Energy storage system cooling method and device, electronic equipment and storage medium

Abstract

The application discloses a cooling method and device of an energy storage system, electronic equipment and a storage medium. The method comprises the following steps: obtaining operation data of the energy storage system; determining a first predicted starting time when the energy storage system enters a charging / discharging state in a first period according to the operation data, the first period being the next operation period closest to the current time; determining a refrigeration starting time of the energy storage system according to the first predicted starting time, the refrigeration starting time being earlier than the first predicted starting time; and performing cooling treatment on the energy storage system according to the refrigeration starting time.

Description

Technical Field

[0001] This invention relates to the field of battery thermal management technology, and in particular to a cooling method, apparatus, electronic device, and storage medium for an energy storage system. Background Technology

[0002] Energy storage products have increasingly stringent energy consumption requirements. During daily operation, these products release a large amount of heat, requiring timely cooling to ensure their normal operation. However, most existing products use hysteresis feedback control, which only activates the unit cooling after the Battery Management System (BMS) issues a cooling request. This causes the battery temperature to rise sharply in the early stages of charging and discharging. By the time the BMS triggers the cooling request, the battery temperature has already exceeded the threshold. After cooling is activated, the battery cools down rapidly, resulting in drastic temperature fluctuations and significant thermal stress impacts. Consequently, existing cooling technologies severely affect the lifespan of energy storage products. Summary of the Invention

[0003] To address the aforementioned problems, embodiments of this application provide a cooling method, apparatus, electronic device, and storage medium for an energy storage system. The solution proposed in this application helps to resolve the issue that existing cooling technologies severely impact the lifespan of energy storage products.

[0004] In a first aspect, embodiments of this application provide a cooling method for an energy storage system. The method includes: acquiring operating data of the energy storage system; determining, based on the operating data, a first predicted start time for the energy storage system to enter a charging / discharging state in a first cycle, wherein the first cycle is the next operating cycle closest to the current time; determining, based on the first predicted start time, a cooling start time for the energy storage system, wherein the cooling start time is earlier than the first predicted start time; and performing cooling treatment on the energy storage system based on the cooling start time.

[0005] As can be seen, the embodiments of this application predict the start time of charging and discharging in the next operating cycle by acquiring operating data and start cooling treatment in advance, realizing the transformation from passive response to active prevention, avoiding a sudden temperature rise in the early stage of charging and discharging, reducing temperature peaks, reducing thermal stress shocks, and effectively improving the service life of energy storage products in the energy storage system.

[0006] In conjunction with the first aspect, in one possible embodiment, determining the first predicted start time for the energy storage system to enter the charge / discharge state in the first cycle based on operating data includes: determining at least one historical start time for the energy storage system to enter the charge / discharge state within at least one historical operating cycle, wherein the historical operating cycle is the operating cycle prior to the current time; and predicting the first predicted start time based on the at least one historical start time.

[0007] As can be seen, the embodiments of this application fully utilize the historical patterns of energy storage system operation by determining the historical start time within at least one historical operating cycle and predicting the charging and discharging start time of the next operating cycle based on these historical start times. This makes the prediction results more consistent with the actual operating mode, provides a reliable time basis for initiating cooling treatment in advance, and improves the accuracy and effectiveness of active temperature control.

[0008] In conjunction with the first aspect, in one possible embodiment, predicting a first predicted start time based on at least one historical start time includes: obtaining a second predicted start time and a first historical start time for the energy storage system to enter a charging / discharging state within a second cycle; wherein the second cycle is the historical operating cycle closest to the current time, and the first historical start time is the actual start time for the energy storage system to enter a charging / discharging state within the second cycle; obtaining a smoothing coefficient, and determining a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; and determining the first predicted start time based on the product of the first historical start time and the first weight, and the product of the second predicted start time and the second weight.

[0009] As can be seen, the embodiments of this application achieve adaptive prediction of the charging and discharging start time by using the actual start time of the most recent historical operating cycle and the predicted start time of the previous cycle for weighted calculation. This prediction method takes into account the influence of the predicted value and the actual value of the previous operating cycle on the predicted value of the next cycle, which improves the accuracy and robustness of the prediction and provides accurate time basis for active temperature control.

[0010] In conjunction with the first aspect, in one possible embodiment, at least one historical operating cycle is multiple, and predicting a first predicted start time based on at least one historical start time includes: obtaining multiple second historical start times of the energy storage system in multiple historical operating cycles, wherein the second historical start time is the actual start time of the energy storage system entering the charging / discharging state in the corresponding historical operating cycle; obtaining a third predicted start time based on the multiple second historical start times; obtaining a smoothing coefficient, and determining a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; determining the first predicted start time based on the product of the second historical start time and the first weight, and the product of the third predicted start time and the second weight.

[0011] As can be seen, the embodiments of this application calculate the baseline prediction value by utilizing the actual start time of multiple historical operating cycles and combine it with the most recent actual start time for weighted prediction. This fully utilizes the stable patterns reflected by long-term historical data and the latest changes reflected by recent data, making the prediction results more accurate and reliable. This provides a more precise time basis for active temperature control and further improves the effectiveness of refrigeration control.

[0012] In conjunction with the first aspect, in one possible embodiment, after cooling the energy storage system according to the cooling start-up time, the method further includes: obtaining the actual start time of the energy storage system entering the charging / discharging state in the first cycle; calculating the prediction error between the first predicted start time and the actual start time; if the prediction error is greater than a first preset threshold, increasing the smoothing coefficient; if the prediction error is greater than or equal to the first preset threshold, decreasing the smoothing coefficient; if the prediction error is greater than a second preset threshold, marking the first predicted start time as abnormal data; wherein the second preset threshold is greater than the first preset threshold, and abnormal data does not participate in the adjustment of the smoothing coefficient.

[0013] As can be seen, the embodiments of this application achieve adaptive optimization of the prediction model by calculating the prediction error after each charge and discharge cycle and dynamically adjusting the smoothing coefficient according to the magnitude of the error. When the prediction deviation is large, the smoothing coefficient is increased to improve the model response speed; when the prediction is accurate, the smoothing coefficient is decreased to maintain model stability. At the same time, abnormal data is identified and excluded through a second preset threshold, avoiding interference from extreme cases on the prediction model. This enables the prediction model to continuously adapt to changes in the operating rules of the energy storage system and maintain high prediction accuracy over a long period.

[0014] In conjunction with the first aspect, in one possible embodiment, determining the cooling start-up time of the energy storage system based on the first predicted start time includes: acquiring the cell status of the energy storage system; determining a pre-cooling duration based on the cell status, wherein the pre-cooling duration is the minimum time required for the energy storage system to reach a preset temperature; and determining the cooling start-up time based on the pre-cooling duration and the first predicted start-up time.

[0015] As can be seen, the embodiments of this application dynamically determine the precooling time by obtaining the cell status, and deduce the cooling start time based on the precooling time and the predicted charging and discharging start time, thereby achieving precise matching of the precooling time. This ensures that the energy storage system has reached the target temperature when charging and discharging begins, while avoiding energy waste caused by excessive precooling, thus improving temperature control efficiency and economy.

[0016] In conjunction with the first aspect, in one possible embodiment, before determining the cooling start-up time of the energy storage system based on the first predicted start time, the method further includes: obtaining the current time; determining the time difference between the first predicted start time and the current time; and determining that the time difference is greater than a preset time difference.

[0017] As can be seen, by introducing a time difference judgment mechanism before determining the cooling start time, the embodiments of this application ensure that the pre-cooling operation has a sufficient execution time window, effectively offsetting the impact of system response delay on the pre-cooling effect, avoiding insufficient cooling effect due to late pre-cooling start, and improving the execution reliability of the pre-cooling strategy and the stability of temperature control effect.

