Liquid cooling temperature control method of energy storage cabinet and energy storage cabinet

Abstract

The invention provides a liquid cooling temperature control method of an energy storage cabinet and the energy storage cabinet. The method comprises the following steps: acquiring predicted operation power of the energy storage cabinet after a first duration, historical operation data and historical environment temperature data of the energy storage cabinet, and current operation data and current environment temperature of the energy storage cabinet; based on the predicted operation power of the energy storage cabinet after the first duration, the historical operation data and the historical environment temperature data of the energy storage cabinet, and the current operation data and the current environment temperature of the energy storage cabinet, predicting the battery cell temperature in the energy storage cabinet after the first duration, wherein the battery cell temperature in the energy storage cabinet after the first duration comprises the highest battery cell temperature and the lowest battery cell temperature; obtaining a first liquid cooling mode, wherein the first liquid cooling mode is the liquid cooling mode of the current energy storage cabinet; and determining a second liquid cooling mode based on the first liquid cooling mode, the highest temperature of the battery cell and / or the lowest temperature of the battery cell, wherein the second liquid cooling mode is a liquid cooling mode of the energy storage cabinet after the first duration. According to the method provided by the invention, the liquid cooling temperature of the energy storage cabinet can be actively controlled, and the energy consumption is reduced.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a liquid cooling temperature control method for an energy storage cabinet and an energy storage cabinet. Background Art

[0002] The energy storage system controls the temperature of the battery through a liquid cooling system to maintain the normal temperature of the battery and ensure the safety of the energy storage system. The liquid cooling system regulates the temperature of the battery through the circulation of coolant. When the battery temperature is too high, the coolant absorbs the heat and transfers it to the external environment, thereby reducing the battery temperature; on the contrary, when the battery temperature is too low, the battery temperature can be increased by heating the coolant.

[0003] In the related technologies, the liquid cooling system is passively started or shut down based on the change of battery temperature. When the battery temperature does not reach the temperature threshold for starting the liquid cooling system, the liquid cooling system cannot be started, or when the battery temperature does not reach the temperature threshold for shutting down the liquid cooling system, the liquid cooling system cannot be shut down. Therefore, the following problems will arise: in the early stage of charging and discharging, the battery temperature is already rising, but the liquid cooling system cannot be started to control the temperature; or after the battery stops charging and discharging, but the temperature does not reach the power reduction threshold, the liquid cooler will continue to run, increasing the energy consumption of the liquid cooling system, and the overall efficiency of the energy storage cabinet is low. Summary of the invention

[0004] The present application provides a liquid cooling temperature control method for an energy storage cabinet and an energy storage cabinet, which are helpful to actively control the liquid cooling temperature of the energy storage cabinet, reduce the energy consumption of the liquid cooling system, improve the overall efficiency of the energy storage cabinet, and better control the battery temperature.

[0005] In a first aspect, the present application provides a liquid cooling temperature control method for an energy storage cabinet, comprising: Obtain the predicted operating power of the energy storage cabinet after the first period of time, the historical operating data and historical ambient temperature data of the energy storage cabinet, and the current operating data and current ambient temperature of the energy storage cabinet; Predicting the temperature of the battery cells in the energy storage cabinet after the first time period based on the predicted operating power of the energy storage cabinet after the first time period, the historical operating data and the historical ambient temperature data of the energy storage cabinet, the current operating data and the current ambient temperature of the energy storage cabinet, wherein the temperature of the battery cells in the energy storage cabinet after the first time period includes the maximum temperature T1 and the minimum temperature T2 of the battery cells; Obtain a first liquid cooling mode, where the first liquid cooling mode is the liquid cooling mode of the current energy storage cabinet; The second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell. The second liquid cooling mode is the liquid cooling mode of the energy storage cabinet after the first time period.

[0006] In a second aspect, the present application provides an energy storage cabinet, including a battery pack, a PCS, and an EMS, wherein the EMS is used to implement the liquid cooling temperature control method of the energy storage cabinet as described in the first aspect.

[0007] Compared with the prior art, this application has at least the following technical effects: The present application provides a liquid cooling temperature control method for an energy storage cabinet, which predicts the operating power of the energy storage cabinet after a first period of time in advance, predicts the temperature of a battery cell in the energy storage cabinet after the first period of time according to the predicted operating power of the energy storage cabinet after the first period of time, historical operating data and historical ambient temperature data of the energy storage cabinet, current operating data of the energy storage cabinet, and current ambient temperature, and then determines the liquid cooling mode of the energy storage cabinet after the first period of time according to the temperature of the battery cell in the energy storage cabinet after the first period of time, so that the energy storage system can execute the liquid cooling strategy in advance, and the liquid cooling device can work in advance, which is helpful to actively control the liquid cooling temperature of the energy storage cabinet, reduce the energy consumption of the liquid cooling system, improve the comprehensive efficiency of the energy storage cabinet, better control the battery temperature, more effectively protect the battery, and improve the service life of the battery and the efficiency of the energy storage station. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flow chart of a liquid cooling control strategy in the related art; Figure 2 A schematic flow chart of a method for controlling the liquid cooling temperature of an energy storage cabinet provided in an embodiment of the present application; Figure 3 A schematic diagram of a flow chart of an energy storage cabinet operation strategy provided in an embodiment of the present application; Figure 4 A schematic diagram of a process for obtaining the predicted operating power of an energy storage cabinet after a first duration provided in an embodiment of the present application; Figure 5 A schematic diagram of the basic structure of a convolutional neural network model provided in an embodiment of the present application; Figure 6 A schematic diagram of the basic structure of the long short-term memory network model provided in the embodiment of the present application; Figure 7 A flow chart of a method for training a load prediction model provided in an embodiment of the present application; Figure 8 A schematic diagram of the structure of the liquid cooling mode conversion provided in the embodiment of the present application; Fig. 9 A schematic diagram of the structure of the energy storage cabinet provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] In the embodiments of the present application, unless otherwise specified, the character " / " indicates that the objects before and after the association are in an or relationship. For example, A / B can represent A or B. "And / or" describes the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone.

