CN109703389B - Vehicle pile network integrated charging scheduling device and method based on new energy bus - Google Patents
Vehicle pile network integrated charging scheduling device and method based on new energy bus Download PDFInfo
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention discloses a new energy bus-based integrated charging scheduling device and method for a pile network, and relates to the technical field of new energy bus scheduling. The device mainly comprises a new energy bus-mounted terminal, a charging pile monitoring terminal and a bus commanding and dispatching system for wireless communication, wherein the cloud computing platform and the bus commanding and dispatching system are in wired communication, and the bus commanding and dispatching system issues dispatching information to the mobile terminal and the new energy bus-mounted terminal through a wireless transmission network. The new energy bus dispatching is realized through the vehicle state information, the charging pile state information, the vehicle line operation information, the vehicle charging and driving mileage prediction model and the dispatching optimization model contained in the cloud computing platform, the defect that an existing bus dispatching system is not designed according to the integrated thinking of the new energy bus charging time, the vehicle pile distance, the map service and the like, and meanwhile, the utilization rate of the bus is improved.
Description
Technical Field
The invention relates to the technical field of new energy bus dispatching, in particular to a vehicle pile network integrated charging dispatching device and method based on a new energy bus.
Background
In the public transportation industry, new energy buses have become a common and indispensable transportation means. It relieves the pressure of urban traffic and reduces traffic pollution. The new energy bus is popular with people, and a series of problems are generated. The dispatching scheduling of the public transport system is the basic guarantee of the normal operation of the public transport system, the original public transport dispatching system mainly adopts a dispatching optimal scheme model based on fuel vehicles, the characteristics of charging duration, charging pile position distribution and the like of new energy vehicles are not considered, the dispatching quality is not high due to the fact that the new energy buses are unreasonably charged under the existing condition, and the utilization rate of the new energy buses is low.
Disclosure of Invention
The invention aims to solve the problems that the scheduling quality is not high and the utilization rate of a new energy bus is low due to unreasonable charging of the new energy bus in the prior art, and therefore the invention provides a vehicle pile network integrated charging scheduling device and method based on the new energy bus.
In order to achieve the purpose, the invention provides the following scheme:
a vehicle pile network integrated charging scheduling device based on a new energy bus comprises a new energy bus vehicle-mounted terminal, a charging pile monitoring terminal, a cloud computing platform, a bus commanding and scheduling system and a mobile terminal; the cloud computing platform comprises a vehicle charging and mileage predicting model, a map service model and a scheduling optimization model;
the new energy bus-mounted terminal and the charging pile monitoring terminal are in data communication with the bus commanding and dispatching system through a wireless transmission network; cloud computing platform with adopt wired transmission network to carry out two-way data communication between the public transit command dispatch system, mobile terminal built-in have with public transit command dispatch system assorted APP software, public transit command dispatch system issues scheduling information through wireless transmission network mobile terminal with in the new forms of energy public transit vehicle mounted terminal, and mobile terminal's APP software with show on the driver operation display screen of new forms of energy public transit vehicle mounted terminal.
Optionally, the new energy bus-mounted terminal comprises a navigation positioning device, a driver operation display screen, a vehicle-mounted communication device and an intelligent scheduling terminal; the new energy bus-mounted terminal is used for collecting the vehicle state information and reporting the vehicle state information to the bus commanding and dispatching system for gathering; the vehicle state information comprises vehicle basic information, vehicle use condition, vehicle residual capacity and vehicle position information; the vehicle basic information comprises a vehicle VIN, a license plate number, a vehicle model, a vehicle weight, a battery model and a battery type; the vehicle service condition comprises the service life of the vehicle, the operating mileage of the vehicle and the total charging times.
Optionally, the charging pile monitoring terminal is configured to collect charging pile status information and report the charging pile status information to the bus commanding and dispatching system for summarizing; the charging pile state information comprises the position of the available charging pile, the number of the available charging piles and the available time of the charging pile.
Optionally, the bus commanding and dispatching system is configured to collect and transmit vehicle state information acquired by the new energy bus-mounted terminal, charging pile state information acquired by the charging pile monitoring terminal, and vehicle route operation information acquired by the bus commanding and dispatching system to the cloud computing platform; the vehicle route operation information comprises a bus route number, a vehicle operation time interval and next charging time.
