CN118082593A - Intelligent charging pile based on energy storage battery power supply and control method thereof - Google Patents
Intelligent charging pile based on energy storage battery power supply and control method thereof Download PDFInfo
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Abstract
The invention provides an intelligent charging pile based on energy storage battery power supply and a control method thereof, relates to the technical field of charging equipment, and aims to provide an efficient, safe and user-friendly electric automobile charging solution. The intelligent charging pile takes the built-in energy storage battery pack as energy buffer, allows energy to be stored when the power grid load is low, and releases energy when the demand is high, so that the efficient utilization of electric energy and the balance of the power grid load are realized. The control method comprises real-time charging demand prediction, intelligent optimal scheduling and dynamic power adjustment mechanisms which cooperate to adapt to different charging demands and power grid conditions. In addition, this intelligent charging stake still possesses environmental monitoring module, can adjust the operating condition who fills electric stake according to environmental change, guarantees the security and the reliability of charging process. The intelligent charging pile is suitable for individuals or public parking places, provides convenient and reliable charging service for electric vehicle users, and supports construction of intelligent city infrastructure.
Description
Technical Field
The invention relates to the technical field of charging equipment, in particular to an intelligent charging pile based on energy storage battery power supply and a control method thereof.
Background
With the increase of environmental protection consciousness and the development of technology, electric vehicles (ELECTRIC VEHICLE, EV) are used as clean vehicles, and the market share of the electric vehicles is increasing year by year. The widespread use of electric vehicles has provided an effective solution to the problems of conventional energy consumption and urban pollution. However, the rapid popularization of electric vehicles also presents new challenges, the most prominent of which is the construction and optimization of the charging infrastructure.
Because traditional electric automobile fills electric pile mainly relies on fixed electric wire netting power supply, in the electric wire netting coverage incomplete or the unstable area of electric power supply, electric automobile's use receives very big restriction. Moreover, as the number of electric vehicles increases, particularly in urban areas, the charging demand during peak hours can place a significant burden on the grid, possibly even resulting in insufficient power, and in remote areas or newly developed commercial and residential areas, the grid infrastructure may be imperfect, which makes deployment of traditional charging piles difficult. Therefore, the traditional charging pile has high dependence on a power grid, and has the problems of overlarge power load in peak time and difficult deployment of charging facilities.
With the progress of energy storage technology, a charging pile using an energy storage battery as a power source becomes a viable solution. The charging pile can work independently of a traditional power grid, has the advantages of flexible deployment and no limitation of power grid coverage, and is particularly suitable for charging requirements of electric vehicles in remote areas, temporary activities or areas with power grid construction lagging behind. In addition, the load of the power grid can be reduced, the pressure of the power grid can be effectively relieved by the charging pile powered by the energy storage battery in the peak load period of the power grid, and the stability and reliability of the whole power system are improved. The energy utilization efficiency can be improved, the charging pile powered by the energy storage battery can store electric energy when the power grid load is low, release the electric energy when the demand is high, optimize energy distribution and improve the energy utilization efficiency.
Although the intelligent charging pile based on the energy storage battery power supply provides the advantages, a series of technical challenges are still faced in practical deployment and operation, wherein, how to intelligently charge and manage, and how to intelligently schedule and charge according to the charging requirement of the electric automobile and the state of the energy storage battery is a key for ensuring the charging efficiency and the service life of the battery and realizing efficient operation, especially when independently operating, the energy distribution is optimized and the energy utilization efficiency is improved on the premise that the stability and the safety of the system can be ensured by controlling the management of the charging pile.
Disclosure of Invention
In view of this, in order to solve the problems faced by the traditional charging infrastructure and solve the problems of how to intelligently schedule charging for the charging requirement of the electric automobile and the state of the energy storage battery, the invention provides an intelligent charging pile based on the power supply of the energy storage battery and a control method thereof, and the charging process is optimized and the charging efficiency and the safety are improved by adopting the manners of charging requirement prediction, intelligent monitoring and dynamic power adjustment.
The invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides an intelligent charging pile based on energy storage battery power supply, comprising:
Energy storage battery: the lithium ion Battery pack consists of a plurality of lithium ion Battery units with high energy density, wherein an energy storage Battery pack is arranged inside a charging pile and is connected with a Battery Management System (BMS) for monitoring and managing the charging and discharging processes of the Battery units;
The vehicle monitoring module: the charging pile device comprises an identifier reading device and an encoder, wherein the identifier reading device and the encoder are used for identifying and monitoring a unique identifier of an accessed vehicle, retrieving historical charging data of the vehicle and monitoring real-time charging state information of the vehicle;
a charging demand prediction module: analyzing the retrieved historical charging data by using a time sequence analysis and deep learning model, predicting the charging demand of the vehicle, wherein the prediction result is used for guiding the energy storage of the charging pile and the charging strategy adjustment;
and (3) an optimal scheduling module: the method comprises the steps of analyzing the emergency degree, the expected charging time length and the expected departure time information of a charging task of a charging pile based on an attention mechanism, distributing different weights for each task, and dynamically adjusting a charging queue and power output according to the weights;
the charging power dynamic adjustment module: the charging device is used for dynamically adjusting the charging power output by the charging pile according to the state of the energy storage battery pack monitored in real time, the power grid load and the predicted charging demand of the vehicle;
and the charging management module is used for: the charging system comprises a charging controller and a central processing unit, wherein the charging controller is connected with a battery management system of an energy storage battery pack and used for controlling charging and discharging of a battery unit, the charging controller is connected with the central processing unit, and the central processing unit is connected with a vehicle monitoring module, a charging demand prediction module, an optimal scheduling module and a charging power dynamic adjustment module and used for monitoring real-time charging state information of a vehicle, predicting charging demands of the vehicle, adjusting charging queues and power output and dynamically adjusting charging power output by a charging pile.
As a further scheme of the invention, the identifier reading device comprises an RFID reader (Radio Frequency Identification, RFID) and a two-dimensional code scanner, which are respectively used for contactlessly reading a unique identifier in an RFID tag carried by an access vehicle and scanning the two-dimensional code tag of the vehicle to obtain the unique identifier of the vehicle.
As a further scheme of the invention, the encoder comprises a digital signal processing unit which is used for decoding the unique identifier read by the RFID reader or the two-dimensional code scanner, retrieving historical charging data of the vehicle, monitoring real-time charging state information of the vehicle, verifying and converting the real-time charging state information into the encoding data in an electronic information format, wherein the real-time charging state information of the vehicle comprises battery capacity and charging rate.
As a further scheme of the invention, the charging demand prediction module comprises a historical data analyzer and a long-short-period memory network model, wherein the historical data analyzer is used for collecting historical charging data of the vehicle, the historical charging data comprises charging time, charging quantity and charging frequency, the long-short-period memory network model is used as a deep learning model, and the historical charging data of the vehicle is used for predicting the charging demand of the future period of the vehicle.
As a further scheme of the invention, the optimizing and dispatching module comprises a dispatching optimizer, which is used for analyzing the emergency degree, the expected charging time length and the expected leaving time of the vehicle task to be charged by using the attention mechanism in deep learning, distributing weight to each charging task, and dynamically adjusting the charging queue and the power distribution according to the weight of each charging task and the current state of the charging pile.
As a further scheme of the invention, the charging power dynamic adjustment module comprises a real-time state monitor and a power adjustment unit, wherein the real-time state monitor is used for monitoring the electric quantity of the energy storage battery pack, the charging state and the load data of the current power grid in real time; the power adjusting unit is used for calculating an optimal charging power output value and adjusting the charging power in real time according to the state of the energy storage battery pack, the power grid load and the real-time and predicted charging demand of the vehicle.
As a further aspect of the present invention, the intelligent charging pile based on energy storage battery power supply further includes:
the environment monitoring module is used for monitoring surrounding environment data of the charging pile based on the temperature sensor and the humidity sensor;
The safety protection module is provided with a protection circuit for overvoltage, overcurrent, short circuit and temperature abnormality;
The communication module is used for data exchange between the charging pile and the central management system and supporting data exchange and communication between the charging pile and the remote server, the user mobile equipment and the charging pile;
The user interface is composed of a touch screen display and physical keys, and is used for displaying the charging state and electric quantity information and allowing a user to set charging parameters.