[0010] It should be pointed out that the words "first", "second", etc. involved in the embodiments of the present application are only used to distinguish the description purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor can they be understood as indicating or implying order.

[0011] In the embodiments of the present application, "at least one" means one or more, and "more" means two or more. In addition, "at least one of the following" or similar expressions refers to any combination of these items, which may include any combination of single items or plural items. For example, at least one of A, B, or C can represent: A, B, C, A and B, A and C, B and C, or A, B and C. Among them, each of A, B, and C can be an element itself, or a set containing one or more elements.

[0012] In the embodiments of the present application, "exemplary", "in some embodiments", "in another embodiment", etc. are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present concepts in a concrete way.

[0013] In the embodiments of the present application, "of", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are consistent. In the embodiments of the present application, communication and transmission can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are consistent. For example, transmission can include sending and / or receiving, which can be a noun or a verb.

[0014] The equal to involved in the embodiments of the present application can be used in conjunction with greater than, and is applicable to the technical solution adopted when greater than, and can also be used in conjunction with less than, and is applicable to the technical solution adopted when less than. It should be noted that when equal to is used in conjunction with greater than, it cannot be used in conjunction with less than; when equal to is used in conjunction with less than, it cannot be used in conjunction with greater than.

[0015] Figure 1 It is a flow chart of the liquid cooling control strategy in the related technology, such as Figure 1As shown, when the battery temperature t≤0℃, the liquid cooling system starts heating, and after starting heating, when t>5℃, the liquid cooling system stops heating; when t≥30℃, the liquid cooling system starts cooling, and after starting cooling, when t<20℃, the liquid cooling system stops cooling. Figure 1 The liquid cooling control strategies in the related technologies shown are all based on the change of battery temperature to passively start or shut down the liquid cooling system. Specifically, when the battery temperature reaches the corresponding temperature threshold for starting heating or stopping heating, or when the battery temperature reaches the corresponding temperature threshold for starting cooling or stopping cooling, the liquid cooling system will execute the corresponding control strategy. Conversely, when the battery temperature does not reach the temperature threshold for starting the liquid cooling system, the liquid cooling system cannot be started, or when the battery temperature does not reach the temperature threshold for shutting down the liquid cooling system, the liquid cooling system cannot be shut down. Therefore, the following problems will arise: in the early stage of charging and discharging, the battery temperature is already rising, but the liquid cooling system cannot be started to control the temperature; or after the battery stops charging and discharging, but the temperature has not entered the power reduction threshold, the liquid cooler will continue to run, increasing the energy consumption of the liquid cooling system, and the overall efficiency of the energy storage cabinet is low.

[0016] Based on the above problems, the embodiment of the present application proposes a liquid cooling temperature control method for an energy storage cabinet, which predicts the temperature of the battery cells in the energy storage cabinet after a first period of time in advance, and then predicts the liquid cooling mode of the energy storage cabinet after the first period of time according to the temperature of the battery cells in the energy storage cabinet after the first period of time, so that the energy storage system can execute the liquid cooling strategy in advance, and let the liquid cooling equipment work in advance, which is helpful to actively control the liquid cooling temperature of the energy storage cabinet, reduce the energy consumption of the liquid cooling system, improve the comprehensive efficiency of the energy storage cabinet, better control the battery temperature, more effectively protect the battery, and increase the battery life and the efficiency of the energy storage station. For example, if it is predicted that the liquid cooling system will shut down in 2 minutes, it will shut down first at the current moment, rely on the natural cooling of the battery pack, and shut down the liquid cooling system in advance, which can reduce the energy consumption of the liquid cooling system. Or if it is predicted that the liquid cooling system will start cooling in 2 minutes, it will start cooling first at the current moment, reduce the temperature rise rate, make the temperature rise not drastic, and reduce the temperature peak to protect the battery.

[0017] Now combined Figure 2-Figure 8 The liquid cooling temperature control method of the energy storage cabinet provided in an embodiment of the present application is described.

[0018] Figure 2 A flow chart of a liquid cooling temperature control method for an energy storage cabinet provided in an embodiment of the present application specifically includes the following steps: Step S21, obtaining the predicted operating power of the energy storage cabinet after a first period of time, the historical operating data and historical ambient temperature data of the energy storage cabinet, and the current operating data and current ambient temperature of the energy storage cabinet.

