CN118061816B

CN118061816B - Preassembled mobile super charging station

Info

Publication number: CN118061816B
Application number: CN202410496028.2A
Authority: CN
Inventors: 张高锋
Original assignee: Nanjing Gufeng Intelligent Technology Co ltd
Current assignee: Nanjing Gufeng Intelligent Technology Co ltd
Priority date: 2024-04-24
Filing date: 2024-04-24
Publication date: 2024-08-02
Anticipated expiration: 2044-04-24
Also published as: CN118061816A

Abstract

The invention belongs to the technical field of mobile charging stations, and discloses a preassembled mobile super charging station; comprising the following steps: the photovoltaic power generation module is used for converting photovoltaic energy into electric energy by adopting a photovoltaic power generation device and storing the electric energy in the charging station; the power grid interconnection module is used for interconnecting and communicating the charging station and the power grid and cooperatively operating with the power grid; the battery detection module is used for detecting the performance of the new energy automobile battery to be charged; the intelligent charging management module is used for formulating an intelligent charging strategy of the new energy automobile and carrying out intelligent management and control on the charging of the new energy automobile; the invention can accurately detect the new energy automobile battery, understand the state and performance of the battery, and provide reference for better management of the service life cycle of the battery; and the optimal charging strategy can be formulated according to the data such as the new energy vehicle type, the vehicle energy type, the vehicle electric quantity and the like, so that the efficient and intelligent distribution of charging resources of the charging station is realized, and the emergency vehicle is supported to be charged quickly.

Description

Preassembled mobile super charging station

Technical Field

The invention relates to the technical field of mobile charging stations, in particular to a preassembled mobile super charging station.

Background

With the increasing popularity and demand of new energy vehicle types (such as pure electric vehicles, hybrid electric vehicles and the like), the construction of charging facilities becomes more and more important; most public charging stations are fixed at present, and a great deal of time and cost are required for layout and construction; the charging requirement is not easy to predict in advance, and the electricity requirements of different areas and places are difficult to meet, so that empty or overload phenomenon is caused; for the areas without public charging stations, when the electric quantity of the new energy vehicle is exhausted, the new energy vehicle cannot be charged, and other vehicles are required to be towed to the areas with the public charging stations for charging, so that time and labor are wasted; in addition, due to the increase of the electricity consumption, the electricity carrying requirement of the power grid can be continuously increased, and the capacity of the power grid is difficult to meet;

There are of course also mobile charging stations, for example the patent publication CN110504721a discloses mobile charging stations; comprising the following steps: the mobile energy storage device, the power conversion device, the direct current charging device, the cable winch and the system control device are arranged on the vehicle body; the system control device controls the mobile energy storage device, the power conversion device and the direct current charging device; the power conversion equipment rectifies electric energy obtained from a power grid side and then charges the mobile energy storage equipment; inverting the electric energy output by the mobile energy storage equipment to perform emergency power supply for the electric load on the power grid side; inverting the electric energy output by the mobile energy storage device and providing the electric energy to the direct current charging device, rectifying and transforming the inverted electric energy by the direct current charging device, and charging the electric automobile to be rescued; the cable winch is used for placing a charging cable of the direct-current charging equipment; the mobile charging station can timely ensure the power utilization of the electric loads of important departments and key posts when the power failure crisis occurs at the power grid side, and can also solve the problem caused by insufficient electric power in the actual running process of the electric automobile;

However, the technology only can solve the problem of insufficient electric quantity of the new energy automobile, and is difficult to detect the battery of the new energy automobile, so that the battery state and performance of the new energy automobile cannot be monitored; meanwhile, intelligent charging management cannot be performed on the new energy automobile, so that charging demand distribution is unreasonable, the new energy automobile with higher charging emergency degree is difficult to rapidly charge, and the charging process is low in efficiency;

In view of the above, the present invention proposes a preloaded mobile super charging station to solve the above-mentioned problems.

