A dynamic data fusion-based electric vehicle charging scheduling method and system, and a storage medium

Abstract

This invention discloses a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion, relating to the field of data processing technology. The method includes the following steps: acquiring the status monitoring parameters of all standby charging interfaces and filtering out available charging piles that meet the requirements; classifying vehicles into charging-priority vehicles and cost-priority vehicles based on the real-time status data and environmental parameters of the electric vehicles requesting charging; for charging-priority vehicles, adjusting the charging pile search range, introducing output power to determine the target charging pile and performing a locking operation; for cost-priority vehicles, calculating the cost optimization coefficient based on arrival time prediction and electricity price fluctuations within the time period, and generating a preferred charging pile sequence. The user finally selects and locks the charging pile, while dynamically adjusting the locking time according to road congestion. This method achieves efficient allocation of charging pile resources, optimizing user experience and economic costs.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion. Background Technology

[0002] With the transformation of the global energy structure and the increasing awareness of environmental protection, electric vehicles, due to their low emissions and energy efficiency, are gradually becoming an important direction in the future transportation sector. The widespread adoption of electric vehicles not only promotes the implementation of green travel concepts but also places higher demands on the construction and operation of charging infrastructure. However, many problems still exist in the charging process. First, the utilization rate of charging stations is low. Currently, many regions have enough charging stations to meet basic needs in terms of quantity, but due to unreasonable layout and insufficient status monitoring methods, some charging stations are idle for a long time, while others experience queuing congestion due to concentrated use, making it impossible to effectively achieve a balanced allocation of resources.

[0003] Existing technologies often employ scheduling methods that allocate resources based on priority according to uniform charging demand information. However, these methods fail to consider the different actual target needs of battery-powered vehicles. Most scheduling schemes fail to achieve classified management of charging-priority users and cost-priority users, resulting in uneven resource allocation and failing to truly meet users' personalized needs. Furthermore, there are delays in updating the status of charging piles, and the availability of idle charging piles is not considered.

[0004] Furthermore, the lack of flexible management with time lag in electricity pricing reduces the effectiveness of charging price forecasts. In addition, the lack of real-time monitoring and forecasting of road traffic conditions during charging scheduling makes it impossible to dynamically adjust users' reservation times. This results in unreasonable arrangements of reservation delay times, which further exacerbates the waste of charging pile resources.

[0005] In the prior art, CN116228295A discloses an intelligent recommendation method and system for charging piles. The method includes: extracting elements from charging demand information to obtain charging demand parameter information; obtaining a list of charging piles within a preset radius area based on user location information; setting a charging pile optimization space based on the charging pile list information; obtaining charging price strategy information for each charging pile in the charging pile list information; determining charging pile performance evaluation parameters based on the charging demand parameter information and the charging price strategy information; performing global optimization within the charging pile optimization space based on the charging pile performance evaluation parameters, outputting preferred charging pile information, and recommending and managing charging piles to target users based on the preferred charging pile information. However, this solution does not consider the different actual target needs of battery-powered vehicles, nor does it consider the fluctuation of charging prices over time. Furthermore, it does not consider the real-time status of charging piles and the dynamic adjustment of user reservation times, thus reducing the real-time performance and effectiveness of the scheduling system.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method for electric vehicle charging scheduling based on dynamic data fusion, comprising the following steps:

[0010] Within the intelligent scheduling area, the status monitoring parameters corresponding to all standby charging interfaces are acquired. Based on the status monitoring parameters, the status of the standby charging interfaces is characterized to form a characterization system that reflects the health status of the charging piles. Based on this system, available charging piles that meet the requirements are selected.

[0011] The system acquires real-time status data and environmental parameters of electric vehicles requesting charging, determines the target demand coefficient of these vehicles, and classifies them into charging-priority vehicles and cost-priority vehicles based on the target demand coefficient.

[0012] For vehicles with charging priority, the search area for available charging piles is adjusted by the vehicle target demand coefficient, the available charging piles in the search area are determined, the output power of the available charging piles is used to determine the target charging pile, the target charging pile is locked and the status of available charging piles is updated.

[0013] For cost-priority vehicles, the current location of each available charging station and the cost-priority vehicle is obtained, the arrival time of the vehicle is predicted, a time window is set based on the arrival time of the vehicle, and the average electricity price data of each charging station within the time window is collected. Based on the average electricity price data and the current location information, the cost optimization coefficient of the cost-priority vehicle and each available charging station is calculated.

[0014] Based on the cost optimization coefficient, the available charging piles are arranged in ascending order to form a preferred charging pile sequence. Users can select and lock the preferred charging piles in the preferred charging pile sequence, update the status of available charging piles, and use the expected arrival time of the vehicle as the initial locking time. At the same time, the locking time is dynamically adjusted according to the road congestion.

[0015] Furthermore, the status monitoring parameters include the internal temperature of the standby charging interface, the input current, and the input voltage;

[0016] Specifically, the detection interval is set, and the status of the standby charging interface is characterized based on the status monitoring parameters. A health offset index is generated based on the difference between the standby charging interface status monitoring parameters and the calibrated status monitoring parameters. This health offset index provides a preliminary characterization of the charging pile's health status. The formula used to calculate the health offset index is as follows:

[0017] ;

[0018] In the formula, Indicates the health deviation index, The internal temperature of the charging port. Input voltage to the charging port. Input current to the charging port. , and These represent the internal temperature, input voltage, and input current, respectively.

[0019] The logic for using the health deviation index to initially characterize the health status of charging piles is as follows:

[0020] like This indicates that the corresponding charging pile is in poor health and cannot perform the charging task normally.

[0021] like This indicates that the health status of the corresponding charging pile has been initially determined to be normal, and a second safety assessment will be conducted. This serves as a preliminary health assessment threshold.

