CN118336047A - Intelligent temperature adjusting method and device for flow battery - Google Patents
Intelligent temperature adjusting method and device for flow battery Download PDFInfo
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Abstract
The application is applicable to the technical field of electrochemical energy storage, and provides an intelligent temperature adjustment method of a flow battery, which comprises the following steps: and acquiring the actual pile temperature and the actual environment temperature. And inputting the actual pile temperature and the actual environment temperature into an optimized control algorithm to obtain an optimal control value. And adjusting the current electric pile working temperature of the flow battery according to the optimal control value. According to the application, the high-efficiency and accurate temperature control of the flow battery is realized based on a neural network method, on one hand, the optimal operation temperature of each electric pile and each battery pack is ensured, the optimization of the battery performance is realized, and the battery efficiency, the effective capacity and the service life are improved; on the other hand, the energy consumption for temperature rise and temperature reduction adjustment is saved through accurate control, the self-power consumption of the system is reduced, and the use cost of the flow battery is reduced.
Description
Technical Field
The application belongs to the technical field of electrochemical energy storage, and particularly relates to an intelligent temperature regulation method and device for a flow battery.
Background
In recent years, flow batteries are increasingly applied in the electrochemical energy storage industry due to the advantages of long cycle life, high safety, environmental friendliness, high life cycle performance price ratio and the like, and become an important technical direction in the field of electric power energy storage. The working temperature ranges of different batteries are different, and the optimal working temperature range of the flow battery is generally 25-35 ℃. The efficiency, the service life and the safety of the battery can be effectively ensured in the optimal working temperature range. Too high or too low a temperature can negatively impact battery performance and even lead to battery failure. The low temperature can weaken the power supply capability of the battery, the high temperature is easy to generate thermal runaway, and the unbalance of the temperature can also lead to the reduction of the effective capacity and the service life of the battery. In the prior art, the whole temperature of an electrolyte circulation system is controlled, the general parameters are fixed values, the temperature control curve is not optimized for the flow battery system, and the model of the flow battery is changed in the working process, so that the flow battery is difficult to accurately control the temperature, and the optimal battery performance and the system self-power consumption are not realized.
Disclosure of Invention
The embodiment of the application provides an intelligent temperature regulation method and device for a flow battery, which can solve the problems that accurate temperature control of the flow battery is difficult, and optimal battery performance and system self-power consumption are not realized.
In a first aspect, an embodiment of the present application provides a method for intelligent temperature adjustment of a flow battery, including:
acquiring an actual galvanic pile temperature and an actual environment temperature;
Inputting the actual pile temperature and the actual environment temperature into an optimized control algorithm to obtain an optimal control value;
And adjusting the current electric pile working temperature of the flow battery according to the optimal control value.
In an implementation embodiment of the present application, the step of constructing the optimized control algorithm includes: the control algorithm is optimized using a back propagation neural network.
In an embodiment of the present application, the optimal control value includes: the adjusting the current stack working temperature of the flow battery according to the optimal control value comprises the following steps:
judging whether the current operating temperature of the electric pile needs to be cooled or not;
If the current electric pile working temperature needs to be cooled, adjusting the current electric pile working temperature to be between a highest electric pile temperature threshold value and a lowest electric pile temperature threshold value according to the optimal cooling control value;
If the current electric pile working temperature needs to be heated, adjusting the current electric pile working temperature to be between the highest electric pile temperature threshold value and the lowest electric pile temperature threshold value according to the optimal heating control value.
In an implementation embodiment of the present application, the determining whether the current operating temperature of the galvanic pile needs to be cooled includes:
Presetting a highest galvanic pile temperature threshold value, a lowest galvanic pile temperature threshold value, a highest environmental temperature threshold value and a lowest environmental temperature threshold value;
Judging whether the current operating temperature of the electric pile needs to be cooled or not according to the highest electric pile temperature threshold value, the lowest electric pile temperature threshold value, the highest environment temperature threshold value and the lowest environment temperature threshold value.
