New energy automobile intelligent charging path planning method and device, terminal and medium

Abstract

The invention provides a new energy automobile intelligent charging path planning method and device, a terminal and a medium, and belongs to the technical field of charging scheduling. The method comprises the steps that after a charging instruction of a user is obtained, state information of a plurality of candidate charging piles is obtained; wherein the state information comprises the position, the charging power, the charging price and the estimated available time period; based on the state information of each candidate charging pile and the user information of the user, the matching degree of each candidate charging pile and the demand of the user is evaluated; wherein the user information comprises a current position, a preferred charging degree and a residual electric quantity state; and based on the demand matching degree of each candidate charging pile, generating corresponding candidate charging paths and sorting the candidate charging paths, so that a user selects a target charging path from the candidate charging paths. According to the invention, the convenience of searching and using the charging pile by a user can be improved, and effective distribution and use of charging pile resources are promoted.

Description

Technical Field

[0001] The present invention relates to the field of charging scheduling technology, and in particular to a method, device, terminal and medium for intelligent charging path planning for new energy vehicles. Background Art

[0002] With the popularization of new energy vehicles, the demand for charging infrastructure is growing, and charging convenience has become a key factor affecting user experience.

[0003] At present, users often face many problems when looking for charging piles. On the one hand, it is difficult to obtain comprehensive and accurate charging pile information, such as location, power, real-time status, queue status, etc., which makes it impossible to quickly find a suitable charging pile; on the other hand, the existing charging path planning methods are often based only on initial data, and cannot adjust the path in time according to real-time changing road conditions, charging pile status and electricity prices. As a result, users may encounter problems such as long waiting time, high charging fees or long driving distances, which reduces charging efficiency and user satisfaction. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, terminal and medium for intelligent charging path planning of a new energy vehicle to solve the problem of planning the charging path.

[0005] In a first aspect, an embodiment of the present invention provides a method for intelligent charging path planning for a new energy vehicle, comprising: After receiving the user's charging instruction, the status information of multiple candidate charging piles is obtained; wherein the status information includes location, charging power, charging price and estimated available time period; Based on the status information of each candidate charging pile and the user's user information, evaluating the degree of matching between each candidate charging pile and the user's needs; wherein the user information includes the current location, the preferred charging level and the remaining power status; Based on the degree of demand matching of each candidate charging pile, corresponding candidate charging paths are generated and sorted, so that the user can select a target charging path from each candidate charging path.

[0006] In a possible implementation, obtaining the estimated available time period of the candidate charging pile includes: Obtaining user information of a user who has made a reservation for a first candidate charging pile; wherein the first candidate charging pile is any candidate charging pile; Based on the location of the first candidate charging pile, the current location of the first reserved user and the real-time traffic conditions, determining the estimated arrival time and estimated power consumption of the first reserved user at the first candidate charging pile; wherein the first reserved user is any reserved user; The later time between the end time of charging of the second reserved user and the estimated arrival time of the first reserved user is used as the start time of charging of the first reserved user; wherein the second reserved user is the previous reserved user of the first reserved user; Determine an estimated charging time for the first reserved user based on the charging power of the first candidate charging pile, the current remaining power state of the first reserved user, the estimated power consumption, and the preferred charging degree; Determining the charging end time based on the charging start time and the estimated charging duration of the first reserved user; The charging end time of each reserved user of the first candidate charging pile is calculated in sequence, and the charging end time of the last reserved user is used as the start time of the estimated available time period of the first candidate charging pile. In a possible implementation, the degree of demand matching includes waiting time, charging cost and charging time; based on the status information of each candidate charging pile and the user information of the user, the degree of matching between each candidate charging pile and the user's demand is evaluated, including: Determine the estimated arrival time and estimated power consumption of the user at the candidate charging pile based on the location of the second candidate charging pile, the current location of the user, and the real-time traffic conditions; wherein the second candidate charging pile is any candidate charging pile; Calculate the waiting time corresponding to the second candidate charging pile based on the difference between the estimated arrival time of the user at the second candidate charging pile and the estimated start time of the available time period; Calculate the charging cost corresponding to the second candidate charging pile based on the charging price of the second candidate charging pile, the estimated power consumption and the user's preferred charging level; The charging time corresponding to the second candidate charging pile is calculated based on the charging power of the second candidate charging pile, the estimated power consumption and the user's preferred charging level.

