CN116587916A - Intelligent charging method, charging pile, computer equipment and storage medium of electric vehicle - Google Patents
Intelligent charging method, charging pile, computer equipment and storage medium of electric vehicle Download PDFInfo
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- 238000004590 computer program Methods 0.000 claims description 12
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/30—Constructional details of charging stations
- B60L53/31—Charging columns specially adapted for electric vehicles
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Abstract
The invention provides an intelligent charging method, a charging pile, computer equipment and a storage medium of an electric vehicle, wherein the method comprises the following steps: acquiring a first parameter set of each electric vehicle at a plurality of time points through a detection unit; calculating a first state vector of each charging cluster according to a first parameter set of the electric vehicle and uploading the first state vector to a second control unit; inputting the received first state vector rewards to the neural network of the first control unit to obtain a first charging power distribution scheme of each second control unit; and repeatedly acquiring the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is smaller than or equal to the preset value. The invention has the beneficial effects that: the charging loss is effectively reduced, the temperature of the electric vehicle during operation is reduced, so that the second control unit can receive a new electric vehicle to be charged, and the charging efficiency is improved.
Description
Technical Field
The invention relates to the field of intelligent electric charging, in particular to an intelligent charging method, a charging pile, computer equipment and a storage medium of an electric vehicle.
Background
The electric vehicle has the advantages of high efficiency, zero emission, simple structure and the like, and has been rapidly developed in recent years. However, the electric vehicle has the defects that when the electric vehicle group is connected to the power grid, the problem of short-time tension and long-time low-efficiency operation of the power distribution facility capacity can be caused, and when the electric vehicle group is charged, certain harm can be possibly caused to the power grid. Therefore, the charging problem of the electric vehicle is particularly important, and a charging method is needed.
Disclosure of Invention
The invention mainly aims to provide an intelligent charging method, a charging pile, computer equipment and a storage medium for an electric vehicle, and aims to solve the problem that a certain harm is brought to a power grid when an electric vehicle group is charged.
The invention provides an intelligent charging method of an electric vehicle, which is realized by a charging pile, wherein the charging pile comprises a first control unit, a plurality of second control units, a plurality of charging clusters and a plurality of detection units, the charging clusters are used for charging the electric vehicle to be charged, the charging clusters are connected with the second control units in a one-to-one correspondence manner, the second control units are used for controlling charging parameters of the electric vehicle to be charged in the corresponding charging clusters, and the second control units are controlled by the first control units and at least one detection unit; the detecting element is used for being connected with the electric motor car that waits to charge one-to-one for obtain the parameter set of electric motor car that waits to charge, detecting element and second control unit data connection, second control unit and first control unit data connection, the intelligent charging method of electric motor car includes:
Acquiring a first parameter set of each electric vehicle at a plurality of time points through a detection unit; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle;
calculating a first state vector of each charging cluster according to a first parameter set of the electric vehicle and uploading the first state vector to a second control unit;
converting each first state vector into a state value, calculating the variance of each charging cluster, and judging whether the variance is larger than a preset value or not;
if yes, the second control unit inputs the first state vector received by the second control unit and the corresponding initial rewarding value into the neural network of the first control unit, and a first charging power distribution scheme of each second control unit is obtained; the neural network is trained by taking different state vectors and corresponding rewards as input and taking a charging power distribution scheme as output; wherein the initial rewarding value is a preset value;
the first control unit sends the first charging power distribution scheme of each second control unit to the second control units and executes the first charging power distribution scheme, calculates a reward value of each first control unit, and collects second parameter sets of each electric vehicle after execution; wherein, the calculation formula for calculating the rewarding value of each first control unit is as follows ，/>，/>Indicate->A prize value of->Indicate->Variance of individual state vectors>Representing a preset value;
and converting the second parameter set into a second state vector and inputting the second state vector into the neural network of the first control unit together with the reward value to obtain a second charging power distribution scheme, and repeatedly obtaining the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is smaller than or equal to the preset value.
Further, after the step that the first control unit sends the first charging power allocation scheme of each second control unit to the second control unit and performs, the method further includes:
the first control unit acquires the states of charge of the electric vehicles to be charged in each charging cluster detected by the detection unit;
and selecting one charge state with the minimum charge state as a second control unit connected with the newly-connected electric vehicle to be charged according to the charge states of the charging clusters.
