Intelligent scheduling management system and method for new energy electric vehicle charging pile

Abstract

The invention discloses a new energy electric vehicle charging pile intelligent scheduling management system and method, and relates to the technical field of charging pile scheduling management. Comprising an acquisition module, an analysis module, a screening module, a selection module and a feedback module, wherein the acquisition module is used for acquiring automobile related data and user portrait data uploaded by a user, and extracting and generating a charging pile requirement from the automobile related data and the user portrait data; and the analysis module is used for searching in the database based on the charging pile requirements, obtaining charging pile data meeting standards, analyzing the charging pile data and calculating the charging pile matching degree. The technical key points are that analysis is carried out based on the automobile related data uploaded by the user and the user portrait data, the charging pile requirements are generated, and the charging pile matching degree is calculated; then, the charging piles in the nearby area are retrieved based on the charging pile requirements, the charging piles screened out through the mode better meet the requirements of the user, and therefore better experience can be provided for the user, the use effect is good, and the good use prospect is achieved.

Description

Technical Field

[0001] This invention relates to the field of charging pile scheduling and management technology, specifically to an intelligent scheduling and management system and method for new energy electric vehicle charging piles. Background Technology

[0002] Charging piles, also known as electric vehicle charging stations or electric vehicle power supply equipment, are devices that provide electrical energy to electric vehicles, enabling them to store enough electricity to support their operation.

[0003] With the development of science and technology, technologies in various industries have been innovated, and the development of new energy vehicles has gradually become the current trend in the automotive industry. Various new energy technologies are emerging like mushrooms after rain.

[0004] As more and more users choose new energy vehicles, electric vehicles and charging infrastructure, which are important components of new energy vehicles, have also attracted more and more attention from consumers. With the increase in the number of charging infrastructure in recent years, how to choose a suitable charging pile for new energy electric vehicles has become a difficult point for people. In order to make it easier for users to choose a suitable charging pile for new energy electric vehicles, some charging piles and vehicle dispatch management systems have been invented.

[0005] The existing patent authorization announcement number CN118469249B, entitled "A Vehicle Range Management Method, Device, Storage Medium and System," describes the following management steps: real-time collection of historical charging data, real-time battery status and user behavior data of the vehicle; prediction of charging demand using machine learning algorithms; dynamic adjustment of charging scheduling to ensure optimal allocation of charging resources; distributed energy storage and sharing among vehicles through wireless energy transmission technology; and generation of optimal charging and energy allocation strategies by comprehensively considering charging station status, vehicle battery level, travel route and energy demand using collaborative optimization algorithms. Through deep integration of distributed energy storage and collaborative optimization applications, optimized charging and energy management is achieved, extending the vehicle's range. The existing patent, CN114254889A, entitled "A Method, System, Electronic Device, and Storage Medium for Vehicle Queuing at Charging Stations," describes a process that includes receiving a reservation request from a user, the request carrying the departure location; obtaining the travel distance between the arrival and departure locations of each charging station in the charging station deployment map and the corresponding charging station identifier based on a preset charging station deployment map and the departure location; obtaining the number of activated charging piles at each charging station identifier to obtain the usage percentage of each charging pile; selecting low-occupancy charging station identifiers with usage percentages less than a preset congestion threshold and shorter distances from the charging station identifiers based on the usage percentage and a preset short-distance priority order; obtaining travel route data based on the charging station deployment map and low-occupancy charging station identifiers; and sending the travel route data to the user. This application has the effect of improving user experience and the overall utilization rate of charging piles. The solution described in the aforementioned patent has certain drawbacks. The solution described in the first patent mainly adopts a collaborative optimization approach, considering the status of the integrated charging station, the battery level of each vehicle, the current route, and real-time energy demand, to generate the optimal charging and energy distribution strategy. By minimizing the overall performance indicators, it dynamically adjusts the charging priority and energy sharing strategy to ensure that each vehicle can obtain the best energy support during driving, thereby improving charging efficiency and energy utilization efficiency. However, due to the power loss of wireless charging and the cost of the equipment, this method is currently not widely applicable to ordinary new energy vehicles. The solution described in Patent 2 is that by obtaining the usage ratio and the short distance priority, the server obtains and sends the route to the user to reach the corresponding charging station, reducing the possibility that the user may choose the charging station with a long queue again, thus improving the user experience; based on the congestion status of the corresponding charging station at different times, the accuracy of the server obtaining the congestion status of the charging station is effectively improved; its main focus is on how to find areas with more available charging piles for charging. However, in actual use, the power of charging piles varies, the charging price varies, and everyone's charging requirements are different. The above-mentioned unified planning method for scheduling vehicles is intended to ensure that users can stably charge their new energy vehicles. However, with the above method, the selected charging locations are basically all slow charging locations. Given the existence of fast charging piles, this method cannot meet the requirements of most people. Therefore, we have developed an intelligent scheduling management system and method for new energy electric vehicle charging piles. Summary of the Invention

[0006] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an intelligent scheduling and management system and method for new energy electric vehicle charging piles. It analyzes user-uploaded vehicle-related data and user profile data to generate charging pile requirements, and then searches for charging piles in nearby areas based on these requirements. This method selects charging piles that better meet user needs, thus providing a better user experience, demonstrating good performance and promising application prospects, and solving the problems mentioned in the background technology.

