Charging information transmission method for mobile new energy vehicles in the Internet of Vehicles environment

Abstract

The present invention proposes a charging information transmission method for mobile new energy vehicles in the Internet of Vehicles environment. First, by using distributed mobile edge computing and available vehicle-mounted ad hoc networks, an efficient heterogeneous communication framework based on hybrid transmission mode is designed for large-scale charging information transmission between mobile new energy vehicles and charging stations; secondly, based on the constructed terminal intersection model and ant colony algorithm, an adaptive routing protocol suitable for the Internet of Vehicles environment is proposed to dynamically plan the optimal charging information transmission path; finally, by using the constructed waiting time model, a charging information forwarding mechanism based on the receiving end is proposed to select the best charging information forwarding vehicle. The present invention has been experimentally verified that the proposed charging information transmission method can effectively reduce wireless network resource overhead, alleviate network congestion, and improve the adaptability of charging data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking data transmission, and in particular to a charging information transmission method for mobile new energy vehicles in a vehicle networking environment. Background Art

[0002] Serious air pollution and traffic noise have attracted great public attention in recent years. New energy vehicles have flourished due to their environmentally friendly characteristics such as no direct carbon emissions, low noise, and efficient energy conversion. However, the rapid development of new energy vehicles has also brought many problems, including local power grid overload and low charging efficiency. Therefore, it is necessary to build a smart grid system with global and efficient charging services, and real-time and reliable charging information interaction between different charging entities is the key to its realization.

[0003] The existing centralized cloud-based communication framework is widely used for optimized charging management of new energy vehicles, but this method has low security and privacy protection and high communication cost. As one of the key components of 5G wireless communication networks, vehicle-mounted ad hoc networks show excellent transmission performance and low communication costs in highly dynamic environments, making them very suitable for charging services for mobile new energy vehicles. However, this method still faces problems such as unstable links and high overhead. In order to mitigate the negative impact of vehicle-mounted networks, existing heterogeneous wireless networks aim to collect real-time charging information packets by integrating vehicle-mounted networks and cellular networks, but a large amount of charging information without any preprocessing will bring a heavy load to wireless communication and may cause severe network congestion. Summary of the invention

[0004] The purpose of the present invention is to provide a charging information transmission method for mobile new energy vehicles in a vehicle networking environment, to provide real-time and reliable charging information interaction between different charging entities, to effectively reduce wireless network resource overhead, to alleviate network congestion, and to improve the adaptability of charging data transmission, so as to build a smart grid system with global and efficient charging services.

[0005] The technical solution to achieve the purpose of the present invention is:

[0006] A charging information transmission method for mobile new energy vehicles in an Internet of Vehicles environment comprises: firstly, utilizing distributed mobile edge computing and available vehicle-mounted ad hoc networks, designing an efficient heterogeneous communication framework based on a hybrid transmission mode for large-scale charging information transmission between mobile new energy vehicles and charging stations; secondly, based on a constructed terminal intersection model and an ant colony algorithm, an adaptive routing protocol suitable for the Internet of Vehicles environment is proposed to dynamically plan the optimal charging information transmission path; finally, utilizing a constructed waiting time model, a charging information forwarding mechanism based on a receiving end is proposed to select the optimal charging information forwarding vehicle.

[0007] Compared with the prior art, the present invention has the following beneficial effects:

[0008] (1) By utilizing distributed mobile edge computing and available vehicle-mounted ad hoc networks, a heterogeneous communication framework based on hybrid transmission methods is designed to ensure the reliability and security of charging information transmission, reduce network load, alleviate communication congestion, increase the transmission rate of data packets, reduce communication costs, and improve the scalability and robustness of large-scale networks. Compared with periodic broadcast communication methods, it can effectively reduce the transmission of redundant packets and avoid broadcast storm effects.

[0009] (2) Based on the designed terminal intersection model and ant colony algorithm, an adaptive routing protocol is proposed to dynamically plan the optimal charging information transmission path. The protocol can use the explored path to directly transmit data packets, which is beneficial to reducing the convergence time of the optimal route exploration, alleviating network congestion and reducing network overhead. It uses global and local pheromones to effectively avoid blind route exploration, which is beneficial to cope with dynamic environments and reduce transmission delays. Pheromone evaporation and route maintenance can effectively prevent the proposed algorithm from quickly converging to suboptimal areas.