Further, before the step of acquiring the first parameter set of each electric vehicle at a plurality of time points by the detection unit, the method further includes:
receiving a charging form selected by a user and forwarding the charging form to a first control unit;
the selected charging form is sent to a corresponding second control unit;
And the second control unit controls the charging clusters to charge the corresponding electric vehicles according to the selected charging mode.
Further, after the step of the first control unit transmitting the first charging power allocation scheme of each second control unit to the second control unit and executing, the method further includes:
receiving charging power for each electric vehicle to be charged,
judging whether the charging power of the electric vehicle is smaller than the lowest starting power or not;
and if the charging power of the electric vehicle is smaller than the lowest starting power, adjusting the charging power to the lowest starting power, and reducing the charging power of other electric vehicles to be charged.
Further, after the step of converting the second parameter set into the second state vector and inputting the second state vector to the neural network of the first control unit together with the reward value to obtain the second charging power distribution scheme, repeating the step of obtaining the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is less than or equal to the preset value, the method further includes:
acquiring the time length of single adjustment when the variance of the state vector of the charging cluster is smaller than or equal to the preset value;
and when the time length of the single adjustment reaches the preset time length, judging that the charging pile needs to be repaired.
The invention also provides a charging pile, comprising: the system comprises a first control unit, a plurality of second control units, a plurality of charging clusters and a plurality of detection units, wherein the charging clusters are used for charging electric vehicles to be charged, the charging clusters are connected with the second control units in a one-to-one correspondence manner, the second control units are used for controlling charging parameters of the electric vehicles to be charged in the corresponding charging clusters, the second control units are controlled by the first control units, and the second control units at least control one detection unit; the detection unit is used for being in one-to-one correspondence with the electric vehicle to be charged, acquiring a parameter set of the electric vehicle to be charged, and being in data connection with the second control unit which is in data connection with the first control unit;
the acquisition module is used for acquiring first parameter sets of all electric vehicles at a plurality of time points through the detection unit; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle;
the calculation module is used for calculating a first state vector of each charging cluster according to a first parameter set of the electric vehicle and uploading the first state vector to the second control unit;
the judging module is used for converting each first state vector into a state value, calculating the variance of each charging cluster and judging whether the variance is larger than a preset value or not;
The input module is used for inputting the first state vector received by the second control unit and the corresponding initial rewarding value into the neural network of the first control unit if the first state vector is received by the second control unit, so as to obtain a first charging power distribution scheme of each second control unit; the neural network is trained by taking different state vectors and corresponding rewards as input and taking a charging power distribution scheme as output; wherein the initial rewarding value is a preset value;
a transmitting module for transmitting the first charging power distribution scheme of each second control unit to the second control unitExecuting, calculating a reward value of each first control unit, and collecting a second parameter set of each electric vehicle after executing; wherein, the calculation formula for calculating the rewarding value of each first control unit is as follows，/>，/>Indicate->A prize value of->Indicate->Variance of individual state vectors>Representing a preset value;
the conversion module is used for converting the second parameter set into a second state vector and inputting the second state vector into the neural network of the first control unit together with the rewarding value to obtain a second charging power distribution scheme, and repeatedly obtaining the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is smaller than or equal to the preset value.
Further, the charging pile further comprises:
the charge state acquisition module is used for acquiring the charge states of the electric vehicles to be charged in the charging clusters detected by the detection units;
the selecting module is used for selecting one charge state with the minimum charge state as a second control unit connected with the newly-accessed electric vehicle to be charged according to the charge states of the charging clusters.
The invention also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of any of the above.
The invention has the beneficial effects that: the first parameter set of the electric vehicle is obtained, the variance value is calculated, the charging distribution scheme is selected by using the state vector and the rewarding system of the rewarding value, repeated adjustment is carried out, loss can be effectively reduced, the temperature during charging is reduced, the second control unit can receive a new electric vehicle to be charged conveniently, and charging efficiency is improved.
Drawings
Fig. 1 is a schematic flow chart of an intelligent charging method of an electric vehicle according to an embodiment of the application;
fig. 2 is a schematic structural view of a charging pile according to an embodiment of the present application;
fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, in the embodiments of the present application, all directional indicators (such as up, down, left, right, front, and rear) are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicators correspondingly change, and the connection may be a direct connection or an indirect connection.
