CN117350519A - Charging station planning method and system based on new energy passenger car charging demand prediction - Google Patents
Charging station planning method and system based on new energy passenger car charging demand prediction Download PDFInfo
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Abstract
The invention provides a charging station planning method and a charging station planning system based on new energy passenger car charging demand prediction, wherein the method comprises the steps of firstly, collecting and processing operation data of a new energy passenger car, constructing a space-time track diagram, and extracting and obtaining charging and driving related information of the vehicle through the space-time track diagram; then, aiming at the battery capacity attenuation condition of the new energy passenger car, carrying out data reconstruction on the operation data of the new energy passenger car; then, a vehicle journey energy consumption prediction model and a vehicle charging energy prediction model are built, a vehicle energy consumption prediction result and a vehicle battery charging energy prediction result are obtained, and space-time prediction is carried out on the charging demand of the target area; and finally, constructing a double-stage rapid site selection scheme based on a traffic network and charging requirements, obtaining the optimal site selection of the charging station according to a charging requirement space-time prediction result, and completing planning of the specification and the number of the charging piles of the charging station according to the historical charging habits of users and the charging requirement peak value data.
Description
Technical Field
The invention belongs to the technical field of new energy, and relates to a planning method and a planning system of a charging station.
Background
The new energy automobile industry has a great breakthrough in the key technical field, the permeability of the new energy automobile is steadily improved, and the matched charging station infrastructure is also required to be gradually perfected along with the gradual improvement of the market share of the new energy automobile.
Although charging stations of new energy automobiles are built on a large scale, the existing charging station infrastructure still has a plurality of problems, such as small number of charging piles, long station searching time, unreasonable distribution of the charging piles, low utilization rate of the charging facilities and the like. It is essential, again because of unreasonable planning of the charging facilities. Therefore, the generation of such problems can be greatly solved by combining computer technology to improve the planning layout of the charging facility.
The existing charging demand prediction mainly starts from charging historical data in a station, and the prediction research of the regional charging demand distribution is performed through historical operation data, so that the method is not suitable for site selection of charging stations without station establishment. Or in combination with existing vehicle inventory, charging demand prediction is achieved from a macroscopic perspective, but this is not applicable to specific charging station planning. The method is also commonly used for researching the regional vehicle charging demand distribution based on the angle of a user travel rule, but the modeling is mostly based on simulation data, few factors are considered, and the final result often has larger deviation from the actual result. In addition, the charging station address selection problem is mainly based on charging requirements, one or two factors in the characteristics of traffic hub and various areas of cities and the travel preference of users are combined, the complexity of the current address selection solving algorithm is high, and the requirement on calculation performance is high.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a charging station planning method and a charging station planning system based on new energy passenger car charging demand prediction.
The method of the invention comprises the following steps:
firstly, collecting operation data of a new energy passenger car in a target area, removing abnormal data in the operation data to obtain vehicle operation data, constructing a space-time track diagram of the new energy passenger car according to the vehicle operation data and a charging and discharging logic of the new energy passenger car, and extracting charging start time, charging completion time, daily average charging frequency, charging duration and driving path information of the vehicle through the space-time track diagram;
step two, aiming at the battery capacity attenuation condition of the new energy passenger car, assuming that the battery degradation accords with the cut-off normal distribution, carrying out data reconstruction on the vehicle operation data in the target area to obtain the reconstructed operation data of the new energy passenger car;
thirdly, constructing a vehicle journey energy consumption prediction model according to a running data set formed by time information interval of the charging starting time, the charging completion time data information, the average charging frequency, the charging duration, the running path information, the battery capacity attenuation condition and the reconstruction running data of the new energy passenger vehicle and combining a Catboost algorithm and an XGBoost algorithm to obtain a vehicle journey energy consumption prediction result;
Step four, a vehicle charging energy prediction model based on a Stacking fusion frame is constructed, abnormal charging energy values are marked and deleted in the model by using a Bayesian Gaussian mixture algorithm, and a prediction result of the vehicle battery charging energy is obtained;
step five, combining a vehicle energy consumption prediction result based on travel energy consumption prediction and a vehicle battery charging energy prediction result to perform space-time prediction on the charging demand of the target area;
step six, constructing a double-stage rapid site selection scheme based on a traffic network and charging demands aiming at a target area according to a charging demand space-time prediction result, wherein the first stage combines thermal distribution to screen out an area which most accords with regional distribution and travel characteristics as a charging station candidate area; the input of the second stage is the charging station candidate region obtained in the first stage, and the K-means algorithm is utilized to carry out iterative calculation on the regional charging demand to obtain the distance average value from the charging station when the charging demand occurs, so as to obtain the optimal address selection of the charging station;
step seven, firstly setting charge duration and parking duration indexes, extracting historical charge habits of the vehicle according to a space-time trajectory graph, determining a charge type, and planning the specification of a charging pile in a station to which the vehicle belongs; and integrating charging demand peak value data of the target area in an hour unit according to a charging demand space-time prediction result of the target area, and constructing an optimal matching scheme of the number of each power charging pile according to a time node to finish planning of the specifications and the number of the charging piles of the charging station.