In an implementation embodiment of the present application, the determining whether the current operating temperature of the electric pile needs to be cooled according to the highest electric pile temperature threshold, the lowest electric pile temperature threshold, the highest environmental temperature threshold and the lowest environmental temperature threshold includes:
if the actual ambient temperature is higher than the highest ambient temperature threshold value and the actual electric pile temperature is higher than the highest electric pile temperature threshold value, determining to cool the current electric pile working temperature;
If the actual ambient temperature is lower than the minimum ambient temperature threshold and the actual electric pile temperature is lower than the maximum electric pile temperature threshold, determining to raise the current electric pile working temperature;
if the actual ambient temperature is between the highest ambient temperature threshold and the lowest ambient temperature threshold and the actual electric pile temperature is lower than the highest electric pile temperature threshold, determining to raise the current electric pile working temperature;
And if the actual ambient temperature is between the highest ambient temperature threshold and the lowest ambient temperature threshold and the actual electric pile temperature is higher than the highest electric pile temperature threshold, determining to cool the current electric pile working temperature.
In an implementation of the present application, the method further includes:
And after the preset time period, if the current operating temperature of the electric pile cannot be regulated to be between the highest electric pile temperature threshold value and the lowest electric pile temperature threshold value, an alarm is sent out.
In a second aspect, an embodiment of the present application provides an intelligent temperature adjustment device for a flow battery, including:
The acquisition unit is used for acquiring the actual pile temperature and the actual environment temperature;
The processing unit is used for inputting the actual pile temperature and the actual environment temperature into an optimized control algorithm to obtain an optimal control value;
and the adjusting unit is used for adjusting the current electric pile working temperature of the flow battery according to the optimal control value.
In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method of any one of the above when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements a method as in any of the above.
In a fifth aspect, an embodiment of the application provides a computer program product for, when run on a terminal device, causing the terminal device to perform the method of any of the first aspects described above.
It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.
Compared with the prior art, the embodiment of the application has the beneficial effects that:
According to the application, the high-efficiency and accurate temperature control of the flow battery is realized based on a neural network method, on one hand, the optimal operation temperature of each electric pile and each battery pack is ensured, the optimization of the battery performance is realized, and the battery efficiency, the effective capacity and the service life are improved; on the other hand, the energy consumption for temperature rise and temperature reduction adjustment is saved through accurate control, the self-power consumption of the system is reduced, and the use cost of the flow battery is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a flow battery intelligent temperature regulation method of the application;
FIG. 2 is a flow chart of the present application for optimizing a control algorithm using a back propagation neural network;
FIG. 3 is a graph of the result of optimizing a control algorithm by a back propagation neural network to produce predictions in accordance with an embodiment of the present application;
FIG. 4 is a flow chart of how the temperature is regulated according to the present application;
FIG. 5 is a flow chart of the application for determining whether cooling is needed;
FIG. 6 is a flow chart for judging whether the current operating temperature of the electric pile is raised or lowered according to the application;
FIG. 7 is a flow chart of how temperature regulation is performed in accordance with one embodiment of the present application;
fig. 8 is a schematic structural diagram of an intelligent temperature adjusting device of a flow battery according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The conventional intelligent temperature regulation method for the flow battery mainly relies on an automatic control system, which monitors the temperature of the battery and automatically adjusts the operating state of the battery according to preset parameters to maintain the temperature within a proper range. The preset parameter is a fixed value, but the model of the flow battery is changed in the working process, so that accurate temperature control of the flow battery is difficult, and the optimal battery performance and the system self-power consumption are not realized. Based on the above, the application provides an intelligent temperature adjustment method of a flow battery, and the method is described below with reference to the embodiment.
As shown in fig. 1, a flow battery intelligent temperature adjustment method includes:
s101, acquiring an actual pile temperature and an actual environment temperature;
S102, inputting the actual pile temperature and the actual environment temperature into an optimized control algorithm to obtain an optimal control value;
And S103, adjusting the current electric pile working temperature of the flow battery according to the optimal control value.
The actual stack temperature and the actual ambient temperature of the stack are monitored, for example, by temperature sensors.
Next, the actual stack temperature and actual ambient temperature data are input into an optimized control algorithm. And calculating an optimal control value according to the historical data, the current conditions and the performance of the battery.
The control algorithm calculates an optimal control value, such as adjusting the flow rate or fan speed of the stack cooling system.
And finally, adjusting the current electric pile working temperature of the flow battery according to the optimal control value given by the control algorithm, namely heating or cooling the current electric pile working temperature.