The term "and/or" is herein merely an association relation describing an associated object, meaning that there may be three relations, e.g., a and B, may represent: a exists alone, A and B exist together, and B exists alone.
Furthermore, descriptions such as those referred to as "first," "second," and the like, are provided for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implying an order of magnitude of the indicated technical features in the present disclosure. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The invention provides an intelligent charging method of an electric vehicle, which is realized through a charging pile, wherein the charging pile comprises a first control unit, a plurality of second control units, a plurality of charging clusters and a plurality of detection units, wherein the charging clusters are used for charging the electric vehicle to be charged, the charging clusters are connected with the second control units in a one-to-one correspondence manner, the second control units are used for controlling charging parameters of the electric vehicle to be charged in the corresponding charging clusters, and the second control units are controlled by the first control units and at least control one detection unit; the detecting element is used for being connected with the electric motor car that waits to charge one-to-one for obtain the parameter set of electric motor car that waits to charge, detecting element and second control unit data connection, second control unit and first control unit data connection, the intelligent charging method of electric motor car includes:
S1: acquiring a first parameter set of each electric vehicle at a plurality of time points through a detection unit; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle;
s2: calculating a first state vector of each charging cluster according to a first parameter set of the electric vehicle and uploading the first state vector to a second control unit;
s3: converting each first state vector into a state value, calculating the variance of each charging cluster, and judging whether the variance is larger than a preset value or not;
s4: if yes, the second control unit inputs the first state vector received by the second control unit and the corresponding initial rewarding value into the neural network of the first control unit, and a first charging power distribution scheme of each second control unit is obtained; the neural network is trained by taking different state vectors and corresponding rewards as input and taking a charging power distribution scheme as output; wherein the initial rewarding value is a preset value;
s5: the first control unit sends the first charging power distribution scheme of each second control unit to the second control units and executes the first charging power distribution scheme, calculates a reward value of each first control unit, and collects second parameter sets of each electric vehicle after execution; wherein, the calculation formula for calculating the rewarding value of each first control unit is as follows ，/>，/>Indicate->A prize value of->Indicate->Variance of individual state vectors>Representing a preset value;
s6: and converting the second parameter set into a second state vector and inputting the second state vector into the neural network of the first control unit together with the reward value to obtain a second charging power distribution scheme, and repeatedly obtaining the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is smaller than or equal to the preset value.
In this embodiment, the first control unit, the plurality of second control units, and the plurality of detection units may communicate with each other through a controller area network (Controller Area Network, CAN), and signals may be transmitted to a CAN bus through a CAN communication interface, and received by a CAN controller through the CAN bus, where each unit (including the first control unit, the second control unit, and the detection units) may specifically be a microprocessor, a CPU, or a processing device. The specific regulation form is not limited, and the electric energy in a part of electric vehicles can be transferred to other electric vehicles through the regulation circuit formed by the inductors, so that the loss can be effectively reduced, the working temperature of the electric vehicles is reduced, the second control unit can receive the new electric vehicle to be charged conveniently, and the charging efficiency is improved. In addition, the charging power can be obtained by measuring voltage and current, and can also be obtained according to other parameters in the parameter set, the application is not limited to the above, and the electric vehicle can be an electric vehicle, an electric bicycle and the like. The charging pile comprises a plurality of charging clusters, each charging cluster comprises a plurality of charging interfaces, each charging interface can be used for charging the electric vehicle, and the charging clusters are similar to a charging box and are mutually independent.
Acquiring, by the detection unit, first parameter sets of respective electric vehicles at a plurality of time points as described in the above step S1; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle; in one embodiment, the parameter set is obtained by a carrier topology identification method capable of communicating by HPLC, which includes the following steps: step 1: continuous voltage sampling is continuously carried out, and sampling data at the previous moment are processed in parallel in a pipeline mode; step 2: carrying out high-density data analysis on the sampled data by adopting a convolutional neural network method based on FFT; step 3: averaging analysis results of the sliding window data, and taking the averaged spectrum modulus value as a judgment basis of the carrier signal; step 4: step classification is carried out according to the spectrum modulus amplitude of the signal, and 16-bit modulation signal codes are output; the communication network topology identification between the edge management device and the ordered charging module is realized by adopting a characteristic current signal detection circuit.
