Control method and device of cooling system in energy storage system and computer equipment

Abstract

The application discloses a kind of control method, device and computer equipment of cooling system in energy storage system.Cold system control method includes: obtaining the running state data of energy storage system at current time and the charge-discharge power strategy corresponding to preset future period;According to charge-discharge power strategy, determine the expected charge-discharge current corresponding to each time in preset future period in combination with future each time running state data, determine the battery heat generation power corresponding to future each time;According to the battery heat generation power, the operating state of cooling system and preset cooling capacity model, determine the battery temperature prediction curve corresponding to preset future period;Battery temperature prediction curve is compared with preset temperature control threshold, to determine the target control state and starting time of cooling system, generates cooling system control instruction and executes.The application solves the technical problems of cooling start lag and poor temperature control stability caused by the lack of future charge-discharge working condition prediction ability of cooling system.

Description

Technical Field

[0001] This invention relates to the field of battery management technology for energy storage power stations, and more specifically, to control methods, devices, and computer equipment for cooling systems in energy storage systems. Background Technology

[0002] As the core energy storage component of energy storage power stations, the operating temperature of lithium-ion batteries directly affects their performance, lifespan, and safety. In existing energy storage systems, the Battery Management System (BMS) and Thermal Management System (TMS) typically operate in independent control modes. The BMS is responsible for battery state monitoring, SOC / SOH estimation, and charge / discharge management, while the TMS controls the operation of air-cooled or liquid-cooled equipment based on real-time temperature feedback. This control method, which relies on real-time temperature feedback, only activates the cooling equipment when the battery temperature exceeds a set threshold, resulting in a significant time lag between detecting a temperature rise and the cooling system taking effect. Furthermore, in passive control mode, the battery temperature often fluctuates repeatedly around the threshold, causing frequent start-stop cycles of the cooling equipment, which not only affects the stability of temperature control but also shortens the lifespan of the cooling equipment. Due to the lack of ability to predict future temperature rise trends, the thermal management system often adopts a conservative strategy, continuously operating the cooling equipment at high power throughout the charge / discharge process, resulting in unnecessary energy loss.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a control method, apparatus, and computer equipment for a cooling system in an energy storage system, to at least solve the technical problems of delayed cooling start-up and poor temperature control stability caused by the lack of predictive ability of the cooling system for future charging and discharging conditions.

[0005] According to one aspect of the present invention, a control method for a cooling system in an energy storage system is provided, comprising: acquiring operating state data of the energy storage system at the current moment and a charge / discharge power strategy corresponding to a preset future time period, wherein the operating state data includes the operating state of the cooling system in the energy storage system; determining the expected charge / discharge current corresponding to each of multiple future moments in the preset future time period according to the charge / discharge power strategy; determining the battery heat generation power corresponding to each of the multiple future moments according to the expected charge / discharge current and the operating state data; determining the battery temperature prediction curve corresponding to the preset future time period according to the battery heat generation power, the operating state of the cooling system, and a preset cooling capacity model; comparing the battery temperature prediction curve with a preset temperature control threshold to determine the target control state and start-up timing of the cooling system; and generating and executing a cooling system control command according to the target control state and start-up timing.

[0006] Optionally, based on the expected charge / discharge current and operating status data for each of the multiple future time points, the battery heat generation power for each of the multiple future time points is determined, including: obtaining the battery internal resistance in the energy storage system; determining the battery temperature in the energy storage system based on the operating status data; determining the Joule heat power for each of the multiple future time points based on the expected charge / discharge current and battery internal resistance for each of the multiple future time points; determining the reaction heat power for each of the multiple future time points based on the expected charge / discharge current, battery temperature, and preset open-circuit voltage temperature coefficient for each of the multiple future time points; and determining the battery heat generation power for each of the multiple future time points based on the Joule heat power and reaction heat power for each of the multiple future time points.

[0007] Optionally, based on the battery heat generation power, cooling system operating status, and preset cooling capacity model corresponding to multiple future moments, a battery temperature prediction curve corresponding to a preset future time period is determined, including: dividing the preset future time period into multiple time steps based on the current moment and multiple future moments, wherein the current moment and multiple future moments each correspond to one of the multiple time steps; performing a temperature prediction step for each of the multiple time steps, starting from the first time step, iterating sequentially until the predicted battery temperature corresponding to each of the multiple time steps is obtained, generating a battery temperature prediction curve, wherein the temperature prediction step is as follows: obtaining the initial battery temperature corresponding to the current time step, wherein, at the current time... When the step size is the first time step, the initial battery temperature is determined based on the operating status data; the battery heat generation power for the current time step is determined based on the battery heat generation power corresponding to the current time step among multiple future moments; the cooling power for the current time step is determined based on the operating status of the cooling system; the net heat change for the current time step is calculated based on the battery heat generation power and cooling power for the current time step; the temperature change for the current time step is calculated based on the net heat change; and the predicted battery temperature for the current time step is determined based on the temperature change and the initial battery temperature, and the predicted battery temperature for the current time step is used as the initial battery temperature for the next time step.

[0008] Optionally, the cooling power corresponding to the current time step is determined according to the operating status of the cooling system, including: determining the equivalent heat transfer coefficient and cooling medium temperature corresponding to the current time step according to the operating status of the cooling system; and calculating the cooling power corresponding to the current time step based on the preset heat transfer area, equivalent heat transfer coefficient, and temperature difference between the cooling medium temperature and the initial battery temperature.

[0009] Optionally, the battery temperature prediction curve is compared with a preset temperature control threshold to determine the target control state of the cooling system. The preset temperature control threshold includes a first threshold, a second threshold, and a third threshold, wherein the first threshold is less than the second threshold, and the second threshold is less than the third threshold. This includes: determining the maximum predicted temperature based on the battery temperature prediction curve; if the maximum predicted temperature is less than the first threshold, determining the target control state as either stopping operation or maintaining a preset minimum power operation; if the maximum predicted temperature is not less than the first threshold and is less than the second threshold, determining the target control state as operating at a first power, where the first power is higher than the preset minimum power; if the maximum predicted temperature is not less than the second threshold and is less than the third threshold, determining the target control state as operating at a second power, where the second power is higher than the first power; and if the maximum predicted temperature is not less than the third threshold, determining the target control state as operating at a preset maximum power, where the preset maximum power is higher than the second power.

