CN116384542A - Method and system for predicting charging load of electric automobile in expressway service area - Google Patents
Method and system for predicting charging load of electric automobile in expressway service area Download PDFInfo
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Abstract
The invention provides a method and a system for predicting charging load of an electric automobile in a highway service area, which belong to the field of energy traffic, and the method comprises the following steps: determining an OD matrix according to road network data of the expressway, and simulating a driving path of the electric automobile; determining the endurance mileage of the electric vehicle before the electric vehicle reaches the anxiety electric quantity based on an electric vehicle energy consumption model according to the initial charge state, the battery capacity, the anxiety electric quantity, the battery output power and the running speed of the electric vehicle; based on the travel path, the predicted charging power and the initial charging time of the electric vehicle in each energy supply service area are determined according to the distance between the electric vehicle and each service area, the endurance mileage, the travel distance, the time of the upper high speed, the charging times, the charging duration and the supply power of the charging pile, so that the charging load of the electric vehicle in each energy supply service area is determined, and finally the charging load of each energy supply service area is determined. The method improves the prediction accuracy of the charging load of the electric automobile on the expressway.
Description
Technical Field
The invention relates to the field of energy traffic, in particular to a method and a system for predicting charging load of an electric vehicle in a highway service area by considering mileage attenuation.
Background
To address the traditional energy shortages, climate changes and environmental issues, the realization of "carbon neutralization" and "carbon peaking" energy architecture is moving towards energy low-carbon conversion. Compared with the traditional automobile, the electric automobile is cleaner and environment-friendly, and the energy utilization efficiency is higher. With the increasing number of electric vehicles, the connection between the traffic system and the power system is becoming more and more compact. The long-distance travel of the electric automobile brings about the energy supply requirement of the expressway, however, the development speed of the electric automobile infrastructure of the expressway network is far slower than the increase speed of the inter-city travel requirement of the electric automobile. In order to slow down the anxiety of the mileage of the driver of the electric automobile and promote the vigorous development of the market of the electric automobile, the development of the supporting infrastructure of the electric automobile on the expressway should be promoted. The electric vehicle charging load prediction result can be used as a basis for selecting the capacity of a construction charging station, and can provide guidance for the energy scheduling of the expressway micro-grid.
The traditional charge load prediction method judges the charge behavior of the electric automobile through the power consumption per kilometer or the endurance mileage of the electric automobile, so as to further predict the charge load of the electric automobile. When the electric automobile runs at a low speed, the power consumption of the automobile is not changed greatly every kilometer, the endurance mileage is accurate, and the method is suitable for urban scenes. When an electric automobile runs at a high speed, serious mileage attenuation is generated by the increase of the energy consumption of the electric automobile, the energy supply requirement of the electric automobile is analyzed by adopting average energy consumption and endurance mileage, and then the charging load of the electric automobile on a highway is predicted, so that larger deviation is generated on the space-time distribution of the charging load of the electric automobile, the traditional charging load prediction method is not suitable for the charging load prediction of the electric automobile in a highway scene, errors are generated, and the determination of the charging station capacity and the energy scheduling of a highway microgrid are difficult to provide guidance.
Disclosure of Invention
The invention aims to provide a method and a system for predicting the charging load of an electric automobile in a highway service area, which can improve the prediction accuracy of the charging load of the electric automobile on a highway.
In order to achieve the above object, the present invention provides the following solutions:
a method for predicting the charging load of an electric automobile in a highway service area comprises the following steps:
obtaining road network data of a highway; the road network data comprise node positions, energy supply service area positions and inter-node traffic flow of each period in a set period;
determining an OD matrix according to the road network data; the elements in the OD matrix are the number of vehicles running from the node i to the node j in two adjacent time periods in a set period; i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to m, and m is the number of nodes of the expressway;
simulating the driving path of each electric automobile based on the OD matrix to determine the travel distance and the travel path of each electric automobile;
determining the endurance mileage of any electric vehicle on the expressway before the electric vehicle reaches the anxiety electric quantity based on an electric vehicle energy consumption model according to the initial charge state, the battery capacity, the anxiety electric quantity, the battery output power and the running speed of the electric vehicle; the energy consumption model of the electric automobile is established in advance according to the energy conversion efficiency and the loss information of the electric automobile; the anxiety electric quantity is the lowest residual electric quantity of the preset electric automobile before being charged on the expressway; the initial state of charge is the state of charge of the electric automobile at a high speed;
Based on the travel path of the electric automobile, determining predicted charging power of the electric automobile in each energy supply service area according to the endurance mileage of the electric automobile before the electric automobile reaches the anxiety electric quantity, the distance between the current position of the electric automobile and each energy supply service area, the travel distance of the electric automobile and the supply power of a charging pile when the electric automobile is charged;
determining the initial charging time of the electric automobile in each energy supply service area according to the high-speed time of the electric automobile, the distance between the current position of the electric automobile and each energy supply service area, the charging times and the charging duration;
determining the charging load of the electric automobile in each energy supply service area according to the predicted charging power and the initial charging time of the electric automobile in each energy supply service area;
and aiming at any energy supply service area, determining the charging load of the energy supply service area according to the charging load of each electric automobile in the energy supply service area.