[0007] In a possible implementation, based on the demand matching degree of each candidate charging pile, corresponding candidate charging paths are generated and sorted, including: The waiting time, charging cost and charging time of each candidate charging pile are normalized, and weighted summed based on the weight corresponding to the recommendation strategy selected by the user to obtain the demand matching degree score of the candidate charging pile; Sort the candidate charging piles according to their demand matching scores from high to low.

[0008] In a possible implementation, after sorting the candidate charging piles according to the demand matching scores from large to small, the following is further included: Based on the distances and demand matching scores between each candidate charging pile and the third candidate charging pile, the additional matching score of the third candidate charging pile is calculated and displayed; wherein the third candidate charging pile is any candidate charging pile, and the calculation formula for the additional matching score is:

[0009] in, is the additional matching score for the third candidate charging station. For the The demand matching degree score of the candidate charging piles is is the number of candidate charging piles except the third candidate charging pile, For the The distance between the first candidate charging pile and the third candidate charging pile.

[0010] In a possible implementation, after sorting the candidate charging piles according to the demand matching scores from large to small, the following is further included: Based on the historical usage data of each candidate charging pile, it is determined whether the candidate charging pile is a frequently used charging pile of the user. If so, the candidate charging pile is marked and displayed.

[0011] In a possible implementation, after generating and sorting corresponding candidate charging paths based on the demand matching degree of each candidate charging pile, the method further includes: During the driving process after the user selects the target charging path, the status information of each candidate charging pile is continuously updated, and the corresponding candidate charging paths are regenerated and sorted based on the updated status information of each candidate charging pile.

[0012] In a second aspect, an embodiment of the present invention provides a new energy vehicle intelligent charging path planning device, comprising: An acquisition module is used to acquire status information of multiple candidate charging piles after acquiring a charging instruction from a user; wherein the status information includes location, charging power, charging price, and estimated available time period; An evaluation module, for evaluating the degree to which each candidate charging pile matches the user's needs based on the status information of each candidate charging pile and the user's user information; wherein the user information includes the current location, the preferred charging level, and the remaining power status; The planning module is used to generate and sort corresponding candidate charging paths based on the demand matching degree of each candidate charging pile, so that the user can select a target charging path from each candidate charging path.

[0013] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0015] The embodiments of the present invention provide a method, device, terminal and medium for intelligent charging path planning of new energy vehicles. The method evaluates the matching degree of each candidate charging pile through the relevant status information of multiple candidate charging piles and the specific needs of users, so that users can find the charging pile that best suits their current situation, avoiding the problem of queuing or insufficient charging due to selecting an inappropriate charging pile, reducing the time and energy consumed by users in searching for charging piles, improving the convenience of users in searching for and using charging piles, and promoting the effective allocation and use of charging pile resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 This is a flow chart of an implementation method of a new energy vehicle intelligent charging path planning method provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a new energy vehicle intelligent charging path planning device provided by an embodiment of the present invention; Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of the implementation of the new energy vehicle intelligent charging path planning method provided by an embodiment of the present invention, which is described in detail as follows: Step 101, after obtaining the user's charging instruction, obtain the status information of multiple candidate charging piles; wherein the status information includes location, charging power, charging price and estimated available time period.

[0021] In this embodiment, the charging instruction is a request from the user to find a charging pile through an application or a vehicle system. After obtaining the user's charging instruction, based on the user's current location, the nearby charging stations or charging piles are searched, and the charging piles within the driving range of the remaining power are selected as candidate charging piles. This can be achieved by accessing the API interfaces provided by the charging network operator, which usually provide information such as the location, status (whether idle), type (fast charging / slow charging), and rate of the charging pile. The status information of the candidate charging pile is used to evaluate whether the charging pile is suitable for the user's current needs.