[0010] Optionally, the battery temperature prediction curve is compared with a preset temperature control threshold to determine the start-up timing of the cooling system, including: determining the control moment when the predicted temperature first reaches the preset temperature control threshold based on the battery temperature prediction curve; obtaining the response time of the cooling system, wherein the response time is the time required from sending the control command to the cooling system reaching the expected cooling capacity; and determining the start-up timing of the cooling system based on the control moment and the response time.

[0011] According to another aspect of the present invention, a control device for a cooling system in an energy storage system is also provided, comprising: an acquisition module, configured to acquire operating status data of the energy storage system at the current moment and a charge / discharge power strategy corresponding to a preset future time period, wherein the operating status data includes the operating status of the cooling system in the energy storage system; a first determination module, configured to determine the expected charge / discharge current corresponding to each of multiple future times in the preset future time period according to the charge / discharge power strategy; a second determination module, configured to determine the battery heat generation power corresponding to each of the multiple future times according to the expected charge / discharge current and operating status data; a third determination module, configured to determine the battery temperature prediction curve corresponding to the preset future time period according to the battery heat generation power corresponding to each of the multiple future times, the operating status of the cooling system, and a preset cooling capacity model; a fourth determination module, configured to compare the battery temperature prediction curve with a preset temperature control threshold to determine the target control state and start-up timing of the cooling system; and a generation module, configured to generate and execute cooling system control commands according to the target control state and start-up timing.

[0012] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute the control method of the cooling system in any of the above-described energy storage systems.

[0013] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program, when running, executes the control method of the cooling system in any of the above-described energy storage systems.

[0014] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the control method for the cooling system in any of the above-described energy storage systems. In this embodiment of the invention, a control method for the cooling system in an energy storage system is adopted. This involves acquiring the current operating status data of the energy storage system and a preset charging / discharging power strategy for future time periods. The operating status data includes the operating status of the cooling system within the energy storage system. Based on the charging / discharging power strategy, the expected charging / discharging current for each of the multiple future times within the preset future time period is determined. Based on the expected charging / discharging current and operating status data for each of the multiple future times, the battery heat generation power for each of the multiple future times is determined. Based on the battery heat generation power for each of the multiple future times, the operating status of the cooling system, and a preset cooling capacity model, a battery temperature prediction curve for the preset future time period is determined. The battery temperature prediction curve is compared with a preset temperature control threshold to determine the target control state and start-up timing of the cooling system. Based on the target control state and start-up timing, a cooling system control command is generated and executed. This achieves the goal of the cooling system issuing commands according to the charging / discharging power strategy for future time periods, thereby realizing the technical effect that the cooling system can predict and control battery temperature based on battery charging / discharging conditions. This solves the technical problems of cooling start-up lag and poor temperature control stability caused by the lack of predictive ability for future charging / discharging conditions in the cooling system. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0016] Figure 1 A hardware block diagram of a computer terminal for implementing a control method for a cooling system in an energy storage system is shown.

[0017] Figure 2 This is a flowchart illustrating a control method for a cooling system in an energy storage system according to an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of a control method for a cooling system in an energy storage system according to an optional embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of the control system architecture of the cooling system in an energy storage system provided by an optional embodiment of the present invention;

[0020] Figure 5 This is a structural block diagram of the control device for the cooling system in an energy storage system provided according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0024] State of charge (SOC): Reflects the current remaining charge of the battery and affects the battery's internal resistance characteristics.

[0025] Bernardi heat generation model: A classic physical model used to calculate the internal heat generation power of lithium-ion batteries. This model posits that battery heat generation consists of two parts: Joule heat generated by the current flowing through the internal resistance, and reaction heat caused by the reversibility of the electrochemical reaction.

[0026] According to an embodiment of the present invention, a method embodiment of a control method for a cooling system in an energy storage system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] The method embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a control method for a cooling system in an energy storage system is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0028] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0029] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the control method of the cooling system in the energy storage system in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the control method of the cooling system in the energy storage system described above. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0030] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0031] Figure 2 This is a flowchart illustrating a control method for a cooling system in an energy storage system according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0032] Step S201: Obtain the operating status data of the energy storage system at the current moment and the charging and discharging power strategy corresponding to the preset future time period. The operating status data includes the operating status of the cooling system in the energy storage system.

[0033] In this step, operational status data may include current operational parameters of the cooling system, such as cooling medium temperature, flow rate, and operating power of the cooling equipment. The charge / discharge power strategy is typically obtained from the energy management system (EMS) and covers a preset future time period, such as the charge / discharge power plan or instructions for each moment within the next 30 minutes to 2 hours. For energy storage power stations participating in grid dispatch, this power plan is issued by the grid dispatch system; for independently operating energy storage systems, future power demand can be predicted based on historical data and operating modes. This strategy reflects the future load demand of the energy storage system and is the foundation for predictive control.

[0034] In addition, operational status data may also include other status parameters of the energy storage system. For example, Figure 3 This is a schematic diagram of a control method for a cooling system in an energy storage system according to an optional embodiment of the present invention, such as... Figure 3 As shown, the operating status data includes: current battery temperature, state of charge (SOC), ambient temperature, etc. The current battery temperature is data collected from temperature monitoring points of each battery cluster or module, and can be the average value, maximum value, or multi-point distribution data.

[0035] Step S202: Based on the charging and discharging power strategy, determine the expected charging and discharging current corresponding to each of the multiple future moments in the preset future time period.

[0036] In this step, the expected charging and discharging current at each future moment can be determined using the power setting value in the charging and discharging power strategy and the voltage parameters of the energy storage system through the formula. The calculation yields the following: I(t) is the expected current at time t, P(t) is the planned power value at time t, and Vsys(t) is the system voltage. The system voltage can be estimated based on the current state of charge (SOC) and cell characteristic curves, or a simplified calculation can be performed using the rated voltage.

[0037] Optionally, for systems containing multiple battery clusters connected in parallel, the expected current of each branch can be further refined based on the state differences of each cluster to improve the accuracy of subsequent calculations.

[0038] Step S203: Determine the battery heat generation power corresponding to each of the multiple future time points based on the expected charge / discharge current and operating status data corresponding to each of the multiple future time points.