[0022] The specific method for secondary safety assessment is as follows: It involves assessing the insulation resistance and contact resistance losses of charging piles initially deemed to be in normal health status. The specific assessment logic is as follows:

[0023] like and This indicates that the charging pile is initially determined to be normal and meets safety requirements, and the status is updated to "available charging pile".

[0024] Otherwise, if the charging station is initially determined to be normal but does not meet safety requirements, its status will be updated to fault.

[0025] in and These are insulation resistance and contact resistance, respectively. and These are the minimum insulation resistance and the maximum contact resistance, respectively.

[0026] Furthermore, the real-time status data includes vehicle battery level and estimated remaining range, and the environmental parameters include ambient temperature and humidity. The specific method for determining the target demand coefficient for the electric vehicle requesting charging is as follows: a predicted demand coefficient is generated based on the vehicle battery level and estimated remaining range data, and the predicted demand coefficient is then adjusted using environmental parameters to obtain the target demand coefficient. The specific formula used to calculate the target demand coefficient is as follows:

[0027] ;

[0028] In the formula, For the target demand coefficient, This is the environmental correction factor. To predict demand coefficients;

[0029] Among them, the predicted demand coefficient The formula used for the calculation is:

[0030] ;

[0031] In the formula, For vehicle battery level, To estimate the remaining range, and These are the weighting coefficients for battery level and remaining battery life, respectively. and and All are greater than 0. This is the battery life reduction factor;

[0032] Among them, environmental correction factor The specific formula used for the calculation is as follows:

[0033] ;

[0034] In the formula, Humidity risk factor, This is the temperature influence coefficient. and These are ambient temperature and the optimal battery operating temperature, respectively. This refers to the relative humidity of the environment.

[0035] Furthermore, the specific logic for classifying electric vehicles with charging requests is as follows:

[0036] when When charging requests are made, electric vehicles will be marked as charging priority vehicles;

[0037] when At that time, electric vehicles requesting charging will be marked as cost-priority vehicles;

[0038] In the formula, The threshold is used for priority division.

[0039] Furthermore, the specific method for adjusting the search area for charging-priority vehicles is as follows: the initial search radius is scaled using the vehicle target demand coefficient. Based on the scaled search radius, the search area is determined with the charging-priority vehicle as the center. The specific formula used to calculate the scaled search radius is as follows:

[0040] ;

[0041] In the formula, This is the scaled search radius. As the initial search radius, Minimum search radius;

[0042] The specific logic for determining the target charging station is as follows: Based on the location information of available charging stations within the search area, the distance data between each available charging station and the vehicle with charging priority is calculated. The output power of the available charging stations is then incorporated to calculate a selection priority coefficient. The available charging station with the highest selection priority coefficient is selected as the target charging station. The specific formula for calculating the selection priority coefficient is as follows:

[0043] ;

[0044] In the formula, Let be the priority coefficient for selecting the i-th available charging station within the search area. Let be the power adaptation coefficient of the i-th available charging pile within the search area. This represents the distance between the i-th available charging pile and the vehicle with charging priority within the search area, where i is the index of the available charging pile within the search area;

[0045] Power compatibility coefficient of the i-th available charging station within the search area The specific expression is:

[0046] ;

[0047] In the formula, Let i be the output power of the i-th available charging station within the search area. Available charging power for vehicles that prioritize charging.

[0048] Furthermore, based on the average electricity price data and current location information, the specific formula used to calculate the cost optimization coefficient between cost-priority vehicles and each available charging station is as follows:

[0049] ;

[0050] In the formula, Let be the cost optimization coefficient between the j-th available charging pile and the cost-priority vehicle. Let be the power adaptation coefficient for the j-th available charging pile. Let be the distance between the j-th available charging station and the cost-priority vehicle. Cost per unit distance The average electricity price data for the j-th available charging pile within a set time window is given. The specific method for setting the time window is as follows: the predicted arrival time of the vehicle is taken as the start time of the time window, and the expected charging time is taken as the length of the time window.

[0051] Furthermore, the preferred charging pile sequence arranges the available charging piles corresponding to the cost preference coefficients greater than 0 in ascending order. The first available charging pile in the preferred charging pile sequence is the charging pile with the lowest cost. As the sequence number increases, the charging cost increases sequentially.

[0052] The initial lock-in time is calculated by the ratio of the distance between the available charging station and the cost-priority vehicle to the vehicle's average driving speed.

[0053] The locking time is dynamically adjusted based on road congestion conditions. The specific formula for adjusting the locking time is as follows:

[0054] ;

[0055] In the formula, This is the adjusted lock time. Let be the vehicle's speed at time t. The time variable for cost-priority vehicles traveling to a designated charging station. The shortest path distance to the locked charging station, where The specific formula used for the calculation is as follows:

[0056] ;

[0057] In the formula, The free-flow velocity of the shortest path, Let be the traffic density of the shortest path at time t. For road congestion density, This is the velocity-density nonlinear factor;

[0058] The maximum value between the initial lock time and the adjusted lock time is taken as the final lock time. The locking of the charging pile ends when the final lock time ends.

[0059] The present invention also provides an electric vehicle charging scheduling system based on dynamic data fusion, the system being used to execute the electric vehicle charging scheduling method based on dynamic data fusion, comprising:

[0060] The working status detection module is used to acquire the status monitoring parameters corresponding to all standby charging interfaces within the intelligent scheduling area. Based on the status monitoring parameters, the status of the standby charging interfaces is characterized to form a characterization system that reflects the health status of the charging pile, and the available charging piles that meet the requirements are selected accordingly.

[0061] The target demand segmentation module is used to acquire real-time status data and environmental parameters of electric vehicles requesting charging, determine the target demand coefficient of electric vehicles requesting charging, and classify electric vehicles requesting charging into charging priority vehicles and cost priority vehicles based on the target demand coefficient.

