Electric vehicle charging planning method, system, device and storage medium

Abstract

The application provides an electric vehicle charging planning method, system, device and storage medium, and belongs to the technical field of vehicle charging. The method comprises the following steps: collecting charging pile information, commuting schedule information and vehicle parameter information, and performing data cleaning; matching the current journey with the commuting schedule information, and determining whether the current journey needs midway charging in combination with the vehicle parameter information; when it is determined that the current journey needs midway charging, screening candidate charging piles along the current journey through charging pile screening conditions, and individually sorting the candidate charging piles based on the commuting schedule information to generate a charging planning scheme. Through the technical scheme in the application, it can be determined whether the vehicle needs midway charging in the current journey, and when it is determined that midway charging is needed, a charging planning scheme is generated in combination with multi-source data information to meet the charging demand of the user.

Description

Technical Field

[0001] This invention relates to the field of vehicle charging technology, and in particular to an electric vehicle charging planning method, system, device and storage medium. Background Technology

[0002] As the number of electric vehicles on the road increases year by year, the charging problem has become a key factor restricting the popularization of electric vehicles. However, due to the uneven distribution of charging piles and the lack of timely information, car owners find it difficult to quickly find charging piles that suit their needs. Traditional charging applications only have basic charging pile location query functions and cannot formulate personalized charging plans based on car owners' commuting schedules, travel habits, and vehicle parameters (such as battery capacity and range), resulting in unreasonable charging time planning by car owners. In addition, traditional charging applications lack dynamic adjustment capabilities. When car owners suddenly change their travel plans (such as working overtime or changing their destination temporarily) or encounter unexpected situations such as traffic congestion or charging pile failures, they cannot update their charging plans in time, further reducing charging efficiency and travel convenience. Furthermore, car owners find it difficult to accurately estimate charging costs.

[0003] Therefore, there is an urgent need for an application solution that can integrate real-time information on charging stations on the map, users' commuting schedules, and combine multi-dimensional data to achieve dynamic and personalized charging planning. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes an electric vehicle charging planning method, system, device and storage medium.

[0005] In a first aspect, embodiments of the present invention provide an electric vehicle charging planning method, comprising:

[0006] Collect charging pile information, commuting schedule information, and vehicle parameter information, and perform data cleaning;

[0007] The current trip is matched with the commuting schedule information, and combined with the vehicle parameter information, it is determined whether the current trip requires charging midway.

[0008] When it is determined that the current trip requires charging, candidate charging stations are selected along the current trip using charging station filtering criteria. The candidate charging stations are then sorted in a personalized manner based on the commuting schedule information to generate a charging plan.

[0009] In some embodiments, the charging pile information includes: the location coordinates of the charging pile, charging type, rated power, real-time status, charging price, operator information, and user evaluation data;

[0010] The commuting schedule information includes: departure time, departure point, destination, commuting route preference, and itinerary change records;

[0011] The vehicle parameter information includes: remaining battery power, battery capacity, driving range, charging speed, and battery health data.

[0012] In some embodiments, the data cleaning process includes:

[0013] The location data of the target vehicle is noise-reduced, and the collected data is cleaned and verified according to pre-set data cleaning rules.

[0014] The data cleaning rules include: outlier removal rules, duplicate data merging rules, and missing value completion rules.

[0015] In some embodiments, determining whether the current trip requires charging midway includes:

[0016] Based on the commuting schedule information, the commuting distance matching the current trip is obtained, and the remaining battery power and driving range in the vehicle parameter information are combined to calculate the battery adequacy coefficient.

[0017] The battery adequacy coefficient is compared with a preset threshold. If the battery adequacy coefficient is less than the preset threshold, it is determined that the current trip requires charging midway.

[0018] In some embodiments, the process of generating a charging plan includes:

[0019] Candidate charging stations can be obtained by having users manually adjust the filtering criteria or by calling the default filtering criteria;

[0020] The candidate charging stations are ranked by weight based on the user's historical usage records and preference settings.

[0021] Based on the weighted ranking of the candidate charging piles, a charging planning scheme is generated.