[0039] In this step, the battery's heat generation power can be calculated based on the battery's heat generation model. For example, according to the Bernardi heat generation model, battery heat generation mainly consists of Joule heat and reaction heat. The battery's heat generation power at each future time point can be obtained by adding the Joule heat power and the reaction heat power at each future time point. This heat generation power... It is an important parameter for predicting battery temperature in the future.

[0040] Step S204: Based on the battery heat generation power, cooling system operation status, and preset cooling capacity model corresponding to multiple future time periods, determine the battery temperature prediction curve corresponding to the preset future time period.

[0041] In this step, the battery temperature prediction curve is achieved through dynamic thermal balance calculation. It uses the battery's heat generation power as the heat source input, combined with a pre-set cooling capacity model of the cooling system's operating state as the heat sink output. These two are substituted into the thermal balance equation to calculate the net heat change, and an iterative algorithm is used to accumulate the temperature change at each moment, thereby generating a battery temperature prediction curve that reflects future temperature trends. This provides a quantitative basis for subsequent judgments on whether the temperature is too high and for early initiation of cooling.

[0042] Step S205: Compare the battery temperature prediction curve with the preset temperature control threshold to determine the target control state and start-up timing of the cooling system.

[0043] In this step, the battery temperature prediction curve serves as forward-looking data input for future temperature evolution, and a preset temperature control threshold is used as the criterion for tiered control. By comparing the predicted temperature peak with multiple thresholds, such as those corresponding to low, medium, and high power levels, the target control state is determined, i.e., the operating power level or start / stop command of the cooling system, to balance heat dissipation requirements and energy consumption. Simultaneously, based on the moment the predicted curve first reaches the threshold, the response lag time and safety margin time of the cooling system are subtracted in reverse to accurately calculate the start-up timing, ensuring that cooling action intervenes before the actual temperature exceeds the limit, thereby eliminating response lag and improving control stability.

[0044] Step S206: Generate and execute cooling system control commands based on the target control state and start-up timing.

[0045] In this step, the cooling system control commands include specific parameters such as equipment start / stop, frequency adjustment, or valve opening. These commands are processed through smoothing mechanisms, such as ramp adjustment, to avoid abrupt changes in control quantities, and are ultimately sent to the execution module at the specified start-up time. Specifically, during the execution phase, the control decision module transforms the abstract control strategy into specific equipment operation commands based on the determined target control state and the calculated start-up timing. For example, for a liquid cooling system, it generates variable frequency setpoints for the cooling pump, speed adjustment commands for the fan, or opening control signals for the cooling valve. Based on the mechanical characteristics and safety regulations of the cooling equipment, it can also use ramp adjustment or smoothing filtering algorithms to gradually process the control quantities to avoid impacts or damage to the equipment caused by abrupt changes in control commands. Subsequently, the smoothed control commands are sent to the actuators of the thermal management system via industrial communication protocols such as CAN bus or Modbus. When the preset start-up timing is reached, the execution action is automatically triggered. During the execution process, the actual response status of the equipment and the real-time changes in battery temperature are monitored in real time. If the actual temperature deviates from the predicted curve or the actuator feedback is abnormal, the closed-loop correction mechanism is immediately activated to re-predict the temperature and dynamically adjust subsequent control commands, thereby ensuring that the cooling system can respond accurately, smoothly, and promptly to the battery's thermal management needs.

[0046] Through the above steps, the cooling system achieves the goal of issuing instructions based on the charging and discharging power strategy corresponding to the future time period. This enables the cooling system to predict and control the battery temperature in conjunction with the battery charging and discharging conditions, thereby solving the technical problems of delayed cooling start-up and poor temperature control stability caused by the lack of predictive ability of the cooling system for future charging and discharging conditions.

[0047] As an optional embodiment, the battery heat generation power corresponding to each of the multiple future time points is determined based on the expected charge / discharge current and operating status data corresponding to each of the multiple future time points, including: obtaining the battery internal resistance in the energy storage system; determining the battery temperature in the energy storage system based on the operating status data; determining the Joule heat power corresponding to each of the multiple future time points based on the expected charge / discharge current and battery internal resistance corresponding to each of the multiple future time points; determining the reaction heat power corresponding to each of the multiple future time points based on the expected charge / discharge current, battery temperature, and preset open-circuit voltage temperature coefficient corresponding to each of the multiple future time points; and determining the battery heat generation power corresponding to each of the multiple future time points based on the Joule heat power and reaction heat power corresponding to each of the multiple future time points.

[0048] Optionally, the process of calculating the battery heat generation power based on the expected charge / discharge current and real-time operating status data corresponding to multiple future moments first involves obtaining the internal resistance characteristics of the battery in the energy storage system at the current and future moments by looking up tables or models. Combined with the battery temperature data obtained from real-time monitoring, the expected charge / discharge current is multiplied by the battery internal resistance using the Joule's law principle to accurately calculate the Joule heat power generated by the current flowing through the internal resistance. At the same time, based on the obtained expected charge / discharge current, current battery temperature, and preset open-circuit voltage temperature coefficient, the reaction heat power generated by the electrochemical reaction is calculated. Finally, the calculated Joule heat power and reaction heat power are superimposed to obtain the total battery heat generation power corresponding to each future moment, thereby providing accurate heat source input data for subsequent thermal balance analysis and temperature prediction.

[0049] Specifically, to accurately quantify the thermal effects of a battery during charging and discharging, the Bernardi heat generation equation can be used as a battery heat generation model. This model decomposes the total heat generation power of the battery into two parts: Joule heat caused by internal resistance and reaction heat caused by entropy change, thus more accurately reflecting the heat generation characteristics under different current and temperature conditions. Figure 3 This is a schematic diagram of a control method for a cooling system in an energy storage system according to an optional embodiment of the present invention, such as... Figure 3 As shown, based on Bernardi's heat production equation, the following formula is used to calculate the heat production power:

[0050]

[0051] Optionally, the heat generation model includes Joule heating. ,in: Battery heat generation power (unit: W). This refers to the charging and discharging current (unit: A). Battery internal resistance (unit: Ω) is related to state of charge and temperature. Battery temperature (unit: K). Temperature coefficient of open-circuit voltage (unit: V / K).