Optionally, the cloud computing platform collects the acquired vehicle state information, charging pile state information and vehicle route operation information, analyzes and calculates the acquired vehicle state information, charging and mileage predicting model, the map service and the scheduling optimization model, dynamically generates scheduling information and sends the scheduling information to the bus commanding and scheduling system to perform scheduling operation on the vehicle; the scheduling information includes a scheduling scheme and navigation information.
Optionally, the map service is configured to obtain real-time traffic information and route navigation information; the map service provides space geographic basic data, position data map display, real-time road condition data and path navigation functions for the vehicle charging and mileage predicting model and the scheduling optimization model;
the input of the vehicle charging and mileage predicting model is vehicle state information, charging pile state information and vehicle line operation information, and the output is predicted driving information data after a plurality of groups of vehicles are charged;
the input of the scheduling optimization model is a plurality of groups of optimized data, and the output is the charging time of the vehicle and the position information of the charging pile; each group of optimized data comprises predicted running information data, vehicle state information, charging pile state information and vehicle line operation information of a group of vehicles after charging;
and the cloud computing platform generates scheduling information according to the vehicle line operation information, the map service and the charging time and charging pile position information of the vehicle output by the scheduling optimization model.
A charging scheduling method applying a new energy bus-based pile-grid integrated charging scheduling device comprises the following steps:
acquiring vehicle state information, charging pile state information and vehicle line operation information;
inputting the vehicle state information, the charging pile state information and the vehicle route operation information into a vehicle charging and driving mileage prediction model, and outputting a plurality of groups of vehicle charging post-prediction driving information data according to each vehicle operation time interval and charging pile position distribution information to form a vehicle charging post-prediction driving information set; each set of the predicted driving information data after the vehicle is charged comprises a predicted no-load running mileage, a predicted no-load running time, a predicted charging starting SOC, a predicted charging ending SOC, a predicted charging electric quantity, a predicted conveying speed (kilometer/hour), a predicted lap ending time, a predicted lap ending SOC, a predicted operation mileage after the current charging, a predicted operation number after the current charging, a predicted next charging time and a predicted next vehicle charging no-load running mileage;
fusing a plurality of groups of predicted driving information data after the vehicle is charged, the vehicle state information, the charging pile state information and the vehicle line operation information to generate a plurality of groups of optimized data;
sequentially inputting a plurality of groups of optimized data into a scheduling optimization model, and optimizing and outputting the charging time and the charging pile position information of the vehicle;
generating scheduling information according to the charging time of the vehicle and the position information of the charging pile by combining map service and vehicle route operation information; the scheduling information comprises a scheduling scheme and navigation information;
and sending the scheduling information to a bus commanding and scheduling system by taking the vehicle VIN as an identifier so as to realize the scheduling operation of the vehicle.
Optionally, before sequentially inputting multiple sets of the optimized data into the scheduling optimization model and optimizing the charging time of the vehicle and the charging pile position information, the charging scheduling method further specifically includes:
determining a decision variable of a target optimization function in the scheduling optimization model; the decision variables comprise predicted no-load running mileage, predicted no-load running time, predicted charging duration, predicted charging starting SOC, predicted charging ending SOC, predicted charging electric quantity, predicted conveying speed (kilometer/hour), predicted trip ending time, predicted trip ending SOC, predicted operation mileage after the current charging, predicted operation number after the current charging, predicted next charging time, predicted next vehicle charging no-load running mileage, vehicle state information, charging pile state information and vehicle line operation information;
fusing a plurality of groups of the predicted driving information data after the vehicle is charged, the vehicle state information, the charging pile state information and the vehicle line operation information to generate a decision variable set; the decision variable set comprises a plurality of groups of decision variable data;
determining the weight of each decision variable by adopting a factor-molecule algorithm according to a plurality of groups of decision variable data;
and determining an objective optimization function according to each decision variable and the weight corresponding to the decision variable.
Optionally, the objective optimization function is: fk＝ω1*X1+ω2*X2......+ωn*Xn；
Wherein, X1、X2、X3… …, Xn indicates that there are n decision variables; ω represents a weight; fkThe optimization score for the k-th vehicle is represented.
