An automobile charging route planning method and device, a storage medium and a terminal device

Abstract

The application discloses a kind of automobile charging route planning method, device, storage medium and terminal equipment, the method includes: obtaining vehicle real-time power, vehicle real-time position, city electronic map and the real-time traffic data of each road section in map;According to the real-time traffic data, the weight function of each road section is constructed, then the weight of each road section in the city electronic map is calculated according to the weight function, and each road section in city electronic map is identified according to weight, to generate real-time traffic network;According to the vehicle real-time position and the real-time traffic network, determine several charging routes and the total weight corresponding to each charging route;According to the vehicle real-time power, the route distance of several charging routes and the total weight corresponding to each charging route are calculated, and the charging route with the lowest energy consumption is selected as the selected route when the vehicle is charged.The application selects new energy automobile charging route, which is beneficial to reduce the energy consumption of new energy automobile.

Description

Technical Field

[0001] This invention relates to the field of new energy, and in particular to a method, apparatus, storage medium and terminal equipment for planning vehicle charging routes. Background Technology

[0002] With rapid social development, the number of motor vehicles has increased dramatically, and problems such as traffic congestion and environmental pollution have become increasingly prominent. In order to build a sustainable urban transportation system, new energy vehicles have joined the ranks of urban transportation, further enriching the transportation network system. Therefore, the charging problem of new energy vehicles has also attracted much attention.

[0003] In existing technologies, the planning of traffic routes for charging new energy vehicles is mainly based on the geographical relationship between the vehicle and numerous charging piles. However, this process ignores the feedback between the vehicle and the surrounding transportation network, resulting in some deviations in traffic route planning. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this invention proposes a vehicle charging route planning method, device, storage medium, and terminal equipment. When selecting charging routes, the method considers the dynamic feedback between the vehicle and the real-time traffic system, making the selection more comprehensive and accurate, which is beneficial to reducing the energy consumption of new energy vehicles.

[0005] An embodiment of the present invention provides a method for planning a car charging route, comprising:

[0006] The system acquires real-time vehicle battery level, real-time vehicle location, city electronic map, and real-time traffic data for each road segment on the map. The real-time traffic data includes road segment length, vehicle speed limit, traffic flow, weather impact factors, congestion level, and the number of new energy vehicles.

[0007] A weight function for each road segment is constructed based on the real-time traffic data. Then, the weight of each road segment in the city electronic map is calculated based on the weight function. Each road segment in the city electronic map is then identified based on the weight to generate a real-time traffic network.

[0008] Based on the real-time location of the vehicle and the real-time traffic network, several charging routes and the total weight corresponding to each charging route are determined.

[0009] The charging route with the lowest energy consumption is selected as the chosen route for vehicle charging based on the vehicle's real-time battery level, the route distance of several charging routes, and the total weight of each charging route.

[0010] Furthermore, the weighting function includes:

[0011]

[0012] Among them, Q i Let be the weight of segment i, l be the segment length, v be the vehicle speed limit, j be the traffic flow, t be the weather impact factor, d be the congestion level, n be the number of new energy vehicles, and Q1, Q2, Q3, Q4, and Q5 be the weight coefficients of the corresponding target layers.

[0013] Furthermore, based on the real-time location of the vehicle and the real-time traffic network, several charging routes and the total weight corresponding to each charging route are determined, including:

[0014] Using the real-time location of the vehicle as the starting point and the locations of each charging pile in the real-time traffic network as the ending point, the various road segments in the real-time traffic network are connected in sequence to obtain several charging lines.

[0015] The weights of each segment of each charging line are multiplied together, and the result of the multiplication is used as the total weight of the corresponding charging line.

[0016] Furthermore, based on the vehicle's real-time battery level, the route distances of several charging routes, and the total weight corresponding to each charging route, the charging route with the lowest energy consumption is selected as the chosen route for vehicle charging, including:

[0017] Based on the vehicle speed limit and road length of each segment in the charging route, the vehicle's pre-consumption power is calculated, and then the pre-consumption power is compared with the vehicle's real-time power. Charging routes with pre-consumption power less than the vehicle's real-time power are selected as candidate routes.