[0018] As can be seen from the methods described in the above embodiments, predicting the charge / discharge start time of the next operating cycle by acquiring operational data enables early initiation of cooling, effectively improving the lifespan of energy storage products in the energy storage system. Determining the historical start time within at least one historical operating cycle provides a reliable time basis for predicting the charge / discharge start time of the next operating cycle, improving the accuracy and effectiveness of active temperature control. Calculating the prediction error after each charge / discharge cycle and dynamically adjusting the smoothing coefficient based on the error magnitude achieves adaptive optimization of the prediction model, enabling it to continuously adapt to changes in the operating patterns of the energy storage system and maintain high prediction accuracy over the long term. Dynamically determining the pre-cooling duration by acquiring the cell status and deducing the cooling start time based on the pre-cooling duration and the predicted charge / discharge start time avoids energy waste caused by excessive pre-cooling. Introducing a time difference judgment mechanism before determining the cooling start time improves the reliability of the pre-cooling strategy and the stability of the temperature control effect.

[0019] Secondly, embodiments of this application provide a cooling device for an energy storage system, used to perform part or all of the method described in the first aspect, the device comprising: Acquisition unit, used to acquire operational data of the energy storage system; The determination unit is used to determine the first predicted start time for the energy storage system to enter the charging / discharging state in the first cycle based on the operating data. The first cycle is the next operating cycle closest to the current time. The determining unit is also used to determine the cooling start-up time of the energy storage system based on the first predicted start-up time, wherein the cooling start-up time is earlier than the first predicted start-up time; The processing unit is used to cool the energy storage system according to the cooling start-up time.

[0020] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, a communication interface, and at least one program, wherein the at least one program is stored in the memory and configured to be executed by the processor, and one or more instructions are adapted to be loaded by the processor and execute part or all of the methods of the first aspect and / or the second aspect.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform part or all of the methods of the first aspect and / or the second aspect.

[0022] Fifthly, this application provides a computer program product that, when read and executed by a computer, causes the computer to perform part or all of the methods of the first aspect and / or the second aspect.

[0023] It is understood that the beneficial effects of the embodiments of the second to fifth aspects can be referred to the beneficial effects of the method of the first aspect, and will not be repeated here. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0025] Figure 1 This is a schematic diagram of the structure of an energy storage system provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating an application scenario of a cooling method for an energy storage system provided in an embodiment of this application. Figure 3 A schematic flowchart illustrating a cooling method for an energy storage system provided in an embodiment of this application; Figure 4 A schematic diagram of a first cycle and a second cycle provided for embodiments of this application; Figure 5 A schematic flowchart of another cooling method for an energy storage system provided in an embodiment of this application; Figure 6 A schematic diagram illustrating the process of determining a third prediction start time provided in an embodiment of this application; Figure 7 A schematic flowchart illustrating another cooling method for an energy storage system provided in this application embodiment; Figure 8 A schematic diagram of the structure of a cooling device for an energy storage system provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0026] Reference numerals: 100: Energy storage system; 101: High-voltage cable; 102: First power conversion device; 103: Second power conversion device; 104: Energy storage device; 200: Application scenario; 201: Cooling control equipment; 202: Cooling equipment; 203: Battery management system; 800: Cooling device of energy storage system; 801: Acquisition unit; 802: Determination unit; 803: Processing unit; 900: Electronic equipment; 901: Memory; 902: Processor; 903: Communication interface; 904: Bus. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application.

[0028] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In some embodiments, please refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of an energy storage system provided in an embodiment of this application, and this application... Figure 1 The embodiments are illustrated using a shared energy storage scenario on the generation / distribution side as an example. The energy storage device in this application is not limited to a prefabricated energy storage module in the generation and distribution energy storage scenario.

[0031] This application provides an energy storage system 100, which includes: a high-voltage cable 101, a first power conversion device 102, a second power conversion device 103, and an energy storage device 104. In some embodiments of the power generation scenario, the second power conversion device 103 can be a wind power conversion device. Since the electricity generated by wind power conversion is volatile, random, and intermittent, the unstable electricity output by the wind power conversion device can be stored in the energy storage device 104 through grid connection. The energy storage device 104 is connected to the high-voltage cable 101 and outputs smooth electricity to the power consumption side of the distribution network, realizing peak shaving and frequency regulation, and stable grid operation; or, wind power... The power conversion device is always connected to the high-voltage cable 101. Under normal power generation conditions, the power output of the wind power conversion device is supplied to the power consumption side of the distribution network through the high-voltage cable. When the current power load is low and the wind power conversion device generates excess power, the excess power is first stored in the energy storage device 104 to improve the problem of new energy power generation absorption. When the power load is high, the power grid issues an instruction to transmit the power stored in the energy storage device 104 together with the high-voltage cable 101 in grid-connected mode to supply power to the power consumption side. This provides the power grid with various services such as peak shaving, frequency regulation, and backup, giving full play to the peak shaving role of the power grid, promoting peak shaving and valley filling, and alleviating the power supply pressure of the power grid.

[0032] In some embodiments on the distribution network side, the first power conversion device 102 can be a photovoltaic panel, and the energy storage device 104 is connected to the high-voltage cable 101 and installed downstream of the high-voltage cable 101 between the user load and the photovoltaic power conversion device. The electrical energy output by the photovoltaic power conversion device is stored in the energy storage device 104, which can respond in a timely manner to act as a backup power source when the power grid / distribution network fails; or, it can provide power supply support to alleviate line congestion when the high-voltage cable 101 transmission line is blocked, and to delay the economic pressure caused by the expansion of the power grid / distribution capacity when the power grid is planned to be expanded.

[0033] Optionally, the first power conversion device may include, but is not limited to, a photovoltaic panel, and the second power conversion device may include, but is not limited to, a wind power conversion device. The first power conversion device 102 and the second power conversion device 103 can convert at least one of solar energy, light energy, wind energy, thermal energy, tidal energy, biomass energy, and mechanical energy into electrical energy.

[0034] Optionally, the energy storage device 104 may include, but is not limited to, energy storage scenarios such as energy storage power stations, hydropower / thermal / wind power generation systems, solar power generation systems, mobile power systems, smart home systems, or temporary power supply systems, and may also be applied in multiple fields such as data centers, charging piles, and electric vehicles.

[0035] Optionally, the energy storage device 104 may include battery modules, battery packs, battery clusters, mobile power supplies, energy storage cabinets / prefabricated energy storage compartments, and other battery integrated systems composed of individual batteries. The actual application form of the energy storage device 104 provided in this application embodiment may be, but is not limited to, the listed products, and may also be other application forms. This application embodiment does not strictly limit the application form of the energy storage system 100.

[0036] Alternatively, the single cell is not limited to at least one of cylindrical, square, prismatic, or other shaped cells.

[0037] Optionally, the single cell can be a rechargeable battery, which refers to a single cell that can be recharged after discharge to activate the active materials and continue to be used. The single cell can be a lithium-ion battery, sodium-ion battery, sodium-lithium-ion battery, lithium metal battery, sodium metal battery, lithium-sulfur battery, magnesium-ion battery, nickel-metal hydride battery, nickel-cadmium battery, lead-acid battery, etc., and this application does not specifically limit it.

[0038] Example 1: Please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating an application scenario of a cooling method for an energy storage system provided in an embodiment of this application, wherein application scenario 200 includes, for example... Figure 1 The energy storage system 100, cooling control device 201, cooling device 202, and battery management system 203 are shown.

[0039] For a detailed description of the energy storage system 100 for storing and releasing electrical energy, please refer to the foregoing content and it will not be repeated here.

[0040] The Battery Management System (BMS) 203 is connected to the Energy Storage System 100 and is used to collect operating data of the Energy Storage System 100 during operation. This operating data includes parameters such as charging and discharging current and battery temperature.

[0041] The cooling control device 201 is communicatively connected to the battery management system 203 and the cooling device 202 respectively. The cooling control device 201 is used to receive messages sent by the battery management system 203 through the controller local area network bus and parse them to obtain operating data.

