Road condition-based charging station intelligent selection method, electronic device and storage medium

Abstract

The application provides a charging station intelligent selection method based on road conditions, an electronic device and a storage medium, and the method comprises the following steps: acquiring a current navigation route of a vehicle, wherein the current navigation route is composed of multiple road segments; acquiring road segment energy consumption of each road segment in the multiple road segments; based on current energy of the vehicle and the road segment energy consumption of each road segment, obtaining first node residual power after each road segment is driven to an end; and based on the first node residual power, searching and determining a charging station to be pushed to a user, and the application can improve the prediction accuracy of the charging station to be pushed to the user and reduce the mileage anxiety of the user by dividing the current navigation route into multiple road segments and pushing the charging station to the user based on the residual power after each road segment is driven to an end.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and particularly to the field of vehicle networking technology, specifically to a method for intelligent selection of charging stations based on road conditions, an electronic device, and a storage medium. Background Technology

[0002] With the rapid development of the automotive industry and the support of 5G and new transportation infrastructure, the environmental pollution and energy shortage problems caused by traditional fuel vehicles are becoming increasingly prominent. Electric vehicles, with their electrification, intelligence, and low pollution, are gaining popularity and becoming a core direction for the future development of the automotive industry. However, due to the long charging time and uneven distribution of charging stations, research on charging route planning for electric vehicles in urban traffic is particularly important. Wireless charging technology for electric vehicles has also received increasing attention in recent years, and the impact of wireless charging must be considered in route planning.

[0003] While significant progress has been made in electric vehicle charging route planning, current methods typically calculate charging routes based on the vehicle's remaining mileage or driving range, combined with charging station information, to select suitable charging stations. However, existing methods suffer from significant prediction bias, which, for safety reasons, can easily lead to charging trips far exceeding the vehicle's actual needs. Summary of the Invention

[0004] One objective of the embodiments of this application is to provide a road condition-based intelligent charging station selection method, electronic device, and storage medium, wherein by dividing the current navigation route into multiple segments and pushing charging stations to the user based on the remaining battery power after each segment is completed, the prediction accuracy of the need to push charging stations to the user can be improved, and the user's range anxiety can be reduced.

[0005] Another objective of the embodiments of this application is to provide a method, electronic device and storage medium for intelligent selection of charging stations based on road conditions. By refining the road condition information and dynamically adjusting the dynamic energy consumption coefficient of the energy consumption prediction model based on the road condition information, the prediction of the energy consumption prediction model is matched with the road condition information of each road segment, thereby effectively improving the prediction accuracy of the charging stations that need to be pushed to users.

[0006] Another objective of the embodiments of this application is to provide a road condition-based intelligent charging station selection method, electronic device, and storage medium, wherein the remaining power can be determined based on the user's selection of any specified location to search for charging stations that meet the user's needs. On the one hand, this satisfies the user's personalized needs for charging locations, and on the other hand, it can push accurate charging station information to the user in advance, alleviating the user's range anxiety.

[0007] Another objective of the embodiments of this application is to provide a road condition-based intelligent charging station selection method, electronic device, and storage medium, wherein a corresponding charging station topology network can be automatically constructed based on the remaining battery power of the vehicle on different road segments of the navigation route, thereby intelligently, comprehensively, and accurately recommending charging stations that meet the user's charging needs.

[0008] To achieve the above objectives, in a first aspect, the present invention provides a road condition-based intelligent charging station selection method, comprising the following steps: obtaining the vehicle's current navigation route, wherein the current navigation route consists of multiple road segments; obtaining the energy consumption of each road segment; based on the vehicle's current energy and the energy consumption of each road segment, obtaining the remaining battery power at a first node after the completion of driving on each road segment; and searching for and determining charging stations to be pushed to the user based on the remaining battery power at the first node. The present invention, by dividing the current navigation route into multiple road segments and pushing charging stations to the user based on the remaining battery power after the completion of driving on each road segment, can improve the prediction accuracy of charging station recommendations to the user and reduce user range anxiety.

[0009] The road condition information includes road segment length, road segment type, road segment level, travel time, and driving speed. The energy consumption prediction model is constructed based on the dynamic energy consumption coefficient, the road segment length, and the travel time. The dynamic energy consumption coefficient is related to the road segment type and / or the road segment level. This invention refines the road condition information and dynamically adjusts the dynamic energy consumption coefficient of the energy consumption prediction model based on the road condition information, so that the prediction of the energy consumption prediction model matches the road condition information of each road segment, thereby effectively improving the prediction accuracy of the need to push charging stations to users.