In a second aspect, the invention also provides a control method of the intelligent charging pile based on energy storage battery power supply, which comprises the following steps:
When the electric automobile is connected to the charging pile, the unique identifier of the connected vehicle is identified and monitored through an identifier reading device equipped with charging pile equipment, historical charging data of the vehicle is fetched, and real-time charging state information of the vehicle is monitored;
analyzing the retrieved historical charging data by using a time sequence analysis and deep learning model, and predicting the vehicle charging requirement;
analyzing the emergency degree, the expected charging time length and the expected departure time information of the charging tasks of the charging pile based on the attention mechanism, distributing different weights for each task, and dynamically adjusting a charging queue and power output according to the weights;
Dynamically adjusting the charging power output by the charging pile according to the state of the energy storage battery pack and the power grid load monitored in real time and the predicted charging demand of the vehicle;
Data exchange is carried out between the charging pile and the remote server, the user mobile equipment and the charging pile through the communication module and the central management system, and the data exchange and the communication between the charging pile and the remote server, the user mobile equipment and the charging pile are supported;
and displaying the charging state and the electric quantity information through a user interface, and allowing a user to set charging parameters.
As a further scheme of the invention, when the unique identifier of the accessed vehicle is identified and monitored through the identifier reading device arranged on the charging pile equipment, the unique identifier in the RFID tag carried by the accessed vehicle is read by the RFID reader of the identifier reading device in a contactless manner, and the unique identifier of the vehicle is obtained by the two-dimensional code scanner of the identifier reading device by scanning the two-dimensional code tag of the vehicle.
As a further scheme of the invention, when the charging power output by the charging pile is dynamically adjusted, the method comprises the following steps:
the real-time state monitor monitors the electric quantity and the charging state of the energy storage battery pack and the load data of the current power grid;
and the power adjusting unit calculates an optimal charging power output value according to the state of the energy storage battery pack, the power grid load and the real-time prediction charging requirement of the vehicle and adjusts the charging power in real time.
As a further scheme of the invention, the deep learning model uses a long-short-term memory network (Autoregressive Integrated Moving Average, LSTM), during training, historical charging data are collected, the time stamp of the historical charging data is converted into numerical characteristics, the processed data are divided into a training set, a verification set and a test set, the training set is utilized to train the model, the performance on the verification set is monitored to adjust parameters, the model is operated on the test set, the latest input data are input into the trained LSTM model, and the charging demand prediction in a future period is obtained.
Compared with the prior art, the intelligent charging pile based on the energy storage battery power supply and the control method thereof have the following beneficial effects:
1. efficient energy management. Through the effective management and the utilization of the energy storage battery pack, the intelligent charging pile can store energy when the power grid load is low, provide stable charging service in a high-demand period, and remarkably improve the utilization efficiency of energy sources and be beneficial to balancing the power grid load through a dynamic energy management mechanism.
2. And (5) predicting the charging demand in real time. And the time sequence analysis and the long-term and short-term memory network model are utilized to predict the charging requirement of the vehicle, so that scientific basis is provided for energy storage and charging strategy adjustment of the charging pile. The charging pile can meet the instant charging requirement of the vehicle, and the configuration and the utilization of energy storage resources are optimized.
3. And (5) intelligent optimization scheduling. The optimal scheduling module based on the attention mechanism can dynamically adjust the charging queue and the power output, ensures that the charging task with high emergency degree or short leaving time is preferably satisfied, and the flexible scheduling strategy remarkably improves the service efficiency and the user satisfaction of the charging pile.
4. Dynamic power adjustment. According to the state of the energy storage battery pack, the power grid load and the charging requirement of the vehicle which are monitored in real time, the charging power is dynamically adjusted, so that the battery health is protected, the power distribution is optimized, and the energy cost is reduced. And moreover, the integrated safety protection module monitors and handles risks such as overvoltage, overcurrent, short circuit and temperature abnormality in real time, so that the safety of the charging process is greatly improved.
In summary, the intelligent charging pile based on the energy storage battery power supply and the control method thereof not only improve the charging efficiency and the safety, but also realize the intelligent management and the optimization of the charging process through advanced algorithms and technologies, provide efficient, safe and convenient charging service for electric automobile users, and simultaneously make contributions to the development of smart cities and smart grids.
These and other aspects of the invention will be more readily apparent from the following description of the embodiments. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the related art, a brief description of the drawings is provided below, which are intended to provide a further understanding of the present invention and constitute a part of the specification, together with the embodiments of the present invention, serve to explain the present invention and not to limit the present invention. In the drawings:
Fig. 1 is a flowchart of a control method of an intelligent charging pile based on energy storage battery power supply according to an embodiment of the invention.