[0062] The charging priority scheduling module is used to adjust the search area of ​​available charging piles for charging priority vehicles by adjusting the vehicle target demand coefficient, determine the available charging piles in the search area, introduce the output power of available charging piles to determine the target charging pile, lock the target charging pile and update the status of available charging piles.

[0063] The cost-priority scheduling module is used to obtain the current location of each available charging pile and the cost-priority vehicle for cost-priority vehicles, predict the arrival time of the vehicle, set a time window based on the arrival time of the vehicle, and collect the average electricity price data of each charging pile within the time window. Based on the average electricity price data and the current location information, the module calculates the cost optimization coefficient between the cost-priority vehicle and each available charging pile.

[0064] The dynamic locking adjustment module is used to sort the available charging piles in ascending order according to the cost optimization coefficient to form a preferred charging pile sequence. Users can select and lock the charging piles in the preferred charging pile sequence, update the status of available charging piles, and use the expected vehicle arrival time as the initial locking time. At the same time, the locking time is dynamically adjusted according to the road congestion.

[0065] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described electric vehicle charging scheduling method based on dynamic data fusion.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] By introducing charging pile status monitoring parameters, a characterization system reflecting the health status of charging piles is constructed. The system can accurately identify and screen out available charging piles that meet the requirements. Compared with the traditional scheduling scheme that treats all charging piles the same, this scheme effectively avoids the problems of reduced efficiency or safety hazards caused by charging pile failures or poor health status.

[0068] This solution categorizes electric vehicles requesting charging into charging-priority and cost-priority vehicles through the design of a target demand coefficient. This fundamentally solves the problem of existing scheduling methods' inability to flexibly address varying user needs. For charging-priority vehicles, by adjusting the search area for available charging stations and introducing charging station output power, the solution quickly locates target charging stations, significantly reducing user waiting time and meeting urgent charging needs. For cost-priority vehicles, this solution dynamically calculates the cost optimization coefficient and generates a preferred charging station sequence by combining the vehicle's current location, estimated arrival time, and average electricity price data for charging stations, further helping users minimize costs.

[0069] The system can dynamically adjust the locking time of charging stations, making scheduling more accurate and flexible. This dynamic adjustment capability not only effectively reduces user waiting time caused by road congestion or environmental changes, but also avoids resource waste and improves the overall operational efficiency of the system. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0071] Figure 2 A chart showing the 24-hour trend of electricity price changes and electricity consumption distribution for charging.

[0072] Figure 3 A comparison chart of the predicted electricity price trend for charging and the actual electricity price;

[0073] Figure 4 A comparison chart of the internal temperature of the charging interface and the calibrated internal temperature;

[0074] Figure 5 A fitted curve of internal temperature versus health offset index;

[0075] Figure 6 A statistical graph showing the mapping of input voltage and current to the health offset index;

[0076] Figure 7 A curve fitting the remaining range versus the predicted demand coefficient;

[0077] Figure 8A statistical chart mapping target demand coefficient, predicted demand coefficient, and environmental coefficient;

[0078] Figure 9 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0080] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0081] Example:

[0082] Please see Figures 1-9 The present invention provides a technical solution:

[0083] A method for electric vehicle charging scheduling based on dynamic data fusion, comprising the following steps:

[0084] Step 1: Within the intelligent scheduling area, acquire the status monitoring parameters corresponding to all standby charging interfaces. Based on the status monitoring parameters, characterize the status of the standby charging interfaces to form a characterization system that reflects the health status of the charging piles, and select available charging piles that meet the requirements accordingly.

[0085] The status monitoring parameters include the internal temperature of the charging interface in standby mode, the input current, and the input voltage.

[0086] The internal temperature of the charging interface is a crucial indicator of the charging pile's health status. High temperatures can accelerate interface wear and even pose safety hazards. Therefore, real-time monitoring of the internal temperature is essential for the stable operation of the charging pile. Temperature sensors, such as thermistors, thermocouples, or integrated temperature sensing chips, are embedded inside the charging interface to detect temperature changes. The real-time data collected by the temperature sensors is transmitted to the back-end management system or cloud server via the charging pile's internal control system. This process can be achieved through communication modules such as CAN bus, RS485, or wireless communication protocols.

[0087] Monitoring the input current of the charging interface is crucial for ensuring the power output and operational stability of the charging pile. Abnormal current conditions may indicate equipment failure or overload risk. Charging interfaces are typically equipped with Hall current sensors to measure the current at the charging pile's input. Hall sensors indirectly calculate the current value by detecting changes in the magnetic field strength flowing through a conductor. Hall current sensors feature non-contact measurement, do not affect the circuit itself, and offer excellent response speed and high accuracy.

[0088] Input voltage is one of the key parameters for the operation of a charging station, directly affecting the safety of the equipment and the charging efficiency of electric vehicles. Monitoring the voltage ensures that it remains within the rated range, preventing equipment damage or charging failure due to overvoltage or undervoltage. Charging stations have built-in voltage sensors, such as voltage divider circuit voltage sensors or integrated voltage detection chips, to monitor the voltage input to the charging interface in real time.

[0089] The specific detection interval is set so that a self-check is performed after each time interval, avoiding the problem that users can only find out whether the charging pile is faulty or occupied after arriving at the charging location due to the delay in updating the charging pile status.

[0090] Based on the status monitoring parameters, the status of the standby charging interface is characterized. Specifically, a health offset index is generated based on the difference between the standby charging interface status monitoring parameters and the calibrated status monitoring parameters. The health offset index is used to initially characterize the health status of the charging pile. The formula used to calculate the health offset index is as follows:

[0091] ;

[0092] In the formula, Indicates the health deviation index, The internal temperature of the charging port. Input voltage to the charging port. Input current to the charging port. , and These represent the internal temperature, input voltage, and input current, respectively.