[0007] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A smart scheduling and management system for charging piles for new energy electric vehicles includes a data acquisition module, an analysis module, a filtering module, a selection module, and a feedback module. Data collection module: Collects vehicle-related data and user profile data uploaded by users, and extracts and generates charging pile requirements from them; Analysis module: Based on the charging pile requirements, it searches the database to obtain charging pile data that meets the standards, analyzes the charging pile data, and calculates the charging pile matching degree; Filtering module: Acquires charging pile matching degree data and external environment data, calculates the regional comprehensive adaptability, and sorts the charging piles based on the comprehensive adaptability and charging pile matching degree to obtain a charging recommendation list; Selection module: Sends the charging recommendation list to the user, obtains the user's selected objects, makes reservations for the corresponding charging piles, predicts charging data, generates power dispatch reports, and transmits them to the charging pile charging system; Feedback module: Obtains charging data from charging piles, calculates the actual matching degree of charging piles based on the charging data, compares the actual matching degree of charging piles with the corrected matching degree of charging piles, adjusts the charging pile data in the database based on the comparison results, obtains user feedback data, adjusts demand data, and stores the demand data in user profile data.

[0008] Furthermore, the vehicle-related data uploaded by users includes vehicle information manually filled in by the user and vehicle data provided by the vehicle battery management system. The steps to extract the requirements for generating a charging station are as follows: Charging data is extracted from vehicle-related data uploaded by users. The charging data includes vehicle location data, battery power data and battery temperature data collected in real time by the vehicle battery management system, and charging speed limit and battery capacity data retrieved from the merchant database based on vehicle information manually filled in by the user. User requirements are extracted from user profile data, including parking space size requirements, charging pile distance requirements, and charging power requirements. By combining charging data with user requirements, charging pile requirements are generated.

[0009] Furthermore, the steps for retrieving information from the database based on the charging pile requirements are as follows: Obtain charging pile distance requirements and vehicle location data, and find charging piles that meet the charging pile distance requirements and are in standby mode; Then, the parking space size requirements are compared with the parking space sizes in the data of charging piles that meet the charging pile distance requirements. Charging piles that meet the size requirements are filtered out, the number of charging piles that meet the size requirements is counted, and the number of charging piles that meet the size requirements is compared with the set number. If the number of charging piles that meet the size requirements is greater than the set number. Then, the upper limit of charging power is obtained from the data of charging piles that meet the size requirements, and the upper limit of charging power is compared with the charging power requirements. Charging piles that meet the power requirements are selected and a charging pile list is generated directly. If the number of charging piles that meet the size requirements is less than or equal to the set number, a charging pile list will be generated directly.

[0010] Furthermore, the steps for analyzing the charging pile data and calculating the charging pile matching degree are as follows: Extract the current location data of charging piles and vehicles to plan K groups of travel routes; Calculate the average distance and average congestion level of the travel route; The system obtains the current battery level data of the vehicle, predicts the battery level data when the vehicle arrives at the charging station, and then calculates the battery charging amount data. The charging time is calculated based on the battery charging capacity, and the charging price is analyzed. Finally, the matching degree of the charging station is calculated based on the battery charging time and the charging price.

[0011] Furthermore, the steps for obtaining charging pile matching data and external environment data to calculate the overall regional adaptability are as follows: Obtain the time data of K groups of charging pile routes and the number of times the charging piles are occupied at the current time in the past 30 days from the charging pile data, calculate the average travel time, and further obtain the external interference value; Acquire external environmental data, including the temperature and humidity of the surrounding space, and analyze the impact on charging. By analyzing the charging pile matching data, external interference values, and charging impact values, the overall regional adaptability is obtained.

[0012] Furthermore, charging piles are sorted based on comprehensive compatibility and charging pile matching degree. When obtaining the charging recommendation list, the ranking value of the charging piles is first calculated by combining the comprehensive compatibility and charging pile matching degree, and then the charging recommendation list is formulated according to the ranking value of the charging piles from large to small.

[0013] Furthermore, the predicted charging data includes calculated battery charging capacity, charging power, and charging duration, while the generated power dispatch report includes the predicted charging data, the charging piles reserved by the user, and a list of recommended charging stations.

[0014] Furthermore, the steps to adjust the charging pile data in the database based on the comparison results are as follows: The standard range is calculated based on the actual matching degree of the charging pile; The corrected charging pile matching degree is compared with the standard range; If the corrected charging pile matching degree is within the standard range, no adjustment is made; If the corrected charging pile matching degree does not fall within the standard range, then update the charging power loss rate used to analyze the charging pile matching degree.

[0015] Further steps to obtain user feedback data and adjust requirement data are as follows: Extract star ratings from user feedback data regarding parking space size, charging station distance, charging speed, and charging price. The feedback star rating is used as a benchmark. If the feedback star rating is higher than one-third of the star rating of the column, the demand data will not be adjusted. When the feedback star rating is less than one-third of the star rating of the column, the required data will be adjusted by the following ratio: (one-third of the column star rating - feedback star rating) × preset ratio.

[0016] Furthermore, a method for intelligent scheduling and management of charging piles for new energy electric vehicles includes the following steps: Collect vehicle-related data and user profile data uploaded by users, and extract and generate charging pile requirements from them; Based on the requirements of charging piles, the database is searched to obtain charging pile data that meets the standards. The charging pile data is then analyzed to calculate the charging pile matching degree. Acquire charging pile matching degree data and external environment data, calculate the regional comprehensive adaptability, and sort the charging piles based on the comprehensive adaptability and charging pile matching degree to obtain a charging recommendation list; The system sends a list of recommended charging stations to users, obtains the users' selected charging stations, makes reservations for the corresponding charging stations, predicts charging data, generates power dispatch reports, and transmits them to the charging station charging system. Obtain charging data from charging piles, calculate the actual matching degree of charging piles based on the charging data, compare the actual matching degree of charging piles with the corrected matching degree of charging piles, adjust the charging pile data in the database based on the comparison results, obtain user feedback data, adjust demand data, and store the demand data in user profile data.

