Charging path planning method, device and server

Abstract

The application provides a charging path planning method and device and a server, and relates to the field of computers. The method comprises the following steps: a server acquires a charging request. The charging request comprises a current position and a remaining power of a vehicle to be charged. The server screens a first charging station set according to the current position and the remaining power of the vehicle to be charged. The first charging station set comprises at least one charging station. The server determines a comprehensive service index of each charging station according to charging station historical information and vehicle historical information of each charging station in the first charging station set. The server determines a target charging station with the maximum comprehensive service index from the first charging station set according to the comprehensive service index. The server plans a charging path according to the current position of the vehicle to be charged and position information of the target charging station. The method improves the charging efficiency of the vehicle to be charged.

Description

Technical Field

[0001] This application relates to the field of computers, and more particularly to a charging path planning method, apparatus, and server. Background Technology

[0002] With the increasing popularity of electric vehicles, they have gradually replaced traditional gasoline-powered cars as one of the most important means of transportation. Similar to gasoline-powered cars needing to refuel at gas stations, electric vehicles need to be charged at charging stations when their battery level is low.

[0003] When the electric vehicle's battery level drops below a certain threshold, the vehicle can alert the user to head to a charging station. Users can view the charging station's location information on their mobile phones, in-vehicle systems, or other devices. Users can select a target charging station and plan their charging route using navigation.

[0004] However, users' choice of charging stations is random, which may lead to problems such as long waiting times and insufficient power in the charging equipment. This cannot guarantee the charging efficiency of users' electric vehicles at charging stations, resulting in low charging efficiency. Summary of the Invention

[0005] This application provides a charging path planning method, device, and server to address the issues of randomness in user selection of charging stations, potential long waiting times, insufficient charging equipment power, and the inability to guarantee the charging efficiency of the user's electric vehicle at the charging station, resulting in low charging efficiency.

[0006] Firstly, this application provides a charging path planning method, including:

[0007] Obtain a charging request, which includes the current location and remaining battery power of the vehicle to be charged;

[0008] Based on the current location and remaining battery power of the vehicle to be charged, a first set of charging stations is obtained, which includes at least one charging station.

[0009] Based on the charging station historical information and vehicle historical information of each charging station in the first charging station set, the comprehensive service index of each charging station is determined.

[0010] Based on the comprehensive service index, the charging station with the largest comprehensive service index is determined as the target charging station from the first set of charging stations;

[0011] A charging route is planned based on the current location of the vehicle to be charged and the location information of the target charging station.

[0012] Optionally, determining the comprehensive service index of each charging station based on the charging station historical information and the vehicle historical information in the first charging station set specifically includes:

[0013] Based on the historical charging records of the vehicles to be charged in the vehicle history information, determine the historical usage parameters of each charging station;

[0014] Based on the location information of each charging station, the current location of the vehicle to be charged, weather information, and road condition information, the driving parameters and estimated arrival time of each charging station are determined.

[0015] Based on the historical information of the charging station, determine the queuing waiting parameters and charging capacity parameters of the charging station at the time corresponding to the estimated arrival time;

[0016] Based on the historical usage parameters, driving parameters, queuing parameters, charging power parameters, and preset weights, the comprehensive service index of each charging station is calculated.

[0017] Optionally, determining the historical usage parameters of each charging station based on the historical charging records of the vehicle to be charged in the vehicle history information specifically includes:

[0018] Based on the historical charging records, the historical usage time of each charging station is determined;

[0019] The time parameters are determined based on the time difference between the historical usage time and the current time.

[0020] The historical usage parameters of each charging station are obtained by superimposing the time parameters of each charging station.

[0021] Optionally, determining the queuing parameters and charging capacity parameters of the charging station at the time corresponding to the estimated arrival time based on the charging station's historical information specifically includes:

[0022] Based on the estimated arrival time, determine the arrival time period;

[0023] The statistics of the charging station's historical information include the number of charging vehicles that arrive at the charging station for charging during the specified arrival time each day within a preset time period, and the charging time for each charging vehicle.

[0024] The queuing waiting parameters are calculated based on the number of vehicles arriving at the charging station for charging during the arrival time each day and the charging time of each vehicle.

[0025] The remaining power of the charging station is obtained from the historical information of the charging station, within a preset time period, at the estimated arrival time each day;

[0026] The charging power parameter is determined based on the remaining power of the charging station at the estimated arrival time each day.

[0027] Optionally, the step of filtering to obtain the first set of charging stations based on the current location and remaining battery power of the vehicle to be charged specifically includes:

[0028] Based on the remaining battery power, weather information, and road condition information, determine the remaining driving distance;

[0029] Based on the current location and the drivable distance, determine multiple charging stations that the vehicle to be charged can reach;

[0030] The first charging station set is composed of multiple charging stations accessible to the vehicle to be charged.

[0031] Optionally, the method further includes:

[0032] A preset number of charging stations with the highest comprehensive service index are selected from the first set of charging stations and then formed into a second set of charging stations.