[0042] The cooling device 202 is connected to the energy storage system 100 via cooling pipes or the like. The cooling device 202 includes one or more of a compressor, a water pump or a fan, and is used to cool the energy storage system 100 under the control of the cooling control device 201.

[0043] In this embodiment of the application, the cooling control device 201 acquires the operating data of the energy storage system 100.

[0044] Specifically, the cooling control device 201 obtains the operating data of the energy storage system 100 either through the battery management system 203 or directly from the energy storage system 100.

[0045] For example, the cooling control device 201 needs to receive real-time messages sent by the battery management system 203 via the controller local area network bus. The messages include timestamps, charging and discharging currents, and battery temperatures. The cooling control device 201 preloads a database CAN matrix configuration file, decomposes the messages using a CAN protocol parsing engine, filters out battery status messages based on CAN identifiers, extracts raw data according to the start bit, length, and byte order defined in the database CAN matrix, and calculates physical values ​​using resolution and offset formulas to obtain the operating data of the energy storage system 100. For example, if the raw charging current is 200, the resolution is 0.1A / bit, and the offset is -40A, then the actual current = 200 × 0.1 - 40 = 20A.

[0046] The cooling control device 201 determines the first predicted start time for the energy storage system 100 to enter the charging / discharging state in the first cycle based on the operating data. The first cycle is the next operating cycle closest to the current time.

[0047] Specifically, the cooling control device 201 performs statistical analysis on the acquired operating data and extracts the current data characteristics of the energy storage system 100 entering the charging / discharging state in historical operating cycles. Based on the preset correspondence between the charging / discharging start time and the current data characteristics, the cooling control device 201 determines the remaining time to enter the charging / discharging state, and thus determines the first predicted start time.

[0048] The cooling control device 201 determines the cooling start time of the energy storage system 100 based on the first predicted start time, and the cooling start time is earlier than the first predicted start time.

[0049] Specifically, the cooling start-up time is determined based on the cooling advance, which is calibrated based on the heat dissipation characteristics of the energy storage system 100 and the target cooling temperature.

[0050] The cooling control device 201 cools the energy storage system 100 according to the cooling start-up time.

[0051] The cooling control device 201 controls the operation of the cooling device 202 according to the cooling start time so that the energy storage system 100 reaches the target temperature before entering the charging / discharging state.

[0052] As can be seen, the embodiments of this application predict the start time of charging and discharging in the next operating cycle by acquiring operating data and start cooling treatment in advance, realizing the transformation from passive response to active prevention, avoiding a sudden temperature rise in the early stage of charging and discharging, reducing temperature peaks, reducing thermal stress shocks, and effectively improving the service life of energy storage products in the energy storage system.

[0053] Please see Figure 3 , Figure 3 This application provides a schematic flowchart of a cooling method for an energy storage system, which can be based on... Figure 3 The application scenario 200 shown is implemented as follows: Figure 3 As shown, it includes steps S301-S304.

[0054] S301: Cooling control equipment acquires operating data of the energy storage system.

[0055] The operational data here includes one or more of the following: timestamps generated by the energy storage system during operation, charging and discharging current, and battery temperature.

[0056] S302: The cooling control equipment determines the first predicted start time for the energy storage system to enter the charging / discharging state in the first cycle based on the operating data. The first cycle is the next operating cycle closest to the current time.

[0057] Optionally, determining the first predicted start time for the energy storage system to enter the charging / discharging state in the first cycle based on operating data includes: determining at least one historical start time for the energy storage system to enter the charging / discharging state within at least one historical operating cycle, wherein the historical operating cycle is the operating cycle prior to the current time; and predicting the first predicted start time based on the at least one historical start time.

[0058] Specifically, in the embodiments of this application, after the cooling control device obtains the operating data of the energy storage system, it identifies the operating mode of the energy storage system in one or more previous historical operating cycles based on the operating data, so as to obtain at least one historical start time when the energy storage system enters the charging / discharging state in at least one historical operating cycle. The historical start time includes the start time when the energy storage system enters the charging state or the discharging state.

[0059] For example, the cooling control device can set a charging current threshold, compare the charging and discharging current with the threshold, and determine the current state of the energy storage system. When the charging and discharging current is greater than the charging current threshold, it is determined to be in a charging state; when the charging and discharging current is less than the discharging current threshold, it is determined to be in a discharging state.

[0060] Based on the status judgment results, the cooling control equipment records the start and end times of each charge / discharge state, and extracts the start time of the charge / discharge state as the historical start time. The historical operating cycle corresponding to this historical start time is at least one operating cycle that has been completed before the current time, such as the charge / discharge start time of the previous day, two days, or week.

[0061] The cooling control equipment will summarize and analyze at least one extracted historical start time to form a historical start time series. Then, charging time will be predicted based on this historical start time series.

[0062] For example, the cooling control device calculates statistical characteristics of the historical start times based on at least one historical start time, such as calculating the average, median, or mode of multiple historical start times, and uses the calculated statistical value as the first predicted start time. Alternatively, the cooling control device identifies patterns in the historical start time series, such as detecting that the historical start times are consistently close to a certain point in multiple consecutive operating cycles, and then using that close point in time as the first predicted start time. The cooling control device can also extrapolate based on the changing trends of historical start times; for example, when the historical start times show a trend of gradually advancing or delaying, the device predicts the start time of the next operating cycle according to this trend.

[0063] As can be seen, the embodiments of this application fully utilize the historical patterns of energy storage system operation by determining the historical start time within at least one historical operating cycle and predicting the charging and discharging start time of the next operating cycle based on these historical start times. This makes the prediction results more consistent with the actual operating mode, provides a reliable time basis for initiating cooling treatment in advance, and improves the accuracy and effectiveness of active temperature control.

[0064] Optionally, predicting a first predicted start time based on at least one historical start time includes: obtaining a second predicted start time and a first historical start time for the energy storage system to enter a charging / discharging state within a second cycle; wherein the second cycle is the historical operating cycle closest to the current time, and the first historical start time is the actual start time for the energy storage system to enter a charging / discharging state within the second cycle; obtaining a smoothing coefficient, and determining a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; and determining the first predicted start time based on the product of the first historical start time and the first weight, and the product of the second predicted start time and the second weight.

[0065] Specifically, the cooling control equipment learns from historical charge and discharge times to predict future charge and discharge time windows.

[0066] The cooling control equipment obtains the second predicted start time and the first historical start time of the energy storage system entering the charging / discharging state during the second cycle.

[0067] The second cycle is the most recent historical running cycle. For example, please refer to [link to example]. Figure 4 , Figure 4 This application provides a schematic diagram of a first cycle and a second cycle, as shown in the embodiments. Figure 4 On the time axis T shown, the operation phase of the energy storage system is divided into multiple equal-length cycles, each of which can last for one day (24 hours), 6 hours, or 1 hour. The first cycle is the next cycle closest to the current time, and the second cycle is the previous historical operating cycle closest to the current time.

[0068] The first historical start time is the actual moment when the energy storage system enters the charging / discharging state within this second cycle.

[0069] The second predicted start time is the predicted value of the charging and discharging start time of the second cycle made by the cooling control device before the start of the second cycle. After obtaining the second predicted start time of the second cycle, the cooling control device will not only perform a pre-cooling operation based on the second predicted start time, but also save the second predicted start time for use in predicting the first predicted start time of the first cycle.

[0070] The cooling control equipment acquires a pre-set smoothing coefficient, which ranges from 0 to 1. The specific value is dynamically adjusted based on the stability of the energy storage system's charging and discharging patterns. When the charging and discharging patterns are relatively stable, the smoothing coefficient is smaller, for example, between 0.1 and 0.2, making the prediction results more biased towards historical experience. When the charging and discharging patterns fluctuate significantly, the smoothing coefficient is larger, for example, between 0.3 and 0.5, allowing the prediction results to adapt quickly to changes.

[0071] The cooling control equipment determines the first weight and the second weight based on the smoothing coefficient. The first weight is positively correlated with the smoothing coefficient (e.g., the first weight is directly taken as the value of the smoothing coefficient), and the second weight is negatively correlated with the smoothing coefficient (e.g., 1 - the smoothing coefficient). The sum of the first weight and the second weight is 1.