[0022] In some embodiments, it also includes:

[0023] When the deviation between the vehicle's real-time location and the planned charging route in the charging plan exceeds a preset distance threshold, or when a change in the user's trip is detected, a new charging plan is generated.

[0024] In some embodiments, the process of generating a charging plan further includes:

[0025] Optimization objectives and constraints are set, and particle optimization is performed on the candidate charging piles. Based on the optimization results, a charging planning scheme is generated.

[0026] In a second aspect, embodiments of the present invention provide an electric vehicle charging planning system, comprising:

[0027] The data acquisition unit is used to collect charging pile information, commuting schedule information, and vehicle parameter information, and to perform data cleaning.

[0028] The charging determination unit is used to match the current trip with the commuting schedule information and, in conjunction with the vehicle parameter information, determine whether the current trip requires charging midway.

[0029] The charging planning unit is used to filter candidate charging stations along the current journey according to the charging station screening criteria when it is determined that the current journey requires charging, and to generate a charging planning scheme by personalized sorting of the candidate charging stations based on the commuting schedule information.

[0030] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0031] At least one processor; and a memory communicatively connected to the at least one processor;

[0032] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the steps of the method according to any embodiment of the present invention.

[0033] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that are used to cause a processor to execute the steps of any embodiment of the method of the present invention.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] The electric vehicle charging planning method provided by this invention first collects charging pile information, commuting schedule information, and vehicle parameter information, and then cleans the data. Next, it matches the current trip with the commuting schedule information and, combined with the vehicle parameter information, determines whether the current trip requires charging en route. Finally, when it is determined that the current trip requires charging en route, it filters candidate charging piles along the current trip using charging pile selection criteria, and then ranks the candidate charging piles based on the commuting schedule information to generate a personalized charging plan. Through the technical solution of this invention, a personalized charging plan can be generated by combining the user's commuting schedule, travel habits, and vehicle parameters. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating an electric vehicle charging planning method provided in an embodiment of the present invention.

[0038] Figure 2 A flowchart illustrating a method for determining charging during a trip, provided by an embodiment of the present invention;

[0039] Figure 3 This is a flowchart illustrating a method for generating a charging planning scheme according to an embodiment of the present invention.

[0040] Figure 4 This is a structural block diagram of an electric vehicle charging planning system provided in an embodiment of the present invention;

[0041] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0044] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0045] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0046] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0047] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0048] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0049] Figure 1 This is a flowchart illustrating an electric vehicle charging planning method provided by an embodiment of the present invention. This method is particularly suitable for scenarios that provide emergency traffic control for vehicles in emergency situations and coordinate with medical resources. This method can be executed by an electric vehicle charging planning system, which can be implemented in software and / or hardware and can be configured in an electronic device.

[0050] like Figure 1 As shown, the method specifically includes:

[0051] S1 collects charging pile information, commuting schedule information, and vehicle parameter information, and performs data cleaning.

[0052] The charging station information includes: location coordinates, charging type (fast charging / slow charging), rated power, real-time status (idle / in use / faulty), charging price (including peak / off-peak electricity prices), operator information, and user reviews. Commuting schedule information includes: departure time, departure point, destination, preferred commuting route, and trip change records. Vehicle parameter information includes: remaining battery power, battery capacity, driving range, charging speed (charging power in fast / slow charging modes), and battery health data.

[0053] It connects with charging station operators, map service providers, and transportation department databases via API interfaces to obtain charging station information in real time. Simultaneously, it synchronizes data with user terminal calendar applications to automatically obtain users' commuting schedules. Furthermore, it connects with in-vehicle systems via Bluetooth or vehicle-to-everything (V2X) technology to obtain vehicle parameter information in real time.

[0054] In some embodiments, the data cleaning process includes:

[0055] The location data of the target vehicle is noise-reduced, and the collected data is cleaned and verified according to pre-set data cleaning rules.

[0056] The data cleaning rules include: outlier removal rules, duplicate data merging rules, and missing value completion rules.