[0033] The charging stations in the second charging station set are sorted according to the comprehensive service index, and the sorted charging stations are output.

[0034] Secondly, this application provides a charging path planning device, comprising:

[0035] The acquisition module is used to acquire a charging request, which includes the current location and remaining battery power of the vehicle to be charged.

[0036] The processing module is configured to: filter and obtain a first set of charging stations based on the current location and remaining battery power of the vehicle to be charged, wherein the first set of charging stations includes at least one charging station; determine the comprehensive service index of each charging station based on the charging station history information and the vehicle history information of each charging station in the first set of charging stations; determine the charging station with the highest comprehensive service index from the first set of charging stations as the target charging station based on the comprehensive service index; and plan a charging route based on the current location of the vehicle to be charged and the location information of the target charging station.

[0037] Optionally, the processing module is specifically used for:

[0038] Based on the historical charging records of the vehicles to be charged in the vehicle history information, determine the historical usage parameters of each charging station;

[0039] Based on the location information of each charging station, the current location of the vehicle to be charged, weather information, and road condition information, the driving parameters and estimated arrival time of each charging station are determined.

[0040] Based on the historical information of the charging station, determine the queuing waiting parameters and charging capacity parameters of the charging station at the time corresponding to the estimated arrival time;

[0041] Based on the historical usage parameters, driving parameters, queuing parameters, charging power parameters, and preset weights, the comprehensive service index of each charging station is calculated.

[0042] Optionally, the processing module is specifically used for:

[0043] Based on the historical charging records, the historical usage time of each charging station is determined;

[0044] The time parameters are determined based on the time difference between the historical usage time and the current time.

[0045] The historical usage parameters of each charging station are obtained by superimposing the time parameters of each charging station.

[0046] Optionally, the processing module is specifically used for:

[0047] Based on the estimated arrival time, determine the arrival time period;

[0048] The statistics of the charging station's historical information include the number of charging vehicles that arrive at the charging station for charging during the specified arrival time each day within a preset time period, and the charging time for each charging vehicle.

[0049] The queuing waiting parameters are calculated based on the number of vehicles arriving at the charging station for charging during the arrival time each day and the charging time of each vehicle.

[0050] The remaining power of the charging station is obtained from the historical information of the charging station, within a preset time period, at the estimated arrival time each day;

[0051] The charging power parameter is determined based on the remaining power of the charging station at the estimated arrival time each day.

[0052] Optionally, the processing module is specifically used for:

[0053] Based on the remaining battery power, weather information, and road condition information, determine the remaining driving distance;

[0054] Based on the current location and the drivable distance, determine multiple charging stations that the vehicle to be charged can reach;

[0055] The first charging station set is composed of multiple charging stations accessible to the vehicle to be charged.

[0056] Optionally, the processing module is further configured to:

[0057] A preset number of charging stations with the highest comprehensive service index are selected from the first set of charging stations and then formed into a second set of charging stations.

[0058] The charging stations in the second charging station set are sorted according to the comprehensive service index, and the sorted charging stations are output.

[0059] Thirdly, this application provides a server, including: a memory and a processor;

[0060] The memory is used to store computer programs; the processor is used to execute the charging path planning method in the first aspect and any possible design of the first aspect according to the computer programs stored in the memory.

[0061] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by at least one processor of a server, performs the charging path planning method of the first aspect and any possible design of the first aspect.

[0062] Fifthly, this application provides a computer program product comprising a computer program that, when at least one processor of a server executes the computer program, enables the server to execute the charging path planning method in the first aspect and any possible design of the first aspect.

[0063] The charging path planning method, apparatus, and server provided in this application acquire a charging request. This request includes the current location and remaining battery power of the vehicle to be charged. Based on the current location and remaining battery power of the vehicle, a first set of charging stations is obtained. This first set of charging stations includes at least one charging station. Based on the charging station's historical information and the vehicle's historical information in the first set of charging stations, a comprehensive service index for each charging station is determined. Based on the comprehensive service index, the charging station with the highest comprehensive service index is selected as the target charging station from the first set of charging stations. By planning a charging path based on the current location of the vehicle and the location information of the target charging station, the charging efficiency of the vehicle at the charging station is improved, thereby enhancing the user experience. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A schematic diagram of a charging path planning scenario provided in an embodiment of this application;

[0066] Figure 2 A flowchart illustrating a charging path planning method provided in one embodiment of this application;

[0067] Figure 3 This is a schematic diagram of the structure of a charging path planning device provided in an embodiment of this application;

[0068] Figure 4 This is a schematic diagram of the hardware structure of a server provided in one embodiment of this application. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] The terms "first," "second," "third," "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0071] Depending on the context, the word "if" as used here can be interpreted as "when," "when," or "in response to determination."

[0072] Furthermore, as used herein, the singular forms “a,” “one,” and “the” are intended to also include the plural forms, unless the context indicates otherwise.