[0017] (III) Beneficial Effects This invention provides an intelligent scheduling and management system and method for charging piles of new energy electric vehicles, which has the following beneficial effects: This invention provides an intelligent scheduling and management system and method for charging piles of new energy electric vehicles. It analyzes vehicle-related data and user profile data uploaded by users to generate charging pile requirements. Then, it searches for charging piles in nearby areas based on the charging pile requirements. The charging piles selected in this way are more in line with the user's requirements, thereby providing the user with a better experience, good performance, and good application prospects.

[0018] This invention provides an intelligent scheduling and management system and method for charging piles of new energy electric vehicles. It filters suitable charging piles based on vehicle conditions and user requirements, then conducts a comprehensive analysis considering road conditions and external environmental influences. Furthermore, it takes into account the occupancy of parking spaces during driving to analyze the overall regional suitability. Further filtering and judgment are then performed to obtain a charging recommendation list. This list fully considers external changes, enabling users to quickly find suitable charging piles, resulting in good performance.

[0019] This invention provides an intelligent scheduling and management system and method for charging piles of new energy electric vehicles. After each charging session, it collects actual charging data and user feedback data, adjusts the parameters for prediction based on the actual charging data, which greatly improves the accuracy of subsequent predictions, and adjusts the user profile by combining user feedback data, so that the order of charging piles in the generated charging recommendation list is more in line with user requirements, which greatly improves user satisfaction. The overall effect is good and it has good application prospects. Attached Figure Description

[0020] Figure 1 This is a flowchart of an intelligent scheduling and management system for charging piles of new energy electric vehicles according to the present invention. Figure 2 This is a diagram showing the charging parking spaces during the use of an intelligent scheduling and management system for new energy electric vehicle charging piles according to the present invention. Detailed Implementation

[0021] 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.

[0022] Research concept: The existing solution focuses on finding more areas with available charging stations for charging. However, in actual use, charging stations have varying power outputs and charging prices, and everyone's charging requirements are different. The above-mentioned unified planning method for vehicle dispatching aims to ensure users can stably charge their new energy vehicles. However, this method primarily selects slow charging locations, which is insufficient to meet the needs of most people given the availability of fast charging stations.

[0023] In the early stages of research and development, the main focus was on finding solutions that could capture user needs. To do this, it was necessary to collect relevant data submitted by users. To better match charging stations with users, it was also necessary to understand vehicle data. Therefore, the solution developed was to install the charging station on the vehicle, which could capture data submitted by users during initial use and data based on the vehicle used by the user. Based on this, user needs could be captured quickly.

[0024] However, there are certain drawbacks in actual use. For example, users' needs change over time, so using the initial user-filled data as the user's normal needs data will gradually fail to meet the user's needs as the years go by.

[0025] Therefore, during the mid-stage of research and development, we studied changes in user needs and developed a solution to adjust user needs based on user feedback after charging. This approach can gradually and accurately collect user needs, making the collected needs more and more accurate, thereby keeping user satisfaction at a high level in the long term.

[0026] However, there are certain drawbacks in actual use. For example, the number of charging spaces varies, the frequency of use varies, and the environmental interference experienced by above-ground and underground charging is different. Moreover, the charging piles will wear out after a long period of use. Therefore, it is not only necessary to understand the user's data, but also to take a holistic approach and make comprehensive calculations to help users select a more suitable charging space.

[0027] Therefore, in the later stages of research and development, taking a holistic approach and considering the main factors affecting charging as well as external interference, a more comprehensive charging pile ranking calculation scheme was developed, which makes the generated list more effective in use. For specific schemes, see Examples 1 and 2.

[0028] Example 1: Please see Figure 1 This embodiment provides an intelligent scheduling and management system for charging piles of new energy electric vehicles, which includes two parts: hardware and software. The hardware component mainly consists of equipment related to the operation of the intelligent scheduling and management system for new energy electric vehicle charging piles, such as servers and network transmission equipment.

[0029] The software component comprises the data acquisition module, analysis module, filtering module, selection module, and feedback module described in this patent.

[0030] Data acquisition: The implementation of this system requires data from charging piles, user demand data, and vehicle battery-related data. Therefore, all data needs to be aggregated and analyzed during use. In actual operation, this system is mainly divided into two parts: a main body and a data acquisition part. The data acquisition part is the acquisition module, while the main body is mainly used for processing and analyzing the acquired data, which is equivalent to a server.

[0031] Data collection module: Collects vehicle-related data and user profile data uploaded by users, and extracts and generates charging pile requirements from them.

[0032] During daily use, the data acquisition module monitors the user's charging behavior in real time. When the user issues a charging command or the user-set charging threshold is reached, the data acquisition program is automatically started to achieve intelligent data collection.

[0033] In actual use, the data collection module uses an APP. The APP is downloaded to the user's car's infotainment system. The APP collects vehicle data. At the same time, users can also manually input their personal needs through the APP. The system integrates this data to generate a more accurate charging demand profile, thereby accurately grasping the user's needs.

[0034] In practical applications, the app can be integrated with existing car-specific mobile applications to extract relevant vehicle data, such as real-time battery temperature, remaining charge, and battery life. This data fusion further enhances the accuracy of demand analysis. Furthermore, the system features real-time updates to ensure data timeliness and accuracy, facilitating subsequent analysis and processing.