[0072] The cooling control device multiplies the first historical start time by the first weight to obtain the first product; it multiplies the second predicted start time by the second weight to obtain the second product; and it adds the first product and the second product to obtain the first predicted start time. This calculation process ensures that the first predicted start time integrates the actual start time of the most recent historical operating cycle and the predicted start time of the previous cycle for that cycle, reflecting both the latest actual operating conditions and retaining the trend information of historical predictions.

[0073] The cooling control equipment uses the calculated first predicted start time as the predicted value for the energy storage system to enter the charging / discharging state in the next operating cycle, and uses it for subsequent cooling start-up decisions.

[0074] As can be seen, the embodiments of this application achieve adaptive prediction of the charging and discharging start time by using the actual start time of the most recent historical operating cycle and the predicted start time of the previous cycle for weighted calculation. This prediction method takes into account the influence of the predicted value and the actual value of the previous operating cycle on the predicted value of the next cycle, which improves the accuracy and robustness of the prediction and provides accurate time basis for active temperature control.

[0075] S303: The cooling control equipment determines the cooling start time of the energy storage system based on the first predicted start time, and the cooling start time is earlier than the first predicted start time.

[0076] Optionally, determining the cooling start-up time of the energy storage system based on the first predicted start time includes: acquiring the cell status of the energy storage system; determining the pre-cooling duration based on the cell status, wherein the pre-cooling duration is the minimum time required for the energy storage system to reach a preset temperature; and determining the cooling start-up time based on the pre-cooling duration and the first predicted start-up time.

[0077] Specifically, in this embodiment of the application, after determining the first predicted start time, the cooling control device further obtains the current cell state of the energy storage system.

[0078] Cell status includes the cell's current temperature, state of charge, health status, and heat dissipation parameters. The cooling control equipment can obtain the cell's current temperature through real-time data collected by the battery management system, and its heat dissipation characteristics through internally stored historical data or preset parameters.

[0079] The cooling control equipment determines the pre-cooling duration based on the acquired cell status. The pre-cooling duration refers to the minimum cooling time required for the energy storage system to reach a preset temperature, where the preset temperature is a target temperature value lower than the normal operating temperature of the energy storage system. For example, the preset temperature might be set to 25 degrees Celsius, while the normal operating temperature range of the energy storage system is 30 to 35 degrees Celsius. The cooling control equipment calculates the cooling time required to reach the preset temperature by considering the temperature difference between the current cell temperature and the preset temperature, the cell's heat dissipation efficiency, and the cooling capacity of the cooling equipment. When the current cell temperature is high or the temperature difference is large, the pre-cooling duration is increased accordingly; when the cooling capacity of the cooling equipment is strong or the cell's heat dissipation efficiency is high, the pre-cooling duration is shortened accordingly. The pre-cooling duration can also be adjusted based on the cell's state of charge and health status. For example, when the cell's health status declines, the pre-cooling duration is appropriately extended to ensure cooling effectiveness.

[0080] The cooling control equipment determines the cooling start time based on the pre-cooling duration and the first predicted start time. Specifically, the cooling control equipment calculates the cooling start time by subtracting the pre-cooling duration from the first predicted start time, ensuring that the energy storage system can reach the preset temperature through pre-cooling before entering the charging / discharging state. For example, if the first predicted start time is 10:00 AM and the pre-cooling duration is 20 minutes, the cooling start time is determined to be 9:40 AM.

[0081] As can be seen, the embodiments of this application dynamically determine the precooling time by obtaining the cell status, and deduce the cooling start time based on the precooling time and the predicted charging and discharging start time, thereby achieving precise matching of the precooling time. This ensures that the energy storage system has reached the target temperature when charging and discharging begins, while avoiding energy waste caused by excessive precooling, thus improving temperature control efficiency and economy.

[0082] Optionally, before determining the cooling start-up time of the energy storage system based on the first predicted start time, the method further includes: obtaining the current time; determining the time difference between the first predicted start time and the current time; and determining that the time difference is greater than a preset time difference.

[0083] Specifically, in this embodiment, before determining the cooling start time based on the first predicted start time, the cooling control device first obtains the current time, which can be obtained in real time through the system clock. The cooling control device calculates the difference between the first predicted start time and the current time to obtain a time difference, which represents the remaining time length between the current moment and the predicted charging / discharging start time.

[0084] The cooling control equipment compares the calculated time difference with the preset time difference to determine whether the time difference is greater than the preset time difference.

[0085] The preset time difference is a pre-defined time threshold used to ensure that the pre-cooling operation can be performed effectively. This preset time difference is calibrated based on the system response delay of the cooling equipment and is typically set to 5 to 10 minutes. The system response delay includes the cumulative time of multiple components, such as the communication delay of the cooling control equipment sending commands, the start-up delay of the cooling equipment after receiving the commands, the transmission delay required to establish the refrigerant circulation, and the response delay of the temperature sensor feedback signal.

[0086] When the time difference is greater than the preset time difference, it indicates that there is still sufficient time before the predicted start time of charging and discharging. The cooling control equipment has enough time window to complete all preparations before pre-cooling start-up, including determining the cooling start-up time, sending control commands, and waiting for the cooling equipment to respond. At this time, the cooling control equipment continues to execute the subsequent cooling start-up time determination steps and starts the cooling process according to the determined cooling start-up time.

[0087] When the time difference is greater than or equal to the preset time difference, it indicates that the current time is too close to the predicted start time of charging and discharging. Even if precooling is started immediately, the cooling equipment cannot complete effective precooling before the start of charging and discharging due to the system response delay. At this time, the cooling control equipment can take other measures, such as directly entering the dynamic cooling mode during the charging and discharging process, or adjusting the precooling strategy according to the actual situation.

[0088] As can be seen, by introducing a time difference judgment mechanism before determining the cooling start time, the embodiments of this application ensure that the pre-cooling operation has a sufficient execution time window, effectively offsetting the impact of system response delay on the pre-cooling effect, avoiding insufficient cooling effect due to late pre-cooling start, and improving the execution reliability of the pre-cooling strategy and the stability of temperature control effect.

[0089] S304: Cooling control equipment cools the energy storage system according to the cooling start-up time.

[0090] Optionally, the energy storage system is cooled according to the cooling start-up time, including: obtaining the optimal operating frequency corresponding to the optimal energy efficiency ratio of the cooling equipment; controlling the cooling equipment to start running at the optimal operating frequency during the cooling start-up time; and controlling the cooling equipment to stop running after the pre-cooling time.

[0091] Specifically, in this embodiment of the application, after determining the cooling start time, the cooling control device further obtains the optimal operating frequency corresponding to the optimal energy efficiency ratio of the cooling device.

[0092] For example, the cooling equipment includes a compressor, and the compressor's operating frequency directly affects the energy efficiency ratio (EER) of the refrigeration system. The EER is the ratio of cooling capacity to input power. Cooling equipment exhibits different EER characteristic curves at different operating frequencies. Typically, there exists a specific operating frequency point where the EER reaches its maximum value; this frequency point is the optimal operating frequency. The cooling control equipment can obtain this optimal operating frequency through a pre-stored equipment performance parameter table. This performance parameter table is calibrated based on the cooling equipment's performance test data before it leaves the factory and records the corresponding EER values ​​at different operating frequencies.

[0093] When the cooling start-up time arrives, the cooling control equipment sends a control command to the cooling equipment, controlling it to start operation at the optimal operating frequency. During the pre-cooling phase, the cooling equipment operates at a constant frequency, without frequency adjustment based on temperature changes, ensuring the compressor continuously operates within its most economical energy efficiency range. Since the optimal operating frequency corresponds to the highest cooling efficiency and the maximum cooling capacity per unit of energy consumption, energy consumption can be minimized while meeting pre-cooling requirements.