In order to achieve the above purpose, the present invention also provides the following solutions:
an electric vehicle charging load prediction system for an expressway service area, comprising:
the data acquisition unit is used for acquiring road network data of the expressway; the road network data comprise node positions, energy supply service area positions and inter-node traffic flow of each period in a set period;
The OD matrix determining unit is connected with the data acquisition unit and is used for determining an OD matrix according to the road network data; the elements in the OD matrix are the number of vehicles running from the node i to the node j in two adjacent time periods in a set period; i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to m, and m is the number of nodes of the expressway;
the travel determining unit is connected with the OD matrix determining unit and is used for simulating the travel path of each electric automobile based on the OD matrix so as to determine the travel distance and the travel path of each electric automobile;
the continuous voyage mileage determining unit is used for determining the continuous voyage mileage of any electric automobile on the expressway according to the initial charge state, the battery capacity, the anxiety electric quantity, the battery output power and the running speed of the electric automobile and based on an electric automobile energy consumption model; the energy consumption model of the electric automobile is established in advance according to the energy conversion efficiency and the loss information of the electric automobile; the anxiety electric quantity is the lowest residual electric quantity of the preset electric automobile before being charged on the expressway; the initial state of charge is the state of charge of the electric automobile at a high speed;
The charging power prediction unit is respectively connected with the travel determination unit and the range determination unit and is used for determining the predicted charging power of the electric vehicle in each energy supply service area according to the travel distance of the electric vehicle before the electric vehicle reaches the anxiety electric quantity, the distance between the current position of the electric vehicle and each energy supply service area, the travel distance of the electric vehicle and the supply power of a charging pile when the electric vehicle is charged based on the travel path of the electric vehicle;
the charging time prediction unit is connected with the endurance mileage determination unit and is used for determining the initial charging time of the electric automobile in each energy supply service area according to the high-speed time of the electric automobile, the distance between the current position of the electric automobile and each energy supply service area, the charging times and the charging duration;
the vehicle load prediction unit is respectively connected with the charging power prediction unit and the charging time prediction unit and is used for determining the charging load of the electric vehicle in each energy supply service area according to the predicted charging power and the initial charging time of the electric vehicle in each energy supply service area;
the service area load prediction unit is connected with the vehicle load prediction unit and is used for determining the charging load of the energy supply service area according to the charging load of each electric automobile in the energy supply service area for any energy supply service area.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the invention, the OD matrix is used for simulating the travel path of the electric automobile, the travel and charging behaviors of the electric automobile on the expressway can be better reflected, the endurance mileage of the electric automobile on the expressway before the electric automobile reaches the anxiety electric quantity is determined based on the electric automobile energy consumption model, the predicted charging power of the electric automobile in each energy supply service area is determined by further combining the travel path, the current position, the endurance mileage of the electric automobile, the position of each energy supply service area and the supply power of the charging pile, and the initial charging time of the electric automobile in each energy supply service area is determined according to the high-speed time, the current position, the charging times and the charging duration of each energy supply service area on the electric automobile; and further determining the charging load of each electric automobile in each energy supply service area, and finally determining the charging load of each energy supply service area. The method solves the problem that the traditional charge load prediction method is not suitable for predicting the charge load of the electric automobile in the expressway scene, and realizes accurate prediction of the charge load of the electric automobile in the expressway scene.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting the charging load of an electric vehicle in a highway service area according to the present invention;
FIG. 2 is a schematic diagram of a highway network model;
FIG. 3 is a schematic diagram of a Model S energy consumption Model;
fig. 4 is an electric vehicle charging load graph of the energy consumption supply service area SE 1;
fig. 5 is an electric vehicle charging load diagram of the energy consumption supply service area SE 2;
fig. 6 is a graph of the charging load of the electric vehicle in the energy consumption supply service area SE 3;
fig. 7 is an electric vehicle charging load graph of the energy consumption supply service area SE 4;
fig. 8 is an electric vehicle charging load graph of the energy consumption supply service area SE 5;
fig. 9 is an electric vehicle charging load graph of the energy consumption supply service area SE 6;
fig. 10 is a schematic block diagram of the electric vehicle charging load prediction system for the expressway service area according to the present invention.
Symbol description:
nodes-1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17;
energy consumption supply areas SE1, SE2, SE3, SE4, SE5, SE6;
the system comprises a data acquisition unit-21, an OD matrix determination unit-22, a travel determination unit-23, a range determination unit-24, a charging power prediction unit-25, a charging time prediction unit-26, a vehicle load prediction unit-27 and a service area load prediction unit-28.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for predicting the charging load of an electric automobile in an expressway service area, which are used for analyzing the running characteristics of the electric automobile in a expressway scene in detail, simulating the travel track of the electric automobile by adopting an OD matrix, better reflecting the travel and the charging behavior of the electric automobile on the expressway, generating the electric automobile model according to the city occupation rate, respectively establishing energy consumption models of different models of the electric automobile, and finally obtaining a charging load prediction result which can better reflect the actual situation and make up the defect that the traditional charging load prediction method is not suitable for the expressway service area scene.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1, the embodiment provides a method for predicting a charging load of an electric vehicle in a highway service area, including:
s1: and obtaining road network data of the expressway. The road network data comprises node positions, energy supply service area positions and inter-node traffic flow of each period in a set period.