Disclosure of Invention

In order to overcome the defects in the prior art, the invention provides the following technical scheme for achieving the purposes: a preloaded mobile super charging station, comprising:

The photovoltaic power generation module is used for converting photovoltaic energy into electric energy by adopting a photovoltaic power generation device and storing the electric energy in the charging station;

The power grid interconnection module is used for interconnecting and communicating the charging station and the power grid and cooperatively operating with the power grid; the charging station and the power grid are interconnected and intercommunicated, and the power transmission and the data transmission are included; the cooperative operation comprises power grid capacity expansion, peak clipping and valley filling, demand response and power grid frequency modulation;

The battery detection module is used for detecting the performance of the new energy automobile battery to be charged; the step of detecting the performance of the new energy automobile battery to be charged comprises the following steps:

step A1: reading battery parameters;

step A2: acquiring initial capacity of the battery according to the battery parameters;

Step A3: calculating a battery coefficient according to the battery parameters and the initial battery capacity;

Step A4: acquiring a charging parameter;

Step A5: calculating a detection coefficient according to the charging parameter;

step A6: calculating a battery performance coefficient according to the battery coefficient and the detection coefficient;

Step A7: judging whether to generate a maintenance instruction or a replacement instruction according to the battery performance coefficient;

the intelligent charging management module is used for formulating an intelligent charging strategy of the new energy automobile and carrying out intelligent management and control on the charging of the new energy automobile; the intelligent charging strategy making step comprises the following steps:

step B1: acquiring vehicle data of m automobiles to be charged, wherein m is an integer greater than 1;

Step B2: calculating priority coefficients of m automobiles to be charged according to vehicle data of the m automobiles to be charged;

step B3: acquiring charging station power data;

Step B4: distributing charging station power data to m automobiles to be charged according to the priority coefficient of the m automobiles to be charged and the charging station power data; and the charging station charges the m automobiles to be charged according to the power respectively distributed by the m automobiles to be charged.

Further, the light energy in the sunlight is converted into direct current by the photovoltaic panel in the photovoltaic power generation device and stored in the capacitor of the charging station.

Further, the interconnection and intercommunication between the charging station and the power grid comprises power transmission and data transmission;

the cooperative operation comprises power grid capacity expansion, peak clipping and valley filling, demand response and power grid frequency modulation;

the capacity expansion of the power grid is that the charging station transmits the stored electric energy to the power grid, so that the load capacity of the power grid is increased;

the peak clipping and valley filling method comprises the following steps:

Presetting an acquisition interval, and acquiring a plurality of power grid loads according to the acquisition interval, wherein the power grid loads are power loads borne by a power grid; drawing a load curve according to the collected multiple power grid loads, and obtaining peak values and valley values of the power grid loads according to the load curve;

When the load of the power grid rises to a peak value, the charging station transmits electric energy to the power grid, and the peak value of the load of the power grid is reduced; when the load of the power grid is reduced to the valley value, the power grid transmits electric energy to the charging station, and the valley value of the load of the power grid is improved;

the demand response is that the charging station adjusts the working state according to the received real-time scheduling instruction of the power grid;

The power grid frequency modulation method comprises the following steps:

When the frequency of the power grid system is reduced, namely the power supplied by the power grid is smaller than the load demand of the power grid; the power grid dispatching center sends a discharge instruction to the charging station; the charging station transmits the stored electric energy to a power grid according to the discharging instruction, and the frequency of the power grid system is increased through an electromagnetic induction effect;

when the frequency of the power grid system increases, namely the power supplied by the power grid is greater than the load demand of the power grid; the power grid dispatching center sends out a charging instruction; and the power grid transmits electric energy to the charging station according to the charging instruction, so that the frequency of the power grid system is reduced.

Further, the step of performing performance detection on the new energy automobile battery to be charged includes:

step A1: reading battery parameters;

Step A4: acquiring a charging parameter;

Step A7: and judging whether to generate a maintenance instruction or a replacement instruction according to the battery performance coefficient.

Further, in the step A1, the method for reading the battery parameter includes:

marking the new energy automobile to be charged as an automobile to be charged;

an OBD interface card reader in the charging station is adopted, and battery parameters are read from a battery management system in the automobile to be charged through an OBD interface on the automobile to be charged;

the battery parameters include battery type, current battery capacity, and charge cycle number; the current capacity of the battery is the current maximum charging capacity of the automobile battery to be charged; the charging cycle times are the charging and discharging cycle times of the automobile battery to be charged;

In the step A2, the method for obtaining the initial capacity of the battery includes:

Inputting battery parameters into a trained capacity prediction model to predict initial capacity of the battery; the initial capacity of the battery is the maximum charging capacity of the automobile battery to be charged when not in use;

the training process of the capacity prediction model comprises the following steps:

the initial battery capacities corresponding to the battery parameters are collected in advance, and the battery parameters and the initial battery capacities are converted into a corresponding set of feature vectors;

Taking each group of feature vectors as input of a capacity prediction model, wherein the capacity prediction model takes a group of predicted battery initial capacities corresponding to each group of battery parameters as output, and takes actual battery initial capacities corresponding to each group of battery parameters as prediction targets, and the actual battery initial capacities are digital labels of preset judging results corresponding to the battery parameters; taking the sum of prediction errors of all battery parameters as a training target; training the capacity prediction model until the sum of prediction errors reaches convergence, and stopping training; the capacity prediction model is a deep neural network model.