[0019] In this step, the battery management system (BMS) obtains the predicted operating power of the energy storage cabinet after the first period of time, the historical operating data and historical ambient temperature data of the energy storage cabinet, the current operating data and the current ambient temperature of the energy storage cabinet. Among them, the historical operating data of the energy storage cabinet includes the historical operating power of the energy storage cabinet, the historical battery state of charge (SOC), the historical battery state of health (SOH) and the historical operating power of the liquid cooler, and the current operating data of the energy storage cabinet includes the current battery state of charge, the current battery state of health and the current operating power of the liquid cooler. The historical ambient temperature data refers to the ambient temperature of the area where the energy storage cabinet is located in the past period of time, which can be the ambient temperature data in the previous week, the previous month, or the previous 3 months. These ambient temperature data are collected by the temperature sensor and stored in the BMS.

[0020] The operating power of the energy storage cabinet is related to the load power, the charging and discharging plan of the energy storage system, the anti-backflow strategy, and the anti-over-demand strategy. Among them, the anti-backflow strategy refers to the strategy to prevent the electric energy of the energy storage system from flowing back into the power grid. In the industrial and commercial scenario, the energy storage cabinet discharges for the industrial and commercial park to consume itself, and it is necessary to prevent the electric energy from flowing back into the power grid. In other words, the energy storage cabinet discharges for internal use in the park and does not transmit electric energy back to the power grid. The discharge amount of the energy storage cabinet must be fully discharged to the equipment in the industrial and commercial park, and cannot be higher than the load of the equipment, otherwise it will flow to the power grid. For example, if the discharge power of the energy storage cabinet is 100kw and the load of the equipment is 80kw, then when the energy storage cabinet discharges, 20kw will flow to the power grid, which will form a reverse flow.

[0021] The anti-overdemand strategy refers to a strategy that prevents the charging and discharging of the energy storage system from causing the grid demand to exceed the specified demand limit. The grid demand can also be called load demand, which refers to the user's demand for electricity on the power system within a set time period. The set time period can be 15 minutes, one hour, or one day, etc., depending on the management and measurement requirements of the power system. During this period, the grid demand is usually the average maximum value of the actual electricity consumed by the user. The dynamic adjustment of the anti-backflow strategy and the anti-overdemand strategy is mainly based on whether the anti-backflow threshold and the anti-overdemand threshold are triggered. The anti-backflow threshold refers to the threshold power of the anti-backflow strategy, and the anti-overdemand threshold refers to the threshold power of the anti-overdemand strategy.

[0022] Specifically, Figure 3 As shown, Figure 3A flow chart of the energy storage cabinet operation strategy provided in an embodiment of the present application, wherein the energy storage system is charged and discharged according to a planned curve, that is, the energy storage system is charged and discharged at a set charging and discharging time. Generally, the energy storage system is charged during the valley period of electricity prices and discharged during the peak period of electricity prices, that is, the energy storage system is charged from the power grid when the electricity price is low (such as in the early morning), and the energy storage system can discharge to user equipment or loads during periods of higher electricity prices (such as during the day or during peak electricity consumption hours) to reduce the need to purchase electricity from the power grid, thereby reducing electricity costs.

[0023] During the charging and discharging process of the energy storage system, it is necessary to monitor the gateway meter in real time and determine whether the power of the gateway meter triggers the anti-reverse flow threshold and the anti-over-demand threshold. The gateway meter usually refers to the meter installed at the connection point between the industrial and commercial energy storage system / load / other system and the power grid. The function of this meter is to measure and record the exchange of electric energy between the energy storage system / load / other system and the power grid. For example, it can measure and record the amount of electricity output when the energy storage system discharges to the grid and the amount of electricity input when charging from the grid.

[0024] When the gateway meter detects the anti-backflow threshold, that is, the gateway meter power (the power of the power grid entering the industrial and commercial park) is less than or equal to the anti-backflow threshold, it is necessary to adjust the operating power of the energy storage cabinet. Or when the gateway meter detects the anti-demand threshold, it is necessary to adjust the operating power of the energy storage cabinet according to the current charging and discharging status of the energy storage cabinet. Specifically, first determine the charging and discharging status of the energy storage cabinet. If the energy storage cabinet is in a charging state, it is necessary to reduce the charging power of the energy storage cabinet; or if the energy storage cabinet is in a discharging state, it is necessary to increase the discharge power of the energy storage cabinet; or if the energy storage cabinet is in a static fully charged state, it is necessary to discharge, and the demand needs to be controlled, that is, it is necessary to control the discharge amount of the energy storage cabinet so that it does not trigger the anti-demand threshold. In the present application, the operating power of the energy storage cabinet specifically refers to the operating power of the energy storage converter (PCS) in the energy storage cabinet.

[0025] Based on this, the predicted operating power of the energy storage cabinet needs to be obtained by combining the predicted load power, the charging and discharging plan, the anti-backflow strategy and the anti-overdemand strategy. Therefore, the present application provides a method for obtaining the predicted operating power of the energy storage cabinet after the first time period, such as Figure 4 As shown, Figure 4 A schematic diagram of a process for obtaining the predicted operating power of an energy storage cabinet after a first period of time provided in an embodiment of the present application.

[0026] Figure 4Explanation of letters in: WY refers to the predicted operating power of the energy storage cabinet after the first time period; W refers to the current operating power of the energy storage cabinet, where discharging is positive and charging is negative (in other embodiments, discharging may be negative and charging may be positive); W1 refers to the predicted load power; W2 refers to the threshold power of the anti-backflow strategy, i.e., the first threshold power; W3 refers to the threshold power of the anti-over-demand strategy, i.e., the second threshold power; n2 refers to the anti-backflow power adjustment step, i.e., the first adjustment step; n3 refers to the anti-over-demand power adjustment step, i.e., the second adjustment step.