[0022] Step 102, based on the status information of each candidate charging post and the user's user information, evaluate the degree of matching between each candidate charging post and the user's needs; wherein the user information includes the current location, the preferred charging level and the remaining power status.

[0023] In this embodiment, the user information includes the user's current location, preference for charging level (such as fast full charging or only charging to a certain percentage), and the current remaining power status of the electric vehicle. Based on the status information of the candidate charging piles and the specific needs of the user (such as location proximity, charging speed requirements, budget constraints, etc.), a comprehensive evaluation can be performed to evaluate the degree of match between each candidate charging pile and the user's needs, and the most suitable charging pile can be intelligently recommended to reduce the time and energy consumed by the user in finding a charging pile.

[0024] Step 103 : Based on the demand matching degree of each candidate charging pile, corresponding candidate charging paths are generated and sorted, so that the user can select a target charging path from each candidate charging path.

[0025] In this embodiment, for charging piles with a high degree of demand matching, the system generates a route for them and displays it to the user in descending order of matching degree. The user selects the best route to go to the charging station according to his or her own situation (such as time urgency, budget, etc.).

[0026] In addition, there may be multiple paths from the user's current location to a charging station. When generating and recommending paths, an optimal path with short distance and low power consumption can be selected for each charging station based on road conditions, simplifying the process of users selecting charging paths.

[0027] The embodiment of the present invention evaluates the matching degree of each candidate charging pile through the relevant status information of multiple candidate charging piles and the specific needs of the user, so that the user can find the charging pile that best suits his current situation, avoiding the problem of waiting in line or insufficient charging due to selecting an inappropriate charging pile, reducing the time and energy consumed by the user in searching for charging piles, improving the convenience of the user in finding and using charging piles, and promoting the effective allocation and use of charging pile resources.

[0028] In a possible implementation, obtaining the estimated available time period of the candidate charging pile includes: Obtaining user information of a user who has made a reservation for a first candidate charging pile; wherein the first candidate charging pile is any candidate charging pile; Based on the location of the first candidate charging pile, the current location of the first reserved user and the real-time traffic conditions, determining the estimated arrival time and estimated power consumption of the first reserved user at the first candidate charging pile; wherein the first reserved user is any reserved user; The later time between the end time of charging of the second reserved user and the estimated arrival time of the first reserved user is used as the start time of charging of the first reserved user; wherein the second reserved user is the previous reserved user of the first reserved user; Determine an estimated charging time for the first reserved user based on the charging power of the first candidate charging pile, the current remaining power state of the first reserved user, the estimated power consumption, and the preferred charging degree; Determining the charging end time based on the charging start time and the estimated charging duration of the first reserved user; The charging end time of each reserved user of the first candidate charging pile is calculated in sequence, and the charging end time of the last reserved user is used as the start time of the estimated available time period of the first candidate charging pile. In this embodiment, due to the uncertainty of road conditions, the actual charging period of the reserved user is different from the reserved charging period. By dynamically calculating the start and end charging time of each reserved user, the available time of the charging pile can be predicted more accurately, thereby improving the utilization efficiency of the charging pile resources.

[0029] Specifically, this embodiment needs to collect the information of all reserved users and their current locations, calculate the time for each user to arrive at the charging pile and the amount of electricity consumed on the way based on real-time traffic data, compare the estimated arrival time of the current user and the end charging time of the previous user, and select the later one as the start charging time for the current user. Then calculate the time required for charging based on the power of the charging pile, the current amount of electricity of the user, the power consumption on the way, and the charging level that the user expects to achieve. Then determine the end charging time for the user based on the start charging time and the estimated charging duration. Finally, perform the above steps for all reserved users in sequence until the end charging time of the last user is determined, which is the start time of the next available time period of the charging pile.

[0030] This dynamic adjustment mechanism can effectively deal with the uncertainties brought about by changes in road conditions, ensuring the efficient use of charging pile resources while also providing users with a more reliable service experience.

