Energy storage converter heat dissipation control method and device and energy storage system

Abstract

This application provides a method, device, and energy storage system for heat dissipation control of an energy storage converter. Based on historical power time-series data of the energy storage converter, an AI model is used to predict the power output of the converter within a preset future time period. The start / stop priority of each cooling fan in the cooling fan cluster is determined based on the cumulative operating time of each fan. Target control parameters for the cooling fan cluster are output based on the start / stop priority and the predicted power output. The rotational speed of some or all cooling fans in the cooling fan cluster is adjusted according to the target control parameters at the current moment. This application enables precise on-demand heat dissipation based on the real-time operating status of the converter, significantly reducing auxiliary power consumption, extending the service life of fans and the converter, and improving the system's thermal safety and intelligence level.

Description

Technical Field

[0001] This application relates to the field of energy storage, specifically to a heat dissipation control method, device, and energy storage system for an energy storage converter. Background Technology

[0002] The heat dissipation of energy storage converters mainly adopts the traditional fixed-speed direct-blowing control method of fans. The fans run at a constant speed and cannot be flexibly adjusted, which causes the fans to continue to run idle when heat dissipation is not needed. This results in high auxiliary power consumption and a shortened fan life. At the same time, this directional heat dissipation method is difficult to achieve precise heat dissipation on demand. Power devices are prone to thermal fatigue due to uneven thermal stress. The overall level of intelligence is low and cannot meet the needs of dense heat dissipation and thermal safety control. Summary of the Invention

[0003] In view of this, this application provides a heat dissipation control method, device and energy storage system for an energy storage converter, in order to solve the problems of high energy consumption and short lifespan caused by constant fan speed operation in the prior art, and the inability to flexibly adjust according to the operating status to achieve precise heat dissipation on demand.

[0004] In a first aspect, embodiments of this application provide a heat dissipation control method for an energy storage converter, including: Based on the historical power time-series data of the energy storage converter, an AI model is used to predict the power of the energy storage converter in a future preset time period. The start / stop priority of each cooling fan is determined based on the cumulative running time of each cooling fan in the cooling fan cluster. Based on the start / stop priority and the power prediction value, output the target control parameters of the cooling fan cluster; Adjust the rotational speed of some or all of the cooling fans in the cooling fan cluster at the current moment according to the target control parameters.

[0005] In one optional embodiment, based on the historical power time-series data of the energy storage converter, an AI model is used to predict the predicted power value of the energy storage converter within a preset future time period, including: Obtain the historical power time-series curve of the energy storage converter sampled at a first preset time interval within a historical period; The actual power time-series curve for the current time period is compared with the historical power time-series curve for the corresponding time period to predict the power forecast value for the future preset time period.

[0006] In one optional embodiment, the start / stop priority of each cooling fan is determined based on the cumulative running time of each cooling fan in the cooling fan cluster, including: Sort the cooling fans in ascending order of cumulative running time; Cooling fans with shorter cumulative running time are assigned a higher start / stop priority.

[0007] In one optional embodiment, the target control parameters of the cooling fan cluster are output based on the start / stop priority and the predicted value, including: The number of target fans that need to be started in the cooling fan cluster is dynamically determined based on the power prediction value. The top N cooling fans with the highest start / stop priority are identified as the cooling fans that need to be started, where N is greater than or equal to the target number of fans.

[0008] In an optional embodiment, it further includes: Real-time monitoring of the operating status of each cooling fan; When any cooling fan is detected to be faulty, the faulty fan is marked as disabled and removed from the start / stop priority queue; Identify the neighboring fans of the faulty fan and increase the speed of the neighboring fans; Report the location information of the faulty fan.

[0009] In an optional embodiment, a round-robin load balancing step is also included: The cooling fan cluster is polled and traversed at a preset time period; The cumulative running time of each cooling fan is compared. When the difference between the cumulative running time of a certain cooling fan and the minimum cumulative running time in the cooling fan cluster exceeds a preset threshold, the current cooling fan is turned off and the cooling fan with the minimum cumulative running time is turned on.

[0010] In one optional embodiment, the target control parameter is the target speed or target PWM duty cycle of each cooling fan; When adjusting the speed according to the target control parameters, the cooling fan is controlled by PWM speed regulation.

[0011] In one optional embodiment, the cooling fan cluster includes multiple cooling fans installed at different functional modules inside the energy storage converter. Each cooling fan has a one-to-one positional relationship with its corresponding functional module, and each cooling fan is configured to provide localized cooling for its corresponding functional module.