[0093] It should be noted that the health deviation index The health deviation index is used to characterize the deviation between the current working state and the calibrated working state of a charging pile. The larger the value, the greater the deviation between the current working state of the charging pile and the ideal working state, the worse the performance of the charging pile, and the possible malfunction.

[0094] The temperature of the charging port is a key parameter affecting its health. If the temperature is too high, it may indicate problems such as poor heat dissipation, overload, or component aging. Therefore, it needs to be taken into account in the evaluation. This item reflects the degree of deviation between the real-time temperature and the calibrated temperature. The greater the temperature difference, the more significantly the health deviation index will increase.

[0095] Voltage and current are core parameters for the operation of a charging interface. Abnormal voltage or current may indicate problems such as overload, poor contact, or electrical faults in the interface. This item reflects the deviation of voltage and current from their calibrated values. By multiplying the two, it assesses whether the electrical condition of the charging interface is abnormal. Multiplying the voltage and current deviations is to reflect their coupling effect. In actual operation, the coordination of voltage and current determines the power output and the operating state of the interface. If both deviate simultaneously, the health condition may deteriorate rapidly.

[0096] When the real-time temperature does not exceed the calibrated temperature, the temperature of the charging interface remains within a safe range and will not significantly affect the health of the device. Therefore, temperature deviation can be disregarded in the health deviation index. However, even if the temperature is within the normal range, deviations in voltage and current may still adversely affect the health of the charging interface. For example, a severely low voltage may result in insufficient charging power, while excessive current may cause the interface to overheat or the insulation material to age.

[0097] This includes calibrating the internal temperature, calibrating the input voltage, and calibrating the input current. , and The specific method for obtaining this information is as follows: During the design, manufacturing, and factory testing phases of the charging pile, the manufacturer will conduct performance tests on the interface portion of the charging pile and record the calibration data of the charging interface under normal working conditions.

[0098] The logic for using the health deviation index to initially characterize the health status of charging piles is as follows:

[0099] like This indicates that the corresponding charging pile is in poor health and cannot perform the charging task normally.

[0100] like This indicates that the health status of the corresponding charging pile has been initially determined to be normal, and a second safety assessment will be conducted. The initial health assessment threshold is set based on expert experience and the actual environment; Table 1 shows some health deviation statistics.

[0101] Table 1: Health Deviation Data Statistics Table

[0102]

[0103] Analysis of the data in Table 1 shows that increased temperature may lead to decreased equipment operating efficiency and even increase the risk of failure. Record number 7, in particular, shows an internal temperature of 70°C, far exceeding the rated value of 40°C, indicating a serious heat dissipation problem.

[0104] The temperature deviation is small; for example, the health deviation index is low in data 4 and 8, indicating that there is a significant relationship between temperature and health status.

[0105] For devices numbered 3, 6, and 7, the input voltages are 210V, 205V, and 200V respectively, which differ significantly from the rated value (220V). This may indicate an abnormality in the power supply system, causing the equipment to malfunction. For devices numbered 8 and 10, although the input current is slightly higher, their health offset index is moderate, indicating that the equipment can adapt to slightly higher currents to a certain extent.

[0106] The specific method for secondary safety assessment is as follows: It involves assessing the insulation resistance and contact resistance losses of charging piles initially deemed to be in normal health status. The specific assessment logic is as follows:

[0107] like and This indicates that the charging pile is initially determined to be normal and meets safety requirements, and the status is updated to "available charging pile".

[0108] Otherwise, if the charging station is initially determined to be normal but does not meet safety requirements, its status will be updated to fault.

[0109] in and These are insulation resistance and contact resistance, respectively. and These are the minimum insulation resistance and the maximum contact resistance, respectively.

[0110] Insulation resistance is a crucial parameter for measuring the insulation performance of a charging pile's internal components and its relationship with the external environment. It is typically tested using an insulation resistance tester on the high-voltage and ground or low-voltage sections of the charging pile. During operation, reliable insulation is required between the internal electrical equipment and cables. A decrease in insulation performance can lead to short circuits, leakage, or equipment malfunction. A high insulation resistance value indicates good insulation performance, effectively preventing abnormal current loss.

[0111] Contact resistance refers to the resistance value between connection points in a circuit, such as charging interfaces, connectors, and wire terminals. In charging stations, low contact resistance must be ensured between the plug and socket, and between the connecting wires and terminals, to guarantee smooth current flow. Excessive contact resistance will reduce power transmission efficiency.

[0112] Therefore, the higher the insulation resistance within the set range, the higher the safety, and the lower the contact resistance within the set range, the higher the transmission efficiency. Thus, potential fault signals can be judged by insulation resistance and contact resistance.

[0113] By monitoring parameters such as temperature, voltage, and current, a preliminary assessment of whether a charging station is in normal working order can be made. This allows for a quick initial detection of any obvious anomalies in the equipment. The collection and calculation of these parameters can be completed in real time through monitoring modules within the equipment, such as sensors and electrical measurement modules, without the need for additional complex testing procedures. The acquisition of this data requires no extra steps and is inexpensive, making it particularly suitable as a preliminary assessment method. It not only saves time but also quickly identifies problem-free individuals from a large pool of devices.

[0114] Each time the charging pile is inspected for insulation resistance and contact resistance, further consideration is given to charging safety. Therefore, for charging piles that are initially determined to be normal, insulation resistance and contact resistance tests are used to further assess whether there are potential faults in the equipment. If the insulation resistance is below the threshold or the contact resistance is outside the range, even if the initial health assessment is normal, there may be safety hazards, and further maintenance or repair is required.