Further, in the step A3, the method for calculating the battery coefficient includes:

；

In the method, in the process of the invention, In order to be a battery coefficient,For the current capacity of the battery,For the initial capacity of the battery,In order to achieve the number of charging cycles,、The weight coefficient is preset;

In the step A4, the method for obtaining the charging parameter includes:

presetting activation electric energy, wherein the activation electric energy is electric energy for activating a battery management system of an automobile to be charged; the charging station transmits electric energy to the automobile to be charged according to the activated electric energy, and the charging parameters are obtained from a battery management system in the automobile to be charged;

the charging parameters comprise a current value curve, a voltage value curve and charging efficiency;

The current value curve is the variation trend of the current value in the process of activating the electric energy to be transmitted to the automobile to be charged; the voltage value curve is the variation trend of the voltage value in the process of activating the electric energy to be transmitted to the automobile to be charged; the charging efficiency is the ratio of the output electric energy of the charging station to the useful electric energy of the automobile battery to be charged.

Further, in the step A5, the method for calculating the detection coefficient includes:

；

In the method, in the process of the invention, In order to detect the coefficient of the light,In order to provide a rate of change of the current value,In order to provide a rate of change of the voltage value,In order to achieve the efficiency of the charge,、、Is a preset proportionality coefficient;

The current value change rate is the difference value of the maximum value minus the minimum value of the current value in the current value curve; the voltage value change rate is the difference value of the maximum value minus the minimum value of the voltage value in the voltage value curve;

in the step A6, the method for calculating the battery performance coefficient includes:

；

In the method, in the process of the invention, In order to be a coefficient of performance of the battery,、Is a preset weight coefficient.

Further, in the step A7, the method for determining whether to generate the maintenance instruction or the replacement instruction includes:

Presetting a coefficient threshold value, wherein the coefficient threshold value comprises a first coefficient threshold value And a second coefficient threshold value，; Comparing the battery performance coefficient with a coefficient threshold;

If it is No maintenance instruction and no replacement instruction are generated;

If it is Generating a maintenance instruction;

If it is A replacement instruction is generated.

Further, the step of formulating the intelligent charging strategy includes:

step B3: acquiring charging station power data;

Further, in the step B1, the vehicle data includes a vehicle electric quantity, a vehicle energy type, and a vehicle type;

The electric quantity of the vehicle is the residual electric quantity of the automobile to be charged; the method for acquiring the electric quantity of the vehicle is consistent with the method for acquiring the battery parameters;

the energy type of the vehicle is the energy type adopted by the running of the vehicle; vehicle energy types include electric vehicles and hybrid electric vehicles; the judging method of the vehicle energy type comprises the following steps:

Collecting automobile images of m automobiles to be charged, and identifying license plate information of the m automobiles to be charged; the charging station is connected with a vehicle registration management department website, and vehicle energy types of the m vehicles to be charged are inquired from the vehicle registration management department website according to license plate information of the m vehicles to be charged;

the method for identifying the license plate information of m automobiles to be charged comprises the following steps:

Sequentially inputting automobile images of m automobiles to be charged into a trained license plate detection model, detecting license plates in the automobile images of the m automobiles to be charged, and obtaining license plate images of the m automobiles to be charged; sequentially inputting the detected license plate images of the m vehicles to be charged into a trained license plate recognition model to recognize license plate information corresponding to the m vehicles to be charged;

The license plate detection model is a target detector, and the target detector is used for detecting the position of a license plate in an automobile image and cutting out the automobile image through a minimum rectangular frame of the license plate; the training method of the license plate recognition model comprises the following steps:

Pre-constructing a character set; generating Chinese and English character information according to the character set, obtaining license plate data information through data augmentation processing, generating a training set for license plate recognition model training through taking a plurality of scene pictures as backgrounds, and taking an image in the training set as a first training image; training the license plate recognition model by using a training set, and outputting the license plate recognition model meeting the prediction error; the license plate recognition model is a CNN neural network model;