[0010] (3) The data forwarding mechanism based on the receiving end is different from the previous charging information relay scheme. The waiting time model is designed based on the forwarding distance and bit error rate. The optimal charging information forwarding vehicle is selected by the candidate vehicles instead of the transmitting vehicle. This scheme effectively reduces network congestion and competition. At the same time, it uses the improved RTS / CTS mode to effectively reduce overhead, alleviate transmission load, and improve the transmission rate of data packets. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flow chart of a charging information transmission method for mobile new energy vehicles in a connected vehicle environment of the present invention.

[0012] Figure 2 Schematic diagram of a heterogeneous communication framework based on a hybrid transmission method.

[0013] Figure 3 Schematic diagram of the hybrid transmission method for charging information transmission in the proposed heterogeneous communication framework.

[0014] Figure 4 Schematic diagram of the specific process of step 2 in the embodiment of the present invention.

[0015] Figure 5 Schematic diagram of the specific process of step 3 in the implementation mode of the present invention. DETAILED DESCRIPTION

[0016] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments. The following specific implementation is helpful for developers to understand the present invention, but does not limit the present invention in any form. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which are all within the protection scope of the present invention.

[0017] like Figure 1 As shown, an embodiment of the present invention proposes a charging information transmission method for mobile new energy vehicles in a vehicle networking environment, including:

[0018] Step 1: Design a heterogeneous communication framework based on a hybrid transmission mode to transmit charging information, reduce network load and reduce network congestion;

[0019] Step 2: dynamically plan the optimal charging information transmission path between the mobile new energy vehicle to be charged and the corresponding edge computing server to accelerate the convergence speed of the optimal route;

[0020] Step 3: Design a data forwarding mechanism based on the receiving end to select the best charging information forwarding vehicle.

[0021] like Figure 2 As shown, the step 1 includes:

[0022] The designed heterogeneous communication framework consists of four parts, namely distributed charging stations, existing basic networks, edge computing servers and vehicle-mounted ad hoc networks. Among them, the existing basic network provides a charging information transmission path between the charging station and the edge computing server; the vehicle-mounted ad hoc network consists of mobile new energy vehicles and gasoline vehicles, which are connected to each other through V2V (Vehicle-to-Vehicle) communication. Figure 3 As shown in the figure, a hybrid transmission method is designed in the heterogeneous communication framework to transmit charging information. An active communication method is adopted between the charging station and the edge computing server, and the latest charging information is periodically released through relatively well-developed methods such as wired connection, 4G, WiFi, WiMax or RFID; a passive communication method is adopted between the mobile new energy vehicle and the edge computing server. Through V2X (Vehicle-to-Everything, vehicle wireless communication technology), the mobile new energy vehicle sends a request message to the corresponding edge computing server when charging is needed. After receiving the request, the edge computing server responds to the vehicle with the charging information cached in its server.

[0023] like Figure 4 As shown, the step 2 includes:

[0024] Step 2-1, based on the transmission delay and delay variance, the problem of exploring the optimal charging information transmission path is formulated as an NP optimization problem;

[0025] Step 2-2, propose a terminal intersection model, requiring the mobile new energy vehicle and the edge computing server to select their respective corresponding terminal intersections;

[0026] Step 2-3, based on the ant colony algorithm, the optimal path is established through forward exploration and backward exploration mechanisms; pheromone evaporation and route maintenance mechanisms are designed during the optimal path selection process.

[0027] like Figure 5 As shown, the step 3 includes:

[0028] Step 3-1, design a waiting time model based on forwarding distance and bit error rate;

[0029] Step 3-2, using the constructed waiting time model, design a data forwarding mechanism based on the receiving end, and the receiving end selects the best charging information forwarding vehicle.