[0052] The heat generation model also includes the heat of reaction. : Heat generated by the flow of current through the battery's internal resistance is the primary source of heat, typically accounting for 85-90% of total heat production. Generated by electrochemical reactions during battery charging and discharging, its sign depends on the direction of charging and discharging and the temperature coefficient. Battery internal resistance. The internal resistance was obtained through experimental calibration and stored as a two-dimensional lookup table (MAP). It is retrieved based on interpolation using the current state of charge (SOC) and temperature. The internal resistance increases with decreasing temperature, exhibiting a U-shaped curve with SOC (smaller in the middle region of SOC and larger at both ends). Open-circuit voltage temperature coefficient. This value can be obtained from the battery specifications or determined experimentally. For lithium iron phosphate batteries, this value is usually negative; for ternary lithium batteries, it may be positive or negative in different SOC ranges.

[0053] As an optional embodiment, based on the battery heat generation power, cooling system operating status, and preset cooling capacity model corresponding to multiple future moments, a battery temperature prediction curve corresponding to a preset future time period is determined, including: dividing the preset future time period into multiple time steps based on the current moment and multiple future moments, wherein the current moment and multiple future moments each correspond to one of the multiple time steps; performing a temperature prediction step for each of the multiple time steps, starting from the first time step, iterating sequentially until the predicted battery temperature corresponding to each of the multiple time steps is obtained, generating a battery temperature prediction curve, wherein the temperature prediction step is as follows: obtaining the initial battery temperature corresponding to the current time step, wherein, in With the current time step being the first time step, the initial battery temperature is determined based on operating status data. The battery heat generation power for the current time step is determined based on the battery heat generation power corresponding to the current time step across multiple future moments. The cooling power for the current time step is determined based on the operating status of the cooling system. The net heat change for the current time step is calculated based on the battery heat generation power and cooling power. The temperature change for the current time step is calculated based on the net heat change. The predicted battery temperature for the current time step is determined based on the temperature change and the initial battery temperature, and this predicted battery temperature is used as the initial battery temperature for the next time step.

[0054] Optionally, the battery temperature prediction curve for a preset future time period is determined based on the battery heat generation power, the operating status of the cooling system, and a preset cooling capacity model corresponding to multiple future moments. Specifically, the preset future time period is first divided into multiple consecutive time steps based on the current moment and the multiple future moments. Then, starting from the first time step, temperature prediction steps are executed sequentially until all time steps are covered to generate a complete temperature prediction curve. When executing the temperature prediction step, if the current time step is the first time step, the initial battery temperature is determined based on real-time operating status data; if it is a subsequent time step, the predicted battery temperature calculated from the previous time step is directly used as the current time step temperature. The system first sets the initial battery temperature, then determines the heat generation at the current time step based on the battery's heat generation power. Simultaneously, it calculates the cooling power at the current time step by combining the real-time operating status of the cooling system and the cooling capacity model. Then, it calculates the net heat change at the current time step using the difference between the heat generation and the cooling power. Based on the battery's thermal capacity parameters, it converts the net heat change into the corresponding temperature change. Finally, it superimposes this temperature change onto the initial battery temperature to obtain the predicted battery temperature at the current time step. This prediction result is then used as the initial input for the next time step. Through this iterative calculation method, the system accurately predicts the battery temperature change trend over the entire preset time period.

[0055] Figure 3 This is a schematic diagram of a control method for a cooling system in an energy storage system according to an optional embodiment of the present invention, such as... Figure 3 As shown, the battery temperature prediction curve is established based on the principle of energy conservation and is calculated iteratively using a discretized form:

[0056]

[0057] in, Let K be the battery temperature at time k (in K or °C). To calculate the step size (in seconds), a typical value is 60 seconds. The equivalent thermal mass of the battery (unit: kg). Specific heat capacity of battery (unit: J / (kg)) K)), Let be the heat generation power at time k (unit: W). This represents the cooling power (in W) at time k. It is calculated based on the current battery temperature. Using the initial value, calculate the heat generation power hourly according to the power plan. The temperature at future times is calculated iteratively using the heat balance equation. This forms a temperature prediction curve.

[0058] As an optional embodiment, determining the cooling power corresponding to the current time step based on the operating status of the cooling system includes: determining the equivalent heat transfer coefficient and cooling medium temperature corresponding to the current time step based on the operating status of the cooling system; and calculating the cooling power corresponding to the current time step based on the preset heat transfer area, equivalent heat transfer coefficient, and temperature difference between the cooling medium temperature and the initial battery temperature.

[0059] Optionally, the equivalent heat transfer coefficient and cooling medium temperature corresponding to the current time step can be determined first based on the real-time operating parameters of the cooling system. The equivalent heat transfer coefficient reflects the influence of the cooling medium flow rate on the heat transfer efficiency. Then, combined with the preset effective heat transfer area between the battery and the cooling medium, the heat transfer area, the determined equivalent heat transfer coefficient, and the temperature difference formed by the initial battery temperature and the cooling medium temperature are multiplied to calculate the heat removed from the battery by the cooling system at the current time step, which is the corresponding cooling power.

[0060] Specifically, the cooling power is calculated based on the current or expected control state of the cooling system. .

[0061]

[0062] in, The equivalent heat transfer coefficient related to the cooling medium flow rate F (unit: W / (m²) K)), The effective heat exchange area between the battery and the cooling medium (unit: m²). Battery temperature (unit: K or °C). Temperature of the cooling medium (unit: K or °C). Equivalent heat transfer coefficient. The heat transfer coefficient exhibits a non-linear relationship with the cooling medium flow rate; increasing the flow rate improves the coefficient, but diminishing marginal returns exist. This parameter is obtained through experimental calibration and stored as a one-dimensional lookup table, retrieved based on the current or expected cooling flow rate. For liquid cooling systems, the cooling medium temperature... The inlet temperature of the coolant can be used as an approximation; for air-cooled systems, the ambient temperature should be used.

[0063] As an optional embodiment, the battery temperature prediction curve is compared with a preset temperature control threshold to determine the target control state of the cooling system. The preset temperature control threshold includes a first threshold, a second threshold, and a third threshold, wherein the first threshold is less than the second threshold, and the second threshold is less than the third threshold. This includes: determining the maximum predicted temperature based on the battery temperature prediction curve; if the maximum predicted temperature is less than the first threshold, determining the target control state as either stopping operation or maintaining a preset minimum power operation; if the maximum predicted temperature is not less than the first threshold and is less than the second threshold, determining the target control state as operating at a first power, where the first power is higher than the preset minimum power; if the maximum predicted temperature is not less than the second threshold and is less than the third threshold, determining the target control state as operating at a second power, where the second power is higher than the first power; and if the maximum predicted temperature is not less than the third threshold, determining the target control state as operating at a preset maximum power, where the preset maximum power is higher than the second power.