[0018] Calculate the ratio of the pre-consumption power of each candidate route to the corresponding total weight, and select the candidate route with the smallest ratio as the charging route with the lowest energy consumption.

[0019] Furthermore, before calculating the ratio of the estimated power consumption of each candidate route to its corresponding total weight, the following steps are also included:

[0020] Obtain traffic light data for each candidate route. The traffic light data includes: the maximum traffic flow that can pass between the opening and closing of each traffic light, the real-time congestion level of the road segment where each traffic light is located, the duration of each traffic light being closed at one time, and the minimum energy consumption required for a vehicle to pass through each traffic light.

[0021] The traffic volume of each traffic light segment for each candidate route is calculated based on the traffic light data.

[0022] The total weight is optimized based on the proportion of vehicle traffic.

[0023] Furthermore, obtaining the real-time location of the vehicle includes:

[0024] The vehicle's three-dimensional position, speed, and corresponding time point are calculated based on the GPS signals fed back by the satellite, and the three-dimensional position is used as the vehicle's real-time position at that time point.

[0025] This invention also provides a vehicle charging route planning device, comprising:

[0026] The traffic data acquisition module is used to acquire real-time vehicle battery level, real-time vehicle location, city electronic map, and real-time traffic data for each road segment in the map; the real-time traffic data includes road segment length, vehicle speed limit, traffic flow, weather impact factors, congestion level, and number of new energy vehicles.

[0027] The traffic network construction module is used to construct a weight function for each road segment based on the real-time traffic data, then calculate the weight of each road segment in the city electronic map based on the weight function, and mark each road segment in the city electronic map according to the weight to generate a real-time traffic network.

[0028] The charging route generation module is used to determine several charging routes and the total weight of each charging route based on the real-time location of the vehicle and the real-time traffic network.

[0029] The charging route selection module is used to calculate and select the charging route with the lowest energy consumption as the selected route when charging the vehicle, based on the vehicle's real-time battery level, the route distance of several charging routes, and the total weight of each charging route.

[0030] This invention also provides a storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device containing the storage medium to perform the vehicle charging route planning method described in any one of this invention.

[0031] This invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the vehicle charging route planning method described in any one of this invention when executing the computer program.

[0032] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0033] This invention acquires real-time traffic data and constructs a weight function for each road segment in a city's electronic map. This weight function incorporates real-time road segment length, vehicle speed limits, traffic flow, weather factors, congestion levels, and the number of new energy vehicles. This data, combined with the city's electronic map, forms a real-time traffic network. By analyzing the vehicle's initial location, several routes to charging stations on the city's electronic map and their corresponding weights can be obtained. When selecting the route with the lowest energy consumption, the acquired vehicle battery data is combined with these weights, taking into account the dynamic feedback between the vehicle and the real-time traffic system. Therefore, the selection of the route with the lowest energy consumption is more comprehensive and accurate, which is beneficial for reducing the energy consumption of new energy vehicles. Attached Figure Description

[0034] Figure 1 A flowchart illustrating the steps of a vehicle charging route planning method provided in an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of a car charging route planning device provided in an embodiment of the present invention; Detailed Implementation

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0037] Please refer to Figure 1 According to one embodiment of the present invention, a method for planning vehicle charging routes is provided, comprising:

[0038] Step S1: Obtain real-time vehicle battery level, real-time vehicle location, city electronic map, and real-time traffic data for each road segment in the map; the real-time traffic data includes road segment length, vehicle speed limit, traffic flow, weather impact factors, congestion level, and number of new energy vehicles.

[0039] Preferably, the vehicle's three-dimensional position, speed, and corresponding time point are calculated based on the GPS signals fed back by the satellite, and the three-dimensional position is used as the vehicle's real-time position corresponding to that time point.