[0030] Wherein, the step 2-1 specifically includes:

[0031] The transmission delay and the corresponding delay variance are selected to evaluate the performance of the established routing path as follows:

[0032]

[0033]

[0034] Where y is the candidate backbone route between the mobile new energy vehicle and the edge computing server, QOS(y) represents the comprehensive service quality of route y, D(y) and DV(y) represent the transmission delay and delay variance of y, λ1 and λ2 are weight values ​​used to adjust the effects of different QOS indicators (λ1+λ2=1); D th and DV th implies the corresponding upper bounds on the routing transmission delay and delay variance; e ij Indicates that from intersection I i To intersection I j The road section between ij ) and DV(e ij ) represent the segments along the road e ij The transmission delay and delay variance of .

[0035] Wherein, the step 2-2 specifically includes:

[0036] Using two parameters, namely the moving direction of the mobile new energy vehicle (or edge computing server) and the length of the road section from the current position of the mobile new energy vehicle (or edge computing server) to its candidate adjacent intersection, the corresponding terminal intersection is comprehensively selected to convert the end-to-end routing exploration scheme into a routing exploration scheme between intersections;

[0037] The formula is as follows:

[0038]

[0039] Where EI is the end intersection, G(I m ) is the evaluation candidate intersection I m Performance evaluation function, δ represents the weight parameter (0<δ<1), l(I m ) indicates that the mobile new energy vehicle or edge computing server is connected to the candidate adjacent intersection I m The distance, L represents the length of the current driving section of the mobile new energy vehicle or edge computing server, dr(I m ) represents the moving direction of the mobile new energy vehicle or edge computing server. If the mobile new energy vehicle or edge computing server moves to I m If moving, dr(I m ) is 1, if the mobile new energy vehicle or edge computing server is far away from I m Then dr(I m ) is 0. If the new energy vehicle or edge computing server is stationary, dr(I m ) is 0.5.

[0040] Wherein, the steps 2-3 specifically include:

[0041] After determining the corresponding terminal intersection, use forward ants to explore feasible paths. When reaching intersection I i , a forward ant first inserts I at the head i The index of the next intersection is then selected based on the probability of the local and global pheromones. j The probability P ij Given as follows:

[0042]

[0043] Where P ij is the forward ant (located at I i )Select I j is the probability of the next intersection, γ1 and γ2 represent the weight values ​​for adjusting the local and global pheromone influences, and n calculates I i The number of adjacent intersections, σ min is a constant value (0<σ min<1), GQ(y ij ) is the global pheromone, representing the i The route y to the intersection corresponding to the selected edge computing server ij The quality of service, this road passes through I j ; LQ(e ij ) represents from I i To I j Section e ij The local pheromone is expressed as follows:

[0044]

[0045] where e ij Indicates that from intersection I i To intersection I j The road section between ij ) and DV(e ij ) represent the segments along the road e ij The transmission delay and delay variance of D are shown in Figure 1. λ1 and λ2 are weight values ​​used to adjust the effects of different pheromone indicators (λ1+λ2=1). th and DV th implies corresponding upper bounds on the transmission delay and delay variance.

[0046] When the forward ant reaches the intersection corresponding to the edge computing server, the path it explores is considered as an available candidate routing path, and the corresponding backward ant is generated, and then returns to the intersection corresponding to the mobile new energy vehicle along the same but opposite movement path of the forward ant; once it reaches the given area I i (from I j The backward ant first collects the local service quality along the route segments through the routing path to obtain the latest global pheromone LGQ (y ij ), LGQ(y ij ) is expressed as

[0047]

[0048] where y ij Indicates that from intersection I i The complete routing path to the intersection corresponding to the selected edge computing server, this path passes through the intersection I j ; D(y ij ) and DV(y ij ) represents y ij The transmission delay and delay variance of D are, λ1 and λ2 are weight values ​​used to adjust the effects of different pheromone indicators (λ1+λ2=1); th and DV th implies corresponding upper bounds on the transmission delay and delay variance.

[0049] Afterwards, the backward ant updates are stored in I i The global pheromone table in , the pheromone update process is as follows:

[0050] GQ(y ij )←(1-χ)·GQ(y ij )+χ·LGQ(y ij )

[0051] Where GQ(y ij ) is the global pheromone, representing the i The route y to the intersection corresponding to the selected edge computing server ij The quality of service, this road passes through I j ; χ is the weight parameter (0<χ<1).