[0035] The vehicle-related data uploaded by users includes vehicle information manually filled in by the user and vehicle data provided by the vehicle battery management system. The steps to extract the requirements for generating a charging station are as follows: Charging data is extracted from vehicle-related data uploaded by users. The charging data includes vehicle location data, battery power data and battery temperature data collected in real time by the vehicle battery management system, and charging speed limit and battery capacity data retrieved from the merchant database based on vehicle information manually filled in by the user. User requirements are extracted from user profile data, including parking space size requirements, charging pile distance requirements, and charging power requirements. User profile data is a collection of user tags. Other tagging technologies can also be used instead, such as building user behavior models or using big data analysis to generate personalized demand profiles, to ensure that the selection of charging piles is more in line with the actual needs of users and improve the charging experience.

[0036] By combining charging data with user requirements, charging pile requirements are generated.

[0037] The aforementioned data can be further enhanced by combining web scraping technology to obtain more relevant data from public online platforms. After filtering and cleaning, the data dimensions are enriched, further improving the breadth and depth of data collection. At the same time, by combining user consumption habits and judging user personality, the statistical user tags will be more accurate, thereby providing users with more personalized charging solutions. This system improves resource utilization, reduces user waiting time, and achieves efficient and convenient charging services by optimizing the way users obtain charging pile needs and the accuracy of their needs.

[0038] Data analysis, after acquiring user needs and related vehicle data, is activated when the user issues a charging command or reaches the user-set charging level. For example, it can be set to automatically trigger a charging request when the battery level of a new energy vehicle drops to 20%-30%, and the system quickly starts the analysis module to perform analysis.

[0039] The analysis module searches the database based on the charging pile requirements to obtain charging pile data that meets the standards, analyzes the charging pile data, and calculates the charging pile matching degree.

[0040] The steps for retrieving information from the database based on charging pile requirements are as follows: Obtain charging pile distance requirements and vehicle location data, and find charging piles that meet the charging pile distance requirements and are in standby mode; The database is a database built in cooperation with parking lots that stores detailed information about charging piles, including the location, power, status, and size of the parking space corresponding to the charging pile. It can also obtain data such as the actual power of the charging pile.

[0041] This invention is mainly used in daily urban charging scenarios, so there are many nearby options when using it, and there is no situation where the number of charging stations is too low to select.

[0042] A charging station in standby mode is one that is not currently supplying power to other vehicles and is not in a reserved state.

[0043] Then, the parking space size requirements are compared with the parking space sizes in the data of charging piles that meet the charging pile distance requirements. Charging piles that meet the size requirements are filtered out, the number of charging piles that meet the size requirements is counted, and the number of charging piles that meet the size requirements is compared with the set number. If the number of charging piles that meet the size requirements is greater than the set number. Currently, most vehicles have relatively weak intelligent parking systems, so they still rely heavily on manual parking. Manual parking depends on the user's parking skills, and since user skill levels vary, even for the same model of vehicle, different drivers may have different opinions. Some drivers have stricter requirements for parking space size. Therefore, when selecting charging stations, it is necessary to consider users' personalized needs for parking space size to ensure that the selected charging stations meet users' actual parking needs and avoid parking difficulties caused by unsuitable parking space size. Thus, precise matching of parking space size is required to further improve the user experience.

[0044] By precisely matching the size of the parking space, this invention can effectively prevent users from experiencing a negative impact on their charging experience due to parking inconvenience, thus providing users with a better experience.

[0045] Then, the upper limit of charging power is obtained from the data of charging piles that meet the size requirements, and the upper limit of charging power is compared with the charging power requirements. Charging piles that meet the power requirements are selected and a charging pile list is generated directly. This situation mainly occurs when there are too many charging piles or during off-peak charging periods. At this time, there are a large number of charging piles. In order to reduce the amount of data calculated for matching charging piles and improve the speed of matching charging piles, further filtering is required to reduce the number of charging piles before generating a charging pile list.

[0046] After testing, when there are 35 charging piles of the required size in the vicinity, after power screening, 7 charging piles remain. The data processing volume is reduced to 16% of that without screening, and the calculation speed is reduced from 27 seconds to 9 seconds, greatly improving efficiency.

[0047] If the number of charging piles that meet the size requirements is less than or equal to the set number, a charging pile list will be generated directly.

[0048] Set the number to 10-15. If it is during peak charging hours or the number of charging stations in the vicinity is too low, you can directly generate a list.

[0049] If the number of charging piles that meet the size requirements is less than or equal to 3, the size requirements will be relaxed, and the number of charging piles in the filtered charging pile list will be adjusted to 6-9 to ensure that users still have enough choices during peak charging periods or when charging piles are scarce.

[0050] The steps for analyzing charging pile data and calculating the charging pile matching degree are as follows: Extract charging station and vehicle current location data, plan K routes (2≤K≤4), and calculate the average distance Lp and average congestion level YDp for each route. , Let be the distance of the i-th group of routes. , The congestion level of the i-th route is calculated by multiplying the congestion level of each segment by the sum of the distances of the segments and dividing by the total distance. For example, if the total route is 5 kilometers long and the route is divided into 3 segments, the first segment is 2 kilometers long with a congestion level of 2, the second segment is 1 kilometer long with a congestion level of 3, and the third segment is 2 kilometers long with a congestion level of 2. The calculated congestion level of the route is 2.2.

[0051] It is a rounding function; For example, the table below shows the parameters of the planned route to a certain charging station;

[0052] The technology for planning K groups of travel routes is based on existing vehicle navigation systems. This invention does not improve upon this technology, so it will not be described in detail. Existing vehicle navigation systems generate driving routes based on user driving habits. Because they are generated based on user habits, they are more in line with user habits, and therefore, the planned routes are more accurate.