[0115] Step 2: Obtain real-time status data and environmental parameters of electric vehicles requesting charging, determine the target demand coefficient of electric vehicles requesting charging, and classify electric vehicles requesting charging into charging priority vehicles and cost priority vehicles based on the target demand coefficient.

[0116] The real-time status data includes vehicle battery level and estimated remaining range. The environmental parameters include ambient temperature and humidity. The specific method for determining the target demand coefficient of the electric vehicle requesting charging is as follows: a predicted demand coefficient is generated based on the vehicle battery level and estimated remaining range data, and the predicted demand coefficient is actually corrected using environmental parameters to obtain the target demand coefficient. The specific formula used to calculate the target demand coefficient is as follows:

[0117] ;

[0118] In the formula, For the target demand coefficient, This is the environmental correction factor. To predict demand coefficients;

[0119] It should be noted that the target demand coefficient The target demand coefficient is used to characterize the urgency of a user's charging needs. The smaller the value, the less urgent the user's need for charging.

[0120] Among them, the predicted demand coefficient The formula used for the calculation is:

[0121] ;

[0122] In the formula, For vehicle battery level, To estimate the remaining range, and These are the weighting coefficients for battery level and remaining battery life, respectively. and and All are greater than 0. This is the battery life reduction factor;

[0123] Among them, the predicted demand coefficient This is used to predict the urgency of a user's charging needs based on the vehicle's current status. Among them... The higher the value, the greater the urgency of the user's charging needs.

[0124] In the formula, This section is used to quantify electricity. The impact. The lower the value, the less charge the vehicle's battery has remaining, which also means a higher charging demand. This is determined through calculations. The percentage of remaining rechargeable capacity is obtained, thus reflecting the urgency of the charging demand. When the battery level approaches 100%, the charging demand coefficient is close to 0, indicating that the battery is fully charged and no charging is needed. When the battery level is close to 0%, the charging demand coefficient is close to its maximum, indicating that there is almost no battery left and a strong need to charge. and Proportional.

[0125] In the formula, Part of it is used to quantify the impact of the estimated remaining range. It directly reflects the vehicle's remaining driving range and can more accurately represent the urgency of the user's charging needs. The lower the remaining range, the higher the charging demand. An exponential decay function is used. This is used to reflect the impact of remaining range on charging demand because the relationship between charging demand and range is non-linear. When the range is short, the charging demand will increase significantly, while when the range is long enough, the increase in charging demand will slow down rapidly.

[0126] Battery life reduction factor This determines the rate of exponential decay, which can be adjusted according to the actual application scenario. For example, in urban commuting scenarios, users have less anxiety about battery life. The battery life can be set relatively low, which increases the risk of insufficient battery life during long-distance travel. It can be set to a relatively large value, generally between 0.004 and 0.02.

[0127] In real-world usage scenarios, remaining battery life directly affects whether a trip can continue. Battery charge level only provides a prediction of the completed trip; the remaining battery life has a more significant impact on charging needs. Therefore, setting... and and All are greater than 0.

[0128] Among them, environmental correction factor The specific formula used for the calculation is as follows:

[0129] ;

[0130] In the formula, Humidity risk factor, This is the temperature influence coefficient. and These are ambient temperature and the optimal battery operating temperature, respectively. This refers to the relative humidity of the environment.

[0131] Among them, environmental correction factor In the formula, the temperature correction part is set based on the lithium battery Arrhenius equation and the temperature efficiency model. When the ambient temperature... Deviating from the battery's optimal operating temperature When the battery is in use, its performance will decrease, limiting its actual range. Users may need to charge it in advance, further increasing the urgency of their charging needs.

[0132] High humidity environments can adversely affect the safety of batteries and charging interfaces, such as accelerating corrosion or increasing the risk of short circuits, further reducing battery range. High humidity environments may also correspond to severe weather conditions such as heavy rain or snow, increasing the energy consumption of electric vehicles and thus further burdening their range. Therefore, an environmental correction factor is needed. and Proportional to environmental parameters, the priority of vehicle charging needs is further determined, making the allocation of charging station resources more rational. Table 2 shows some statistical data on target demand coefficients.

[0133] Table 2: Statistical Table of Target Demand Coefficient Analysis

[0134]

[0135] The humidity risk coefficient and temperature influence coefficient can be set according to expert experience. The temperature influence coefficient is adjusted according to different temperature influences, specifically, the value corresponding to the influence at low temperatures is greater than the value corresponding to the influence at high temperatures.

[0136] The specific logic for classifying electric vehicles with charging requests is as follows:

[0137] when When charging requests are made, electric vehicles will be marked as charging priority vehicles;

[0138] when At that time, electric vehicles requesting charging will be marked as cost-priority vehicles;

[0139] In the formula, The threshold for priority classification is set based on expert experience.

[0140] Step 3: For vehicles with charging priority, adjust the search area of ​​available charging piles by adjusting the vehicle target demand coefficient, determine the available charging piles in the search area, use the output power of available charging piles to determine the target charging pile, lock the target charging pile and update the status of available charging piles.

[0141] The specific method for adjusting the search area for charging-priority vehicles is as follows: The initial search radius is scaled using the vehicle's target demand coefficient. Based on the scaled search radius, the search area is determined with the charging-priority vehicle as the center. The specific formula used to calculate the scaled search radius is as follows:

[0142] ;

[0143] In the formula, This is the scaled search radius. As the initial search radius, Minimum search radius;

[0144] The initial search radius is a system-preset default value, representing the search range under normal circumstances. The minimum search radius ensures that the search range is not lower than a certain minimum value, avoiding the inability to find charging stations due to excessive scaling. The more urgent the vehicle's situation, the more likely it is to be detected. The larger the value, the smaller the search range. The system prioritizes scheduling charging stations closer to the vehicle to meet the needs of vehicles with priority charging, using the natural logarithm function. As a scaling factor, it can smoothly adjust the search radius and avoid [problems caused by] [other factors]. The changes caused the search scope to fluctuate drastically.