[0010] The dynamic energy consumption coefficient includes a first energy consumption coefficient corresponding to the road segment length and a second energy consumption coefficient corresponding to the travel time. The first energy consumption coefficient is determined based on the ratio between the energy consumption test value when traveling at the default speed on the test road segment and the road segment length. Different road segment types and / or road segment levels correspond to different first energy consumption coefficients. The second energy consumption coefficient is determined based on the ratio between the energy consumption test difference when traveling at the default speed and non-default speed on the test road segment and the travel time of the test road segment. Different road segment types and / or road segment levels correspond to different second energy consumption coefficients. This allows the present invention to further configure the dynamic energy consumption coefficient based on the road segment length and travel time, thereby further improving the accuracy of the energy consumption prediction model.

[0011] The energy consumption prediction model further includes a speed index for adjusting the dynamic energy consumption coefficient. The speed index is determined based on the ratio between the driving speed and the default driving speed corresponding to the dynamic energy consumption coefficient. This allows the present invention to further adjust the dynamic energy consumption coefficient based on the ratio between the driving speed and the default driving speed corresponding to the dynamic energy consumption coefficient, so that the dynamic energy consumption coefficient is closely aligned with the driving speed, thereby further improving the accuracy of the energy consumption prediction model.

[0012] Specifically, based on the designated location of the vehicle, a charging station topology network is constructed with the designated location as the center and a first preset distance as the radius; the remaining battery power of the second node after the end of driving on the road segment where each charging station is located in the charging station topology network is determined; and charging stations whose remaining battery power of the second node meets the first preset condition are pushed to the user. This invention allows the user to determine the corresponding remaining battery power based on any designated location selected by the user to search for charging stations that meet the user's needs. On the one hand, it meets the user's personalized needs for charging location, and on the other hand, it can push accurate charging station information to the user in advance, alleviating the user's range anxiety.

[0013] Specifically, based on the remaining battery power of the first node, the road segment in which the remaining battery power of the first node meets the second preset condition is determined; based on the end point of the road segment, a charging station topology network is constructed with the end point as the center and the second preset distance as the radius; the remaining battery power of the second node in the charging station topology network is determined after the end of the journey on the road segment where each charging station is located is determined; and charging stations in which the remaining battery power of the second node meets the first preset condition are pushed to the user. This allows the present invention to automatically construct a corresponding charging station topology network based on the remaining battery power of the vehicle on different road segments of the navigation route, thereby intelligently, comprehensively and accurately recommending charging stations that meet the user's charging needs.

[0014] Secondly, the present invention provides an electronic device for intelligent selection of charging stations based on road conditions. The electronic device includes at least one processor, wherein the at least one processor is configured to: acquire the current navigation route of a vehicle, the current navigation route consisting of multiple road segments; acquire the energy consumption of each of the multiple road segments; obtain the remaining battery power of a first node after the end of driving on each of the road segments based on the current battery power of the vehicle and the energy consumption of each of the road segments; and search for and determine charging stations to be pushed to the user based on the remaining battery power of the first node. By dividing the current navigation route into multiple road segments and pushing charging stations to the user based on the remaining battery power after the end of driving on each road segment, the prediction accuracy of the need to push charging stations to the user can be improved, and the user's range anxiety can be reduced.

[0015] Thirdly, the present invention provides a computer storage medium storing program instructions, which, when executed, implement the method described above. Attached Figure Description

[0016] Figure 1 The diagram shows an application of the road condition-based intelligent charging station selection method of the present invention in one embodiment;

[0017] Figure 2 The diagram shown is a schematic flowchart of one embodiment of the intelligent charging station selection method based on road conditions of the present invention.

[0018] Figure 3 The diagram shows a schematic of obtaining the energy consumption of each road segment in a plurality of road segments in one embodiment of the intelligent charging station selection method based on road conditions of the present invention.

[0019] Figure 4 The flowchart shown is a process for searching and determining charging stations to be pushed to the user in one embodiment of the road condition-based intelligent charging station selection method of the present invention.

[0020] Figure 5 The diagram shown is a schematic diagram of a charging station topology network in one embodiment of the road condition-based intelligent charging station selection method of the present invention.