Optionally, the optimizing data of the plurality of groups are sequentially input into the scheduling optimization model, and charging time of the vehicle and charging pile position information are optimized and output, specifically including:
sequentially inputting a plurality of groups of optimized data into the target optimization function to obtain a plurality of vehicle optimization scores;
when the vehicle optimization scores are all smaller than 0, the vehicle does not carry out charging in the vehicle operation time interval;
when the vehicle optimization score is larger than or equal to 0, the highest vehicle optimization score is selected from the vehicle optimization scores larger than or equal to 0, the vehicle operation time interval in the optimization data corresponding to the highest vehicle optimization score is determined as the charging time of the vehicle, and the available charging pile position in the optimization data corresponding to the highest vehicle optimization score is determined as the charging pile position information of the vehicle.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a vehicle pile network integrated charging scheduling device and method based on a new energy bus. The device comprises a new energy bus-mounted terminal, a charging pile monitoring terminal, a cloud computing platform, a bus commanding and scheduling system and a mobile terminal. The cloud computing platform comprises a vehicle charging and mileage forecasting model, a map service model and a scheduling optimization model. The new energy bus-mounted terminal and the charging pile monitoring terminal are in data communication with the bus commanding and dispatching system through a wireless transmission network, the cloud computing platform is in wired communication with the bus commanding and dispatching system, the mobile terminal is internally provided with an APP matched with the bus commanding and dispatching system, and the bus commanding and dispatching system issues dispatching information to the mobile terminal and the new energy bus-mounted terminal through the wireless transmission network. According to the new energy bus dispatching device and method, the new energy bus dispatching device and method are established through the vehicle state information, the charging pile state information, the vehicle route operation information, the driving mileage prediction model and the dispatching optimization model when the vehicle is charged, the defect that a bus dispatching system in the prior art is not designed according to the integrated concept of 'bus pile net' such as long charging time, distance of bus piles, map service and the like of the new energy bus is overcome, the problems that the dispatching quality is low and the utilization rate of the new energy bus is low due to unreasonable charging of the new energy bus in the prior art are solved, and meanwhile, the utilization rate of the bus is improved.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions of the present invention, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained by those skilled in the art without inventive efforts.
Fig. 1 is a block diagram of a vehicle pile-network integrated charging scheduling device based on a new energy bus according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a vehicle pile network integrated charging scheduling method based on a new energy bus according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a vehicle charging and mileage predicting model according to an embodiment of the present invention;
FIG. 4 is a block diagram of a map service according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of an optimized scheduling model according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The national standard GB/T32960 technical Specification of electric vehicle remote service and management System and 2016, 1 month and 10 days are officially implemented, and the system is widely applied and popularized nationwide at present, and provides a good support platform for monitoring new energy vehicles. The method is used for issuing a notice (issue change basis [ 2016 ] 1681 ]) of an implementation scheme for promoting the development of the intelligent traffic by promoting the 'Internet plus' convenient traffic, and provides a system implementation scheme reference for 'vehicle pile net' integrated scheduling.
Example one
Fig. 1 is a block diagram of a structure of a post network integrated charging scheduling device based on a new energy bus according to an embodiment of the present invention, and as shown in fig. 1, the post network integrated charging scheduling device provided in the embodiment of the present invention includes a new energy bus-mounted terminal, a charging pile monitoring terminal, a cloud computing platform, a bus commanding and scheduling system, and a mobile terminal. The cloud computing platform comprises a vehicle charging and mileage predicting model, a map service model and a scheduling optimization model.
And the new energy bus-mounted terminal and the charging pile monitoring terminal are in data communication with the bus commanding and dispatching system through a wireless transmission network. And the cloud computing platform and the bus commanding and dispatching system are in bidirectional data communication by adopting a wired transmission network. The mobile terminal is internally provided with APP software matched with the bus commanding and dispatching system, the bus commanding and dispatching system issues dispatching information to the mobile terminal and the new energy bus-mounted terminal through a wireless transmission network, and the APP software of the mobile terminal and a driver operation display screen of the new energy bus-mounted terminal display. The scheduling information includes a scheduling scheme and navigation information.
The new energy bus-mounted terminal comprises a navigation positioning device, a driver operation display screen, a vehicle-mounted communication device and an intelligent scheduling terminal.
And the new energy bus-mounted terminal is used for acquiring the vehicle state information and reporting the vehicle state information to the bus commanding and dispatching system for summarizing. The vehicle state information includes vehicle basic information (vehicle VIN, license plate number, vehicle model, vehicle weight, battery model, battery type), vehicle service condition (vehicle service life, vehicle operating mileage, total charging times), vehicle remaining capacity, and vehicle position information.
The charging pile monitoring terminal is used for collecting charging pile state information and reporting the charging pile state information to the bus commanding and dispatching system for gathering. The charging pile state information comprises the position of the available charging pile, the number of the available charging piles and the available time of the charging pile.