Through the automatic regulation process, the flow battery can be ensured to keep the optimal performance under different environmental conditions, and the service life of the flow battery is prolonged.
In an implementation embodiment of the present application, the step of constructing the optimized control algorithm includes:
The control algorithm is optimized using a back propagation neural network.
Since the back propagation neural network optimizes the weights and biases of the entire network, optimization of global performance can be achieved. In the control algorithm, it is the final objective to obtain the optimal control strategy, so that the overall performance of the system can be optimized. Through training and optimization of the back propagation neural network, the more globally optimal control performance is obtained.
In an embodiment of the present application, as shown in fig. 2, the optimizing the control algorithm using the back propagation neural network includes:
s201, determining an error according to the obtained actual output value and a preset control value;
S202, calculating parameters of a control algorithm by using the error and the back propagation neural network, and obtaining an optimized control value through a self-learning optimization neural network model of the neural network.
Exemplary, the transfer function of the object is controlled with time delay and time variationFor example, where a 0 is a slow time-varying coefficient. Three layers of BP neural networks are adopted, and M input layer nodes and Q hidden layer nodes are arranged.
Wherein the input layer is expressed as:
Wherein, E (k-j) is the control error for the output of the input layer.
The hidden layer is expressed as:
Wherein, For the implicit layer weighting coefficients,For hidden layer output, f is a hidden layer activation function. Output layer:
Wherein the method comprises the steps of As the weighting coefficient(s),For the threshold, the activation functions f and g are as follows
f[·]＝tanh(x)，g[·]＝[1+tanh(x)]/2
The performance index is as follows:
where r (k+1) is a control target, and y (k+1) is an output signal.
The output layer weighting factor correction formula is:
Wherein the method comprises the steps of Gradient of error, alpha is inertia coefficient, eta is learning efficiency
For replacing unknownsThe resulting error is compensated by adjusting the learning efficiency η.
Hidden layer weighting coefficient correction formula:
The control value is calculated as:
u(k)＝u(k-1)+KP(k)[e(k)-e(k-1)]+KI(k)e(k)+KD(k)[e(k)-2e(k-1)+e(k-2)]
u (k) is a given direct control value, the control effect is as shown in fig. 3, the output y (k) can well track a given control target r (k) on the control of a time delay and time-varying object, and the transient process is fast and stable without overshoot.
In an embodiment of the application as shown in fig. 4, the optimal control values include: the adjusting the current stack working temperature of the flow battery according to the optimal control value comprises the following steps:
s401, judging whether the current operating temperature of the electric pile needs to be cooled or not;
s402, if the current electric pile working temperature needs to be cooled, adjusting the current electric pile working temperature to be between a highest electric pile temperature threshold value and a lowest electric pile temperature threshold value according to the optimal cooling control value;
s403, if the current electric pile working temperature needs to be heated, adjusting the current electric pile working temperature to be between the highest electric pile temperature threshold value and the lowest electric pile temperature threshold value according to the optimal heating control value.
Illustratively, the current operating temperature of the stack is first obtained. Assuming a current operating temperature of 48 c, which exceeds the maximum stack temperature threshold of 45 c, the stack needs to be cooled.
And in the cooling process, calculating an optimal cooling control value according to an optimized control algorithm.
Assuming that the optimal cooling control value is 40 ℃, cooling the electric pile according to the control value. After a period of time, the stack temperature gradually drops, eventually stabilizing at 40 ℃, meeting the safe operating temperature between the highest and lowest stack temperature thresholds.
It is assumed that at a certain moment, the stack temperature is lowered to 20c below the lowest stack temperature threshold of 25 c due to a decrease in the external ambient temperature or excessive discharge of the battery, and at this time, a temperature raising operation of the stack is required.
Assuming that the optimal cooling control value is 30 ℃, cooling the electric pile according to the control value. After a period of time of regulation, the temperature of the galvanic pile gradually rises, and finally, the temperature is stabilized at 30 ℃ and accords with the safe working temperature between the highest and lowest galvanic pile temperature thresholds.
Through the automatic regulation process, the operating temperature of the electric pile of the flow battery is ensured to be always kept in the optimal range, so that the performance and the service life of the battery are improved.
