinations refer to all pairwise pairings between each candidate gear within the water pump's operating reference range and each candidate gear within the fan's operating reference range. Since the operating reference range defines the range of gear values ​​for both the water pump and the fan, each candidate gear is an integer value within that range. Each candidate gear for the water pump is paired with each candidate gear for the fan one by one, enumerating all candidate gear combinations. The preset candidate conditions include the water pump's candidate speed being within its operating reference range, the fan's candidate speed being within its operating reference range, and the charging connection components of the charging device having temperatures below a preset upper temperature threshold within a preset short-term time domain. Target operating parameters characterize the energy consumption operating parameters representing the minimum sum of power consumption of the water pump and fan that satisfies the constraints; these target operating parameters include the water pump's operating parameters and the fan's operating parameters.

[0150] Continuing with the example above and referring to Table 1-a, after determining the water pump's operating baseline range as speed 4 to 5 and the fan's operating baseline range as speed 3 to 5, the candidate speeds for the water pump are speed 4 and 5, and the candidate speeds for the fan are speed 3, 4, and 5. Assume the water pump's operating baseline range is speed 4 to 5. Further, based on Table 1-b, we determine that: the power consumption corresponding to water pump speed 4 is 800W, and the power consumption corresponding to water pump speed 5 is 1000W; the power consumption corresponding to fan speed 3 is 80W, the power consumption corresponding to fan speed 4 is 150W, and the power consumption corresponding to fan speed 5 is 300W. Pairing each candidate speed of the water pump with each candidate speed of the fan, we enumerate all possible combinations. At this point, the candidate speed combinations can include water pump speed 4 and fan speed 3, water pump speed 4 and fan speed 4, water pump speed 4 and fan speed 5, water pump speed 5 and fan speed 3, water pump speed 5 and fan speed 4, and water pump speed 5 and fan speed 5. For each candidate power level combination, determine whether it meets the temperature constraint. Among all combinations that meet the temperature constraint, select the combination with the lowest total power consumption as the optimal combination. For example, if all combinations meet the temperature constraint, the combination with the lowest total power consumption is water pump at speed 4 (800W) and fan at speed 3 (80W), with a total power consumption of 880W. The target power level for the water pump is determined to be speed 4, and the target power level for the fan is determined to be speed 3.

[0151] S1013: The short-time domain MPC inner loop unit generates control commands based on the water pump operating parameters and fan operating parameters.

[0152] In one implementation, the pump operating parameter is the pump speed, and the control command is the fan speed. Based on the determined target pump speed and fan speed, and referring to Tables 1-a and 1-b (the correspondence between pump speed and fan speed), the short-time domain MPC inner loop unit determines the target fan speed and fan speed respectively to generate control commands. These control commands include a first sub-control command and a second sub-control command. The first sub-control command is the pump control command, and the second sub-control command is the fan control command. The first sub-control command includes the target pump speed, and the second sub-control command includes the target fan speed. Continuing with the example above, the target pump speed is determined to be 4, and the target fan speed is determined to be 3. Based on the correspondence between pump speed and fan speed in Table 1, the target pump speed corresponding to pump speed 4 is N4, and the target fan speed corresponding to fan speed 3 is M3. The short-time domain MPC inner loop unit generates the first sub-control command (target speed N4) and the second sub-control command (target speed M3).

[0153] S1014: The short-time domain MPC sends the first sub-control command and the second sub-control command to the water pump control unit and the fan control unit, respectively.

[0154] Continuing with the example above, the short-time domain MPC sends the first sub-control command to the pump control unit: the target speed is N4, and sends the second sub-control command to the fan control unit: the target speed is M3.

[0155] It should be noted that the water pump operating parameters and fan operating parameters support dynamic updates, and the control commands are dynamically adjusted. This will be explained in detail in step S1015.

[0156] S1015: The short-time domain MPC inner loop unit updates the pump operating parameters and fan operating parameters on a rolling basis according to a preset period to generate update control commands.

[0157] Because water pumps and fans differ in their physical response speed, control precision requirements, system energy consumption optimization, and thermal inertia, they employ different replacement cycles. For example, water pumps drive coolant circulation; the liquid has a large heat capacity and a fast response, requiring adjustment to quickly suppress temperature rise. In contrast, fans drive airflow; air has low thermal inertia but a slow flow field establishment and a lag in mechanical response. Excessively high replacement frequencies can easily cause oscillations or noise; therefore, lower frequency replacements are preferable.