[0012] In one optional embodiment, the target control parameters of the cooling fan cluster are output based on the start / stop priority and the power prediction value, including: Obtain the distribution data of heat points in each functional module; The heat generation of each functional module under different operating power ranges is determined based on the heat point distribution data. Based on the different power prediction values ​​and corresponding heat generation of each functional module, the target speed of each cooling fan is dynamically determined.

[0013] In an optional embodiment, it further includes: Calculate the cumulative thermal stress data of each functional module inside the energy storage converter; Based on the accumulated thermal stress data and the accumulated operating time of each cooling fan, the estimated remaining lifespan of each cooling fan is determined. The start / stop priority of each cooling fan is adjusted based on the estimated remaining lifespan, wherein the start / stop priority is positively correlated with the estimated remaining lifespan.

[0014] Secondly, embodiments of this application provide a heat dissipation control device for an energy storage converter, comprising: The AI ​​prediction module is used to predict the power of the energy storage converter within a preset future time period based on the historical power time series data of the energy storage converter and using an AI model. The determination module is used to determine the start / stop priority of each cooling fan based on the cumulative running time of each cooling fan in the cooling fan cluster; The determining module is also used to output target control parameters for the cooling fan cluster based on the start / stop priority and the power prediction value; The control module is used to adjust the rotational speed of some or all of the cooling fans in the cooling fan cluster at the current moment according to the target control parameters.

[0015] In an optional embodiment, the device further includes: The fault handling module is used to monitor the operating status of each cooling fan in real time; when any cooling fan is detected to be faulty, the faulty fan is marked as disabled and removed from the start / stop priority queue; the neighboring fans of the faulty fan are identified and the speed of the neighboring fans is increased; and the location information of the faulty fan is reported.

[0016] In an optional embodiment, the device further includes: The life monitoring module is used to calculate the cumulative thermal stress data of each functional module inside the energy storage converter; based on the cumulative thermal stress data and the cumulative operating time of each cooling fan, it determines the estimated remaining life of each cooling fan; and adjusts the start-stop priority of each cooling fan based on the estimated remaining life, wherein the start-stop priority is positively correlated with the estimated remaining life.

[0017] Thirdly, embodiments of this application provide an energy storage system, including: An electronic device includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method described in any of the first aspects above. A computer-readable storage medium comprising a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method described in any of the first aspects.

[0018] This application uses historical power time-series data of the energy storage converter and an AI model to predict the power output of the energy storage converter within a preset future time period. It determines the start / stop priority of each cooling fan in the cooling fan cluster based on the cumulative operating time of each fan. Based on the start / stop priority and the predicted power output, it outputs target control parameters for the cooling fan cluster. The application then adjusts the speed of some or all of the cooling fans in the cooling fan cluster at the current moment according to the target control parameters. This application enables precise on-demand cooling based on the real-time operating status of the converter, significantly reducing auxiliary power consumption, extending the service life of the fans and the converter, and improving the system's thermal safety and intelligence level. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic flowchart illustrating a heat dissipation control method for an energy storage converter provided in an embodiment of this application; Figure 2 A schematic diagram illustrating an example of a heat dissipation control method for an energy storage converter provided in this application embodiment; Figure 3 A schematic diagram of the structure of a heat dissipation control device for an energy storage converter provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0022] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0023] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0025] The method described in this application embodiment can be applied to energy storage systems to perform AI prediction and dynamic collaborative control of fan clusters. Figure 1 A flowchart illustrating a heat dissipation control method for an energy storage converter provided in this application embodiment is shown below. Figure 1 As shown, the method may include: Step 101: Based on the historical power time series data of the energy storage converter, use an AI model to predict the power forecast value of the energy storage converter in the future preset time period.

[0026] The energy storage system continuously collects and stores historical power time-series data, such as historical power time-series curves sampled at a first preset time interval (e.g., every 1 minute, 3 minutes, or 15 minutes). These curves record the power changes of the energy storage converter over a past period (e.g., the previous 24 hours). The AI ​​model uses this historical power time-series data to identify the patterns and trends in the periodic changes of the energy storage converter's power over time. Specifically, the model compares the actual power time-series curve for the current time period with the historical power time-series curve for the same time period the previous day, and combines this with slowly varying characteristics such as temperature and load to predict the power forecast for a short period in the future (e.g., the next 5-15 minutes). For example, the AI ​​model predicts that at 10:00 AM the next day, the converter will perform a 30-minute full-power (100%) discharge task. This prediction result will be generated in advance (e.g., 09:59) to provide a basis for subsequent fan pre-start.