[0053] This system can also have a separate route planning module. However, in this case, to accurately plan routes to meet user needs, it would require collecting user habits and interfacing with commonly used navigation software to obtain and analyze location data. This approach increases the amount of data processed and results in poor performance. Therefore, this invention utilizes existing navigation systems, simplifying the data processing flow, ensuring route planning accuracy, avoiding the inconvenience of additional data collection, and improving the user experience.

[0054] Obtain the current battery charge data Q1 of the car, where Q = Q × JKZ × A, Q is the battery capacity, JKZ is the battery health value, and A is the current remaining charge percentage. For example, if the total battery capacity Q is 100Ah, the health value JKZ is 90%, and the current remaining charge percentage A is 50%, then Q1 = 100Ah × 0.9 × 0.5 = 45Ah. Predict the battery charge data Q2 when the car reaches the charging station, where Q2 = Q1 - Lp × ed. For example, if the energy consumption on a road with congestion level YDp of 2 is 0.682Ah / km and Lp is 5km, then Q2 = 45Ah - 5km × 0.682Ah / km = 39.09Ah. h, the congestion level YDp is determined based on real-time traffic data displayed on the map, generally including level 1 (basically no cars), level 2 (smooth traffic), level 3 (slow traffic), level 4 (congested), and level 5 (severe congestion). The energy consumption of vehicles corresponding to different levels is different, and the energy consumption of vehicles corresponding to different levels is calculated based on the historical driving data of the vehicles. ed is the energy consumption of the car driving on the road with the average congestion level YDp. Then, the battery charging capacity data Q3 is calculated as Q×JKZ-Q2. After the above calculation, Q3=100Ah×0.9-39.09Ah=45.81Ah, that is, the amount of electricity that needs to be charged under normal circumstances is 45.81Ah. The energy consumption in kilowatt-hours can also be used as the calculation formula.

[0055] The battery charging time TZ is calculated based on the battery charging capacity, then the battery charging price PZ is calculated, and finally the charging pile matching degree MD is calculated based on the battery charging time TZ and the battery charging price PZ. ; In this formula, under normal circumstances, the vehicle will not charge when the battery level is 80%, therefore, there will be no issue. Therefore, there is no need to consider this situation.

[0056] The above calculations do not take into account the influence of the external environment. They are mainly based on the data of the compatibility calculation of charging piles and vehicles. The main purpose is to reduce data clutter in the same step to improve the calculation speed. After analysis, the influence of the environment is included in the calculation of charging time. The calculation time of charging time is increased from 3.6 seconds to 8.4 seconds, which greatly reduces the speed. Therefore, the analysis of this step does not consider the influence of the external environment.

[0057] This invention provides an intelligent scheduling and management system and method for charging piles of new energy electric vehicles. It analyzes vehicle-related data and user profile data uploaded by users to generate charging pile requirements. Then, it searches for charging piles in nearby areas based on the charging pile requirements. The charging piles selected in this way are more in line with the user's requirements, thereby providing the user with a better experience, good performance, and good application prospects.

[0058] In this invention, charging is divided into two stages: before 80% and after 80%. This is mainly due to the characteristics of new energy vehicles when charging. Before 80%, it is a fast charging mode, which charges quickly, while after 80%, it is a slow charging mode, which charges more slowly.

[0059] P represents the charging power of the charging station for the user's vehicle, and SH1 represents the charging power loss rate when the vehicle's battery level is below 80%. SH1 represents the average power output when the vehicle's battery level is above 80%, SH2 represents the charging power loss rate when the vehicle's battery level is above 80%, and M represents the number of price tiers for this charging station at different time periods. Let be the price of the charging station in the i-th time period. Let TZmin be the charging capacity of the charging station in the i-th stage, TZmin be the shortest battery charging time among all charging stations in the charging station list, and N be the total number of charging stations recorded in the charging station list. Let P1 be the battery charging time of the i-th charging station recorded in the charging station list, P1 be the weight ratio corresponding to the battery charging time set based on user profile data, and PZmin be the lowest battery charging price among all charging stations in the charging station list. P1 represents the battery charging price of the i-th charging station recorded in the charging station list, and P2 represents the weight ratio of the battery charging price set based on user profile data.

[0060] To reduce the amount of data required for calculation, instead of using a precise and specific algorithm, the average data from two stages is used as a substitute. This calculation method can significantly reduce the amount of data to be processed, improve the data processing speed, and has good performance. At the same time, this simplified calculation method has a smaller error in practical applications and can meet the needs of most scenarios.

[0061] If a precise and specific algorithm is used, it is necessary to collect the vehicle's historical charging data, analyze the historical charging data and charging power, and fully consider the influence of the external environment. Therefore, in this case, the amount of data to be processed is relatively large and the processing efficiency is not high.

[0062] Charging station ranking: After understanding the basic compatibility between charging stations and vehicles, a comprehensive evaluation needs to be conducted by combining the external environment and data of surrounding charging stations. Based on the evaluation results, suitable charging stations are evaluated for users. This method recommends charging stations that meet user requirements and can effectively avoid situations where charging stations are occupied or there are no charging stations nearby, requiring users to search for charging stations again. This process is based on the filtering module.

[0063] Filtering module: Acquires charging pile matching degree data and external environment data, calculates the regional comprehensive adaptability, and sorts the charging piles based on the comprehensive adaptability and charging pile matching degree to obtain a charging recommendation list.