And step S2, calculating a first state vector of each charging cluster according to the first parameter set of the electric vehicle, and uploading the first state vector to the second control unit, wherein the calculating of the state vector according to the first parameter set is to arrange the parameters of each dimension, so as to obtain a corresponding first state vector.
As described in step S3, each of the first state vectors is converted into a state value, the variance of each charging cluster is calculated, and whether the variance is larger than a preset value is determined, wherein the conversion is performed by multiplying the values of each dimension of the state vector by corresponding preset parameters and adding the values, and the preset parameters are preset values according to each dimension.
As described in the above steps S4-S6, the neural network of the first control unit performs the selection of the charging power allocation scheme according to the corresponding obtained state vector and the reward value, specifically, the selection of the charging power allocation scheme may be regarded as a markov decision process, that is, the allocation scheme of multiple charging powers is preset, the allocation scheme of the charging power is selected according to each state vector, and then sent to the second control unit and executed, so that the state of each charging cluster is changed, and the reward value is sent to the first control vector, the first control vector updates the selected parameter according to the magnitude of the reward, and then selects a new allocation scheme in the current state, which should be interpreted that the updated selected parameter needs to maximize the rewardWherein- >For the number of times of selection of allocation scheme +.>Indicating maximum rewards, i.e. get +.>Because the objective of the neural model is to maximize rewards, the neural network will tend to select better actions, i.e. actions that can get more rewards, in each state, so that state balance of each charging cluster can be realized as soon as possible, loss of charging electric quantity is effectively reduced, and charging efficiency of the electric vehicle is improved.
In one embodiment, after step S5, the first control unit sends the first charging power allocation scheme of each second control unit to the second control unit and performs the step S, the method further includes:
s601: the first control unit acquires the states of charge of the electric vehicles to be charged in each charging cluster detected by the detection unit;
s602: and selecting one charge state with the minimum charge state as a second control unit connected with the newly-connected electric vehicle to be charged according to the charge states of the charging clusters.
The steps S601-S602 mentioned above realize the access to the new electric vehicle to be charged, and the obtaining of the state of charge may be calculated by the existing neural network method, and the principle of applying the neural network method to the state of charge detection of the lithium electric vehicle is as follows: and (3) taking a large amount of corresponding external data such as voltage and current and the state of charge data of the electric vehicle as training samples, repeatedly training and modifying through forward propagation of input information and reverse propagation of error transmission in the self-learning process of the neural network, and obtaining a predicted state of charge value of the electric vehicle through inputting new data when the predicted state of charge reaches the error range of the design requirement. Because the equalization of a part of electric vehicles aims at individual electric vehicles, the equalization mode can lead the energy equalization of the electric vehicles to be slower, is quite unfavorable for equipment with a plurality of single electric vehicles, and is therefore grouped, and certainly, if the voltage difference between the electric vehicles in a certain electric vehicle group is overlarge, the equalization of electric energy of the electric vehicles can be carried out in the electric vehicle group, and one second control unit with the smallest charge state is selected as a newly connected second control unit connected with the electric vehicle to be charged according to the charge state of each second control unit, so that the charging efficiency can be improved.
In one embodiment, the second control unit further comprises a power distribution subunit and a control subunit, the power distribution subunit is connected with the control subunit, the control subunit is connected with the first control unit, the power distribution subunit is used for being connected with the electric vehicles to be charged so as to obtain parameter sets of the electric vehicles and charging forms of the electric vehicles, and charging power is set for the electric vehicles to be charged according to the parameter sets and the charging forms of the electric vehicles.
In this embodiment, the charging form may be a form set by the first control unit for each electric vehicle according to a model of each electric vehicle, or may be a charging form set according to a selection of a user, where the charging form of the electric vehicle is information obtained in advance, and the charging power is set for the electric vehicle to be charged according to the parameter set and the charging form of the electric vehicle, specifically, a correspondence between the parameter set and the charging form and the charging power of the electric vehicle may be pre-established, and then the setting of the charging power is performed.
In one embodiment, before the step S1 of acquiring the first parameter set of each electric vehicle at a plurality of time points by the detection unit, the method further includes:
S001: receiving a charging form selected by a user and forwarding the charging form to a first control unit;
s002: the selected charging form is sent to a corresponding second control unit;
and S003, the second control unit controls the charging clusters to charge the corresponding electric vehicles according to the selected charging modes.