[0064] Optionally, Figure 3 This is a schematic diagram of a control method for a cooling system in an energy storage system according to an optional embodiment of the present invention, such as... Figure 3 As shown, the temperature control threshold is divided into multiple levels, forming a hierarchical control strategy:

[0065] (1) Divide the temperature control threshold into the first threshold. Second threshold and the third threshold ,in Typical settings are: , , Safety limits The specific threshold can be adjusted according to the battery type and operating requirements.

[0066] (2) Based on the maximum value in the temperature prediction curve Determine the target control state of the cooling system:

[0067] when When necessary, the cooling system should be controlled to operate at minimum power or stop operating to reduce energy consumption;

[0068] when At this time, the cooling system is controlled to operate at low power, corresponding to a cooling medium flow rate of 30-50% of the rated flow rate;

[0069] when At this time, the cooling system is controlled to operate at medium power, corresponding to a cooling medium flow rate of 50-70% of the rated flow rate;

[0070] when At this time, the cooling system is controlled to operate at maximum power, and the cooling medium flow rate reaches 100% of the rated flow rate.

[0071] (3) When the predicted temperature will exceed the safety limit even at maximum cooling power. When this occurs, a power limiting mechanism is triggered, sending a power limiting request to the battery management system or energy management system to reduce heat generation at the source by reducing the charging and discharging power.

[0072] As an optional embodiment, the battery temperature prediction curve is compared with a preset temperature control threshold to determine the start-up timing of the cooling system, including: determining the control moment when the predicted temperature first reaches the preset temperature control threshold based on the battery temperature prediction curve; obtaining the response time of the cooling system, wherein the response time is the time required from sending the control command to the cooling system reaching the expected cooling capacity; and determining the start-up timing of the cooling system based on the control moment and the response time.

[0073] Optionally, the specific moment when the predicted temperature first reaches the preset temperature control threshold is determined based on the battery temperature prediction curve. Then, the response time required for the cooling system to actually reach the expected cooling capacity from receiving the command is obtained. Finally, the response time and the preset safety margin time are subtracted from the temperature reaching moment to calculate the specific time when the cooling system should be started in advance, so as to ensure that the cooling system has been fully operating and has sufficient heat dissipation capacity before the battery temperature actually reaches the dangerous threshold.

[0074] Specifically, the following logical steps can be used:

[0075] (1) Based on the generated battery temperature prediction curve, the system identifies when the predicted battery temperature first reaches or exceeds the preset temperature control threshold (e.g., the third threshold). The time point at which the control moment is defined is the time point at which the control moment is defined. From this moment on, the battery enters a state requiring powerful cooling or maximum power cooling.

[0076] (2) The system obtains the response time of the cooling system. The response time is a physical parameter pre-calibrated or monitored in real time. It represents the time lag required from the moment the cooling system receives a control command (such as starting the cooling pump, adjusting the fan speed, or opening a valve) until the cooling medium flow rate, heat exchange efficiency, etc., reach the expected target state. For example, for a liquid cooling system, the response time may include the total time required for the water pump to accelerate to the set frequency, for the coolant circulation to be established, and for heat exchange to reach a steady state.

[0077] (3) The system is based on the control time. and response time Calculate the start-up timing of the cooling system The specific calculation formula can be expressed as:

[0078]

[0079] in, This is a preset safety margin time to accommodate model prediction errors or sudden changes in operating conditions. It is implemented by advancing the control time... The start command is sent at the appropriate time to ensure that the battery temperature actually reaches the threshold. At that time, the cooling system is already operating at full load or target power, thus effectively preventing the battery temperature from rising further due to cooling lag, achieving precise predictive thermal management.

[0080] The cooling system control commands may include: equipment start / stop commands, such as starting or stopping the cooling pump or fan; continuous adjustment commands, such as adjusting the cooling pump frequency, fan speed, and valve opening; and mode switching commands, such as switching between multiple cooling modes, like switching from air cooling to liquid cooling.

[0081] To avoid shocking the equipment due to abrupt changes in the control quantity, ramp adjustment or PID control can be used to achieve a smooth transition. For example, when adjusting the cooling pump frequency from 30Hz to 40Hz, the adjustment should be done gradually at a rate of 1-2Hz per second.

[0082] Control commands can be sent to the thermal management system actuators via communication interfaces (such as CAN bus, Modbus, etc.), and execution feedback can be monitored. During control execution, the actual temperature change is continuously monitored and compared with the predicted temperature. If the actual temperature deviates from the predicted curve by more than a set threshold (such as ±2℃), temperature prediction and control decisions are re-performed to achieve closed-loop control.

[0083] In conjunction with the above optional embodiments, a control method for the cooling system in an energy storage system is also proposed. Figure 3 This is a schematic diagram of a control method for a cooling system in an energy storage system according to an optional embodiment of the present invention, such as... Figure 3As shown, firstly, real-time operating status data of the energy storage system and the charging / discharging power plan for a future preset period are acquired. The real-time operating status data includes information such as the current battery temperature, state of charge, and ambient temperature. Next, based on the acquired charging / discharging power plan, the expected charging / discharging current at each moment within the future preset period is calculated. Subsequently, based on the battery heat generation model, combined with the expected charging / discharging current and the current battery state, the battery heat generation power at each moment within the future preset period is calculated. Based on this, combined with the current operating status of the cooling system and the cooling capacity model, the battery temperature prediction curve for the future preset period is iteratively calculated based on the heat balance equation. Then, the temperature prediction curve is compared with a preset temperature control threshold. Based on the comparison result, the target control state and activation timing of the cooling system are determined. The activation timing considers the response lag of the cooling system to ensure that action is taken before the predicted temperature exceeds the threshold. Finally, based on the determined target control state and activation timing, corresponding cooling system control commands are generated and executed to achieve coordinated control from passive response to active prediction.

[0084] According to an optional embodiment of the present invention, a control system for a cooling system in an energy storage system is also provided. Figure 4 This is a schematic diagram of the control system architecture of the cooling system in an energy storage system provided by an optional embodiment of the present invention, such as... Figure 4 As shown, the control system includes the following modules: data acquisition module, power plan acquisition module, temperature prediction module, control decision module, and execution module.

