Temperature prediction method, electronic device, storage medium and energy storage system

Abstract

The present application provides a temperature prediction method, electronic device, storage medium and energy storage system, the method comprising: obtaining the ambient temperature and battery parameters of the energy storage device; obtaining the current first power, third power and fourth power of the energy storage device, and if the difference between the second power and the current first power is not less than the fourth power, judging the state of the energy storage device; outputting the predicted first power according to the state of the energy storage device; predicting the battery temperature of the energy storage device based on the ambient temperature, the battery parameters and the predicted first power. The method provided by the present application helps to solve the problem that the energy storage device cannot accurately predict the battery temperature change of the energy storage device under the anti-backflow and anti-overdemand strategies.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a temperature prediction method, electronic equipment, storage medium and energy storage system. Background Art

[0002] In the energy storage system, it is necessary to ensure that the battery cells of the energy storage cabinet and the supporting electrical equipment operate within a safe temperature range.

[0003] In the prior art, the temperature change of the battery cell is predicted based on the changes in various data of the energy storage cabinet, and the battery is charged and discharged according to the long-term stable planning curve. However, accurate temperature prediction cannot be achieved under the strategies of preventing backflow and over-demand in specific scenarios. Summary of the invention

[0004] In view of this, the present application provides a temperature prediction method, an electronic device and a storage medium, which are helpful to solve the problem that the energy storage device cannot accurately predict the temperature change of the battery of the energy storage device under the anti-backflow and anti-overdemand strategies.

[0005] In a first aspect, the present application provides a temperature prediction method, which is applied to an energy storage device, comprising:

[0006] Acquiring ambient temperature and battery parameters of the energy storage device;

[0007] Acquire a current first power, a third power, and a fourth power of the energy storage device, wherein the current first power is used to characterize the current operating power of the energy storage device, the third power is used to characterize the anti-backflow threshold power of the energy storage device, and the fourth power is used to characterize the anti-over-demand threshold power of the energy storage device;

[0008] Calculating a second power of the energy storage device, where the second power is used to characterize the load power of the energy storage device; if a difference between the second power and the current first power is not less than the fourth power, determining the state of the energy storage device;

[0009] If the energy storage device is in a charging state, the predicted first power is compensated based on the current first power, and the predicted first power is used to characterize the predicted operating power of the energy storage device; it is determined whether the difference between the second power and the predicted first power after compensation is less than the fourth power; if the difference between the second power and the predicted first power after compensation is less than the fourth power, the predicted first power is output; if the difference between the second power and the predicted first power is not less than the fourth power, the predicted first power is added with a preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and the adjusted predicted first power is output;

[0010] If the energy storage device is in a discharging state, the predicted first power is compensated based on the current first power; it is determined whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, the predicted first power is output; if the difference between the second power and the predicted first power is not less than the fourth power, the predicted first power is added to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and the adjusted predicted first power is output;

[0011] If the energy storage device is in a static fully charged state, the predicted first power is set to zero; determine whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, output the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, add the predicted first power to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and output the adjusted predicted first power;

[0012] The battery temperature of the energy storage device is predicted based on the ambient temperature, the battery parameter, and the predicted first power.

[0013] In one possible implementation manner, the battery parameters include an SOC parameter and an SOH parameter.

[0014] In one possible implementation, the calculating the second power of the energy storage device includes: calculating the second power of the energy storage device based on historical power data, date data, weather conditions and air quality of the energy storage device.

[0015] In one possible implementation, historical power data, date data, weather conditions and air quality of the energy storage device are input into a preset model, the power load demand of the energy storage device is output, and the second power of the energy storage device is calculated.

[0016] In one possible implementation manner, if the difference between the second power and the current first power is not greater than the third power, the predicted first power is adjusted based on the current first power and a preset first adjustment value.

[0017] In one possible implementation, determining the charge and discharge state of the energy storage device and adjusting the first power includes: if the energy storage device is in a charging state, increasing the predicted first power until the charging state is stopped; if the energy storage device is in a discharging state, increasing the predicted first power for discharging; if the energy storage device is in a static fully charged state, increasing the predicted first power for discharging.

[0018] In one possible implementation, adjusting the predicted first power based on the current first power and the preset first adjustment value includes: if the difference between the second power and the current first power is not less than the fourth power, compensating the predicted first power based on the current first power; judging whether the difference between the second power and the predicted first power is greater than the third power; if the difference between the second power and the predicted first power is greater than the third power; outputting the predicted first power; if the difference between the second power and the predicted first power is not greater than the third power, subtracting the preset second adjustment value from the predicted first power until the difference between the second power and the current first power is greater than the third power, and outputting the adjusted predicted first power.