[0027] Step 102: Determine the start / stop priority of each cooling fan based on the cumulative running time of each cooling fan in the cooling fan cluster.

[0028] The energy storage converter is internally equipped with a fan cluster, which consists of multiple cooling fans installed in different functional modules (such as DC filter modules, rectifier-inverter modules, AC filter modules, etc.). The energy storage system maintains an operating log for each cooling fan, recording its cumulative operating time in real time. When a fan needs to be started, the energy storage system reads the cumulative operating time of each cooling fan and sorts them in ascending order. The shorter the cumulative operating time of a fan, the greater its remaining lifespan potential, and therefore it is assigned a higher start / stop priority. This strategy aims to balance the workload of all fans, preventing individual fans from aging prematurely due to prolonged operation, thereby extending the overall lifespan of the entire fan cluster.

[0029] Step 103: Output the target control parameters for the cooling fan cluster based on start / stop priority and power prediction values.

[0030] The energy storage system combines the predicted power value output by the AI ​​model with the start / stop priority determined based on cumulative operating time to generate precise control commands. First, the energy storage system dynamically determines the target number of fans to be activated in the cooling fan cluster based on the predicted power value generated in step 101, to meet future cooling demands. For example, if the predicted power value is 20% of the rated power (less than 30% can be considered a light load), the energy storage system determines that only one cooling fan needs to be activated for low-speed disturbance cooling; if the predicted power value is 80% of the rated power (greater than 70% can be considered a heavy load), the energy storage system determines that all four cooling fans need to be activated for forced cooling.

[0031] Secondly, the energy storage system identifies the top N cooling fans with the highest start / stop priority as the fans to be activated, where N is greater than or equal to the target number of fans. For example, if two fans need to be activated, the energy storage system obtains the fan identifiers of the two fans with the shortest and second-longest cumulative running time. Finally, based on the predicted power value and a preset "power-PWM duty cycle" mapping table, the energy storage system calculates the target PWM duty cycle for each cooling fan to be activated. This duty cycle directly determines the target fan speed, airflow, and air pressure. For example, when the predicted power value is 60% of the rated power, the corresponding PWM duty cycle is 60%, and the target speed is 3500 RPM. When the predicted power value is 20% of the rated power, the energy storage system outputs a PWM duty cycle of 30%, driving the corresponding fan to run at a low speed of approximately 2000 RPM. At this speed, the airflow is low, the noise is low, and the system primarily maintains basic cooling and reduces auxiliary power consumption. When the predicted power value is 90% of the rated power, the energy storage system outputs a PWM duty cycle of 100%, driving the corresponding fan to run at full speed of 5000RPM. At this time, the air volume is at its maximum and forced heat dissipation is achieved, ensuring that the power devices do not overheat under heavy load conditions.

[0032] Through the above method, the target control parameters in this embodiment include two parts: first, the fan identifier of the cooling fan to be started, which is determined by the start / stop priority ranking result, that is, selecting the top N fans with the shortest cumulative running time from the cooling fan cluster as the execution targets of this cooling task; second, the target PWM duty cycle corresponding to the cooling fan to be started, which is calculated from the power prediction value through the "power-PWM duty cycle" mapping table, and is used to precisely control the speed, airflow, and air pressure of each fan. The combination of the two parts solves both the start / stop decision problem of which fan to work and the speed control problem of how hard the fan should work, thereby ensuring that the energy storage system meets the cooling requirements while achieving balanced fan life and optimized energy consumption.

[0033] Step 104: Adjust the speed of some or all of the cooling fans in the cooling fan cluster at the current moment according to the target control parameters.

[0034] The energy storage system sends a PWM signal with a corresponding duty cycle to the fan drive module, for example, a PWM signal with a 60% duty cycle and a frequency of 25kHz. Upon receiving this signal, the fan drive module immediately adjusts the terminal voltage of the fan motor, driving the fan to begin smooth operation at the target speed (3500 RPM). This adjustment can be completed before the predicted high power output (e.g., one minute in advance). When the full-power discharge task actually begins at 10:00 AM the next day, the ambient temperature inside the energy storage converter and the temperature of critical power components have already been reduced to a lower initial level due to the pre-operation of the cooling fan, effectively suppressing temperature spikes and achieving feedforward control of the energy storage converter.