[0085] Optionally, the data acquisition module is used to collect various data required for the operation of the energy storage system, including battery data, thermal management system data, environmental data, and power data. Specifically, battery data is collected via a CAN bus to communicate with the battery management system (BMS), acquiring status data such as voltage, current, temperature, SOC, and SOH of each battery cluster, with a collection frequency of at least 1Hz, which can be increased to 10Hz for key parameters such as temperature. Thermal management system data is collected via Modbus, CAN, and other protocols to communicate with the thermal management system (TMS), acquiring data such as cooling medium temperature, flow rate, cooling equipment operating status (e.g., pump frequency, fan speed), and valve opening. Environmental data is collected via environmental sensors to acquire data such as ambient temperature and humidity. Power data is used to collect data such as the current actual charge / discharge power and grid-side power. This data acquisition module has data preprocessing functions, including data filtering to remove noise and outliers, data interpolation to compensate for inconsistent sampling periods, and data synchronization to align timestamps from different sources.

[0086] Optionally, the power plan acquisition module is used to obtain the charging and discharging power plan for a future preset time period from the energy management system (EMS). This module supports an active acquisition mode, communicating with the EMS via Ethernet and various communication protocols (such as Modbus TCP, IEC61850, OPC UA, etc.) to obtain the power plan from the scheduling instructions in real time; it supports a passive prediction mode, predicting future power demand based on historical operating data and time period characteristics when there is no explicit power plan, and using time series analysis methods to identify the periodic patterns of the operating mode (such as daily peak shaving periods); and it supports a hybrid mode, combining the scheduling plan and historical patterns to correct and supplement the power plan. This power plan includes information such as power setpoints, duration, and priority for each time period, and its time domain range is configurable, typically ranging from 30 minutes to 2 hours.

[0087] Optionally, the temperature prediction module is used to calculate the predicted battery temperature curve for a preset future period based on the power plan, battery heat generation model, and thermal balance equation. This module includes a model parameter library storing parameters required by the battery heat generation model, including the battery internal resistance MAP table, open-circuit voltage temperature coefficient, battery heat capacity parameters, heat transfer coefficient curve, etc. Parameters can be loaded via configuration files and support online updates. It also includes a prediction algorithm engine that performs iterative temperature calculations based on the thermal balance equation, employing a discretized time-domain calculation method with configurable calculation step size (typically 60 seconds) and configurable prediction time domain (typically 30 minutes to 2 hours). Furthermore, it includes a multi-scenario prediction function, capable of predicting temperature for different cooling control strategies (such as maintaining the current state or switching to different power levels), generating multiple prediction curves for decision comparison. Finally, it includes an uncertainty handling function, considering uncertainties such as model errors, parameter deviations, and external interference, and performing confidence interval estimation or robustness analysis on the prediction results. In addition, the temperature prediction module also supports adaptive adjustment of model parameters. By comparing the historical predicted temperature with the actual temperature, the model deviation is identified, and key parameters (such as internal resistance and heat transfer coefficient) are corrected online using methods such as recursive least squares and Kalman filtering.

[0088] Optionally, the control decision module is used to determine the target control state and start-up timing of the cooling system based on the comparison results between the temperature prediction curve and the temperature control threshold. This module includes a threshold management unit that stores and manages temperature control threshold configurations, supports multi-level threshold settings, and can dynamically adjust thresholds based on factors such as season, ambient temperature, and battery aging status; a control strategy unit that implements hierarchical control logic, determines the target cooling power level based on the predicted maximum temperature, and supports configurable switching of various control strategies (such as aggressive, conservative, and economical strategies); a timing calculation unit that calculates the optimal start-up time based on the prediction curve, response time, and safety margin, and considers the state transition path of the cooling system (e.g., step-by-step adjustment may be required from stop to high power); an optimization decision unit that makes optimization decisions with the goal of minimizing cooling energy consumption while meeting temperature constraints, and can employ optimization algorithms such as dynamic programming and model predictive control; an anomaly handling unit that triggers a power limiting request when the predicted temperature will exceed the safety limit even at maximum cooling power, calculates the required power limiting amplitude, generates a power limiting command, and sends it to the energy management system or battery management system; and a conflict coordination unit that performs priority coordination and trade-off processing when thermal management requirements conflict with other control requirements (such as power response and frequency regulation).

[0089] Optionally, the execution module generates and sends cooling system control commands based on the target control state and start-up timing output by the control decision module. This module includes a command generation unit that converts the abstract target control state into specific equipment control commands, such as converting "medium-power cooling" into specific parameters like "cooling pump frequency 40Hz, valve opening 60%"; a smoothing control unit that smooths the control input to avoid abrupt changes, using ramp functions, filters, or PID controllers for gradual adjustment; a communication interface unit that communicates with the thermal management system's actuators via CAN bus, Modbus, Ethernet, or other communication protocols, sending control commands and receiving execution feedback; a status monitoring unit that monitors the execution of control commands, determines whether the cooling system responds as expected, and promptly feeds back to the control decision module when execution deviations or equipment malfunctions are detected; and a data recording unit that records all control commands, execution states, temperature changes, and other data for post-analysis and model optimization. Furthermore, the execution module includes safety protection functions; when abnormal temperature increases or cooling system malfunctions are detected, it can bypass the normal control flow and directly trigger emergency cooling or power cut-off protection actions.

[0090] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that the control method of the cooling system in the energy storage system according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0092] According to embodiments of the present invention, a control device for the cooling system in an energy storage system is also provided for implementing the control method for the cooling system in the above-described energy storage system. Figure 5 This is a structural block diagram of the control device for the cooling system in an energy storage system according to an embodiment of the present invention. Figure 5 This is a structural block diagram of the control device for the cooling system in an energy storage system according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes: an acquisition module 51, a first determination module 52, a second determination module 53, a third determination module 54, a fourth determination module 55, and a generation module 56. The device will be described below.

[0093] The acquisition module 51 is used to acquire real-time operating status data of the energy storage system and the charging and discharging power plan for a future preset period. The real-time operating status data includes the current battery temperature, state of charge, ambient temperature, and current status of the cooling system.

[0094] The first determining module 52, connected to the acquiring module 51, is used to calculate the expected charging and discharging current at each moment within a future preset time period based on the charging and discharging power plan.