Vehicle types include private vehicles and emergency services vehicles; the vehicle type judging method comprises the following steps:

using a trained type analysis model to identify automobile images of m automobiles to be charged, and outputting identification results, wherein the identification results comprise private vehicles and emergency service vehicles;

The type analysis model training process includes:

Collecting a plurality of automobile images in advance, marking each automobile image as a second training image, marking automobiles to be charged in each second training image, and marking the automobiles to be charged, wherein the marking comprises private vehicles and emergency service vehicles; respectively converting the private vehicle and the emergency service vehicle into digital labels; dividing the marked second training image into a training set and a testing set; training the type analysis model by using a training set, and testing the type analysis model by using a testing set; presetting an error threshold, and outputting a type analysis model when the average value of the prediction errors of all the second training images in the test set is smaller than the error threshold; the type analysis model is a convolutional neural network model.

Further, in the step B2, the calculating method of the priority coefficient of the m vehicles to be charged includes:

；

In the method, in the process of the invention, The priority coefficient of the ith car to be charged,For the vehicle type number of the i-th vehicle to be charged,For the vehicle charge of the ith car to be charged,For the vehicle energy type value of the ith vehicle to be charged,、Is a preset proportion coefficient, and the ratio coefficient is a preset proportion coefficient,。

Further, in the step B3, the charging station power data is the total power that the charging station can provide;

In the step B4, the method for distributing charging station power data to m vehicles to be charged includes:

adding the priority coefficients of m automobiles to be charged to obtain a priority coefficient sum; calculating the charging power of each vehicle to be charged, wherein the charging power of each vehicle to be charged is the power distributed to each vehicle to be charged by the charging station power data;

the calculation method of the charging power of each automobile to be charged comprises the following steps:

；

In the method, in the process of the invention, The charging power of the ith car to be charged,As the sum of the priority coefficients,For charging station power data.

The preassembled mobile super charging station has the technical effects and advantages that:

The photovoltaic power generation technology is adopted to provide power for the mobile charging station, and mutual transmission of electric energy is realized through interconnection and intercommunication with the power grid, so that the cooperative operation of the charging station and the power grid, such as capacity expansion, peak clipping, valley filling and the like of the power grid, is realized, and the problems of power grid load and frequency modulation are relieved; meanwhile, the new energy automobile battery can be accurately detected, the state and the performance of the battery can be known, and a reference is provided for better management of the service life cycle of the battery; in addition, an optimized charging strategy can be formulated according to the data such as the new energy vehicle type, the vehicle energy type and the vehicle electric quantity, so that the efficient and intelligent distribution of charging resources of the charging station is realized, and the emergency vehicle is supported to be charged quickly; most importantly, mobile charging can be realized, the charging requirements of different sites and time can be rapidly met, and the application range of the new energy automobile is enlarged.

Drawings

FIG. 1 is a schematic diagram of a preloaded mobile super charging station module according to an embodiment of the present invention;

Fig. 2 is a schematic diagram of a preloaded mobile super charging station according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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 be within the scope of the invention.

Referring to fig. 1 and 2, the preassembled mobile super charging station according to the present embodiment includes a photovoltaic power generation module, a power grid interconnection module, a battery detection module, and an intelligent charging management module; each module is connected in a wired and/or wireless mode, so that data transmission among the modules is realized;

All modules in the charging station are integrated in advance, and seamless connection is realized through a standardized interface; the modularized container transportation mode is adopted to carry out integral transportation on the charging station, and the charging station is rapidly deployed to an unmanned area (namely an area without a power supply) or a temporary power utilization area according to the power requirement, so that the charging of a new energy automobile and the capacity expansion of a power grid are realized, and the charging system is convenient and rapid;

the photovoltaic panel in the photovoltaic power generation device is used for converting light energy in sunlight into direct current, and the direct current is stored in the capacitor of the charging station to provide renewable power for the charging station; to reduce dependence on conventional fossil fuels, reduce air pollution and environmental impact;

the power grid interconnection module is used for interconnecting and communicating the charging station and the power grid and cooperatively operating with the power grid;

the charging station and the power grid are interconnected and intercommunicated, and the power transmission and the data transmission are included; the charging station realizes power transmission with a power grid through PCS (power conversion system) inverter equipment; the charging station realizes data transmission with the power grid through a wireless communication network (such as 4G/5G);

the cooperative operation comprises capacity expansion, peak clipping and valley filling of the power grid, demand response and frequency modulation of the power grid;

the capacity expansion of the power grid is that the charging station transmits the stored electric energy to the power grid, so that the whole load capacity of the power grid is enlarged, and the load peak of the power grid is relieved;

the method for peak clipping and valley filling comprises the following steps:

Presetting an acquisition interval, and acquiring a plurality of power grid loads according to the acquisition interval, wherein the power grid loads are power loads borne by a power grid; drawing a load curve according to the collected multiple power grid loads, and obtaining peak values and valley values of the power grid loads according to the load curve; the power grid load is obtained by a power sensor arranged at the power grid side; the acquisition interval is preset by a person skilled in the art according to actual experience;

When the load of the power grid rises to a peak value, the charging station transmits electric energy to the power grid, and the peak value of the load of the power grid is reduced; when the load of the power grid is reduced to the valley value, the power grid transmits electric energy to the charging station, and the valley value of the load of the power grid is improved; thereby relieving the change rate of the load curve of the power grid from the valley value to the peak value, and enabling the load curve to more smoothly transition from the valley value to the peak value;

scheduling instructions such as adjusting charge rate, delaying charge time period, stopping charging, etc.;

adjusting the operating state such as decreasing the charge rate, delaying the charge start time, stopping the charge, etc.;

the method for power grid frequency modulation comprises the following steps:

When the frequency of the power grid system is reduced, namely the power supplied by the power grid is insufficient to meet the load demand of the power grid; the power grid dispatching center sends a discharge instruction to the charging station; the charging station transmits the stored electric energy to the power grid according to the discharging instruction, so that the electric energy supplied by the power grid can meet the load demand of the power grid, and the frequency of a power grid system is increased through the electromagnetic induction effect;

When the frequency of the power grid system increases, namely the power supplied by the power grid exceeds the load demand of the power grid; the power grid dispatching center sends out a charging instruction; the power grid transmits electric energy to the charging station according to the charging instruction, so that the electric energy supplied by the power grid is close to the load demand of the power grid, and the frequency of a power grid system is reduced;

The charging station can be combined with the power grid to adjust the frequency of the power grid system, so that the stable operation of the frequency of the power grid system is ensured;

the battery detection module is used for detecting the performance of the new energy automobile battery to be charged;

the performance detection method for the new energy automobile battery to be charged comprises the following steps:

step A1: reading battery parameters;

The method for reading the battery parameters comprises the following steps:

marking the new energy automobile to be charged as an automobile to be charged;

the battery parameters include battery type, current battery capacity, and charge cycle number;

Battery types such as lithium ion batteries, lithium polymer batteries, lithium iron phosphorus batteries, and the like; the current capacity of the battery is the current maximum charging capacity of the automobile battery to be charged; the charging cycle times are the charging and discharging cycle times of the automobile battery to be charged;

The method for acquiring the initial capacity of the battery comprises the following steps:

The specific training process of the capacity prediction model comprises the following steps:

Taking each group of feature vectors as input of a capacity prediction model, wherein the capacity prediction model takes a group of predicted battery initial capacities corresponding to each group of battery parameters as output, and takes actual battery initial capacities corresponding to each group of battery parameters as prediction targets, and the actual battery initial capacities are digital labels of preset judging results corresponding to the battery parameters; taking the sum of prediction errors of all battery parameters as a training target; wherein, the calculation formula of the prediction error is as follows WhereinFor prediction error, k is the group number of the corresponding feature vector of the battery parameter,The initial capacity of the battery is predicted for the corresponding k-th battery parameter,The initial capacity of the actual battery corresponding to the k group of battery parameters; training the capacity prediction model until the sum of prediction errors reaches convergence, and stopping training;

The capacity prediction model is specifically a deep neural network model;

the calculation method of the battery coefficient comprises the following steps:

；

The specific numerical value of the weight coefficient in the formula can be set according to actual conditions, the weight coefficient reflects the importance of the current capacity, the charging cycle number and the initial capacity of the battery, and a person skilled in the art can preset the corresponding weight coefficient according to the importance of the current capacity, the charging cycle number and the initial capacity of the battery so as to accurately acquire the battery coefficient;