[0021] Figure 6 The diagram shown is another flowchart illustrating how the road condition-based intelligent charging station selection method of the present invention searches for and determines charging stations to be pushed to the user in one embodiment.

[0022] Figure 7 The diagram shows the overall implementation process of the intelligent charging station selection method based on road conditions according to one embodiment of the present invention.

[0023] Figure 8 The diagram shown is a schematic representation of the principle structure of an electronic device for intelligent selection of charging stations based on road conditions according to an embodiment of the present invention.

[0024] Component designation explanation

[0025] 100 Electronic devices that intelligently select charging stations based on road conditions

[0026] 101 processor

[0027] 102 Memory

[0028] S100~S400 Steps

[0029] Steps S210~S220

[0030] Steps S411~S413

[0031] Steps S421~S424 Detailed Implementation

[0032] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0033] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0034] This embodiment provides a method, electronic device, and storage medium for intelligent selection of charging stations based on road conditions. The current navigation route is divided into multiple segments, the energy consumption of each segment is obtained, the remaining power of the first node after the end of each segment is obtained, and charging stations are searched and pushed to the user based on the remaining power after the end of each segment. This can improve the prediction accuracy of the charging station push to the user and reduce the user's range anxiety.

[0035] Figure 1 The diagram shown illustrates the application of the road condition-based intelligent charging station selection method in this embodiment. Figure 1 The system generates navigation routes based on the input origin (e.g., the vehicle's current location) and destination. These routes can be generated using the vehicle's in-vehicle terminal or user devices within the vehicle, such as smartphones, tablets, smartwatches, and smart glasses. After generating the navigation route, it can be divided into multiple segments. Traffic information for each segment is then acquired and input into an energy consumption prediction model to obtain the energy consumption for each segment. Based on the vehicle's current energy and the energy consumption of each segment, the remaining battery power at the first node after each segment's journey is completed is calculated. Based on this remaining battery power, charging stations are searched and identified to recommend to the user, effectively improving the accuracy of determining whether the vehicle needs charging and thus providing accurate charging station information to the user, reducing range anxiety.

[0036] The following will describe in detail the principles and implementation methods of the road condition-based intelligent charging station selection method, electronic device, and storage medium of this embodiment, so that those skilled in the art can understand the road condition-based intelligent charging station selection method, electronic device, and storage medium of this embodiment without creative effort.

[0037] This embodiment provides a method for intelligent selection of charging stations based on road conditions. Figure 2 The diagram shown illustrates the principle flowchart of the road condition-based intelligent charging station selection method in this embodiment. This method can be executed by the vehicle's onboard terminal. Figure 2 As shown, the intelligent charging station selection method based on road conditions includes the following steps:

[0038] Step S100: Obtain the vehicle's current navigation route, which consists of multiple road segments;

[0039] Step S200: Obtain the energy consumption of each of the multiple road segments;

[0040] Step S300: Based on the vehicle's current energy and the energy consumption of each road segment, obtain the remaining power of the first node after the completion of each road segment; and

[0041] Step S400: Search for and determine the charging stations to be pushed to the user based on the remaining power of the first node.

[0042] The road condition-based intelligent charging station selection method in this embodiment divides the current navigation route into multiple segments and pushes charging stations to the user based on the remaining battery power after each segment is completed. This can improve the prediction accuracy of when charging stations need to be pushed to the user and reduce the user's range anxiety.

[0043] The steps S100 to S400 of the intelligent charging station selection method based on road conditions in this embodiment will be described in detail below.

[0044] Step S100: Obtain the vehicle's current navigation route, which consists of multiple road segments.

[0045] Specifically, in this embodiment, a navigation route is generated based on the origin (e.g., the vehicle's current location) and the destination. This navigation route can be generated based on the vehicle's in-vehicle terminal or on in-vehicle user devices such as smartphones, tablets, smartwatches, and smart glasses. After generating the vehicle's navigation route, in some implementations, the current navigation route can be directly divided into multiple segments based on road links (the smallest digital units that make up a road), thereby obtaining more refined segment energy consumption data later. In other implementations, the current navigation route can be divided into multiple segments based on road type, road length, road grade, road location points (e.g., service area locations, highway entrance / exit locations, gas station locations, scenic area locations, and merchant locations), and user selections. Each segment can contain a set of links consisting of one or more road links, which facilitates the grouping and merging of road links with similar characteristics, ensuring prediction accuracy without disrupting or altering the link structure, while also improving computational efficiency.