Fig. 2 is a schematic block diagram of an intelligent charging pile based on energy storage battery power supply according to an embodiment of the present invention.
Description of the drawings:
100-an energy storage battery; a 101-lithium ion battery cell; 102-a battery management system; 200-a vehicle monitoring module; 201-identifier reading means; 202-an encoder; 300-a charging demand prediction module; 400-optimizing a scheduling module; 500-a dynamic charging power adjustment module; 600-a charge management module; 601-a central processing unit; 602-a charge controller.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In some of the flows described in the specification and claims of the present invention and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
Technical solutions in exemplary embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in exemplary embodiments of the present invention, and it is apparent that the described exemplary embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
In order to solve the problems of the traditional charging infrastructure and the intelligent scheduling charging of the charging requirements of the electric automobile and the state of the energy storage battery, the invention provides an intelligent charging pile based on the power supply of the energy storage battery and a control method thereof.
The technical scheme of the invention is further described below with reference to specific embodiments:
referring to fig. 2, fig. 2 is a schematic block diagram of an intelligent charging pile based on energy storage battery power supply according to the present invention. An intelligent charging pile based on energy storage battery power supply provided in an embodiment of the invention comprises an energy storage battery pack 101, a vehicle monitoring module 200, a charging demand prediction module 300, an optimal scheduling module 400, a charging power dynamic adjustment module 500 and a charging management module 600. The energy storage battery pack 101 is composed of a plurality of lithium ion battery cells 102 with high energy density, and the energy storage battery pack 101 is arranged inside a charging pile and is connected with a battery management system 103 (BMS) for monitoring and managing the charging and discharging processes of the battery cells.
The vehicle monitoring module 200 includes an identifier reading device 201 and an encoder 202 equipped with a charging stake apparatus for identifying and monitoring a unique identifier of an accessed vehicle, retrieving historical charging data of the vehicle, and monitoring real-time charging state information of the vehicle.
In this embodiment, the identifier reading device 201 includes an RFID reader and a two-dimensional code scanner, which are respectively configured to contactlessly read a unique identifier in an RFID tag carried by an access vehicle and scan a two-dimensional code tag of the vehicle, so as to obtain the unique identifier of the vehicle.
The charging demand prediction module 300 uses a time series analysis and deep learning model to analyze the retrieved historical charging data, predicts the vehicle charging demand, and the prediction result is used for guiding the energy reserve and charging strategy adjustment of the charging pile. The optimization scheduling module 400 is configured to analyze the urgency degree, the expected charging duration and the expected departure time information of the charging task of the charging pile based on the attention mechanism, allocate different weights to each task, and dynamically adjust the charging queue and the power output according to the weights.
In this embodiment, the optimization scheduling module 400 includes a scheduling optimizer, which is configured to analyze the emergency degree, the expected charging duration and the expected departure time of the vehicle task to be charged by using the attention mechanism in deep learning, allocate a weight to each charging task, and dynamically adjust the charging queue and the power allocation according to the weight of each charging task and the current state of the charging pile.
When the charging demand prediction module 300 uses time series analysis, a time series prediction model (Autoregressive Integrated Moving Average, ARIMA) model is used to perform time series analysis, and a long-short-term memory network (LSTM) is used to predict the charging demand in a certain time period in the future. Wherein, when the ARIMA model is adopted to carry out time sequence analysis, the time sequence prediction model (ARIMA) predicts the predicted value at the time t when the vehicle charging demand is predictedThe calculation formula of (2) is as follows:
In the method, in the process of the invention, .../>Is historical charging demand data,/>.../>Is a historical error term; /(I)Constant terms for the time series prediction model; /(I),/>.../>Is an autoregressive coefficient; /(I)The direct influence data of the predicted value of the current moment at the previous moment is obtained; /(I)Direct influence data of the predicted value of the current moment for the first two moments; and so on,For the front/>The time directly affects the data of the predicted value of the current time; /(I)，/>.../>Is a moving average coefficient; the influence data of the error at the previous moment on the current prediction is obtained; by analogy,/> For the front/>Influence data of errors at each moment on current prediction; /(I)The order of the autoregressive term; /(I)Is the order of the moving average term.