It should be noted that, the ratio of the current capacity of the battery to the initial capacity of the battery, and the number of charging cycles are the influencing parameters of the battery coefficient; the larger the ratio of the current capacity of the battery to the initial capacity of the battery is, the smaller the capacity loss and attenuation of the battery are, the better the battery performance is, namely the larger the battery coefficient is, and the opposite is the case; because each charge and discharge cycle causes a certain degree of loss to the battery, the internal resistance of the battery is increased, so that the more the number of charge cycles is, the better the battery performance is, namely the smaller the battery coefficient is, and vice versa; meanwhile, the battery coefficient is only a parameter reflecting the battery performance, and the calculation of the battery coefficient is dimension-removing calculation;

Step A4: acquiring a charging parameter;

the method for acquiring the charging parameters comprises the following steps:

Presetting activation electric energy, wherein the activation electric energy is electric energy for activating a battery management system of an automobile to be charged, and the activation electric energy is set according to a communication protocol and a safety standard between a charging station and the automobile to be charged; the charging station transmits electric energy to the automobile to be charged according to the activated electric energy, and the charging parameters are obtained from a battery management system in the automobile to be charged;

The current value curve is the variation trend of the current value in the process of activating the electric energy to be transmitted to the automobile to be charged; the voltage value curve is the variation trend of the voltage value in the process of activating the electric energy to be transmitted to the automobile to be charged; the charging efficiency is the ratio of the output electric energy of the charging station to the useful electric energy of the automobile battery to be charged;

The method for calculating the detection coefficient comprises the following steps:

；

the specific numerical value of the proportionality coefficient in the formula can be set according to actual conditions, the proportionality coefficient reflects the importance of the current value change rate, the voltage value change rate and the charging efficiency, and a person skilled in the art can preset the corresponding proportionality coefficient according to the importance of the current value change rate, the voltage value change rate and the charging efficiency so as to accurately acquire the detection coefficient;

The current value change rate, the voltage value change rate and the charging efficiency are the influence parameters of the detection coefficient; the larger the current value change rate and the voltage value change rate, the larger the fluctuation of the battery in the charging process, the lower the energy conversion efficiency, the worse the battery performance, namely the smaller the detection coefficient, and the opposite is the opposite; the higher the charging efficiency, the less the battery system loses in receiving the input electrical energy and effectively converting it into stored energy, i.e. more electrical energy is effectively stored in the battery, the better the battery performance, i.e. the greater the detection coefficient, and vice versa; meanwhile, the detection coefficient is only a parameter reflecting the performance of the battery, and the calculation of the detection coefficient is dimension removal calculation;

the calculation method of the battery performance coefficient comprises the following steps:

；

In the method, in the process of the invention, In order to be a coefficient of performance of the battery,、The weight coefficient is preset;

The specific numerical value of the weight coefficient in the formula can be set according to the actual situation, the weight coefficient reflects the importance of the battery coefficient and the detection coefficient, and a person skilled in the art can preset the corresponding weight coefficient according to the importance of the battery coefficient and the detection coefficient so as to accurately evaluate the battery performance;

the method for judging whether to generate the maintenance instruction or the replacement instruction comprises the following steps:

If it is No maintenance instruction and no replacement instruction are generated; the battery performance coefficient is higher, the battery performance is better, and other operations are not needed;

If it is Generating a maintenance instruction; the battery performance coefficient is lower, the battery performance is poorer, and the automobile owner to be charged is prompted to maintain the battery after the charging is completed, so that the battery performance and the running reliability of the automobile to be charged are ensured;

If it is Generating a replacement instruction; the method has the advantages that the performance coefficient of the battery is too low, the performance of the battery is too poor, the automobile owner to be charged is prompted to replace the battery in time after the charging is completed, so that the performance of the battery and the running reliability of the automobile to be charged are ensured, and the personal safety of the automobile owner to be charged is ensured;

the first coefficient threshold value And a second coefficient threshold valueWhen the historical new energy automobile is subjected to battery maintenance and battery replacement, the new energy automobile subjected to battery maintenance is marked as a maintenance automobile, and the new energy automobile subjected to battery replacement is marked as a replacement automobile; calculating battery performance coefficients of different maintenance vehicles for a plurality of times, calculating battery performance coefficients of a plurality of different maintenance vehicles each time, marking the maximum battery performance coefficient of the plurality of battery performance coefficients calculated each time as the maximum coefficient, taking the average value of the obtained maximum coefficients as a first coefficient threshold value; Similarly, a first coefficient threshold value is obtainedThe maintenance car in the method is replaced by a replacement car, and a second coefficient threshold value is obtained；