[0027] The anti-backflow power adjustment step (n2) means that in order to achieve the anti-backflow function in the energy storage system, the power control system of the energy storage system will adjust the operating power with a fixed power change. This fixed size is the step size, the smallest unit of power change, that is, the step size n2. For example, if n2 is set to 10kW, then each time the system is adjusted, the output power of the inverter will increase or decrease by 10kW. When the BMS detects a backflow phenomenon, it will gradually adjust the operating power of the energy storage cabinet in steps by adjusting the output power of the inverter until the backflow phenomenon disappears.

[0028] Similarly, the power adjustment step length (n3) for preventing over-demand means that in order to achieve the function of preventing over-demand, the power control system of the energy storage system will adjust the operating power with a fixed power change. This fixed size is the step length, the smallest unit of power change, that is, the step length n3.

[0029] Figure 4 The process diagram shown specifically includes the following steps: Step S41 , predicting the user's load power after a first period of time based on the user's historical power load data, date type information, weather status information and air quality information, to obtain a predicted load power W1.

[0030] The BMS inputs the user's historical power load data, date type information, weather status information and air quality information into a preset load prediction model for prediction, and outputs the predicted load power W1.

[0031] Among them, historical power load data refers to power load data in the past period of time, which can be power load data in the previous week, previous month, previous 3 months, etc. Date type refers to different date types (such as weekdays, weekends, holidays), and different date types will cause significant changes in power load. For example, holidays usually have lower industrial and commercial loads, while residential loads may increase because people are at home. Weather conditions refer to weather conditions (such as temperature, humidity, wind speed, precipitation), which have a direct impact on power load. For example, hot weather may increase the use of air conditioning, thereby increasing electricity demand; while cold weather may increase the use of heating systems. In addition to the above factors, other factors that affect power load can also be added to the load forecasting model, such as seasonal temperature changes, charging and discharging plans, etc.

[0032] In the present application, the load prediction model may be an LSTM (Long Short-Term Memory) model or a CNN-LSTM (Convolutional Neural Network- Long Short-Term Memory) model. Figure 5 A schematic diagram of the basic structure of a convolutional neural network model provided in an embodiment of the present application, Figure 6 A schematic diagram of the basic structure of the long short-term memory network model provided in the embodiment of the present application is shown in FIG. Figure 5 As shown in Figure 1, the basic structure of the convolutional neural network model includes convolutional layer, pooling layer, convolutional layer, pooling layer and fully connected layer. The convolutional layer and pooling layer of CNN are used to extract effective features from the input data, fully explore its internal connections and reduce noise. Figure 6 As shown in Figure 1, the basic structure of the long short-term memory network model has three gates: forget gate, input gate and output gate, as well as a cell state and hidden state. The forget gate determines which old information is forgotten, the input gate controls the degree of acceptance of new information, and the output gate determines how much information in the cell state will be used to generate the hidden state. Figure 6 Here h_t represents the hidden state of the current time step, h_(t-1) represents the hidden state of the previous time step, C_t represents the cell state of the current time step, C_(t-1) represents the cell state of the previous time step, x_t represents the input of the current time step, σ is the sigmoid activation function, and tanh is the activation function.

[0033] In some embodiments, the preset load prediction model is a CNN-LSTM model. Combining CNN and LSTM models for load prediction can not only use the convolution layer and pooling layer of the CNN model to extract effective features of the input data, fully explore its internal connections, improve prediction accuracy, and strengthen the coupling between data, but also use the LSTM model to process time series data. The CNN-LSTM model makes full use of the input load data and influencing factors, combines the advantages of CNN and LSTM, and improves the prediction effect.

[0034] In some embodiments, the model can be trained by the cloud, and the trained model can be sent to the energy storage cabinet to perform load prediction locally. By using the cloud-edge collaboration approach, the advantages of the cloud in processing large amounts of data, fast processing speed, large storage capacity and computing power can be fully utilized, which helps to accelerate model training.

[0035] like Figure 7 As shown, Figure 7 A flow chart of the training method of the load prediction model provided in the embodiment of the present application, including collecting required data, data preprocessing (including abnormal data identification and processing, data normalization and encoding), determining model parameters, applying the model for prediction (including using CNN for feature extraction and inputting LSTM for prediction), evaluating the prediction results (including calculating the prediction error and determining whether the prediction error meets the requirements), outputting the prediction results if the requirements are met, and modifying the model parameters and redetermining the model parameters if the requirements are not met.

[0036] This application uses a load prediction model based on CNN-LSTM to accurately obtain the user's power load demand, and uses the predicted load power W1 as the basis for judging the activation of the anti-backflow strategy and the anti-overdemand strategy, thereby predicting the operating power of the energy storage cabinet, and the operating power of the energy storage cabinet will directly affect the temperature change of the battery cells in the energy storage cabinet. Therefore, effective prediction of the load power will effectively predict the operating power of the energy storage cabinet, and based on the predicted operating power of the energy storage cabinet, the temperature change of the battery cell will be effectively predicted, so that the liquid cooling mode of the energy storage cabinet can be adjusted according to the battery cell temperature.

[0037] Step S42, obtaining a first threshold power W2, a second threshold power W3 and a current operating power W of the energy storage cabinet.

[0038] The first threshold power W2 is used to characterize the threshold power of the anti-backflow strategy, and the second threshold power W3 is used to characterize the threshold power of the anti-over-demand strategy. W2 and W3 are threshold powers pre-set by technicians according to actual conditions.