[0040] In a preferred embodiment, the new energy vehicle contains various sensors that collect data on the vehicle's voltage, current, and battery temperature during operation. This data is transmitted in real-time to the vehicle's microcontroller processing unit via relays, enabling the microcontroller to process the data and generate real-time battery data. Additionally, the vehicle receives GPS positioning signals transmitted by satellites in real-time, and calculates its three-dimensional position, speed, and corresponding time based on the positioning messages within the signals. Besides acquiring real-time vehicle data, this solution also utilizes urban traffic data. Through big data mining techniques, it extracts data such as road length, vehicle speed limits, traffic flow, weather influencing factors, congestion levels, and the number of new energy vehicles from meteorological bureaus, transportation bureaus, or third parties.

[0041] Step S2: Construct a weight function for each road segment based on the real-time traffic data, then calculate the weight of each road segment in the city electronic map based on the weight function, and mark each road segment in the city electronic map according to the weight to generate a real-time traffic network.

[0042] Preferably, the weighting function includes:

[0043]

[0044] Among them, Q i Let be the weight of segment i, l be the segment length, v be the vehicle speed limit, j be the traffic flow, t be the weather impact factor, d be the congestion level, n be the number of new energy vehicles, and Q1, Q2, Q3, Q4, and Q5 be the weight coefficients of the corresponding target layers.

[0045] In a preferred embodiment, the influence of various factors in the traffic system is quantified by the analytic hierarchy process (AHP). The target layer can be selected as road segment length, vehicle speed limit, traffic flow, weather influence factors, congestion level, and the number of new energy vehicles. When selecting weight coefficients, big data technology can be used to calculate the relative coefficients of various factors that may affect traffic in the pre-driving area of ​​new energy vehicles, thereby ensuring a balanced analysis.

[0046] Step S3: Based on the real-time location of the vehicle and the real-time traffic network, determine several charging routes and the total weight corresponding to each charging route.

[0047] Preferably, based on the real-time location of the vehicle and the real-time traffic network, several charging routes and the total weight corresponding to each charging route are determined, including:

[0048] Using the real-time location of the vehicle as the starting point and the locations of each charging pile in the real-time traffic network as the ending point, the various road segments in the real-time traffic network are connected in sequence to obtain several charging lines.

[0049] The weights of each segment of each charging line are multiplied together, and the result of the multiplication is used as the total weight of the corresponding charging line.

[0050] In a preferred embodiment, the real-time traffic network can be understood as a dynamic map with implicit weights. The map contains various charging piles and various connecting road segments. After obtaining the real-time location of the new energy vehicle, the location is used as the starting point and the charging pile is used as the destination. The vehicle continuously traverses the real-time traffic network to obtain various charging routes within the city area. Each charging route consists of multiple road segments, and each road segment contains a weight value. By multiplying the weight values ​​of these road segments together, the total weight value corresponding to the route can be obtained.

[0051] Step S4: Calculate based on the vehicle's real-time battery level, the route distance of several charging routes, and the total weight corresponding to each charging route, and select the charging route with the lowest energy consumption as the selected route for vehicle charging.

[0052] Preferably, the charging route with the lowest energy consumption is selected as the chosen route for vehicle charging based on the vehicle's real-time battery level, the route distance of several charging routes, and the total weight corresponding to each charging route, including:

[0053] Based on the vehicle speed limit and road length of each segment in the charging route, the vehicle's pre-consumption power is calculated, and then the pre-consumption power is compared with the vehicle's real-time power. Charging routes with pre-consumption power less than the vehicle's real-time power are selected as candidate routes.

[0054] Calculate the ratio of the pre-consumption power of each candidate route to the corresponding total weight, and select the candidate route with the smallest ratio as the charging route with the lowest energy consumption.

[0055] In a preferred embodiment, the possible routes for a new energy vehicle to reach a charging station are known. However, due to the current power limitations of the new energy vehicle, the number of routes needs to be divided. By using the relationship curve between speed limit, road segment length, and vehicle power consumption, candidate routes for the new energy vehicle can be obtained. Each candidate route corresponds to a pre-consumption power consumption, and the total weight of each candidate route is used as the expected value of the route. The pre-consumption power consumption is balanced by calculating the ratio of the pre-consumption power consumption of each candidate route to the corresponding total weight. The candidate route with the smallest ratio is selected as the charging route with the lowest energy consumption.

