An electric vehicle charging optimization method based on the integration of transportation three networks

Abstract

The present invention discloses an electric vehicle charging optimization method based on the integration of three transportation networks, which is applied to a cloud server and relates to the technical field of electric vehicle charging. The cloud server is connected to the Internet of Vehicles, the power grid and the transportation network. When the remaining power of the target electric vehicle is lower than a preset threshold or the remaining power cannot meet the target electric vehicle's driving from the current position to the driving destination, the target charging pile is matched in real time between the current position and the driving destination, so that the estimated time for the target electric vehicle to travel to the driving destination is the shortest. The cloud server is connected to the Internet of Vehicles, the power grid and the transportation network to realize the integration of the three networks. Vehicle data, charging pile distribution operation status and traffic data related to electric vehicle charging optimization can be obtained, the operation status of the charging pile is monitored in real time, and the operation status of the electric vehicle is dynamically analyzed, and the best charging pile is matched for charging, thereby improving the efficiency and experience of user charging.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to an electric vehicle charging optimization method based on the integration of three transportation networks. Background Art

[0002] As society moves towards reducing carbon emissions and promoting sustainable transportation, the use of electric vehicles is gradually increasing. However, charging the battery of an electric vehicle is a time-consuming process, which greatly affects the user experience. As supporting facilities for electric vehicles, public charging stations can provide fast charging services for electric vehicles, greatly reducing the charging time of electric vehicles.

[0003] However, as the number of electric vehicles increases, there may be queues for charging at public charging stations. In the prior art, it is not possible to monitor the operation of public charging stations and their charging piles in real time and dynamically analyze the operation of electric vehicles to match the best charging pile for charging, resulting in low charging efficiency and poor user experience. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned background technical problems and to propose an electric vehicle charging optimization method based on the integration of three transportation networks.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The embodiment of the present invention provides an electric vehicle charging optimization method based on the integration of transportation three networks, which is applied to a cloud server, wherein the cloud server is connected to a vehicle network, an electric energy network, and a transportation network, and the method includes:

[0007] When receiving first driving data transmitted by the target electric vehicle according to a preset period, judging whether the target electric vehicle needs to be charged according to the first driving data; the first driving data includes the current position, driving destination and remaining power of the target electric vehicle;

[0008] If the remaining power is lower than a preset threshold or the remaining power is insufficient for the target electric vehicle to travel from the current position to the driving destination, the target charging pile is matched in real time between the current position and the driving destination based on the Internet of Vehicles, the power network and the transportation network, so that the estimated time for the target electric vehicle to travel to the driving destination is the shortest; the estimated time includes the estimated driving time and the estimated charging time.

[0009] Optionally, matching a target charging pile in real time between the current location and the driving destination according to the Internet of Vehicles, the power network, and the transportation network includes:

[0010] Acquire a planned route from the current location to the driving destination according to the transportation network;

[0011] Determining pending charging piles on the planned route according to the electric energy network;

[0012] For each pending charging pile, according to the transportation network and the vehicle network, calculating the driving time from the current position to the driving destination via the pending charging pile as the first driving time;

[0013] For each pending charging pile, according to the electric energy network and the Internet of Vehicles, calculating the charging time of the target electric vehicle at the charging pile as the first charging time;

[0014] The undetermined charging pile with the smallest sum of the first driving time and the first charging time is taken as the target charging pile.

[0015] Optionally, for each pending charging pile, the driving time from the current position to the driving destination via the pending charging pile is calculated according to the transportation network and the vehicle network, and the first driving time includes:

[0016] For each pending charging pile, determining a target route including the pending charging pile in the planned route according to the transportation network;

[0017] Acquire intersection information of the target route according to the traffic network, and divide the target route into a plurality of sections according to the intersection information;

[0018] Acquire the traffic flow at each intersection of the target route and the vehicle density of each road section of the target route according to the vehicle network, and calculate the average road speed of each road section of the target route according to the traffic flow and the vehicle density;

[0019] The travel time of the target electric vehicle along the target route is calculated as the first travel time by combining the average road speed of each section of the target route and the historical average speed of the target electric vehicle.

[0020] Optionally, calculating the average road speed of each section of the target route according to the traffic flow and the vehicle density includes:

[0021]

[0022] in, The target route Average road speed for the section, For the The vehicle density of the road segment, For the Traffic flow at the intersection of the road segment, are preset parameters.