The intelligent charging management module is used for formulating an intelligent charging strategy of the new energy automobile and carrying out intelligent management and control on the charging of the new energy automobile;

the intelligent charging strategy making steps comprise:

step B3: acquiring charging station power data;

Step B4: distributing charging station power data to m automobiles to be charged according to the priority coefficient of the m automobiles to be charged and the charging station power data; the charging station charges the m automobiles to be charged according to the power respectively distributed by the m automobiles to be charged;

In the step B1, the vehicle data includes a vehicle electric quantity, a vehicle energy type and a vehicle type;

Collecting automobile images of m automobiles to be charged, and identifying license plate information of the m automobiles to be charged; the charging station is connected with a vehicle registration management department website, and vehicle energy types of the m vehicles to be charged are inquired from the vehicle registration management department website according to license plate information of the m vehicles to be charged; the method comprises the steps that automobile images of m automobiles to be charged are obtained by a first image sensor arranged beside each charging pile in a charging station, and the first image sensor is opposite to a license plate of the automobiles to be charged;

The license plate detection model is set as a target detector for realizing license plate detection, the target detector is used for detecting the position of a license plate in an automobile image, and the automobile image is cut out through the minimum rectangular frame of the license plate; target detectors such as SSD, centerNet, etc.;

The training method of the license plate recognition model comprises the following steps:

Pre-constructing character sets, such as provincial abbreviations, capital English letters and numbers; generating Chinese and English character information according to the character set, acquiring diversified license plate data information through data augmentation processing, generating a training set for license plate recognition model training through taking a plurality of scene pictures as backgrounds, and taking an image in the training set as a first training image; training a license plate recognition model by using a training set, and outputting the license plate recognition model meeting a prediction error, wherein a calculation formula of the prediction error is as follows Wherein isPrediction error, Y is the number of the first training image,The predicted license plate information corresponding to the Y-group first training image is obtained,The actual license plate information corresponding to the Y-group first training image is obtained;

the license plate recognition model is specifically a CNN neural network model;

vehicle types include private vehicles and emergency services vehicles; private vehicles are vehicles owned by individuals, and emergency service vehicles such as fire trucks, ambulances, police cars, and the like;

the vehicle type judging method comprises the following steps:

the specific training process of the type analysis model comprises the following steps:

Collecting a plurality of automobile images in advance, marking each automobile image as a second training image, marking automobiles to be charged in each second training image, and marking the automobiles to be charged, wherein the marking comprises private vehicles and emergency service vehicles; converting the private vehicle and the emergency service vehicle to digital labels, respectively, and converting the private vehicle to 0 and the emergency service vehicle to 1 by way of example; dividing the marked second training image into a training set and a testing set, taking 70% of the second training image as the training set, and taking 30% of the second training image as the testing set; training the type analysis model by using a training set, and testing the type analysis model by using a testing set; presetting an error threshold, and outputting a type analysis model when the average value of the prediction errors of all the second training images in the test set is smaller than the error threshold; wherein, the calculation formula of the prediction error mean value is that WhereinIn order to predict the error of the signal,For the number of the second training image,Is the firstThe prediction labels corresponding to the second training images of the group,Is the firstThe actual labels corresponding to the second training images are set, and U is the number of the second training images in the test set; the error threshold value is preset according to the precision required by the type analysis model;

the type analysis model is specifically a convolutional neural network model;

in the step B2, the method for calculating the priority coefficients of the m vehicles to be charged includes:

；

In the method, in the process of the invention, The priority coefficient of the ith car to be charged,For the vehicle type number of the i-th vehicle to be charged,For the vehicle charge of the ith car to be charged,For the vehicle energy type value of the ith vehicle to be charged,、Is a preset proportion coefficient, and the ratio coefficient is a preset proportion coefficient,；

The specific numerical value of the proportionality coefficient in the formula can be set according to actual conditions, the proportionality coefficient reflects the importance of the numerical value of the vehicle electric quantity and the vehicle energy source type, and a person skilled in the art can preset the corresponding proportionality coefficient according to the importance of the numerical value of the vehicle electric quantity and the vehicle energy source type so as to accurately acquire the priority coefficient;