[0019] In one possible implementation, the energy storage device also includes a liquid cooler, and predicting the battery temperature of the energy storage device based on the ambient temperature, the battery parameters and the predicted first power includes: training a temperature prediction model through the second power, the ambient temperature, the battery parameters and the power of the liquid cooler; and inputting the ambient temperature, the battery parameters and the predicted first power into the temperature prediction model to predict the battery temperature of the energy storage device.

[0020] In a second aspect, the present application provides a temperature prediction device for an energy storage device, comprising:

[0021] An acquisition module, the acquisition module is used to acquire the ambient temperature and the battery parameters of the energy storage device; the acquisition module is also used to acquire the current first power, third power and fourth power of the energy storage device, the current first power is used to characterize the current operating power of the energy storage device, the third power is used to characterize the anti-backflow threshold power of the energy storage device, and the fourth power is used to characterize the anti-over-demand threshold power of the energy storage device;

[0022] A calculation module, the calculation module is used to calculate the second power of the energy storage device, the second power is used to characterize the load power of the energy storage device; if the difference between the second power and the current first power is not less than the fourth power, the state of the energy storage device is determined;

[0023] A compensation module, wherein if the energy storage device is in a charging state, the compensation module is used to compensate the predicted first power based on the current first power, and the predicted first power is used to characterize the predicted operating power of the energy storage device; determine whether the difference between the second power and the predicted first power after compensation is less than the fourth power; if the difference between the second power and the predicted first power after compensation is less than the fourth power, output the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, add the predicted first power to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and output the adjusted predicted first power;

[0024] The compensation module is also used for compensating the predicted first power based on the current first power if the energy storage device is in a discharging state; judging whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, outputting the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, adding the preset first adjustment value to the predicted first power until the difference between the second power and the current first power is less than the fourth power, and outputting the adjusted predicted first power;

[0025] The compensation module is also used to, if the energy storage device is in a static full-charge state, set the predicted first power to zero; determine whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, output the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, add the predicted first power to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and output the adjusted predicted first power;

[0026] A prediction module is further used to predict the battery temperature of the energy storage device based on the ambient temperature, the battery parameters and the predicted first power.

[0027] In one possible implementation manner, the battery parameters include an SOC parameter and an SOH parameter.

[0028] In one possible implementation, the calculation module is further used to calculate the second power of the energy storage device based on historical power data, date data, weather conditions and air quality of the energy storage device.

[0029] In one possible implementation, the calculation module is also used to input the historical power data, date data, weather conditions and air quality of the energy storage device into a preset model, output the power load demand of the energy storage device, and calculate the second power of the energy storage device.

[0030] In one possible implementation, the temperature prediction device also includes an adjustment module, and the adjustment module is used to adjust the predicted first power based on the current first power and a preset first adjustment value if the difference between the second power and the current first power is not greater than the third power.

[0031] In one possible implementation, the adjustment module is used to increase the predicted first power if the energy storage device is in a charging state until the charging state is stopped; if the energy storage device is in a discharging state, increase the predicted first power for discharging; if the energy storage device is in a static fully charged state, increase the predicted first power for discharging.

[0032] In one possible implementation, the compensation module is used to compensate the predicted first power based on the current first power if the difference between the second power and the current first power is not less than the fourth power; determine whether the difference between the second power and the predicted first power is greater than the third power; if the difference between the second power and the predicted first power is greater than the third power; output the predicted first power; if the difference between the second power and the predicted first power is not greater than the third power, subtract a preset second adjustment value from the predicted first power until the difference between the second power and the current first power is greater than the third power, and output the adjusted predicted first power.

[0033] In one possible implementation, the prediction module is also used to train a temperature prediction model through the second power, the ambient temperature, the battery parameters and the power of the liquid cooler; and input the ambient temperature, the battery parameters and the predicted first power into the temperature prediction model to predict the battery temperature of the energy storage device.

[0034] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store a computer program; the processor is used to run the computer program to implement the temperature prediction method as described in the first aspect.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is executed on a computer, the computer implements the temperature prediction method as described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a flow chart of an operation strategy of an energy storage device provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a temperature prediction method according to an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a power prediction method according to an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of the structure of a temperature prediction device provided in this application;

[0040] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] In the prior art, the temperature prediction model cannot predict the temperature change within ten minutes after the anti-backflow and anti-over-demand strategies are triggered and stopped. Only when the anti-backflow and anti-over-demand strategies are running for a period of time and stopped for a period of time, can the next temperature change be predicted based on the current power after the power changes.