[0035] Through the above steps, the embodiments of this application realize closed-loop, intelligent, and feedforward control of the heat dissipation of the energy storage converter, which significantly improves the heat dissipation efficiency, reduces the energy consumption of the auxiliary power supply, and extends the service life of the fan and the entire converter.

[0036] In one optional embodiment, the energy storage system can monitor the operating status of each cooling fan in the cooling fan cluster in real time. Specifically, the cooling fans are equipped with speed feedback signal lines or current detection circuits. The energy storage system collects the actual speed of each cooling fan at a fixed period (e.g., every 100 milliseconds) and compares it with the target speed corresponding to the drive PWM signal. If the actual speed of a fan is continuously lower than 80% of the target speed and exceeds a preset judgment time (e.g., 2 seconds), or its feedback signal is completely lost, the fan is determined to have failed.

[0037] When a cooling fan failure is detected, the energy storage system performs the following actions: First, it marks the failed fan as "disabled" and removes it from the dynamically maintained start / stop priority queue, excluding it from all subsequent fan start / stop decisions. Second, the system identifies the neighboring fans of the failed fan and increases their speeds. Specifically, the system identifies the neighboring fans based on a pre-stored list containing the actual installation locations of each fan (e.g., row and column numbers in the chassis, relative distances, or heat dissipation area associations). For example, if fan F2, located directly above the "rectifier-inverter module," fails, the system automatically looks up its neighboring fans as F1 (left) and F3 (right). Then, the system increases their speeds by increasing the PWM duty cycle sent to F1 and F3, for example, by 30% for each, to compensate for the lost airflow and pressure from F2, ensuring that the cooling requirements of critical modules are still met. Finally, the energy storage system generates alarm information including the faulty fan number, fault type, and precise location (such as "fan above the rectifier-inverter module on the second layer of the chassis"), and reports it to the host computer or operation and maintenance platform of the energy storage system in real time, prompting maintenance personnel to replace the faulty fan in a timely manner.

[0038] This application embodiment achieves rapid response and accurate alarm for fan failures by accurately identifying and disabling faulty fans and relying on nearby fans to maintain the cooling effect, thereby enhancing the reliability and maintainability of the energy storage system.

[0039] In one optional embodiment, the energy storage system can perform fan polling balancing control during the operation of the energy storage converter to balance the cumulative operating time of each cooling fan, thereby extending the lifespan of the entire fan cluster. Specifically, the energy storage system triggers a balancing detection task at preset time intervals (e.g., every hour). Within each cycle, the energy storage system iterates through all fans in the cooling fan cluster (including currently running and stopped fans) using a polling method, reading the cumulative operating time of each fan. The energy storage system first selects the fan with the minimum current cumulative operating time from the cluster. Then, the cumulative running time of each currently running fan is compared one by one. and The difference ΔT between them. When the ΔT of a certain running fan exceeds a preset threshold (e.g., 20 hours), the energy storage system determines that the fan has been over-running and needs to be rotated. The energy storage system can shut down the current cooling fan and start the cooling fan with the shortest cumulative running time.

[0040] Specifically, the energy storage system identifies the fan with the shortest cumulative running time from among the currently inactive fans (i.e., the corresponding fan). The system selects a target fan (the one with the longest cumulative running time). Then, it sends a start command to the target fan and gradually increases its PWM duty cycle to the required cooling level. Once the target fan is running normally and the airflow meets the requirements, it sends a shutdown command to the fan with the longest cumulative running time, causing it to stop completely. Finally, the system updates the cumulative running time records of both cooling fans. If multiple running fans exceed the threshold during a single iteration, the system can rotate them sequentially according to ΔT from largest to smallest, rotating only one pair of fans at a time to avoid large-scale switching in a short period causing fluctuations in cooling capacity.

[0041] Through the polling and balancing steps described above, the cumulative operating time of all fans is dynamically maintained at a similar level, preventing a few fans from aging and failing prematurely due to long-term continuous operation, thereby improving the overall lifespan balance of the fan cluster and the reliability of the energy storage system.