[0145] Simultaneously, minimum search radius This system is designed to locate charging stations within a certain range, even when the vehicle is in an extremely urgent situation.

[0146] The specific logic for determining the target charging station is as follows: Based on the location information of available charging stations within the search area, the distance data between each available charging station and the vehicle with charging priority is calculated. The output power of the available charging stations is then incorporated to calculate a selection priority coefficient. The available charging station with the highest selection priority coefficient is selected as the target charging station. The specific formula for calculating the selection priority coefficient is as follows:

[0147] ;

[0148] In the formula, Let be the priority coefficient for selecting the i-th available charging station within the search area. Let be the power adaptation coefficient of the i-th available charging pile within the search area. This represents the distance between the i-th available charging pile and the vehicle with charging priority within the search area, where i is the index of the available charging pile within the search area;

[0149] Power compatibility coefficient of the i-th available charging station within the search area The specific expression is:

[0150] ;

[0151] In the formula, Let i be the output power of the i-th available charging station within the search area. Available charging power for vehicles that prioritize charging.

[0152] The power adaptation coefficient of the i-th available charging station is used to determine the available charging stations for vehicles with charging priority.

[0153] Step 4: For cost-priority vehicles, obtain the current location of each available charging station and the cost-priority vehicle, predict the vehicle's arrival time, set a time window based on the vehicle's arrival time, and collect the average electricity price data of each charging station within the time window. Based on the average electricity price data and the current location information, calculate the cost optimization coefficient between the cost-priority vehicle and each available charging station.

[0154] Based on the average electricity price data and current location information, the specific formula used to calculate the cost optimization coefficient between cost-priority vehicles and each available charging station is as follows:

[0155] ;

[0156] In the formula, Let be the cost optimization coefficient between the j-th available charging pile and the cost-priority vehicle. Let be the power adaptation coefficient for the j-th available charging pile. Let be the distance between the j-th available charging station and the cost-priority vehicle. Cost per unit distance The average electricity price data for the j-th available charging pile within a set time window is given. The specific method for setting the time window is as follows: the predicted arrival time of the vehicle is taken as the start time of the time window, and the expected charging time is taken as the length of the time window.

[0157] It should be noted that the cost optimization coefficient between the j-th available charging pile and the cost-priority vehicle is... Used to characterize the cost of travel in conjunction with the actual electricity price. The larger the value, the greater the cost of the entire charging process.

[0158] The distance to a charging station affects energy consumption and time costs; therefore, charging stations further away require higher travel costs in cost assessments. Furthermore, the electricity price at the charging station directly determines the vehicle's charging cost, making it a core parameter in cost optimization. This value is typically dynamic, depending on real-time electricity prices or the average of historical electricity prices. and and Proportional.

[0159] The average electricity price of the j-th available charging pile within a set time window can be predicted using historical data combined with a deep learning network.

[0160] The specific method for setting the time window is as follows: the predicted arrival time of the vehicle is used as the starting time of the time window, and the estimated charging time is used as the length of the time window. The specific electricity price for vehicle charging should be the electricity price for the period from the start of charging after the vehicle arrives until the completion of charging; therefore, a time window is set to represent accurate electricity price data.

[0161] Step 5: Based on the cost optimization coefficient, the available charging piles are arranged in ascending order to form a preferred charging pile sequence. Users select and lock the preferred charging piles in the preferred charging pile sequence, update the status of available charging piles, and use the expected vehicle arrival time as the initial locking time. At the same time, the locking time is dynamically adjusted according to road congestion.

[0162] The preferred charging pile sequence arranges the available charging piles corresponding to the cost preference coefficient greater than 0 in ascending order. The first available charging pile in the preferred charging pile sequence is the charging pile with the lowest cost. As the sequence number increases, the charging cost increases sequentially.

[0163] The initial lock-in time is calculated by the ratio of the distance between the available charging station and the cost-priority vehicle to the vehicle's average driving speed.

[0164] The locking time is dynamically adjusted based on road congestion conditions. The specific formula for adjusting the locking time is as follows:

[0165] ;

[0166] In the formula, This is the adjusted lock time. Let be the vehicle's speed at time t. The time variable for cost-priority vehicles traveling to a designated charging station. The shortest path distance to the locked charging station, where The specific formula used for the calculation is as follows:

[0167] ;

[0168] In the formula, The free-flow velocity of the shortest path, Let be the traffic density of the shortest path at time t. For road congestion density, This is the velocity-density nonlinear factor;

[0169] It should be noted that the speed-density relationship model is an important research topic in traffic engineering. The basic structure of the formula comes from the Lighthill-Whitham-Richards model, in which the nonlinear characteristic of speed decrease caused by density increase is one of the core assumptions.

[0170] Wherein represents The speed of vehicles on road segment t at a specific time is affected by the traffic density of the road segment, i.e., the degree of congestion. With traffic density The speed of a vehicle changes with the density of its surroundings; when the density is low, the vehicle speed is close to the free-flow speed; while when the density approaches the congested density, the speed drops to near zero.

[0171] Traffic flow velocity refers to the maximum speed a vehicle can reach in the absence of vehicle interference, i.e., under extremely low traffic density conditions. It is usually determined by road design, such as speed limits and environmental conditions.

[0172] Traffic density represents the number of vehicles per unit distance. It is a core parameter of traffic flow and reflects the degree of congestion on a road segment. As density increases, interference between vehicles intensifies, leading to a gradual decrease in speed.

[0173] Traffic density refers to the maximum number of vehicles per unit distance when a road segment is completely congested; it is also known as "traffic saturation density".

[0174] When traffic density reaches At that moment, the vehicle could not move and its speed dropped to 0.