[0158] The preset period refers to the time interval pre-set by the system for periodically updating the operating parameters of the water pump and the fan. The preset period includes two different sub-periods: the first update period and the second update period. The first update period is used for rolling updates of the water pump operating parameters, for example, every 100 milliseconds; the second update period is used for rolling updates of the fan operating parameters, for example, every 1 second, and the first update period is shorter than the second update period.

[0159] Among them, the pump operating parameters and / or fan operating parameters corresponding to the update control command exceed the operating reference range.

[0160] S5: Based on control commands, adjust the speed of the water pump and fan in the charging device to perform heat dissipation control.

[0161] Continue to combine Figure 5 As shown, step S5 includes steps S1016-S1017.

[0162] S1016: The water pump control unit adjusts the water pump speed in the charging device based on the first sub-control command.

[0163] Continuing with the example above, the water pump control unit adjusts the water pump speed in the charging device to level 3 based on the first sub-control command.

[0164] S1017: The fan control unit adjusts the fan speed in the charging device based on the second sub-control command.

[0165] Continuing with the example above, the fan control unit adjusts the fan speed in the charging device to level 4 based on the second sub-control command.

[0166] In summary, by predicting overall heat dissipation demand in the long-term domain to plan the optimal operating baseline for energy efficiency, and then combining this with short-term domain prediction to make real-time fine adjustments to heat dissipation parameters such as fans and water pumps, we can ensure effective heat dissipation while significantly reducing the power consumption of the cooling distribution unit itself, thus achieving a balance between heat dissipation performance and system energy efficiency.

[0167] The processes S1-S5 described above are executed under the constraint that none of them exceed the preset upper temperature threshold. If they do exceed it, it is directly marked as an over-temperature warning state, and the heat dissipation power is forcibly increased. The following section will combine... Figure 8 An example is provided.

[0168] Figure 8 This is a second flowchart illustrating a heat dissipation method for a charging device provided in an embodiment of this application.

[0169] like Figure 8 As shown, another method for heat dissipation of charging devices includes the following steps: S6: Under the constraint that any predicted temperature value in the temperature prediction sequence exceeds the preset upper temperature threshold, determine that the charging device is in an over-temperature warning state, and based on the water pump operating reference range, update the water pump operating parameters to the upper limit of the water pump operating reference range, and update the fan operating parameters to the upper limit of the fan operating reference range.

[0170] Following Table 1 in the first embodiment above, assuming the water pump operating reference range is [4, 5] and the fan operating reference range is [3, 5], under the constraint that any predicted temperature value in the temperature prediction sequence exceeds the preset upper limit threshold, the fan operating parameters are updated to the upper limit value of the fan operating reference range, level 5; the water pump operating parameters are updated to the upper limit value of the water pump operating reference range, level 5, and power consumption is not considered at this time.

[0171] In summary, when the temperature exceeds the preset upper limit threshold at any moment in the temperature prediction sequence, it indicates that the system is at risk of overheating. Pushing the water pump and fan to the highest level at the same time can maximize the coolant flow and air velocity, achieve the strongest heat dissipation effect, and prevent electronic components (such as power semiconductors, batteries, capacitors, etc.) from degrading in performance, shortening their lifespan, or even thermal runaway due to high temperature.

[0172] It should be noted that in the execution logic of the above embodiments, the system also integrates fault tolerance and fallback mechanisms for various abnormal operating conditions to ensure the continuity and robustness of the heat dissipation control closed loop: In the event of communication interruption or message anomaly, the system automatically switches to a closed-loop control strategy based on real-time sampled current within a short time domain. This switches to a simple mode of "adjusting the fan and water pump based on the current current level" to ensure temperature control. If the anomaly persists into a long time domain, the system switches to a backup thermal prediction model based on historical charging curves with the same SOC. Simultaneously, it triggers charging power derating to ensure uninterrupted control. The system then uses historical data from previous charging at the same SOC to predict heat generation and simultaneously reduces charging power (derating) to prevent overheating.

[0173] When a sudden request is made to reduce power or terminate charging, the short-time domain temperature prediction sequence is refreshed within 100 milliseconds, and the heat dissipation power is dynamically adjusted accordingly. At the same time, the original energy consumption planning task is immediately terminated at the long-time domain level, and the system seamlessly switches to the waste heat dissipation mode to avoid overcooling or energy waste. The waste heat dissipation mode continues to dissipate heat, releasing the remaining heat in the charging device to prevent temperature rebound after shutdown.