[0048] Based on the above problems, an embodiment of the present application proposes a temperature prediction method, which is applied to energy storage equipment.

[0049] Now combined Figure 1-Figure 2 The temperature prediction method provided in the embodiment of the present application is described.

[0050] Figure 1 A schematic diagram of the operation strategy of the energy storage device provided in the embodiment of the present application. Figure 1 As shown, the above operation strategy includes the operation strategy of energy storage equipment in industrial and commercial scenarios. First, charge and discharge are performed according to the charge and discharge time set in the planned curve. Generally, charging is performed when the electricity price is low; discharging is performed when the electricity price is high. Then, the gateway meter is monitored in real time. When the gateway meter detects the anti-backflow threshold power, the anti-backflow strategy is executed, and the energy storage device reduces the power to operate; when the gateway meter detects the anti-over-demand threshold power, the anti-over-demand strategy is executed, and the power of the energy storage device is adjusted according to the current charging status of the energy storage device; if the energy storage device is in a charging state, the power is increased until the charging state is stopped; if the energy storage device is in a discharging state, the prediction is increased for discharge; if the energy storage device is in a static fully charged state, the power is increased for discharge to control the power demand.

[0051] like Figure 2 The figure shows a flow chart of an embodiment of the temperature prediction method provided by the present application, which specifically includes the following steps:

[0052] Step 210, obtaining the ambient temperature and battery parameters of the energy storage device.

[0053] Specifically, an embodiment of the present application proposes to first obtain the ambient temperature of the energy storage device and the battery parameters of the energy storage device, wherein the energy storage device may include multiple temperature sensors, and the temperature sensors may be arranged inside the energy storage device or outside the energy storage device, and the energy storage device obtains the ambient temperature through the temperature sensor. The specific acquisition method is not specifically limited in the embodiments of the present application. The battery parameters of the energy storage device proposed in the present application may include: SOC (battery state of charge, State of Charge) parameter, SOH (battery state of health, State of Health) parameter, battery capacity, rated voltage, charge and discharge rate, charge and discharge depth, maximum charge and discharge power and battery rated voltage, etc. Specifically, the SOC parameter is the percentage of the remaining battery power to the rated capacity of the battery, which is used to reflect the remaining capacity of the battery. The range of SOC is generally 0 to 100%; the SOH parameter is the ratio of the performance parameter to the nominal parameter after the battery has been used for a period of time. According to the IEEE standard, when the capacity of a fully charged battery is less than 80% of the rated capacity, that is, when the SOH is less than 80%, the battery should be replaced; battery capacity is one of the important performance indicators for measuring battery performance, indicating the amount of electricity discharged by the battery under certain conditions; the rated voltage of the battery in the energy storage device refers to its designed or nominal operating voltage, expressed in volts (V). The battery in the energy storage device is composed of single cells connected in series, which increases the voltage in series and increases the capacity in parallel; the battery charge and discharge rate is a measure of the charging speed, which affects the continuous current and peak current of the battery when it is working; the charge and discharge depth is used to measure the percentage between the battery discharge amount and the rated capacity of the battery. The deeper the discharge depth, the shorter the battery cycle life; the maximum charge and discharge power is the maximum current and power of the battery during charging and discharging; the rated voltage of the battery will change under different discharge currents and ambient temperatures. The larger the discharge current, the lower the voltage; the lower the temperature, the lower the voltage of the battery with the same capacity.

[0054] Step 220: Obtain the current first power, third power, and fourth power of the energy storage device.