[0046] Step S200: Obtain the energy consumption of each of the multiple road segments.

[0047] Figure 3 The diagram illustrates, in one embodiment of the road condition-based intelligent charging station selection method of this example, the acquisition of road segment energy consumption for each of multiple road segments. Figure 3 As shown, obtaining the energy consumption of each of the multiple road segments includes the following steps:

[0048] Step S210: Obtain traffic condition information for each road segment; and

[0049] Step S220: Input the road condition information into the energy consumption prediction model to obtain the road segment energy consumption for each road segment.

[0050] In this embodiment, the traffic information of each road segment is obtained from the generated navigation route, or the traffic information of each road segment can be obtained from the vehicle terminal or user terminal via network or broadcast.

[0051] In this embodiment, the road condition information includes, but is not limited to, road segment length, road segment type, road segment grade, travel time, and driving speed. The road segment type includes ramps, auxiliary roads, interchanges, tunnels, bridges, closed roads, pedestrian streets, roundabouts, etc.; the road segment grade includes expressways, urban expressways, national highways, provincial highways, county roads, township roads, other roads, Class 9 roads, ferries, pedestrian walkways, etc. Specific road segment types and grades can be adjusted based on data from different map providers, and this application does not impose any restrictions on this. The travel time includes the estimated time to pass through the road segment at a preset driving speed, and the congestion time of passing through congested sections within the road segment.

[0052] In addition, the traffic information also includes location points on the road, which include one or more of the following: service area locations, highway entrance / exit locations, gas station locations, scenic spot locations, and business locations. By inputting the above traffic information, the accuracy of energy consumption prediction in different scenarios can be further improved and optimized.

[0053] In this embodiment, the road condition information is input into the energy consumption prediction model. Specifically, the road segment length, road segment type, road segment level, travel time, and driving speed are input into the energy consumption prediction model, and the energy consumption prediction model outputs the energy consumption of the corresponding road segment.

[0054] Therefore, the intelligent charging station selection method based on road conditions in this embodiment refines the road condition information and dynamically adjusts the input of the energy consumption prediction model based on the road condition information, so that the prediction of the energy consumption prediction model matches the road condition information of each road segment, thereby effectively improving the prediction accuracy of the charging stations that need to be pushed to users.

[0055] Specifically, in this embodiment, the energy consumption prediction model is constructed based on the dynamic energy consumption coefficient, the road segment length, and the travel time.

[0056] In this embodiment, the dynamic energy consumption coefficient includes a first energy consumption coefficient corresponding to the road segment length and a second energy consumption coefficient corresponding to the travel time. Specifically, in this embodiment, one way to construct the energy consumption prediction model based on the dynamic energy consumption coefficient, the road segment length, and the travel time is as follows:

[0057] F(energy) = Road segment length * r1 + Travel time * r2;

[0058] Wherein, F (energy) is the energy consumption of the road segment, r1 is the first energy consumption coefficient corresponding to the length of the road segment, and r2 is the second energy consumption coefficient corresponding to the travel time.

[0059] In this embodiment, the dynamic energy consumption coefficient is associated with the road segment type and / or the road segment grade.

[0060] Specifically, the first energy consumption coefficient is determined based on the ratio between the energy consumption test value when driving at the default speed on the test road segment and the length of the test road segment. Different road segment types and / or road segment grades correspond to different first energy consumption coefficients.

[0061] In this embodiment, the first energy consumption coefficient r1 corresponding to the road segment length is calculated as follows: r1 = F1 / L1; where F1 is the energy consumption test value when driving at the default speed on the test road segment, and L1 is the road segment length of the test road segment.

[0062] Specifically, the second energy consumption coefficient is determined based on the ratio between the energy consumption test difference when driving at the default driving speed and the non-default driving speed on the test road segment and the travel time of the test road segment. Different road segment types and / or road segment grades correspond to different second energy consumption coefficients.

[0063] In this embodiment, the second energy consumption coefficient r2 corresponding to the travel time is calculated as follows: r2 = (F2 - F0) / T1; where F2 is the energy consumption test value when traveling at a non-default speed on the test road segment, F0 is the energy consumption test value when traveling at the default speed on the test road segment, and T1 is the travel time when traveling at a non-default speed.