As described in the above steps S001-S003, the method may be implemented by a system platform, specifically, the system platform is connected to a first control unit, the system platform is connected to a user terminal, the system platform is configured to receive a charging form selected by a user and forward the charging form to the first control unit, and the first control unit sends the selected charging form to a corresponding second control unit. The user can select charging modes such as immediate charging, ordered charging and the like through the user terminal, then the system platform transmits the information to the first control unit according to the generated corresponding order information, and the first control unit forwards the information to the corresponding second control unit, so that the second control unit can conveniently set charging power of the electric vehicle.
In one embodiment, after step S5, the first control unit sends the first charging power allocation scheme of each second control unit to the second control unit and performs the step S, the method further includes:
S611: receiving charging power for each electric vehicle to be charged,
s612: judging whether the charging power of the electric vehicle is smaller than the lowest starting power or not;
s613: and if the charging power of the electric vehicle is smaller than the lowest starting power, adjusting the charging power to the lowest starting power, and reducing the charging power of other electric vehicles to be charged.
As described in the above steps S611-S613, when the current charging power is less than the lowest starting power of the charging gun, the charging power of the second control unit may be reduced according to the charging duration of the charging power of the other electric vehicles in the second control unit, so as to satisfy the current charging request of the lowest starting power of the charging gun; and according to different factory stations, charging is preferably regulated and controlled, for example, the district charging station preferably reduces the power of the direct current charging pile, and the commercial factory station preferably reduces the charging power of the alternating current pile, so that the lowest charging power of the electric vehicle is ensured, and the waste of electric energy is avoided.
In one embodiment, after the step S6 of converting the second parameter set into the second state vector and inputting the second state vector to the neural network of the first control unit together with the reward value to obtain the second charging power allocation scheme, repeating the obtaining of the parameter set and the charging power allocation for a plurality of times until the variance of the state vector of each charging cluster is less than or equal to the preset value, the method further includes: the method comprises the steps of carrying out a first treatment on the surface of the
S701: acquiring the time length of single adjustment when the variance of the state vector of the charging cluster is smaller than or equal to the preset value;
s702: and when the time length of the single adjustment reaches the preset time length, judging that the charging pile needs to be repaired.
As described in the above steps S701-S702, the duration of the single adjustment is the duration taken to adjust the variance to be less than or equal to the preset value, and when the single adjustment time is too long, it is indicated that the adjustment of the charging power of the electric vehicle has not improved the charging efficiency, because the influence caused by the duration of the adjustment is much greater than the influence caused by the insufficient charging power, the repair of the charging pile is required, and the preset duration is the duration set in advance.
Referring to fig. 2, the present invention provides a charging pile including: the system comprises a first control unit, a plurality of second control units, a plurality of charging clusters and a plurality of detection units, wherein the charging clusters are used for charging electric vehicles to be charged, the charging clusters are connected with the second control units in a one-to-one correspondence manner, the second control units are used for controlling charging parameters of the electric vehicles to be charged in the corresponding charging clusters, the second control units are controlled by the first control units, and the second control units at least control one detection unit; the detection unit is used for being in one-to-one correspondence with the electric vehicle to be charged, acquiring a parameter set of the electric vehicle to be charged, and being in data connection with the second control unit which is in data connection with the first control unit;
An acquisition module 10 for acquiring, by the detection unit, first parameter sets of respective electric vehicles at a plurality of time points; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle;
a calculating module 20, configured to calculate a first state vector of each charging cluster according to a first parameter set of the electric vehicle and upload the first state vector to a second control unit;
a judging module 30, configured to convert each of the first state vectors into a state value, calculate a variance of each of the charging clusters, and judge whether the variance is greater than a preset value;
the input module 40 is configured to, if yes, input the first state vectors received by the second control units and the corresponding initial prize values to the neural network of the first control unit, so as to obtain a first charging power allocation scheme of each second control unit; the neural network is trained by taking different state vectors and corresponding rewards as input and taking a charging power distribution scheme as output; wherein the initial rewarding value is a preset value;
a sending module 50, configured to send the first charging power allocation schemes of the second control units to the second control units and execute the first charging power allocation schemes, calculate a prize value of each first control unit, and collect second parameter sets of each electric vehicle after execution; wherein, the calculation formula for calculating the rewarding value of each first control unit is as follows ，/>，/>Indicate->A prize value of->Indicate->Variance of individual state vectors>Representing a preset value;
the conversion module 60 is configured to convert the second parameter set into a second state vector and input the second state vector to the neural network of the first control unit together with the reward value to obtain a second charging power allocation scheme, and repeatedly obtain the parameter set and allocate the charging power for multiple times until the variance of the state vector of each charging cluster is less than or equal to the preset value.