[0073] The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Therefore, “A, B, or C” or “A, B, and / or C” means “any one of the following: A; B; C; A and B; A and C; B and C; A, B, and C”. Exceptions to this definition occur only when combinations of elements, functions, steps, or operations are inherently mutually exclusive in some way.

[0074] With the increasing popularity of electric vehicles, they have gradually replaced traditional gasoline-powered cars as one of the most important means of transportation. Electric vehicle infotainment systems can be set with a battery level threshold. When the battery level falls below this threshold, the system can remind the user to charge. If a user cannot immediately reach a private charging station, they can go to a charging station. Users can view the location information of nearby charging stations on their mobile phones, in-vehicle infotainment systems, and other terminal devices. Users can select a target charging station from these options and plan their charging route based on that target station. While the terminal device may display some charging station information, this information is usually fixed, such as location and the number of charging stations. Users typically cannot obtain information about the current usage status of a charging station or its estimated future usage when viewing nearby charging stations. Therefore, the user's choice of charging station is somewhat random, potentially leading to long waiting times or insufficient battery power upon arrival. Thus, existing technologies cannot guarantee the charging efficiency of electric vehicles after they arrive at a charging station. The main reason for this problem is that the target charging station selected by the user is not the optimal one. To solve this problem, this application provides a charging route planning method. This method can filter for the optimal target charging station based on the charging station history information of nearby charging stations and the vehicle history information of the vehicle to be charged. Then, this application can plan a charging route based on the target charging station. When the user travels to the charging station according to the charging route, the charging efficiency of the vehicle to be charged can be effectively improved, thereby enhancing the user experience.

[0075] The following describes exemplary application scenarios of embodiments of this application. In the following examples, "vehicle" refers to electric vehicles.

[0076] Figure 1 This illustration shows a scenario diagram of charging path planning according to an embodiment of this application. Figure 1 As shown, when a vehicle's battery is low, the vehicle's infotainment system can generate a charging request. The vehicle can then send this request to the server. Upon receiving the request, the server can retrieve the vehicle's current location and remaining battery level. Based on the current location, the server can search for nearby charging stations. Alternatively, the server can determine the vehicle's remaining driving distance based on the remaining battery level. Based on this driving distance, the server can determine the charging station the vehicle can reach before its battery depletes. Figure 1 As shown, the server can display the location relationship between these charging stations and the vehicle to be charged in terminal devices such as vehicle-mounted systems.

[0077] In this application, a server is used as the execution entity to perform the charging path planning method of the following embodiments. Specifically, the execution entity can be a server hardware device, a software application implementing the following embodiments in the server, a computer-readable storage medium installed with the software application implementing the following embodiments, or code implementing the software application.

[0078] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0079] Figure 2 A flowchart illustrating a charging path planning method according to an embodiment of this application is shown. Figure 1 Based on the illustrated embodiments, as Figure 2 As shown, with the server as the execution entity, the method in this embodiment may include the following steps:

[0080] S101. Obtain a charging request, which includes the current location and remaining battery power of the vehicle to be charged.

[0081] In this embodiment, the server can obtain charging requests sent by terminal devices. The terminal device can be the vehicle's infotainment system. Alternatively, the terminal device can be a mobile phone linked to the vehicle. The charging request can include the current location and remaining battery power of the vehicle to be charged. Optionally, the charging request can also include vehicle information, including a vehicle identifier. This vehicle identifier is used to uniquely identify the vehicle to be charged.

[0082] In one example, the server can also store the current location, remaining battery power, and vehicle information of the vehicle to be charged in a database. The vehicle identifier is used to locate various information corresponding to the vehicle to be charged in the database.

[0083] S102. Based on the current location and remaining battery power of the vehicle to be charged, a first set of charging stations is obtained, which includes at least one charging station.

[0084] In this embodiment, the server can determine the driving distance of the vehicle to be charged based on its remaining battery power. The server can also determine the selection range of charging stations based on the vehicle's current location and the driving distance. The server can acquire all charging stations within the selection range. The server can add these charging stations to a first charging station set. Optionally, the server can also acquire charging station information when determining these charging stations. This charging station information may include historical charging station information, charging station location information, and charging pile information within the charging station.

[0085] In one example, the specific process by which the server filters to obtain the first set of charging stations may include:

[0086] Step 1: Determine the driving distance based on the remaining battery power, weather information, and road conditions.

[0087] In this step, the main factor affecting the vehicle's remaining driving range is the remaining battery power. During vehicle operation, under stable driving conditions, the energy consumption per kilometer is relatively stable. Therefore, the server can obtain the energy consumption parameters per kilometer set by technicians based on experience. The server can then estimate the driving range of the vehicle to be charged based on these parameters.