Specifically, a road network model of the analyzed expressway is established, and road data is collected. The highway road network model is shown in fig. 2, and comprises actual road segments, highway entrances and exits, service areas capable of supplying power to automobiles for energy supply, road segment traffic capacity, traffic flow among road network nodes, distance among road network nodes and the like.
The invention is based on a Transcad simulation platform, and obtains the data such as the road section traffic capacity, the traffic flow among road network nodes, the distance among road network nodes and the like of the expressway according to a road network model.
S2: and determining an OD (origin-destination) matrix according to the road network data. The elements in the OD matrix are the number of vehicles running from the node i to the node j in two adjacent time periods in a set period; and i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to m, and m is the number of nodes of the expressway.
In this embodiment, the OD matrix is back-pushed according to the inter-node traffic flow. Specifically, after collecting data such as traffic flow, traffic capacity and road length of each road section of the expressway, the reverse thrust of the OD matrix is performed through the Transcad software. The OD matrix reflects the traffic of vehicles among nodes of the road network, can be used for describing the travel characteristics of the vehicles in the road network and is used for simulating the travel paths of the vehicles in the road network. The vehicle journey is simulated by adopting an OD analysis method on the basis of a road network model.
S3: and simulating the driving path of each electric automobile based on the OD matrix to determine the travel distance and the travel path of each electric automobile.
Specifically, an OD probability matrix is first determined from the OD matrix. The elements in the OD probability matrix are probabilities that the electric automobile runs from the node i to the node j in two adjacent time periods in a set period. And then, simulating the driving paths of the electric vehicles based on the OD probability matrix, and determining the stroke paths and the origin-destination points of the electric vehicles. And finally, determining the travel distance of any electric automobile according to the position of the origin-destination of the electric automobile. The method comprises the steps of simulating the driving path of each electric automobile by means of an OD probability matrix and combining with the traffic flow of the expressway network, determining the driving origin-destination and the route service area of the automobile, and determining the travel distance of the automobile through the driving origin-destination.
In this embodiment, a day is divided into 24 time periods in units of hours, and the OD matrix is composed of 24 submatrices
Composition is prepared. Wherein m is road networkNode number, T is 0, 1, 2, …, 23, < >>
Representing the traffic flow between the start and stop points in the period T and the period T+1. OD matrix->
Can be converted into an OD probability matrix +.>
Compared with an OD matrix, the OD probability matrix can more intuitively reflect probability distribution of a vehicle travel path in the road network.
The probability that the electric automobile runs from the node i to the node j in the period from the period T to the period T+1 is determined by adopting the following formula:
wherein,,
probability of electric vehicle driving from node i to node j for T period to T+1 period, +.>
The number of vehicles traveling from node i to node j for the period T to period t+1.
Before S4, the method also analyzes the mileage anxiety mind of the electric automobile driver, and combines the electric automobile energy supply requirement and the expressway scene reality to determine the charging decision principle of the electric automobile driver in the expressway scene. Specifically, when the electric vehicle arrives at any energy supply service area, charging decision information is determined according to the current state of charge and the travel path of the electric vehicle. The charging decision information is whether the electric automobile is charged in the current energy supply service area. And (3) analyzing the travel characteristics of the electric automobile in the expressway scene, and predicting the charging decision of the driver of the electric automobile in the expressway scene.
The electric automobile has a certain distance from the departure place to the expressway toll station, and the initial SOC when the electric automobile arrives at the toll station is considered to be compliant with the normal distribution N (0.75,0.1) by considering the energy loss in the journey and combining the investigation result of the electric automobile data:
wherein SOC is start The electric vehicle is charged at a high speed, mu is an expected value of an initial charged state at the high speed, and sigma is a standard deviation.
When the electric vehicle runs in a city, user initial charge SOC (state-of-charge) is intensively distributed in a 20% -60% interval, and when the SOC is 40%, the probability density is highest, but the electric vehicle in the city has relatively perfect supporting infrastructure, so that the electric vehicle is convenient to charge, and an electric vehicle driver can relatively easily maintain the vehicle SOC at a comfortable level. When the vehicle is located on the expressway, the electric vehicle is difficult to supply energy on the expressway, serious mileage attenuation can be generated when the electric vehicle runs at the expressway, a driver generally does not tend to charge in an expressway service area due to time and economic factors, although the expressway service area is built up by a quick charging pile, the quick charging still needs half an hour to charge the electric vehicle to 80%, and the electric vehicle driver is considered to find the service area to charge when the SOC of the electric vehicle is lower than 30% in combination with mileage anxiety of the electric vehicle driver and actual conditions of the expressway.
When the electric automobile reaches any energy supply service area, judging whether the current charge state of the electric automobile is smaller than or equal to the anxiety electric quantity.
And if the current charge state of the electric automobile is smaller than or equal to the anxiety electric quantity, charging the electric automobile in a current energy supply service area.
If the current state of charge of the electric automobile is greater than the anxiety electric quantity, judging whether other energy supply service areas exist before the electric automobile reaches the destination from the current position, if so, the electric automobile is not charged in the current energy supply service area and continues to run, if not, judging whether the state of charge of the electric automobile is less than the anxiety electric quantity when the electric automobile reaches the destination from the current position, if so, the electric automobile is charged in the current energy supply service area, otherwise, the electric automobile is not charged in the current energy supply service area and continues to run.