In this embodiment, the first control unit, the plurality of second control units, and the plurality of detection units included in the charging pile may communicate with each other through a controller area network (Controller Area Network, CAN), and signals may be transmitted to a CAN bus through a CAN communication interface, and received by a CAN controller through the CAN bus, where each unit (including the first control unit, the second control unit, and the detection units) may specifically be a microprocessor, a CPU, or a processing device. The specific regulation form is not limited, and the electric energy in a part of electric vehicles can be transferred to other electric vehicles through the regulation circuit formed by the inductors, so that the loss can be effectively reduced, the working temperature of the electric vehicles is reduced, the second control unit can receive the new electric vehicle to be charged conveniently, and the charging efficiency is improved. In addition, the charging power can be obtained by measuring voltage and current, and can also be obtained according to other parameters in the parameter set, the application is not limited to the above, and the electric vehicle can be an electric vehicle, an electric bicycle and the like. The charging pile comprises a plurality of charging clusters, each charging cluster comprises a plurality of charging interfaces, each charging interface can be used for charging the electric vehicle, and the charging clusters are similar to a charging box and are mutually independent.
Acquiring, by the detection unit, a first set of parameters for each electric vehicle at a plurality of points in time, as described in module 10 above; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle; in one embodiment, the parameter set is obtained by a carrier topology identification method capable of communicating by HPLC, which includes the following steps: step 1: continuous voltage sampling is continuously carried out, and sampling data at the previous moment are processed in parallel in a pipeline mode; step 2: carrying out high-density data analysis on the sampled data by adopting a convolutional neural network method based on FFT; step 3: averaging analysis results of the sliding window data, and taking the averaged spectrum modulus value as a judgment basis of the carrier signal; step 4: step classification is carried out according to the spectrum modulus amplitude of the signal, and 16-bit modulation signal codes are output; the communication network topology identification between the edge management device and the ordered charging module is realized by adopting a characteristic current signal detection circuit.
As described in the above module 20, the first state vector of each charging cluster is calculated according to the first parameter set of the electric vehicle and uploaded to the second control unit, where the state vector is calculated according to the first parameter set and the parameters of each dimension are arranged, so as to obtain the corresponding first state vector.
As described in the above block 30, each of the first state vectors is converted into a state value, and the variance of each charging cluster is calculated, and whether the variance is greater than a preset value is determined, where the conversion is performed by multiplying the values of each dimension of the state vector by corresponding preset parameters and adding the values, and the preset parameters are preset values according to each dimension. As described in the above modules 40-60, the neural network of the first control unit performs a selection of a charging power allocation scheme according to the corresponding obtained state vector and the reward value, specifically, the selection of the charging power allocation scheme may be regarded as a markov decision process, that is, a scheme of presetting a plurality of charging power allocation schemes, selecting the charging power allocation scheme according to each state vector, and then sending the charging power allocation scheme to the second control unit and performing the operation, so as to change the state of each charging cluster, and send the reward value to the first control vector, where the first control vector updates the selected parameter according to the magnitude of the reward, and then selects a new allocation scheme in the current state, where it is required to maximize the reward, that is, the newly selected parameter is required to be updatedWherein- >For the number of times of selection of allocation scheme +.>Indicating maximum rewards, i.e. get +.>Because the goal of the neural model is to maximize rewards, the neural network will tend to choose better actions, i.eThe method can obtain more rewards, so that the state balance of each charging cluster can be realized as soon as possible, the loss of the charging electric quantity is effectively reduced, and the charging efficiency of the electric vehicle is improved.
In one embodiment, the charging pile further comprises:
the charge state acquisition module is used for acquiring the charge states of the electric vehicles to be charged in the charging clusters detected by the detection units;
the selecting module is used for selecting one charge state with the minimum charge state as a second control unit connected with the newly-accessed electric vehicle to be charged according to the charge states of the charging clusters.