In the first step, the operation data of the new energy passenger car include a vehicle number, a data acquisition time, a vehicle state, a charging state, a vehicle speed, a driving mileage, a total voltage, a total current, a vehicle electric quantity, a highest voltage of a battery cell, a lowest voltage of the battery cell, a highest temperature value, a lowest temperature value, a longitude of vehicle running, and a latitude of vehicle running; the abnormal data comprise missing data, jump data and state marking error data; the historical charging habit of the vehicle is the charging type of the vehicle in the space-time track diagram; the charging starting time of the vehicle is the stopping starting time of the vehicle at a charging station in the space-time track diagram; the charging completion time of the vehicle is the stop time of the vehicle at a charging station in the space-time track diagram; the average daily charging frequency of the vehicle is the stay frequency of the vehicle at a charging station in a space-time track diagram; the charging duration time of the vehicle is the total stay time of the vehicle at the charging station in the space-time track diagram; the travel path information of the vehicle is a summary of the longitude of the vehicle travel and the latitude of the vehicle travel in the space-time trajectory graph.
In the second step, the data reconstruction process comprises the steps of firstly completing the identification of the charging attribute to determine whether the charging pile is a public charging pile, then analyzing the battery capacity and the battery capacity attenuation condition of the new energy passenger car, completing the assessment of the battery state of the vehicle by means of a machine learning algorithm and an optimizing algorithm, and completing the identification of the charging attribute and the data reconstruction of the battery state of the vehicle; and the charging attribute identification is to determine whether the attribute of the charging pile is a public charging pile or a private charging pile according to the charging behavior, the position area and the charging power of the passenger car. The data reconstruction of the vehicle battery state is combined with the truncated normal distribution and the battery state which are in accordance with the battery degradation, so that the coupling of the vehicle journey energy consumption prediction model to the battery capacity of the new energy passenger vehicle is completed, and the sample data is more in accordance with the actual vehicle battery state distribution.
The truncated normal distribution is represented as follows:
，
，
wherein,probability density function as normal distribution, +.>Probability distribution function as normal distribution, +.>And->Mean and standard deviation, respectively>And->Respectively represent the upper and lower bounds of the random variable.
Further, in the third step, the vehicle journey energy consumption prediction model includes a path driving state prediction model and a core energy consumption prediction model; the construction method of the path running state prediction model comprises the following steps: constructing various energy consumption factors by utilizing a data mining method, classifying and screening vehicle characteristics by combining a pearson correlation coefficient and a random forest, and dividing the vehicle characteristics into vehicle state characteristics and road condition dynamic characteristics, wherein the vehicle state characteristics are vehicle use static information including the driving mileage and longitude and latitude of a vehicle at the current moment and vehicle current battery information including the current battery capacity, the current battery SOC and the current battery SOH, and the road condition dynamic characteristics are average current and average speed of a battery of the vehicle driving at the current road section; constructing a path running state prediction model of a Catboost algorithm under a Boosting integration algorithm frame, and predicting to obtain path vehicle energy consumption by taking vehicle state characteristics and road condition dynamic characteristics as inputs; the method for constructing the core energy consumption prediction model comprises the following steps: and calculating actual energy consumption in the running process of the vehicle by using an interval mapping energy consumption estimation method, integrating the output of a path running state prediction model and vehicle state characteristics as input, using an XGBoost algorithm under a Boosting integration algorithm frame, establishing a core energy consumption prediction model, and outputting a vehicle energy consumption prediction result.
Further, in the fourth step, the method for constructing the vehicle charging energy prediction model includes:
firstly, marking and deleting abnormal charge energy values in vehicle charge energy by using a Bayesian Gaussian mixture algorithm, and carrying out distribution statistics on the vehicle charge energy after deleting the abnormal charge energy values, so that the overall distribution of the vehicle charge energy approximately obeys normal distribution, and taking the vehicle charge energy as the input of a vehicle charge energy prediction model;
the calculation of the vehicle charge energy is represented as follows:
，
，
wherein,and->SOC values at the present time and at the start of charging, respectively,/->For coulombic efficiency, +.>And->The battery charging current and the battery capacity are respectively; />Accumulating charging energy for the first k charging moments,/->And->The charging voltage and the charging current of the vehicle at the current moment are respectively;
then selecting a random forest suitable for a linear model in an Embedded embedding method and carrying out feature screening in an L1 regularization mode, wherein the features comprise a battery SOC, a battery capacity value, a battery average temperature value, a battery consistency, a charging power level, a charging average current and a charging starting time, and the screened features comprise the battery SOC, the capacity value and the charging average current;
And taking the Bagging-based random frame and the Boosting-based LGBM as a Stacking base model, taking Lasso as a Stacking meta-model, establishing a charging energy prediction model under a Stacking fusion frame, and predicting the battery charging energy of the vehicle.
Further, in the fifth step, the specific method for performing space-time prediction on the charging requirement of the target area is as follows: firstly, a charging behavior decision is completed through a vehicle journey energy consumption prediction model, a charging demand is acquired, and the flow is as follows: firstly, judging according to the current residual SOC value, judging that charging is needed when the SOC value is smaller than a certain threshold value, and judging that potential charging needs exist when the SOC value is larger than the certain threshold value and the parking time is longer than 40 min; meanwhile, the current residual SOC value is input into a stroke energy consumption prediction model, the next stroke residual SOC value is obtained, whether the next stroke starting SOC value is smaller than 0 or not is judged, when the next stroke starting SOC value is smaller than 0, the current residual SOC value is judged to be required to be charged, and otherwise, the current residual SOC value is judged to be not required to be charged;
determining the charging demand of the vehicle through a charging demand prediction result, and carrying out space-time statistics on the points with the demand to obtain the charging peak period demand time distribution; and respectively carrying out charge demand measurement and calculation on the four charge power levels by combining a charge demand decision result and a charge energy prediction model to obtain a daily peak load charge quantity predicted value.