[0056] Preferably, before calculating the ratio of the pre-consumption power of each candidate route to the corresponding total weight, the method further includes:

[0057] Obtain traffic light data for each candidate route. The traffic light data includes: the maximum traffic flow that can pass between the opening and closing of each traffic light, the real-time congestion level of the road segment where each traffic light is located, the duration of each traffic light being closed at one time, and the minimum energy consumption required for a vehicle to pass through each traffic light.

[0058] The traffic volume of each traffic light segment for each candidate route is calculated based on the traffic light data.

[0059] The total weight is optimized based on the proportion of vehicle traffic.

[0060] In a preferred embodiment, traffic light data has a certain impact on the total weight of each candidate route, which may affect drivers' driving expectations. Therefore, optimization of the total weight is considered, and the vehicle passage ratio of traffic lights is calculated using the following formula:

[0061]

[0062] Where Vn is the maximum traffic flow that can pass between the opening and closing of each traffic light, Yn is the real-time congestion level of the road segment where each traffic light is located, and T on L is the duration of each traffic light's off period. min The minimum energy consumption required for a vehicle to pass through each traffic light.

[0063] Based on the method embodiments of the present invention, corresponding apparatus embodiments are provided:

[0064] Please refer to Figure 2 Another embodiment of the present invention provides a vehicle charging route planning device, comprising:

[0065] The traffic data acquisition module is used to acquire real-time vehicle battery level, real-time vehicle location, city electronic map, and real-time traffic data for each road segment in the map; the real-time traffic data includes road segment length, vehicle speed limit, traffic flow, weather impact factors, congestion level, and number of new energy vehicles.

[0066] The traffic network construction module is used to construct a weight function for each road segment based on the real-time traffic data, then calculate the weight of each road segment in the city electronic map based on the weight function, and mark each road segment in the city electronic map according to the weight to generate a real-time traffic network.

[0067] The charging route generation module is used to determine several charging routes and the total weight of each charging route based on the real-time location of the vehicle and the real-time traffic network.

[0068] The charging route selection module is used to calculate and select the charging route with the lowest energy consumption as the selected route when charging the vehicle, based on the vehicle's real-time battery level, the route distance of several charging routes, and the total weight of each charging route.

[0069] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0070] Those skilled in the art will clearly understand that, for convenience and simplicity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0071] In another embodiment of the present invention described above, a storage medium is provided, the storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the vehicle charging route planning method according to any one of the method embodiments of the present invention.

[0072] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0073] Based on the various embodiments described above, the present invention provides corresponding embodiments for terminal devices.

[0074] One embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the vehicle charging route planning method according to any embodiment of the present invention.

[0075] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0076] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0077] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart memory card (SMC), secure digital card (SD), flash memory card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0078] The embodiments of the present invention have the following beneficial effects:

[0079] This invention acquires real-time traffic data and constructs a weight function for each road segment in a city's electronic map. This weight function incorporates real-time road segment length, vehicle speed limits, traffic flow, weather factors, congestion levels, and the number of new energy vehicles. This data, combined with the city's electronic map, forms a real-time traffic network. By analyzing the vehicle's initial location, several routes to charging stations on the city's electronic map and their corresponding weights can be obtained. When selecting the route with the lowest energy consumption, the acquired vehicle battery data is combined with these weights, taking into account the dynamic feedback between the vehicle and the real-time traffic system. Therefore, the selection of the route with the lowest energy consumption is more comprehensive and accurate, which is beneficial for reducing the energy consumption of new energy vehicles.

[0080] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for planning vehicle charging routes, characterized in that, include: Obtain real-time vehicle battery level, real-time vehicle location, city electronic map, and real-time traffic data for each road segment in the map; The real-time traffic data includes road segment length, vehicle speed limit, traffic flow, weather impact factors, congestion level, and number of new energy vehicles. A weight function for each road segment is constructed based on the real-time traffic data. Then, the weight of each road segment in the city electronic map is calculated based on the weight function. Each road segment in the city electronic map is then identified based on the weight to generate a real-time traffic network. Based on the real-time location of the vehicle and the real-time traffic network, several charging routes and the total weight corresponding to each charging route are determined. The calculation is performed based on the vehicle's real-time battery level, the route distance of several charging routes, and the total weight of each charging route. The charging route with the lowest energy consumption is selected as the chosen route for vehicle charging. The weighting function includes: in, for The weight of road segments For the length of the road segment, To limit vehicle speed, For traffic flow, Weather influencing factors Congestion level, For the number of new energy vehicles, For summation and counting variables, used to calculate... The number of new energy vehicles is calculated by iterating through them. , , , , These are the weight coefficients for the corresponding target layer.