Therefore, the access to a new electric vehicle to be charged is realized, the charge state can be obtained by calculating through the existing neural network method, and the principle that the neural network method is applied to the charge state detection of the lithium electric vehicle is as follows: and (3) taking a large amount of corresponding external data such as voltage and current and the state of charge data of the electric vehicle as training samples, repeatedly training and modifying through forward propagation of input information and reverse propagation of error transmission in the self-learning process of the neural network, and obtaining a predicted state of charge value of the electric vehicle through inputting new data when the predicted state of charge reaches the error range of the design requirement. Because the equalization of a part of electric vehicles aims at individual electric vehicles, the equalization mode can lead the energy equalization of the electric vehicles to be slower, is quite unfavorable for equipment with a plurality of single electric vehicles, and is therefore grouped, and certainly, if the voltage difference between the electric vehicles in a certain electric vehicle group is overlarge, the equalization of electric energy of the electric vehicles can be carried out in the electric vehicle group, and one second control unit with the smallest charge state is selected as a newly connected second control unit connected with the electric vehicle to be charged according to the charge state of each second control unit, so that the charging efficiency can be improved.
The application has the beneficial effects that: the first parameter set of the electric vehicle is obtained, the variance value is calculated, the charging distribution scheme is selected by using the state vector and the rewarding system of the rewarding value, repeated adjustment is carried out, loss can be effectively reduced, the temperature of the electric vehicle during operation is reduced, the second control unit can receive a new electric vehicle to be charged conveniently, and charging efficiency is improved.
Referring to fig. 3, in an embodiment of the present application, there is further provided a computer device, which may be a server, and an internal structure thereof may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing various parameter sets and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, may implement the intelligent charging method of the electric vehicle of any of the above embodiments.
It will be appreciated by those skilled in the art that the architecture shown in fig. 3 is merely a block diagram of a portion of the architecture in connection with the present inventive arrangements and is not intended to limit the computer devices to which the present inventive arrangements are applicable.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor can implement the intelligent charging method of the electric vehicle of any embodiment.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present application and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (9)
1. The intelligent charging method of the electric vehicle is realized through a charging pile, the charging pile comprises a first control unit, a plurality of second control units, a plurality of charging clusters and a plurality of detection units, wherein the charging clusters are used for charging the electric vehicle to be charged, the charging clusters are connected with the second control units in a one-to-one correspondence manner, the second control units are used for controlling charging parameters of the electric vehicle to be charged in the corresponding charging clusters, the second control units are controlled by the first control units, and the second control units at least control one detection unit; the detection unit is used for being connected with the electric vehicle to be charged in a one-to-one correspondence manner and used for acquiring a parameter set of the electric vehicle to be charged, the detection unit is in data connection with the second control unit, and the second control unit is in data connection with the first control unit.
Acquiring a first parameter set of each electric vehicle at a plurality of time points through a detection unit; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle;
calculating a first state vector of each charging cluster according to a first parameter set of the electric vehicle and uploading the first state vector to a second control unit;
converting each first state vector into a state value, calculating the variance of each charging cluster, and judging whether the variance is larger than a preset value or not;
if yes, the second control unit inputs the first state vector received by the second control unit and the corresponding initial rewarding value into the neural network of the first control unit, and a first charging power distribution scheme of each second control unit is obtained; the neural network is trained by taking different state vectors and corresponding rewards as input and taking a charging power distribution scheme as output; wherein the initial rewarding value is a preset value;
the first control unit sends the first charging power distribution scheme of each second control unit to the second control units and executes the first charging power distribution scheme, calculates a reward value of each first control unit, and collects second parameter sets of each electric vehicle after execution; wherein, the calculation formula for calculating the rewarding value of each first control unit is as follows ，/>，/>Represent the firstA prize value of->Indicate->Variance of individual state vectors>Representing a preset value;
and converting the second parameter set into a second state vector and inputting the second state vector into the neural network of the first control unit together with the reward value to obtain a second charging power distribution scheme, and repeatedly obtaining the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is smaller than or equal to the preset value.