[0031] The accurate calculation of power consumption is a complex process. In this embodiment, the road conditions, vehicle types and driving habits can be simply classified. Each category is used as an influencing factor of power consumption. The three categories are combined, and the power consumption is estimated through a neural network model or a regression fitting equation to simplify the calculation process. Specifically, the current and predicted traffic conditions (such as congestion level, average vehicle speed, etc.) can be obtained through the traffic information service platform, combined with vehicle parameters (basic information such as vehicle weight, air resistance coefficient, tire rolling resistance coefficient, etc.), driving behavior data (such as sudden acceleration, sudden braking frequency, etc.), environmental conditions (weather conditions (rain, snow, wind, etc.), temperature and other factors), and the historical data training model is used to predict the energy consumption of a specific section of road. The input features may include vehicle speed, acceleration, road type, weather conditions, etc., and the output is the estimated power consumption of the section.

[0032] In a possible implementation, the degree of demand matching includes waiting time, charging cost and charging time; based on the status information of each candidate charging pile and the user information of the user, the degree of matching between each candidate charging pile and the user's demand is evaluated, including: Determine the estimated arrival time and estimated power consumption of the user at the candidate charging pile based on the location of the second candidate charging pile, the current location of the user, and the real-time traffic conditions; wherein the second candidate charging pile is any candidate charging pile; Calculate the waiting time corresponding to the second candidate charging pile based on the difference between the estimated arrival time of the user at the second candidate charging pile and the estimated start time of the available time period; Calculate the charging cost corresponding to the second candidate charging pile based on the charging price of the second candidate charging pile, the estimated power consumption and the user's preferred charging level; The charging time corresponding to the second candidate charging pile is calculated based on the charging power of the second candidate charging pile, the estimated power consumption and the user's preferred charging level.

[0033] In this embodiment, the degree of demand matching can be evaluated by waiting time, charging cost and charging time. In order to optimize the spatiotemporal distribution of electric vehicle charging load, different incentives are usually adopted to guide electric vehicle users to charge at designated charging stations, and electricity price is one of the incentives. Therefore, users' consideration of electricity price is also a response to power load scheduling.

[0034] Specifically, assume that user A is located in the city center and plans to go to a nearby shopping mall and wants to charge his electric car during this time. The system identifies two candidate charging stations B and C.

[0035] For the second candidate charging pile B: Obtain status information: B's location, charging power of 50 kW, charging price of 1 yuan / kWh, and estimated start time of the available time period of 14:00.

[0036] Determine the estimated arrival time and power consumption: Based on the real-time traffic conditions, the estimated arrival time of user A at B is calculated to be 13:30, and the estimated power consumption on the way is 3kWh.

[0037] Calculate the waiting time: Since the estimated arrival time is 13:30 and B's estimated available time starts at 14:00, the waiting time is 30 minutes.

[0038] Evaluate charging costs: If user A wants to charge to 80% and the current remaining power is 20%, an additional 60% of power is required. Assuming the battery capacity is 60kWh, 36kWh (60kWh*60%) needs to be charged. Adding 3kWh of power consumed on the way, a total of 39kWh needs to be charged. At a price of 1 yuan / kWh, the total cost is 39 yuan.

[0039] Estimated charging time: Charging at 50kW to replenish 39kWh of power theoretically takes about 47 minutes (ignoring other factors such as charging efficiency loss).

[0040] For the second candidate charging pile C: Repeat the above steps using their specific parameters for comparative analysis.

[0041] In this embodiment, the utilization rate and service efficiency of the charging piles are improved by accurately matching user needs with the characteristics of the charging piles.

[0042] In a possible implementation, based on the demand matching degree of each candidate charging pile, corresponding candidate charging paths are generated and sorted, including: The waiting time, charging cost and charging time of each candidate charging pile are normalized, and weighted summed based on the weight corresponding to the recommendation strategy selected by the user to obtain the demand matching degree score of the candidate charging pile; Sort the candidate charging piles according to their demand matching scores from high to low.