[0023] Optionally, calculating the travel time of the target electric vehicle according to the target route includes:

[0024]

[0025] in, is the travel time according to the target route, is the number of sections included in the target route, The target line The length of the road section, is the historical average speed of the target electric vehicle, It is the speed compensation parameter.

[0026] Optionally, for each pending charging pile, the charging time of the target electric vehicle at the charging pile is calculated according to the electric energy network and the Internet of Vehicles as the first charging time, including:

[0027] For each pending charging pile, determine the rated charging power of the pending charging pile and the number of other pending charging piles within a first preset range of the pending charging pile according to the electric energy network;

[0028] Acquire second driving data of vehicles within a second preset range of the to-be-determined charging pile according to the Internet of Vehicles, and determine the number of vehicles to be charged and the charging probability of each vehicle to be charged according to the second driving data; the current power of the vehicle to be charged is less than a preset threshold;

[0029] According to the rated charging power of the pending charging pile, the number of other pending charging piles, the number of vehicles to be charged and the charging probability, the charging time of the target electric vehicle at the charging pile is calculated as the first charging time.

[0030] Optionally, calculating the charging time of the target electric vehicle at the charging pile includes:

[0031]

[0032] in, is the charging time of the target electric vehicle at the charging pile, is the maximum battery capacity of the target electric vehicle, is the remaining power, is the power consumption of the target electric vehicle when driving to the charging pile, is the rated charging power of the pending charging pile, is the number of vehicles to be charged, For the The charging probability of the vehicle to be charged is For the The maximum battery capacity of the vehicle to be charged, For the The current charge level of the vehicle to be charged.

[0033] The embodiment of the present invention provides an electric vehicle charging optimization method based on the integration of traffic three networks, which is applied to a cloud server, and the cloud server is connected to the Internet of Vehicles, the power network and the traffic network. The method includes: when receiving the first driving data transmitted by the target electric vehicle according to the preset period, judging whether the target electric vehicle needs to be charged according to the first driving data; the first driving data includes the current position, the driving destination and the remaining power of the target electric vehicle; if the remaining power is lower than the preset threshold or the remaining power cannot meet the target electric vehicle to travel from the current position to the driving destination, then according to the Internet of Vehicles, the power network and the traffic network, the target charging pile is matched in real time between the current position and the driving destination, so that the estimated time for the target electric vehicle to travel to the driving destination is the shortest; the estimated time includes the estimated driving time and the estimated charging time. The cloud server is connected to the Internet of Vehicles, the power network and the traffic network to realize the integration of the three networks, and can obtain vehicle data, charging pile distribution operation status and traffic data related to electric vehicle charging optimization, monitor the operation status of the charging pile in real time and dynamically analyze it with the operation status of the electric vehicle, match the best charging pile for charging, and improve the efficiency and experience of user charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] Figure 1 A flow chart of an electric vehicle charging optimization method based on the integration of three transportation networks is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The embodiment of the present invention provides an electric vehicle charging optimization method based on the integration of transportation three networks. Figure 1 , Figure 1 A flowchart of an electric vehicle charging optimization method based on the integration of transportation three networks provided in an embodiment of the present invention is applied to a cloud server, and the cloud server is connected to the Internet of Vehicles, the power network and the transportation network. The method includes:

[0038] S101, when first driving data transmitted by a target electric vehicle according to a preset period is received, it is determined whether the target electric vehicle needs to be charged according to the first driving data.

[0039] S102, if the remaining power is lower than a preset threshold or the remaining power is insufficient for the target electric vehicle to travel from the current location to the destination, a target charging pile is matched between the current location and the destination based on the Internet of Vehicles, the power grid and the transportation network, so that the estimated time for the target electric vehicle to travel to the destination is the shortest.

[0040] The first driving data includes the current position, driving destination and remaining power of the target electric vehicle. The estimated time includes the estimated driving time and the estimated charging time.

[0041] An electric vehicle charging optimization method based on the integration of three transportation networks is provided according to an embodiment of the present invention. The cloud server is connected to the Internet of Vehicles, the power network and the transportation network to achieve the integration of three networks. The vehicle data, the distribution and operation status of charging piles and the traffic data related to the optimization of electric vehicle charging can be obtained, the operation status of the charging piles can be monitored in real time, and the operation status of the electric vehicle can be dynamically analyzed, and the best charging piles can be matched for charging, so as to improve the efficiency and experience of user charging.