[0064] The steps to obtain charging pile matching data and external environment data, and calculate the overall regional adaptability are as follows: Obtain the time data of K groups of charging station routes and the number of times the charging station is occupied (C) at the current moment in the past 30 days. The higher the number of times the charging station is occupied (C), the more popular the charging station is. Even if it is not currently in use, it may be used in the next moment. Therefore, this aspect needs to be fully considered to calculate the average travel time (Txp). , For the time data of the i-th group of travel routes, calculate the external interference value using the following formula: ; In the formula, Disb is the external interference value, djc is the preset level difference, 3 < djc < 8, sjqz is the weight coefficient of the travel time, B is a constant, B > 1, and csqz is the weight coefficient of the probability that the charging pile is occupied. The above Txp≤10 refers to the fact that existing charging stations are generally reserved for 10 minutes. During these 10 minutes, the charging station is locked and cannot be used by other people. However, there is a certain probability that other vehicles may not know this and occupy the parking space.

[0065] When the charging pile does not have the predetermined function, the calculation formula for the case of Txp≤10 is cancelled, and the formula for Txp>10 is adopted as the general formula.

[0066] Acquire external environmental data, including the temperature and humidity of the surrounding space, and calculate the charging impact value. The specific formula is as follows: ; In the formula, The current space temperature, The current space temperature, For standard temperature range, Standard humidity range This refers to the distance between the current ambient temperature monitored by the charging station and the standard temperature range. This refers to the distance between the current ambient humidity, as monitored by the charging station, and the standard humidity range. Weighting factors for the effect of temperature on car charging. Weighting coefficients for the impact of humidity on car charging; When sorting, the influence of the external environment needs to be considered, mainly because the outdoor and underground environments often differ significantly, especially in summer and winter. Therefore, additional analysis is required. Additional settings can be added, such as setting a range for the external temperature and humidity. When the weather is within this range, the influence of the external environment is small, and the effects of temperature and humidity are not considered. This method can greatly reduce the amount of data processing and effectively reduce the influence of the external environment.

[0067] However, due to the varying distribution of charging stations, some locations are poorly ventilated and exposed to direct sunlight, resulting in high temperatures and overall high temperatures. This leads to lower charging efficiency in summer, severely impacting the effectiveness of fast charging. In contrast, underground parking lots maintain a relatively constant temperature, resulting in more stable charging efficiency. Therefore, when ranking charging stations, it is necessary to consider the impact of environmental factors and recommend suitable charging stations to improve user satisfaction.

[0068] The comprehensive regional adaptability Qzsp is calculated based on charging pile matching data, external interference values, and charging impact values. The specific formula is as follows: In the formula, L represents the number of charging piles in the list of charging piles included in the closed charging area. Let i be the charging pile matching degree between the i-th charging pile in the closed charging area and the charging pile list. Let be the external interference value of the charging pile located in the closed charging area and the i-th charging pile in the charging pile list. This represents the charging impact value of the i-th charging pile located in the closed charging area and the charging pile list.

[0069] Qzsp considers multiple aspects and conducts a comprehensive analysis to determine the impact of charging piles in the area.

[0070] The charging piles are sorted based on their overall compatibility and matching degree. To obtain the recommended charging list, a ranking value is first calculated by combining these two factors. Then, the recommended charging list is compiled from highest to lowest ranking value. The formula for calculating the charging pile ranking value is as follows: In the formula, This is the ranking value for charging stations. The weighting coefficients represent the conditions of the charging pile itself. This is the difference in regional bonus levels. This is the regional weighting coefficient.

[0071] With a success rate of over 75%, the regional bonus is limited, and the ranking value mainly depends on the conditions of the charging pile itself. This method selects charging piles that better meet the needs of users.

[0072] The weighting coefficients are determined using the coefficient of variation method, which assigns weights to each indicator based on the degree of variation between the current value and the target value. If the numerical difference of an indicator is large, clearly distinguishing each evaluated object, it indicates that the indicator has rich discriminative information and should therefore be given a larger weight. Conversely, if the numerical difference of each evaluated object on a certain indicator is small, then the indicator's ability to distinguish each evaluated object is weak, and therefore it should be given a smaller weight. This method directly utilizes the information contained in each indicator to calculate the weight of the indicator, thus possessing objectivity.

[0073] This invention provides an intelligent scheduling and management system and method for charging piles of new energy electric vehicles. It filters suitable charging piles based on vehicle conditions and user requirements, then conducts a comprehensive analysis considering road conditions and external environmental influences. Furthermore, it takes into account the occupancy of parking spaces during driving to analyze the overall regional suitability. Further filtering and judgment are then performed to obtain a charging recommendation list. This list fully considers external changes, enabling users to quickly find suitable charging piles, resulting in good performance.

[0074] User selection: After generating the charging recommendation list, the list will be sent to the user so that the user can choose a suitable charging station. This process is based on the selection module.

[0075] like Figure 2 As shown, when the charging recommendation list is sent to the user, it will be displayed in conjunction with a map. In the map, P represents the number of charging piles that meet the requirements in the area, where Pan is the highest ranking value of the charging piles that meet the requirements in the area, and LP is the distance. When the user selects a parking area, the system will automatically reserve the charging pile with the highest ranking value in the area and send the charging pile parking space number to the user to facilitate finding a parking space.

[0076] Similar to the data acquisition module, the selection module provides system feedback, allowing users to choose a suitable charging station based on this feedback.

[0077] Selection Module: Sends a list of recommended charging stations to users, obtains the users' selected charging stations, makes reservations for the corresponding charging stations, predicts charging data, generates power dispatch reports, and transmits them to the charging station charging system.

[0078] Predictive charging data includes calculated battery charging capacity, charging power, and charging time. A power dispatch report is generated, which records the battery charging capacity, charging power, and charging time. This report is then sent to the relevant management system in the area corresponding to the charging pile, allowing the management system to make advance arrangements and reduce the impact of rapid power consumption at the charging pile.