[0043] In this embodiment, users can set their preferences for recommendation strategies in advance on the charging route planning platform. Based on the different recommendation strategies selected by users (shortest time, lowest price, closest distance), corresponding weights are assigned to different matching items (waiting time, charging cost, and charging time) to reflect their importance in the final score.

[0044] Specifically, suppose a user wants to find a charging station. If the recommendation strategy set by the user is "lowest price", the system will perform the following operations: Data preparation: Collect information such as the location, price, waiting time, and charging time of several candidate charging stations.

[0045] Normalization processing: Convert the waiting time, charging cost and charging time of each candidate charging pile into a value between 0 and 1.

[0046] Set weight: Since the user selected "lowest price", the weight of the higher charging cost is given, such as wcost=0.7 wcost =0.7, while the weights of waiting time and charging time are relatively low, for example, wwait=0.2 respectively. wwait = 0.2 and wduration = 0.1 wDuration =0.1.

[0047] Calculate the score: For each candidate charging station, use the formula: Score=0.2×Nwait+0.7×Ncost+0.1×Nduration Score =0.2× Nwait +0.7× Ncost +0.1× Nduration Calculate the score.

[0048] Sorting and display: Finally, sort by score and show the best option to the user.

[0049] This approach not only takes into account the specific needs of users, but also provides flexible customization options, making the entire charging experience more convenient and efficient.

[0050] In a possible implementation, after sorting the candidate charging piles according to the demand matching scores from large to small, the following is further included: Based on the distances and demand matching scores between each candidate charging pile and the third candidate charging pile, the additional matching score of the third candidate charging pile is calculated and displayed; wherein the third candidate charging pile is any candidate charging pile, and the calculation formula for the additional matching score is:

[0051] in, is the additional matching score for the third candidate charging station. For the The demand matching degree score of the candidate charging piles is is the number of candidate charging piles except the third candidate charging pile, For the The distance between the first candidate charging pile and the third candidate charging pile.

[0052] In this embodiment, the additional matching score is an additional score calculated for a specific charging pile (i.e., the third candidate charging pile) taking into account the influence of other surrounding charging piles. This score reflects the actual value of the charging pile environment around the charging pile relative to the user. The more other charging piles there are near the third candidate charging pile, the closer the distance is, and the higher the demand matching score is, the higher the additional matching score of the third candidate charging pile is.

[0053] This embodiment provides more comprehensive information to help users make better choices by considering the impact of surrounding charging piles. For example, when there are multiple high-quality but crowded charging piles nearby, a charging pile that is slightly farther away but more likely to be immediately available is recommended.

[0054] In a possible implementation, after sorting the candidate charging piles according to the demand matching scores from large to small, the following is further included: Based on the historical usage data of each candidate charging pile, it is determined whether the candidate charging pile is a frequently used charging pile of the user. If so, the candidate charging pile is marked and displayed.

[0055] In this embodiment, the frequently used charging piles refer to the charging piles that the user has frequently used in the past, which usually means that these charging piles are more in line with the user's preferences or habits in terms of geographical location, service quality, etc. The historical usage data records the user's past behavior information on the use of charging piles, including but not limited to the frequency, time point, duration, etc. of use.

[0056] Specifically, suppose a user is looking for a nearby electric vehicle charging station, and the system has ranked the five candidate charging stations around him according to the demand matching scores. Next, the system will do the following: Get historical usage data: Query the user's history to understand the charging stations he / she has frequently visited and their usage frequency.

[0057] Determine frequently used charging piles: Set standards, for example, if a charging pile has been used more than five times in the past three months, it will be considered a frequently used charging pile.

[0058] Mark commonly used charging piles: Among the five candidate charging piles mentioned above, two charging piles are found to meet the commonly used standards. The system adds a "common" mark next to these two charging piles.

[0059] Display results: The results finally displayed to the user are not only sorted by the demand matching score, but also specifically highlight the two charging piles with the "commonly used" logo.

[0060] The advantage of this is that even if some charging piles have a slightly lower demand matching score, they will still be given priority by users because they are their commonly used charging piles. This method combines objective scores with personal preferences to provide a more comprehensive and humane recommendation solution.