[0042] In one implementation, the cloud server can obtain the driving data of each vehicle on the road through the Internet of Vehicles, the cloud server can obtain the distribution and current operation status of charging piles through the power network, and the cloud server can perform route planning through the transportation network.

[0043] In one implementation, the target electric vehicle may transmit first driving data to a cloud server via the Internet of Vehicles at a preset period, and the cloud server may determine whether the target electric vehicle currently needs to be charged.

[0044] In another implementation, the target electric vehicle may send a charging request including first driving data to a cloud server through the Internet of Vehicles, and the cloud server may match a target charging pile for the target electric vehicle based on the first driving data.

[0045] In one embodiment, matching the target charging pile between the current location and the driving destination according to the Internet of Vehicles, the power network and the transportation network includes:

[0046] Step 1: Obtain a planned route from the current location to the driving destination based on the traffic network.

[0047] Step 2: Determine the pending charging piles on the planned route based on the power grid.

[0048] Step three: for each pending charging pile, calculate the driving time from the current position through the pending charging pile to the driving destination according to the transportation network and the Internet of Vehicles, as the first driving time.

[0049] Step 4: For each pending charging pile, the charging time of the target electric vehicle at the charging pile is calculated according to the electric energy grid and the Internet of Vehicles as the first charging time.

[0050] Step 5: The undetermined charging pile with the smallest sum of the first driving time and the first charging time is selected as the target charging pile.

[0051] In one implementation, the remaining power of the target electric vehicle is sufficient for the target electric vehicle to travel from a current location to a to-be-determined charging pile.

[0052] In one embodiment, for each pending charging pile, the driving time from the current location to the driving destination via the pending charging pile is calculated according to the traffic network and the vehicle network, and the first driving time includes:

[0053] Step 1: for each pending charging pile, determine a target route including the pending charging pile in the planned route according to the traffic network.

[0054] Step 2: Obtain intersection information of the target route based on the traffic network, and divide the target route into multiple sections based on the intersection information.

[0055] Step three, obtain the traffic flow at each intersection of the target route and the vehicle density of each section of the target route based on the vehicle network, and calculate the average road speed of each section of the target route based on the traffic flow and vehicle density.

[0056] Step 4: combining the average road speed of each section of the target route and the historical average speed of the target electric vehicle, calculate the travel time of the target electric vehicle along the target route as the first travel time.

[0057] In one implementation, the intersection information of the target route includes the number and location of traffic lights on the target route, and the target route can be divided into multiple sections according to the location of the traffic lights. Factors affecting the average road speed of each section include the vehicle density of the section and the traffic flow at the intersection.

[0058] In one embodiment, the calculation of the average road speed of each section of the target route according to the traffic flow and vehicle density includes:

[0059] (1)

[0060] in, The target route Average road speed for the section, For the The vehicle density of the road segment, For the Traffic flow at the intersection of the road segment, are preset parameters.

[0061] In one implementation, based on the relationship between traffic flow, vehicle density and average road speed, Can be set to 4.

[0062] In one embodiment, the above-mentioned calculation of the travel time of the target electric vehicle according to the target route includes:

[0063]

[0064] in, is the travel time according to the target route, is the number of sections included in the target route. For the target line The length of the road section, is the historical average speed of the target electric vehicle, It is the speed compensation parameter.

[0065] In one implementation, Determined from average road speeds and historical average speeds:

[0066]

[0067] In one embodiment, for each pending charging pile, the charging time of the target electric vehicle at the charging pile is calculated according to the electric energy grid and the Internet of Vehicles as the first charging time, including:

[0068] Step 1: for each pending charging pile, determine the rated charging power of the pending charging pile and the number of other pending charging piles within a first preset range of the pending charging pile according to the electric power grid.

[0069] Step 2: Obtain second driving data of vehicles within a second preset range of the pending charging pile according to the Internet of Vehicles, determine the number of vehicles to be charged and the charging probability of each vehicle to be charged according to the second driving data, and the current power of the vehicles to be charged is less than a preset threshold.

[0070] Step three, according to the rated charging power of the pending charging pile, the number of other pending charging piles, the number of vehicles to be charged and the charging probability, calculate the charging time of the target electric vehicle at the charging pile as the first charging time.