The optimization scheduling module 400 is responsible for optimizing the allocation of the charging pile resources, scheduling charging tasks according to the charging demand prediction, the energy storage battery state and the power grid condition, and adopting a genetic algorithm to allocate the charging pile resources during optimization, wherein the fitness function among the charging efficiency, the battery life and the user satisfaction is as follows:
In the method, in the process of the invention, Is a fitness function; /(I)、/>、/>The weight parameters corresponding to the charging efficiency, battery life and user satisfaction, respectively, are used to balance different optimization objectives.
The deep learning model of the charge demand prediction module 300 selects LSTM, and when the LSTM model is used for prediction, the LSTM model uses historical charge dataAs input, the charging demand/>, for the next time period is predicted. The optimization scheduling module 400 analyzes multiple dimension information of the vehicle task to be charged through an attention mechanism in deep learning, wherein the multiple dimension information comprises an emergency degree, an expected charging duration and an expected departure time, dynamically distributes weight for each charging task, and adjusts a charging queue and power distribution according to the weight, so that optimal utilization of resources is realized. Wherein, when attention weight is distributed, task/>Weights/>Can be based on the emergency level/>Expected charge duration/>And predicted departure time/>Calculated by the attention model:
Wherein, The function obtained through the deep learning training is used for calculating the priority of each task.
The charging power dynamic adjustment module 500 is configured to dynamically adjust the charging power output by the charging pile according to the state of the energy storage battery pack 101 and the power grid load monitored in real time, and the predicted charging requirement of the vehicle, so as to optimize the charging process and protect the health of the battery.
In this embodiment, the charging power dynamic adjustment module 500 includes a real-time status monitor and a power adjustment unit, where the real-time status monitor is used to monitor the electric quantity of the energy storage battery pack 101, the charging status and the load data of the current power grid in real time; the power adjustment unit is configured to calculate an optimal charging power output value and adjust the charging power in real time according to the state of the energy storage battery pack 101, the grid load, and the real-time and predicted charging demand of the vehicle.
When the dynamic charging power adjustment module 500 performs power adjustment, the calculation formula of the adjusted charging power is:
Wherein, Adjusted charging power,/>Is the charging requirement,/>Is the residual electric quantity of the energy storage battery,/>Is the battery temperature,/>Is the grid voltage.
The charging management module 600 includes a charging controller 602 and a central processing unit 601, the charging controller 602 is connected with the battery management system 103 of the energy storage battery pack 101 and is used for controlling charging and discharging of the battery unit, the charging controller 602 is connected with the central processing unit 601, and the central processing unit 601 is connected with the vehicle monitoring module 200, the charging demand prediction module 300, the optimal scheduling module 400 and the charging power dynamic adjustment module 500 and is used for monitoring real-time charging state information of the vehicle, predicting charging demands of the vehicle, adjusting charging queues and power output and dynamically adjusting charging power output by the charging pile.
For example, assuming that the charging demand of a certain charging pile in a specific period of time is predicted to be 40kWh by LSTM, the current State of Charge (SOC) of the energy storage battery pack 101 is 60%, the battery temperature is normal, and the grid voltage is stable. Then, the charge power dynamic adjustment module 500 will determine an optimal charge power output based on this information. If the power grid is predicted to be in a high-load state, the charging pile can charge to enough electric quantity in advance to meet the requirements of the peak period.
According to the intelligent charging pile based on the energy storage battery power supply, through the modules, the data analysis, the monitoring and the optimal scheduling are combined to realize the fine management of the charging process of the electric automobile, so that the user experience can be improved, the load of a power grid is lightened, the service life of the battery and the charging safety can be improved, and the development of an intelligent charging technology is promoted.
In this embodiment, the intelligent charging pile based on energy storage battery power supply further includes:
the environment monitoring module is used for monitoring surrounding environment data of the charging pile based on the temperature sensor and the humidity sensor;
The safety protection module is provided with a protection circuit for overvoltage, overcurrent, short circuit and temperature abnormality;
The communication module is used for data exchange between the charging pile and the central management system and supporting data exchange and communication between the charging pile and the remote server, the user mobile equipment and the charging pile;
The user interface is composed of a touch screen display and physical keys, and is used for displaying the charging state and electric quantity information and allowing a user to set charging parameters.