[0061] In a possible implementation, after generating and sorting corresponding candidate charging paths based on the demand matching degree of each candidate charging pile, the method further includes: During the driving process after the user selects the target charging path, the status information of each candidate charging pile is continuously updated, and the corresponding candidate charging paths are regenerated and sorted based on the updated status information of each candidate charging pile.

[0062] In this embodiment, while the user is on the way to the selected charging pile, the system continuously monitors the changes in the status information of all candidate charging piles, including but not limited to the availability of charging piles, queue length, changes in electricity prices, etc., and pays attention to whether there are new charging piles available in the area along the way. Once a change in the status of a charging pile is detected or a new charging pile is found, the demand matching score of each candidate charging pile is immediately recalculated using the latest data, and the candidate charging path list is regenerated and sorted accordingly. If the newly generated sorting result indicates that there is a better option than the current choice, the user is notified in time and a suggestion to switch to a better path is provided.

[0063] Specifically, assuming that a user is on the way to the target charging station C1 that he initially selected, the system will perform the following operations: Initial sorting: The system recommends several candidate charging piles to the user based on the initial data, and sorts them according to the scores of the degree of matching with the needs. Among them, C1 with the highest score is selected as the target.

[0064] Status information update: During driving, the system detects that C1 suddenly becomes unavailable (for example due to a fault or full load).

[0065] At the same time, the system discovered a new charging station C6 that was closer and had a higher demand matching score.

[0066] Re-evaluation and ranking: Based on this new information, the system recalculates the demand matching scores of all candidate charging piles and updates the ranking. At this time, the newly discovered C6 may be ranked first due to its superior location and high matching degree.

[0067] Provide update suggestions: The system sends a notification to the user, informing him that the original charging station C1 is no longer available, and recommends that the user change to the new optimal option C6, while providing an updated navigation route.

[0068] This approach not only improves the efficiency of users finding charging stations, but also ensures a smoother and more satisfying experience throughout the process.

[0069] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0070] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0071] Figure 2 The structure diagram of the intelligent charging path planning device for new energy vehicles provided by an embodiment of the present invention is shown. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows: like Figure 2 As shown, the new energy vehicle intelligent charging path planning device 2 includes: The acquisition module 21 is used to acquire the status information of multiple candidate charging piles after acquiring the user's charging instruction; wherein the status information includes the location, charging power, charging price and estimated available time period; An evaluation module 22, for evaluating the degree of matching between each candidate charging pile and the user's needs based on the status information of each candidate charging pile and the user's user information; wherein the user information includes the current location, the preferred charging level and the remaining power status; The planning module 23 is used to generate and sort corresponding candidate charging paths based on the demand matching degree of each candidate charging pile, so that the user can select a target charging path from each candidate charging path.

[0072] In a possible implementation, the acquisition module 21 is specifically used for: Obtaining user information of a user who has made a reservation for a first candidate charging pile; wherein the first candidate charging pile is any candidate charging pile; Based on the location of the first candidate charging pile, the current location of the first reserved user and the real-time traffic conditions, determining the estimated arrival time and estimated power consumption of the first reserved user at the first candidate charging pile; wherein the first reserved user is any reserved user; The later time between the end time of charging of the second reserved user and the estimated arrival time of the first reserved user is used as the start time of charging of the first reserved user; wherein the second reserved user is the previous reserved user of the first reserved user; Determine an estimated charging time for the first reserved user based on the charging power of the first candidate charging pile, the current remaining power state of the first reserved user, the estimated power consumption, and the preferred charging degree; Determining the charging end time based on the charging start time and the estimated charging duration of the first reserved user; The charging end time of each reserved user of the first candidate charging pile is calculated in sequence, and the charging end time of the last reserved user is used as the start time of the estimated available time period of the first candidate charging pile. In a possible implementation, the demand matching degree includes waiting time, charging cost and charging time; the evaluation module 22 is specifically used for: Determine the estimated arrival time and estimated power consumption of the user at the candidate charging pile based on the location of the second candidate charging pile, the current location of the user, and the real-time traffic conditions; wherein the second candidate charging pile is any candidate charging pile; Calculate the waiting time corresponding to the second candidate charging pile based on the difference between the estimated arrival time of the user at the second candidate charging pile and the estimated start time of the available time period; Calculate the charging cost corresponding to the second candidate charging pile based on the charging price of the second candidate charging pile, the estimated power consumption and the user's preferred charging level; The charging time corresponding to the second candidate charging pile is calculated based on the charging power of the second candidate charging pile, the estimated power consumption and the user's preferred charging level.