[0095] The second determining module 53, connected to the first determining module 52, is used to calculate the battery heat generation power at each time within a preset future time period based on the battery heat generation model and the expected charging and discharging current and the current state of the battery at multiple future times.

[0096] The third determining module 54, connected to the second determining module 53, is used to combine the current cooling system status and cooling capacity model to iteratively calculate the battery temperature prediction curve within a future preset time period based on the thermal balance equation.

[0097] The fourth determining module 55, connected to the third determining module 54, is used to compare the battery temperature prediction curve with the preset temperature control threshold to determine the target control state and start-up timing of the cooling system. When the predicted temperature will exceed the temperature control threshold, the cooling is started or enhanced in advance before the predicted over-limit time.

[0098] The generation module 56, connected to the fourth determination module 55, is used to generate and execute cooling system control commands based on the target control state and start-up timing.

[0099] It should be noted that the aforementioned acquisition module 51, first determination module 52, second determination module 53, third determination module 54, fourth determination module 55, and generation module 56 correspond to steps S201 to S206 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the aforementioned modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0100] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0101] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the control method and device for the cooling system in the energy storage system of this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the control method for the cooling system in the energy storage system described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] The processor can access information and application programs stored in the memory via a transmission device to perform the following steps: acquiring the current operating status data of the energy storage system and the charging / discharging power strategy corresponding to a preset future time period, wherein the operating status data includes the operating status of the cooling system in the energy storage system; determining the expected charging / discharging current corresponding to each of the multiple future times within the preset future time period based on the charging / discharging power strategy; determining the battery heat generation power corresponding to each of the multiple future times based on the expected charging / discharging current and operating status data; determining the battery temperature prediction curve corresponding to the preset future time period based on the battery heat generation power, the operating status of the cooling system, and a preset cooling capacity model; comparing the battery temperature prediction curve with a preset temperature control threshold to determine the target control state and start-up timing of the cooling system; and generating and executing cooling system control commands based on the target control state and start-up timing.

[0103] Optionally, the processor may also execute program code for the following steps: obtaining the battery internal resistance in the energy storage system; determining the battery temperature in the energy storage system based on operating status data; determining the Joule thermal power corresponding to each of the multiple future time points based on the expected charge / discharge current and battery internal resistance corresponding to each of the multiple future time points; determining the reaction thermal power corresponding to each of the multiple future time points based on the expected charge / discharge current, battery temperature, and preset open-circuit voltage temperature coefficient corresponding to each of the multiple future time points; and determining the battery heat generation power corresponding to each of the multiple future time points based on the Joule thermal power and reaction thermal power corresponding to each of the multiple future time points.

[0104] Optionally, the processor may also execute program code with the following steps: Dividing a preset future time period into multiple time steps based on the current time and multiple future times, where the current time and multiple future times each correspond to one of the multiple time steps; performing a temperature prediction step for each of the multiple time steps, starting from the first time step, iterating sequentially until the predicted battery temperature for each of the multiple time steps is obtained, generating a battery temperature prediction curve, wherein the temperature prediction steps are as follows: obtaining the initial battery temperature corresponding to the current time step, wherein, when the current time step is the first time step, the initial battery temperature is determined based on operating status data; determining the battery heat generation power of the current time step based on the battery heat generation power corresponding to the time corresponding to the current time step among the multiple future times; determining the cooling power corresponding to the current time step based on the operating status of the cooling system; calculating the net heat change of the current time step based on the battery heat generation power and cooling power corresponding to the current time step; calculating the temperature change of the current time step based on the net heat change; determining the predicted battery temperature of the current time step based on the temperature change and the initial battery temperature, and using the predicted battery temperature of the current time step as the initial battery temperature of the next time step.

[0105] Optionally, the processor may also execute program code that performs the following steps: determining the equivalent heat transfer coefficient and cooling medium temperature corresponding to the current time step based on the operating status of the cooling system; and calculating the cooling power corresponding to the current time step based on the preset heat transfer area, equivalent heat transfer coefficient, and temperature difference between the cooling medium temperature and the initial battery temperature.

[0106] Optionally, the processor may also execute program code for the following steps: determining the maximum predicted temperature based on the battery temperature prediction curve; determining the target control state as either stopping operation or maintaining a preset minimum power operation when the maximum predicted temperature is less than a first threshold; determining the target control state as operating at a first power when the maximum predicted temperature is not less than the first threshold and less than a second threshold, wherein the first power is higher than the preset minimum power; determining the target control state as operating at a second power when the maximum predicted temperature is not less than the second threshold and less than a third threshold, wherein the second power is higher than the first power; and determining the target control state as operating at a preset maximum power when the maximum predicted temperature is not less than the third threshold, wherein the preset maximum power is higher than the second power.

[0107] Optionally, the processor may also execute program code for the following steps: determining the control moment when the predicted temperature first reaches the preset temperature control threshold based on the battery temperature prediction curve; obtaining the response time of the cooling system, wherein the response time is the time required from sending the control command to the cooling system reaching the expected cooling capacity; and determining the start-up timing of the cooling system based on the control moment and the response time.

[0108] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0109] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the control method of the cooling system in the energy storage system provided in the above embodiments.

[0110] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0111] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring the operating status data of the energy storage system at the current moment and the charge / discharge power strategy corresponding to a preset future time period, wherein the operating status data includes the operating status of the cooling system in the energy storage system; determining the expected charge / discharge current corresponding to each of the multiple future times in the preset future time period according to the charge / discharge power strategy; determining the battery heat generation power corresponding to each of the multiple future times according to the expected charge / discharge current and the operating status data; determining the battery temperature prediction curve corresponding to the preset future time period according to the battery heat generation power, the operating status of the cooling system, and a preset cooling capacity model; comparing the battery temperature prediction curve with a preset temperature control threshold to determine the target control state and start-up timing of the cooling system; and generating and executing cooling system control commands according to the target control state and start-up timing.

[0112] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire the operating status data of the energy storage system at the current moment and the charging and discharging power strategy corresponding to a preset future time period, wherein the operating status data includes the operating status of the cooling system in the energy storage system; determine the expected charging and discharging current corresponding to each of the multiple future times in the preset future time period according to the charging and discharging power strategy; determine the battery heat generation power corresponding to each of the multiple future times according to the expected charging and discharging current and the operating status data; determine the battery temperature prediction curve corresponding to the preset future time period according to the battery heat generation power, the operating status of the cooling system, and a preset cooling capacity model; compare the battery temperature prediction curve with a preset temperature control threshold to determine the target control state and start-up timing of the cooling system; and generate and execute cooling system control commands according to the target control state and start-up timing.