[0039] Step S43: if W1-W≤W2, then reduce the current operating power W of the energy storage cabinet in units of a first adjustment step until W1-W>W2, and obtain the predicted operating power of the energy storage cabinet after the first time period.

[0040] In this step, the difference between the predicted load power W1 obtained in the above step S41 and the current operating power W of the energy storage cabinet is calculated. If the difference is less than or equal to the first threshold power W2, the anti-backflow strategy is triggered, and the current operating power W of the energy storage cabinet needs to be reduced by the first adjustment step n2 until W1-W>W2, and the predicted operating power WY of the energy storage cabinet after the first time length is output. It should be noted that Figure 4 In step S43 shown, WY=W, WY=WY-n2, which means that the initial value of WY is set to W, and W is reduced by n2 units, which is equivalent to reducing WY by n2 units, so when W1-WY>W2, WY is output.

[0041] Among them, the current operating power of the energy storage cabinet is W, discharge is positive, and charging is negative. For example, if W=100 kW, it means that the current discharge power of the energy storage cabinet is 100 kW. If W=-100 kW, it means that the current charging power of the energy storage cabinet is 100 kW.

[0042] Step S44: if W2<W1-W<W3, the predicted operating power of the energy storage cabinet after the first period of time is the predicted load power W1.

[0043] In this step, if the difference of W1-W is greater than the first threshold power W2 and less than the second threshold power W3, then WY=W1, that is, the predicted operating power WY of the energy storage cabinet after the first time period is the predicted load power W1.

[0044] Step S45: if W1-W≥W3, and the energy storage cabinet is in a charging state, the current operating power W of the energy storage cabinet is reduced in units of a second adjustment step until W1-W<W3, thereby obtaining the predicted operating power of the energy storage cabinet after the first time period.

[0045] In this step, if the difference between W1-W is greater than or equal to the second threshold power W3, it indicates that the demand prevention strategy is triggered, and the operating power of the energy storage cabinet needs to be adjusted in combination with the current charging and discharging state of the energy storage cabinet. Specifically, if the energy storage cabinet is in a charging state, the current operating power W of the energy storage cabinet is reduced in units of the second adjustment step n3 until W1-W<W3, and the predicted operating power WY of the energy storage cabinet after the first time length is output. It should be noted that Figure 4In step S45, WY=W, WY=WY+n3, which means that the initial value of WY is set to W, and W is reduced by n3, which is equivalent to reducing WY by n3. Therefore, when W1-WY<W3, WY is output. Because the charging power of the energy storage cabinet is negative, adding a positive number n3 to the charging power indicates reducing the charging power.

[0046] Step S46: if W1-W≥W3, and the energy storage cabinet is in a discharging state, the current operating power W of the energy storage cabinet is increased in units of a second adjustment step until W1-W<W3, thereby obtaining the predicted operating power of the energy storage cabinet after the first time period.

[0047] In this step, if the difference between W1-W is greater than or equal to the second threshold power W3, it indicates that the demand prevention strategy is triggered, and the operating power of the energy storage cabinet needs to be adjusted in combination with the current charge and discharge state of the energy storage cabinet. Specifically, if the energy storage cabinet is in a discharge state, the current operating power W of the energy storage cabinet is increased in units of the second adjustment step n3 until W1-W<W3, and the predicted operating power WY of the energy storage cabinet after the first time length is output. It should be noted that Figure 4 In step S46, WY=W, WY=WY+n3, which means that the initial value of WY is set to W, and W is increased by n3 units, which is equivalent to increasing WY by n3 units, so when W1-WY<W3, WY is output. Because the discharge power of the energy storage cabinet is negative, adding a positive number n3 to the discharge power indicates increasing the discharge power.

[0048] Step S47: if W1-W≥W3, and the energy storage cabinet is in a static fully charged state, the current operating power W of the energy storage cabinet is 0, and the current operating power W of the energy storage cabinet is increased in units of a second adjustment step until W1-W<W3, thereby obtaining the predicted operating power of the energy storage cabinet after the first time period.

[0049] In this step, if the difference between W1 and W is greater than or equal to the second threshold power W3, it indicates that the demand prevention strategy is triggered, and the operating power of the energy storage cabinet needs to be adjusted in combination with the current charge and discharge state of the energy storage cabinet. Specifically, if the energy storage cabinet is in a static full charge state, the energy storage cabinet does not charge or discharge at this time, and the current operating power W of the energy storage cabinet is 0. It is necessary to increase the current operating power W of the energy storage cabinet by the second adjustment step n3. It should be noted that Figure 4 In step S47 shown, WY=W=0, WY=WY+n3, which means that the initial value of WY is set to W and its value is equal to 0, and W is increased by n3 units, which is equivalent to increasing WY by n3 units, so when W1-WY<W3, WY is output.

[0050] In actual operation, in order to ensure the stability of the power grid and avoid unnecessary burden on the power grid, the energy storage system needs to accurately control its charging and discharging power. By setting the appropriate adjustment step size (n2 and n3), the system can control the power change more finely, thereby managing the power more effectively and preventing reverse flow. Optionally, n2=10 kW, n3=10 kW.

[0051] Optionally, the BMS can also directly adjust the power in one step, for example, directly adjusting the power from 180 kW to 100 kW in one step.