[0042] In one optional embodiment, multiple cooling fans in the cooling fan cluster can be installed at different functional modules inside the energy storage converter. Due to differences in module characteristics, the heat generation of different functional modules at different power levels is not uniform. The energy storage system can determine the fan speed corresponding to each functional module based on the power and heat generation of each module. Heat generation refers to the heat generated per unit time, reflecting the intensity of current heat generation and the temperature level. Specifically, the energy storage converter may include: a DC filter module, an AC filter module, a rectifier-inverter module, an AC output module, an auxiliary power supply module, and a converter control module. One or more dedicated cooling fans are installed near or directly above each functional module, forming a one-to-one positional relationship. (Refer to...) Figure 2 The rectifier-inverter module corresponds to fan F1, the DC filter module to fan F2, the AC filter module to fan F3, the AC output module to fan F4, and the auxiliary power supply module and converter control module to fan Fn, etc. Each fan is configured to primarily provide localized cooling for its corresponding functional module, while also providing auxiliary cooling for adjacent areas.

[0043] The energy storage system pre-acquires and stores heat dissipation data for each functional module within the energy storage converter. This data includes the typical heat generation of each module at different operating power ranges and the coordinates of its hotspot locations. For example, the rectifier-inverter module experiences a surge in switching losses above 70% power, resulting in heat generation that can be more than three times that of other modules. While the DC filter module generates relatively little heat under light load, its heat generation increases at high power due to increased current ripple. The energy storage system can pre-store a "power-heat generation" mapping table for each functional module, allowing users to look up the heat generation at different power levels.

[0044] The energy storage system independently calculates the required airflow to suppress the temperature rise of each functional module based on its predicted power and heat generation, and then deduces the target rotational speed. For example, when the predicted power is 60%, the rectifier-inverter module generates 200W of heat and requires a speed of 3500 RPM; the AC filter module generates 80W of heat and requires 2000 RPM; the auxiliary power supply module generates only 20W of heat and can be shut down or maintained at an extremely low speed.

[0045] Energy storage systems can optimize start-up and shutdown priorities based on heat generation, comprehensively considering heat generation and cumulative operating time to determine start-up and shutdown priorities. When cumulative operating times are similar, the lower the heat generation of a functional module, the lower the start-up and shutdown priority of its corresponding cooling fan; conversely, the higher the heat generation, the higher the start-up and shutdown priority. When heat generation is similar, the longer the cumulative operating time, the lower the corresponding start-up and shutdown priority. Specifically, if a module requires low cooling, the energy storage system can prioritize shutting down that fan, allowing nearby fans to handle the cooling. If multiple fans need to be started simultaneously, the energy storage system, while meeting the minimum cooling requirements of each module, prioritizes starting the fan with the shorter cumulative operating time, assigning heavier cooling tasks to fans with more remaining lifespan. For example, when both F1 and F3 need to run at 3500 RPM, but F1 has 300 more cumulative operating hours than F3, the energy storage system automatically increases the priority of F3, allowing F3 to handle the higher load. If one fan needs to be removed, F1 is shut down first.

[0046] The energy storage system outputs an independent target speed for each fan that needs to be started, thereby dynamically and accurately achieving the effect of multi-fan collaborative control with on-demand heat dissipation, local controllability, and balanced lifespan.

[0047] In one optional embodiment, the energy storage system can further adjust the dynamic control strategy of the cooling fan based on accumulated thermal stress data. Accumulated thermal stress data refers to the fatigue damage that power devices gradually accumulate due to long-term temperature fluctuations (such as temperature difference ΔT and cycle count), and can be used to reflect the lifespan of functional modules and their adjacent fans. Specifically, the energy storage system can monitor the fluctuations in the ambient temperature of power devices and modules in real time using temperature sensors deployed on each functional module (such as rectifier-inverter modules, DC filter modules, etc.). The energy storage system records the magnitude of each temperature rise and fall, as well as the number of temperature cycles, storing this data as accumulated thermal stress data to reflect the degree of fatigue damage to each functional module and its corresponding cooling fan.

[0048] The energy storage system combines the cumulative operating time of each fan with the accumulated thermal stress data of the module served by that fan to comprehensively estimate the remaining lifespan of each fan. Generally speaking, fans that operate in high-temperature or large-temperature-fluctuation environments for extended periods experience faster bearing aging and grease evaporation, resulting in a shorter remaining lifespan; while fans operating in milder environments have longer lifespans. Based on the estimated remaining lifespan, the energy storage system dynamically adjusts the start-up and shutdown priorities of each fan. Priority is positively correlated with the estimated remaining lifespan: fans with longer remaining lifespans receive higher start-up and shutdown priorities and are activated first when cooling is needed; conversely, fans nearing the end of their lifespan are prioritized for shutdown or used as backups.