In this embodiment, the environmental monitoring module uses the temperature and humidity sensor to monitor the environmental condition around the charging pile in real time, and the safety protection module detects the overvoltage, overcurrent, short circuit and abnormal temperature conditions in the charging process in real time, and takes corresponding protection measures. Wherein, when the environmental adaptation is adjusted, the temperature is based on the environmental temperatureAnd humidity/>Adjusting charging power/>：
In the method, in the process of the invention,Is a coefficient function regulated according to environmental conditions, ensures the safety and efficiency of the charging process under different environments, and is suitable for the charging of the batteryIs the new charging power obtained after adjustment according to the current ambient temperature and humidity.
The intelligent charging pile based on the energy storage battery power supply in the embodiment provides a comprehensive electric automobile charging solution through integrating advanced charging demand prediction, optimal scheduling, environment monitoring, safety protection and efficient communication technologies. The charging strategy can be intelligently adjusted according to the power grid and the environmental conditions, the safety of the charging process can be ensured, and meanwhile, efficient and convenient charging service is provided for users. In this way, the invention aims to optimize the charging experience of the electric vehicle, promote the wide adoption of the electric vehicle and support the construction of the smart city infrastructure.
In one embodiment, referring to fig. 1, the invention further provides a control method of the intelligent charging pile based on energy storage battery power supply, which comprises the following steps:
And step S10, when the electric automobile is connected into the charging pile, the unique identifier of the connected vehicle is identified and monitored through the identifier reading device of the charging pile equipment, the historical charging data of the vehicle is called, and the real-time charging state information of the vehicle is monitored.
In the above step, when the electric vehicle enters the charging station and is connected to the charging post, the RFID reader or the two-dimensional code scanner automatically recognizes a unique identifier (RFID tag or two-dimensional code) on the vehicle at the time of vehicle access recognition. Then, the system queries the database through the identifier, retrieves the historical charging data of the vehicle, and monitors the current charging state of the vehicle in real time, including the battery capacity and the connection state.
And S20, analyzing the retrieved historical charging data by using a time sequence analysis and a deep learning model, and predicting the vehicle charging requirement.
In the above step, when the LSTM model is used for predicting the charging demand, the LSTM model uses the historical charging dataAs input, the charging demand/>, for the next time period is predicted。
Wherein, when the ARIMA model is adopted to carry out time sequence analysis, the time sequence prediction model (ARIMA) predicts the predicted value at the time t when the vehicle charging demand is predictedThe calculation formula of (2) is as follows:
In the method, in the process of the invention, .../>Is historical charging demand data,/>.../>Is a historical error term; /(I)Constant terms for the time series prediction model; /(I),/>.../>Is an autoregressive coefficient; /(I)The direct influence data of the predicted value of the current moment at the previous moment is obtained; /(I)Direct influence data of the predicted value of the current moment for the first two moments; and so on,For the front/>The time directly affects the data of the predicted value of the current time; /(I)，/>.../>Is a moving average coefficient; the influence data of the error at the previous moment on the current prediction is obtained; by analogy,/> For the front/>Influence data of errors at each moment on current prediction; /(I)The order of the autoregressive term; /(I)Is the order of the moving average term.
And step S30, analyzing the emergency degree, the expected charging duration and the expected departure time information of the charging tasks of the charging pile based on the attention mechanism, distributing different weights for each task, and dynamically adjusting the charging queue and the power output according to the weights.
In the above steps, tasks are performed during attention weight allocationWeights/>Can be based on the emergency level/>Expected charge duration/>And predicted departure time/>Calculated by the attention model:
Wherein, The function obtained through the deep learning training is used for calculating the priority of each task.
And S40, dynamically adjusting the charging power output by the charging pile according to the state of the energy storage battery pack and the power grid load monitored in real time and the predicted charging demand of the vehicle.
In the above step, when the dynamic adjustment module of charging power performs power adjustment, the calculation formula of the adjusted charging power is:
Wherein, Adjusted charging power,/>Is the charging requirement,/>Is the residual electric quantity of the energy storage battery,/>Is the battery temperature,/>Is the grid voltage.
And S50, carrying out data exchange with a central management system through a communication module, and supporting data exchange and communication between the charging pile and a remote server, user mobile equipment and the charging pile.
In the above steps, the communication module can perform real-time data exchange with the central management system through the 4G/5G or Wi-Fi network, so as to support remote monitoring, fault diagnosis and firmware updating, and simultaneously support interaction with the mobile equipment of the user, such as payment and reservation service.
And step S60, displaying the charging state and the electric quantity information through a user interface, and allowing a user to set charging parameters.