As shown in fig. 5, in an embodiment of the present application, the determining whether the current operating temperature of the galvanic pile needs to be reduced includes:
s501, presetting a highest electric pile temperature threshold value, a lowest electric pile temperature threshold value, a highest environment temperature threshold value and a lowest environment temperature threshold value;
S502, judging whether the current operating temperature of the electric pile needs to be cooled or not according to the highest electric pile temperature threshold value, the lowest electric pile temperature threshold value, the highest environment temperature threshold value and the lowest environment temperature threshold value.
As shown in fig. 6, in an embodiment of the present application, the determining, according to the highest stack temperature threshold, the lowest stack temperature threshold, the highest ambient temperature threshold, and the lowest ambient temperature threshold, whether the current stack operating temperature needs to be cooled includes:
S601, if the actual ambient temperature is higher than the highest ambient temperature threshold value and the actual pile temperature is higher than the highest pile temperature threshold value, determining to cool the current pile working temperature;
s602, if the actual ambient temperature is lower than the minimum ambient temperature threshold and the actual pile temperature is lower than the maximum pile temperature threshold, determining to raise the current pile working temperature;
S603, if the actual environmental temperature is between the highest environmental temperature threshold and the lowest environmental temperature threshold, and the actual electric pile temperature is lower than the highest electric pile temperature threshold, determining to raise the current electric pile working temperature;
s604, if the actual environmental temperature is between the highest environmental temperature threshold and the lowest environmental temperature threshold, and the actual electric pile temperature is higher than the highest electric pile temperature threshold, determining to cool the current electric pile working temperature.
In an implementation of the present application, the method further includes:
And after the preset time period, if the current operating temperature of the electric pile cannot be regulated to be between the highest electric pile temperature threshold value and the lowest electric pile temperature threshold value, an alarm is sent out.
As shown in fig. 7, the present application sets initial parameters of the system, such as a high-low temperature threshold T BH、TBL of the stack temperature and a high-low temperature threshold T AH、TAL of the ambient temperature.
The actual cell stack temperature and the actual environment temperature are obtained online, the temperature of a group of n cell stacks is respectively marked as t 11、…、t1n、ta by taking the example, and the temperature of one cell stack is denoted by t 1x.
Under normal conditions, when the actual ambient temperature T a is higher than T AH, the temperature-rising related component can be kept out of operation, and the temperature-lowering related component is operated; if T 1x deviates from the highest stack temperature threshold T BH, setting a control value u 1x of the cooling component by a control algorithm based on a BP neural network according to T a、t1x, until t 1x stabilizes between the highest stack temperature threshold and the lowest stack temperature threshold; If t 1x is still unable to be stabilized between the highest stack temperature threshold and the lowest stack temperature threshold after the preset time, an alarm is sent. When the ambient temperature T a is lower than T AL, the cooling related component can be standby and not operated, and the heating related component is operated; If T 1x is lower than the lowest stack temperature threshold T BL, setting a control value u 2x of the temperature raising component by a control algorithm based on a BP neural network according to T a、t1x, until t 1x stabilizes between the highest stack temperature threshold and the lowest stack temperature threshold; if t 1x is still unable to be stabilized between the highest stack temperature threshold and the lowest stack temperature threshold after the preset time period, an alarm is sent. When the ambient temperature T a is between T AL and T AH, the temperature-raising related components and the temperature-lowering related components operate; If t 1x deviates from the optimal working temperature interval, setting a temperature raising component control value u 2x or a temperature lowering component control value u 1x by a control algorithm based on a BP neural network according to t a、t1x, until t 1x stabilizes between the highest stack temperature threshold and the lowest stack temperature threshold; If t 1x cannot be stabilized within the optimal working temperature range after the preset time period, an alarm is sent out. In abnormal situations, when the ambient temperature T a is higher than T AH, the situation that T 1x is lower than T BL occurs, Sending out an alarm; When the ambient temperature T a is lower than T AL, the condition that T 1x is higher than T BH occurs, and an alarm is sent.
As shown in fig. 8, in an embodiment of the present application, a flow battery intelligent temperature adjustment device includes:
an acquiring unit 801, configured to acquire an actual stack temperature and an actual environmental temperature;
The processing unit 802 is configured to input the actual stack temperature and the actual environmental temperature into an optimized control algorithm to obtain an optimal control value;
And the adjusting unit 803 is used for adjusting the current electric pile working temperature of the flow battery according to the optimal control value.