[0052] Optionally, you can also set a power hysteresis value. When the power / load fluctuates to a certain extent, the system will not immediately make an adjustment response, but will wait until the fluctuation exceeds a preset threshold (i.e., the hysteresis value) before taking action. This can avoid frequent triggering of the adjustment mechanism due to small fluctuations, thereby reducing algorithm oscillations and unnecessary adjustments, and improving the overall stability and response efficiency of the system.

[0053] It should be noted that the above steps S43 to S47 are parallel judgment conditions, and there is an "or" relationship between the steps. The judgment order is not limited, and the judgment order can be swapped or executed simultaneously.

[0054] For ease of understanding Figure 4 The method for obtaining the predicted operating power of the energy storage cabinet shown is now illustrated by examples. It should be understood that the following examples are only used for explanation and do not limit the method of the embodiment of the present application.

[0055] Example 1: W1=100 kW, W=95 kW (discharge state) W2=10 kW, W3=200 kW, n2=10 kW, n3=10 kW W1-W=100-95=5 kW <10 kW, triggering the anti-backflow strategy WY=W=95 kW WY=WY- n2=95-10=85 kW W1-WY=100-85=15 kW >10 kW Then the output WY=85 kW Example 2: W1=100 kW, W=-120 kW (charging state), W2=10 kW, W3=200 kW, n2=10 kW, n3=10kW W1-W=100-(-120)=220 kW >200 Kw, triggering the over-demand prevention strategy WY=W=-120 kW After 3 adjustments WY = WY + 3*n3 = -120 + 3*10 = -90 kW At this time W1-WY=100-(-90)=190 kW <200 kW Then the output WY=-90 kW Step S22, predicting the temperature of the battery cells in the energy storage cabinet after the first period of time based on the predicted operating power of the energy storage cabinet after the first period of time, the historical operating data and historical ambient temperature data of the energy storage cabinet, the current operating data of the energy storage cabinet, and the current ambient temperature, wherein the temperature of the battery cells in the energy storage cabinet after the first period of time includes a maximum battery cell temperature T1 and a minimum battery cell temperature T2.

[0056] Specifically, the BMS inputs the parameters acquired in step S21 (including the predicted operating power of the energy storage cabinet after the first period of time, the historical operating data and historical ambient temperature data of the energy storage cabinet, the current operating data and the current ambient temperature of the energy storage cabinet) into a preset battery cell temperature prediction model, and predicts the temperatures of all battery cells in the energy storage cabinet, thereby obtaining the highest battery cell temperature T1 and the lowest battery cell temperature T2 among the multiple battery cells.

[0057] The first duration refers to the prediction duration, which can be any value between 2 minutes and 10 minutes. Optionally, the first duration is 2 minutes, mainly considering predicting the battery cell temperature 2 minutes in advance. The algorithm accuracy of the prediction model is higher, and the longer the duration, the lower the accuracy.

[0058] The battery cell temperature prediction model can be an LSTM model or a CNN-LSTM model. The basic structure of the CNN model and the LSTM model can be found in the description of the load prediction model in step S21, which will not be repeated here. Optionally, the battery cell temperature prediction model is a CNN-LSTM model, which can not only use the convolution layer and pooling layer of the CNN model to extract effective features of the input data, fully explore its internal connections, improve prediction accuracy, and strengthen the coupling between data, but also use the LSTM model to process time series data, combining the advantages of CNN and LSTM to improve the prediction effect.

[0059] Step S23, obtaining a first liquid cooling mode, where the first liquid cooling mode is the current liquid cooling mode of the energy storage cabinet.

[0060] The liquid cooling modes of the energy storage cabinet include shutdown mode, first-stage cooling mode, second-stage cooling mode, third-stage cooling mode, self-circulation mode and heating mode. Among them, the target water temperatures of the heating mode, first-stage cooling mode, second-stage cooling mode and third-stage cooling mode decrease successively, and the target water temperature refers to the cooling water outlet temperature set when the liquid cooling system is designed, that is, the design temperature of the cooling water coming out of the condenser. Optionally, the target water temperature of the heating mode is 27°C, the target water temperature of the first-stage cooling mode is 22°C, the target water temperature of the second-stage cooling mode is 18°C, and the target water temperature of the third-stage cooling mode is 15°C. The shutdown mode refers to the liquid cooling system stopping starting. The self-circulation mode refers to the temperature control when the battery is at a more suitable temperature without cooling or heating, that is, the self-circulation of the heat transfer medium is used to control the temperature of the battery, further reducing the energy consumption of the liquid cooling unit.

[0061] It can be understood that the first liquid cooling mode and the second liquid cooling mode are both one of the liquid cooling modes of the energy storage cabinet.

[0062] Step S24: determining a second liquid cooling mode based on the first liquid cooling mode, the battery cell maximum temperature T1 and / or the battery cell minimum temperature T2, wherein the second liquid cooling mode is the liquid cooling mode of the energy storage cabinet after the first time period.

[0063] Specifically, Figure 8 As shown, Figure 8 The schematic diagram of the structure of the liquid cooling mode conversion provided in the embodiment of the present application, step S24 may include: (1) When the first liquid cooling mode is the shutdown mode, If T1≥38℃ and T2≥12℃, the second liquid cooling mode is the three-stage cooling mode; or If the energy storage cabinet is in the discharging or standby state and 35℃≤T1<38℃, T2≥12℃, or the energy storage cabinet is in the charging state and 33℃≤T1<38℃, T2≥12℃, the second liquid cooling mode is the secondary cooling mode; or If 27℃≤T1<33℃ and T2≥12℃, the second liquid cooling mode is the primary cooling mode; or If T1-T2≥5℃, the second liquid cooling mode is the self-circulation mode; or If T1<27°C and T2≤12°C, the second liquid cooling mode is the heating mode.