In the above steps, the user can view the current charging state (charging progress, remaining time) and electric quantity information through the touch screen interface, and can set the charging parameters (charging power and charging time).
In this embodiment, when the unique identifier of the access vehicle is identified and detected by the identifier reading device equipped in the charging pile device, the RFID reader of the identifier reading device reads the unique identifier in the RFID tag carried by the access vehicle in a contactless manner, and the two-dimensional code scanner of the identifier reading device acquires the unique identifier of the vehicle by scanning the two-dimensional code tag of the vehicle.
Wherein, when the power that charges that the electric pile output is filled in dynamic adjustment, include:
the real-time state monitor monitors the electric quantity and the charging state of the energy storage battery pack and the load data of the current power grid;
and the power adjusting unit calculates an optimal charging power output value according to the state of the energy storage battery pack, the power grid load and the real-time prediction charging requirement of the vehicle and adjusts the charging power in real time.
In this embodiment, the deep learning model uses a long-short-term memory network (LSTM), during training, collects historical charging data, converts a time stamp of the historical charging data into a numerical feature, divides the processed data into a training set, a verification set and a test set, trains the model by using the training set data, monitors performance on the verification set to adjust parameters, runs the model on the test set, inputs the latest input data into the trained LSTM model, and obtains a prediction of charging demands in a future period of time.
The control method of the intelligent charging pile based on the energy storage battery power supply realizes the efficient management of the charging process of the electric automobile and the optimization of user experience. By means of accurate vehicle identification and historical data retrieval and combining advanced charging demand prediction technology and dynamic power adjustment strategies, the method can intelligently schedule charging tasks according to real-time power grid load and energy storage battery states, and optimize energy use. The interactivity and usability of the system are further enhanced by the communication module and the user-friendly interface, and convenient and reliable charging is provided for the users of the electric automobiles.
It should be understood that although described in a certain order, the steps are not necessarily performed sequentially in the order described. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, some steps of the present embodiment may include a plurality of steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily sequential, but may be performed alternately or alternately with at least a part of the steps or stages in other steps or other steps.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. Intelligent charging stake based on energy storage battery power supply, its characterized in that includes:
Energy storage battery: the lithium ion battery pack consists of a plurality of lithium ion battery units with high energy density, and an energy storage battery pack is arranged inside a charging pile and is connected with a battery management system for monitoring and managing the charging and discharging processes of the battery units;
The vehicle monitoring module: the charging pile device comprises an identifier reading device and an encoder, wherein the identifier reading device and the encoder are used for identifying and monitoring a unique identifier of an accessed vehicle, retrieving historical charging data of the vehicle and monitoring real-time charging state information of the vehicle;
a charging demand prediction module: analyzing the retrieved historical charging data by using a time sequence analysis and deep learning model, predicting the charging demand of the vehicle, wherein the prediction result is used for guiding the energy storage of the charging pile and the charging strategy adjustment;
and (3) an optimal scheduling module: the method comprises the steps of analyzing the emergency degree, the expected charging time length and the expected departure time information of a charging task of a charging pile based on an attention mechanism, distributing different weights for each task, and dynamically adjusting a charging queue and power output according to the weights;
the charging power dynamic adjustment module: the charging device is used for dynamically adjusting the charging power output by the charging pile according to the state of the energy storage battery pack monitored in real time, the power grid load and the predicted charging demand of the vehicle;
and the charging management module is used for: the charging system comprises a charging controller and a central processing unit, wherein the charging controller is connected with a battery management system of an energy storage battery pack and used for controlling charging and discharging of a battery unit, the charging controller is connected with the central processing unit, and the central processing unit is connected with a vehicle monitoring module, a charging demand prediction module, an optimal scheduling module and a charging power dynamic adjustment module and used for monitoring real-time charging state information of a vehicle, predicting charging demands of the vehicle, adjusting charging queues and power output and dynamically adjusting charging power output by a charging pile.
2. The intelligent charging stake based on energy storage battery power supply of claim 1, wherein the identifier reading device comprises an RFID reader and a two-dimension code scanner, which are respectively used for contactlessly reading a unique identifier in an RFID tag carried by an access vehicle and scanning the two-dimension code tag of the vehicle to obtain the unique identifier of the vehicle.