And the bus commanding and dispatching system is responsible for transmitting the collected vehicle state information, the charging pile state information and the vehicle line operation information to the cloud computing platform. The bus line operation information is directly collected and summarized by the bus commanding and dispatching system, and comprises bus line numbers, bus operation time intervals and next charging time.
The cloud computing platform collects the acquired vehicle state information, charging pile state information and vehicle route operation information, analyzes and calculates by combining the vehicle charging and mileage predicting model, the map service and the scheduling optimization model, dynamically generates scheduling information and sends the scheduling information to the bus commanding and scheduling system so as to perform scheduling operation on the vehicle.
The map service is used for acquiring real-time road condition information and path navigation information; the map service provides the vehicle charging and mileage predicting model and the scheduling optimization model with functions of space geographic basic data, position data map display, real-time road condition data and path navigation.
The input of the vehicle charging and mileage predicting model is vehicle state information, charging pile state information and vehicle line operation information, and the predicted driving information data after the plurality of groups of vehicles are charged is output.
The input of the scheduling optimization model is a plurality of groups of optimized data, and the output is the charging time of the vehicle and the position information of the charging pile; each group of optimized data comprises predicted running information data, vehicle state information, charging pile state information and vehicle line operation information of a group of vehicles after charging.
And the cloud computing platform generates scheduling information according to the vehicle line operation information, the map service and the charging time and charging pile position information of the vehicle output by the scheduling optimization model.
The mobile terminal comprises a tablet and a mobile phone, wherein the APP software in the tablet and the mobile phone is internally provided with a navigation function and used for receiving and displaying the dispatching information issued by the bus commanding and dispatching system.
And the driver operation display screen of the new energy bus-mounted terminal is used for displaying the charging scheduling information and the charging navigation information issued by the bus commanding and scheduling system.
Example two
Fig. 2 is a schematic flow chart of a method for scheduling integrated charging of a vehicle pile network based on a new energy bus according to an embodiment of the present invention, and as shown in fig. 2, the method for scheduling integrated charging of a vehicle pile network according to an embodiment of the present invention is applied to a device described in the first embodiment, and includes the following steps.
Step 101: acquiring vehicle state information, charging pile state information and vehicle line operation information; the vehicle state information comprises vehicle basic information (vehicle VIN, license plate number, vehicle model, vehicle weight, battery model and battery type), vehicle use condition (vehicle service life, vehicle operation mileage and total charging times), vehicle residual capacity and vehicle position information; the charging pile state information comprises the position of the available charging piles, the number of the available charging piles and the available time of the charging piles; the vehicle route operation information comprises a bus route number, a vehicle operation time interval and next charging time.
Step 102: inputting the vehicle state information, the charging pile state information and the vehicle route operation information into a vehicle charging and driving mileage prediction model, and outputting a plurality of groups of vehicle charging post-prediction driving information data according to each vehicle operation time interval and charging pile position distribution information to form a vehicle charging post-prediction driving information set; each set of the vehicle post-charging predicted driving information data comprises a predicted no-load running mileage, a predicted no-load running time, a predicted charging starting SOC, a predicted charging ending SOC, a predicted charging electric quantity, a predicted conveying speed (kilometer/hour), a predicted lap ending time, a predicted lap ending SOC, a predicted post-charging running mileage, a predicted post-charging running number of circles, a predicted next charging time and a predicted next vehicle charging no-load running mileage.
Step 103: and fusing the plurality of groups of the predicted running information data after the vehicle is charged, the vehicle state information, the charging pile state information and the vehicle line operation information to generate a plurality of groups of optimized data. The number of sets of the predicted travel information data after the vehicle is charged is the same as the number of sets of the optimized data.
Step 104: and sequentially inputting the multiple groups of optimized data into a scheduling optimization model, and optimizing and outputting the charging time of the vehicle and the position information of the charging pile.
Step 105: generating scheduling information according to the charging time of the vehicle and the position information of the charging pile by combining map service and vehicle route operation information; the scheduling information includes a scheduling scheme and navigation information.
Step 106: and sending the scheduling information to a bus commanding and scheduling system by taking the vehicle VIN as an identifier so as to realize the scheduling operation of the vehicle.
Wherein, before executing step 104, the method further comprises:
determining a decision variable of a target optimization function in the scheduling optimization model; the decision variables comprise predicted no-load running mileage, predicted no-load running time, predicted charging duration, predicted charging starting SOC, predicted charging ending SOC, predicted charging electric quantity, predicted conveying speed (kilometer/hour), predicted trip ending time, predicted trip ending SOC, predicted charging post-running mileage, predicted charging post-running circles, predicted next charging time, predicted next vehicle charging no-load running mileage, vehicle state information, charging pile state information and vehicle line operation information.