[0057] After acquiring charging station information, commuting schedule information, and vehicle parameter information, multi-source data fusion and cleaning are performed. For example, the Kalman filter algorithm is used to reduce noise in the target vehicle's positioning data to ensure that the location information accuracy error is within the allowable range; at the same time, the collected data is cleaned and verified according to pre-set data cleaning rules to ensure that the data accuracy meets a certain standard.

[0058] In some embodiments, after data cleaning, the method further includes:

[0059] Establish cloud-based and local databases;

[0060] All charging station information, commuting schedule information, and charging records are stored in a cloud database;

[0061] The charging station information and commuting schedule information within a certain time range are stored in a local database.

[0062] In this embodiment, a dual storage architecture of cloud + local is adopted. The cloud database uses a MySQL cluster to store massive amounts of charging pile data, user historical trip data and charging records, while the local database uses an SQLite database to store users' recent commuting schedules and frequently used charging pile information, ensuring that basic data can still be viewed even in a network-free environment.

[0063] In some embodiments, the method further includes: establishing a real-time data update mechanism to update the collected charging pile information, commuting schedule information, and vehicle parameter information in real time.

[0064] The real-time data update mechanism includes:

[0065] The charging pile information is updated at the first preset cycle;

[0066] In addition, update the commuting schedule information after any changes are made;

[0067] In addition, the vehicle parameter information is updated at a second preset cycle.

[0068] In this embodiment, considering that charging pile information may change, such as changes in real-time status and charging prices, which could affect charging planning, the charging pile information can be updated every 30 seconds. Similarly, commuting schedule information will also change after the user finishes their trip, and vehicle parameter information also needs to be updated in real time. Therefore, the information can be synchronized to the system within 10 seconds after the commuting schedule information changes, and vehicle parameter information can be collected and updated every minute to ensure data timeliness.

[0069] S2 matches the current trip with commuting schedule information and, in conjunction with vehicle parameter information, determines whether the current trip requires charging midway.

[0070] Figure 2 This is a flowchart illustrating a method for determining charging during a trip, as provided in an embodiment of the present invention. Figure 2 As shown, in some embodiments, determining whether the current journey requires charging includes:

[0071] S201 calculates the commuting distance matching the current trip based on commuting schedule information, and combines the remaining battery power and driving range in the vehicle parameter information to calculate the battery adequacy coefficient.

[0072] Extract key information from the commuting schedule, including departure time, origin and destination, to obtain the commuting distance. Combine this with the vehicle's current remaining battery power and driving range to calculate the battery adequacy coefficient.

[0073] The battery adequacy coefficient can be expressed as: K = (E / M) × L. K represents the battery adequacy coefficient, E represents the remaining battery power, M represents the driving range, and L represents the commuting distance.

[0074] S202, compare the battery adequacy coefficient with the preset coefficient threshold. If the battery adequacy coefficient is less than the preset coefficient threshold, it is determined that the current trip needs to be charged midway.

[0075] For example, the preset coefficient threshold is set to 0.8. When K < 0.8, it is determined that charging is required midway.

[0076] S3, when it is determined that the current trip requires charging, the system filters candidate charging stations along the current trip according to the charging station selection criteria, and sorts the candidate charging stations in a personalized manner based on the commuting schedule information to generate a charging plan.

[0077] Figure 3 This is a flowchart illustrating a charging planning scheme generation method provided in an embodiment of the present invention, as shown below. Figure 3 As shown, in some embodiments, the process of generating a charging plan includes:

[0078] S301 allows users to manually adjust the filtering conditions or call the default filtering conditions to obtain candidate charging piles;

[0079] S302, Based on the user's historical usage records and preference settings of the candidate charging piles, the candidate charging piles are sorted by weight;

[0080] S303 generates a charging plan based on the weighted ranking of candidate charging piles.

[0081] Based on the vehicle's real-time location and charging station information, the distribution of nearby charging stations is displayed intuitively on the map interface. Users can quickly filter charging stations by adjusting charging speed (fast / slow charging), price range, carrier brand, and availability status. Charging stations are personalized by using a weighted sorting method based on user history (such as usage frequency in the past 3 months) and user preferences (such as prioritizing charging stations with free parking). The weights are allocated as follows: user preference, distance, price, and rating.