Further, in the sixth step, the specific method in the first stage is: taking the traveling parking situation and the regional distribution of the vehicles as guidance, evaluating and sorting the regions needing to be built from the aspects of traffic network congestion degree, vehicle parking hot spot distribution and surrounding POI hot spot distribution, and selecting the region which most accords with the regional distribution and traveling characteristics as a charging station candidate region; the specific method of the second stage is as follows: selecting a K-means algorithm in a machine learning clustering algorithm, taking a charging station candidate area obtained in the first stage as initial data of the K-means algorithm, iteratively calculating a demand-to-station distance average value by combining the charging demand prediction result until each station meets 95% of charging demand, ending calculation, and outputting a final site selection result.
The invention provides a charging station planning system based on new energy passenger car charging demand prediction, which comprises a data acquisition module, a data reconstruction module, a vehicle journey energy consumption prediction module, a vehicle charging energy prediction module, a space-time prediction module of charging demand, a charging station address selection module and a charging station planning module.
The data acquisition module is used for acquiring the operation data of the new energy passenger car in the target area and removing abnormal data in the new energy passenger car to obtain vehicle operation data, a space-time track diagram of the new energy passenger car is constructed according to the vehicle operation data and the charge-discharge logic of the new energy passenger car, and the charge start time, the charge completion time, the daily charge frequency, the charge duration and the travel path information of the vehicle are extracted through the space-time track diagram.
The data reconstruction module is used for carrying out data reconstruction on the collected operation data of the new energy passenger car in the target area according to the battery capacity attenuation condition of the new energy passenger car, assuming that the battery degradation accords with the cut-off normal distribution, and obtaining the reconstruction operation data of the new energy passenger car.
The vehicle journey energy consumption prediction module constructs a vehicle journey energy consumption prediction model according to a travel data set formed by time information interval of charging start time, charging completion time data information, daily charging frequency, charging duration time, travel path information, battery capacity attenuation conditions and reconstruction operation data of a new energy passenger vehicle, and combines a Catboost algorithm and an XGBoost algorithm to obtain a vehicle journey energy consumption prediction result;
the vehicle charging energy prediction module is used for constructing a vehicle charging energy prediction model based on a Stacking fusion frame, and marking and deleting abnormal charging energy values in the model by using a Bayesian Gaussian mixture algorithm to obtain a prediction result of the vehicle battery charging energy.
The space-time prediction module of the charging demand performs space-time prediction on the charging demand of the target area by combining a vehicle energy consumption prediction result based on travel energy consumption prediction and a vehicle battery charging energy prediction result.
The method comprises the steps that an addressing module of the charging station builds a double-stage rapid addressing scheme based on a traffic network and charging requirements aiming at a target area, and the first stage combines thermal distribution to screen out an area which most accords with regional distribution and travel characteristics as a charging station candidate area; and the input of the second stage is the charging station candidate region obtained in the first stage, and the K-means algorithm is utilized to carry out iterative calculation on the regional charging demand to obtain the distance average value from the charging station when the charging demand occurs, so as to obtain the optimal address selection of the charging station.
The charging station planning module firstly sets charging duration and parking duration indexes, extracts historical charging habits of vehicles according to a space-time trajectory graph, determines charging types and plans the specifications of charging piles in a site; and integrating charging demand peak value data of the target area in an hour unit according to a charging demand space-time prediction result of the target area, and constructing an optimal matching scheme of the number of each power charging pile according to a time node to finish planning of the specifications and the number of the charging piles of the charging station.
The invention also proposes a computer device for charging station planning based on new energy passenger vehicle charging demand prediction, comprising a memory, a processor and program instructions stored in the memory for execution by the processor, wherein the processor executes the program instructions to implement the steps in the method and the system described above.
The invention also proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method described above and the system described above.
Compared with the prior art, the method has the advantages that firstly, the operation data of the new energy passenger car are collected and processed, a space-time track diagram is constructed, and the charging and driving related information of the car is obtained through extraction of the space-time track diagram; then, aiming at the battery capacity attenuation condition of the new energy passenger car, carrying out data reconstruction on the operation data of the new energy passenger car; then, a vehicle journey energy consumption prediction model and a vehicle charging energy prediction model are built, a vehicle energy consumption prediction result and a vehicle battery charging energy prediction result are obtained, and space-time prediction is carried out on the charging demand of the target area; and finally, constructing a double-stage rapid site selection scheme based on a traffic network and charging requirements, obtaining the optimal site selection of the charging station according to a charging requirement space-time prediction result, and completing planning of the specification and the number of the charging piles of the charging station according to the historical charging habits of users and the charging requirement peak value data. According to the method, the influence of the vehicle battery capacity attenuation on the charging requirement is considered, and the data reconstruction is carried out on the running data of the vehicle, so that the prediction accuracy of the charging requirement is greatly improved; according to the invention, the battery state and the actual running state of the vehicle are considered, the vehicle journey energy consumption prediction model and the vehicle charging energy prediction model are respectively constructed, the charging demand of the target area is predicted in a space-time manner based on the prediction result, the accurate prediction of the optimal site selection of the charging station is completed, the specification and the number of the charging piles of the charging station are planned according to the user demand, the station searching distance of the user is shortened to the greatest extent, and the utilization rate of the charging station is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart for constructing a vehicle trip energy consumption prediction model.