[0064] Therefore, the intelligent charging station selection method based on road conditions in this embodiment can further configure the dynamic energy consumption coefficient based on the road segment length and travel time, thereby further improving the accuracy of the energy consumption prediction model.

[0065] Furthermore, in this embodiment, the energy consumption coefficient corresponding to the road segment length and the energy consumption coefficient corresponding to the travel time are adjusted according to the proportional relationship between the driving speeds of different tests.

[0066] Specifically, the energy consumption prediction model also includes a speed index for adjusting the dynamic energy consumption coefficient, the speed index being determined based on the ratio between the driving speed and the default driving speed corresponding to the dynamic energy consumption coefficient.

[0067] For example, ignoring congestion, if you travel 10 kilometers at 120 km / h on a highway, the energy consumed is Q1. The value of r1 is calculated as: normal energy (Q1) / distance (10 km). When traveling at another speed (e.g., 100 km / h), the energy consumed over the same 10 kilometers is Q2. The value of r2 is calculated as: [actual energy (Q2) - normal energy (Q1)] / travel time at the other speed. If the test value is taken at 120 km / h when the highway speed is 100 km / h, then r1 and r2 are adjusted proportionally: r1 = r1 * 100 / 120; r2 = r2 * 100 / 120.

[0068] The road condition-based intelligent charging station selection method in this embodiment can further adjust the dynamic energy consumption coefficient based on the ratio between the driving speed and the default driving speed corresponding to the dynamic energy consumption coefficient, so that the dynamic energy consumption coefficient is closer to the driving speed, thereby further improving the accuracy of the energy consumption prediction model.

[0069] Step S300: Based on the current energy of the vehicle and the energy consumption of each road segment, obtain the remaining power of the first node after the end of each road segment.

[0070] Specifically, in this embodiment, the remaining power after the end of each road segment is obtained based on the difference between the vehicle's current energy and the cumulative energy consumption of each preceding road segment. In this embodiment, this is denoted as the remaining power of the first node.

[0071] Step S400: Search for and determine the charging stations to be pushed to the user based on the remaining power of the first node.

[0072] In this embodiment, the system searches for nearby charging stations based on the remaining battery level of the node to obtain the optimal charging location. Specifically, in this embodiment, the process of searching for and determining the charging station to push to the user based on the remaining battery level of the first node includes the following steps:

[0073] Based on the vehicle's designated location and the remaining battery power of the first node corresponding to the road segment where the designated location is located, the system searches and determines the charging station to push to the user.

[0074] Figure 4 This is a flowchart illustrating a method for intelligently selecting charging stations based on road conditions, as described in this embodiment, of searching for and determining charging stations to push to the user. Specifically, in this embodiment, as follows... Figure 4 As shown, the process of searching for and determining the charging station to push to the user based on the vehicle's specified location and the remaining battery power of the first node corresponding to the road segment where the specified location is located includes the following steps:

[0075] Step S411: Based on the designated location of the vehicle, construct a charging station topology network with the designated location as the center and a first preset distance as the radius.

[0076] The designated location of the vehicle can be the vehicle's current location, or it can be selected by the user by clicking on a map or entering a search term. For example, clicking or searching for a service area specified by the user can be used as the designated location of the vehicle.

[0077] like Figure 5 As shown, a circular map area is formed with the vehicle's designated location entered by the user on the map as the center and a first preset distance of 5 kilometers as the radius. The charging stations within the circular map area are then obtained to form a charging station topology network.

[0078] In this embodiment, the first preset distance is pre-configured or determined by the user, for example, 5 kilometers.

[0079] Step S412: Determine the remaining battery power of the second node after the end of the journey for each charging station in the charging station topology network.

[0080] Step S413: Push charging stations whose remaining power of the second node meets the first preset condition to the user.

[0081] The first preset condition includes a power threshold for the remaining power of the charging station, in case the searched charging station cannot meet the vehicle's charging needs.

[0082] It should be noted that if there are no charging stations meeting the first preset conditions within the charging station topology network centered at the specified location and with a radius of the first preset distance, a prompt may be made via pop-up window or other means to indicate whether to increase the first preset distance. The method for increasing the first preset distance is pre-configured, for example, by a proportional coefficient (e.g., 1.2, 1.5 times). It should also be noted that the upper limit of the increase in the first preset distance cannot exceed the distance that the vehicle's remaining range can reach.