[0174] When temperature or related sensors drift or data becomes abnormal, the system calculates equivalent temperature parameters based on a real-time thermodynamic model to replace the faulty sensor data. That is, it uses the built-in "thermal model" (a mathematical formula that simulates heat generation and dissipation) to estimate a reasonable temperature value to replace the faulty sensor data; and simultaneously starts a parameter self-calibration process to attempt to repair or calibrate the sensor.

[0175] Corresponding to the above embodiments, this application also provides a complete embodiment of a heat dissipation method for a charging device. The following is a detailed description... Figure 9 An example is provided.

[0176] Figure 9 This is a schematic diagram of a complete heat dissipation method for a charging device provided in an embodiment of this application.

[0177] like Figure 9 As shown, the heat dissipation method for the charging device includes the following steps: S901: Collects operating parameters and historical charging data parameters of the charging equipment.

[0178] The specific details of step S901 can be found in step S1 above, and will not be repeated here.

[0179] S902: Determine the unique constraint boundary.

[0180] The specific details of step S901 can be found in formulas (4) and (5) under step S1004 above, and will not be repeated here.

[0181] S903: Short-time domain prediction of temperature prediction sequence and long-time domain prediction of heating power curve.

[0182] The specific details of step S903 can be found in step S2 above, and will not be repeated here.

[0183] S904: Determine whether the temperature prediction values ​​in the temperature prediction sequence exceed the constraint boundary.

[0184] S905: When the constraint boundary is exceeded, determine that the charging equipment is in an over-temperature warning state, and update the water pump operating parameters to the upper limit of the water pump operating reference range, and update the fan operating parameters to the upper limit of the fan operating reference range, based on the water pump operating reference range.

[0185] The specific details of steps S904-S905 can be found in step S6 above, and will not be repeated here.

[0186] S906: Without exceeding the constraint boundary, determine the operating reference range of the charging equipment during the entire charging cycle based on the heat generation power curve.

[0187] The specific details of step S906 can be found in step S3 above, and will not be repeated here.

[0188] S907: Based on the operating reference range and temperature prediction sequence within the full charging cycle, and under the constraint that each predicted temperature value in the temperature prediction sequence does not exceed the preset upper temperature threshold, the control command is determined.

[0189] The specific details of step S907 can be found in step S4 above, and will not be repeated here.

[0190] S908: Based on control commands, it adjusts the speed of the fan and water pump to perform heat dissipation operations.

[0191] The specific details of step S908 can be found in step S5 above, and will not be repeated here.

[0192] In summary, by predicting overall heat dissipation demand in the long-term domain to plan the optimal operating baseline for energy efficiency, and then combining this with short-term domain prediction to make real-time fine adjustments to heat dissipation parameters such as fans and water pumps, we can ensure effective heat dissipation while significantly reducing the power consumption of the cooling distribution unit itself, thus achieving a balance between heat dissipation performance and system energy efficiency.

[0193] Corresponding to the above embodiments, this application also provides a heat dissipation device for a charging device.

[0194] Figure 10 This is a schematic diagram of a heat dissipation device for a charging device provided in an embodiment of this application.

[0195] like Figure 10 As shown, the heat dissipation device 1000 of the charging equipment includes: an acquisition module 1001, a determination module 1002, an execution module 1003, and an update module 1004.

[0196] Specifically, the acquisition module 1001 corresponds to the data acquisition unit; the determination module 1002 corresponds to the short-term prediction unit 221, the long-term prediction unit 222, the long-term MPC outer loop unit 231, and the short-term MPC inner loop unit 232; the execution module 1003 corresponds to the water pump control unit 241 and the fan control unit 242; and the update module 1004 corresponds to the short-term prediction unit 221.

[0197] The acquisition module 1001 is used to acquire the operating parameters of the charging equipment; the determination module 1002 is used to determine the temperature prediction sequence and the heat generation power curve based on the operating parameters; wherein, the temperature prediction sequence is used to characterize multiple predicted temperature values ​​in a preset short time domain after the current control moment; the heat generation power curve is used to characterize the heat generation change trend of the charging equipment during the entire charging cycle; the determination module is also used to determine the operating reference range of the water pump and fan of the charging equipment during the entire charging cycle based on the heat generation power curve; wherein, the operating reference range includes the speed range of the water pump and the speed range of the fan; the determination module 1002 is also used to determine the control command based on the operating reference range and the temperature prediction sequence, under the constraint that each predicted temperature value in the temperature prediction sequence does not exceed a preset upper temperature threshold; the execution module 1003 is used to adjust the speed of the water pump and fan in the charging equipment based on the control command to perform heat dissipation control.