[0052] The above steps are repeated until the backward ants reach the intersection corresponding to the mobile new energy vehicle; when all the backward ants arrive and complete the global pheromone calculation and update operations, we only need to compare the different values ​​of the latest global pheromone QOS(y) of all available candidate routes, and then determine the best routing path, which is expressed as

[0053]

[0054] where y opt The path with the maximum QOS(y) is the optimal routing path.

[0055] The mobile new energy vehicle sends a confirmation message to the mobile new energy vehicle at the corresponding intersection to start the charging information transmission.

[0056] In the proposed routing protocol, a simple mathematical expression is established to simulate the pheromone evaporation mechanism, which is derived as follows:

[0057]

[0058] Where μ represents the pheromone evaporation parameter (0<μ<1), T ev represents the time interval, GQ(y)(t+T ev ) and GQ(y)(t) respectively represent the ev and the global pheromone on the routing path y at time t, σ min is a constant value (0<σ min <1).

[0059] When the optimal routing path y between two intersections based on quality of service opt After the establishment, the charging information transmission operation starts through the intersection; when reaching the intersection I iWhen the charging packet is dynamically forwarded to the next intersection based on the latest global pheromone, and if the road segment e ij Compared with other adjacent road segments, it has the largest global pheromone GQ(y ij ), I j Being I i Select the next intersection as the most suitable for packet transmission. ij ) is given as

[0060]

[0061] Where N represents I i The number of adjacent intersections, GQ(y in ) is the global pheromone, representing the i The route y to the intersection corresponding to the selected edge computing server in The quality of service, this road passes through I n .

[0062] Wherein, the step 3-1 specifically includes:

[0063] The waiting time model designed based on forwarding distance and bit error rate is as follows:

[0064]

[0065] where t k represents the waiting time, d sk represents the forwarding distance, and BER represents the bit error rate. The derived waiting time is limited to [0,T max ] time interval to mitigate the negative impact on data forwarding delay, T max Indicates t k The maximum value of R represents the wireless communication range of the forwarding vehicle and d sk ≤R; σ1, σ2 and σ3 represent weight values, which are responsible for adjusting the relative priorities of various evaluation parameters and extending the waiting time interval of potential charging information forwarding vehicles; if d sk ≥0, indicating candidate charging information forwarding vehicle C k Located in the packet forwarding direction, and it is the current transmitting vehicle C s is a feasible option; if d sk <0 means C k In C s After that, the charging information packet is transmitted in the opposite direction, so C k Not a viable option, t k Set to +∞.

[0066] Wherein, the step 3-2 specifically includes:

[0067] The data forwarding mechanism based on the receiving end is as follows: the transmitting vehicle currently carrying the charging information uses the improved RTS / CTS scheme to select the charging information forwarding vehicle, and broadcasts the RTS frame including the transmitting vehicle, the current position of the target forwarding intersection, and the sign information (so that all candidate charging information forwarding vehicles process the RTS frame) to all vehicles within the communication range of the transmitting vehicle; after receiving the frame, each candidate charging information forwarding vehicle replies with a CTS frame to the transmitting vehicle in a broadcast manner after its estimated waiting time. When the transmitting vehicle receives the first CTS frame, the candidate vehicle that replies to the frame is selected as the best charging information forwarding vehicle, and other candidate vehicles terminate their responses.

[0068] Experimental verification shows that the charging information transmission method proposed in the present invention can effectively reduce wireless network resource overhead, alleviate network congestion, and improve the adaptability of charging data transmission.

[0069] The above is a detailed description of the specific implementation examples of the present invention. It should be noted that the present invention is not limited to the above specific implementation examples, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essential content of the present invention.