[0082] In some embodiments, the method further includes: regenerating the charging plan when the deviation between the vehicle's real-time location and the planned charging route in the charging plan exceeds a preset distance threshold, or when a change in the user's trip is detected.

[0083] For example, by setting charging pile filtering conditions, candidate charging piles that match the charging speed, meet the idle status, and are ≤1 km away from the current trip route can be selected from the charging piles along the route; at the same time, a trip change monitoring mechanism is established, which triggers the replanning of the charging plan by comparing the deviation between the user's real-time location and the planned charging route. For example, if the deviation from the planned charging route is >2 km and lasts for 5 minutes, the charging plan is replanned; or, trip change records in the calendar application are captured, triggering the replanning of the charging plan and pushing change reminders.

[0084] In some embodiments, the process of generating a charging plan further includes:

[0085] Optimization objectives and constraints are set, and particle optimization is performed on candidate charging piles. Based on the optimization results, a charging planning scheme is generated.

[0086] For example, with the optimization goals of lowest charging cost, shortest charging time, and highest travel convenience, the system takes as input vehicle parameter information (battery capacity C, fast charging power P1, slow charging power P2), charging station information (charging price P, idle time T0), commuting time constraint (latest arrival time T1), and other parameters, and uses the particle swarm optimization algorithm to solve for the optimal charging scheme, and outputs recommended charging stations, suggested charging time (accurate to the minute), estimated charging time, and estimated remaining power.

[0087] In some embodiments, the method further includes: introducing a charging pile busyness prediction model to predict the busyness of candidate charging piles; and adjusting the charging planning scheme based on the busyness of candidate charging piles.

[0088] For long-distance travel scenarios, an additional charging pile busyness prediction model is introduced. Based on the charging pile usage data of the same time period (such as 10 am to 12 pm on weekends) within the past 3 months, the busyness of each charging pile in the next 2 hours is predicted (idle probability > 70% is judged as "low busyness"). Charging piles with low busyness are recommended first, and reasonable time allocation for "charging + rest" is planned (such as recommending nearby rest areas and convenience stores during a 40-minute charging period).

[0089] In some embodiments, the method further includes: receiving real-time road congestion information and adjusting the charging plan when the estimated arrival time at the charging station exceeds a preset threshold or the status of the target charging station changes.

[0090] For example, real-time road congestion information is received and combined with changes in the status of charging piles. When road congestion causes the estimated arrival time at the charging pile to be delayed by more than 15 minutes, or when the target charging pile changes from "idle" to "in use", the "alternative solution generation logic" is automatically triggered to re-select the optimal charging pile from the candidate charging pile list and update the charging plan to ensure the feasibility of the solution.

[0091] In some embodiments, the method further includes: displaying charging pile information using vector map technology. The display process supports 2D / 3D view switching, clearly marking the location, status (distinguished by different colored icons: green - idle, red - in use, gray - faulty) and key information (such as price, distance, and charging speed). Clicking on the charging pile icon allows viewing detailed information, such as the charging pile number, charging interface type, and user review list.

[0092] In some embodiments, the method further includes displaying the charging plan in the form of a timeline combined with a route map. For example, it may include marking "departure time, estimated arrival time at charging station, charging duration, estimated departure time from charging station, and estimated arrival time at destination" and highlighting the charging route segments.

[0093] In some embodiments, the system further includes: setting up an information reminder mechanism. For example, pop-up reminders, voice reminders (in-vehicle terminal), and message push (mobile phone notification bar) can be used. The reminder types include "charging plan generation reminder, trip change reminder, charging pile status change reminder, and charging completion reminder". The reminder trigger conditions can be customized by the user (such as reminding the user to leave for charging 30 minutes in advance).

[0094] In some embodiments, the method further includes: estimating the cost of candidate charging stations. Based on the user-selected charging station price P and the expected charging amount Q (Q = (target charge - current charge) / charging efficiency, with fast charging calculated at 90% and slow charging at 85%), the charging cost is accurately estimated using the formula "charging cost = P × Q + additional costs (such as parking fees, if any)", and a cost comparison table of different charging options (such as "charging to 80% charge" or "charging to full charge") is provided.

