CN116995784B - Ship energy storage and discharge control method and device, electronic equipment and readable medium - Google Patents
Ship energy storage and discharge control method and device, electronic equipment and readable medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000004146 energy storage Methods 0.000 title claims abstract description 23
- 239000003990 capacitor Substances 0.000 claims abstract description 111
- 239000000446 fuel Substances 0.000 claims abstract description 89
- 238000012545 processing Methods 0.000 claims description 18
- 230000004044 response Effects 0.000 claims description 15
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- 230000006870 function Effects 0.000 description 10
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 8
- 229910052744 lithium Inorganic materials 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0063—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with circuits adapted for supplying loads from the battery
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J2207/50—Charging of capacitors, supercapacitors, ultra-capacitors or double layer capacitors
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/30—The power source being a fuel cell
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/40—The network being an on-board power network, i.e. within a vehicle
- H02J2310/42—The network being an on-board power network, i.e. within a vehicle for ships or vessels
Abstract
The embodiment of the disclosure discloses a ship energy storage discharge control method, a ship energy storage discharge control device, electronic equipment and a readable medium. One embodiment of the method comprises the following steps: responding to the current time as a preset acquisition time, and acquiring a power signal of a target ship; determining the required power of a target ship; generating predicted power of the storage battery, predicted power of the capacitor and predicted power of the fuel cell according to the required power and the power signal; acquiring a first charge state of a target storage battery and a second charge state of a target super capacitor; determining battery operating power, capacitor operating power and fuel cell operating power according to the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge and the second state of charge; and controlling the target ship to run according to the storage battery running power, the capacitor running power and the fuel cell running power. This embodiment avoids situations where the vessel is not operational.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a ship energy storage and discharge control method, a device, electronic equipment and a readable medium.
Background
In the sailing process, various electric energy power source ships generally use different power sources for sailing. These include fuel cells, batteries, and supercapacitors. How to control the discharge power between different power sources to reduce the waste of electric power resources is an important research topic. Currently, when controlling the discharge power between different power sources, the following methods are generally adopted: and predicting the discharge power of different power sources by using a prediction model, and controlling the ship to navigate by using the predicted discharge power.
However, when the above manner is adopted to control the discharge power between different power sources, there are often the following technical problems:
first, when the prediction model is used to predict the discharge power of the power source, the states of charge of the lithium battery and the super capacitor are not considered, so that the predicted discharge power may not be maintained due to the low states of charge of the lithium battery and the super capacitor, and the ship may not voyage.
Secondly, when the prediction model is used for prediction, long time is required to be consumed for prediction, and when the power of the ship demand changes rapidly, the discharge power which cannot be predicted in time is caused, so that the degree of circuit change in the circuit is large, the damage of a battery is caused, the service life is shortened, and further the waste of battery resources is caused.
Third, when determining the discharge power of different power sources, the determined discharge power may not meet the discharge power of the actual requirement due to the reduction of the service lives of the different power sources, thereby resulting in the reduction of the service lives of the power sources and the waste of electric power resources.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose a ship energy storage discharge control method, apparatus, electronic device, and computer readable medium to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a ship energy storage discharge control method, the method comprising: responding to the current time as a preset acquisition time, and acquiring a power signal of a target ship; determining the required power of the target ship; generating a predicted power of the storage battery, a predicted power of the capacitor and a predicted power of the fuel cell according to the required power and the power signal; acquiring a first charge state of a target storage battery and a second charge state of a target super capacitor; determining a battery operating power, a capacitor operating power, and a fuel cell operating power based on the demand power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge, and the second state of charge; and controlling the target ship to run according to the battery running power, the capacitor running power and the fuel cell running power.