[0088] Furthermore, changes in weather and road conditions can also affect a vehicle's energy consumption during driving. For example, in cold or hot weather, drivers typically need to turn on the air conditioning. Air conditioning use increases the vehicle's energy consumption. Since the energy consumption varies at different temperatures depending on the air conditioning's mode (cold air, hot air, dehumidification, ventilation, etc.), the server can determine the air conditioning's energy consumption per minute by obtaining temperature and humidity information from the weather data and, based on the mapping relationship between the air conditioning mode, temperature, and energy consumption parameters set by technicians based on experience. Similarly, in congested traffic, vehicles travel at slower speeds and may brake and start more frequently. Therefore, energy consumption increases during driving in congested conditions. The server can also obtain additional energy consumption parameters per kilometer of travel set by technicians based on experience.

[0089] The server can comprehensively calculate the driving distance of the vehicle to be charged based on the power consumption parameters per kilometer of driving, the power consumption of the air conditioner per minute, and the additional power consumption parameters per kilometer of driving.

[0090] Step 2: Based on the current location and driving distance, determine the multiple charging stations that the vehicle to be charged can reach.

[0091] In this step, the server can obtain all charging stations whose straight-line distance from the current location to the vehicle to be charged is less than or equal to the preset distance threshold, based on a preset distance threshold. These charging stations can be those near the vehicle. The preset distance threshold can be determined based on the drivable distance. For example, when the drivable distance is 20 kilometers, the preset distance threshold can be 20 kilometers. Since the actual driving distance is usually greater than the straight-line distance, the server can also obtain the navigation distance from these charging stations to the vehicle. The server can retain charging stations whose navigation distance is less than or equal to the drivable distance. Furthermore, after the vehicle reaches the charging station, it usually needs to find a charging pile in the parking lot corresponding to the charging station and start charging. Therefore, the server can also reserve a certain buffer distance for the vehicle to travel within the charging station. The server can further delete charging stations where the sum of the navigation distance and the buffer distance is greater than the drivable distance. The server can determine that the remaining charging stations are those that the vehicle can reach.

[0092] Step 3: Combine the multiple charging stations accessible to the vehicle to be charged into a first charging station set.

[0093] In this step, the server can add these reachable charging stations to a first charging station set. This first charging station set can include the charging station name or charging station ID of each reachable charging station. The charging station name or charging station ID is typically used to uniquely identify a charging station.

[0094] S103. Based on the historical charging station information and vehicle information of each charging station in the first charging station set, determine the comprehensive service index of each charging station.

[0095] In this embodiment, the server can evaluate a charging station based on its historical charging station information to determine the quality of its service. The server can assess the service quality of the charging station using comprehensive service indicators. Furthermore, the server can use vehicle history information to determine the probability of a user visiting the charging station, thereby increasing the effectiveness of the comprehensive service indicator calculation.

[0096] In one example, the calculation process for this comprehensive service indicator may specifically include the following steps:

[0097] Step 1: Determine the historical usage parameters of each charging station based on the historical charging records of the vehicles to be charged in the vehicle history information.

[0098] In this step, the server retrieves the charging habits of the vehicle to be charged from the vehicle's historical information. Based on these charging habits, the server calculates the historical usage parameters for each charging station. When the vehicle frequently visits a particular charging station, the historical usage parameters for that station are relatively high. Conversely, for charging stations that the vehicle has never visited, the historical usage parameters can be 0.

[0099] Specifically, the server can calculate historical usage parameters according to the following steps:

[0100] Step 11: Determine the historical usage time of each charging station based on historical charging records.

[0101] In this step, the server can obtain the historical charging records of the vehicle to be charged within a preset time period. This preset time period is a default value. For example, it could be the past 5 years. Alternatively, the server can also obtain historical charging records starting from a preset start time. This preset start time can be a default value. For example, it could be the time the user purchased the vehicle. The historical charging records must include at least the historical usage times. Furthermore, the historical charging records may also include information such as historical charging stations, historical charging amounts, and historical charging durations.

[0102] Step 12: Determine the time parameters based on the time difference between the historical usage time and the current time.

[0103] In this step, the server can calculate the time difference between historical usage times and the current time. This time difference can be measured in days. The server can be configured with a mapping table or formula that maps time differences to time parameters. The server can then calculate the time parameter based on this mapping table or formula. This mapping table or formula can be preset by technical personnel. Since charging records closer to the current time have greater reference value, in this step, a larger time difference results in a smaller time parameter, and vice versa. For example, the time difference and the time parameter can be inversely proportional. For instance, when the time difference is 1 day, the time parameter can be 1. Similarly, when the time difference is 389 days, the time parameter can be 1 / 389.

[0104] Step 13: Overlay the time parameters of each charging station to obtain the historical usage parameters of each charging station.

[0105] In this step, the server can sum the time parameters of the same charging station to obtain the historical usage parameters of that charging station. For example, the vehicle to be charged may have gone to charging station A 5 days ago and 10 days ago, respectively. Then, the historical usage parameter of charging station A is 0.2 + 0.1 = 0.3. As another example, the first set of charging stations may include charging station B. If the vehicle to be charged has not gone to charging station B, then the historical usage parameter of charging station B can be 0.