[0055] Specifically, an embodiment of the present application proposes to obtain the current first power, third power and fourth power of the energy storage device, wherein the current first power is used to characterize the current operating power of the energy storage device, the third power is used to characterize the anti-reverse flow threshold power of the energy storage device, and the fourth power is used to characterize the anti-over-demand threshold power of the energy storage device. The anti-reverse flow threshold power is a key parameter in the energy storage system, and the anti-reverse flow threshold power defines the maximum amount of power that the energy storage system can provide to the power grid or load during the discharge process. During the discharge process of the energy storage system, due to power fluctuations, changes in load power consumption, etc., the electric energy flows back to the power grid, which may affect the stability and safety of the power grid. The reverse flow may also cause damage to the energy storage device, and improper charging and discharging process may shorten the battery life. By setting the anti-reverse flow threshold power, it is helpful to protect the energy storage device and extend the service life of the energy storage device. Further, it can ensure that the energy storage system does not inject too much energy into the power grid, thereby maintaining the stability of the power grid. The anti-over-demand threshold power refers to preventing the power provided by the energy storage system during the discharge process within a specific time period from exceeding the actual demand of the power grid or the load. This 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 of time, the grid demand is usually the average maximum value of the electricity actually consumed by users. When the gateway meter calculates that the over-demand threshold is reached, the strategy of the energy storage device will be adjusted according to the current state of the energy storage device. The dynamic adjustment of the anti-backflow strategy and the anti-over-demand strategy proposed in this application is mainly based on whether the anti-backflow threshold power and the anti-over-demand threshold power are triggered. Based on the current first power, third power and fourth power, the load power prediction can be effectively completed, and the power change of the energy storage device can be effectively predicted. Based on the charging and discharging power of the energy storage cabinet, the temperature change of the battery cell in the energy storage device will be effectively predicted.

[0056] Step 230, calculating the second power of the energy storage device, and if the difference between the second power and the current first power is not less than the fourth power, determining the state of the energy storage device.

[0057] Specifically, an embodiment of the present application proposes to calculate the second power of the energy storage device, and if the difference between the second power and the current first power is not less than the fourth power, the state of the energy storage device is judged. Wherein, the second power is used to characterize the load power of the energy storage device. Calculating the second power of the energy storage device includes calculating the second power of the energy storage device based on the historical power data, date data, weather conditions and air quality of the energy storage device. Further, an embodiment of the present application proposes to input the historical power data, date data, weather conditions and air quality of the energy storage device into the power load calculation model based on the historical power data, date data, weather conditions and air quality of the energy storage device, output the power load demand of the energy storage device, and then calculate the second power. Wherein, the power load calculation model proposed in the present application may include: convolutional neural network and long short-term memory network (Convolutional Neural Networks-Long Short-Term Memory, CNN-LSTM) model. It can be understood that the convolutional neural network usually includes an input layer, multiple convolutional layers, a pooling layer, a fully connected layer, etc.; the local features of the image are automatically extracted through the convolutional layer, and the local spatial structure information of the image can be captured. The long short-term memory network model unit contains three gates, namely, an input gate, a forget gate, an output gate and a cell state, through which the flow of information is controlled. The long short-term memory network model is mainly used for processing and predicting tasks of time series data, such as language modeling, text generation, speech recognition, etc. The convolutional neural network and the long short-term memory network proposed in the embodiment of the present application combine the convolutional neural network and the long short-term memory network. First, the convolutional neural network is used to extract features of the input object and extract the features of each frame. Optionally, the spatial dimension of the features is further reduced to extract more abstract features. Then, the LSTM layer is used to take the feature sequence extracted by CNN as input, and the LSTM is used to process the time series relationship of these features, capture the dynamic changes in time, and predict the power load. Finally, the fully connected layer is used to perform classification or regression tasks and evaluate the prediction results.

[0058] Optionally, Figure 3 A schematic diagram of a flow chart of an embodiment of the power prediction method provided in this application is as follows: Figure 3As shown, W is the current first power, WY is the predicted first power predicted based on the above-mentioned convolutional neural network and long short-term memory network model, W1 is the second power, W2 is the third power, W3 is the fourth power, n3 is the preset first adjustment value, and n2 is the preset second adjustment value. An embodiment of the present application proposes that if the difference between the second power and the current first power is not less than the fourth power, the over-demand threshold power is triggered, and the over-demand prevention strategy is executed, and the state of the energy storage device is first determined. If the energy storage device is in a charging state, the predicted first power is compensated based on the current first power, and the predicted first power is used to characterize the predicted operating power of the energy storage device; it is determined whether the difference between the second power and the compensated predicted first power is less than the fourth power; if the difference between the second power and the compensated predicted first power is less than the fourth power, the predicted first power is output; if the difference between the second power and the predicted first power is not less than the fourth power, the predicted first power is added to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and the adjusted predicted first power is output.

[0059] Optionally, if the energy storage device is in a discharging state, the predicted first power is compensated based on the current first power; it is determined whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, the predicted first power is output; if the difference between the second power and the predicted first power is not less than the fourth power, the predicted first power is added to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and the adjusted predicted first power is output.