[0049] By employing the above methods, the energy storage system avoids assigning heavy heat dissipation tasks to fans that are about to fail, effectively reducing the risk of heat dissipation failure due to sudden fan malfunctions. At the same time, it extends the balanced service life of the entire fan cluster and can issue maintenance warnings in advance when the remaining service life is too low.

[0050] In one optional embodiment, the energy storage system can collect the real-time load rate of the energy storage converter and the temperature of the key power modules, and perform piecewise linear speed regulation based on the combined state of the two to achieve the optimal balance between heat dissipation capacity, energy consumption and noise. Specifically, the energy storage system calculates the real-time load rate (i.e., the ratio of the current output power to the rated power) through the current and voltage signals of the energy storage converter, and obtains the real-time temperature T through the temperature sensor. The energy storage system presets three speed regulation intervals: (1) When the load rate is <30% and the temperature T is <45℃, it is determined to be a light load and low temperature state. At this time, the heat generation is extremely low, the PWM duty cycle of the energy storage system is 0%, and all related fans stop running to achieve energy saving and noise reduction.

[0051] (2) When 30% ≤ load rate < 70% or 45℃ ≤ T < 75℃, the energy storage system enters the linear speed regulation zone. The target speed control quantity is calculated using a bivariate linear combination formula: target speed = 30% + (load rate - 30%) × m + (T - 45) × p. Wherein, 30% is the initial duty cycle, the load rate coefficient m reflects the dominant influence of power, and the temperature coefficient p reflects the high weight of over-temperature risk. For example, when the load rate is 50%, the temperature is 60℃, m is 1.0, and p is 3%, the speed control quantity = 30% + 20% + 45% = 95%, corresponding to a PWM duty cycle of 95%, and the fan is close to full speed operation.

[0052] (3) When the load rate is ≥ 70% or the temperature T is ≥ 75℃, it is determined to be a heavy load or high temperature state. The energy storage system directly outputs PWM duty cycle of 100%, and all associated fans run at full speed to force temperature control with maximum heat dissipation capacity.

[0053] The limit values ​​for each speed regulation range mentioned above are merely illustrative descriptions. Other values ​​may be used in other embodiments. For example, when the load rate is <20% and the temperature T is <35℃, it is determined to be a light load low temperature state; or, when the load rate is ≥60%, it is determined to be a heavy load state. The specific limit values ​​are not limited. In addition, when the energy storage converter switches from the running state to the shutdown state, the energy storage system determines whether the fan should be delayed in shutting down based on the load rate before shutdown: if the load rate before shutdown is >1 threshold (e.g., 50%), it indicates that the converter has just experienced a heavy load condition and there is still residual heat inside. The energy storage system controls the fan to continue running at the current speed for a first duration (e.g., 2 minutes) and then gradually shuts it down; if the load rate is ≤1 threshold, the fan is shut down immediately.

[0054] By using the above-mentioned dual-variable combined speed regulation, this embodiment can adaptively adjust the heat dissipation intensity under different operating conditions, avoiding the response lag or over-adjustment problems that may be caused by single power signal or single-point temperature threshold control.

[0055] In one optional embodiment, the energy storage system can acquire real-time temperature data of each functional module (such as a DC filter module, a rectifier-inverter module, an AC filter module, etc.) to construct a three-dimensional temperature distribution map inside the energy storage converter. When the temperature difference between two adjacent modules exceeds a second threshold (e.g., 8°C) and persists for a certain period of time (e.g., 30 seconds), the energy storage system can determine that local heat accumulation has occurred and perform a disturbance heat dissipation step.

[0056] Specifically, the energy storage system can increase the speed of the high-temperature module fan by a disturbance increment (e.g., 10% to 20%) based on the current target speed, while simultaneously reducing the speed of the low-temperature module fan by the same proportion for a duration of T1 (e.g., 15 seconds). After T1, the speeds of both fans are restored to their original target values, and the disturbance directions are swapped: the original high-temperature module fan decreases its speed, and the original low-temperature module fan increases its speed for a duration of T2 (e.g., 10 seconds). This process is repeated 2 to 3 times, creating airflow disturbances within the energy storage converter, forcing a change in the internal airflow path, thereby transferring heat from the high-temperature region to the low-temperature region.