[0106] In one implementation, the server can further optimize the historical usage parameters based on the historical driving trajectory. The specific process may include the following steps:

[0107] Step 14: Obtain the historical driving trajectory of the vehicle to be charged from the vehicle history information, and filter the historical driving trajectory.

[0108] In this step, the server can obtain the historical driving trajectory of the vehicle to be charged within a preset time period. This preset time period is a default value. For example, it could be the past 5 years. Alternatively, the server can also obtain the historical driving trajectory starting from a preset start time. This preset start time can be a default value. For example, it could be the time the user purchased the vehicle. The historical driving trajectory can specifically include the driving route and driving time.

[0109] Optionally, the server can retain all historical driving trajectories within a radius of the drivable distance centered on the current location.

[0110] Optionally, the server can also filter the historical driving trajectory based on the map information at the current time to delete road segments that are no longer drivable.

[0111] Step 15: Based on the historical driving trajectory and the location information of each charging station in the first charging station set, determine the historical driving trajectory that is closest to each charging station.

[0112] In this step, the server can calculate the closest driving trajectory to each charging station in the first charging station set based on the location information of each charging station. In this calculation, the server can determine the closest driving trajectory to each charging station by calculating the distance from a point to a line. It is important to note that multiple historical driving trajectories may overlap, but with different travel times. When the closest driving trajectory to a charging station corresponds to multiple overlapping historical driving trajectories with different travel times, all of these historical driving trajectories are considered the closest historical driving trajectory to that charging station.

[0113] Step 16: Determine the distance parameter and weight parameter based on the historical driving trajectory closest to each charging station.

[0114] In this step, the server can obtain the travel time of at least one historical driving trajectory for each charging station. The server can calculate the time difference between this travel time and the current time. The server can calculate a time parameter based on this time difference. The server can accumulate at least one time parameter for each charging station to obtain a weight parameter. The server can calculate the additional distance required to detour to the charging station based on the historical driving trajectory. The server can calculate a distance parameter based on this distance. Since the greater the additional distance, the less the server recommends that the vehicle going to the charging station, the larger the distance, the smaller the distance parameter can be. For example, the distance can be inversely proportional to the distance parameter.

[0115] Step 17: Optimize historical usage parameters based on distance and weight parameters.

[0116] In this step, the server can add the product of the distance parameter and the weight parameter to the historical usage parameters to optimize the historical usage parameters.

[0117] Step 2: Based on the location information of each charging station, the current location of the vehicle to be charged, weather information, and road condition information, determine the driving parameters and estimated arrival time of each charging station.

[0118] In this step, the server can determine the distance between each charging station and the current location using a map based on the location information of each charging station. The server can be configured with an average driving speed. This average driving speed can be preset by technicians, or it can be calculated based on the historical driving speed of the vehicle to be charged. In weather conditions such as rain, snow, or heavy fog, vehicle speeds typically decrease. Therefore, the server can adjust the average driving speed based on weather information. For example, in heavy fog, the average driving speed can decrease by 20%. Vehicle speeds also change under different road conditions, such as congestion or smooth traffic. Therefore, the server can also adjust the average driving speed based on road condition information. For example, in congested conditions, the server can adjust the average driving speed on congested sections based on the degree of congestion. The server can calculate driving parameters based on the vehicle's average driving speed. Based on the adjusted average driving speed, the server can calculate the travel time for the vehicle to travel from its current location to each charging station. The server can then determine the estimated arrival time based on this travel time and the current time.

[0119] Step 3: Based on the historical information of the charging station, determine the queuing waiting parameters and charging capacity parameters of the charging station at the time corresponding to the estimated arrival time.

[0120] In this step, the server can also obtain historical charging station information within a preset time period. This preset time period is a default value. For example, the preset time period could be the past 5 years. Alternatively, the server can also obtain historical charging station information starting from a preset start time. This preset start time can be a default value. For example, the preset start time could be the charging station's establishment time. This historical charging station information can include the queuing status of the charging station at the estimated arrival time and the remaining battery power of the charging station. The server can calculate queuing waiting parameters based on the queuing status. The server can also calculate charging power parameters based on the remaining battery power of the charging station.

[0121] Specifically, the steps for the server to calculate the queuing waiting parameters and charging power parameters may include:

[0122] Step 31: Determine the arrival time period based on the estimated arrival time.

[0123] In this step, the server can divide a day into multiple time periods. The server can determine the time period corresponding to the estimated arrival time based on the estimated arrival time. This time period is the arrival time period. For example, the server can divide a day into four time periods: 0:00-6:00 is the first time period, 6:00-12:00 is the second time period, 12:00-18:00 is the third time period, and 18:00-24:00 is the fourth time period.