Namely, there are 3 kinds of scenes as follows:
scenario 1) user arrives at certain energy replenishment service area and SOC of electric automobile is lower than anxiety electric quantity SOC current And theta is less than or equal to, and the user selects the energy supply service area to charge.
Scenario 2) user arrives at certain energy supply service area the SOC of electric car is greater than anxiety electric quantity SOC current > θ, and there is still a service area in front where energy replenishment can be performed, the user continues to advance.
Scenario 3) the SOC of the electric vehicle is greater than the anxiety electric quantity when the user arrives at a certain energy replenishment service area, but the no service area can carry out energy replenishment before the arrival at the destination, and the SOC of the electric vehicle is less than the anxiety electric quantity when the user arrives at the destination: SOC (State of Charge) end ＜θ＜SOC current At this point the user chooses to charge in the service area.
Wherein SOC is current Is the current SOC of the electric automobile, theta is the anxiety electric quantity, and SOC end Is the SOC when the electric vehicle leaves the high speed.
S4: and aiming at any electric automobile on the expressway, determining the endurance mileage of the electric automobile before the electric automobile reaches the anxiety electric quantity based on an electric automobile energy consumption model according to the initial charge state, the battery capacity, the anxiety electric quantity, the battery output power and the running speed of the electric automobile. The initial state of charge is the state of charge at high speed on an electric vehicle. The anxiety electric quantity is the lowest residual electric quantity of the preset electric automobile before being charged on the expressway. The energy consumption model of the electric automobile is established in advance according to the energy conversion efficiency and the loss information of the electric automobile.
The occurrence of mileage decay of an electric vehicle in a high-speed driving state is related to wind resistance, an electric vehicle energy recovery system and motor characteristics.
The charging load of the electric automobile is distributed in a space-time mode, and if deviation is generated in calculation of the endurance mileage of the electric automobile, the space-time distribution of the electric automobile can generate large errors, so that the charging load prediction is inaccurate. In order to better predict the electric vehicle charging requirements in expressway scenes, the invention firstly establishes an electric vehicle energy consumption model by combining factors influencing the electric vehicle energy consumption, and analyzes the electric vehicle energy consumption at different speeds.
The running equation of the automobile is introduced:
wherein F is the driving force of the electric vehicle, m 'is the weight of the electric vehicle, g is the gravitational acceleration, F is the rolling resistance coefficient, i' is the road gradient coefficient, delta is the rotational mass conversion coefficient, v is the driving speed of the electric vehicle, A is the windward area of the electric vehicle, and C D The wind resistance coefficient is ρ is air density, and t is time.
The influence of wind resistance on the automobile is considered in the automobile running equation, the influence of an electric automobile energy recovery system and energy efficiency is considered according to the analysis, and the influence of the automobile energy recovery system on energy consumption is ignored in consideration of the fact that the braking times of the automobile is small when the automobile runs on an expressway. The invention adds the energy efficiency of the motor into the running equation of the automobile:
Where η is the energy conversion efficiency of the motor.
The equipment such as the inside air conditioner of electric automobile, well accuse system and headlight wiper all are through electric energy drive, and the loss that this part operation caused has certain influence to electric automobile mileage, in order to further promote the precision of model, adds the loss of this part in the electric automobile energy consumption model.
Adding additional loss of the electric automobile and combining a power equation to obtain a final electric automobile energy consumption model:
wherein P is the running power of the electric automobile, and P system Is an additional loss of the electric automobile.
According to the method, parameters of the electric automobile with high urban occupation rate are collected, energy consumption models of the vehicles of various types are respectively built, and the generation probability of the vehicles of various types is respectively determined according to the urban occupation rate.
Further, as more random factors are used in the prediction of the charging load of the electric automobile, in order to reduce the influence of the random factors on the result and improve the progress of the prediction result of the charging load, the method simulates the random factors in the prediction of the charging load of the electric automobile by adopting a Monte Carlo method, and can complete the prediction of the charging load of the electric automobile in a highway scene by combining an OD matrix and a charging decision of a driver of the electric automobile.
Specifically, according to mileage anxiety and vehicle energy supply requirements, the high-speed cruising mileage and cruising distance of the electric automobile are calculated. After an electric automobile energy consumption model is built and a charging decision of an electric automobile driver is analyzed, the distance and the time that the electric automobile can travel before reaching mileage anxiety electric quantity can be calculated.
When the electric automobile runs on the expressway, the speed of the automobile is kept unchanged within a certain range, the automobile speed is considered to be subject to uniform distribution in the intervals of [100,120], after the automobile speed is determined, the running distance (endurance mileage) of the electric automobile before the electric automobile reaches the anxiety electric quantity can be calculated according to the electric automobile energy consumption model:
l＝νt；
wherein l is the endurance mileage of the electric vehicle before the electric vehicle reaches the anxiety electric quantity, v is the running speed of the electric vehicle, t is the endurance time (running time) of the electric vehicle before the electric vehicle reaches the anxiety electric quantity, C is the battery capacity of the electric vehicle, and SOC start Is the charge state of the electric automobile at high speed, theta is anxiety electric quantity and P out And outputs power for the battery.