[0198] In one possible implementation, the determining module 1002 is further configured to determine the target operating parameters of the water pump and the fan within the speed range defined by the operating reference interval, based on the temperature prediction sequence and with a preset upper temperature threshold as a constraint; the target operating parameters are used to characterize the energy consumption operating parameters that minimize the sum of the power consumption of the water pump and the power consumption of the fan under the condition of satisfying the constraint; and to generate control commands based on the water pump operating parameters and the fan operating parameters.

[0199] In another possible implementation, the update module 1004 is used to perform rolling updates on the water pump operating parameters and fan operating parameters based on a preset cycle to generate update control commands. The water pump operating parameters are updated according to a first update cycle, and the fan operating parameters are updated according to a second update cycle, with the first update cycle being shorter than the second update cycle. The update control command corresponds to a target gear exceeding the operating reference range. The target gear includes the target gear for both the water pump and the fan.

[0200] In another possible implementation, the determining module 1002 is further configured to generate a heat generation power sequence within a preset short time domain after the current control moment based on the target current, current change rate, multiple time points within a preset short time domain, total equivalent resistance, and real-time heat generation power of the charging device; use the heat generation power sequence as input to the dynamic thermal balance model, and use the dynamic thermal balance model to obtain a temperature prediction sequence within the preset short time domain; and generate a heat generation power curve within the entire charging cycle based on the historical charging data parameters of the charging device.

[0201] In another possible implementation, the determining module 1002 is also used to fit the current in the preset short time domain to the target current, current change rate, and multiple time points in the preset short time domain of the charging device, thereby obtaining a transient current sequence in the preset short time domain; and to generate a heating power sequence in the preset short time domain based on the transient current sequence, the total equivalent resistance, and the real-time heating power.

[0202] In another possible implementation, the determining module 1002 is also used to fit the current planning curve for the entire charging cycle based on the battery's state of charge, constant current to constant voltage switching point, charging termination point, and upper limit charging current in the historical charging data parameters; and to generate the heat generation power curve for the entire charging cycle based on the current planning curve and the ambient temperature.

[0203] In another possible implementation, the determining module 1002 is further used to divide the entire charging cycle into multiple time nodes; each time node corresponds to a water pump operating parameter and a fan operating parameter; the heat generation power curve is input into the long-term domain model predictive control model to obtain the control sequence of the water pump operating parameters and fan operating parameters corresponding to each time node in the entire charging cycle; wherein the long-term domain model predictive control model adopts an optimization strategy with the goal of minimizing the total heat dissipation energy consumption of the charging device, and the model is constrained by the temperature of the charging connection component of the charging device being less than a preset upper temperature threshold; based on the control sequence, the water pump operating reference interval and the fan operating reference interval in the entire charging cycle are generated.

[0204] In another possible implementation, the determining module 1002 is further configured to divide a preset short-time domain into multiple consecutive control nodes; for each control node, based on a preset power consumption ratio coefficient and the coolant volume flow rate of the water pump, the instantaneous power consumption of the water pump is obtained, and based on a preset power consumption ratio coefficient and the candidate speed of the fan, the instantaneous power consumption of the fan is obtained; based on the instantaneous power consumption of the water pump and the instantaneous power consumption of the fan, the water pump operating parameters and the fan operating parameters are determined from the candidate speed combinations that meet the preset candidate conditions; wherein, the preset candidate conditions include the candidate speed of the water pump being within the water pump operating reference range, the candidate speed of the fan being within the fan operating reference range, and the temperature of the charging connection component of the charging device being less than the preset upper temperature threshold within the preset short-time domain.

[0205] In another possible implementation, the determining module 1002 is further configured to determine that the charging device is in an over-temperature warning state under the hard constraint that any predicted temperature value in the temperature prediction sequence exceeds the preset upper temperature threshold, and update the target gear of the water pump to the upper limit value of the water pump operating reference range and update the target gear of the fan to the upper limit value of the fan operating reference range based on the water pump operating reference range.

[0206] Figure 11This is a schematic diagram of a computing device provided in an embodiment of this application.