[0073] In a possible implementation, the planning module 23 is specifically configured to: The waiting time, charging cost and charging time of each candidate charging pile are normalized, and weighted summed based on the weight corresponding to the recommendation strategy selected by the user to obtain the demand matching degree score of the candidate charging pile; Sort the candidate charging piles according to their demand matching scores from high to low.

[0074] In a possible implementation, the planning module 23 is further configured to: After sorting the candidate charging piles from large to small according to the demand matching degree score, the additional matching score of the third candidate charging pile is calculated and displayed based on the distance between each candidate charging pile and the third candidate charging pile and the demand matching degree score; wherein the third candidate charging pile is any candidate charging pile, and the calculation formula of the additional matching score is:

[0075] in, is the additional matching score for the third candidate charging station. For the The demand matching degree score of the candidate charging piles is is the number of candidate charging piles except the third candidate charging pile, For the The distance between the first candidate charging pile and the third candidate charging pile.

[0076] In a possible implementation, the planning module 23 is further configured to: After sorting the candidate charging piles from high to low according to the demand matching degree score, it is determined whether the candidate charging pile is a frequently used charging pile of the user based on the historical usage data of each candidate charging pile. If so, the candidate charging pile is marked and displayed.

[0077] In a possible implementation, the planning module 23 is further configured to: After generating and sorting corresponding candidate charging paths based on the degree of demand matching of each candidate charging pile, during the driving process after the user selects the target charging path, the status information of each candidate charging pile is continuously updated, and the corresponding candidate charging paths are regenerated and sorted based on the updated status information of each candidate charging pile.

[0078] The embodiment of the present invention evaluates the matching degree of each candidate charging pile through the relevant status information of multiple candidate charging piles and the specific needs of the user, so that the user can find the charging pile that best suits his current situation, avoiding the problem of waiting in line or insufficient charging due to selecting an inappropriate charging pile, reducing the time and energy consumed by the user in searching for charging piles, improving the convenience of the user in finding and using charging piles, and promoting the effective allocation and use of charging pile resources.

[0079] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned embodiments of the intelligent charging path planning method for new energy vehicles are implemented, such as Figure 1Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 2 Functions of modules / units 21 to 23 are shown.

[0080] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into Figure 2 Modules / units 21 to 23 are shown.

[0081] The terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0082] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0083] The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0084] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0085] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0087] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0090] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned embodiments of the intelligent charging path planning method for new energy vehicles can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0091] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for intelligent charging path planning for new energy vehicles, characterized in that: include: After obtaining the user's charging instruction, obtaining the status information of multiple candidate charging piles; wherein the status information includes location, charging power, charging price and estimated available time period; Based on the status information of each candidate charging pile and the user information of the user, evaluating the degree of matching between each candidate charging pile and the needs of the user; wherein the user information includes the current location, the preferred charging level and the remaining power status; Based on the degree of demand matching of each candidate charging pile, corresponding candidate charging paths are generated and sorted, so that the user can select a target charging path from each candidate charging path.