[0060] Optionally, if the energy storage device is in a static fully charged state, the predicted first power is set to zero; determine whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, the predicted first power is output; if the difference between the second power and the predicted first power is not less than the fourth power, the predicted first power is added to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and the adjusted predicted first power is output. The preset first adjustment value can be different values ​​in different application scenarios, and can be set according to different over-demand prevention strategies. This application does not impose any special restrictions on the specific value of the preset first adjustment value.

[0061] Optionally, another embodiment in the present application proposes that if the difference between the second power and the current first power is not greater than the third power, the anti-counterflow threshold power is triggered, and the anti-counterflow strategy is executed. Based on the current first power and the preset first adjustment value, the predicted first power is adjusted. First, the working state of the energy storage device is judged. If the energy storage device is in the charging state, the predicted first power is increased until the charging state stops; if the energy storage device is in the discharging state, the predicted first power is increased for discharging; if the energy storage device is in the static fully charged state, the predicted first power is increased for discharging. It can be understood that in the present application, the predicted first power is adjusted according to the anti-counterflow strategy. Specifically, if the difference between the second power and the current first power is not less than the fourth power, the predicted first power is compensated based on the current first power; it is judged whether the difference between the second power and the predicted first power is greater than the third power; if the difference between the second power and the predicted first power is greater than the third power, the predicted first power is output; if the difference between the second power and the predicted first power is not greater than the third power, the predicted first power is subtracted by the preset second adjustment value until the difference between the second power and the current first power is greater than the third power, and the adjusted predicted first power is output. The preset second adjustment value can be different values in different application scenarios and can be set to vary with different anti-counterflow strategies. The present application does not make special limitations on the specific value of the preset second adjustment value.

[0062] Exemplarily, an embodiment in the present application proposes that the energy storage device has a negative power value during charging and a positive power value during discharging. Specifically, an embodiment in the present application proposes that W2 can be 20kW, W can be 100kW, and WY can be 100kW; W1 can be 100kW, that is, W1 - W is 0, triggering counterflow, and the anti-counterflow strategy is executed. WY is subtracted by the preset second adjustment value, where the preset second adjustment value can be 5kW, 10kW or any value not greater than W2 until W1 - WY > W2. When W1 is 200Kw and W is 100Kw, that is, W1 - W is 100kW which is greater than W2, counterflow is not triggered and the anti-counterflow strategy is not executed.

[0063] Another embodiment in the present application proposes that if the energy storage device is in the charging state, W3 can be 300kW, W can be -100kW, and WY can be -100kW; W1 can be 240kW, that is, W1 - W is 340kW which is greater than W3, triggering over-demand, and the anti-over-demand strategy is executed. WY is added with the preset first adjustment value, where the preset first adjustment value can be 5kW, 10kW; until W1 - WY < W3. When W1 is 150kW and W is 100kW, that is, W1 - W is 100kW which is less than W3, over-demand is not triggered and the anti-over-demand strategy is not executed.

[0064] Another embodiment in the present application proposes that if the energy storage device is in a discharging state, W3 can be 300 kW, W can be -100 kW, and WY can be -100 kW; W1 can be 240 kW, that is, W1 - W is 340 kW which is greater than W3, triggering over-demand, and the anti-over-demand strategy is executed. Add a preset first adjustment value to WY, where the preset first adjustment value can be 5 kW, 10 kW, until W1 - WY < W3. When W1 is 150 kW and W is 100 Kw, that is, W1 - W is 100 Kw which is less than W3, over-demand is not triggered and the anti-over-demand strategy is not executed.

[0065] Another embodiment in the present application proposes that if the energy storage device is in a static fully charged state, W3 can be 300 kW, W can be -100 kW, and WY can be 0; W1 can be 240 kW, that is, W1 - W is 340 which is greater than W3, triggering over-demand, and the anti-over-demand strategy is executed. Add a preset first adjustment value to WY, where the preset first adjustment value can be 5 kW, 10 kW, until W1 - WY < W3. When W1 is 150 kW and W is 100 kW, that is, W1 - W is 100 Kw which is less than W3, over-demand is not triggered and the anti-over-demand strategy is not executed. The power prediction method proposed in the present application can predict the power change within ten minutes after and after the anti-counterflow strategy or anti-over-demand strategy is triggered and stopped. Without the anti-counterflow strategy or anti-over-demand strategy running for a period of time and stopping for a period of time, the power change of the next energy storage device can be directly predicted based on the predicted first power. Compared with the prior art, the second power of the energy storage device can be predicted more efficiently and accurately within ten minutes after and after the anti-counterflow strategy or anti-over-demand strategy is triggered.