[0083] If the first preset distance has been expanded to the maximum and there is still no charging station that meets the first preset condition, it indicates that the specified location may be unreasonable. This embodiment provides a prompt through pop-up windows or other means, including the following prompts:

[0084] 1) If the specified location is specified by the user, the user will be prompted that there is no suitable charging station at the specified location and that the specified location needs to be changed, or that a charging station should be selected on the road section before reaching the specified location.

[0085] 2) If the specified location is the vehicle's current location, it will be prompted that there are no suitable charging stations for the next leg of its journey, and it will be asked whether to call for charging or roadside assistance.

[0086] Therefore, the road condition-based intelligent charging station selection method in this embodiment can determine the remaining power based on the user's selection of any specified location in order to search for charging stations that meet the user's needs. On the one hand, it meets the user's personalized needs for charging location, and on the other hand, it can push accurate charging station information to the user in advance, alleviating the user's range anxiety.

[0087] Furthermore, the road condition-based intelligent charging station selection method in this embodiment can also automatically construct a corresponding charging station topology network based on the vehicle's remaining battery power on different road segments of the navigation route, thereby intelligently, comprehensively and accurately recommending charging stations that meet the user's charging needs.

[0088] Figure 6 This is shown as another flowchart illustrating the road condition-based intelligent charging station selection method of this embodiment, in one example, for searching and determining charging stations to push to the user. Specifically, in this embodiment, as... Figure 6 As shown, the process of searching for and determining charging stations to push to users based on the remaining power of the first node includes the following steps:

[0089] Step S421: Based on the remaining battery power of the first node, determine the road segment where the remaining battery power of the first node meets the second preset condition; wherein, the second preset condition is a low battery threshold, which is set by the user or uses the system default value, such as 20% battery power. That is, after the vehicle has traveled through the road segment, the remaining battery power of the node in that road segment is no more than 20% battery power, and the vehicle should go to the charging station to charge after traveling through the road segment.

[0090] Step S422: Based on the end point of the road segment, construct a charging station topology network with the end point as the center and a second preset distance as the radius; wherein, the second preset distance is determined based on the remaining power of the first node.

[0091] Step S423: Determine the remaining battery power of the second node after the completion of driving on the road segment where each charging station is located in the charging station topology network; and

[0092] Step S424: Push charging stations whose remaining power of the second node meets the first preset condition to the user.

[0093] The first preset condition is a power threshold of the remaining power at the charging station, and the second preset condition is a power threshold of the remaining power at the node where the vehicle arrives at the charging station. The power threshold of the first preset condition can be lower than the power threshold of the second preset condition.

[0094] Therefore, the road condition-based intelligent charging station selection method in this embodiment can automatically construct a corresponding charging station topology network based on the vehicle's remaining battery power on different road segments of the navigation route, thereby intelligently, comprehensively and accurately recommending charging stations that meet the user's charging needs.

[0095] It should be noted that when pushing charging stations that meet the first preset condition to users, there may be multiple charging stations that meet the first preset condition at the same time. Therefore, the intelligent charging station selection method based on road conditions in this embodiment also includes:

[0096] In response to receiving a charging station selection instruction, the system retrieves the charging station corresponding to the instruction, i.e., the user confirms the final charging station. In this embodiment, it may also include automatically generating a charging navigation route with the selected charging station as the destination.

[0097] Furthermore, in this embodiment, when searching for charging stations, the priority of search factors can be pre-configured. For example, priority can be given to searching for charging stations within service areas; if no suitable charging stations are found, then charging stations after exiting the highway can be searched. For instance, if there is no service area for charging within 10 kilometers of the charging point, then charging within 5 kilometers of exiting the highway can be selected; if no charging station is found, the distance can be increased further.

[0098] Figure 7 This is a flowchart illustrating the overall implementation process of the road condition-based intelligent charging station selection method in one embodiment of this invention. To enable those skilled in the art to further understand the principle and implementation method of the road condition-based intelligent charging station selection method in this embodiment, the following is combined with... Figure 7 The overall implementation process of the road condition-based intelligent charging station selection method in this embodiment is described.