Fig. 3 is a flow chart of a charging behavior decision.
FIG. 4 is a flow chart of a dual stage fast addressing scheme
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved by the present application more clear, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
A flow chart of a charging station planning method based on new energy passenger vehicle charging demand prediction is shown in fig. 1, and the specific content is as follows.
Firstly, collecting operation data of a new energy passenger car in a target area, removing abnormal data in the operation data to obtain vehicle operation data, constructing a space-time track diagram of the new energy passenger car according to the vehicle operation data and a charging and discharging logic of the new energy passenger car, and extracting charging start time, charging completion time, average daily charging frequency, charging duration and driving path information of the vehicle through the space-time track diagram.
Specifically, the operation data of the new energy passenger car include a vehicle number, a data acquisition time, a vehicle state, a charging state, a vehicle speed, a driving mileage, a total voltage, a total current, a vehicle electric quantity, a highest voltage of a battery cell, a lowest voltage of the battery cell, a highest temperature value, a lowest temperature value, a longitude of vehicle driving and a latitude of vehicle driving.
The exception data includes missing data, transition data, and status flag error data. The missing data mainly occurs in longitude of vehicle running and latitude data of vehicle running, the jump data mainly occurs in driving mileage data, and the state marking error data mainly occurs in vehicle state and charging state data.
The historical charging habits of the vehicle are the type of charge of the vehicle in the spatiotemporal trace map.
The charging start time of the vehicle is the stop start time of the vehicle at the charging station in the space-time trajectory diagram.
The charging completion time of the vehicle is the stop time of the vehicle at the charging station in the space-time trajectory diagram.
The average daily charging frequency of the vehicle is the number of stops of the vehicle at the charging station in the space-time trajectory graph.
The charging duration of the vehicle is the total length of stay of the vehicle at the charging station in the space-time trajectory diagram.
The travel path information of the vehicle is a summary of the longitude of the travel of the vehicle and the latitude of the travel of the vehicle in the spatiotemporal track diagram.
And secondly, aiming at the battery capacity attenuation condition of the new energy passenger car, assuming that the battery degradation accords with the cut-off normal distribution, carrying out data reconstruction on the vehicle operation data in the target area to obtain the reconstructed operation data of the new energy passenger car.
The data reconstruction process comprises the steps of firstly completing charging attribute identification to determine whether a charging pile is a public charging pile, then analyzing the battery capacity and the battery capacity attenuation condition of a new energy passenger car, completing vehicle battery state assessment by means of a machine learning algorithm and an optimizing algorithm, and completing charging attribute identification and vehicle battery state data reconstruction.
The charging attribute identification is to determine whether the attribute of the charging pile is a public charging pile or a private charging pile according to the charging behavior, the position area and the charging power of the passenger car.
And the data reconstruction of the vehicle battery state is combined with the truncated normal distribution and the battery state which are in accordance with the battery degradation to finish the coupling of the vehicle journey energy consumption prediction model to the battery capacity of the new energy passenger vehicle, so that the sample data is more in accordance with the actual vehicle battery state distribution.
The truncated normal distribution is expressed as follows:
，
，
wherein,probability density function as normal distribution, +.>Probability distribution function as normal distribution, +.>And->Mean and standard deviation, respectively>And->Respectively represent the upper and lower bounds of the random variable.
And thirdly, constructing a vehicle journey energy consumption prediction model according to a travel data set formed by time information interval according to the charging start time, the charging completion time data information, the average charging frequency, the charging duration, the travel path information, the battery capacity attenuation condition and the reconstruction operation data of the new energy passenger vehicle, and combining a Catboost algorithm and an XGBoost algorithm to obtain a vehicle journey energy consumption prediction result.
Specifically, the vehicle journey energy consumption prediction model is composed of a path running state prediction model and a core energy consumption prediction model, and the construction flow chart thereof is shown in fig. 2.
The construction method of the path running state prediction model comprises the following steps: and constructing various energy consumption factors by using a data mining method, and classifying vehicle characteristics by combining a Pearson correlation coefficient (Pearson) and a random forest (RF-OOB) together to divide the vehicle characteristics into vehicle state characteristics and road condition dynamic characteristics. The vehicle state features include the running mileage at the current moment of the vehicle, the vehicle use static information of longitude and latitude and the vehicle current battery information including the current battery capacity, the current battery SOC and the current battery SOH, and the road condition dynamic features refer to the average current and the average speed of the battery of the vehicle running on the current road section. And constructing a path running state prediction model of a Catboost algorithm under the Boosting integrated algorithm framework, and predicting to obtain the path vehicle energy consumption by taking vehicle state characteristics and road condition dynamic characteristics as inputs.