Fusing a plurality of groups of the predicted driving information data after the vehicle is charged, the vehicle state information, the charging pile state information and the vehicle line operation information to generate a decision variable set; the set of decision variables includes a plurality of sets of decision variable data.
And determining the weight of each decision variable by adopting a factor-molecule algorithm according to the plurality of groups of decision variable data.
And determining an objective optimization function according to each decision variable and the weight corresponding to the decision variable. The objective optimization function is: fk＝ω1*X1+ω2*X2......+ωn*Xn(ii) a Wherein, X1、X2、X3… …, Xn indicates that there are n decision variables; ω represents a weight; fkThe optimization score for the k-th vehicle is represented.
Step 104 specifically includes: and sequentially inputting the multiple groups of optimized data into the target optimization function to obtain multiple vehicle optimization scores.
And when the vehicle optimization scores are all smaller than 0, the vehicle does not carry out charging in the vehicle operation time interval.
When the vehicle optimization score is larger than or equal to 0, the highest vehicle optimization score is selected from the vehicle optimization scores larger than or equal to 0, the vehicle operation time interval in the optimization data corresponding to the highest vehicle optimization score is determined as the charging time of the vehicle, and the available charging pile position in the optimization data corresponding to the highest vehicle optimization score is determined as the charging pile position information of the vehicle.
EXAMPLE III
The integrated charging scheduling device for the vehicle pile network comprises a new energy bus-mounted terminal, a charging pile monitoring terminal, a cloud computing platform, a bus commanding and scheduling system and a mobile terminal. The cloud computing platform comprises a vehicle charging and mileage predicting model, a map service model and a scheduling optimization model.
And the new energy bus-mounted terminal and the charging pile monitoring terminal are in data communication with the bus commanding and dispatching system through a wireless transmission network. And the cloud computing platform and the bus commanding and dispatching system are in bidirectional data communication by adopting a wired transmission network. The mobile terminal is internally provided with APP software matched with the bus commanding and dispatching system, the bus commanding and dispatching system issues dispatching information to the mobile terminal and the new energy bus-mounted terminal through a wireless transmission network, and the APP software of the mobile terminal and a driver operation display screen of the new energy bus-mounted terminal display. The scheduling information includes charging scheduling information and charging navigation information.
As a preferred scheme of the invention, the bus commanding and dispatching system collects and provides vehicle state information, charging pile state information and vehicle route operation information.
The vehicle state information includes vehicle basic information (vehicle VIN, license plate number, vehicle model, vehicle weight, battery model, battery type), vehicle usage (vehicle life, vehicle operating mileage, total number of charges), vehicle remaining capacity, and vehicle position information.
The charging pile state information comprises the position and the quantity of the available charging piles and the available time of the charging piles.
The vehicle route operation information comprises the number of the bus route, the time interval of the vehicle trip and the next charging time
As a preferred aspect of the present invention, the vehicle charging and mileage predicting model performs predictive planning on the charging behavior of the vehicle within the vehicle trip interval time period, as shown in fig. 3, and the input information of the vehicle charging and mileage predicting model includes vehicle state information, available charging pile positions, and vehicle trip intervals.
The map service provides basic map data service, bus route data service, weather information service, real-time road condition data service and path navigation service.
The output result of the vehicle charging and driving mileage prediction model comprises a predicted no-load running mileage, a predicted no-load running time, a predicted charging starting SOC, a predicted charging ending SOC, a predicted charging electric quantity, a predicted conveying speed (kilometer/hour), a predicted trip ending time, a predicted trip ending SOC, a predicted operation mileage after the current charging, a predicted operation number after the current charging, a predicted next charging time and a predicted vehicle charging no-load running mileage next time.
And outputting a plurality of groups of output results according to different available charging pile positions at the interval of each vehicle.
As a preferred scheme of the present invention, the map service is as shown in fig. 4, and a Baidu map open platform javascriptAPI v2.0 is adopted to obtain services such as basic map data service, bus route data service, weather information service, real-time traffic data service, and route navigation service, and the services are packaged to form a vehicle scheduling map service dedicated for a bus scheduling system.