[0057] Through the aforementioned multi-fan coordinated perturbation heat dissipation, this embodiment of the application effectively improves internal thermal balance and reduces local hot spot temperature by using this simple time-alternating perturbation (e.g., switching between high and low speeds every 15 seconds) without continuously increasing the speed of all fans, thereby further enhancing the thermal safety of power devices and the overall lifespan of the device.

[0058] Figure 3 This is a schematic diagram of the structure of a heat dissipation control device for an energy storage converter provided in an embodiment of this application, as shown below. Figure 3 As shown, the device may include: The AI ​​prediction module 310 is used to predict the power of the energy storage converter within a preset future period based on the historical power time series data of the energy storage converter and using an AI model.

[0059] The determination module 320 is used to determine the start and stop priority of each cooling fan based on the cumulative running time of each cooling fan in the cooling fan cluster.

[0060] The determination module 320 is also used to output target control parameters for the cooling fan cluster based on the start / stop priority and the power prediction value.

[0061] The control module 330 is used to adjust the speed of some or all of the cooling fans in the cooling fan cluster at the current moment according to the target control parameters.

[0062] In an optional embodiment, the device further includes: The fault handling module is used to monitor the operating status of each cooling fan in real time; when any cooling fan is detected to be faulty, the faulty fan is marked as disabled and removed from the start / stop priority queue; the neighboring fans of the faulty fan are identified and their speeds are increased; and the location information of the faulty fan is reported.

[0063] In an optional embodiment, the device further includes: The life monitoring module is used to calculate the cumulative thermal stress data of each functional module inside the energy storage converter; based on the cumulative thermal stress data and the cumulative operating time of each cooling fan, the remaining life estimate of each cooling fan is determined; and the start-stop priority of each cooling fan is adjusted based on the remaining life estimate, wherein the start-stop priority is positively correlated with the remaining life estimate.

[0064] In practical applications, the energy storage system of this application embodiment may include electronic devices and computer storage media. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 400 may include a processor 401, a memory 402, and a communication unit 403. These components communicate through one or more buses. Those skilled in the art will understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiment of this application. It may be a bus-shaped structure or a star-shaped structure, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0065] The communication unit 403 is used to establish a communication channel, enabling the electronic device to communicate with other devices. It can receive user data sent by other devices or send user data to other devices.

[0066] The processor 401 serves as the control center of the electronic device, connecting various parts of the device via interfaces and lines. It executes software programs, instructions, and / or modules stored in the memory 402, and calls data stored in the memory to perform various functions and / or process data. The processor may be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 401 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.

[0067] The memory 402 is used to store the execution instructions of the processor 401. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0068] When the execution instructions in memory 402 are executed by processor 401, the electronic device 400 is able to perform some or all of the steps in the above embodiments.

[0069] The computer storage medium may store a program, which, when executed, may include some or all of the steps in the various embodiments of the energy storage converter heat dissipation control method provided in this application. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0070] In a specific implementation, this application also provides a computer program product, wherein the computer program product includes executable instructions, which, when executed on a computer, cause the computer to perform some or all of the steps in various embodiments of the energy storage converter heat dissipation control method provided in this application.

[0071] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

Claims

1. A heat dissipation control method for an energy storage converter, characterized in that, include: Based on the historical power time-series data of the energy storage converter, an AI model is used to predict the power of the energy storage converter in a future preset time period. The start / stop priority of each cooling fan is determined based on the cumulative running time of each cooling fan in the cooling fan cluster. Based on the start / stop priority and the power prediction value, output the target control parameters of the cooling fan cluster; Adjust the rotational speed of some or all of the cooling fans in the cooling fan cluster at the current moment according to the target control parameters.

2. The heat dissipation control method for an energy storage converter according to claim 1, characterized in that, Based on historical power time-series data of the energy storage converter, an AI model is used to predict the power forecast value of the energy storage converter within a preset future time period, including: Obtain the historical power time-series curve of the energy storage converter sampled at a first preset time interval within a historical period; The actual power time-series curve for the current time period is compared with the historical power time-series curve for the corresponding time period to predict the power forecast value for the future preset time period.

3. The heat dissipation control method for an energy storage converter according to claim 1, characterized in that, The start / stop priority of each cooling fan is determined based on the cumulative running time of each cooling fan in the cooling fan cluster, including: Sort the cooling fans in ascending order of cumulative running time; Cooling fans with shorter cumulative running time are assigned a higher start / stop priority.