[0095] In addition, it can automatically record detailed data such as "charging time, charging station, charging amount, charging cost, and payment method" for each charge. It supports generating charging cost reports by "day, week, month, and year". The report types include "bar chart (daily cost comparison), line chart (monthly cost trend), and pie chart (cost percentage of different operators)".

[0096] In addition, based on historical charging data, analyze users' charging habits (such as "preferential fast charging percentage" or "peak charging percentage"), and combine them with electricity pricing policies (peak-valley electricity price differences) to give cost optimization suggestions (such as "it is recommended to schedule 70% of charging during off-peak hours (23:00-7:00) to save charging costs").

[0097] To illustrate the technical solutions of this invention in more detail, further explanation is provided below in conjunction with specific application scenarios.

[0098] 1) Users first need to enter their electric vehicle parameters in the vehicle management interface of the system: battery capacity 60kWh, fast charging power 60kW, slow charging power 6kW, and full-charge range 400km; then set the following in the preference settings interface: prioritize fast charging piles, charging piles with prices ≤1.8 yuan / kWh, and free parking, and push charging reminders 30 minutes in advance.

[0099] 2) The system first obtains information on charging stations within a 3km radius of the user's commute route via API, including: Charging Station A (fast charging, 1.6 yuan / kWh, available, 2.1km away, free parking), Charging Station B (slow charging, 1.5 yuan / kWh, in use, 1.8km away, paid parking), and Charging Station C (fast charging, 1.9 yuan / kWh, available, 1.5km away, free parking). Then, it synchronizes the user's commute schedule from their calendar: departing home at 8:00 AM and arriving at the company at 8:30 AM daily, a commute distance of 15km. Finally, it obtains the user's vehicle's current remaining battery power via Bluetooth: 35kWh, and calculates the current range: (35 / 60) × 400 ≈ 233km.

[0100] 3) The system analyzes the commuting information: departure time 8:00, commuting distance 15km, current range 233km, calculates the battery adequacy coefficient K=(35 / 60)×15≈8.75>0.8, initially determining "no need to charge during the journey"; however, considering the user's itinerary for the next day (the calendar shows that the user needs to depart 1 hour earlier the next day, and needs to go to the customer's company 20km away after the commute), it is predicted that the remaining battery will be about 25kWh and the range will be about 167km when departing the next day. The total distance of the commute + customer trip is 35km, K=(25 / 60)×35≈14.58>0.8, but considering that the return trip to the company after the customer trip will still require 20km, it is finally determined that "the battery needs to be replenished to 40kWh during the commute the next day".

[0101] 4) The system filters charging piles based on user preferences: excluding slow-charging charging pile B, leaving charging piles A and C; scores are calculated using a weighted sorting algorithm (preference 40% + distance 30% + price 20% + rating 10%): Charging pile A score = 40 (meets fast charging and free parking) + (3-2.1) / 3×30≈9 + 12 (1.6 yuan / kWh is lower than 1.8 yuan) + 10 (rating 4.8) = 71 points; Charging pile C score = 40 (meets fast charging and free parking) + (3-1.5) / 3×30 = 15 + 8 (1.9 yuan / kWh is close to 1.8 yuan) + 9 (rating 4.6) = 72 points; Charging pile C is ultimately recommended first.

[0102] 5) Input vehicle parameters into the system: target battery capacity 40kWh, current battery capacity 35kWh, fast charging power 60kW, charging efficiency 90%, C charging station price 1.9 yuan / kWh, commute departure time 7:00; calculate charging amount Q = (40-35) / 0.9≈5.56kWh, charging time = 5.56kWh / 60kW≈5.56 minutes (about 6 minutes), charging cost = 1.9×5.56≈10.56 yuan; considering that the C charging station is 1.5 kilometers away from home (5 minutes by car), the planned scheme is: depart from home at 6:49, arrive at the C charging station at 6:54, charge from 6:54 to 7:00, and depart for the company on time at 7:00.