The construction method of the core energy consumption prediction model comprises the following steps: and calculating actual energy consumption in the running process of the vehicle by using an interval mapping energy consumption estimation method, integrating the output of a path running state prediction model and vehicle state characteristics as input, using an XGBoost algorithm under a Boosting integration algorithm frame, establishing a core energy consumption prediction model, and outputting a vehicle energy consumption prediction result.
And fourthly, constructing a vehicle charging energy prediction model based on a Stacking fusion frame, marking and deleting abnormal charging energy values in the model by using a Bayesian Gaussian mixture algorithm, and obtaining a prediction result of the vehicle battery charging energy.
Specifically, the construction method of the vehicle charging energy prediction model comprises the following steps:
firstly, marking and deleting abnormal charge energy values in vehicle charge energy by using a Bayesian Gaussian mixture algorithm, and carrying out distribution statistics on the vehicle charge energy after deleting the abnormal charge energy values, so that the overall distribution of the vehicle charge energy approximately obeys normal distribution, and the overall distribution of the vehicle charge energy approximately obeys the normal distribution and is used as input of a vehicle charge energy prediction model.
The calculation of the vehicle charge energy is expressed as follows:
，
，
wherein,and->Respectively the current time andSOC value at start of charge +.>For coulombic efficiency, +.>And->The battery charging current and the battery capacity are respectively; />Accumulating charging energy for the first k charging moments,/->And->The charging voltage and the charging current of the vehicle at the present time are respectively.
Then selecting a random forest suitable for a linear model in an Embedded embedding method and carrying out feature screening in an L1 regularization mode, wherein the features comprise a battery SOC, a battery capacity value, a battery average temperature value, a battery consistency, a charging power level, a charging average current and a charging starting time, and the screened features comprise the battery SOC, the capacity value and the charging average current;
And finally, taking the Bagging-based random frame and the Boosting-based LGBM as a Stacking base model, taking Lasso as a Stacking meta-model, establishing a charging energy prediction model under a Stacking fusion frame, and predicting the battery charging energy of the vehicle.
And fifthly, carrying out space-time prediction on the charging requirement of the target area by combining a vehicle energy consumption prediction result based on travel energy consumption prediction and a vehicle battery charging energy prediction result.
Specifically, the specific method for performing space-time prediction on the charging demand of the target area comprises the following steps: firstly, a charging behavior decision is completed through a vehicle journey energy consumption prediction model, a charging requirement is acquired, and a flow chart of the charging behavior decision is shown in fig. 3, specifically: firstly, judging according to the current residual SOC value, judging that charging is needed when the SOC value is smaller than a certain threshold value, and judging that potential charging needs exist when the SOC value is larger than the certain threshold value and the parking time is longer than 40 min; and simultaneously, inputting the current residual SOC value into a stroke energy consumption prediction model, obtaining the residual SOC value of the next stroke, judging whether the initial SOC value of the next stroke is smaller than 0, and judging that the initial SOC value of the next stroke is required to be charged when the initial SOC value of the next stroke is smaller than 0, otherwise, judging that the initial SOC value of the next stroke is not required to be charged.
Determining the charging demand of the vehicle through a charging demand prediction result, and carrying out space-time statistics on the points with the demand to obtain the charging peak period demand time distribution; and respectively carrying out charge demand measurement and calculation on the four charge power levels by combining a charge demand decision result and a charge energy prediction model to obtain a daily peak load charge quantity predicted value.
Step six, constructing a double-stage rapid site selection scheme based on a traffic network and charging demands aiming at a target area according to a charging demand space-time prediction result, wherein the first stage combines thermal distribution to screen out an area which most accords with regional distribution and travel characteristics as a charging station candidate area; and the input of the second stage is the charging station candidate region obtained in the first stage, and the K-means algorithm is utilized to carry out iterative calculation on the regional charging demand to obtain the distance average value from the charging station when the charging demand occurs, so as to obtain the optimal address selection of the charging station.
A flow chart of the dual stage fast addressing scheme is shown in particular in fig. 4.
The specific method of the first stage is as follows: taking the vehicle travel parking situation and the regional distribution as guidance, evaluating and sorting the regions needing to be built from the aspects of traffic network congestion degree, vehicle parking hot spot distribution and surrounding POI hot spot distribution, and selecting the region which is most in line with the regional distribution and travel characteristics as a charging station candidate region.
The specific method of the second stage is as follows: selecting a K-means algorithm in a machine learning clustering algorithm, taking a charging station candidate area obtained in the first stage as initial data of the K-means algorithm, iteratively calculating a demand-to-station distance average value by combining the charging demand prediction result until each station meets 95% of charging demand, ending calculation, and outputting a final site selection result.
Step seven, firstly setting charge duration and parking duration indexes, extracting historical charge habits of the vehicle according to a space-time trajectory graph, determining a charge type, and planning the specification of a charging pile in a station to which the vehicle belongs; and integrating charging demand peak value data of the target area in an hour unit according to a charging demand space-time prediction result of the target area, and constructing an optimal matching scheme of the number of each power charging pile according to a time node to finish planning of the specifications and the number of the charging piles of the charging station.
The invention also provides a charging station planning system based on the new energy passenger car charging demand prediction, which consists of a data acquisition module, a data reconstruction module, a vehicle journey energy consumption prediction module, a vehicle charging energy prediction module, a charging demand space-time prediction module, a charging station address selection module and a charging station planning module.