[0079] Feedback Summary: After a user charges their phone, the system will collect actual charging data from the charging station and the user's feedback on the charging session.

[0080] The feedback module obtains charging data from charging piles, calculates the actual matching degree of the charging piles based on the charging data, compares the actual matching degree of the charging piles with the corrected matching degree of the charging piles, adjusts the charging pile data in the database based on the comparison results, obtains user feedback data, adjusts the demand data, and stores the demand data in the user profile data.

[0081] The formula for calculating the actual matching degree of the charging pile is: ; In the formula, The actual matching degree of the charging pile is calculated, TZs is the actual charging time of the new energy vehicle, and PZs is the actual charging price of the new energy vehicle.

[0082] The actual matching degree can be calculated based on the actual charging data of the charging piles mentioned above.

[0083] Since the initial charging pile matching degree did not take into account the interference of the external environment, it is necessary to take into account the interference of the external environment in subsequent comparisons. Therefore, it is necessary to correct the charging pile matching degree based on the interference data and calculate the corrected charging pile matching degree MDj.

[0084] The formula for calculating the corrected charging pile matching degree MDj is: ; The steps to adjust the charging pile data in the database based on the comparison results are as follows: The standard range is calculated based on the actual matching degree of the charging pile. , The range fluctuation ratio; Take 5%-8%.

[0085] Compare the corrected charging pile matching degree MDj with the standard range; If the corrected charging pile matching degree is within the standard range, no adjustment is needed. In this case, it means that the charging pile data used when calculating the matching degree data is relatively accurate and no additional adjustment is required. This step is mainly because charging piles are subject to wear and tear and maintenance during use. Therefore, their data fluctuates to some extent and needs to be updated in real time to eliminate the impact of fluctuations, so as to make the subsequent use effect better and the calculation more accurate. If the corrected charging pile matching degree is not within the standard range, then update SH1 to SH11 and update SH2 to SH21; Updated Ps1 represents the average power output of the vehicle when its battery level is below 80% during actual charging at this charging station. (Updated version) Ps2 is the average power when the vehicle's battery level is above 80% during actual charging at this charging station.

[0086] This invention provides an intelligent scheduling and management system and method for charging piles of new energy electric vehicles. After each charging session, it collects actual charging data and user feedback data, adjusts the parameters for prediction based on the actual charging data, which greatly improves the accuracy of subsequent predictions, and adjusts the user profile by combining user feedback data, so that the order of charging piles in the generated charging recommendation list is more in line with user requirements, which greatly improves user satisfaction. The overall effect is good and it has good application prospects.

[0087] The steps to obtain user feedback data and adjust the required data are as follows: Extract star ratings from user feedback data regarding parking space size, charging station distance, charging speed, and charging price. Like existing rating software, the feedback module evaluates parking space size, charging pile distance, charging speed, and charging price separately, thus collecting relatively comprehensive feedback data.

[0088] The feedback star rating is used as a benchmark. If the feedback star rating is higher than one-third of the star rating of the column, the demand data will not be adjusted. When the feedback star rating is less than one-third of the star rating of the column, the required data will be adjusted by the following ratio: (one-third of the column star rating - feedback star rating) × preset ratio.

[0089] For example, the original preset parking space is 5 meters long, 2.5 meters wide, and has a total star rating of 6 stars, with a preset ratio of 2%. However, when the user feedback is 0.5 stars, the adjusted ratio is (2-0.5)×2=3%. After the adjustment, the parking space is 5.15 meters long and 2.575 meters wide.

[0090] In the above, unless the user sets the adjustment ratio individually, the default adjustment is to adjust the parking space upwards, the charging pile distance downwards, the charging speed upwards, and the charging price downwards.

[0091] Example 2: Based on Example 1, this example also provides an intelligent scheduling and management method for charging piles for new energy electric vehicles, including the following specific steps: A method for intelligent scheduling and management of charging piles for new energy electric vehicles includes the following steps: Collect vehicle-related data and user profile data uploaded by users, and extract and generate charging pile requirements from them; Based on the requirements of charging piles, the database is searched to obtain charging pile data that meets the standards. The charging pile data is then analyzed to calculate the charging pile matching degree. Acquire charging pile matching degree data and external environment data, calculate the regional comprehensive adaptability, and sort the charging piles based on the comprehensive adaptability and charging pile matching degree to obtain a charging recommendation list; The system sends a list of recommended charging stations to users, obtains the users' selected charging stations, makes reservations for the corresponding charging stations, predicts charging data, generates power dispatch reports, and transmits them to the charging station charging system. Obtain charging data from charging piles, calculate the actual matching degree of charging piles based on the charging data, compare the actual matching degree of charging piles with the corrected matching degree of charging piles, adjust the charging pile data in the database based on the comparison results, obtain user feedback data, adjust demand data, and store the demand data in user profile data.

[0092] In the application, the various formulas mentioned are all calculated by removing dimensions and taking their numerical values. The formulas are established by collecting a large amount of data and simulating the most recent real situation. Some coefficients or weights in the formulas are set by those skilled in the art according to the actual situation, so they will not be elaborated here.