[0064] (2) When the first liquid cooling mode is the three-stage cooling mode, If T1≤36°C, the second liquid cooling mode is a secondary cooling mode.

[0065] (3) When the first liquid cooling mode is the secondary cooling mode, If T1 ≥ 38°C, the second liquid cooling mode is the three-stage cooling mode; or If the energy storage cabinet is in a discharging or standby state and T1≤33°C, or the energy storage cabinet is in a charging state and T1≤31°C, the second liquid cooling mode is a primary cooling mode.

[0066] (4) When the first liquid cooling mode is the first-stage cooling mode, If the energy storage cabinet is in the discharging or standby state and T1≥35℃, or the energy storage cabinet is in the charging state and T1≥33℃, the second liquid cooling mode is the secondary cooling mode; or If T1≤25°C or T2≤9°C, the second liquid cooling mode is the self-circulation mode.

[0067] (5) When the first liquid cooling mode is the self-circulation mode, If T1≥27℃ and T2≤9℃, the second liquid cooling mode is the primary cooling mode; or If T1<27℃ and T2≤12℃, the second liquid cooling mode is heating mode; or If T1-T2≤5°C, the second liquid cooling mode is the shutdown mode.

[0068] (6) When the first liquid cooling mode is the heating mode, If T1≥30°C or T2≤15°C, the second liquid cooling mode is the self-circulation mode.

[0069] The method provided in the present application predicts the operating power of the energy storage cabinet after a first period of time in advance, predicts the temperature of the battery cells in the energy storage cabinet after the first period of time according to the predicted operating power of the energy storage cabinet after the first period of time, the historical operating data and the historical ambient temperature data of the energy storage cabinet, the current operating data and the current ambient temperature of the energy storage cabinet, and then determines the liquid cooling mode of the energy storage cabinet after the first period of time according to the temperature of the battery cells in the energy storage cabinet after the first period of time, so that the energy storage system can execute the liquid cooling strategy in advance and let the liquid cooling equipment work in advance, which is helpful to actively control the liquid cooling temperature of the energy storage cabinet, reduce the energy consumption of the liquid cooling system, improve the comprehensive efficiency of the energy storage cabinet, better control the battery temperature, more effectively protect the battery, and increase the service life of the battery and the efficiency of the energy storage station.

[0070] Fig. 9 A schematic diagram of the structure of the energy storage cabinet provided in the embodiment of the present application is shown in FIG. Fig. 9 As shown, the energy storage cabinet 90 includes a battery pack 91, a PCS 92 and an EMS 93, wherein the EMS 93 is used to implement the liquid cooling temperature control method of the energy storage cabinet provided in the embodiment shown in the present application. Fig. 9 The energy storage cabinet is merely used as an example, and the specific structure is not limited. The position and quantity of the battery pack 91, PCS 92, and EMS 93 included in the energy storage cabinet are also not limited.

[0071] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A liquid cooling temperature control method for an energy storage cabinet, It is characterized in that The method comprises: Acquire the predicted operating power of the energy storage cabinet after a first period of time, historical operating data and historical ambient temperature data of the energy storage cabinet, and current operating data and current ambient temperature of the energy storage cabinet; Predicting the temperature of the battery cells in the energy storage cabinet after the first time period based on the predicted operating power of the energy storage cabinet after the first time period, the historical operating data and the historical ambient temperature data of the energy storage cabinet, the current operating data and the current ambient temperature of the energy storage cabinet, wherein the temperature of the battery cells in the energy storage cabinet after the first time period includes a maximum battery cell temperature T1 and a minimum battery cell temperature T2; Acquire a first liquid cooling mode, where the first liquid cooling mode is the current liquid cooling mode of the energy storage cabinet; A second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell, where the second liquid cooling mode is the liquid cooling mode of the energy storage cabinet after the first time period.

2. The method according to claim 1, It is characterized in that The liquid cooling mode of the energy storage cabinet includes a shutdown mode, a primary cooling mode, a secondary cooling mode, a tertiary cooling mode, a self-circulation mode and a heating mode. The target water temperatures of the heating mode, the primary cooling mode, the secondary cooling mode and the tertiary cooling mode decrease in sequence. The second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell, including: When the first liquid cooling mode is the shutdown mode, If T1≥38°C and T2≥12°C, the second liquid cooling mode is a three-stage cooling mode; or If the energy storage cabinet is in a discharging or standby state and 35°C ≤ T1 < 38°C, T2 ≥ 12°C, or the energy storage cabinet is in a charging state and 33°C ≤ T1 < 38°C, T2 ≥ 12°C, then the second liquid cooling mode is a secondary cooling mode; or If 27°C≤T1<33°C and T2≥12°C, the second liquid cooling mode is the primary cooling mode; or If T1-T2≥5°C, the second liquid cooling mode is the self-circulation mode; or If T1<27° C. and T2≤12° C., the second liquid cooling mode is a heating mode.