In a second aspect, some embodiments of the present disclosure provide a ship energy storage discharge control apparatus, the apparatus comprising: the acquisition unit is configured to acquire a power signal of the target ship in response to the current time being a preset acquisition time; a first determination unit configured to determine a required power of the target ship; a generating unit configured to generate a battery predicted power, a capacitance predicted power, and a fuel cell predicted power based on the required power and the power signal; an acquisition unit configured to acquire a first state of charge of a target storage battery and a second state of charge of a target supercapacitor; a second determination unit configured to determine a battery operation power, a capacitor operation power, and a fuel cell operation power based on the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge, and the second state of charge; and a control unit configured to control the target ship to travel based on the battery operating power, the capacitor operating power, and the fuel cell operating power.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantages: according to the ship energy storage and discharge control method, the situation that the ship cannot run can be avoided. Specifically, the reason for the failure of the ship is that: when the prediction model is used for predicting the discharge power of the power source, the charge states of the lithium battery and the super capacitor are not considered, so that the predicted discharge power can not be maintained due to the fact that the charge states of the lithium battery and the super capacitor are low, and the ship can not navigate. Based on this, in the ship energy storage discharge control method of some embodiments of the present disclosure, first, a power signal of a target ship is acquired in response to a current time being a preset acquisition time. Thus, the power signal transmitted to the power source during the operation of the ship can be acquired. And secondly, determining the required power of the target ship. Thus, the total power required for the operation of the vessel can be determined. Then, a battery predicted power, a capacitor predicted power, and a fuel cell predicted power are generated based on the demand power and the power signal. Thus, the discharge power of different power sources can be predicted. And then, acquiring the first charge state of the target storage battery and the second charge state of the target super capacitor. From this, the state of charge of the battery and the supercapacitor can be determined. And then, determining the battery operation power, the capacitor operation power and the fuel cell operation power according to the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first charge state and the second charge state. Therefore, the situation that the ship cannot navigate due to the fact that the predicted discharge power cannot be maintained because the charge states of the lithium battery and the super capacitor are low can be avoided through the charge states of the storage battery and the super capacitor. And finally, controlling the target ship to run according to the storage battery running power, the capacitor running power and the fuel cell running power. Therefore, the discharge power of different power sources of the ship is regulated and controlled, and the situation that the ship cannot run is avoided.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of a ship energy storage discharge control method according to the present disclosure;
FIG. 2 is a schematic structural view of some embodiments of a marine vessel energy storage and discharge control device according to the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates a flow 100 of some embodiments of a ship energy storage discharge control method according to the present disclosure. The ship energy storage discharge control method comprises the following steps:
And step 101, responding to the current time as a preset acquisition time, and acquiring a power signal of the target ship.
In some embodiments, an executing body (e.g., a server) of the ship energy storage discharge control method may collect a power signal of the target ship in response to the current time being a preset collection time. The preset acquisition time may be a preset time for acquiring a power signal. The target vessel may be a variety of electric power source vessels requiring discharge control. The power source of the target vessel may include, but is not limited to, at least one of: fuel cells, batteries, and supercapacitors. The power signal may be a signal for controlling the discharge power of each power source by the target ship.
Step 102, determining the required power of the target ship.
In some embodiments, the executing entity may determine the required power of the target vessel. In practice, the above-described execution subject may determine the sum of the power of the electric appliances that the target ship needs to operate as the required power. The required power may be a sum of powers of respective electric appliances connected to the respective power sources.
And 103, generating the predicted power of the storage battery, the predicted power of the capacitor and the predicted power of the fuel cell according to the required power and the power signal.
In some embodiments, the execution body may generate the battery predicted power, the capacitor predicted power, and the fuel cell predicted power according to the required power and the power signal.
In practice, the required power and the power signal may be input to a pre-trained power prediction model to obtain a predicted battery power, a predicted capacitor power, and a predicted fuel cell power. The power prediction model may be a neural network model that is trained in advance, takes the required power and the power signal as input, and takes the predicted power of the storage battery, the predicted power of the capacitor and the predicted power of the fuel cell as output.
Alternatively, the environmental information analysis model may be trained by:
first, a sample set is obtained.
In some embodiments, the execution body may obtain a sample set. The samples in the sample set comprise sample required power and sample power signals, and sample storage battery predicted power, sample capacitance predicted power and sample fuel cell predicted power corresponding to the sample required power and the sample power signals.
And a second step of selecting samples from the sample set.
In some embodiments, the execution body may select a sample from the sample set. Here, the execution subject may randomly select a sample from the sample set.
And thirdly, inputting the sample into an initial network model to obtain the predicted power of the storage battery, the predicted power of the capacitor and the predicted power of the fuel cell corresponding to the sample.