[0094] The cooling control equipment starts timing after the cooling equipment is started. When the running time reaches the pre-cooling time, the cooling equipment stops running. The pre-cooling time is the minimum cooling time required for the energy storage system to reach the preset temperature, determined based on the cell condition. When the cooling equipment runs at the optimal operating frequency for the full pre-cooling time, the energy storage system has reached the preset target temperature. Stopping pre-cooling at this point avoids energy waste caused by over-cooling.

[0095] After the precooling is stopped, the cooling control equipment continues to monitor the status of the energy storage system. Once the charging and discharging phase begins, it switches to dynamic cooling mode and adjusts the working status of the cooling equipment in real time according to the cooling requests issued by the battery management system.

[0096] As can be seen, the embodiments of this application maximize energy efficiency during the precooling process by controlling the cooling equipment to operate at the optimal working frequency corresponding to the optimal energy efficiency ratio during the precooling stage and stopping it in time after the precooling time is reached. This ensures that the energy storage system reaches the target temperature before charging and discharging begins, significantly reduces the energy consumption cost during the precooling stage, and improves the economy of the entire refrigeration control system.

[0097] Optionally, after cooling the energy storage system according to the cooling start-up time, the method further includes: obtaining the actual start time of the energy storage system entering the charging / discharging state in the first cycle; calculating the prediction error between the first predicted start time and the actual start time; if the prediction error is greater than a first preset threshold, increasing the smoothing coefficient; if the prediction error is greater than or equal to the first preset threshold, decreasing the smoothing coefficient; if the prediction error is greater than a second preset threshold, marking the first predicted start time as abnormal data; wherein, the second preset threshold is greater than the first preset threshold, and abnormal data does not participate in the adjustment of the smoothing coefficient.

[0098] Specifically, in this embodiment, after completing the cooling process of the energy storage system, the cooling control device continuously monitors the operating status of the energy storage system. When the energy storage system enters the charging / discharging state, the cooling control device obtains the actual start time of the energy storage system entering the charging / discharging state within the first cycle. This actual start time is the real time value recorded at the end of the first cycle or at the beginning of charging / discharging, and is used to compare and verify with the previously predicted first start time.

[0099] The cooling control equipment calculates the difference between the predicted start time and the actual start time to obtain the prediction error. This prediction error is measured in time units, such as minutes, and reflects the accuracy of the prediction model's prediction of the charge / discharge start time. The smaller the prediction error, the more accurate the prediction; the larger the prediction error, the more serious the deviation between the actual operation and the predicted pattern.

[0100] The cooling control device compares the calculated prediction error with a first preset threshold. The first preset threshold is a pre-defined error tolerance value, such as 5 minutes or 10 minutes, used to determine if the prediction error is within an acceptable range. If the prediction error exceeds the first preset threshold, it indicates that the current prediction model has a large deviation in predicting the charge / discharge start time, requiring model adjustment to improve the accuracy of subsequent predictions. The cooling control device increases the smoothing coefficient. Increasing the smoothing coefficient increases the weight of the most recent actual start time in subsequent predictions, enabling the prediction model to respond more quickly to changes in the actual operating mode.

[0101] If the prediction error is greater than or equal to the first preset threshold, it indicates that the prediction accuracy of the current prediction model is within an acceptable range and the model is relatively stable. The cooling control device then reduces the smoothing coefficient. Reducing the smoothing coefficient increases the weight of historical statistical patterns in subsequent predictions, keeping the prediction model smooth and stable and avoiding over-response to short-term fluctuations.

[0102] The cooling control equipment also compares the prediction error with a second preset threshold, which is greater than the first preset threshold, for example, 30 minutes. If the prediction error is greater than the second preset threshold, it indicates that the prediction result deviates significantly from the actual start time, possibly due to a special event or abnormal situation, such as system maintenance, changes in power grid dispatch instructions, or equipment failure. In this case, the cooling control equipment marks the first predicted start time as abnormal data.

[0103] The first prediction start time of data marked as outliers is not included in the subsequent smoothing coefficient adjustment process, nor is it used as historical data for subsequent prediction calculations. By excluding outliers, the interference of extreme biases on the prediction model can be avoided, ensuring the stability of the model.

[0104] As can be seen, the embodiments of this application achieve adaptive optimization of the prediction model by calculating the prediction error after each charge and discharge cycle and dynamically adjusting the smoothing coefficient according to the magnitude of the error. When the prediction deviation is large, the smoothing coefficient is increased to improve the model response speed; when the prediction is accurate, the smoothing coefficient is decreased to maintain model stability. At the same time, abnormal data is identified and excluded through a second preset threshold, avoiding interference from extreme cases on the prediction model. This enables the prediction model to continuously adapt to changes in the operating rules of the energy storage system and maintain high prediction accuracy over a long period.

[0105] Example 2: The above-mentioned application embodiments provide a cooling method for an energy storage system that determines the start time of pre-cooling by using the predicted start time and actual start time of the energy storage device entering the charging / discharging state in the previous operating phase. Based on this, this application embodiment also provides another cooling method for an energy storage system. Please refer to... Figure 5 , Figure 5A schematic flowchart of another cooling method for an energy storage system provided in this application embodiment is available, which can be based on... Figure 2 The application scenario 200 shown is implemented as follows: Figure 5 As shown, it includes steps S501-S505.

[0106] S501: Cooling control equipment acquires operating data of the energy storage system.

[0107] S502: The cooling control equipment determines, based on the operating data, at least one historical start time when the energy storage system enters the charging / discharging state within at least one historical operating cycle, wherein the historical operating cycle is the operating cycle prior to the current time.

[0108] S503: The cooling control device predicts a first predicted start time based on at least one historical start time.

[0109] Optionally, at least one historical operating cycle may be multiple. Predicting a first predicted start time based on at least one historical start time includes: obtaining multiple second historical start times for the energy storage system in multiple historical operating cycles, where the second historical start time is the actual start time when the energy storage system enters the charging / discharging state within the corresponding historical operating cycle; obtaining a third predicted start time based on the multiple second historical start times; obtaining a smoothing coefficient and determining a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; determining the first predicted start time based on the product of the second historical start time and the first weight, and the product of the third predicted start time and the second weight.

[0110] Specifically, in the embodiments of this application, when there are multiple historical operating cycles for prediction, the cooling control device obtains multiple second historical start times when the energy storage system enters the charging / discharging state within multiple historical operating cycles.

[0111] The historical operating cycle refers to multiple completed operating cycles prior to the current time, such as a past day, 12 hours, or 6 hours. Each historical operating cycle corresponds to a second historical start time, which is the actual moment when the energy storage system enters the charging / discharging state within that cycle. The cooling control equipment records and maintains these second historical start times through the historical data storage module, forming a historical start time sequence.

[0112] The cooling control equipment calculates the third predicted start time based on the multiple second historical start times obtained.

[0113] In some possible implementations, the third predicted start time is a baseline prediction obtained through statistical analysis of the historical start time series. Specifically, it can be obtained by calculating the arithmetic mean, weighted average, or median of multiple second historical start times. For example, when the charge / discharge start times of the most recent five days are selected as the second historical start times, the cooling control equipment calculates the average of these five times as the third predicted start time. This average reflects the overall level of the charge / discharge start times of the energy storage system over several recent operating cycles.

[0114] For example, please refer to Figure 6 , Figure 6 This application provides a schematic diagram of a process for determining a third prediction start time, wherein... Figure 6 The timeline T' shown illustrates three historical running cycles; other implementations may include more or fewer historical running cycles. Here, t4 is obtained by taking the arithmetic mean of the second historical start times (including t1, t2, and t3) from the three historical running cycles, and t4 is used as the third predicted start time for the first cycle. The final first predicted start time t5 is then determined using subsequent smoothing coefficients, t3, and t4.