As a preferred embodiment of the present invention, the scheduling optimization model is shown in fig. 5, which is an objective optimization function of the scheduling optimization model. Described in mathematical language, the objective optimization function u ═ f (x), x ═ x (x)1，...，xn) X ∈ Ω under constraint hi(x) 0, i 1, 2, 1, m and gi(x)≥0(gi(x) ≦ 0), i ≦ 1, 2, ·., maximum or minimum under p, where f (x) is the objective optimization function, x is the decision variable, and Ω is the feasible region.
Assuming that the value of the objective optimization function corresponding to the benefit over the disadvantage, the benefit over the disadvantage and the disadvantage equal, and the benefit over the disadvantage respectively is more than 0, equal to 0, and less than 0, whether the charging scheduling needs to be executed is judged according to the value of the objective optimization function. And selecting the charging pile corresponding to the maximum value according to the score larger than 0 to determine the charging pile used in the charging scheduling.
The decision variables comprise vehicle state information, charging pile state information, vehicle line operation information and all output information of a vehicle charging and mileage predicting model.
Part of the information in the state monitoring information of the vehicle, which is contained in the decision variables, is vehicle basic information (vehicle model, vehicle weight, battery model, battery type), vehicle service conditions (vehicle service life, vehicle operating mileage, total charging times), vehicle remaining capacity and vehicle position information.
And the decision variables comprise the positions and the quantity of the available charging piles and the available time of the charging piles in part of the state monitoring information of the charging piles.
The decision variables comprise all output information of the vehicle charging and driving mileage prediction model, namely predicted no-load running mileage, predicted no-load running time, predicted charging duration, predicted charging starting SOC, predicted charging ending SOC, predicted charging electric quantity, predicted conveying speed (kilometer/hour), predicted trip ending time, predicted trip ending SOC, predicted operation mileage after the charging, predicted operation number after the charging, predicted next charging time and predicted vehicle charging no-load running mileage of the next time.
The objective optimization function determines the weight of each decision variable by using a factor analysis method of SPSS (Statistical Product and Service Solutions software). The method mainly comprises the following steps:
(1) the sets of decision variable data are first normalized, which takes into account the dimensional inconsistencies between the different data, and thus must be non-dimensionalized.
(2) The normalized sets of decision variable data are subjected to a factor analysis (principal component method) using variance maximization rotation.
(3) The prime score and the equation contribution rate for each prime are written.
Fj＝β1j*X1+β2j*X2......+βnj*Xn；
Wherein, FjIs a main factor (j is 1, 2, … …, m), X1、X2、X3… …, Xn are individual decision variables, β1j、β2j、β3j、……、βnjFor each decision variable at a main factor FjThe equation contribution rate of Fj is represented by ej.
(4) And (5) calculating the index weight.
(5) Obtaining a target optimization function;
the objective optimization function is: fk＝ω1*X1+ω2*X2......+ωn*Xn；
Wherein, X1、X2、X3… …, Xn indicates that there are n decision variables; ω represents a weight; fkExpressing the optimization score of the k-th vehicle
Determining input data participating in a SPSS factor analysis method by sampling, analyzing and counting similar historical data of a local area; considering that the factor weights of different scenes are different, the actual weights support manual additional adjustment under the condition that the input data and the factor analysis method of the SPSS are not changed.
And aiming at a plurality of groups of results output by the vehicle charging and driving mileage prediction model of each vehicle and the vehicle operation time interval, performing score calculation according to the scheduling optimization model, and acquiring the score of each combined target function so as to obtain the highest score of the vehicle in the specified vehicle operation interval.
And judging whether the highest score is greater than 0, if so, generating a scheduling scheme and navigation information according to the scheme with the highest score by combining map service.
And sending the scheduling scheme and the navigation information to a bus commanding and scheduling system by taking the vehicle VIN as an identifier.
And the bus commanding and dispatching system issues a dispatching instruction to the mobile terminal.
As a preferred scheme of the invention, the data acquisition and transmission frequency of the bus commanding and dispatching system is once in 10 seconds, and the dispatching instruction is issued in real time.
As a preferred scheme of the invention, the mobile terminal APP is realized by adopting a built-in Baidu map SDK and comprises two operating systems of Android/iOS. The map SDK provides development tools for maps, positioning, tracking, navigation, and the like.
Compared with the prior art, the invention has the beneficial effects that:
1. aiming at the new characteristics of new energy vehicle scheduling, the current state of the vehicle, the charging pile information, the route plan scheduling information and the road condition information are integrated, an optimal scheduling scheme is designed, the optimal scheduling scheme of the bus is calculated and analyzed through the system, and the scheduling timeliness and the scheduling rationality can be improved by combining with a manual intervention result.