In some embodiments, the execution entity may input the sample to an initial network model to obtain a predicted battery power, a predicted capacitor power, and a predicted fuel cell power corresponding to the sample. The initial neural network may be a classification model capable of obtaining a predicted power of the storage battery, a predicted power of the capacitor, and a predicted power of the fuel cell according to the required power and the power signal. The initial neural network may be a convolutional neural network model.
And a fourth step of determining loss values between the battery predicted power, the capacitor predicted power and the fuel cell predicted power and the sample battery predicted power, the sample capacitor predicted power and the sample fuel cell predicted power included in the sample, respectively, to obtain a loss value set.
In some embodiments, the executing entity may determine loss values between the battery predicted power, the capacitor predicted power, and the fuel cell predicted power and the sample battery predicted power, the sample capacitor predicted power, and the sample fuel cell predicted power included in the sample, respectively, to obtain a loss value set. In practice, loss values between the battery predicted power, the capacitor predicted power, and the fuel cell predicted power and the sample battery predicted power, the sample capacitor predicted power, and the sample fuel cell predicted power included in the sample may be determined based on a preset loss function, respectively, to obtain a loss value set. For example, the predetermined loss function may be a cross entropy loss function.
And fifthly, adjusting network parameters of the initial network model in response to the loss value set not meeting a preset loss condition.
In some embodiments, the executing entity may adjust the network parameters of the initial network model in response to the set of loss values not meeting a preset loss condition. The preset loss condition may be that no loss value greater than or equal to a corresponding preset threshold value exists in the loss value set. Here, the setting of the preset threshold is not limited. For example, the loss value and the corresponding preset threshold value may be differentiated to obtain the loss difference value. On this basis, the error value is transmitted forward from the last layer of the model by using back propagation, random gradient descent and the like to adjust the parameters of each layer. Of course, a network freezing (dropout) method may be used as needed, and network parameters of some layers therein may be kept unchanged and not adjusted, which is not limited in any way.
Optionally, in response to the set of loss values satisfying a preset loss condition, determining the initial network model as a power prediction model.
In some embodiments, the executing entity may determine the initial network model as a power prediction model in response to the set of loss values satisfying a preset loss condition.
In some optional implementations of some embodiments, the above-described execution entity may generate the battery predicted power, the capacitance predicted power, and the fuel cell predicted power by:
and a first step of decomposing the power signal to generate a first high-frequency signal and a first low-frequency signal. The decomposing process may be to input the power signal to a high-pass filter and a low-pass filter, respectively, to decompose the power signal, and generate a first high-frequency signal and a first low-frequency signal.
And a second step of decomposing the first low-frequency signal to generate a second high-frequency signal and a second low-frequency signal.
And thirdly, decomposing the second low-frequency signal to generate a third high-frequency signal and a third low-frequency signal.
And a fourth step of performing downsampling processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal, and the third low-frequency signal, respectively, to generate a sampled first high-frequency signal, a sampled second high-frequency signal, a sampled third high-frequency signal, and a sampled third low-frequency signal.
And fifthly, respectively carrying out reconstruction processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal and the third low-frequency signal to generate a first high-frequency signal after reconstruction, a second high-frequency signal after reconstruction, a third high-frequency signal after reconstruction and a third low-frequency signal after reconstruction. In practice, the above-mentioned respective signals may be subjected to reconstruction processing by convolution in the time domain.
And a sixth step of generating a battery predicted power, a capacitor predicted power and a fuel cell predicted power based on the reconstructed first high-frequency signal, the reconstructed second high-frequency signal, the reconstructed third high-frequency signal and the reconstructed third low-frequency signal.
In practice, the battery predicted power, the capacitance predicted power, and the fuel cell predicted power may be generated by the following sub-steps:
a first sub-step of determining an approximate power signal and a detail power signal. Here, the reconstructed low frequency signal may be determined as an approximate power signal, and the sum of the reconstructed first high frequency signal, the reconstructed second high frequency signal, and the reconstructed third high frequency signal may be determined as a detail power signal.
A second sub-step determines the power characterized by the approximated power signal as the capacitance predicted power.