3. The method according to claim 1, It is characterized in that The liquid cooling mode of the energy storage cabinet includes a shutdown mode, a primary cooling mode, a secondary cooling mode, a tertiary cooling mode, a self-circulation mode and a heating mode. The target water temperatures of the heating mode, the primary cooling mode, the secondary cooling mode and the tertiary cooling mode decrease in sequence. The second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell, including: When the first liquid cooling mode is a three-stage cooling mode, If T1≤36°C, the second liquid cooling mode is a secondary cooling mode.

4. The method according to claim 1, It is characterized in that The liquid cooling mode of the energy storage cabinet includes a shutdown mode, a primary cooling mode, a secondary cooling mode, a tertiary cooling mode, a self-circulation mode and a heating mode. The target water temperatures of the heating mode, the primary cooling mode, the secondary cooling mode and the tertiary cooling mode decrease in sequence. The second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell, including: When the first liquid cooling mode is a two-stage cooling mode, If T1 ≥ 38°C, the second liquid cooling mode is a three-stage cooling mode; or If the energy storage cabinet is in a discharging or standby state and T1≤33°C, or the energy storage cabinet is in a charging state and T1≤31°C, the second liquid cooling mode is a primary cooling mode.

5. The method according to claim 1, It is characterized in that The liquid cooling mode of the energy storage cabinet includes a shutdown mode, a primary cooling mode, a secondary cooling mode, a tertiary cooling mode, a self-circulation mode and a heating mode. The target water temperatures of the heating mode, the primary cooling mode, the secondary cooling mode and the tertiary cooling mode decrease in sequence. The second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell, including: When the first liquid cooling mode is a primary cooling mode, If the energy storage cabinet is in a discharging or standby state and T1≥35°C, or the energy storage cabinet is in a charging state and T1≥33°C, the second liquid cooling mode is a secondary cooling mode; or If T1≤25°C or T2≤9°C, the second liquid cooling mode is a self-circulation mode.

6. The method according to claim 1, It is characterized in that The liquid cooling mode of the energy storage cabinet includes a shutdown mode, a primary cooling mode, a secondary cooling mode, a tertiary cooling mode, a self-circulation mode and a heating mode. The target water temperatures of the heating mode, the primary cooling mode, the secondary cooling mode and the tertiary cooling mode decrease in sequence. The second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell, including: When the first liquid cooling mode is the self-circulation mode, If T1≥27°C and T2≤9°C, the second liquid cooling mode is the primary cooling mode; or If T1<27℃ and T2≤12℃, the second liquid cooling mode is heating mode; or If T1-T2≤5°C, the second liquid cooling mode is the shutdown mode.

7. The method according to claim 1, It is characterized in that The liquid cooling mode of the energy storage cabinet includes a shutdown mode, a primary cooling mode, a secondary cooling mode, a tertiary cooling mode, a self-circulation mode and a heating mode. The target water temperatures of the heating mode, the primary cooling mode, the secondary cooling mode and the tertiary cooling mode decrease in sequence. The second liquid cooling mode is determined based on the first liquid cooling mode, the maximum temperature T1 of the battery cell and / or the minimum temperature T2 of the battery cell, including: When the first liquid cooling mode is a heating mode, If T1≥30°C or T2≤15°C, the second liquid cooling mode is a self-circulation mode.

8. The method according to any one of claims 1 to 7, It is characterized in that The predicted operating power of the energy storage cabinet after obtaining the first duration includes: Based on the user's historical power load data, date type information, weather status information and air quality information, the user's load power after the first period is predicted to obtain the predicted load power W1; Obtaining a first threshold power W2, a second threshold power W3, and the current operating power W of the energy storage cabinet, wherein the first threshold power W2 is used to characterize the threshold power of the anti-backflow strategy, and the second threshold power W3 is used to characterize the threshold power of the anti-overdemand strategy; If W1-W≤W2, the current operating power W of the energy storage cabinet is reduced in units of the first adjustment step until W1-W>W2, and the predicted operating power of the energy storage cabinet after the first time period is obtained; or If W2<W1-W<W3, the predicted operating power of the energy storage cabinet after the first period of time is the predicted load power W1; or If W1-W≥W3, and the energy storage cabinet is in a charging state, the current operating power W of the energy storage cabinet is reduced in units of a second adjustment step until W1-W<W3, and the predicted operating power of the energy storage cabinet after the first time period is obtained; or If W1-W≥W3, and the energy storage cabinet is in a discharging state, the current operating power W of the energy storage cabinet is increased in units of a second adjustment step until W1-W<W3, and the predicted operating power of the energy storage cabinet after the first time period is obtained; or If W1-W≥W3, and the energy storage cabinet is in a static fully charged state, the current operating power W of the energy storage cabinet is 0, and the current operating power W of the energy storage cabinet is increased in units of a second adjustment step until W1-W<W3, thereby obtaining the predicted operating power of the energy storage cabinet after the first time period.

9. The method according to claim 1, It is characterized in that The historical operating data of the energy storage cabinet includes the historical operating power of the energy storage cabinet, the historical battery state of charge, the historical battery health status and the historical operating power of the liquid cooler, and the current operating data of the energy storage cabinet includes the current battery state of charge, the current battery health status and the current operating power of the liquid cooler.

10. An energy storage cabinet, It is characterized in that It includes a battery pack, a PCS, and an EMS, and the EMS is used to implement the liquid cooling temperature control method of the energy storage cabinet as described in any one of claims 1 to 9.

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