[0103] 6) Real-time system monitoring: At 6:45 the next day, feedback showed that charging pile C was in use and the estimated idle time was 20 minutes; the alternative plan was immediately triggered, the score of charging pile A was recalculated (71 points), and the plan was adjusted to: leave home at 6:48, arrive at charging pile A at 6:53, charge from 6:53 to 6:59 (charging amount 5.56kWh, duration 6 minutes), leave for the company at 6:59, and remind the user via a pop-up window on their mobile phone that "charging pile status has changed and charging plan has been updated".

[0104] 7) After charging is complete, the system automatically records: charging time 6:53-6:59, charging station A, charging amount 5.6kWh, charging cost 1.6×5.6=8.96 yuan, free parking; at the end of the month, a cost report is generated, showing that Xiao Li charged 12 times that month, with a total cost of 112.3 yuan, of which 8 times were charged during off-peak hours, accounting for 66.7%, and giving the suggestion that "continuing to charge during off-peak hours can further reduce costs".

[0105] To illustrate the technical solution of this invention in more detail, we will now provide a further explanation in conjunction with another specific application scenario.

[0106] 1) The user plans to drive their electric vehicle over the weekend (approximately 120 kilometers). Vehicle specifications: 80kWh battery capacity, 80kW fast charging power, 500km range on a full charge; current remaining battery capacity is 50kWh; planned departure time is Saturday at 9:00 AM.

[0107] 2) The system obtains charging station information along the current journey and filters out multiple candidate charging stations (D1-D5), including the location (distance from the highway exit), real-time status, price, and historical traffic data of each charging station. The system records the user's journey information: departure point, destination, departure time 9:00, estimated arrival time 11:00, and preference for "prioritizing charging stations within service areas".

[0108] 3) The system calculates: current range = (50 / 80) × 500 = 312.5km > 120km, no need to charge midway; however, considering that the user plans to take a detour to add an extra 50km on the return trip, the total return distance is 170km, and it is predicted that the remaining battery power at the start of the return trip is about 30kWh, with a range of about 187.5km, so it is determined that "charging is required on the return trip".

[0109] 4) The system analyzes historical data (Saturdays from 14:00 to 16:00 in the past 3 months): the D3 charging pile has an 82% idle probability and the D4 charging pile has a 65% idle probability, and generates a charging plan.

[0110] The technical solution in this invention achieves accurate and real-time acquisition of charging pile information through multi-source data integration and real-time update mechanisms. This ensures the accuracy and timeliness of information such as charging pile location, status, and price. Combined with intelligent filtering and personalized sorting, it helps users quickly find charging piles that meet their needs, solving the problems of difficulty and slowness in finding charging piles. Through schedule analysis and trip change monitoring, charging planning is integrated into the user's daily commute, achieving a deep and dynamic correlation between charging planning and commuting schedules. It also has dynamic adjustment capabilities to cope with unexpected situations such as trip changes and road congestion, avoiding charging conflicts with travel and improving travel convenience. Based on multi-objective optimization and particle swarm optimization algorithms, personalized and optimized charging solutions are achieved. Combining vehicle parameters, user preferences, and charging pile status, low-cost, short-time, and highly convenient charging solutions are developed. Especially for long-distance travel scenarios, busyness prediction further reduces waiting time, ensuring a smooth journey. Through accurate cost estimation and systematic statistical analysis, charging costs can be made transparent and controllable, helping users clearly understand charging costs, optimize charging habits, and effectively control vehicle expenses. By improving charging convenience, the popularization of electric vehicles and the optimization of energy resources can be promoted, alleviating users' charging anxiety and promoting the promotion of electric vehicles. At the same time, by guiding users to charge during off-peak hours (in conjunction with peak and off-peak electricity pricing), the peak load on the power grid can be reduced, the utilization rate of charging pile equipment can be improved, and the rational allocation of energy resources can be achieved.