[0124] Step 32: In the historical information of the charging station, count the number of vehicles that arrive at the charging station for charging within the preset time period each day and the charging time of each vehicle.

[0125] In this step, the server can statistically analyze the historical information of charging stations to record the number of vehicles that arrived at the station within the specified arrival time period. The server can also count the number of vehicles that arrived at the station and charged within the specified arrival time period each day. Furthermore, the server can obtain the charging duration and charging amount for each vehicle that arrived at the station and charged within the specified arrival time period each day.

[0126] In one implementation, if the current time corresponds to a special date with special meaning, such as a holiday, the server can separate the charging station historical information corresponding to that special date from all the charging station historical information. Optionally, the server can process only the charging station historical information corresponding to these special dates. Optionally, the server can process the charging station historical information corresponding to these special dates separately from the charging station historical information corresponding to other dates.

[0127] Step 33: Calculate the queuing parameters based on the number of vehicles arriving at the charging station during the daily arrival time and the charging time of each vehicle.

[0128] In this step, the server can calculate the first waiting parameter for each day based on the number of vehicles arriving at the charging station during the arrival time period and the charging time of each vehicle. Optionally, the server can accumulate the charging times of vehicles arriving within the arrival time period each day to obtain the total charging time. The server can use the ratio of this total charging time to the number of vehicles as the queuing waiting parameter.

[0129] In one implementation, after the server processes the historical charging station information corresponding to these special dates and the historical charging station information corresponding to other dates respectively, and obtains the queuing waiting parameters for the special dates and the queuing waiting parameters for other dates, the server can use a preset weight index to calculate the weighted sum of the two queuing waiting parameters. This weighted sum is the final queuing waiting parameter used.

[0130] Step 34: Obtain the charging station's historical information, which shows the remaining battery power at the estimated arrival time each day within a preset time period.

[0131] In this step, the server can obtain the remaining power of each charging station at the estimated arrival time of each day from the charging station's historical information.

[0132] Step 35: Determine the charging power parameters based on the remaining power at the charging station at the estimated arrival time each day.

[0133] In this step, the server can calculate the average value of the remaining battery power. This average value is the charging capacity parameter.

[0134] In one implementation, after the server processes the historical charging station information corresponding to these special dates and the historical charging station information corresponding to other dates, respectively, to obtain the charging capacity parameters corresponding to the special dates and the charging capacity parameters corresponding to other dates, the server can use a preset weight index to calculate the weighted sum of the two charging capacity parameters. This weighted sum is the final charging capacity parameter used.

[0135] Step 4: Calculate the comprehensive service index of each charging station based on historical usage parameters, driving parameters, queuing parameters, charging power parameters, and preset weights.

[0136] In this step, the server can have preset weights for historical usage parameters, driving parameters, queuing parameters, and charging power parameters. The server can determine the comprehensive service index of each charging station by calculating the weighted sum of these parameters.

[0137] S104. Based on the comprehensive service index, determine the charging station with the largest comprehensive service index from the first set of charging stations as the target charging station.

[0138] In this embodiment, after calculating the comprehensive service index of each charging station in the first charging station set, the server can determine the charging station with the highest comprehensive service index. The server can then use this charging station as the target charging station.

[0139] In one example, the server can also select a preset number of charging stations with the highest comprehensive service indicators from the first charging station set to form a second charging station set. This preset number can be set by technicians based on experience. For example, the preset number could be 6. The server can sort the charging stations in the second charging station set according to the comprehensive service indicators. The server can output the preset number of sorted charging stations. Users can view these charging stations on mobile phones, in-vehicle systems, and other terminal devices. The server can obtain the selection command generated when the user selects a charging station. The server can determine the target charging station based on this selection command.

[0140] S105. Based on the current location of the vehicle to be charged and the location information of the target charging station, plan the charging route.

[0141] In this embodiment, after identifying a target charging station, the server can determine the location information of that target charging station. Based on this location information and the current location, the server can plan a charging route on a map. The server can then send the planned charging route to terminal devices such as mobile phones and in-vehicle systems.

[0142] The charging route planning method provided in this application allows the server to obtain charging requests. These requests may include the current location and remaining battery power of the vehicle to be charged. The server can filter and obtain a first set of charging stations based on the vehicle's current location and remaining battery power. This first set of charging stations includes at least one charging station. The server can determine the comprehensive service index of each charging station based on its historical charging station information and the vehicle's historical information. Based on the comprehensive service index, the server can determine the charging station with the highest comprehensive service index from the first set of charging stations as the target charging station. The server can plan a charging route based on the vehicle's current location and the target charging station's location information. In this application, by calculating the comprehensive service index of each charging station to determine the target charging station, the server can improve the charging efficiency of the vehicle at the charging station by guiding the user to the target charging station, thereby improving the user experience.