[0207] like Figure 11 As shown, the computing device 1100 includes a processor 1101 and a memory 1102. Exemplarily, the computing device 1100 may also include a communications interface 1103 and a communications bus 1104.

[0208] The processor 1101, memory 1102, and communication interface 1103 communicate with each other via communication bus 1104. The communication interface 1103 may include a transmitter and receiver for communicating with other devices or communication networks. It can be a wired interface (port), such as a fiber distributed data interface (FDDI) or a gigabit Ethernet interface (GE).

[0209] In some embodiments, the processor 1101 is used to execute program 1105, which may specifically execute the relevant steps in the above-described embodiments of the heat dissipation method for the charging device. Specifically, program 1105 may include program code, which includes computer-executable instructions.

[0210] For example, processor 1101 may be a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement some embodiments of this application. Computing device 1100 may include one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs. The CPU may be a single-core CPU or a multi-core CPU.

[0211] In some embodiments, memory 1102 is used to store program 1105. Memory 1102 may include high-speed random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0212] Specifically, program 1105 can be called by processor 1101 to cause computing device 1100 to perform heat dissipation operations on charging device.

[0213] Some embodiments of this application provide a computer-readable storage medium storing at least one executable instruction that, when executed on a computing device 1100, causes the computing device 1100 to perform the heat dissipation method of the charging device described in the above embodiments.

[0214] For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device.

[0215] This application provides a chip system in several embodiments, which is applied to a server. The chip system includes one or more interface circuits and one or more processors. The interface circuits and processors are interconnected via lines. The interface circuits are used to receive signals from the server's memory and send signals to the processors, the signals including computer instructions stored in the memory. When the server processor executes the computer instructions, the server performs various steps in the heat dissipation method of the charging device shown in the above-described method embodiments.

[0216] The beneficial effects that the readable storage medium provided in some embodiments of this application can achieve can be referred to the beneficial effects in the corresponding task reasoning and execution methods provided above, and will not be repeated here.

[0217] It should be noted that, in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0218] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0219] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0220] For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0221] More specific examples of computer-readable media (a non-exhaustive list) include the following: electrical connections having one or more wires (electronic devices), portable computer disks (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM).

[0222] Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory. It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof.

[0223] In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. The above embodiments are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made based on the technical solutions of this application should be included within the scope of protection of this application.

Claims

1. A heat dissipation method for a charging device, characterized in that, include: Obtain the operating parameters of the charging device; Based on the operating parameters, a temperature prediction sequence and a heat generation power curve are determined; wherein, the temperature prediction sequence is used to characterize multiple predicted temperature values ​​within a preset short time domain after the current control moment; the heat generation power curve is used to characterize the heat generation change trend of the charging device during the entire charging cycle; Based on the heat generation power curve, the operating reference range of the water pump and fan of the charging equipment during the entire charging cycle is determined; wherein, the operating reference range includes the speed range of the water pump and the speed range of the fan. Based on the operating reference range and the temperature prediction sequence, and under the constraint that each predicted temperature value in the temperature prediction sequence does not exceed a preset upper temperature threshold, a control command is determined. Based on the control command, the speed of the water pump and fan in the charging device is adjusted to perform heat dissipation control.

2. The heat dissipation method for the charging device according to claim 1, characterized in that, The control commands determined based on the operating reference interval and the temperature prediction sequence, under the constraint that each predicted temperature value in the temperature prediction sequence does not exceed a preset upper temperature threshold, include: Based on the temperature prediction sequence, and with the preset upper temperature threshold as a constraint, the target operating parameters of the water pump and the fan are determined within the speed range defined by the operating reference interval. The target operating parameters are used to characterize the energy consumption operating parameters that minimize the sum of the power consumption of the water pump and the power consumption of the fan while satisfying the constraint. The target operating parameters include the water pump operating parameters and the fan operating parameters. The control command is generated based on the water pump operating parameters and the fan operating parameters.

3. The heat dissipation method for the charging device according to claim 2, characterized in that, Also includes: Based on a preset cycle, the operating parameters of the water pump and the operating parameters of the fan are updated on a rolling basis to generate an update control command; wherein, the operating parameters of the water pump are updated on a rolling basis according to a first update cycle, and the operating parameters of the fan are updated on a rolling basis according to a second update cycle, and the first update cycle is shorter than the second update cycle; wherein, the target gear corresponding to the update control command exceeds the operating reference range.