A third sub-step of determining the product of the power characterized by the detail power signal and the first weight as the predicted power of the fuel cell and the product of the power characterized by the detail power signal and the second weight as the predicted power of the storage battery. Wherein, the first weight and the second weight are real numbers which are equal to or greater than zero and the sum is 1. Here, the setting of the first weight and the second weight is not limited, and may be a weight obtained by experiment.
The related content in the first step to the sixth step is taken as an invention point of the disclosure, so that the second technical problem mentioned in the background art is solved, when the prediction model is used for prediction, a long time is required to be consumed for prediction, and when the change of the ship required power is fast, the discharge power which cannot be predicted in time is caused, so that the change degree of a circuit in the circuit is large, the damage of a battery is caused or the service life is reduced, and further the waste of battery resources is caused. Factors that cause damage to the battery or reduce the life, and thus waste of battery resources, are often as follows: when the prediction model is used for prediction, long time is required to be consumed for prediction, and when the power required by the ship changes rapidly, the discharge power which cannot be predicted in time is caused, so that the degree of circuit change in the circuit is large, the damage of a battery is caused, the service life is shortened, and further the waste of battery resources is caused. If the above factors are solved, the effects of avoiding damage to the battery or reducing the service life and avoiding waste of battery resources can be achieved. To achieve this effect, first, the above power signal is subjected to a decomposition process to generate a first high-frequency signal and a first low-frequency signal; decomposing the first low-frequency signal to generate a second high-frequency signal and a second low-frequency signal; and decomposing the second low-frequency signal to generate a third high-frequency signal and a third low-frequency signal. Therefore, the power signal is decomposed into a high-frequency signal and a low-frequency signal three times, so that different power sources can be regulated and controlled through different signals. And secondly, respectively performing downsampling processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal and the third low-frequency signal to generate a sampled first high-frequency signal, a sampled second high-frequency signal, a sampled third high-frequency signal and a sampled third low-frequency signal. Thus, the respective signals can be sampled. And thirdly, respectively carrying out reconstruction processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal and the third low-frequency signal to generate a first high-frequency signal after reconstruction, a second high-frequency signal after reconstruction, a third high-frequency signal after reconstruction and a third low-frequency signal after reconstruction. Thus, the individual signals may be reconstructed, thereby enabling noise reduction of the signals, and enhancing the characteristics of the signals, thereby reducing the error values. Fourth, according to the above-mentioned first high-frequency signal after reconstruction, above-mentioned second high-frequency signal after reconstruction, above-mentioned third high-frequency signal after reconstruction and above-mentioned third low-frequency signal after reconstruction, produce the predicted power of storage battery, predicted power of electric capacity and predicted power of fuel cell. Thus, prediction of the power of different power sources is completed. The collected power signals are used for directly predicting the power of different power sources without using a prediction model, so that the damage to the battery or the reduction of the service life of the battery are avoided, and the waste of battery resources is avoided
Step 104, obtaining a first charge state of the target storage battery and a second charge state of the target super capacitor.
In some embodiments, the executing entity may obtain the first state of charge of the target storage battery and the second state of charge of the target supercapacitor. The target battery may be a battery that participates in discharging. The target supercapacitor can be a supercapacitor participating in discharging. In practice, the executing body may acquire the first state of charge of the target storage battery and the second state of charge of the target supercapacitor from the database storing the states of charge. Here, the first state of charge and the second state of charge may be determined in various ways. For example, the first state of charge and the second state of charge may be determined by methods such as discharge experiments and kalman filtering.
Step 105, determining the battery operating power, the capacitor operating power and the fuel cell operating power according to the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge and the second state of charge.
In some embodiments, the execution body may determine the battery operating power, the capacitor operating power, and the fuel cell operating power based on the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge, and the second state of charge.
In practice, the above-described execution subject may determine the battery operating power, the capacitor operating power, and the fuel cell operating power by:
and in the first step, blurring processing is respectively carried out on the required power, the first charge state and the first charge state so as to generate a required power blurring amount, a storage battery power blurring amount and a capacitor power blurring amount. Wherein, the blurring process can be performed by using a macaroni control method.