[0093] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A smart scheduling and management system for charging piles of new energy electric vehicles, characterized in that: include: Data collection module: Collects vehicle-related data and user profile data uploaded by users, and extracts and generates charging pile requirements from them; Analysis module: Based on the charging pile requirements, it searches the database to obtain charging pile data that meets the standards, analyzes the charging pile data, and calculates the charging pile matching degree; Filtering module: Acquires charging pile matching degree data and external environment data, calculates the regional comprehensive adaptability, and sorts the charging piles based on the comprehensive adaptability and charging pile matching degree to obtain a charging recommendation list; Selection module: Sends the charging recommendation list to the user, obtains the user's selected objects, makes reservations for the corresponding charging piles, predicts charging data, generates power dispatch reports, and transmits them to the charging pile charging system; Feedback module: Obtains charging data from charging piles, calculates the actual matching degree of charging piles based on the charging data, compares the actual matching degree of charging piles with the corrected matching degree of charging piles, adjusts the charging pile data in the database based on the comparison results, obtains user feedback data, adjusts demand data, and stores the demand data in user profile data.

2. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 1, characterized in that: The vehicle-related data uploaded by users includes vehicle information manually filled in by the user and vehicle data provided by the vehicle battery management system. The steps to extract the requirements for generating a charging station are as follows: Charging data is extracted from vehicle-related data uploaded by users. The charging data includes vehicle location data, battery power data and battery temperature data collected in real time by the vehicle battery management system, and charging speed limit and battery capacity data retrieved from the merchant database based on vehicle information manually filled in by the user. User requirements are extracted from user profile data, including parking space size requirements, charging pile distance requirements, and charging power requirements. By combining charging data with user requirements, charging pile requirements are generated.

3. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 2, characterized in that: The steps for retrieving information from the database based on charging pile requirements are as follows: Obtain charging pile distance requirements and vehicle location data, and find charging piles that meet the charging pile distance requirements and are in standby mode; Then, the parking space size requirements are compared with the parking space sizes in the data of charging piles that meet the charging pile distance requirements. Charging piles that meet the size requirements are filtered out, the number of charging piles that meet the size requirements is counted, and the number of charging piles that meet the size requirements is compared with the set number. If the number of charging piles that meet the size requirements is greater than the set number. Then, the upper limit of charging power is obtained from the data of charging piles that meet the size requirements, and the upper limit of charging power is compared with the charging power requirements. Charging piles that meet the power requirements are selected and a charging pile list is generated directly. If the number of charging piles that meet the size requirements is less than or equal to the set number, a charging pile list will be generated directly.

4. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 3, characterized in that: The steps for analyzing charging pile data and calculating the charging pile matching degree are as follows: Extract the current location data of charging piles and vehicles to plan K groups of travel routes; Calculate the average distance and average congestion level of the travel route; The system obtains the current battery level data of the vehicle, predicts the battery level data when the vehicle arrives at the charging station, and then calculates the battery charging amount data. The charging time is calculated based on the battery charging capacity, and the charging price is analyzed. Finally, the matching degree of the charging station is calculated based on the battery charging time and the charging price.

5. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 4, characterized in that: The steps to obtain charging pile matching data and external environment data, and calculate the overall regional adaptability are as follows: Obtain the time data of K groups of charging pile routes and the number of times the charging piles are occupied at the current time in the past 30 days from the charging pile data, calculate the average travel time, and further obtain the external interference value; Acquire external environmental data, including the temperature and humidity of the surrounding space, and analyze the impact on charging. By analyzing the charging pile matching data, external interference values, and charging impact values, the overall regional adaptability is obtained.

6. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 5, characterized in that: The charging piles are sorted based on their overall compatibility and matching degree. When obtaining the charging recommendation list, the ranking value of the charging piles is first calculated by combining the overall compatibility and matching degree, and then the charging recommendation list is formulated according to the ranking value of the charging piles from large to small.

7. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 6, characterized in that: The predicted charging data includes calculated battery charging capacity, charging power, and charging time. The generated power dispatch report includes the predicted charging data, the charging piles reserved by users, and a list of recommended charging stations.

8. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 7, characterized in that: The steps to adjust the charging pile data in the database based on the comparison results are as follows: The standard range is calculated based on the actual matching degree of the charging pile; The corrected charging pile matching degree is compared with the standard range; If the corrected charging pile matching degree is within the standard range, no adjustment is made; If the corrected charging pile matching degree does not fall within the standard range, then update the charging power loss rate used to analyze the charging pile matching degree.

9. The intelligent scheduling and management system for new energy electric vehicle charging piles according to claim 8, characterized in that: The steps to obtain user feedback data and adjust the required data are as follows: Extract star ratings from user feedback data regarding parking space size, charging station distance, charging speed, and charging price. The feedback star rating is used as a benchmark. If the feedback star rating is higher than one-third of the star rating of the column, the required data will not be adjusted. When the feedback star rating is less than one-third of the star rating of the column, the required data will be adjusted by the following ratio: (one-third of the column star rating - feedback star rating) × preset ratio.

10. A smart scheduling and management method for charging piles of new energy electric vehicles, using the system described in any one of claims 1 to 9, characterized in that: Includes the following steps: Collect vehicle-related data and user profile data uploaded by users, and extract and generate charging pile requirements from them; Based on the requirements of charging piles, the database is searched to obtain charging pile data that meets the standards. The charging pile data is then analyzed to calculate the charging pile matching degree. Acquire charging pile matching degree data and external environment data, calculate the regional comprehensive adaptability, and sort the charging piles based on the comprehensive adaptability and charging pile matching degree to obtain a charging recommendation list; The system sends a list of recommended charging stations to users, obtains the users' selected charging stations, makes reservations for the corresponding charging stations, predicts charging data, generates power dispatch reports, and transmits them to the charging station charging system. Obtain charging data from charging piles, calculate the actual matching degree of charging piles based on the charging data, compare the actual matching degree of charging piles with the corrected matching degree of charging piles, adjust the charging pile data in the database based on the comparison results, obtain user feedback data, adjust demand data, and store the demand data in user profile data.

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