[0175] The maximum value between the initial lock time and the adjusted lock time is taken as the final lock time. When the final lock time ends, the charging pile lock ends and the user is reminded that the current lock has ended. The user can choose to lock again, and the lock time is a fixed time that does not exceed the final lock time. If the lock time ends again, locking is no longer supported.

[0176] Please see Figure 9 The present invention also provides an electric vehicle charging scheduling system based on dynamic data fusion, the system being used to execute the electric vehicle charging scheduling method based on dynamic data fusion, comprising:

[0177] The working status detection module is used to acquire the status monitoring parameters corresponding to all standby charging interfaces within the intelligent scheduling area. Based on the status monitoring parameters, the status of the standby charging interfaces is characterized to form a characterization system that reflects the health status of the charging pile, and the available charging piles that meet the requirements are selected accordingly.

[0178] The target demand segmentation module is used to acquire real-time status data and environmental parameters of electric vehicles requesting charging, determine the target demand coefficient of electric vehicles requesting charging, and classify electric vehicles requesting charging into charging priority vehicles and cost priority vehicles based on the target demand coefficient.

[0179] The charging priority scheduling module is used to adjust the search area of ​​available charging piles for charging priority vehicles by adjusting the vehicle target demand coefficient, determine the available charging piles in the search area, introduce the output power of available charging piles to determine the target charging pile, lock the target charging pile and update the status of available charging piles.

[0180] The cost-priority scheduling module is used to obtain the current location of each available charging pile and the cost-priority vehicle for cost-priority vehicles, predict the arrival time of the vehicle, set a time window based on the arrival time of the vehicle, and collect the average electricity price data of each charging pile within the time window. Based on the average electricity price data and the current location information, the module calculates the cost optimization coefficient between the cost-priority vehicle and each available charging pile.

[0181] The dynamic locking adjustment module is used to sort the available charging piles in ascending order according to the cost optimization coefficient to form a preferred charging pile sequence. Users can select and lock the charging piles in the preferred charging pile sequence, update the status of available charging piles, and use the expected vehicle arrival time as the initial locking time. At the same time, the locking time is dynamically adjusted according to the road congestion.

[0182] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described electric vehicle charging scheduling method based on dynamic data fusion.

[0183] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0184] 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 by 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.

[0185] 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.

[0186] 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 method for electric vehicle charging scheduling based on dynamic data fusion, characterized in that, The specific steps include: Within the intelligent scheduling area, the status monitoring parameters corresponding to all standby charging interfaces are acquired. Based on the status monitoring parameters, the status of the standby charging interfaces is characterized to form a characterization system that reflects the health status of the charging piles. Based on this system, available charging piles that meet the requirements are selected. The system acquires real-time status data and environmental parameters of electric vehicles requesting charging, determines the target demand coefficient of these vehicles, and classifies them into charging-priority vehicles and cost-priority vehicles based on the target demand coefficient. For vehicles with charging priority, the search area for available charging piles is adjusted by the vehicle target demand coefficient, the available charging piles in the search area are determined, the output power of the available charging piles is used to determine the target charging pile, the target charging pile is locked and the status of available charging piles is updated. For cost-priority vehicles, the current location of each available charging station and the cost-priority vehicle is obtained, the arrival time of the vehicle is predicted, a time window is set based on the arrival time of the vehicle, and the average electricity price data of each charging station within the time window is collected. Based on the average electricity price data and the current location information, the cost optimization coefficient of the cost-priority vehicle and each available charging station is calculated. Based on the cost optimization coefficient, the available charging piles are arranged in ascending order to form a preferred charging pile sequence. Users can select and lock the preferred charging piles in the preferred charging pile sequence, update the status of available charging piles, and use the expected vehicle arrival time as the initial locking time. At the same time, the locking time is dynamically adjusted according to road congestion. The status monitoring parameters include the internal temperature of the charging interface in standby mode, the input current, and the input voltage. Specifically, the detection interval is set, and the status of the standby charging interface is characterized based on the status monitoring parameters. A health offset index is generated based on the difference between the standby charging interface status monitoring parameters and the calibrated status monitoring parameters. This health offset index provides a preliminary characterization of the charging pile's health status. The formula used to calculate the health offset index is as follows: In the formula, Indicates the health deviation index, The internal temperature of the charging port. Input voltage to the charging port. Input current to the charging port. , and These represent the internal temperature, input voltage, and input current, respectively. The logic for using the health deviation index to initially characterize the health status of charging piles is as follows: like This indicates that the corresponding charging pile is in poor health and cannot perform the charging task normally. like This indicates that the health status of the corresponding charging pile has been initially determined to be normal, and a second safety assessment will be conducted. Thresholds for preliminary health assessment; The specific method for secondary safety assessment is as follows: It involves assessing the insulation resistance and contact resistance losses of charging piles initially deemed to be in normal health status. The specific assessment logic is as follows: like and This indicates that the charging pile is initially determined to be normal and meets safety requirements, and the status is updated to "available charging pile". Otherwise, if the charging station is initially determined to be normal but does not meet safety requirements, its status will be updated to fault. in and These are insulation resistance and contact resistance, respectively. and These are the minimum insulation resistance and the maximum contact resistance, respectively.

2. The electric vehicle charging scheduling method based on dynamic data fusion according to claim 1, characterized in that: The real-time status data includes vehicle battery level and estimated remaining range. The environmental parameters include ambient temperature and humidity. The specific method for determining the target demand coefficient of the electric vehicle requesting charging is as follows: a predicted demand coefficient is generated based on the vehicle battery level and estimated remaining range data, and the predicted demand coefficient is actually corrected using environmental parameters to obtain the target demand coefficient. The specific formula used to calculate the target demand coefficient is as follows: In the formula, For the target demand coefficient, This is the environmental correction factor. To predict demand coefficients; Among them, the predicted demand coefficient The formula used for the calculation is: In the formula, For vehicle battery level, To estimate the remaining range, and These are the weighting coefficients for battery level and remaining battery life, respectively. and and All are greater than 0. This is the battery life reduction factor; Among them, environmental correction factor The specific formula used for the calculation is as follows: In the formula, Humidity risk factor, This is the temperature influence coefficient. and These are ambient temperature and the optimal battery operating temperature, respectively. This refers to the relative humidity of the environment.