[0115] The cooling control equipment obtains a smoothing coefficient, which ranges from 0 to 1. This coefficient is used to balance the weighting of historical actual data and historical predicted data in the prediction process. The smoothing coefficient can be dynamically adjusted based on the stability of the energy storage system's charging and discharging patterns; it takes a smaller value when the patterns are relatively stable and a larger value when the patterns fluctuate significantly. The cooling control equipment determines a first weight and a second weight based on the smoothing coefficient. The first weight equals the smoothing coefficient, and the second weight equals 1 minus the smoothing coefficient. The first weight is positively correlated with the smoothing coefficient, and the second weight is inversely correlated with the smoothing coefficient. The sum of the first and second weights is 1.

[0116] The cooling control device multiplies the most recent second historical start time among multiple second historical start times with a first weight to obtain a first product; it multiplies the third predicted start time with a second weight to obtain a second product; and it adds the first product and the second product to obtain the first predicted start time.

[0117] This calculation process integrates the latest operating status reflected by the most recent actual charge and discharge start time, as well as the overall pattern derived from the statistical analysis of actual start times of multiple historical cycles. This allows the prediction results to respond promptly to recent operational changes while maintaining stability based on long-term historical data.

[0118] As can be seen, the embodiments of this application calculate the baseline prediction value by utilizing the actual start time of multiple historical operating cycles and combine it with the most recent actual start time for weighted prediction. This fully utilizes the stable patterns reflected by long-term historical data and the latest changes reflected by recent data, making the prediction results more accurate and reliable. This provides a more precise time basis for active temperature control and further improves the effectiveness of refrigeration control.

[0119] S504: The cooling control equipment determines the cooling start time of the energy storage system based on the first predicted start time, and the cooling start time is earlier than the first predicted start time.

[0120] S505: Cooling control equipment cools the energy storage system according to the cooling start-up time.

[0121] For detailed explanations of the other steps in steps S501-S505, please refer to the relevant content of steps S301-S304, which will not be repeated here.

[0122] Example 3: The above-described embodiments mainly describe the determination of cooling time for energy storage systems. Based on this, and taking into account electricity consumption periods, this embodiment also provides another, more detailed cooling method for energy storage systems. Please refer to... Figure 7 , Figure 7 A schematic flowchart of another cooling method for an energy storage system provided in this application embodiment can be based on... Figure 2 The application scenario 200 shown is implemented as follows: Figure 7 As shown, it includes steps S701-S706.

[0123] S701: Cooling control equipment acquires operating data of the energy storage system.

[0124] S702: The cooling control equipment determines the first predicted start time for the energy storage system to enter the charging / discharging state in the first cycle based on the operating data. The first cycle is the next operating cycle closest to the current time.

[0125] S703: The cooling control equipment determines the cooling start time of the energy storage system based on the first predicted start time, and the cooling start time is earlier than the first predicted start time.

[0126] S704: Get the off-peak electricity period and the current time.

[0127] Optionally, obtaining off-peak electricity time periods includes: obtaining multiple third historical start times for multiple third cycles, and historical operating cycles prior to the current moment of the third cycle; determining the charging and discharging time trend of the energy storage system based on the multiple third historical start times; determining the current season based on the charging and discharging time trend; and obtaining off-peak electricity time periods based on the current season.

[0128] S705: Determine that the current time is during off-peak electricity hours.

[0129] Specifically, in this embodiment of the application, the cooling control device acquires the off-peak electricity period and the current time before performing the cooling process.

[0130] Off-peak electricity hours refer to the periods when electricity prices are lower in the peak-valley time-of-use pricing system, which is divided according to load characteristics. These periods are typically defined and published by the local power management department or grid company. The specific division of off-peak electricity hours may vary in different regions and seasons. For example, a common setting defines off-peak electricity hours as the period from 10 PM to 6 AM the following morning, during which electricity demand is lower and prices are significantly lower than during peak and flat daytime hours.

[0131] The cooling control equipment acquires the current time, which can be obtained in real time from the system clock. It compares the current time with the start and end times of the off-peak electricity period to determine if the current time falls within that period. If the current time is within the off-peak period, it indicates that the electricity price is lower during this time, making it suitable for pre-cooling operations.

[0132] As can be seen, the embodiments of this application combine the pre-cooling operation with peak and off-peak electricity prices by introducing a mechanism for judging off-peak electricity periods. This allows the pre-cooling process to be scheduled to be performed during periods with lower electricity prices, making full use of the cost advantage of low electricity prices during off-peak periods. While ensuring the temperature control effect, this significantly reduces the operating electricity cost of the cooling process and improves the overall economic efficiency of the energy storage system.

[0133] S706: Cooling control equipment cools the energy storage system according to the cooling start-up time.

[0134] Optionally, if the current time is outside of a peak electricity period, the method further includes: obtaining the confidence level of the first predicted start time; obtaining the time difference between the current time and the first predicted start time; adjusting the precooling duration according to the confidence level and the time difference to obtain the adjusted precooling duration; and determining the cooling start time according to the adjusted precooling duration and the first predicted start time.

[0135] Specifically, in this embodiment of the application, after determining that the current time is not during a period of off-peak electricity, the cooling control device further obtains the confidence level of the first predicted start time.

[0136] Confidence level is a quantitative indicator used to characterize the reliability of prediction results, reflecting the degree of credibility between the currently predicted charge / discharge start time and the actual possible charge / discharge start time.

[0137] In some possible implementations, the cooling control device can calculate the confidence level based on the statistical characteristics of historical prediction errors. For example, it can calculate the variance or standard deviation of the prediction error over several recent operating cycles; the smaller the variance, the higher the confidence level. Alternatively, it can be evaluated based on the stability of the charge / discharge pattern. A higher confidence level is achieved when the charge / discharge start time remains stable over several consecutive cycles, while a lower confidence level is achieved when there are large fluctuations. The confidence level can be expressed as a percentage, ranging from 0 to 100%, with a higher value indicating a more reliable prediction result.

[0138] The cooling control device obtains the time difference between the current time and the first predicted start time, which represents the remaining time length between the current moment and the predicted start time of charging and discharging.

[0139] The cooling control equipment adjusts the precooling time based on the confidence level and time difference to obtain the adjusted precooling time.

[0140] When the confidence level is higher than the third preset threshold, it indicates that the prediction result is highly reliable, and the pre-cooling operation can be performed according to the original pre-cooling duration or close to the original duration. When the confidence level is not higher than the third preset threshold, it indicates that the prediction result may have a large deviation. If the pre-cooling is still performed according to the original duration, there may be a risk of inaccurate prediction leading to ineffective pre-cooling or excessive pre-cooling. Therefore, the pre-cooling duration should be appropriately shortened to reduce energy waste caused by inaccurate prediction during periods of high electricity prices.

[0141] For example, if the original pre-cooling time is 30 minutes, the first predicted start time is 9:00 AM, the current time is 8:15 AM, and the time difference is 45 minutes. If the confidence level is 90%, the adjusted pre-cooling time remains 30 minutes; if the confidence level is 50%, the adjusted pre-cooling time is shortened to 15 minutes; if the confidence level is only 20%, the adjusted pre-cooling time is shortened to 5 minutes or pre-cooling is cancelled, and the system is instead allowed to dynamically respond based on the actual temperature after charging and discharging begins.

[0142] The cooling control equipment determines the cooling start time based on the adjusted precooling duration and the first predicted start time. It then backtracks the first predicted start time by adjusting the precooling duration to obtain the cooling start time, and starts the cooling equipment for precooling when the first predicted start time is reached.

[0143] As can be seen, the embodiments of this application introduce a prediction confidence assessment mechanism during non-off-peak electricity periods, dynamically adjust the pre-cooling duration according to the reliability of the prediction results, maintain sufficient pre-cooling guarantee when the prediction confidence is high, and appropriately reduce pre-cooling investment when the prediction confidence is low to avoid energy waste caused by inaccurate prediction. This effectively solves the energy cost risk caused by prediction uncertainty during high electricity price periods, and improves the economy and robustness of the cooling control strategy while ensuring temperature control effect.