S5: based on the travel path of the electric automobile, the predicted charging power of the electric automobile in each energy supply service area is determined according to the endurance mileage of the electric automobile before the electric automobile reaches the anxiety electric quantity, the distance between the current position of the electric automobile and each energy supply service area, the travel distance of the electric automobile and the supply power of the charging pile when the electric automobile is charged.
Specifically, the charging load curves of the electric vehicles are calculated, and the charging load curves of the electric vehicles are overlapped to obtain the charging load of the electric vehicle in the expressway service area.
The predicted charging power of the electric automobile in the energy supply service area d is determined by adopting the following formula:
wherein P is d,predict The predicted charging power of the electric automobile in the energy supply service area d is l, which is the endurance mileage of the electric automobile before the electric automobile reaches the anxiety electric quantity, and l base For the distance between the current position of the electric car and the energy supply service area d, S is the number of the energy supply service areas between the current position and the destination of the electric car, l last Distance l between the current position of the electric automobile and the energy supply service area d before the energy supply service area end Is the travel distance of the electric automobile, P d,service And (5) supplying power to the charging pile when the electric automobile is charged in the energy supply service area d.
Namely, 4 situations exist in the predicted charging power of the electric automobile in the energy supply service area:
(1) When the endurance mileage l is greater than the distance between the starting point and a certain energy supply service area, and the energy supply service area capable of performing energy supply is provided in front of the endurance mileage l, the electric automobile driver is considered to not select the energy supply service area for charging.
(2) When the endurance mileage l exceeds the distance between the starting point and a certain energy supply service area, and the endurance mileage l is smaller than the travel distance of the electric vehicle, and before reaching the destination, no energy supply service area for energy supply can be performed, the electric vehicle driver is considered to select the energy supply service area for charging.
(3) When the endurance mileage l is smaller than the distance between the starting point and a certain energy supply service area and is larger than the distance to the last energy supply service area except the energy supply service area, the electric automobile driver is considered to charge in the energy supply service area.
(4) When the endurance mileage l is smaller than the distance from the starting point to the last energy supply service area except the certain energy supply service area, the electric automobile driver is considered to perform a charging action before reaching the energy supply service area, and the energy supply service area is not charged.
Further, the invention also calculates the charging time of the electric vehicle by combining the rapid charging characteristic, the BMS (Battery management system) of the electric vehicle and the power of the charging pile.
The fast charging piles constructed in the certain energy supply service area of the expressway are assumed to be common 90kW charging piles, and 90kW is the upper limit of the power of the charging piles, so that the output power of the 90kW charging piles is not 90kW when the 90kW charging piles serve each electric automobile. The charging speed of an automobile is determined by the BMS of the vehicle and the power of the charging pile, the power level of which determines the upper limit of its supplied energy, and does not mean a constant output power.
The magnitude of the fast charge power allowed by BMSs of different model electric vehicles is different. When the power of the charging pile is larger than the quick charging power allowed by the BMS of the electric automobile, the charging power is determined by the BMS of the electric automobile; when the fast charging power allowed by the BMS of the electric automobile is larger than the charging pile power, the charging power is determined by the charging pile power.
According to the invention, after the BMS of each electric automobile is collected and the quick charge power is allowed, the quick charge pile can acquire the current SOC of the electric automobile, and the charging time of the electric automobile can be predicted by combining the power of the quick charge pile. The charging time length required by each charging of the electric automobile is determined by adopting the following formula:
wherein T is c Charging duration and SOC required by each charging of electric automobile 2 SOC value and SOC at the end of charging of electric automobile 1 The SOC value when the electric automobile starts to charge is C is the battery capacity of the electric automobile, P BMS Fast charging power, P, for electric vehicle charge Is the power of the charging pile.
S6: and determining the initial charging time of the electric automobile in each energy supply service area according to the high-speed time of the electric automobile, the distance between the current position of the electric automobile and each energy supply service area, the charging times and the charging duration.
Specifically, the initial charging time of the electric vehicle in the energy replenishment service area d is determined by adopting the following formula;
t predict ＝t start +t 1 +T c *n；
wherein t is predict Initial charging time t of electric automobile in energy supply service area d start T is the time of high speed on the electric automobile c The charging time length required by each charging of the electric automobile is l c The distance between the current position of the electric automobile and the energy supply service area d is v the running speed of the electric automobile, n is the number of charging times before the electric automobile reaches the energy supply service area d, and t 1 Is the vehicle travel time.
S7: and determining the charging load of the electric automobile in each energy supply service area according to the predicted charging power and the initial charging time of the electric automobile in each energy supply service area.
Specifically, after the charge load space-time distribution of each electric automobile is calculated, the charge load curves of all the time periods are overlapped, and then a prediction result of the charge load space-time distribution of the electric automobile in the expressway service area can be obtained.
According to the method, the running path of the electric automobile is combined, the high-speed endurance mileage of the electric automobile is calculated according to the energy consumption model of the electric automobile, and the charging load of the energy supply service area on the running path of the electric automobile is predicted by adopting a charging load judging method of each electric automobile path energy supply service area. And determining the starting time and the duration of the charging load curve of the energy supply service area generating the charging demand in the electric vehicle journey by combining with the calculation method of the starting charging time of the electric vehicle, and endowing the electric vehicle with the time characteristic of the charging load curve, so that the charging load curve generated aiming at different service areas in the running process of the single electric vehicle can be obtained.