The data acquisition module is used for acquiring the operation data of the new energy passenger car in the target area and removing abnormal data in the new energy passenger car to obtain vehicle operation data, a space-time track diagram of the new energy passenger car is constructed according to the vehicle operation data and the charge-discharge logic of the new energy passenger car, and the charge start time, the charge completion time, the daily charge frequency, the charge duration and the travel path information of the vehicle are extracted through the space-time track diagram.
The data reconstruction module is used for carrying out data reconstruction on the collected operation data of the new energy passenger car in the target area according to the battery capacity attenuation condition of the new energy passenger car, assuming that the battery degradation accords with the cut-off normal distribution, and obtaining the reconstruction operation data of the new energy passenger car.
The vehicle journey energy consumption prediction module constructs a vehicle journey energy consumption prediction model according to a travel data set formed by time information interval of the charging start time, the charging completion time data information, the daily charging frequency, the charging duration, the travel path information, the battery capacity attenuation condition and the reconstruction operation data of the new energy passenger vehicle, and combines a Catboost algorithm and an XGBoost algorithm to obtain a vehicle journey energy consumption prediction result.
The vehicle charging energy prediction module is used for constructing a vehicle charging energy prediction model based on a Stacking fusion frame, and marking and deleting abnormal charging energy values in the model by using a Bayesian Gaussian mixture algorithm to obtain a prediction result of the vehicle battery charging energy.
The space-time prediction module of the charging demand performs space-time prediction on the charging demand of the target area by combining a prediction result of the vehicle energy consumption based on the travel energy consumption prediction and a prediction result of the vehicle battery charging energy.
The method comprises the steps that an addressing module of the charging station builds a double-stage rapid addressing scheme based on a traffic network and charging requirements aiming at a target area, and the first stage combines thermal distribution to screen out an area which most accords with regional distribution and travel characteristics as a charging station candidate area; and the input of the second stage is the charging station candidate region obtained in the first stage, and the K-means algorithm is utilized to carry out iterative calculation on the regional charging demand to obtain the distance average value from the charging station when the charging demand occurs, so as to obtain the optimal address selection of the charging station.
The charging station planning module firstly sets charging duration and parking duration indexes, extracts historical charging habits of the vehicle according to the space-time trajectory graph, determines charging types and plans the specifications of charging piles in the station; and integrating charging demand peak value data of the target area in an hour unit according to a charging demand space-time prediction result of the target area, and constructing an optimal matching scheme of the number of each power charging pile according to a time node to finish planning of the specifications and the number of the charging piles of the charging station.
The specific operation method of each module in the system is described in the steps in the method, and is not described herein.
The invention also proposes a computer device for charging station planning based on new energy passenger vehicle charging demand prediction, comprising a memory, a processor and program instructions stored in the memory for execution by the processor, the processor executing the program instructions to implement the steps in the method and the system described above.
The invention also proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method described above and the system described above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. The charging station planning method based on the new energy passenger car charging demand prediction is characterized by comprising the following steps of:
Firstly, collecting operation data of a new energy passenger car in a target area, removing abnormal data in the operation data to obtain vehicle operation data, constructing a space-time track diagram of the new energy passenger car according to the vehicle operation data and combining charging and discharging logic of the new energy passenger car, and extracting historical charging habit, charging starting time, charging completion time, daily charging frequency, charging duration and driving path information of the vehicle through the space-time track diagram;
step two, aiming at the battery capacity attenuation condition of the new energy passenger car, assuming that the battery degradation accords with the cut-off normal distribution, carrying out data reconstruction on the vehicle operation data in the target area to obtain the reconstructed operation data of the new energy passenger car;
thirdly, constructing a vehicle journey energy consumption prediction model according to a running data set formed by time information interval of the charging starting time, the charging completion time data information, the average charging frequency, the charging duration, the running path information, the battery capacity attenuation condition and the reconstruction running data of the new energy passenger vehicle and combining a Catboost algorithm and an XGBoost algorithm to obtain a vehicle journey energy consumption prediction result;
step four, a vehicle charging energy prediction model based on a Stacking fusion frame is constructed, abnormal charging energy values are marked and deleted in the model by using a Bayesian Gaussian mixture algorithm, and a prediction result of the vehicle battery charging energy is obtained;
Step five, combining a vehicle energy consumption prediction result based on travel energy consumption prediction and a vehicle battery charging energy prediction result to perform space-time prediction on the charging demand of the target area;
step six, constructing a double-stage rapid site selection scheme based on a traffic network and charging demands aiming at a target area according to a charging demand space-time prediction result, wherein the first stage combines thermal distribution to screen out an area which most accords with regional distribution and travel characteristics as a charging station candidate area; the input of the second stage is the charging station candidate region obtained in the first stage, and the K-means algorithm is utilized to carry out iterative calculation on the regional charging demand to obtain the distance average value from the charging station when the charging demand occurs, so as to obtain the optimal address selection of the charging station;
step seven, firstly setting charge duration and parking duration indexes, extracting historical charge habits of the vehicle according to a space-time trajectory graph, determining a charge type, and planning the specification of a charging pile in a station to which the vehicle belongs; and integrating charging demand peak value data of the target area in an hour unit according to a charging demand space-time prediction result of the target area, and constructing an optimal matching scheme of the number of each power charging pile according to a time node to finish planning of the specifications and the number of the charging piles of the charging station.