[0113] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0114] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be 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. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0116] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0119] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for a cooling system in an energy storage system, characterized in that, include: The system acquires the operating status data of the energy storage system at the current moment and the charging and discharging power strategy corresponding to the preset future time period, wherein the operating status data includes the operating status of the cooling system in the energy storage system. Based on the charging and discharging power strategy, determine the expected charging and discharging current corresponding to each of the multiple future moments in the preset future time period; Based on the expected charge and discharge current corresponding to each of the multiple future times and the operating status data, determine the battery heat generation power corresponding to each of the multiple future times; Based on the battery heat generation power corresponding to each of the multiple future times, the operating status of the cooling system, and the preset cooling capacity model, determine the battery temperature prediction curve corresponding to the preset future time period; The battery temperature prediction curve is compared with a preset temperature control threshold to determine the target control state and start-up timing of the cooling system. Based on the target control state and the start-up timing, a cooling system control command is generated and executed.

2. The method according to claim 1, characterized in that, The step of determining the battery heat generation power corresponding to each of the plurality of future time moments based on the expected charge / discharge current and the operating status data includes: Obtain the internal resistance of the battery in the energy storage system; Based on the operating status data, the battery temperature in the energy storage system is determined; Based on the expected charge / discharge current and the battery internal resistance at each of the multiple future times, the Joule thermal power at each of the multiple future times is determined. Based on the expected charge / discharge current, battery temperature, and preset open-circuit voltage temperature coefficient corresponding to each of the multiple future times, the reaction heat power corresponding to each of the multiple future times is determined. The battery heat generation power corresponding to each of the multiple future moments is determined based on the Joule heat power and reaction heat power corresponding to each of the multiple future moments.

3. The method according to claim 1, characterized in that, The step of determining the battery temperature prediction curve corresponding to the preset future time period based on the battery heat generation power corresponding to each of the multiple future time periods, the operating status of the cooling system, and the preset cooling capacity model includes: Based on the current time and the plurality of future times, the preset future time period is divided into a plurality of time steps, wherein the current time and the plurality of future times each correspond to one of the plurality of time steps; For the multiple time steps, a temperature prediction step is performed starting from the first time step, iterating sequentially until the predicted battery temperature corresponding to each of the multiple time steps is obtained, generating the battery temperature prediction curve. The temperature prediction steps are as follows: Obtain the initial battery temperature corresponding to the current time step, wherein, when the current time step is the first time step, the initial battery temperature is determined based on the operating status data; The battery heat generation power at the current time step is determined based on the battery heat generation power corresponding to the time corresponding to the current time step among the plurality of future times. Based on the operating status of the cooling system, determine the cooling power corresponding to the current time step; Calculate the net heat change at the current time step based on the battery heat generation power and cooling power corresponding to the current time step; Based on the net heat change, calculate the temperature change corresponding to the current time step; Based on the temperature change and the initial battery temperature, the predicted battery temperature for the current time step is determined, and the predicted battery temperature for the current time step is used as the initial battery temperature for the next time step.

4. The method according to claim 3, characterized in that, Determining the cooling power corresponding to the current time step based on the operating status of the cooling system includes: Based on the operating status of the cooling system, determine the equivalent heat transfer coefficient and cooling medium temperature corresponding to the current time step; Based on the preset heat exchange area, the equivalent heat transfer coefficient, and the temperature difference between the cooling medium temperature and the initial battery temperature, the cooling power corresponding to the current time step is calculated.

5. The method according to claim 1, characterized in that, The step of comparing the battery temperature prediction curve with a preset temperature control threshold to determine the target control state of the cooling system, wherein the preset temperature control threshold includes a first threshold, a second threshold, and a third threshold, wherein the first threshold is less than the second threshold, and the second threshold is less than the third threshold, includes: Based on the battery temperature prediction curve, determine the maximum predicted temperature. If the predicted maximum temperature is less than the first threshold, the target control state is determined to be either stop operation or maintain preset minimum power operation. If the predicted maximum temperature is not less than the first threshold and less than the second threshold, the target control state is determined to be operating at a first power, wherein the first power is higher than the preset minimum power; If the predicted maximum temperature is not less than the second threshold and less than the third threshold, the target control state is determined to be operating at the second power, wherein the second power is higher than the first power; If the predicted maximum temperature is not less than the third threshold, the target control state is determined to be operating at a preset maximum power, wherein the preset maximum power is higher than the second power.

6. The method according to any one of claims 1 to 5, characterized in that, The step of comparing the battery temperature prediction curve with a preset temperature control threshold to determine the start-up timing of the cooling system includes: Based on the battery temperature prediction curve, determine the control time when the predicted temperature first reaches the preset temperature control threshold. The response time of the cooling system is obtained, wherein the response time is the time required from sending a control command to the cooling system reaching the expected cooling capacity; The start-up timing of the cooling system is determined based on the control time and the response duration.

7. A control device for a cooling system in an energy storage system, characterized in that, include: The acquisition module is used to acquire the operating status data of the energy storage system at the current moment and the charging and discharging power strategy corresponding to the preset future time period, wherein the operating status data includes the operating status of the cooling system in the energy storage system; The first determining module is used to determine the expected charging and discharging current corresponding to each of the multiple future moments in the preset future time period according to the charging and discharging power strategy. The second determining module is used to determine the battery heat generation power corresponding to each of the multiple future times based on the expected charging and discharging current corresponding to each of the multiple future times and the operating status data. The third determining module is used to determine the battery temperature prediction curve corresponding to the preset future time period based on the battery heat generation power corresponding to each of the multiple future times, the operating status of the cooling system, and the preset cooling capacity model. The fourth determining module is used to compare the battery temperature prediction curve with the preset temperature control threshold to determine the target control state and start-up timing of the cooling system. The generation module is used to generate and execute cooling system control commands based on the target control state and the start-up timing.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the control method of the cooling system in any one of claims 1 to 6 of the energy storage system.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs a control method for the cooling system in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the control method of the cooling system in the energy storage system according to any one of claims 1 to 6.

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