It should be noted that, the vehicle type value, the vehicle electric quantity and the vehicle energy type value are the influencing parameters of the priority coefficient; the larger the electric quantity of the vehicle is, the more the residual electric quantity in the automobile battery to be charged is, the smaller the charging emergency degree is, the smaller the priority coefficient is, and the opposite is true; the vehicle type is assigned in advance by a person skilled in the art, a smaller value is assigned to the private vehicle, a larger value is assigned to the emergency service vehicle, and the vehicle type corresponding to the private vehicle is 1 and the vehicle type corresponding to the emergency service vehicle is 3 by way of example; the reason is that emergency service vehicles need to provide emergency services, and the charging emergency degree is greater than that of private vehicles; the vehicle energy type value is assigned to the vehicle energy type in advance by a person skilled in the art, a larger value is assigned to the pure electric vehicle, a smaller value is assigned to the hybrid electric vehicle, and the vehicle energy type value corresponding to the pure electric vehicle is 10 and the vehicle energy type value corresponding to the hybrid electric vehicle is 5; the reason is that the hybrid electric vehicle can be powered by the internal fuel engine, and the emergency degree of charging is smaller than that of the pure electric vehicle; meanwhile, the priority coefficient is only a parameter reflecting the charging rate of the automobile to be charged, and the calculation of the priority coefficient is dimension-removing calculation;

In the step B3, the charging station power data is the total power that the charging station can provide, and the charging station power is obtained according to the specification list, the equipment manual or the technical specification of the charging station;

in the above step B4, the method for distributing the charging station power data to the m vehicles to be charged includes:

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In the method, in the process of the invention, The charging power of the ith car to be charged,As the sum of the priority coefficients,Power data for the charging station;

The embodiment adopts a photovoltaic power generation technology to provide power for the mobile charging station, and realizes mutual transmission of electric energy by interconnection and intercommunication with a power grid, so that the cooperative operation of the charging station and the power grid, such as capacity expansion, peak clipping, valley filling and the like of the power grid, is realized, and the problems of power grid load and frequency modulation are relieved; meanwhile, the new energy automobile battery can be accurately detected, the state and the performance of the battery can be known, and a reference is provided for better management of the service life cycle of the battery; in addition, an optimized charging strategy can be formulated according to the data such as the new energy vehicle type, the vehicle energy type and the vehicle electric quantity, so that the efficient and intelligent distribution of charging resources of the charging station is realized, and the emergency vehicle is supported to be charged quickly; most importantly, mobile charging can be realized, the charging requirements of different sites and time can be rapidly met, and the application range of the new energy automobile is enlarged.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims

1. Preassembled mobile super charging station, characterized by comprising:

Step A1: reading battery parameters; the battery parameters include battery type, current battery capacity, and charge cycle number;

Step A4: acquiring a charging parameter; the charging parameters comprise a current value curve, a voltage value curve and charging efficiency;

in the step A3, the method for calculating the battery coefficient includes:

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in the step A5, the method for calculating the detection coefficient includes:

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step B3: acquiring charging station power data;

2. The preloaded mobile super charging station according to claim 1, wherein the light energy in the sunlight is converted to direct current by the photovoltaic panel in the photovoltaic power generation device and stored in the capacitor of the charging station.

3. The preloaded mobile super charging station of claim 2, wherein the grid expansion is a charging station delivering stored electrical energy to the grid for expanding grid load capacity;

the peak clipping and valley filling method comprises the following steps:

The power grid frequency modulation method comprises the following steps:

4. A preloaded mobile super-charging station as claimed in claim 3, characterised in that in step A1 the method of reading battery parameters comprises:

marking the new energy automobile to be charged as an automobile to be charged;

The current capacity of the battery is the current maximum charging capacity of the automobile battery to be charged; the charging cycle times are the charging and discharging cycle times of the automobile battery to be charged;

5. The preloaded mobile super charging station of claim 4, wherein in step A4, the method for obtaining the charging parameters comprises:

6. The preloaded mobile super-charging station of claim 5, wherein in step A7, the method for determining whether to generate a maintenance instruction or a replacement instruction comprises:

If it is Generating a maintenance instruction;

If it is A replacement instruction is generated.

7. The preloaded mobile super-charging station of claim 6, wherein in step B1, the vehicle data comprises vehicle charge, vehicle energy type, and vehicle type;

The type analysis model training process includes:

8. The preassembled mobile super charging station according to claim 7, wherein in the step B2, the calculating method of the priority coefficient of the m vehicles to be charged comprises:

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9. The preloaded mobile super charging station of claim 8, wherein in step B3, the charging station power data is the total power that the charging station can provide;

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