[0111] Based on the same inventive concept, embodiments of the present invention also provide an electric vehicle charging planning system. Figure 4 This is a structural block diagram of an electric vehicle charging planning system provided in an embodiment of the present invention, such as... Figure 4 As shown, the system specifically includes:

[0112] The data acquisition unit 100 is used to collect charging pile information, commuting schedule information and vehicle parameter information, and to perform data cleaning.

[0113] The charging determination unit 200 is used to match the current trip with the commuting schedule information and, in conjunction with the vehicle parameter information, determine whether the current trip requires charging midway.

[0114] The charging planning unit 300 is used to filter candidate charging stations along the current journey according to the charging station screening conditions when it is determined that the current journey requires charging, and to generate a charging planning scheme by personalized sorting of the candidate charging stations based on the commuting schedule information.

[0115] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the electric vehicle charging planning methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0116] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (BUS).

[0117] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0118] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0119] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the electric vehicle charging planning methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.

[0120] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described electric vehicle charging planning method.

[0121] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0122] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0123] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0124] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0125] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0126] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0127] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0128] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0130] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for electric vehicle charging planning, characterized in that, include: Collect charging pile information, commuting schedule information, and vehicle parameter information, and perform data cleaning; The current trip is matched with the commuting schedule information, and combined with the vehicle parameter information, it is determined whether the current trip requires charging midway. When it is determined that the current trip requires charging, candidate charging stations are selected along the current trip using charging station filtering criteria. The candidate charging stations are then sorted in a personalized manner based on the commuting schedule information to generate a charging plan.

2. The method according to claim 1, characterized in that, The charging pile information includes: the location coordinates of the charging pile, charging type, rated power, real-time status, charging price, operator information, and user evaluation data. The commuting schedule information includes: departure time, departure point, destination, commuting route preference, and itinerary change records; The vehicle parameter information includes: remaining battery power, battery capacity, driving range, charging speed, and battery health data.

3. The method according to claim 1, characterized in that, The data cleaning process includes: The location data of the target vehicle is noise-reduced, and the collected data is cleaned and verified according to pre-set data cleaning rules. The data cleaning rules include: outlier removal rules, duplicate data merging rules, and missing value completion rules.

4. The method according to claim 1, characterized in that, Determining whether the current trip requires charging includes: Based on the commuting schedule information, the commuting distance matching the current trip is obtained, and the remaining battery power and driving range in the vehicle parameter information are combined to calculate the battery adequacy coefficient. The battery adequacy coefficient is compared with a preset threshold. If the battery adequacy coefficient is less than the preset threshold, it is determined that the current trip requires charging midway.

5. The method according to claim 1, characterized in that, The process of generating a charging plan includes: Candidate charging stations can be obtained by having users manually adjust the filtering criteria or by calling the default filtering criteria; The candidate charging stations are ranked by weight based on the user's historical usage records and preference settings. Based on the weighted ranking of the candidate charging piles, a charging planning scheme is generated.

6. The method according to claim 1, characterized in that, Also includes: When the deviation between the vehicle's real-time location and the planned charging route in the charging plan exceeds a preset distance threshold, or when a change in the user's trip is detected, a new charging plan is generated.

7. The method according to claim 1, characterized in that, The process of generating a charging plan also includes: Optimization objectives and constraints are set, and particle optimization is performed on the candidate charging piles. Based on the optimization results, a charging planning scheme is generated.

8. An electric vehicle charging planning system, characterized in that, The system is configured to implement the method according to any one of claims 1-7, the system comprising: The data acquisition unit is used to collect charging pile information, commuting schedule information, and vehicle parameter information, and to perform data cleaning. The charging determination unit is used to match the current trip with the commuting schedule information and, in conjunction with the vehicle parameter information, determine whether the current trip requires charging midway. The charging planning unit is used to filter candidate charging stations along the current journey according to the charging station screening criteria when it is determined that the current journey requires charging, and to generate a charging planning scheme by personalized sorting of the candidate charging stations based on the commuting schedule information.

9. An electronic device, characterized in that, The electronic device includes: At least one processor, and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to perform the steps of the method according to any one of claims 1-7.

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