2. The vehicle charging route planning method as described in claim 1, characterized in that, Based on the real-time location of the vehicle and the real-time traffic network, several charging routes and the total weight corresponding to each charging route are determined, including: Using the real-time location of the vehicle as the starting point and the locations of each charging pile in the real-time traffic network as the ending point, the various road segments in the real-time traffic network are connected in sequence to obtain several charging lines. The weights of each segment of each charging line are multiplied together, and the result of the multiplication is used as the total weight of the corresponding charging line.

3. The vehicle charging route planning method as described in claim 1, characterized in that, The charging route with the lowest energy consumption is selected as the chosen route for vehicle charging based on the vehicle's real-time battery level, the route distance of several charging routes, and the total weight corresponding to each charging route. This includes: Based on the vehicle speed limit and road length of each segment in the charging route, the vehicle's pre-consumption power is calculated, and then the pre-consumption power is compared with the vehicle's real-time power. Charging routes with pre-consumption power less than the vehicle's real-time power are selected as candidate routes. Calculate the ratio of the pre-consumption power of each candidate route to the corresponding total weight, and select the candidate route with the smallest ratio as the charging route with the lowest energy consumption.

4. The vehicle charging route planning method as described in claim 3, characterized in that, Before calculating the ratio of the estimated power consumption of each candidate route to its corresponding total weight, the following steps are also included: Obtain traffic light data for each candidate route. The traffic light data includes: the maximum traffic flow that can pass between the opening and closing of each traffic light, the real-time congestion level of the road segment where each traffic light is located, the duration of each traffic light being closed at one time, and the minimum energy consumption required for a vehicle to pass through each traffic light. The traffic volume of each traffic light segment for each candidate route is calculated based on the traffic light data. The total weight is optimized based on the proportion of vehicle traffic.

5. The vehicle charging route planning method as described in claim 1, characterized in that, The process of obtaining the real-time location of the vehicle includes: The vehicle's three-dimensional position, speed, and corresponding time point are calculated based on the GPS signals fed back by the satellite, and the three-dimensional position is used as the vehicle's real-time position at that time point.

6. A vehicle charging route planning device, characterized in that, include: The traffic data acquisition module is used to acquire real-time vehicle battery level, real-time vehicle location, city electronic map, and real-time traffic data for each road segment in the map; the real-time traffic data includes road segment length, vehicle speed limit, traffic flow, weather impact factors, congestion level, and number of new energy vehicles. The traffic network construction module is used to construct a weight function for each road segment based on the real-time traffic data, then calculate the weight of each road segment in the city electronic map based on the weight function, and mark each road segment in the city electronic map according to the weight to generate a real-time traffic network. The charging route generation module is used to determine several charging routes and the total weight of each charging route based on the real-time location of the vehicle and the real-time traffic network. The charging route selection module is used to calculate and select the charging route with the lowest energy consumption as the selected route when charging the vehicle, based on the real-time battery level of the vehicle, the route distance of several charging routes and the total weight of each charging route. The weighting function includes: in, for The weight of road segments For the length of the road segment, To limit vehicle speed, For traffic flow, Weather influencing factors Congestion level, For the number of new energy vehicles, For summation and counting variables, used to calculate... The number of new energy vehicles is calculated by iterating through them. , , , , These are the weight coefficients for the corresponding target layer.

7. A storage medium, characterized in that, The storage medium includes a stored computer program; wherein, when the computer program is running, it controls the device where the storage medium is located to execute the vehicle charging route planning method as described in any one of claims 1-5.

8. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the vehicle charging route planning method as described in any one of claims 1-5.

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