Claims

1. A charging information transmission method for mobile new energy vehicles in a vehicle networking environment, characterized in that: Includes steps: A heterogeneous communication framework based on a hybrid transmission mode is designed to transmit charging information; the heterogeneous communication framework includes a distributed charging station, a basic network, an edge computing server and a vehicle-mounted ad hoc network, wherein the basic network provides a charging information transmission path between the charging station and the edge computing server; the vehicle-mounted ad hoc network is composed of a mobile new energy vehicle and a gasoline vehicle, which are interconnected through V2V communication; Dynamically plan the optimal charging information transmission path between the mobile new energy vehicle to be charged and the corresponding edge computing server; Design a data forwarding mechanism based on the receiving end and select the best vehicle to forward charging information; The dynamic planning of the optimal charging information transmission path between the mobile new energy vehicle to be charged and the corresponding edge computing server specifically includes: Step 2-1, based on the transmission delay and delay variance, the problem of exploring the optimal charging information transmission path is formulated as an NP optimization problem; Step 2-2, constructing a terminal intersection model, and the mobile new energy vehicle and the edge computing server select their respective corresponding terminal intersections; Step 2-3, based on the ant colony algorithm, the optimal path is established through forward exploration and backward exploration mechanisms; in the process of selecting the optimal path, the pheromone evaporation and route maintenance mechanism is designed; The step 2-1 specifically includes: The transmission delay and the corresponding delay variance are chosen to evaluate the performance of the established routing path as: Where y is the candidate backbone route between the mobile new energy vehicle and the edge computing server, QOS(y) represents the comprehensive service quality of route y, D(y) and DV(y) represent the transmission delay and delay variance of y, λ1 and λ2 are weight values, λ1+λ2=1, D th and DV th denote the corresponding upper bounds of routing transmission delay and delay variance, respectively; e ij Indicates that from intersection I i To intersection I j The road section between ij ) and DV(e ij ) represent the segments along the road e ij The transmission delay and delay variance of .

2. The charging information transmission method for mobile new energy vehicles in the vehicle networking environment according to claim 1 is characterized in that: An active communication method is adopted between the charging station and the edge computing server, and the latest charging information is periodically released through wired connection, 4G, WiFi, WiMax or RFID; a passive communication method is adopted between the mobile new energy vehicle and the edge computing server. Through V2X communication, the mobile new energy vehicle sends a request message to the corresponding edge computing server when charging is needed. After receiving the request message, the edge computing server responds to the vehicle with the cached charging information.

3. The charging information transmission method for mobile new energy vehicles in the vehicle networking environment according to claim 1 is characterized in that: The step 2-2 specifically includes: According to the moving direction of the mobile new energy vehicle or edge computing server and the length of the road section from the current position of the mobile new energy vehicle or edge computing server to its candidate adjacent intersection, the corresponding terminal intersection is selected to convert the end-to-end route exploration into the route exploration between intersections, that is: Where EI is the end intersection, G(I m ) is the evaluation candidate intersection I m Performance evaluation function, δ represents the weight parameter, 0<δ<1, l(I m ) represents the mobile new energy vehicle or edge computing server and the adjacent candidate intersection I m The distance, L represents the length of the current driving section of the mobile new energy vehicle or edge computing server, dr(I m ) represents the moving direction of the mobile new energy vehicle or edge computing server. If the mobile new energy vehicle or edge computing server moves to I m If moving, dr(I m ) is 1, if the mobile new energy vehicle or edge computing server is far away from I m Then dr(I m ) is 0. If the new energy vehicle or edge computing server is stationary, dr(I m ) is 0.

5.