3. The intelligent charging stake based on energy storage battery power supply as claimed in claim 2, wherein the encoder includes a digital signal processing unit therein for decoding the unique identifier read by the RFID reader or the two-dimensional code scanner, retrieving historical charging data of the vehicle, monitoring real-time charging state information of the vehicle, verifying and converting the real-time charging state information of the vehicle into encoding data in an electronic information format, wherein the real-time charging state information of the vehicle includes battery capacity and charging rate.
4. The intelligent charging stake based on energy storage battery power as claimed in claim 3, wherein the charging demand prediction module includes a historical data analyzer for collecting historical charging data of the vehicle, including charging time, charging amount and charging frequency, and a long and short term memory network model as a deep learning model for predicting the charging demand of the vehicle in a future period using the historical charging data of the vehicle.
5. The intelligent charging stake based on energy storage battery power as claimed in claim 4, wherein the optimization scheduling module includes a scheduling optimizer for analyzing the urgency, expected charging duration and expected departure time of the vehicle task to be charged using a deep learning attention mechanism, assigning a weight to each charging task, and dynamically adjusting the charging queue and power allocation based on the weight of each charging task and the current charging stake status.
6. The intelligent charging pile based on energy storage battery power supply of claim 5, wherein the charging power dynamic adjustment module comprises a real-time state monitor and a power adjustment unit, the real-time state monitor is used for monitoring the electric quantity, the charging state and the load data of the current power grid of the energy storage battery pack in real time; the power adjusting unit is used for calculating an optimal charging power output value and adjusting the charging power in real time according to the state of the energy storage battery pack, the power grid load and the real-time and predicted charging demand of the vehicle.
7. The intelligent charging stake based on energy storage battery power as set forth in claim 1, wherein the intelligent charging stake based on energy storage battery power further comprises:
the environment monitoring module is used for monitoring surrounding environment data of the charging pile based on the temperature sensor and the humidity sensor;
The safety protection module is provided with a protection circuit for overvoltage, overcurrent, short circuit and temperature abnormality;
The communication module is used for data exchange between the charging pile and the central management system and supporting data exchange and communication between the charging pile and the remote server, the user mobile equipment and the charging pile;
The user interface is composed of a touch screen display and physical keys, and is used for displaying the charging state and electric quantity information and allowing a user to set charging parameters.
8. A control method of an intelligent charging pile based on energy storage battery power supply, characterized in that the control method is performed based on the intelligent charging pile based on energy storage battery power supply according to any one of claims 1-7, the control method comprising the steps of:
When the electric automobile is connected to the charging pile, the unique identifier of the connected vehicle is identified and monitored through an identifier reading device equipped with charging pile equipment, historical charging data of the vehicle is fetched, and real-time charging state information of the vehicle is monitored;
analyzing the retrieved historical charging data by using a time sequence analysis and deep learning model, and predicting the vehicle charging requirement;
analyzing the emergency degree, the expected charging time length and the expected departure time information of the charging tasks of the charging pile based on the attention mechanism, distributing different weights for each task, and dynamically adjusting a charging queue and power output according to the weights;
Dynamically adjusting the charging power output by the charging pile according to the state of the energy storage battery pack and the power grid load monitored in real time and the predicted charging demand of the vehicle;
Data exchange is carried out between the charging pile and the remote server, the user mobile equipment and the charging pile through the communication module and the central management system, and the data exchange and the communication between the charging pile and the remote server, the user mobile equipment and the charging pile are supported;
and displaying the charging state and the electric quantity information through a user interface, and allowing a user to set charging parameters.
9. The control method of intelligent charging pile based on energy storage battery power supply according to claim 8, wherein when the unique identifier of the access vehicle is identified and monitored by the identifier reading device equipped with the charging pile device, the unique identifier in the RFID tag carried by the access vehicle is read by the RFID reader of the identifier reading device in a contactless manner, and the unique identifier of the vehicle is obtained by scanning the two-dimensional code tag of the vehicle by the two-dimensional code scanner of the identifier reading device.
10. The method for controlling an intelligent charging pile based on energy storage battery power supply according to claim 9, wherein dynamically adjusting the charging power output by the charging pile comprises:
the real-time state monitor monitors the electric quantity and the charging state of the energy storage battery pack and the load data of the current power grid;
and the power adjusting unit calculates an optimal charging power output value according to the state of the energy storage battery pack, the power grid load and the real-time prediction charging requirement of the vehicle and adjusts the charging power in real time.
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