2. The intelligent charging method of an electric vehicle according to claim 1, wherein after the step of the first control unit transmitting the first charging power allocation scheme of each second control unit to the second control unit and performing, the first control unit further comprises:
the first control unit acquires the states of charge of the electric vehicles to be charged in each charging cluster detected by the detection unit;
and selecting one charge state with the minimum charge state as a second control unit connected with the newly-connected electric vehicle to be charged according to the charge states of the charging clusters.
3. The intelligent charging method of an electric vehicle according to claim 1, wherein before the step of acquiring the first parameter set of each electric vehicle at a plurality of time points by the detection unit, further comprises:
Receiving a charging form selected by a user and forwarding the charging form to a first control unit;
the selected charging form is sent to a corresponding second control unit;
and the second control unit controls the charging clusters to charge the corresponding electric vehicles according to the selected charging mode.
4. The intelligent charging method of an electric vehicle according to claim 1, wherein after the step of the first control unit transmitting the first charging power allocation scheme of each second control unit to the second control unit and performing, the first control unit further comprises:
receiving charging power for each electric vehicle to be charged,
judging whether the charging power of the electric vehicle is smaller than the lowest starting power or not;
and if the charging power of the electric vehicle is smaller than the lowest starting power, adjusting the charging power to the lowest starting power, and reducing the charging power of other electric vehicles to be charged.
5. The intelligent charging method of the electric vehicle according to claim 1, wherein after the step of converting the second parameter set into the second state vector and inputting the second state vector into the neural network of the first control unit together with the reward value to obtain the second charging power distribution scheme, repeating the step of obtaining the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is less than or equal to the preset value, the method further comprises:
Acquiring the time length of single adjustment when the variance of the state vector of the charging cluster is smaller than or equal to the preset value;
and when the time length of the single adjustment reaches the preset time length, judging that the charging pile needs to be repaired.
6. A charging pile, comprising: the system comprises a first control unit, a plurality of second control units, a plurality of charging clusters and a plurality of detection units, wherein the charging clusters are used for charging electric vehicles to be charged, the charging clusters are connected with the second control units in a one-to-one correspondence manner, the second control units are used for controlling charging parameters of the electric vehicles to be charged in the corresponding charging clusters, the second control units are controlled by the first control units, and the second control units at least control one detection unit; the detection unit is used for being in one-to-one correspondence with the electric vehicle to be charged, acquiring a parameter set of the electric vehicle to be charged, and being in data connection with the second control unit which is in data connection with the first control unit;
the acquisition module is used for acquiring first parameter sets of all electric vehicles at a plurality of time points through the detection unit; the parameter set at least comprises the charging quantity of the electric vehicle and the charging power of the electric vehicle;
The calculation module is used for calculating a first state vector of each charging cluster according to a first parameter set of the electric vehicle and uploading the first state vector to the second control unit;
the judging module is used for converting each first state vector into a state value, calculating the variance of each charging cluster and judging whether the variance is larger than a preset value or not;
the input module is used for inputting the first state vector received by the second control unit and the corresponding initial rewarding value into the neural network of the first control unit if the first state vector is received by the second control unit, so as to obtain a first charging power distribution scheme of each second control unit; the neural network is trained by taking different state vectors and corresponding rewards as input and taking a charging power distribution scheme as output; wherein the initial rewarding value is a preset value;
the sending module is used for sending the first charging power distribution scheme of each second control unit to the second control unit and executing the first charging power distribution scheme, calculating the rewarding value of each first control unit and collecting the second parameter set of each electric vehicle after executing the second charging power distribution scheme; wherein, the calculation formula for calculating the rewarding value of each first control unit is as follows ，/>，/>Represent the firstA prize value of->Indicate->Variance of individual state vectors>Representing a preset value;
the conversion module is used for converting the second parameter set into a second state vector and inputting the second state vector into the neural network of the first control unit together with the rewarding value to obtain a second charging power distribution scheme, and repeatedly obtaining the parameter set and distributing the charging power for a plurality of times until the variance of the state vector of each charging cluster is smaller than or equal to the preset value.
7. The charging stake of claim 6, wherein the charging stake further comprises:
the charge state acquisition module is used for acquiring the charge states of the electric vehicles to be charged in the charging clusters detected by the detection units;
the selecting module is used for selecting one charge state with the minimum charge state as a second control unit connected with the newly-accessed electric vehicle to be charged according to the charge states of the charging clusters.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1-5 when executing the computer program.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1-5.
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