[0143] Figure 3 This illustration shows a structural schematic diagram of a charging path planning device according to an embodiment of this application, as shown below. Figure 3 As shown, the charging path planning device 10 of this embodiment is used to implement the operation corresponding to the server in any of the above method embodiments. The charging path planning device 10 of this embodiment includes:

[0144] The acquisition module 11 is used to acquire a charging request, which includes the current location and remaining battery power of the vehicle to be charged.

[0145] Processing module 12 is used to filter and obtain a first set of charging stations based on the current location and remaining battery power of the vehicle to be charged. The first set of charging stations includes at least one charging station. Based on the charging station history information and vehicle history information of each charging station in the first set of charging stations, a comprehensive service index is determined for each charging station. Based on the comprehensive service index, the charging station with the highest comprehensive service index is selected as the target charging station from the first set of charging stations. Based on the current location of the vehicle to be charged and the location information of the target charging station, a charging route is planned.

[0146] In one example, processing module 12 is specifically used for:

[0147] Based on the historical charging records of the vehicles to be charged in the vehicle history information, determine the historical usage parameters of each charging station.

[0148] Based on the location information of each charging station, the current location of the vehicle to be charged, weather information, and road condition information, the driving parameters and estimated arrival time of each charging station are determined.

[0149] Based on historical information from the charging station, determine the queuing parameters and charging capacity parameters of the charging station at the time corresponding to the estimated arrival time.

[0150] Based on historical usage parameters, driving parameters, queuing parameters, charging power parameters, and preset weights, the comprehensive service index of each charging station is calculated.

[0151] In one example, processing module 12 is specifically used for:

[0152] Based on historical charging records, determine the historical usage time of each charging station.

[0153] The time parameters are determined based on the time difference between the historical usage time and the current time.

[0154] By superimposing the time parameters of each charging station, the historical usage parameters of each charging station can be obtained.

[0155] In one example, processing module 12 is specifically used for:

[0156] Determine the arrival time period based on the estimated arrival time.

[0157] The statistics on historical information of charging stations include the number of vehicles that arrive at the charging station for charging within a preset time period each day, as well as the charging time for each vehicle.

[0158] The number of vehicles arriving at the charging station each day during the designated time slot and the charging time for each vehicle are used to calculate queuing parameters.

[0159] The system retrieves historical information about charging stations, including the remaining battery power at the estimated arrival time each day within a preset timeframe.

[0160] The charging capacity parameters are determined based on the remaining power at the charging station at the estimated arrival time each day.

[0161] In one example, processing module 12 is specifically used for:

[0162] Determine the remaining driving distance based on the remaining battery power, weather information, and road conditions.

[0163] Based on the current location and driving distance, determine the multiple charging stations that the vehicle to be charged can reach.

[0164] The first charging station set is formed by combining multiple charging stations that the vehicle to be charged can reach.

[0165] In one example, processing module 12 is also used for:

[0166] A predetermined number of charging stations with the highest comprehensive service indicators are selected from the first set of charging stations and then formed into a second set of charging stations.

[0167] The charging stations in the second charging station set are sorted according to the comprehensive service index, and the sorted charging stations are output.

[0168] The charging path planning device 10 provided in this application embodiment can execute the above method embodiment. Its specific implementation principle and technical effect can be found in the above method embodiment, and will not be repeated here.

[0169] Figure 4 A schematic diagram of the hardware structure of a server provided in an embodiment of this application is shown. Figure 4 As shown, the server 20 is used to implement the operations corresponding to the server in any of the above method embodiments. The server 20 in this embodiment may include: a memory 21, a processor 22, and a communication interface 24.

[0170] The memory 21 is used to store computer programs. The memory 21 may include high-speed random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0171] Processor 22 is used to execute the computer program stored in the memory to implement the charging path planning method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments. The processor 22 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by the hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0172] Alternatively, the memory 21 can be either standalone or integrated with the processor 22.

[0173] When the memory 21 is a device independent of the processor 22, the server 20 may also include a bus 23. This bus 23 is used to connect the memory 21 and the processor 22. The bus 23 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0174] Communication interface 24 is used to interact with a terminal device, which can be an in-vehicle infotainment system. Server 20 can obtain charging requests uploaded by the in-vehicle infotainment system through this communication interface 24. Alternatively, server 20 can also send a charging path to the in-vehicle infotainment system through this communication interface 24, enabling the system to autonomously drive to the charging station. Alternatively, server 20 can also send a charging path to the in-vehicle infotainment system through this interface, allowing the driver to navigate to the charging station. Alternatively, the terminal device can also be a mobile phone. Server 20 can obtain charging requests uploaded by the mobile phone through this communication interface 24. The mobile phone can obtain the vehicle's remaining battery power by interacting with the in-vehicle infotainment system. Server 20 can also send a charging path to the mobile phone through this interface, allowing the driver to navigate to the charging station.

[0175] The server provided in this embodiment can be used to execute the charging path planning method described above. Its implementation and technical effects are similar, and will not be described again here.

[0176] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the methods provided in the various embodiments described above.