[0066] Step 240, predict the battery temperature of the energy storage device based on the ambient temperature, battery parameters, and the predicted first power.

[0067] Specifically, an embodiment of the present application proposes to predict the battery temperature of the energy storage device based on the ambient temperature, the battery parameters and the predicted first power. Among them, firstly, the temperature prediction model is trained by the second power, the ambient temperature, the battery parameters and the power of the liquid cooler; the temperature prediction model proposed in the present application can be a long short-term memory network (Long Short-Term Memory, LSTM) model, or it can be other temperature prediction models with the same prediction function. The present application does not make special restrictions on this temperature prediction model. Before training the temperature prediction model, the data such as the second power, the ambient temperature, the battery parameters and the power of the liquid cooler must be processed, and the data processing includes missing value processing, charging segment extraction processing and normalization processing. Among them, data missing can include continuous data missing and individual time point data missing. Continuous data missing cannot be supplemented by connecting the upper and lower time data, so the data can be directly removed; individual time point data missing can be processed by mean interpolation method. When the battery system sensor acquisition is abnormal, resulting in abnormal voltage or temperature data, for example, the single cell voltage of a lithium battery cell is far beyond the temperature range of the lithium battery, it is determined that the sensor data reading is abnormal. If the abnormal value appears continuously (number of times ≥ 3), it will be processed according to the data missing method to remove the abnormal data. If the number of consecutive occurrences of the abnormal value is less than 3, the normal value of the previous moment is used to replace the abnormal value to ensure that there is no abnormal jump in the battery temperature value within 30 seconds. In order to more accurately predict the temperature change during the charging period, the charging fragment is extracted. This application does not specifically limit the method for extracting the charging fragment. Then the ambient temperature, battery parameters and the predicted first power are input into the temperature prediction model to predict the battery temperature of the energy storage device. The temperature prediction model in this application can predict the temperature change within ten minutes after the anti-backflow strategy or the anti-over-demand strategy is triggered and stopped. There is no need for the anti-backflow strategy or the anti-over-demand strategy to run for a period of time and stop for a period of time. The battery temperature change of the next energy storage device can be predicted directly based on the predicted first power.

[0068] The temperature prediction method provided in the embodiment of the present application can predict the temperature change within ten minutes after the anti-backflow strategy or the anti-over-demand strategy is triggered and stopped. There is no need for the anti-backflow strategy or the anti-over-demand strategy to run for a period of time and stop for a period of time. The battery temperature change of the next energy storage device can be predicted directly based on the predicted first power. The temperature prediction method provided in the embodiment of the present application combines the specific charging and discharging strategies of industrial and commercial energy storage equipment, adopts the power load prediction algorithm to dynamically process the power adjustment of the energy storage equipment, and predicts the battery temperature of the energy storage equipment based on the predicted first power, so as to more efficiently and accurately predict the battery temperature change.

[0069] The temperature prediction device for energy storage equipment provided in the embodiment of the present invention can execute the temperature prediction method for energy storage equipment provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. The similarities can be referred to the above description of the temperature prediction method for energy storage equipment provided in the embodiment of the present invention, which will not be repeated here.

[0070] Figure 4 This is a schematic diagram of the structure of an embodiment of the device of the present application, such as Figure 4 As shown, the above device 40 may include:

[0071] An acquisition module 41, the acquisition module 41 is used to acquire the ambient temperature and the battery parameters of the energy storage device; the acquisition module is also used to acquire the current first power, third power and fourth power of the energy storage device, the current first power is used to characterize the current operating power of the energy storage device, the third power is used to characterize the anti-backflow threshold power of the energy storage device, and the fourth power is used to characterize the anti-over-demand threshold power of the energy storage device;

[0072] A calculation module 42, the calculation module 42 is used to calculate the second power of the energy storage device, the second power is used to characterize the load power of the energy storage device; if the difference between the second power and the current first power is not less than the fourth power, the state of the energy storage device is determined;

[0073] A compensation module 43, wherein the compensation module 43 is used to compensate the predicted first power based on the current first power if the energy storage device is in a charging state, and the predicted first power is used to characterize the predicted operating power of the energy storage device; determine whether the difference between the second power and the predicted first power after compensation is less than the fourth power; if the difference between the second power and the predicted first power after compensation is less than the fourth power, output the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, add the predicted first power to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and output the adjusted predicted first power;

[0074] The compensation module 43 is also used for compensating the predicted first power based on the current first power if the energy storage device is in a discharging state; judging whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, outputting the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, adding the predicted first power to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and outputting the adjusted predicted first power;

[0075] The compensation module 43 is also used to set the predicted first power to zero if the energy storage device is in a static full-charge state; determine whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, output the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, add the predicted first power to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and output the adjusted predicted first power;

[0076] The prediction module 44 is further configured to predict the battery temperature of the energy storage device based on the ambient temperature, the battery parameters and the predicted first power.