2. The charging station planning method based on new energy passenger vehicle charging demand prediction according to claim 1, wherein the charging station planning method comprises the following steps: in the first step, the operation data of the new energy passenger car include a vehicle number, a data acquisition time, a vehicle state, a charging state, a vehicle speed, a driving mileage, a total voltage, a total current, a vehicle electric quantity, a highest voltage of a battery cell, a lowest voltage of the battery cell, a highest temperature value, a lowest temperature value, a longitude of vehicle running and a latitude of vehicle running;
the abnormal data comprise missing data, jump data and state marking error data;
the historical charging habit of the vehicle is the charging type of the vehicle in the space-time track diagram;
the charging starting time of the vehicle is the stopping starting time of the vehicle at a charging station in the space-time track diagram;
the charging completion time of the vehicle is the stop time of the vehicle at a charging station in the space-time track diagram;
the average daily charging frequency of the vehicle is the stay frequency of the vehicle at a charging station in a space-time track diagram;
the charging duration time of the vehicle is the total stay time of the vehicle at the charging station in the space-time track diagram;
the travel path information of the vehicle is a summary of the longitude of the vehicle travel and the latitude of the vehicle travel in the space-time trajectory graph.
3. The charging station planning method based on new energy passenger vehicle charging demand prediction according to claim 2, wherein: firstly, completing charging attribute identification to determine whether a charging pile is a public charging pile, then analyzing the battery capacity and the battery capacity attenuation condition of a new energy passenger car, completing vehicle battery state assessment by means of a machine learning algorithm and an optimizing algorithm, and completing charging attribute identification and vehicle battery state data reconstruction;
the charging attribute identification is to determine whether the attribute of the charging pile is a public charging pile or a private charging pile according to the charging behavior, the position area and the charging power of the passenger car;
the data reconstruction of the vehicle battery state is combined with the truncated normal distribution and the battery state which are in accordance with the battery degradation, so that the coupling of the vehicle travel energy consumption prediction model to the battery capacity of the new energy passenger vehicle is completed, and the sample data is more in accordance with the real vehicle battery state distribution;
the truncated normal distribution is represented as follows:
，
，
wherein,probability density function as normal distribution, +.>Is a probability distribution function of a normal distribution,and->Mean and standard deviation, respectively >And->Respectively represent the upper and lower bounds of the random variable.
4. The charging station planning method based on new energy passenger vehicle charging demand prediction according to claim 3, wherein: in the third step, the vehicle journey energy consumption prediction model comprises a path running state prediction model and a core energy consumption prediction model;
the construction method of the path running state prediction model comprises the following steps: constructing various energy consumption factors by utilizing a data mining method, classifying and screening vehicle characteristics by combining a pearson correlation coefficient and a random forest, and dividing the vehicle characteristics into vehicle state characteristics and road condition dynamic characteristics, wherein the vehicle state characteristics are vehicle use static information including the driving mileage and longitude and latitude of a vehicle at the current moment and vehicle current battery information including the current battery capacity, the current battery SOC and the current battery SOH, and the road condition dynamic characteristics are average current and average speed of a battery of the vehicle driving at the current road section; constructing a path running state prediction model of a Catboost algorithm under a Boosting integration algorithm frame, and predicting to obtain path vehicle energy consumption by taking vehicle state characteristics and road condition dynamic characteristics as inputs;
The method for constructing the core energy consumption prediction model comprises the following steps: and calculating actual energy consumption in the running process of the vehicle by using an interval mapping energy consumption estimation method, integrating the output of a path running state prediction model and vehicle state characteristics as input, using an XGBoost algorithm under a Boosting integration algorithm frame, establishing a core energy consumption prediction model, and outputting a vehicle energy consumption prediction result.
5. The charging station planning method based on new energy passenger vehicle charging demand prediction according to claim 1, wherein the charging station planning method comprises the following steps: in the fourth step, the method for constructing the vehicle charging energy prediction model comprises the following steps:
firstly, marking and deleting abnormal charge energy values in vehicle charge energy by using a Bayesian Gaussian mixture algorithm, and carrying out distribution statistics on the vehicle charge energy after deleting the abnormal charge energy values, so that the overall distribution of the vehicle charge energy approximately obeys normal distribution, and taking the vehicle charge energy as the input of a vehicle charge energy prediction model;
the calculation of the vehicle charge energy is represented as follows:
，
，
wherein,and->SOC values at the present time and at the start of charging, respectively,/->In order to achieve a coulombic efficiency,and->The battery charging current and the battery capacity are respectively; />Charging energy is accumulated for the first k charging moments, TRepresents the total charging time of the first k charging moments, < >>And->The charging voltage and the charging current of the vehicle at the current moment are respectively;
then selecting a random forest suitable for a linear model in an Embedded embedding method and carrying out feature screening in an L1 regularization mode, wherein the features comprise a battery SOC, a battery capacity value, a battery average temperature value, a battery consistency, a charging power level, a charging average current and a charging starting time, and the screened features comprise the battery SOC, the capacity value and the charging average current;
and taking the Bagging-based random frame and the Boosting-based LGBM as a Stacking base model, taking Lasso as a Stacking meta-model, establishing a charging energy prediction model under a Stacking fusion frame, and predicting the battery charging energy of the vehicle.