2. And the scheduling data is summarized through the operation condition, so that the method is beneficial to optimizing and adjusting the vehicle type selection, the vehicle stop station and the charging pile distribution in the public transportation industry, the service quality is finally improved, and the operation cost is reduced.
3. The system can also be used for generating, evaluating, optimizing and other scenes of the scheduling plan of the public transport system, and the application can also be expanded to the scheduling of new energy logistics vehicles and special vehicles.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (9)
1. A vehicle pile network integrated charging scheduling device based on a new energy bus is characterized by comprising a new energy bus vehicle-mounted terminal, a charging pile monitoring terminal, a cloud computing platform, a bus commanding and scheduling system and a mobile terminal; the cloud computing platform comprises a vehicle charging and mileage predicting model, a map service model and a scheduling optimization model;
the new energy bus-mounted terminal and the charging pile monitoring terminal are in data communication with the bus commanding and dispatching system through a wireless transmission network; the cloud computing platform and the bus commanding and dispatching system are in bidirectional data communication by adopting a wired transmission network, APP software matched with the bus commanding and dispatching system is arranged in the mobile terminal, the bus commanding and dispatching system issues dispatching information to the mobile terminal and the new energy bus-mounted terminal through a wireless transmission network, and the dispatching information is displayed on the APP software of the mobile terminal and a driver operation display screen of the new energy bus-mounted terminal;
the map service is used for acquiring real-time road condition information and path navigation information; the map service provides space geographic basic data, position data map display, real-time road condition data and path navigation functions for the vehicle charging and mileage predicting model and the scheduling optimization model;
the input of the vehicle charging and mileage predicting model is vehicle state information, charging pile state information and vehicle line operation information, and the output is predicted driving information data after a plurality of groups of vehicles are charged;
the input of the scheduling optimization model is a plurality of groups of optimized data, and the output is the charging time of the vehicle and the position information of the charging pile; each group of optimized data comprises predicted running information data, vehicle state information, charging pile state information and vehicle line operation information of a group of vehicles after charging;
and the cloud computing platform is used for generating scheduling information according to the vehicle line operation information, the map service and the charging time and charging pile position information of the vehicle output by the scheduling optimization model.
2. The integrated charging scheduling device of stake net of claim 1, wherein the new energy bus-mounted terminal comprises navigation positioning, a driver operation display screen, vehicle-mounted communication equipment and an intelligent scheduling terminal; the new energy bus-mounted terminal is used for collecting the vehicle state information and reporting the vehicle state information to the bus commanding and dispatching system for gathering; the vehicle state information comprises vehicle basic information, vehicle use condition, vehicle residual capacity and vehicle position information; the vehicle basic information comprises a vehicle VIN, a license plate number, a vehicle model, a vehicle weight, a battery model and a battery type; the vehicle service condition comprises the service life of the vehicle, the operating mileage of the vehicle and the total charging times.
3. The integrated charging and dispatching device for the vehicle pile network according to claim 1, wherein the charging pile monitoring terminal is used for acquiring charging pile state information and reporting the charging pile state information to the bus commanding and dispatching system for gathering; the charging pile state information comprises the position of the available charging pile, the number of the available charging piles and the available time of the charging pile.
4. The integrated charging and dispatching device for the pile network of the new energy bus according to claim 1, wherein the bus commanding and dispatching system is used for collecting and transmitting the vehicle state information collected by the new energy bus-mounted terminal, the charging pile state information collected by the charging pile monitoring terminal and the vehicle line operation information collected by the bus commanding and dispatching system to the cloud computing platform; the vehicle route operation information comprises a bus route number, a vehicle operation time interval and next charging time.
5. The vehicle pile-network integrated charging scheduling device of claim 4, wherein the cloud computing platform collects the acquired vehicle state information, charging pile state information and vehicle route operation information, analyzes and calculates in combination with the vehicle charging and mileage predicting model, the map service and the scheduling optimization model, dynamically generates scheduling information and sends the scheduling information to the bus commanding and scheduling system to perform scheduling operation on the vehicle; the scheduling information includes a scheduling scheme and navigation information.