2. The method for intelligent charging path planning of new energy vehicles according to claim 1, characterized in that: Get the estimated available time of candidate charging piles, including: Obtaining user information of a user who has made a reservation for a first candidate charging pile; wherein the first candidate charging pile is any candidate charging pile; Based on the location of the first candidate charging pile, the current location of the first reserved user and the real-time traffic conditions, determining the estimated arrival time and estimated power consumption of the first reserved user at the first candidate charging pile; wherein the first reserved user is any reserved user; The later time between the end time of charging of the second reserved user and the estimated arrival time of the first reserved user is used as the start time of charging of the first reserved user; wherein the second reserved user is the previous reserved user of the first reserved user; Determining an estimated charging time for the first reserved user based on the charging power of the first candidate charging pile, the current remaining power state, the estimated power consumption, and the preferred charging degree of the first reserved user; Determining the charging end time based on the charging start time and the estimated charging duration of the first reserved user; The charging end time of each reserved user of the first candidate charging pile is calculated in sequence, and the charging end time of the last reserved user is used as the start time of the estimated available time period of the first candidate charging pile.

3. The method for intelligent charging path planning of new energy vehicles according to claim 1, characterized in that: The degree of demand matching includes waiting time, charging cost and charging time; the evaluating the degree of matching between each candidate charging pile and the user's demand based on the status information of each candidate charging pile and the user information of the user includes: Determine the estimated arrival time and estimated power consumption of the user at the candidate charging pile based on the location of the second candidate charging pile, the current location of the user and the real-time road conditions; wherein the second candidate charging pile is any candidate charging pile; Calculating the waiting time corresponding to the second candidate charging post based on the difference between the estimated arrival time of the user at the second candidate charging post and the estimated start time of the available time period; Calculate the charging cost corresponding to the second candidate charging pile based on the charging price of the second candidate charging pile, the estimated power consumption and the user's preferred charging level; The charging time corresponding to the second candidate charging pile is calculated based on the charging power of the second candidate charging pile, the estimated power consumption and the user's preferred charging level.

4. The method for intelligent charging path planning of new energy vehicles according to claim 3 is characterized in that: The generating and sorting of corresponding candidate charging paths based on the demand matching degree of each candidate charging pile includes: The waiting time, charging cost and charging time of each candidate charging pile are normalized, and weighted summed based on the weight corresponding to the recommendation strategy selected by the user to obtain the demand matching degree score of the candidate charging pile; Sort the candidate charging piles according to their demand matching scores from high to low.

5. The method for intelligent charging path planning of new energy vehicles according to claim 4 is characterized in that: After sorting the candidate charging piles according to the demand matching scores from large to small, the method further includes: Based on the distances and demand matching scores between each candidate charging pile and the third candidate charging pile, the additional matching score of the third candidate charging pile is calculated and displayed; wherein the third candidate charging pile is any candidate charging pile, and the calculation formula of the additional matching score is: in, is the additional matching score of the third candidate charging pile, For the The demand matching degree score of the candidate charging piles is is the number of candidate charging piles other than the third candidate charging pile, For the The distance between the first candidate charging pile and the third candidate charging pile.

6. The method for intelligent charging path planning of new energy vehicles according to claim 4, characterized in that: After sorting the candidate charging piles according to the demand matching scores from large to small, the method further includes: Based on the historical usage data of each candidate charging pile, it is determined whether the candidate charging pile is a frequently used charging pile of the user. If so, the candidate charging pile is marked and displayed.

7. The method for intelligent charging path planning of new energy vehicles according to claim 1, characterized in that: After generating and sorting corresponding candidate charging paths based on the demand matching degree of each candidate charging pile, the method further includes: During the driving process after the user selects the target charging path, the status information of each candidate charging pile is continuously updated, and the corresponding candidate charging paths are regenerated and sorted based on the updated status information of each candidate charging pile.

8. A new energy vehicle intelligent charging path planning device, characterized in that: include: An acquisition module, configured to acquire status information of a plurality of candidate charging piles after acquiring a charging instruction from a user; wherein the status information includes location, charging power, charging price, and estimated available time period; An evaluation module, configured to evaluate the degree to which each candidate charging pile matches the needs of the user based on the status information of each candidate charging pile and the user information of the user; wherein the user information includes the current location, the preferred charging level and the remaining power status; The planning module is used to generate and sort corresponding candidate charging paths based on the demand matching degree of each candidate charging pile, so that the user can select a target charging path from each candidate charging path.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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