4. The heat dissipation control method for an energy storage converter according to claim 1, characterized in that, Based on the start / stop priority and the predicted value, the target control parameters of the cooling fan cluster are output, including: The number of target fans that need to be started in the cooling fan cluster is dynamically determined based on the power prediction value. The top N cooling fans with the highest start / stop priority are identified as the cooling fans that need to be started, where N is greater than or equal to the target number of fans.

5. The heat dissipation control method for an energy storage converter according to claim 1, characterized in that, Also includes: Monitor the operating status of each cooling fan in real time; When any cooling fan is detected to be faulty, the faulty fan is marked as disabled and removed from the start / stop priority queue; Identify the neighboring fans of the faulty fan and increase the speed of the neighboring fans; Report the location information of the faulty fan.

6. The heat dissipation control method for an energy storage converter according to claim 3, characterized in that, It also includes the round-robin load balancing step: During the process of controlling the operation of the fans according to the start-stop priority, the cooling fan cluster is traversed at a preset time period. The cumulative running time of each cooling fan is compared. When the difference between the cumulative running time of a running cooling fan and the cumulative running time of the cooling fan with the shortest cumulative running time among the currently non-running cooling fans exceeds a preset threshold, the running cooling fan is turned off and the cooling fan with the shortest cumulative running time is turned on.

7. The heat dissipation control method for an energy storage converter according to claim 4, characterized in that, The target control parameters include the fan identifier of the cooling fan to be started and the target PWM duty cycle; The step of outputting the target control parameters for the cooling fan cluster based on the start / stop priority and the predicted value includes: Get the fan identifiers of the N cooling fans with the highest start / stop priority; The target PWM duty cycle for each cooling fan to be activated is determined based on the power prediction value of the cooling fan.

8. The heat dissipation control method for an energy storage converter according to claim 1, characterized in that, The cooling fan cluster includes multiple cooling fans installed at different functional modules inside the energy storage converter. Each cooling fan has a one-to-one positional relationship with its corresponding functional module, and each cooling fan is configured to provide localized cooling for its corresponding functional module.

9. The heat dissipation control method for an energy storage converter according to claim 8, characterized in that, Based on the start / stop priority and the power prediction value, the target control parameters of the cooling fan cluster are output, including: Obtain the distribution data of heat points in each functional module; The heat generation of each functional module under different operating power ranges is determined based on the heat point distribution data. Based on the different power prediction values ​​and corresponding heat generation of each functional module, the target speed of each cooling fan is dynamically determined.

10. The heat dissipation control method for an energy storage converter according to claim 1, characterized in that, Also includes: Calculate the cumulative thermal stress data of each functional module inside the energy storage converter; Based on the accumulated thermal stress data and the accumulated operating time of each cooling fan, the estimated remaining lifespan of each cooling fan is determined. The start / stop priority of each cooling fan is adjusted based on the estimated remaining lifespan, wherein the start / stop priority is positively correlated with the estimated remaining lifespan.

11. A heat dissipation control device for an energy storage converter, characterized in that, include: The AI ​​prediction module is used to predict the power of the energy storage converter within a preset future time period based on the historical power time series data of the energy storage converter and using an AI model. The determination module is used to determine the start / stop priority of each cooling fan based on the cumulative running time of each cooling fan in the cooling fan cluster; The determining module is also used to output target control parameters for the cooling fan cluster based on the start / stop priority and the power prediction value; The control module is used to adjust the rotational speed of some or all of the cooling fans in the cooling fan cluster at the current moment according to the target control parameters.

12. The heat dissipation control device for an energy storage converter according to claim 11, characterized in that, The device further includes: The fault handling module is used to monitor the operating status of each cooling fan in real time; when any cooling fan is detected to be faulty, the faulty fan is marked as disabled and removed from the start / stop priority queue; the neighboring fans of the faulty fan are identified and the speed of the neighboring fans is increased; and the location information of the faulty fan is reported.

13. The heat dissipation control device for an energy storage converter according to claim 11, characterized in that, The device further includes: The life monitoring module is used to calculate the cumulative thermal stress data of each functional module inside the energy storage converter; based on the cumulative thermal stress data and the cumulative operating time of each cooling fan, it determines the estimated remaining life of each cooling fan; and adjusts the start-stop priority of each cooling fan based on the estimated remaining life, wherein the start-stop priority is positively correlated with the estimated remaining life.

14. An energy storage system, characterized in that, include: An electronic device includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device performs the method according to any one of claims 1 to 10; A computer-readable storage medium comprising a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 10.

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