3. The electric vehicle charging scheduling method based on dynamic data fusion according to claim 2, characterized in that: The specific logic for classifying electric vehicles with charging requests is as follows: when When charging requests are made, electric vehicles will be marked as charging priority vehicles; when At that time, electric vehicles requesting charging will be marked as cost-priority vehicles; In the formula, The threshold is used for priority division.

4. The electric vehicle charging scheduling method based on dynamic data fusion according to claim 2, characterized in that: The specific method for adjusting the search area for charging-priority vehicles is as follows: The initial search radius is scaled using the vehicle's target demand coefficient. Based on the scaled search radius, the search area is determined with the charging-priority vehicle as the center. The specific formula used to calculate the scaled search radius is as follows: In the formula, This is the scaled search radius. As the initial search radius, Minimum search radius; The specific logic for determining the target charging station is as follows: Based on the location information of available charging stations within the search area, the distance data between each available charging station and the vehicle with charging priority is calculated. The output power of the available charging stations is then incorporated to calculate a selection priority coefficient. The available charging station with the highest selection priority coefficient is selected as the target charging station. The specific formula for calculating the selection priority coefficient is as follows: In the formula, Let be the priority coefficient for selecting the i-th available charging station within the search area. Let be the power adaptation coefficient of the i-th available charging pile within the search area. This represents the distance between the i-th available charging pile and the vehicle with charging priority within the search area, where i is the index of the available charging pile within the search area; Power compatibility coefficient of the i-th available charging station within the search area The specific expression is: In the formula, Let i be the output power of the i-th available charging station within the search area. Available charging power for vehicles that prioritize charging.

5. The electric vehicle charging scheduling method based on dynamic data fusion according to claim 4, characterized in that: Based on the average electricity price data and current location information, the specific formula used to calculate the cost optimization coefficient between cost-priority vehicles and each available charging station is as follows: In the formula, Let be the cost optimization coefficient between the j-th available charging pile and the cost-priority vehicle. Let be the power adaptation coefficient for the j-th available charging pile. Let be the distance between the j-th available charging station and the cost-priority vehicle. Cost per unit distance The average electricity price data for the j-th available charging pile within a set time window is given. The specific method for setting the time window is as follows: the predicted arrival time of the vehicle is taken as the start time of the time window, and the expected charging time is taken as the length of the time window.

6. The electric vehicle charging scheduling method based on dynamic data fusion according to claim 5, characterized in that: The preferred charging pile sequence arranges the available charging piles corresponding to the cost preference coefficient greater than 0 in ascending order. The first available charging pile in the preferred charging pile sequence is the charging pile with the lowest cost. As the sequence number increases, the charging cost increases sequentially. The initial lock-in time is calculated by the ratio of the distance between the available charging station and the cost-priority vehicle to the vehicle's average driving speed. The locking time is dynamically adjusted based on road congestion conditions. The specific formula for adjusting the locking time is as follows: In the formula, This is the adjusted lock time. Let be the vehicle's speed at time t. The time variable for cost-priority vehicles traveling to a designated charging station. The shortest path distance to the locked charging station, where The specific formula used for the calculation is as follows: In the formula, The free-flow velocity of the shortest path. Let be the traffic density of the shortest path at time t. For road congestion density, This is the velocity-density nonlinear factor; The maximum value between the initial lock time and the adjusted lock time is taken as the final lock time. The locking of the charging pile ends when the final lock time ends.

7. An electric vehicle charging scheduling system based on dynamic data fusion, characterized in that: The electric vehicle charging scheduling system based on dynamic data fusion is used to execute the electric vehicle charging scheduling method based on dynamic data fusion as described in any one of claims 1-6, including: The working status detection module is used to acquire the status monitoring parameters corresponding to all standby charging interfaces within the intelligent scheduling area. Based on the status monitoring parameters, the status of the standby charging interfaces is characterized to form a characterization system that reflects the health status of the charging pile, and the available charging piles that meet the requirements are selected accordingly. The target demand segmentation module is used to acquire real-time status data and environmental parameters of electric vehicles requesting charging, determine the target demand coefficient of electric vehicles requesting charging, and classify electric vehicles requesting charging into charging priority vehicles and cost priority vehicles based on the target demand coefficient. The charging priority scheduling module is used to adjust the search area of ​​available charging piles for charging priority vehicles by adjusting the vehicle target demand coefficient, determine the available charging piles in the search area, introduce the output power of available charging piles to determine the target charging pile, lock the target charging pile and update the status of available charging piles. The cost-priority scheduling module is used to obtain the current location of each available charging pile and the cost-priority vehicle for cost-priority vehicles, predict the arrival time of the vehicle, set a time window based on the arrival time of the vehicle, and collect the average electricity price data of each charging pile within the time window. Based on the average electricity price data and the current location information, the module calculates the cost optimization coefficient between the cost-priority vehicle and each available charging pile. The dynamic locking adjustment module is used to sort the available charging piles in ascending order according to the cost optimization coefficient to form a preferred charging pile sequence. Users can select and lock the charging piles in the preferred charging pile sequence, update the status of available charging piles, and use the expected vehicle arrival time as the initial locking time. At the same time, the locking time is dynamically adjusted according to the road congestion.

8. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle charging scheduling method based on dynamic data fusion as described in any one of claims 1 to 6.

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