[0099] like Figure 7 As shown, the user first launches the navigation application on the in-vehicle terminal or user device, inputs the starting point (e.g., the vehicle's current location) and destination to generate a navigation route. After generating the vehicle's navigation route, the current navigation route can be directly divided into multiple segments based on road links (the smallest digital units that make up a road). This allows for more refined segment energy consumption data to be obtained subsequently. Alternatively, the current navigation route can be divided into multiple segments based on road type, road length, road grade, road location points (e.g., service area locations, highway entrance / exit locations, gas station locations, scenic area locations, and business locations), and user selections. Each segment can contain a set of links consisting of one or more road links. This facilitates the grouping and merging of road links with similar characteristics, thereby ensuring prediction accuracy without destroying or changing the link structure, while also improving computational efficiency.

[0100] Traffic condition information for each road segment is acquired and input into an energy consumption prediction model. The model, based on a dynamic energy consumption coefficient, the road segment length, and the travel time, outputs the energy consumption for the corresponding road segment. Then, based on the difference between the vehicle's current energy and the cumulative energy consumption of the preceding road segments, the remaining battery power after each road segment is determined. Based on this remaining battery power, charging stations are searched and selected to be pushed to the user.

[0101] Among these features, the system can determine the remaining battery power based on any specified location selected by the user to search for charging stations that meet the user's needs. On the one hand, it satisfies the user's personalized needs for charging locations, and on the other hand, it can push accurate charging station information to the user in advance, alleviating the user's range anxiety. It can also automatically construct a corresponding charging station topology network based on the remaining battery power of the vehicle on different sections of the navigation route, thereby intelligently, comprehensively and accurately recommending charging stations that meet the user's charging needs.

[0102] This embodiment also provides an electronic device for intelligent selection of charging stations based on road conditions. Figure 8 The diagram shown illustrates the principle structure of the electronic device 10 for intelligent selection of charging stations based on road conditions in one embodiment of this invention. Figure 8 As shown, the electronic device 10 for intelligent selection of charging stations based on road conditions includes at least one processor 101 and a memory 102, wherein the at least one processor 101 is configured to:

[0103] Obtain the vehicle's current navigation route, which consists of multiple road segments;

[0104] Obtain the energy consumption of each of the multiple road segments;

[0105] Based on the vehicle's current energy and the energy consumption of each road segment, the remaining battery power of the first node after the completion of each road segment is obtained; and

[0106] Based on the remaining battery power of the first node, search and determine the charging station to push to the user.

[0107] Furthermore, this embodiment also provides a computer storage medium storing program instructions, which, when executed by a processor, implement the aforementioned intelligent charging station selection method based on road conditions. The intelligent charging station selection method based on road conditions has already been described in detail above and will not be repeated here.

[0108] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented using computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0109] In summary, this invention improves the accuracy of charging station recommendations by dividing the current navigation route into multiple segments and pushing charging station information to users based on the remaining battery power after each segment's completion, thus reducing range anxiety. Furthermore, by refining road condition information and dynamically adjusting the dynamic energy consumption coefficient of the energy consumption prediction model based on this information, the invention ensures that the model's predictions match the road condition information of each segment, effectively improving the accuracy of charging station recommendations. Additionally, this invention can further configure the dynamic energy consumption coefficient based on segment length and travel time, further enhancing the accuracy of the energy consumption prediction model. Finally, this invention can adjust the dynamic energy consumption coefficient based on driving speed and dynamic energy consumption. The ratio between the coefficient and the default driving speed is further adjusted to refine the dynamic energy consumption coefficient, making it more closely aligned with the driving speed and further improving the accuracy of the energy consumption prediction model. This invention can determine the remaining battery power based on any specified location selected by the user to search for charging stations that meet their needs. This satisfies users' personalized charging location requirements and provides accurate charging station information in advance, alleviating range anxiety. Furthermore, this invention can automatically construct a corresponding charging station topology network based on the vehicle's remaining battery power on different segments of the navigation route, intelligently, comprehensively, and accurately recommending charging stations that meet the user's charging needs. Therefore, this invention effectively overcomes the shortcomings of existing technologies and has high industrial application value.