6. The charging scheduling method based on the new energy bus pile-grid integrated charging scheduling device is characterized by comprising the following steps of:
acquiring vehicle state information, charging pile state information and vehicle line operation information;
inputting the vehicle state information, the charging pile state information and the vehicle route operation information into a vehicle charging and driving mileage prediction model, and outputting a plurality of groups of vehicle charging post-prediction driving information data according to each vehicle operation time interval and charging pile position distribution information to form a vehicle charging post-prediction driving information set; each set of the predicted driving information data after the vehicle is charged comprises a predicted no-load running mileage, a predicted no-load running time, a predicted charging starting SOC, a predicted charging ending SOC, a predicted charging electric quantity, a predicted conveying speed (kilometer/hour), a predicted lap ending time, a predicted lap ending SOC, a predicted operation mileage after the current charging, a predicted operation number after the current charging, a predicted next charging time and a predicted next vehicle charging no-load running mileage;
fusing a plurality of groups of predicted driving information data after the vehicle is charged, the vehicle state information, the charging pile state information and the vehicle line operation information to generate a plurality of groups of optimized data;
sequentially inputting a plurality of groups of optimized data into a scheduling optimization model, and optimizing and outputting the charging time and the charging pile position information of the vehicle;
generating scheduling information according to the charging time of the vehicle and the position information of the charging pile by combining map service and vehicle route operation information; the scheduling information comprises a scheduling scheme and navigation information;
and sending the scheduling information to a bus commanding and scheduling system by taking the vehicle VIN as an identifier so as to realize the scheduling operation of the vehicle.
7. The charge scheduling method according to claim 6, wherein before the optimized data sets are sequentially input into the scheduling optimization model and the charging time and the charging pile position information of the vehicle are optimized and output, the charge scheduling method further comprises:
determining a decision variable of a target optimization function in the scheduling optimization model; the decision variables comprise predicted no-load running mileage, predicted no-load running time, predicted charging duration, predicted charging starting SOC, predicted charging ending SOC, predicted charging electric quantity, predicted conveying speed (kilometer/hour), predicted trip ending time, predicted trip ending SOC, predicted operation mileage after the current charging, predicted operation number after the current charging, predicted next charging time, predicted next vehicle charging no-load running mileage, vehicle state information, charging pile state information and vehicle line operation information;
fusing a plurality of groups of the predicted driving information data after the vehicle is charged, the vehicle state information, the charging pile state information and the vehicle line operation information to generate a decision variable set; the decision variable set comprises a plurality of groups of decision variable data;
determining the weight of each decision variable by adopting a factor-molecule algorithm according to a plurality of groups of decision variable data;
and determining an objective optimization function according to each decision variable and the weight corresponding to the decision variable.
8. The charge scheduling method of claim 7, wherein the objective optimization function is: fk＝ω1*X1+ω2*X2......+ωn*Xn；
Wherein, X1、X2、X3… …, Xn indicates that there are n decision variables; ω represents a weight; fkThe optimization score for the k-th vehicle is represented.
9. The charge scheduling method according to claim 8, wherein the sequentially inputting the multiple sets of the optimized data into the scheduling optimization model to optimize and output the charging time and the charging pile position information of the vehicle specifically comprises:
sequentially inputting a plurality of groups of optimized data into the target optimization function to obtain a plurality of vehicle optimization scores;
when the vehicle optimization scores are all smaller than 0, the vehicle does not carry out charging in the vehicle operation time interval;
when the vehicle optimization score is larger than or equal to 0, the highest vehicle optimization score is selected from the vehicle optimization scores larger than or equal to 0, the vehicle operation time interval in the optimization data corresponding to the highest vehicle optimization score is determined as the charging time of the vehicle, and the available charging pile position in the optimization data corresponding to the highest vehicle optimization score is determined as the charging pile position information of the vehicle.
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| CN113673069B (en) * | 2020-05-14 | 2023-11-03 | 南京行者易智能交通科技有限公司 | New energy bus charging model design method and device and mobile terminal equipment |
| CN113012460B (en) * | 2021-02-24 | 2022-03-11 | 华录智达科技股份有限公司 | Single-line bus charging control system and method |
| CN112907083B (en) * | 2021-02-24 | 2024-01-23 | 华录智达科技股份有限公司 | Multi-line bus charging control system and method |
| CN113400960B (en) * | 2021-08-05 | 2022-04-26 | 安徽交欣科技股份有限公司 | Charging scheduling method based on electric bus |
| CN114257961B (en) * | 2021-12-22 | 2023-12-19 | 安必圣汽车科技(常熟)有限公司 | UWB positioning and guiding system for charging new energy bus |
| CN115130824A (en) * | 2022-05-30 | 2022-09-30 | 广东天枢新能源科技有限公司 | Pure electric bus scheduling method and device, electronic equipment and storage medium |
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