S8: and aiming at any energy supply service area, determining the charging load of the energy supply service area according to the charging load of each electric automobile in the energy supply service area.
And for a certain energy supply service area, after charging load curves generated for different service areas in the running process of all the electric vehicles are obtained, the charging load curves of the energy supply service areas in different time periods are overlapped, so that the charging load curve of the electric vehicle in each energy supply service area in the expressway network can be obtained.
P total ＝∑P predict ；
Wherein P is total And supplying the energy to the total charging load of the service area.
The calculation process of the invention is realized based on Matlab.
In summary, the invention first establishes a road network model of a high-speed road section; then collecting the traffic flow of the high-speed road section, and reversely pushing the OD matrix according to the traffic flow of the road section; according to an electric automobile running equation, combining electric automobile characteristics, and establishing an electric automobile energy consumption model; based on the initial state of charge of the electric automobile, determining a charging decision principle of the electric automobile driver in the expressway scene by considering the mileage anxiety influence of the driver; then calculating the charging time length of the electric automobile based on the charging power of the electric automobile; and finally, simulating random factors in the prediction of the charging load of the electric automobile by adopting a Monte Carlo method, and completing the prediction of the charging load of the electric automobile in a highway scene by combining an OD matrix and a charging decision principle of an electric automobile driver.
The charging load of the electric automobile is a power load with time-space variation, the time-space distribution of the charging load is influenced by vehicle parameters such as the state of charge, the endurance mileage, the battery capacity and the like of the electric automobile, and is also influenced by a charging decision and a travel path of a driver, so that the randomness of the charging load of the electric automobile is very difficult to predict, and the operation result of the traditional charging load prediction method can have larger deviation when the electric automobile operates at a high speed. According to the invention, a Monte Carlo method is adopted to simulate random factors in the prediction of the charging load of the electric vehicle, so that the influence of the random factors on a prediction result is reduced, an OD matrix is used to simulate a travel path of the electric vehicle, an electric vehicle energy consumption model is established, the problem that the traditional charging load prediction method is not suitable for predicting the charging load of the electric vehicle in a highway scene is solved, the charging load of the electric vehicle in the highway scene can be accurately predicted, the prediction result is used as a basis for selecting the capacity of a construction charging station, and guidance can be provided for the energy scheduling of the highway micro-grid.
As shown in FIG. 3, the energy consumption Model of Tesla Model S is shown, the energy consumption Model can obtain that the Model S keeps 120km/h of speed and runs for one hour to consume 30.1kwh, the Model S keeps 120km/h of speed and runs for about 400km according to the calculation of the capacity of a battery carried by the Model S, the continuous voyage mileage of the Model S is almost equal to that of a continuous voyage mileage in a Tesla providing Model S100D continuous voyage chart, and the energy consumption Model can better reflect the energy consumption of an electric automobile in actual operation in consideration of the fact that the actual working condition is worse than the test result.
Fig. 4-9 are prediction curves of charging loads of service areas of expressway networks, and it can be seen from prediction results of different service areas that the charging loads of electric vehicles in the service areas are greatly affected by the positions of the service areas, and under the condition that the traffic flow is the same, the charging loads of the service areas far away from the expressway entrance are larger than the charging loads of the service areas near the expressway entrance, so that the influence of the road network on the charging loads of the electric vehicles should be fully considered when the charging loads of the expressway service areas are predicted, and the charging loads of the electric vehicles are predicted by combining the road network.
Example two
In order to execute the corresponding method of the above embodiment to achieve the corresponding functions and technical effects, a system for predicting the charging load of an electric vehicle in a highway service area is provided below.
As shown in fig. 10, the system for predicting the charging load of an electric vehicle in a highway service area provided in this embodiment includes: the data acquisition unit 21, the OD matrix determination unit 22, the trip determination unit 23, the range determination unit 24, the charging power prediction unit 25, the charging time prediction unit 26, the vehicle load prediction unit 27, and the service area load prediction unit 28.
Wherein the data acquisition unit 21 is used for acquiring road network data of the expressway. The road network data comprises node positions, energy supply service area positions and inter-node traffic flow of each period in a set period.
The OD matrix determining unit 22 is connected with the data obtaining unit 21, and the OD matrix determining unit 22 is configured to determine an OD matrix according to the road network data; the elements in the OD matrix are the number of vehicles running from the node i to the node j in two adjacent time periods in a set period; i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to m, and m is the number of nodes of the expressway;
the travel determining unit 23 is connected to the OD matrix determining unit 22, and the travel determining unit 23 is configured to simulate a travel path of each electric vehicle based on the OD matrix, so as to determine a travel distance and a travel path of each electric vehicle.
The endurance mileage determining unit 24 is configured to determine, for any electric vehicle on the expressway, an endurance mileage of the electric vehicle before reaching the anxiety electric quantity based on an electric vehicle energy consumption model according to an initial state of charge, a battery capacity, the anxiety electric quantity, a battery output power and a driving speed of the electric vehicle. The energy consumption model of the electric automobile is established in advance according to the energy conversion efficiency and the loss information of the electric automobile; the anxiety electric quantity is the lowest residual electric quantity of the preset electric automobile before being charged on the expressway; the initial state of charge is the state of charge at high speed on an electric vehicle.