[0177] The computer-readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of a computer program from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the ASIC can reside in a user equipment. Of course, the processor and the computer-readable storage medium can also exist as discrete components in a communication device.

[0178] Specifically, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0179] This application also provides a computer program product comprising a computer program stored in a computer-readable storage medium. At least one processor of the device can read the computer program from the computer-readable storage medium, and the at least one processor executes the computer program to cause the device to implement the methods provided in the various embodiments described above.

[0180] This application also provides a chip including a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that a device with the chip installed performs the methods described in the various possible implementations above.

[0181] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0182] The modules can be physically separate, for example, installed in different locations within a single device, installed on different devices, distributed across multiple network units, or distributed across multiple processors. Alternatively, the modules can be integrated, for example, installed in the same device, or integrated into a single codebase. The modules can exist in hardware form, software form, or a combination of both. This application can select some or all of the modules to achieve the objectives of this embodiment based on actual needs.

[0183] When the various modules are implemented as integrated software functional modules, they can be stored in a computer-readable storage medium. The aforementioned software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0184] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A charging path planning method characterized by, The method comprises: acquiring a charging request, the charging request comprising a current position and a remaining power of a vehicle to be charged; screening a first charging station set according to the current position and the remaining power of the vehicle to be charged, the first charging station set comprising at least one charging station; determining a comprehensive service index of each charging station in the first charging station set according to charging station historical information and vehicle historical information of each charging station, wherein the vehicle historical information is used to determine a historical use parameter of each charging station, the historical use parameter of each charging station being obtained by superimposing a time parameter of each charging station, the time parameter being obtained according to a time difference between a historical use time and a current time, and the historical use time being obtained according to historical charging records; determining a charging station with the largest comprehensive service index from the first charging station set as a target charging station according to the comprehensive service index; planning a charging path according to the current position of the vehicle to be charged and position information of the target charging station.

2. The method of claim 1, wherein, The method comprises: determining a historical use parameter of each charging station according to historical charging records of the vehicle to be charged in the vehicle historical information; determining a driving parameter and an estimated arrival time of each charging station according to position information of each charging station, the current position of the vehicle to be charged, weather information and road condition information; determining a queuing waiting parameter and a charging power parameter of each charging station at a time corresponding to the estimated arrival time according to the charging station historical information; calculating a comprehensive service index of each charging station according to the historical use parameter, the driving parameter, the queuing waiting parameter, the charging power parameter and a preset weight.

3. The method of claim 2, wherein, The method comprises: determining an arrival time period according to the estimated arrival time; counting a number of charging vehicles and a charging time of each charging vehicle in the charging station historical information, which arrive at the charging station for charging in the arrival time period every day within a preset time length; calculating the queuing waiting parameter according to the number of charging vehicles and the charging time of each charging vehicle in the arrival time period every day within the preset time length; acquiring the remaining power of the charging station at the estimated arrival time every day within the preset time length in the charging station historical information; determining the charging power parameter according to the remaining power of the charging station at the estimated arrival time every day.

4. The method according to any one of claims 1-3, characterized in that, The method comprises: determining a drivable distance according to the remaining power, weather information and road condition information; determining a plurality of charging stations reachable by the vehicle to be charged according to the current position and the drivable distance. The first charging station set is composed of a plurality of charging stations that can be reached by the vehicle to be charged.

5. The method according to any one of claims 1-3, characterized in that, The method further comprises: The second charging station set is composed of a preset number of charging stations with the maximum comprehensive service index from the first charging station set. The charging stations in the second charging station set are sorted according to the comprehensive service index, and the sorted charging stations are output.

6. A charging route planning device characterized by comprising: The device comprises: The acquisition module is configured to acquire a charging request, wherein the charging request comprises a current position and a remaining power of a vehicle to be charged. The processing module is configured to filter a first charging station set according to the current position and the remaining power of the vehicle to be charged, wherein the first charging station set comprises at least one charging station; determine a comprehensive service index of each charging station according to charging station historical information and vehicle historical information of each charging station, wherein the vehicle historical information is used to determine a historical use parameter of each charging station, the historical use parameter of each charging station is obtained by superimposing a time parameter of each charging station, the time parameter is obtained according to a time difference between a historical use time and a current time, and the historical use time is obtained according to a historical charging record; determine a target charging station with the maximum comprehensive service index from the first charging station set according to the comprehensive service index; and plan a charging path according to the current position of the vehicle to be charged and position information of the target charging station. The server comprises a memory and a processor.

7. A server, characterized by The memory is configured to store a computer program, and the processor is configured to implement the charging path planning method according to the computer program stored in the memory. The computer readable storage medium stores a computer program, and the computer program is configured to implement the charging path planning method when executed by a processor.

8. A computer-readable storage medium, characterized in that, The computer program product comprises a computer program, and the computer program is configured to implement the charging path planning method when executed by a processor.

9. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is configured to implement the charging path planning method when executed by a processor.

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