6. The charging station planning method based on new energy passenger vehicle charging demand prediction according to claim 1, wherein the charging station planning method comprises the following steps: in the fifth step, the specific method for performing space-time prediction on the charging requirement of the target area is as follows: firstly, a charging behavior decision is completed through a vehicle journey energy consumption prediction model, a charging demand is acquired, and the flow is as follows: firstly, judging according to the current residual SOC value, judging that charging is needed when the SOC value is smaller than a certain threshold value, and judging that potential charging needs exist when the SOC value is larger than the certain threshold value and the parking time is longer than 40 min; meanwhile, the current residual SOC value is input into a stroke energy consumption prediction model, the next stroke residual SOC value is obtained, whether the next stroke starting SOC value is smaller than 0 or not is judged, when the next stroke starting SOC value is smaller than 0, the current residual SOC value is judged to be required to be charged, and otherwise, the current residual SOC value is judged to be not required to be charged;
Determining the charging demand of the vehicle through a charging demand prediction result, and carrying out space-time statistics on the points with the demand to obtain the charging peak period demand time distribution; and respectively carrying out charge demand measurement and calculation on the four charge power levels by combining a charge demand decision result and a charge energy prediction model to obtain a daily peak load charge quantity predicted value.
7. The charging station planning method based on new energy passenger vehicle charging demand prediction according to claim 1, wherein the charging station planning method comprises the following steps: in the sixth step, the specific method in the first stage is as follows: taking the traveling parking situation and the regional distribution of the vehicles as guidance, evaluating and sorting the regions needing to be built from the aspects of traffic network congestion degree, vehicle parking hot spot distribution and surrounding POI hot spot distribution, and selecting the region which most accords with the regional distribution and traveling characteristics as a charging station candidate region;
the specific method of the second stage is as follows: selecting a K-means algorithm in a machine learning clustering algorithm, taking a charging station candidate area obtained in the first stage as initial data of the K-means algorithm, iteratively calculating a demand-to-station distance average value by combining the charging demand prediction result until each station meets 95% of charging demand, ending calculation, and outputting a final site selection result.
8. Charging station planning system based on new forms of energy passenger car demand prediction that charges, its characterized in that: the system comprises a data acquisition module, a data reconstruction module, a vehicle journey energy consumption prediction module, a vehicle charging energy prediction module, a space-time prediction module of charging requirements, a site selection module of a charging station and a charging station planning module;
the data acquisition module is used for acquiring the operation data of the new energy passenger car in the target area and removing abnormal data in the new energy passenger car to obtain vehicle operation data, constructing a space-time track diagram of the new energy passenger car according to the vehicle operation data and the charge-discharge logic of the new energy passenger car, and extracting the charge start time, the charge completion time, the daily charge frequency, the charge duration and the travel path information of the vehicle through the space-time track diagram;
the data reconstruction module is used for carrying out data reconstruction on the collected operation data of the new energy passenger car in the target area according to the battery capacity attenuation condition of the new energy passenger car, assuming that the battery degradation accords with the cut-off normal distribution, and obtaining the reconstruction operation data of the new energy passenger car;
the vehicle journey energy consumption prediction module constructs a vehicle journey energy consumption prediction model according to a travel data set formed by time information interval of charging start time, charging completion time data information, daily charging frequency, charging duration time, travel path information, battery capacity attenuation conditions and reconstruction operation data of a new energy passenger vehicle, and combines a Catboost algorithm and an XGBoost algorithm to obtain a vehicle journey energy consumption prediction result;
The vehicle charging energy prediction module is used for constructing a vehicle charging energy prediction model based on a Stacking fusion frame, marking and deleting abnormal charging energy values in the model by using a Bayesian Gaussian mixture algorithm, and obtaining a prediction result of the vehicle battery charging energy;
the space-time prediction module of the charging demand performs space-time prediction on the charging demand of the target area by combining a vehicle energy consumption prediction result based on travel energy consumption prediction and a vehicle battery charging energy prediction result;
the method comprises the steps that an addressing module of the charging station builds a double-stage rapid addressing scheme based on a traffic network and charging requirements aiming at a target area, and the first stage combines thermal distribution to screen out an area which most accords with regional distribution and travel characteristics as a charging station candidate area; the input of the second stage is the charging station candidate region obtained in the first stage, and the K-means algorithm is utilized to carry out iterative calculation on the regional charging demand to obtain the distance average value from the charging station when the charging demand occurs, so as to obtain the optimal address selection of the charging station;
the charging station planning module firstly sets charging duration and parking duration indexes, extracts historical charging habits of vehicles according to a space-time trajectory graph, determines charging types and plans the specifications of charging piles in a site; and integrating charging demand peak value data of the target area in an hour unit according to a charging demand space-time prediction result of the target area, and constructing an optimal matching scheme of the number of each power charging pile according to a time node to finish planning of the specifications and the number of the charging piles of the charging station.
9. A computer device for charging station planning based on new energy passenger vehicle charging demand prediction, comprising a memory, a processor and program instructions stored in the memory for the processor to run, characterized in that: the processor executes the program instructions to implement the steps in the method of any one of claims 1 to 7 and the system of claim 8.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 7 and the system of claim 8.
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