[0077] In one possible implementation manner, the battery parameters include an SOC parameter and an SOH parameter.

[0078] In one possible implementation, the calculation module 42 is further configured to calculate the second power of the energy storage device based on historical power data, date data, weather conditions, and air quality of the energy storage device.

[0079] In one possible implementation, the calculation module 42 is also used to input the historical power data, date data, weather conditions and air quality of the energy storage device into a preset model, output the power load demand of the energy storage device, and calculate the second power of the energy storage device.

[0080] In one possible implementation, the temperature prediction device also includes an adjustment module 45, and the adjustment module 45 is used to adjust the predicted first power based on the current first power and a preset first adjustment value if the difference between the second power and the current first power is not greater than the third power.

[0081] In one possible implementation, the adjustment module 45 is used to increase the predicted first power if the energy storage device is in a charging state until the charging state is stopped; if the energy storage device is in a discharging state, increase the predicted first power for discharging; if the energy storage device is in a static fully charged state, increase the predicted first power for discharging.

[0082] In one possible implementation, the compensation module 43 is used to compensate the predicted first power based on the current first power if the difference between the second power and the current first power is not less than the fourth power; determine whether the difference between the second power and the predicted first power is greater than the third power; if the difference between the second power and the predicted first power is greater than the third power; output the predicted first power; if the difference between the second power and the predicted first power is not greater than the third power, subtract a preset second adjustment value from the predicted first power until the difference between the second power and the current first power is greater than the third power, and output the adjusted predicted first power.

[0083] In one possible implementation, the prediction module 44 is also used to train a temperature prediction model through the second power, the ambient temperature, the battery parameters and the power of the liquid cooler; and input the ambient temperature, the battery parameters and the predicted first power into the temperature prediction model to predict the battery temperature of the energy storage device.

[0084] Combine the following Figure 5 The exemplary electronic device provided in the embodiments of the present application is further introduced. Figure 5 A schematic structural diagram of an electronic device 500 is shown.

[0085] The electronic device 500 may include at least one processor and at least one memory connected to the processor, wherein the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the temperature prediction method for the energy storage device provided in the embodiment shown in the present application.

[0086] Figure 5 A block diagram of an exemplary electronic device 500 suitable for implementing the embodiments of the present application is shown. Figure 5 The electronic device 500 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0087] like Figure 5As shown, the electronic device 500 is in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: one or more processors 510, storage 520, a communication bus 540 connecting different system components (including the storage 520 and the processor 510), and a communication interface 530.

[0088] The communication bus 540 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnection (PCI) bus.

[0089] The electronic device 500 typically includes a variety of computer system readable media, which can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0090] The memory 520 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Figure 3 Not shown, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the communication bus 540 via one or more data medium interfaces. The memory 520 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0091] A program / utility having a set (at least one) of program modules may be stored in memory 520, such program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules generally perform the functions and / or methods of the embodiments described herein.

[0092] The electronic device 500 may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via the communication interface 530. Furthermore, the electronic device 500 may also communicate with the network adapter ( Figure 5 The network adapter may communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via the communication bus 540. It should be understood that although Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays of Independent Drives; hereinafter referred to as: RAID) systems, tape drives, and data backup storage systems.

[0093] The processor 510 executes various functional applications and data processing by running the programs stored in the memory 520, such as implementing the method provided in the embodiment of the present application.

[0094] It is understandable that the interface connection relationship between the modules illustrated in the embodiment of the present application is only a schematic illustration and does not constitute a structural limitation on the electronic device 500. In other embodiments of the present application, the electronic device 500 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

[0095] In the above embodiments, the processor involved may include, for example, a CPU, a DSP, a microcontroller or a digital signal processor, and may also include a GPU, an embedded neural network processor (Neural-network Process Units; hereinafter referred to as: NPU) and an image signal processor (Image Signal Processing; hereinafter referred to as: ISP). The processor may also include necessary hardware accelerators or logic processing hardware circuits, such as ASIC, or one or more integrated circuits for controlling the execution of the program of the technical solution of the present application. In addition, the processor may have the function of operating one or more software programs, and the software programs may be stored in a storage medium.