And secondly, acquiring a preset fuzzy rule set. The preset fuzzy rule in the preset fuzzy rule set may be a preset fuzzy rule. For example, the preset fuzzy rule may be to allocate the required power to the fuel cell, the storage battery and the super capacitor in response to the required power being equal to or greater than a preset threshold.
And thirdly, performing defuzzification processing on the storage battery power fuzzy quantity and the capacitor power fuzzy quantity according to the required power fuzzy quantity and the preset fuzzy rule set so as to generate a storage battery power offset and a capacitor power offset. In practice, first, the discharge power of each power source can be adjusted through each preset fuzzy rule included in the preset fuzzy rule set. And secondly, performing defuzzification processing on the storage battery power fuzzy amount and the capacitance power fuzzy amount to generate a storage battery power offset amount and a capacitance power offset amount.
And a fourth step of determining battery operation power, capacitor operation power and fuel cell operation power based on the fuel cell predicted power, the battery predicted power, the capacitor predicted power, the battery power offset and the capacitor power offset. In practice, first, the difference between the battery predicted power and the battery power offset may be determined as the battery operating power. Second, the difference between the capacitance prediction power and the capacitance power offset may be determined as the capacitance running power. Third, the difference between the required power and the battery operating power and the capacitor operating power may be determined as the fuel cell operating power.
Optionally, before step 106, the following steps may also be performed:
and the first step is to perform a preset number of charge and discharge operations on the target storage battery and the target super capacitor so as to generate a first charge and discharge group set and a second charge and discharge group set.
In some embodiments, the executing body may perform a preset number of charge and discharge operations on the target storage battery and the target supercapacitor to generate a first charge and discharge data set and a second charge and discharge data set. The number of the first charge and discharge data sets included in the first charge and discharge data set and the number of the second charge and discharge data sets in the second charge and discharge data set are equal to the preset number. The first charge-discharge data set includes two first charge-discharge data, which respectively represent charge data and discharge data. The second charge-discharge data set includes two second charge-discharge data, which respectively represent charge data and discharge data. The charging data may include a charging voltage, a charging current, and a charging time. The discharge data may include a discharge voltage, a discharge current, and a discharge time.
And secondly, inputting the first charge-discharge group set, the second charge-discharge group set, the storage battery operation power, the capacitor operation power and the fuel cell operation power into a custom model to obtain the storage battery actual power, the capacitor actual power and the fuel cell actual power, and taking the storage battery actual power, the capacitor operation power and the fuel cell operation power as new battery operation power, capacitor operation power and fuel cell operation power.
In some embodiments, the executing body may input the first charge-discharge set, the second charge-discharge set, the battery operating power, the capacitor operating power, and the fuel cell operating power into a custom model to obtain the battery actual power, the capacitor actual power, and the fuel cell actual power as the new battery operating power, the capacitor operating power, and the fuel cell operating power.
Here, the custom model may include a first sub-model and a second sub-model. The first sub-model may be a neural network model that takes the first charge-discharge group set and the second charge-discharge group set as inputs and takes the battery life ratio and the capacitor life ratio as outputs. The first sub-model includes the following 10 layers: the first layer, the input layer for deconvolute first charge-discharge group set and second charge-discharge group set, increase feature size, reduce feature dimension. The second layer-the sixth layer, the convolution layer, is used for convolving the result of the input layer. Here, the convolution layers of the second layer to the sixth layer are 1D convolution layers. And a seventh layer, a flattening layer, for unifying the multidimensional result output by the sixth layer to generate a one-dimensional convolution result. And an eighth layer, a Dropout layer, for regularizing the input to a convolution result so as to avoid model overfitting. And the ninth layer is a full-connection layer and is used for classifying and integrating the regularized one-dimensional convolution results to generate integrated features. Tenth layer, output battery life ratio and electric capacity life ratio. The second sub-model can represent a model of the linear relationship between the operating power of the storage battery and the actual power of the storage battery, and between the operating power of the capacitor and the actual power of the capacitor. For example, the second sub-model may be a linear equation. The product of the battery operating power and the battery life ratio can be determined as the battery actual power, the product of the capacitor operating power and the capacitor life ratio can be determined as the capacitor actual power, and the difference between the required power and the battery actual power and the capacitor actual power can be determined as the fuel cell actual power.