4. The heat dissipation method for the charging device according to any one of claims 1-3, characterized in that, in, The operating parameters include the target current of the charging device, the rate of change of current, the total equivalent resistance of the charging connection components of the charging device, and the real-time heating power of the charging device. The step of determining the temperature prediction sequence and the heating power curve based on the operating parameters includes: Based on the target current of the charging device, the rate of change of current, multiple time points in the preset short time domain, the total equivalent resistance, and the real-time heating power, a heating power sequence in the preset short time domain after the current moment is generated. Using the heat generation power sequence as input to the dynamic heat balance model, the temperature prediction sequence within the preset short time domain is obtained using the dynamic heat balance model. Based on the historical charging data parameters of the charging device, the heat generation power curve is generated over the entire charging cycle.

5. The heat dissipation method for the charging device according to claim 4, characterized in that, The step of generating a heat generation power sequence within the preset short-time domain after the current control moment, based on the target current of the charging device, the rate of change of current, multiple time points within the preset short-time domain, the total equivalent resistance, and the real-time heat generation power, includes: The target current of the charging device, the rate of change of current, and multiple time points within the preset short time domain are used to fit the current within the preset short time domain to obtain the transient current sequence within the preset short time domain. Based on the transient current sequence, the total equivalent resistance, and the real-time heating power, the heating power sequence within the preset short time domain is generated.

6. The heat dissipation method for the charging device according to claim 4 or 5, characterized in that, The operating parameters also include the battery's state of charge, constant current to constant voltage switching point, charging termination point, upper limit charging current, and ambient temperature. The process of generating the heat generation power curve over the entire charging cycle based on the operating parameters of the charging equipment includes: Based on the battery's state of charge, constant current to constant voltage switching point, charging termination point, and upper limit charging current in the operating parameters, a current planning curve for the entire charging cycle is obtained by fitting. The heat generation power curve for the entire charging cycle is generated based on the current planning curve and the ambient temperature.

7. The heat dissipation method for the charging device according to claim 1, characterized in that, The step of determining the operating reference range of the charging device within the entire charging cycle based on the heat generation power curve includes: The full charging cycle is divided into multiple time nodes; each time node corresponds to a water pump operating parameter and a fan operating parameter. The heat generation power curve is input into the long-term domain model predictive control model to obtain the control sequence of the water pump operating parameters and the fan operating parameters corresponding to each time node in the full charging cycle; wherein, the long-term domain model predictive control model adopts an optimization strategy with the goal of minimizing the total heat dissipation energy consumption of the charging device, and the model is constrained by the temperature of the charging connection component of the charging device being less than the preset upper temperature threshold. Based on the control sequence, the water pump operating reference range and the fan operating reference range are generated during the full charging cycle.

8. The heat dissipation method for the charging device according to claim 2, characterized in that, Based on the temperature prediction sequence, and with the preset upper temperature threshold as a constraint, the target operating parameters of the water pump and fan are determined within the speed range defined by the operating reference interval, including: The preset short time domain is divided into multiple consecutive control nodes; For each control node, the instantaneous power consumption of the water pump is obtained based on a preset power consumption ratio coefficient and the coolant volume flow rate of the water pump, and the instantaneous power consumption of the fan is obtained based on a preset power consumption ratio coefficient and the candidate speed of the fan. Based on the instantaneous power consumption of the water pump and the instantaneous power consumption of the fan, the water pump operating parameters and the fan operating parameters are determined from the candidate speed combinations that meet the preset candidate conditions; wherein, the preset candidate conditions include the candidate speed of the water pump being within the water pump operating reference range, the candidate speed of the fan being within the fan operating reference range, and the temperature of the charging connection component of the charging device being less than the preset temperature upper limit threshold in a preset short time domain.

9. The heat dissipation method for the charging device according to any one of claims 1-8, characterized in that, Also includes: Under the hard constraint that any predicted temperature value in the temperature prediction sequence exceeds the preset upper temperature threshold, the charging device is determined to be in an over-temperature warning state. Based on the water pump operating reference range, the target speed of the water pump is updated to the upper limit of the water pump operating reference range, and the target speed of the fan is updated to the upper limit of the fan operating reference range.

10. A charging device, characterized in that, The charging device includes a memory and a processor; the memory and the processor are coupled; the memory is used to store computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the charging device to apply the heat dissipation method of the charging device as described in any one of claims 1 to 8.

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