The device can realize high-efficiency and accurate temperature control of the flow battery, on one hand, the optimal operation temperature of each electric pile and each battery pack is ensured, the optimization of the battery performance is realized, and the battery efficiency, the effective capacity and the service life are improved; on the other hand, the energy consumption for temperature rise and temperature reduction adjustment is saved through accurate control, the self-power consumption of the system is reduced, and the use cost of the flow battery is reduced.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The embodiment of the application also provides a network device, which comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, which when executed by the processor performs the steps of any of the various method embodiments described above.
Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps for implementing the various method embodiments described above.
Embodiments of the present application provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that enable the implementation of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (9)
1. An intelligent temperature regulation method for a flow battery is characterized by comprising the following steps:
acquiring an actual galvanic pile temperature and an actual environment temperature;
Inputting the actual pile temperature and the actual environment temperature into an optimized control algorithm to obtain an optimal control value;
And adjusting the current electric pile working temperature of the flow battery according to the optimal control value.
2. The intelligent temperature regulation method of a flow battery of claim 1 wherein the step of constructing the optimized control algorithm comprises:
The control algorithm is optimized using a back propagation neural network.
3. The intelligent temperature regulation method of a flow battery of claim 1, wherein the optimal control value comprises: the adjusting the current stack working temperature of the flow battery according to the optimal control value comprises the following steps:
judging whether the current operating temperature of the electric pile needs to be cooled or not;
If the current electric pile working temperature needs to be cooled, adjusting the current electric pile working temperature to be between a highest electric pile temperature threshold value and a lowest electric pile temperature threshold value according to the optimal cooling control value;
If the current electric pile working temperature needs to be heated, adjusting the current electric pile working temperature to be between the highest electric pile temperature threshold value and the lowest electric pile temperature threshold value according to the optimal heating control value.
4. The intelligent temperature adjustment method for a flow battery according to claim 3, wherein the determining whether the current operating temperature of the stack needs to be cooled comprises:
Presetting a highest galvanic pile temperature threshold value, a lowest galvanic pile temperature threshold value, a highest environmental temperature threshold value and a lowest environmental temperature threshold value;
Judging whether the current operating temperature of the electric pile needs to be cooled or not according to the highest electric pile temperature threshold value, the lowest electric pile temperature threshold value, the highest environment temperature threshold value and the lowest environment temperature threshold value.
5. The intelligent temperature adjustment method of the flow battery according to claim 4, wherein the determining whether the current operating temperature of the electric pile needs to be cooled according to the highest electric pile temperature threshold, the lowest electric pile temperature threshold, the highest ambient temperature threshold and the lowest ambient temperature threshold comprises:
if the actual ambient temperature is higher than the highest ambient temperature threshold value and the actual electric pile temperature is higher than the highest electric pile temperature threshold value, determining to cool the current electric pile working temperature;
If the actual ambient temperature is lower than the minimum ambient temperature threshold and the actual electric pile temperature is lower than the maximum electric pile temperature threshold, determining to raise the current electric pile working temperature;
if the actual ambient temperature is between the highest ambient temperature threshold and the lowest ambient temperature threshold and the actual electric pile temperature is lower than the highest electric pile temperature threshold, determining to raise the current electric pile working temperature;
And if the actual ambient temperature is between the highest ambient temperature threshold and the lowest ambient temperature threshold and the actual electric pile temperature is higher than the highest electric pile temperature threshold, determining to cool the current electric pile working temperature.
6. The intelligent temperature regulation method of a flow battery of any one of claims 1-5, further comprising:
And after the preset time period, if the current operating temperature of the electric pile cannot be regulated to be between the highest electric pile temperature threshold value and the lowest electric pile temperature threshold value, an alarm is sent out.
7. Intelligent temperature regulating device of flow battery, its characterized in that includes:
The acquisition unit is used for acquiring the actual pile temperature and the actual environment temperature;
The processing unit is used for inputting the actual pile temperature and the actual environment temperature into an optimized control algorithm to obtain an optimal control value;
and the adjusting unit is used for adjusting the current electric pile working temperature of the flow battery according to the optimal control value.
8. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 6 when executing the computer program.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410355526.5A CN118336047A (en) | 2024-03-27 | 2024-03-27 | Intelligent temperature adjusting method and device for flow battery |
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