[0144] For detailed explanations of the other steps in steps S701-S706, please refer to the relevant content of steps S301-S304 and S501-S505, which will not be repeated here.

[0145] As can be seen from the methods described in the above embodiments, predicting the charge / discharge start time of the next operating cycle by acquiring operational data enables early initiation of cooling, effectively improving the lifespan of energy storage products in the energy storage system. Determining the historical start time within at least one historical operating cycle provides a reliable time basis for predicting the charge / discharge start time of the next operating cycle, improving the accuracy and effectiveness of active temperature control. Calculating the prediction error after each charge / discharge cycle and dynamically adjusting the smoothing coefficient based on the error magnitude achieves adaptive optimization of the prediction model, enabling it to continuously adapt to changes in the operating patterns of the energy storage system and maintain high prediction accuracy over the long term. Dynamically determining the pre-cooling duration by acquiring the cell status and deducing the cooling start time based on the pre-cooling duration and the predicted charge / discharge start time avoids energy waste caused by excessive pre-cooling. Introducing a time difference judgment mechanism before determining the cooling start time improves the reliability of the pre-cooling strategy and the stability of the temperature control effect.

[0146] Based on the description of the above configuration method embodiments, this application also provides a cooling device 800 for an energy storage system, which can operate in... Figure 2 A computer program (including program code) for the cooling control device 201 shown, and used for execution as described above. Figure 3 , Figure 5 and Figure 7 The method shown. See also Figure 8 , Figure 8 This is a schematic diagram of the structure of a cooling device for an energy storage system provided in an embodiment of this application. The cooling device 800 of the energy storage system includes: Acquisition unit 801 is used to acquire operating data of the energy storage system; The determining unit 802 is used to determine the first predicted start time for the energy storage system to enter the charging / discharging state in the first cycle based on the operating data. The first cycle is the next operating cycle closest to the current time. The determining unit 802 is also used to determine the cooling start time of the energy storage system based on the first predicted start time, wherein the cooling start time is earlier than the first predicted start time. The processing unit 803 is used to cool the energy storage system according to the cooling start-up time.

[0147] In one possible embodiment, in determining the first predicted start time for the energy storage system to enter the charge / discharge state in the first cycle based on the operating data, the determining unit 802 is further specifically configured to: determine, based on the operating data, at least one historical start time for the energy storage system to enter the charge / discharge state within at least one historical operating cycle, wherein the historical operating cycle is the operating cycle prior to the current time; and predict the first predicted start time based on the at least one historical start time.

[0148] In one possible embodiment, in predicting the first predicted start time based on at least one historical start time, the acquisition unit 801 is further specifically configured to: acquire the second predicted start time and the first historical start time of the energy storage system entering the charging / discharging state within a second cycle; wherein the second cycle is the historical operating cycle closest to the current time, and the first historical start time is the actual start time of the energy storage system entering the charging / discharging state within the second cycle; acquire a smoothing coefficient, and determine a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; and determine the first predicted start time based on the product of the first historical start time and the first weight, and the product of the second predicted start time and the second weight.

[0149] In one possible embodiment, at least one historical operating cycle is multiple. Regarding predicting a first predicted start time based on at least one historical start time, the acquisition unit 801 is further specifically configured to: acquire multiple second historical start times of the energy storage system in multiple historical operating cycles, where the second historical start time is the actual start time of the energy storage system entering the charging / discharging state within the corresponding historical operating cycle; acquire a third predicted start time based on the multiple second historical start times; acquire a smoothing coefficient, and determine a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; and determine the first predicted start time based on the product of the second historical start time and the first weight, and the product of the third predicted start time and the second weight.

[0150] In one possible embodiment, after cooling the energy storage system according to the cooling start-up time, the acquisition unit 801 is further specifically used to: acquire the actual start time of the energy storage system entering the charging / discharging state in the first cycle; calculate the prediction error between the first predicted start time and the actual start time; if the prediction error is greater than a first preset threshold, increase the smoothing coefficient; if the prediction error is greater than or equal to the first preset threshold, decrease the smoothing coefficient; if the prediction error is greater than a second preset threshold, mark the first predicted start time as abnormal data; wherein, the second preset threshold is greater than the first preset threshold, and abnormal data does not participate in the adjustment of the smoothing coefficient.

[0151] In one possible embodiment, in determining the cooling start-up time of the energy storage system based on the first predicted start time, the acquisition unit 801 is further specifically configured to: acquire the cell status of the energy storage system; determine the pre-cooling duration based on the cell status, wherein the pre-cooling duration is the minimum duration for the energy storage system to reach a preset temperature; and determine the cooling start-up time based on the pre-cooling duration and the first predicted start time.

[0152] In one possible embodiment, before determining the cooling start time of the energy storage system based on the first predicted start time, the acquisition unit 801 is further specifically used to: acquire the current time; determine the time difference between the first predicted start time and the current time; and determine that the time difference is greater than a preset time difference.

[0153] Based on the description of the above method and device embodiments, please refer to... Figure 9 , Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 The electronic device 900 shown (specifically, the electronic device 900 may be a computer device, Figure 2 The cooling control device 201 shown includes a memory 901, a processor 902, a communication interface 903, and a bus 904. The memory 901, processor 902, and communication interface 903 are interconnected via the bus 904.

[0154] The memory 901 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).

[0155] The memory 901 can store programs. When the program code stored in the memory 901 is executed by the processor 902, the processor 902 and the communication interface 903 are used to execute the various steps of the cooling method of the energy storage system of the present application embodiment.

[0156] The processor 902 may be a general-purpose central processing unit (CPU), microcontroller, application-specific integrated circuit (ASIC), graphics processing unit (GPU), or at least one integrated circuit, for executing related programs to achieve the functions required by the units in the electronic device 900 of this application embodiment, or to execute the cooling method of the energy storage system of this application method embodiment.

[0157] The processor 902 can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the cooling method for the energy storage system of this application can be completed by the integrated logic circuitry in the hardware of the processor 902 or by instructions in software form. The processor 902 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microcontroller or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory 901. The processor 902 reads the information in the memory 901 and, in conjunction with its hardware, performs the functions required by the units included in the electronic device 900 of this application embodiment, or performs the cooling method of the energy storage system of the method embodiment of this application.

[0158] The communication interface 903 uses transceiver devices, such as, but not limited to, transceivers, to enable communication between the electronic device 900 and other devices or communication networks. For example, data can be acquired through the communication interface 903.

[0159] Bus 904 may include a pathway for transmitting information between various components of electronic device 900 (e.g., memory 901, processor 902, communication interface 903).

[0160] It should be noted that, although Figure 9 The illustrated electronic device 900 only shows a memory 901, a processor 902, and a communication interface 903. However, those skilled in the art should understand that in specific implementations, the electronic device 900 may also include other devices necessary for normal operation. Furthermore, depending on specific needs, those skilled in the art should understand that the electronic device 900 may also include hardware devices for implementing other additional functions. Moreover, those skilled in the art should understand that the electronic device 900 may only include the devices necessary for implementing the embodiments of this application, and may not necessarily include... Figure 9 All the devices shown.

[0161] This application embodiment also provides a chip, the chip including a processor and a data interface, the processor reading instructions stored in the memory through the data interface to implement the cooling method of the energy storage system.

[0162] Optionally, as one implementation, the chip may further include a memory storing instructions, and the processor is used to execute the instructions stored in the memory. When the instructions are executed, the processor is used to execute the cooling method of the energy storage system.

[0163] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform at least one step of any of the above methods.

[0164] This application also provides a computer program product containing instructions. When the computer program product is run on a computer or processor, it causes the computer or processor to perform at least one step of any of the methods described above.