The charging power prediction unit 25 is respectively connected to the trip determination unit 23 and the range determination unit 24, and the charging power prediction unit 25 is configured to determine, based on a trip path of the electric vehicle, a predicted charging power of the electric vehicle in each energy supply service area according to a range of the electric vehicle before the electric vehicle reaches the anxiety electric quantity, a distance between a current position of the electric vehicle and each energy supply service area, a trip distance of the electric vehicle, and a supply power of a charging pile when the electric vehicle is charged.
The charging time prediction unit 26 is connected to the endurance mileage determination unit 24, and the charging time prediction unit 26 is configured to determine an initial charging time of the electric vehicle in each energy supply service area according to a high-speed time on the electric vehicle, a distance between a current position of the electric vehicle and each energy supply service area, a charging frequency, and a charging duration.
The vehicle load prediction unit 27 is connected to the charging power prediction unit 25 and the charging time prediction unit 26, respectively, and the vehicle load prediction unit 27 is configured to determine a charging load of the electric vehicle in each energy replenishment service area according to a predicted charging power and an initial charging time of the electric vehicle in each energy replenishment service area.
The service area load prediction unit 28 is connected to the vehicle load prediction unit 27, and the service area load prediction unit 28 is configured to determine, for any energy supply service area, a charging load of the energy supply service area according to a charging load of each electric vehicle in the energy supply service area.
Compared with the prior art, the electric vehicle charging load prediction system for the expressway service area provided by the embodiment has the same beneficial effects as the electric vehicle charging load prediction method for the expressway service area provided by the embodiment, and is not described in detail herein.
Example III
The embodiment provides an electronic device, which includes a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the method for predicting the charging load of the electric automobile in the expressway service area.
Alternatively, the electronic device may be a server.
In addition, the embodiment of the invention also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the method for predicting the charging load of the electric automobile in the expressway service area when being executed by a processor.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. The method for predicting the charging load of the electric automobile in the expressway service area is characterized by comprising the following steps of:
obtaining road network data of a highway; the road network data comprise node positions, energy supply service area positions and inter-node traffic flow of each period in a set period;
determining an OD matrix according to the road network data; the elements in the OD matrix are the number of vehicles running from the node i to the node j in two adjacent time periods in a set period; i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to m, and m is the number of nodes of the expressway;
simulating the driving path of each electric automobile based on the OD matrix to determine the travel distance and the travel path of each electric automobile;
Determining the endurance mileage of any electric vehicle on the expressway before the electric vehicle reaches the anxiety electric quantity based on an electric vehicle energy consumption model according to the initial charge state, the battery capacity, the anxiety electric quantity, the battery output power and the running speed of the electric vehicle; the energy consumption model of the electric automobile is established in advance according to the energy conversion efficiency and the loss information of the electric automobile; the anxiety electric quantity is the lowest residual electric quantity of the preset electric automobile before being charged on the expressway; the initial state of charge is the state of charge of the electric automobile at a high speed;
based on the travel path of the electric automobile, determining predicted charging power of the electric automobile in each energy supply service area according to the endurance mileage of the electric automobile before the electric automobile reaches the anxiety electric quantity, the distance between the current position of the electric automobile and each energy supply service area, the travel distance of the electric automobile and the supply power of a charging pile when the electric automobile is charged;
determining the initial charging time of the electric automobile in each energy supply service area according to the high-speed time of the electric automobile, the distance between the current position of the electric automobile and each energy supply service area, the charging times and the charging duration;
Determining the charging load of the electric automobile in each energy supply service area according to the predicted charging power and the initial charging time of the electric automobile in each energy supply service area;
and aiming at any energy supply service area, determining the charging load of the energy supply service area according to the charging load of each electric automobile in the energy supply service area.
2. The method for predicting the charging load of electric vehicles in an expressway service area according to claim 1, wherein the simulating the driving path of each electric vehicle based on the OD matrix to determine the travel distance and the travel path of each electric vehicle specifically comprises:
determining an OD probability matrix according to the OD matrix; the elements in the OD probability matrix are probabilities of the electric automobile running from the node i to the node j in two adjacent time periods in a set period;
simulating the driving paths of the electric vehicles based on the OD probability matrix, and determining the stroke paths and the starting points of the electric vehicles;
and determining the travel distance of any electric automobile according to the position of the start and stop points of the electric automobile.
3. The method for predicting the charging load of an electric vehicle in a highway service area according to claim 2, wherein the probability of the electric vehicle traveling from node i to node j in the period T to period t+1 is determined by using the following formula:
Wherein,,
probability of electric vehicle driving from node i to node j for T period to T+1 period, +.>
The number of vehicles traveling from node i to node j for the period T to period t+1.
4. The method for predicting the charging load of an electric vehicle in a highway service area according to claim 1, wherein the electric vehicle energy consumption model is as follows:
wherein P is the running power of the electric vehicle, v is the running speed of the electric vehicle, eta is the energy conversion efficiency of the motor, m 'is the weight of the electric vehicle, g is the gravitational acceleration, f is the rolling resistance coefficient, i' is the road gradient coefficient, delta is the rotational mass conversion coefficient, rho is the air density, C D A is windage resistance coefficient, A is windage area of electric automobile, P system The additional loss of the electric automobile is represented by t, which is the time.