As an invention point of the present disclosure, the above-mentioned related matters in the first step and the second step solve the third technical problem mentioned in the background art, in determining the discharge power of different power sources, the determined discharge power may not meet the discharge power of the actual requirement due to the reduction of the service lives of the different power sources, thereby resulting in the reduction of the service lives of the power sources and the waste of electric power resources. Factors that cause damage to the battery or decrease the life, and thus the life of the power source, and waste of electric power resources are often as follows: when the discharge power of different power sources is determined, the determined discharge power does not meet the discharge power of the actual requirement due to the reduction of the service lives of the different power sources, so that the service lives of the power sources are reduced, and the electric power resources are wasted. If the above factors are solved, the effects of avoiding the reduction of the life of the power source and avoiding the waste of the electric power resource can be achieved. To achieve the above-mentioned effect, some embodiments of the present disclosure record charge and discharge data by charging and discharging two power sources of a storage battery and a super capacitor, determine the service lives of the storage battery and the super capacitor by a custom model, and adjust the running power of each power source in time according to the service lives of the storage battery and the super capacitor, thereby avoiding the situation that the determined discharge power does not meet the discharge power of the actual demand, and avoiding the reduction of the service lives of the power sources and the waste of electric power resources.
And 106, controlling the target ship to run according to the storage battery running power, the capacitor running power and the fuel cell running power.
And controlling the target ship to run according to the battery operation power, the capacitor operation power and the fuel cell operation power.
The above embodiments of the present disclosure have the following advantages: according to the ship energy storage and discharge control method, the situation that the ship cannot run can be avoided. Specifically, the reason for the failure of the ship is that: when the prediction model is used for predicting the discharge power of the power source, the charge states of the lithium battery and the super capacitor are not considered, so that the predicted discharge power can not be maintained due to the fact that the charge states of the lithium battery and the super capacitor are low, and the ship can not navigate. Based on this, in the ship energy storage discharge control method of some embodiments of the present disclosure, first, a power signal of a target ship is acquired in response to a current time being a preset acquisition time. Thus, the power signal transmitted to the power source during the operation of the ship can be acquired. And secondly, determining the required power of the target ship. Thus, the total power required for the operation of the vessel can be determined. Then, a battery predicted power, a capacitor predicted power, and a fuel cell predicted power are generated based on the demand power and the power signal. Thus, the discharge power of different power sources can be predicted. And then, acquiring the first charge state of the target storage battery and the second charge state of the target super capacitor. From this, the state of charge of the battery and the supercapacitor can be determined. And then, determining the battery operation power, the capacitor operation power and the fuel cell operation power according to the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first charge state and the second charge state. Therefore, the situation that the ship cannot navigate due to the fact that the predicted discharge power cannot be maintained because the charge states of the lithium battery and the super capacitor are low can be avoided through the charge states of the storage battery and the super capacitor. And finally, controlling the target ship to run according to the storage battery running power, the capacitor running power and the fuel cell running power. Therefore, the discharge power of different power sources of the ship is regulated and controlled, and the situation that the ship cannot run is avoided.
Further predicting fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of a ship energy storage and discharge control apparatus, which correspond to those method embodiments shown in fig. 1, and which may be applied to various electronic devices in particular.
As shown in fig. 2, the ship energy storage discharge control device 200 of some embodiments includes: an acquisition unit 201, a first determination unit 202, a generation unit 203, an acquisition unit 204, a second determination unit 205, and a control unit 206. Wherein the acquisition unit 201 is configured to acquire the power signal of the target vessel in response to the current time being a preset acquisition time; the first determination unit 202 is configured to determine the required power of the target ship; the generating unit 203 is configured to generate a battery predicted power, a capacitance predicted power, and a fuel cell predicted power based on the above-described required power and the above-described power signal; the acquisition unit 204 is configured to acquire a first state of charge of the target storage battery and a second state of charge of the target supercapacitor; the second determining unit 205 is configured to determine a battery operating power, a capacitor operating power, and a fuel cell operating power based on the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge, and the second state of charge; the control unit 206 is configured to control the target ship to travel based on the battery operating power, the capacitor operating power, and the fuel cell operating power.