[0071] In one implementation, the preset threshold, the first preset range, and the second preset range can be set by a technician, and are not limited here. For example, the preset threshold can be 50%, 30%, etc., the first preset range can be 100m, 1km, etc., and the second preset range can be 1km, 3km, etc.

[0072] In one embodiment, the above calculation of the charging time of the target electric vehicle at the charging pile includes:

[0073]

[0074] in, is the charging time of the target electric vehicle at the charging pile, is the maximum battery capacity of the target electric vehicle, is the remaining power, is the power consumption of the target electric vehicle when driving to the charging pile, is the rated charging power of the pending charging pile, is the number of vehicles to be charged, For the The charging probability of the vehicle to be charged is For the The maximum battery capacity of the vehicle to be charged, For the The current charge level of the vehicle to be charged.

[0075] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An electric vehicle charging optimization method based on the integration of three transportation networks, characterized in that: Applied to a cloud server, the cloud server is connected to a vehicle network, an electric power network and a transportation network, and the method includes: When receiving first driving data transmitted by the target electric vehicle according to a preset period, judging whether the target electric vehicle needs to be charged according to the first driving data; the first driving data includes the current position, driving destination and remaining power of the target electric vehicle; If the remaining power is lower than a preset threshold or the remaining power cannot satisfy the target electric vehicle to travel from the current position to the travel destination, a target charging pile is matched in real time between the current position and the travel destination according to the Internet of Vehicles, the power grid and the transportation network, so that the estimated time for the target electric vehicle to travel to the travel destination is the shortest; the estimated time includes an estimated travel time and an estimated charging time; Matching a target charging pile in real time between the current location and the driving destination according to the Internet of Vehicles, the power network, and the transportation network includes: Acquire a planned route from the current location to the driving destination according to the transportation network; Determining pending charging piles on the planned route according to the electric energy network; For each pending charging pile, according to the transportation network and the vehicle network, calculating the driving time from the current position to the driving destination via the pending charging pile as the first driving time; For each pending charging pile, according to the electric energy network and the Internet of Vehicles, calculating the charging time of the target electric vehicle at the charging pile as the first charging time; The undetermined charging pile having the smallest sum of the first driving time and the first charging time is used as the target charging pile; For each pending charging pile, the driving time from the current position to the driving destination via the pending charging pile is calculated according to the traffic network and the vehicle network, and the first driving time includes: For each pending charging pile, determining a target route including the pending charging pile in the planned route according to the transportation network; Acquire intersection information of the target route according to the traffic network, and divide the target route into a plurality of sections according to the intersection information; Acquire the traffic flow at each intersection of the target route and the vehicle density of each road section of the target route according to the vehicle network, and calculate the average road speed of each road section of the target route according to the traffic flow and the vehicle density; Calculate the travel time of the target electric vehicle along the target route by combining the average road speed of each section of the target route and the historical average speed of the target electric vehicle as a first travel time; For each pending charging pile, according to the electric energy network and the Internet of Vehicles, calculating the charging time of the target electric vehicle at the charging pile as the first charging time, including: For each pending charging pile, determine the rated charging power of the pending charging pile and the number of other pending charging piles within a first preset range of the pending charging pile according to the electric energy network; Acquire second driving data of vehicles within a second preset range of the to-be-determined charging pile according to the Internet of Vehicles, and determine the number of vehicles to be charged and the charging probability of each vehicle to be charged according to the second driving data; the current power of the vehicle to be charged is less than a preset threshold; According to the rated charging power of the pending charging pile, the number of other pending charging piles, the number of vehicles to be charged and the charging probability, the charging time of the target electric vehicle at the charging pile is calculated as the first charging time.

2. The electric vehicle charging optimization method based on the integration of transportation three networks according to claim 1 is characterized in that: Calculating the average road speed for each section of the target route based on traffic flow and vehicle density includes: Among them, V i is the average road speed of the i-th section of the target route, m i is the vehicle density of the i-th road section, f i is the traffic flow at the intersection of the i-th road section, and ω is a preset parameter.

3. The electric vehicle charging optimization method based on the integration of transportation three networks according to claim 2 is characterized in that: Calculating the travel time of the target electric vehicle according to the target route includes: Among them, T s is the travel time according to the target route, n is the number of sections included in the target route, Li is the road length of the i-th section of the target route, Vs is the historical average speed of the target electric vehicle, and α is a speed compensation parameter.

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