5. The method for predicting the charge load of an electric vehicle in a highway service area according to claim 1, wherein the following formula is adopted to determine the range of the electric vehicle before the electric vehicle reaches the anxiety electric quantity:
wherein l is the endurance mileage of the electric vehicle before the electric vehicle reaches the anxiety electric quantity, v is the running speed of the electric vehicle, C is the battery capacity of the electric vehicle, and SOC start Is the charge state of the electric automobile at high speed, theta is anxiety electric quantity and P out And outputs power for the battery.
6. The method for predicting the charge load of an electric vehicle in an expressway service area according to claim 1, wherein the predicted charge power of the electric vehicle in the energy replenishment service area d is determined using the following formula:
wherein P is d,predict The predicted charging power of the electric automobile in the energy supply service area d is l, which is the endurance mileage of the electric automobile before the electric automobile reaches the anxiety electric quantity, and l base The distance between the current position of the electric automobile and the energy supply service area d is S, which is the current position of the electric automobileThe number of energy replenishment service areas between the location and destination, l last Distance l between the current position of the electric automobile and the energy supply service area d before the energy supply service area end Is the travel distance of the electric automobile, P d,service And (5) supplying power to the charging pile when the electric automobile is charged in the energy supply service area d.
7. The method for predicting the charge load of an electric vehicle in an expressway service area according to claim 1, wherein the initial charge time of the electric vehicle in the energy replenishment service area d is determined using the following formula;
wherein t is predict Initial charging time t of electric automobile in energy supply service area d start T is the time of high speed on the electric automobile c The charging time length required by each charging of the electric automobile is l c And v is the running speed of the electric automobile, and n is the charging times before the electric automobile reaches the energy supply service area d.
8. The method for predicting the charging load of an electric vehicle in a highway service area according to claim 1, wherein the method for predicting the charging load of an electric vehicle in a highway service area further comprises:
when the electric automobile reaches any energy supply service area, determining charging decision information according to the current charge state and travel path of the electric automobile; the charging decision information is whether the electric automobile is charged in the current energy supply service area.
9. The method for predicting the charging load of an electric vehicle in an expressway service area according to claim 8, wherein determining charging decision information according to a current state of charge and a trip path of the electric vehicle when the electric vehicle arrives at any energy supply service area comprises:
when an electric automobile reaches any energy supply service area, judging whether the current state of charge of the electric automobile is smaller than or equal to anxiety electric quantity;
if the current charge state of the electric automobile is smaller than or equal to the anxiety electric quantity, the electric automobile is charged in a current energy supply service area;
If the current state of charge of the electric automobile is greater than the anxiety electric quantity, judging whether other energy supply service areas exist before the electric automobile reaches the destination from the current position, if so, the electric automobile is not charged in the current energy supply service area and continues to run, if not, judging whether the state of charge of the electric automobile is less than the anxiety electric quantity when the electric automobile reaches the destination from the current position, if so, the electric automobile is charged in the current energy supply service area, otherwise, the electric automobile is not charged in the current energy supply service area and continues to run.
10. The utility model provides a highway service area electric automobile charge load prediction system which characterized in that, highway service area electric automobile charge load prediction system includes:
the data acquisition unit is used for acquiring road network data of the expressway; the road network data comprise node positions, energy supply service area positions and inter-node traffic flow of each period in a set period;
the OD matrix determining unit is connected with the data acquisition unit and is used for determining an OD matrix according to the road network data; the elements in the OD matrix are the number of vehicles running from the node i to the node j in two adjacent time periods in a set period; i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to m, and m is the number of nodes of the expressway;
The travel determining unit is connected with the OD matrix determining unit and is used for simulating the travel path of each electric automobile based on the OD matrix so as to determine the travel distance and the travel path of each electric automobile;
the continuous voyage mileage determining unit is used for determining the continuous voyage mileage of any electric automobile on the expressway according to the initial charge state, the battery capacity, the anxiety electric quantity, the battery output power and the running speed of the electric automobile and based on an electric automobile energy consumption model; the energy consumption model of the electric automobile is established in advance according to the energy conversion efficiency and the loss information of the electric automobile; the anxiety electric quantity is the lowest residual electric quantity of the preset electric automobile before being charged on the expressway; the initial state of charge is the state of charge of the electric automobile at a high speed;
the charging power prediction unit is respectively connected with the travel determination unit and the range determination unit and is used for determining the predicted charging power of the electric vehicle in each energy supply service area according to the travel distance of the electric vehicle before the electric vehicle reaches the anxiety electric quantity, the distance between the current position of the electric vehicle and each energy supply service area, the travel distance of the electric vehicle and the supply power of a charging pile when the electric vehicle is charged based on the travel path of the electric vehicle;
The charging time prediction unit is connected with the endurance mileage determination unit and is used for determining the initial charging time of the electric automobile in each energy supply service area according to the high-speed time of the electric automobile, the distance between the current position of the electric automobile and each energy supply service area, the charging times and the charging duration;
the vehicle load prediction unit is respectively connected with the charging power prediction unit and the charging time prediction unit and is used for determining the charging load of the electric vehicle in each energy supply service area according to the predicted charging power and the initial charging time of the electric vehicle in each energy supply service area;
the service area load prediction unit is connected with the vehicle load prediction unit and is used for determining the charging load of the energy supply service area according to the charging load of each electric automobile in the energy supply service area for any energy supply service area.
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