It will be appreciated that the elements described in the ship energy storage discharge control device 200 correspond to the various steps in the method described in predicting fig. 1. Thus, the operations, features and advantages described above with respect to the method are equally applicable to the ship energy storage and discharge control device 200 and the units contained therein, and are not described herein.
Fig. 3 is a block diagram illustrating a configuration of an electronic device 300 suitable for use in implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, as well as stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means 301 (e.g., a central processing unit, a graphics processor, etc.) that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the process described in the predictive flowcharts above may be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: and acquiring a power signal of the target ship in response to the current time being a preset acquisition time. And determining the required power of the target ship. And generating a storage battery predicted power, a capacitance predicted power and a fuel cell predicted power according to the required power and the power signal. And acquiring a first charge state of the target storage battery and a second charge state of the target super capacitor. And determining a battery operating power, a capacitor operating power and a fuel cell operating power according to the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge and the second state of charge. And controlling the target ship to run according to the battery running power, the capacitor running power and the fuel cell running power.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit, a first determination unit, a generation unit, an acquisition unit, a second determination unit, and a control unit. The names of these units do not constitute a limitation of the unit itself in some cases, for example, the acquisition unit may also be described as "a unit that acquires a power signal of a target ship in response to the current time being a preset acquisition time".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (8)
1. A ship energy storage discharge control method, comprising:
responding to the current time as a preset acquisition time, and acquiring a power signal of a target ship;
determining the required power of the target ship;
generating a predicted power of the storage battery, a predicted power of the capacitor and a predicted power of the fuel cell according to the required power and the power signal;
acquiring a first charge state of a target storage battery and a second charge state of a target super capacitor;
determining a battery operating power, a capacitor operating power and a fuel cell operating power according to the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge and the second state of charge;
controlling the target ship to run according to the storage battery running power, the capacitor running power and the fuel cell running power;
wherein the generating of the battery predicted power, the capacitor predicted power, and the fuel cell predicted power based on the demand power and the power signal comprises:
decomposing the power signal to generate a first high-frequency signal and a first low-frequency signal;
decomposing the first low-frequency signal to generate a second high-frequency signal and a second low-frequency signal;
Decomposing the second low-frequency signal to generate a third high-frequency signal and a third low-frequency signal;
respectively performing downsampling processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal and the third low-frequency signal to generate a sampled first high-frequency signal, a sampled second high-frequency signal, a sampled third high-frequency signal and a sampled third low-frequency signal;
respectively carrying out reconstruction processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal and the third low-frequency signal to generate a first high-frequency signal after reconstruction, a second high-frequency signal after reconstruction, a third high-frequency signal after reconstruction and a third low-frequency signal after reconstruction;
and generating the predicted power of the storage battery, the predicted power of the capacitor and the predicted power of the fuel cell according to the first high-frequency signal after reconstruction, the second high-frequency signal after reconstruction, the third high-frequency signal after reconstruction and the third low-frequency signal after reconstruction.
2. The method of claim 1, wherein the generating battery predicted power, capacitance predicted power, and fuel cell predicted power from the demand power and the power signal comprises:
And inputting the required power and the power signal into a pre-trained power prediction model to obtain the predicted power of the storage battery, the predicted power of the capacitor and the predicted power of the fuel cell.
3. The method of claim 2, wherein the power prediction model is trained by:
obtaining a sample set, wherein samples in the sample set comprise sample required power and sample power signals, and sample storage battery predicted power, sample capacitance predicted power and sample fuel cell predicted power corresponding to the sample required power and the sample power signals;
selecting a sample from the set of samples;
inputting the sample into an initial network model to obtain the predicted power of a storage battery, the predicted power of a capacitor and the predicted power of a fuel cell corresponding to the sample;
respectively determining loss values between the predicted power of the storage battery, the predicted power of the capacitor and the predicted power of the fuel cell corresponding to the sample and the predicted power of the storage battery, the predicted power of the capacitor and the predicted power of the fuel cell included in the sample, so as to obtain a loss value set;
and adjusting network parameters of the initial network model in response to the loss value set not meeting a preset loss condition.