[0110] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for intelligent selection of charging stations based on road conditions, characterized in that, Includes the following steps: Obtain the vehicle's current navigation route, which consists of multiple road segments; Obtain traffic information for each road segment, including road segment length, road segment type, road segment grade, travel time, and travel speed; The road condition information is input into the energy consumption prediction model to obtain the energy consumption of each road segment. The energy consumption prediction model is constructed based on the dynamic energy consumption coefficient, the road segment length, and the travel time. The dynamic energy consumption coefficient is associated with the road segment type and / or the road segment level. Based on the vehicle's current energy and the energy consumption of each road segment, the remaining power of the first node after the end of each road segment is obtained; as well as Based on the remaining power of the first node, search for and determine the charging stations to push to the user; The dynamic energy consumption coefficient includes a first energy consumption coefficient corresponding to the road segment length and a second energy consumption coefficient corresponding to the travel time. The first energy consumption coefficient is determined based on the ratio between the energy consumption test value when driving at the default driving speed on the test road segment and the length of the test road segment. Different road segment types and / or road segment grades correspond to different first energy consumption coefficients. The second energy consumption coefficient is determined based on the ratio between the energy consumption test difference when driving at the default driving speed and the non-default driving speed on the test road segment and the travel time of the test road segment. Different road segment types and / or road segment grades correspond to different second energy consumption coefficients.

2. The method of claim 1, wherein, The energy consumption prediction model also includes a speed index for adjusting the dynamic energy consumption coefficient, the speed index being determined based on the ratio between the driving speed and the default driving speed corresponding to the dynamic energy consumption coefficient.

3. The method of claim 1, wherein, The traffic information also includes location points on the road, which include one or more of the following: service area location, highway entrance / exit location, gas station location, scenic spot location, and business location.

4. The method of claim 1, wherein, The process of searching for and determining charging stations to push to users based on the remaining power of the first node includes the following steps: Based on the vehicle's designated location and the remaining battery power of the first node corresponding to the road segment where the designated location is located, the system searches and determines which charging stations to push to the user.

5. The method of claim 4, wherein, The step of searching for and determining the charging station to push to the user based on the vehicle's specified location and the remaining battery power of the first node corresponding to the road segment where the specified location is located includes the following steps: Based on the designated location of the vehicle, a charging station topology network is constructed with the designated location as the center and a first preset distance as the radius. Determine the remaining battery power of the second node after the end of the journey for each charging station segment in the charging station topology network; and The system pushes charging stations whose remaining battery power at the second node meets the first preset condition to the user.

6. The method of claim 1, wherein, The process of searching for and determining charging stations to push to users based on the remaining power of the first node includes the following steps: Based on the remaining power of the first node, determine the road segment in which the remaining power of the first node meets the second preset condition; Based on the endpoint of the road segment, a charging station topology network is constructed with the endpoint as the center and a second preset distance as the radius. Determine the remaining battery power of the second node after the end of the journey for each charging station segment in the charging station topology network; and The system pushes charging stations whose remaining battery power at the second node meets the first preset condition to the user.

7. The method of claim 6, wherein, The second preset distance is determined based on the remaining power of the first node. 8.An electronic device for intelligent selection of a charging station based on road conditions, the electronic device comprising: The electronic device includes at least one processor, wherein the at least one processor is configured to: Obtain the vehicle's current navigation route, which consists of multiple road segments; Obtain traffic information for each road segment, including road segment length, road segment type, road segment grade, travel time, and travel speed; The road condition information is input into the energy consumption prediction model to obtain the energy consumption of each road segment. The energy consumption prediction model is constructed based on the dynamic energy consumption coefficient, the road segment length, and the travel time. The dynamic energy consumption coefficient is associated with the road segment type and / or the road segment level. Based on the vehicle's current energy and the energy consumption of each road segment, the remaining battery power of the first node after the completion of each road segment is obtained; and Based on the remaining power of the first node, search for and determine the charging stations to push to the user; The dynamic energy consumption coefficient includes a first energy consumption coefficient corresponding to the road segment length and a second energy consumption coefficient corresponding to the travel time. The first energy consumption coefficient is determined based on the ratio between the energy consumption test value when driving at the default driving speed on the test road segment and the length of the test road segment. Different road segment types and / or road segment grades correspond to different first energy consumption coefficients. The second energy consumption coefficient is determined based on the ratio between the energy consumption test difference when driving at the default driving speed and the non-default driving speed on the test road segment and the travel time of the test road segment. Different road segment types and / or road segment grades correspond to different second energy consumption coefficients.

9. A computer storage medium storing program instructions, wherein, When the program instructions are executed, they implement the method as described in any one of claims 1 to 7.

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