4. The charging information transmission method for mobile new energy vehicles in the vehicle networking environment according to claim 1 is characterized in that: The steps 2-3 of establishing the optimal path through the forward exploration and backward exploration mechanisms specifically include: Based on the determined corresponding terminal intersection, the forward ants explore feasible paths. When they reach the intersection I i , a forward ant first inserts I at the head i The index of the next intersection is then selected based on the probability of the local and global pheromones. j The probability P ij for: Where P ij The forward ant chooses I j is the probability of the next intersection, γ1 and γ2 represent the weight values ​​for adjusting the local and global pheromone influences, and n is the value for calculating I i The number of adjacent intersections, σ min is a constant value 0<σ min <1, GQ(y ij ) is the global pheromone, representing the i The route y to the intersection corresponding to the selected edge computing server ij The quality of service, this road passes through I j , LQ(e ij ) represents from I i To I j Section e ij The local pheromone, LQ(e ij )for: When the forward ant reaches the intersection corresponding to the edge computing server, the path it explores is considered as an available candidate routing path, and the corresponding backward ant is generated, and then returns to the intersection corresponding to the mobile new energy vehicle along the same but opposite movement path of the forward ant. Once it reaches the given area I i , the backward ants first collect the local service quality along the route segments through the routing path to obtain the latest global pheromone LGQ(y ij ), LGQ(y ij )for: where y ij Indicates that from intersection I i The complete routing path to the intersection corresponding to the selected edge computing server, this path passes through the intersection I j , D(y ij ) and DV(y ij ) represents y ij The transmission delay and delay variance of The backward ant updates are stored in I i The global pheromone table in , its pheromone update process is: GQ(y ij )←(1-X)·GQ(y ij )+χ·LGQ(y ij ) Where X is the weight parameter, 0<χ<1; Repeat the above steps until the backward ants reach the intersection corresponding to the mobile new energy vehicle. When all backward ants arrive and complete the global pheromone calculation and update operations, compare the different values ​​of QOS(y) of all available candidate routes, and then determine the best routing path: where y opt The path with the maximum QOS(y) is the optimal routing path.

5. The charging information transmission method for mobile new energy vehicles in the vehicle networking environment according to claim 4 is characterized in that: Designing the pheromone evaporation and route maintenance mechanism in the optimal path selection process in step 2-3 specifically includes: In the routing protocol, a mathematical expression is established to simulate the pheromone evaporation mechanism, which is derived as follows: Where μ represents the pheromone evaporation parameter, 0<μ<1, T ev represents the time interval, GQ(y)(t+T ev ) and GQ(y)(t) respectively represent the ev and the global pheromone on the routing path y at time t, σ min is a constant value, 0<σ min <1; When the optimal routing path y between two intersections based on quality of service opt After the establishment, the charging information transmission operation starts through the intersection. When reaching the intersection I i When the charging packet is dynamically forwarded to the next intersection based on the latest global pheromone, and if the road segment e ij Compared with other adjacent road segments, it has the largest global pheromone GQ(y ij ), I j Being I i Select the next intersection as the most suitable for packet transmission, GQ(y ij ) is given as: Where N represents I i The number of adjacent intersections, the road passes through I n .

6. The charging information transmission method for mobile new energy vehicles in a vehicle networking environment according to claim 1 is characterized in that: The design is based on the data forwarding mechanism at the receiving end, and the selection of the best charging information forwarding vehicle specifically includes: Step 3-1, design a waiting time model based on forwarding distance and bit error rate; Step 3-2, based on the waiting time model, a data forwarding mechanism based on the receiving end is designed, and the receiving end selects the best charging information forwarding vehicle.

7. The charging information transmission method for mobile new energy vehicles in the vehicle networking environment according to claim 6 is characterized in that: The waiting time model in step 3-1 is: where t k represents the waiting time, d sk represents the forwarding distance, BER represents the bit error rate; the waiting time is limited to [0,T max ] time interval, T max Indicates t k The maximum value of R represents the wireless communication range of the forwarding vehicle and d sk ≤R; σ1, σ2 and σ3 represent weight values; if d sk ≥0, indicating candidate charging information forwarding vehicle C k Located in the packet forwarding direction, and it is the current transmitting vehicle C s is a feasible option; if d sk <0 means C k In C s After that, the charging information packet is transmitted in the opposite direction. k Not a viable option, t k Set to +∞.

8. The charging information transmission method for mobile new energy vehicles in the vehicle networking environment according to claim 7 is characterized in that: The step 3-2 specifically includes: the transmitting vehicle currently carrying the charging information uses the improved RTS / CTS scheme to select a charging information forwarding vehicle, and broadcasts an RTS frame including the transmitting vehicle, the current position of the target forwarding intersection, and the sign information to all vehicles within the communication range of the transmitting vehicle. After receiving the frame, each candidate charging information forwarding vehicle replies with a CTS frame to the transmitting vehicle in a broadcast manner after its estimated waiting time. When the transmitting vehicle receives the first CTS frame, the candidate vehicle that replies to the frame is selected as the best charging information forwarding vehicle, and other candidate vehicles terminate their responses.

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