[0096] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the temperature prediction method provided in the embodiment shown in the present application.

[0097] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is run on a computer, the computer executes the temperature prediction method provided by the embodiment shown in the present application.

[0098] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A temperature prediction method, characterized in that: Applied to energy storage equipment, the method comprises: Acquiring ambient temperature and battery parameters of the energy storage device; Acquire a current first power, a third power, and a fourth power of the energy storage device, wherein the current first power is used to characterize the current operating power of the energy storage device, the third power is used to characterize the anti-backflow threshold power of the energy storage device, and the fourth power is used to characterize the anti-over-demand threshold power of the energy storage device; Calculating a second power of the energy storage device, where the second power is used to characterize the load power of the energy storage device; if a difference between the second power and the current first power is not less than the fourth power, determining the state of the energy storage device; If the energy storage device is in a charging state, the predicted first power is compensated based on the current first power, and the predicted first power is used to characterize the predicted operating power of the energy storage device; it is determined whether the difference between the second power and the predicted first power after compensation is less than the fourth power; if the difference between the second power and the predicted first power after compensation is less than the fourth power, the predicted first power is output; if the difference between the second power and the predicted first power is not less than the fourth power, the predicted first power is added with a preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and the adjusted predicted first power is output; If the energy storage device is in a discharging state, the predicted first power is compensated based on the current first power; it is determined whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, the predicted first power is output; if the difference between the second power and the predicted first power is not less than the fourth power, the predicted first power is added to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and the adjusted predicted first power is output; If the energy storage device is in a static fully charged state, the predicted first power is set to zero; determine whether the difference between the second power and the predicted first power is less than the fourth power; if the difference between the second power and the predicted first power is less than the fourth power, output the predicted first power; if the difference between the second power and the predicted first power is not less than the fourth power, add the predicted first power to the preset first adjustment value until the difference between the second power and the current first power is less than the fourth power, and output the adjusted predicted first power; Predicting a battery temperature of the energy storage device based on the ambient temperature, the battery parameter, and the predicted first power; The calculating the second power of the energy storage device comprises: Calculating a second power of the energy storage device based on historical power data, date data, weather conditions, and air quality of the energy storage device; The calculating the second power of the energy storage device based on the historical power data, date data, weather conditions and air quality of the energy storage device comprises: Inputting historical power data, date data, weather conditions and air quality of the energy storage device into a preset model, outputting the power load demand of the energy storage device, and calculating the second power of the energy storage device; The method further comprises: If the difference between the second power and the current first power is not greater than the third power, adjusting the predicted first power based on the current first power and a preset second adjustment value; The adjusting the predicted first power based on the current first power and the preset second adjustment value includes: If the difference between the second power and the current first power is not greater than the third power, compensating the predicted first power based on the current first power; Determine whether a difference between the second power and the predicted first power is greater than the third power; If the difference between the second power and the predicted first power is greater than the third power; outputting the predicted first power; If the difference between the second power and the predicted first power is not greater than the third power, subtracting a preset second adjustment value from the predicted first power until the difference between the second power and the current first power is greater than the third power, and outputting the adjusted predicted first power; The energy storage device further includes a liquid cooler, and predicting the battery temperature of the energy storage device based on the ambient temperature, the battery parameter, and the predicted first power includes: Training a temperature prediction model through the second power, the ambient temperature, the battery parameters, and the power of the liquid cooler; The ambient temperature, the battery parameters and the predicted first power are input into the temperature prediction model to predict the battery temperature of the energy storage device.

2. The temperature prediction method according to claim 1, characterized in that: The battery parameters include SOC parameters and SOH parameters.

3. The temperature prediction method according to claim 1, characterized in that: The determining the state of the energy storage device and adjusting the first power includes: If the energy storage device is in a charging state, increasing the predicted first power until the charging state is stopped; If the energy storage device is in a discharging state, increasing the predicted first power for discharging; If the energy storage device is in a static fully charged state, the predicted first power is increased for discharge.

4. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store a computer program; the processor is used to run the computer program, and when the computer program is run on a computer, the temperature prediction method according to any one of claims 1 to 3 is implemented.

5. An energy storage system, characterized in that: include: A battery pack, a PCS and an EMS, wherein the EMS implements the temperature prediction method as described in any one of claims 1 to 3.

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