4. A method according to claim 3, wherein the method further comprises:
and determining the initial network model as a power prediction model in response to the loss value set meeting a preset loss condition.
5. The method of claim 1, wherein the determining battery operating power, capacitor operating power, and fuel cell operating power from the demand power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge, and the second state of charge comprises:
respectively carrying out fuzzification processing on the required power, the first charge state and the first charge state to generate a required power fuzzification amount, a storage battery power fuzzification amount and a capacitor power fuzzification amount;
acquiring a preset fuzzy rule set;
performing defuzzification processing on the storage battery power fuzzy quantity and the capacitor power fuzzy quantity according to the required power fuzzy quantity and the preset fuzzy rule set to generate a storage battery power offset and a capacitor power offset;
and determining the battery operating power, the capacitor operating power and the fuel cell operating power according to the fuel cell predicted power, the battery predicted power, the capacitor predicted power, the battery power offset and the capacitor power offset.
6. A ship energy storage discharge control device, comprising:
the acquisition unit is configured to acquire a power signal of the target ship in response to the current time being a preset acquisition time;
a first determination unit configured to determine a required power of the target ship;
a generation unit configured to generate a battery predicted power, a capacitance predicted power, and a fuel cell predicted power from the demand power and the power signal;
the generating unit is further configured to: decomposing the power signal to generate a first high-frequency signal and a first low-frequency signal;
decomposing the first low-frequency signal to generate a second high-frequency signal and a second low-frequency signal;
decomposing the second low-frequency signal to generate a third high-frequency signal and a third low-frequency signal;
respectively performing downsampling processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal and the third low-frequency signal to generate a sampled first high-frequency signal, a sampled second high-frequency signal, a sampled third high-frequency signal and a sampled third low-frequency signal;
respectively carrying out reconstruction processing on the first high-frequency signal, the second high-frequency signal, the third high-frequency signal and the third low-frequency signal to generate a first high-frequency signal after reconstruction, a second high-frequency signal after reconstruction, a third high-frequency signal after reconstruction and a third low-frequency signal after reconstruction;
Generating a storage battery predicted power, a capacitor predicted power and a fuel cell predicted power according to the reconstructed first high-frequency signal, the reconstructed second high-frequency signal, the reconstructed third high-frequency signal and the reconstructed third low-frequency signal;
an acquisition unit configured to acquire a first state of charge of a target storage battery and a second state of charge of a target supercapacitor;
a second determination unit configured to determine a battery operation power, a capacitor operation power, and a fuel cell operation power based on the required power, the battery predicted power, the capacitor predicted power, the fuel cell predicted power, the first state of charge, and the second state of charge;
and a control unit configured to control the target ship to travel according to the battery operation power, the capacitor operation power, and the fuel cell operation power.
7. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1 to 5.
8. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1 to 5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107140168A (en) * | 2017-04-26 | 2017-09-08 | 武汉理工大学 | A kind of hybrid power ship EMS and control method based on WAVELET FUZZY logic |
| CN107748498A (en) * | 2017-10-09 | 2018-03-02 | 上海海事大学 | A kind of energy management method of the hybrid power ship based on Model Predictive Control |
| CN109823235A (en) * | 2019-02-20 | 2019-05-31 | 中国科学技术大学 | Battery, super capacitor and fuel cell hybrid accumulator Energy Management System |
| CN113723161A (en) * | 2021-03-08 | 2021-11-30 | 腾讯科技（深圳）有限公司 | Neural network model training method, storage medium and electronic device |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107140168A (en) * | 2017-04-26 | 2017-09-08 | 武汉理工大学 | A kind of hybrid power ship EMS and control method based on WAVELET FUZZY logic |
| CN107748498A (en) * | 2017-10-09 | 2018-03-02 | 上海海事大学 | A kind of energy management method of the hybrid power ship based on Model Predictive Control |
| CN109823235A (en) * | 2019-02-20 | 2019-05-31 | 中国科学技术大学 | Battery, super capacitor and fuel cell hybrid accumulator Energy Management System |
| CN113723161A (en) * | 2021-03-08 | 2021-11-30 | 腾讯科技（深圳）有限公司 | Neural network model training method, storage medium and electronic device |
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