[0165] Those skilled in the art will appreciate that the functionality described in conjunction with the various illustrative logic blocks, modules, and algorithmic steps disclosed herein can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality described by the various illustrative logic blocks, modules, and steps can be stored or transmitted as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may comprise a computer-readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or a communication medium that includes any medium facilitating the transfer of a computer program from one place to another (e.g., based on a communication protocol). In this way, the computer-readable medium may substantially correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium, such as a signal or carrier wave. The data storage medium may be any available medium accessible by one or more computers or one or more processors to retrieve instructions, code, and / or data structures for implementing the techniques described in this application. A computer program product may comprise a computer-readable medium.

[0166] By way of example and not limitation, such computer-readable storage media may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory, or any other media that can be used to store desired program code in the form of instructions or data structures and is accessible by a computer. Furthermore, any connection is properly referred to as computer-readable media. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of media. However, it should be understood that the computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other temporary media, but are specifically addressed to non-temporary tangible storage media. As used herein, disks and optical discs include compact optical discs (CDs), laser optical discs, optical discs, digital versatile optical discs (DVDs), and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs reproduce data optically using lasers. The combination of the above items should also be included in the scope of computer-readable media.

[0167] Instructions can be executed by one or more processors, such as digital signal processors (DSPs), general-purpose microcontrollers, application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Therefore, the term "processor" as used herein can refer to any of the foregoing structures or any other structures suitable for implementing the techniques described herein. Furthermore, in some aspects, the functionality described in the various illustrative logic blocks, modules, and steps described herein can be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated into combined codecs. Moreover, the techniques can be fully implemented within one or more circuit or logic elements.

[0168] The technology of this application can be implemented in a wide variety of devices or apparatuses, including wireless handheld devices, integrated circuits (ICs), or a set of ICs (e.g., chipsets). The various components, modules, or units described in this application are intended to emphasize functional aspects of the apparatus for performing the disclosed technology, but do not necessarily need to be implemented by different hardware units. In fact, as described above, the various units can be combined with suitable software and / or firmware within coded hardware units, or provided via interoperable hardware units (containing one or more processors as described above).

[0169] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the specific descriptions of the corresponding steps in the foregoing method embodiments, and will not be repeated here.

[0170] It should be understood that in the description of this application, unless otherwise stated, " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B can represent A or B; where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily imply difference. In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0171] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection shown or discussed between each other may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes at least one computer instruction. When the computer program instruction is loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instruction can be stored in or transmitted through a computer-readable storage medium. The computer instruction can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates at least one available medium. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid state disks (SSDs).

[0174] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

[0175] The device embodiments described above are merely illustrative. The units and modules described as separate components may or may not be physically separate. Furthermore, some or all of the units and modules can be selected to achieve the purpose of this embodiment, depending on actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0176] The above description is only a specific embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A cooling method for an energy storage system, characterized in that, The method includes: Acquire operational data of the energy storage system, the operational data including one or more of the following: timestamps generated by the energy storage system during operation, charging and discharging current, or battery temperature; Determining the first predicted start time for the energy storage system to enter the charging / discharging state in the first cycle based on the operating data, wherein the first cycle is the next operating cycle closest to the current time, specifically includes: determining at least one historical start time for the energy storage system to enter the charging / discharging state within at least one historical operating cycle based on the operating data, wherein the historical operating cycle is the operating cycle before the current time; and predicting the first predicted start time based on the at least one historical start time. The cooling start-up time of the energy storage system is determined based on the first predicted start time, wherein the cooling start-up time is earlier than the first predicted start time; the determination of the cooling start-up time of the energy storage system based on the first predicted start time includes: acquiring the cell status of the energy storage system; determining the pre-cooling duration based on the cell status, wherein the pre-cooling duration is the minimum time required for the energy storage system to reach a preset temperature; and determining the cooling start-up time based on the pre-cooling duration and the first predicted start time. If the current time is not during off-peak hours, obtain the confidence level of the first predicted start time; obtain the time difference between the current time and the first predicted start time; adjust the precooling duration according to the confidence level and the time difference to obtain the adjusted precooling duration; determine the cooling start time according to the adjusted precooling duration and the first predicted start time. The energy storage system is cooled according to the cooling start-up time.

2. The method according to claim 1, characterized in that, The step of predicting the first predicted start time based on at least one historical start time includes: The second predicted start time and the first historical start time of the energy storage system entering the charging / discharging state within the second cycle are obtained; wherein, the second cycle is the historical operating cycle closest to the current time, and the first historical start time is the actual start time of the energy storage system entering the charging / discharging state within the second cycle; Obtain a smoothing coefficient, and determine a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; The first prediction start time is determined based on the product of the first historical start time and the first weight, and the product of the second prediction start time and the second weight.

3. The method according to claim 1, characterized in that, The at least one historical operating cycle can be multiple, and the step of predicting the first predicted start time based on at least one historical start time includes: The energy storage system is obtained at multiple second historical start times in multiple historical operating cycles. The second historical start time is the actual start time of the energy storage system entering the charging / discharging state in the corresponding historical operating cycle. A third predicted start time is obtained based on the plurality of second historical start times; Obtain a smoothing coefficient, and determine a first weight and a second weight based on the smoothing coefficient; wherein the first weight is positively correlated with the smoothing coefficient, the second weight is negatively correlated with the smoothing coefficient, and the sum of the first weight and the second weight is 1; The first prediction start time is determined based on the product of the second historical start time and the first weight, and the product of the third prediction start time and the second weight.

4. The method according to claim 2 or 3, characterized in that, After cooling the energy storage system according to the cooling start-up time, the method further includes: Obtain the actual start time of the energy storage system entering the charging / discharging state in the first cycle; Calculate the prediction error between the first predicted start time and the actual start time; If the prediction error is greater than a first preset threshold, then the smoothing coefficient is increased; If the prediction error is less than or equal to the first preset threshold, then the smoothing coefficient is reduced; If the prediction error is greater than the second preset threshold, the first prediction start time is marked as abnormal data; wherein the second preset threshold is greater than the first preset threshold, and the abnormal data does not participate in the adjustment of the smoothing coefficient.

5. The method according to claim 1, characterized in that, Before determining the cooling start-up time of the energy storage system based on the first predicted start time, the method further includes: Get the current time; Determine the time difference between the first prediction start time and the current time; It is determined that the time difference is greater than a preset time difference.

6. A cooling device for an energy storage system, characterized in that, The apparatus for performing the cooling method of the energy storage system as described in any one of claims 1-5 includes: An acquisition unit is used to acquire the operating data of the energy storage system, the operating data including one or more of the timestamps, charging and discharging currents, or battery temperatures generated by the energy storage system during operation; The determining unit is configured to determine, based on the operating data, a first predicted start time for the energy storage system to enter a charging / discharging state in a first cycle, wherein the first cycle is the next operating cycle closest to the current time. Specifically, this includes: determining, based on the operating data, at least one historical start time for the energy storage system to enter a charging / discharging state within at least one historical operating cycle, wherein the historical operating cycle is an operating cycle prior to the current time; and predicting the first predicted start time based on the at least one historical start time. The determining unit is further configured to determine the cooling start-up time of the energy storage system based on the first predicted start time, wherein the cooling start-up time is earlier than the first predicted start time; the step of determining the cooling start-up time of the energy storage system based on the first predicted start time includes: acquiring the cell status of the energy storage system; determining a pre-cooling duration based on the cell status, wherein the pre-cooling duration is the minimum time required for the energy storage system to reach a preset temperature; and determining the cooling start-up time based on the pre-cooling duration and the first predicted start time. The acquisition unit is further configured to: acquire the confidence level of the first predicted start time if the current time is not during a low-electricity period; acquire the time difference between the current time and the first predicted start time; adjust the precooling duration according to the confidence level and the time difference to obtain the adjusted precooling duration; and determine the cooling start time according to the adjusted precooling duration and the first predicted start time. A processing unit is used to cool the energy storage system according to the cooling start-up time.

7. An electronic device, characterized in that, The method includes a processor, a memory, a communication interface, and at least one program, said at least one program being stored in the memory and configured to be executed by the processor, said program including instructions for performing the steps of the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the method as described in any one of claims 1-5.

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