A charging scheduling method for an electric heavy truck

Abstract

The application provides a charging scheduling method of an electric heavy truck, and relates to the field of vehicle scheduling. The method comprises the following steps: acquiring real-time vehicle operation data of a current electric heavy truck; the real-time vehicle operation data comprises charging behavior statistical data of a preset historical time period before the current time; determining a target charging station according to the charging behavior statistical data and a preset charging station database; the preset charging station database stores charging facility parameters of a plurality of charging stations; creating a reservation charging order of the current electric heavy truck for the target charging station according to the charging behavior statistical data; and locking a parking space of a target charging pile corresponding to the reservation charging order when it is detected that the current electric heavy truck enters a preset distance range around the target charging station. Through accurate acquisition of real-time vehicle operation data of the current electric heavy truck, the target charging station is matched in combination with the preset charging station database, a targeted reservation charging order is created, and the parking space is locked when the current electric heavy truck approaches.

Description

Technical Field

[0001] This invention relates to the field of vehicle dispatching technology, and more specifically, to a charging dispatching method for electric heavy trucks. Background Technology

[0002] With the increasing prevalence of electric heavy-duty trucks in the logistics and transportation sector, the demand for efficient energy replenishment is becoming increasingly prominent. Due to their large battery capacity and high drive power, electric heavy-duty trucks typically require DC fast charging facilities of 250kW or higher to achieve efficient charging. For example, a 400kWh battery model can achieve a charging power of 390kW at 600A current, while a 600kWh model can achieve 455kW and 585kW at 700A and 900A currents, respectively. However, most existing charging infrastructure is designed for passenger vehicles, resulting in multiple compatibility conflicts: First, in terms of power, passenger vehicle charging piles generally have a power of only around 120kW, which cannot meet the high power requirements of heavy trucks, leading to excessively long charging times and severely restricting operational efficiency; second, in terms of physical space, passenger vehicle charging stations have narrow sites, short passages, and height restrictions for rain shelters, and the charging cables are usually only 2-2.5 meters long, which cannot be connected to the charging interfaces of heavy trucks. At the same time, the turning radius and parking space size are insufficient, making it impossible for heavy trucks to use the charging piles; finally, in terms of information, navigation maps do not distinguish between charging pile power levels, vehicle compatibility, and key site parameters, and the data updates are lagging, often marking unused or faulty charging piles as available, thus misleading drivers.

[0003] Currently, charging for electric heavy-duty trucks relies on driver experience or ad-hoc searches, lacking a mechanism for automatically identifying and dynamically maintaining dedicated charging facilities. On the one hand, truly suitable charging stations for heavy-duty trucks—high-power, spacious, and open to the public—are scarce and scattered. On the other hand, existing technologies cannot perform intelligent matching and route planning based on real-time vehicle operation data. This often results in drivers making wasted trips due to misjudging site size, power mismatch, or station closures, increasing operating costs and impacting transportation timeliness. Therefore, there is an urgent need for a solution that can automatically identify dedicated charging facilities for heavy-duty trucks, dynamically assess site suitability, and provide intelligent navigation to resolve the core conflicts of existing technologies: facility mismatch, inaccurate information, and unintelligent navigation. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a charging scheduling method for electric heavy-duty trucks. This method accurately obtains the real-time vehicle operation data of the current electric heavy-duty truck, matches the target charging station with a preset charging station database, creates a targeted pre-ordered charging order, and locks the parking space when the current electric heavy-duty truck approaches. This effectively solves the problems of unclear facility compatibility, easy occupation of parking spaces, and blind charging planning when charging electric heavy-duty trucks.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a charging scheduling method for electric heavy-duty trucks, the method comprising:

[0007] Obtain real-time vehicle operation data of the current electric heavy-duty truck; the real-time vehicle operation data includes: charging behavior statistics for a preset historical time period before the current time, the charging behavior statistics are the charging behavior statistics determined by the on-board intelligent terminal on the current electric heavy-duty truck according to the exclusive feature template of the current electric heavy-duty truck model and the original vehicle operation data of the current electric heavy-duty truck, the exclusive feature template includes the power change curve, battery state rise rate and voltage-current matching law of the current electric heavy-duty truck model under standard conditions;

[0008] Based on the charging behavior statistics and the preset charging station database, the target charging station is determined; the preset charging station database stores the charging facility parameters of multiple charging stations.

[0009] Based on the charging behavior statistics, create a pre-order charging order for the current electric heavy truck at the target charging station;

[0010] When the current electric heavy truck is detected to have entered a preset distance range around the target charging station, the parking space of the target charging pile corresponding to the reserved charging order is locked.

[0011] In an optional implementation, determining the target charging station based on the charging behavior statistics and a preset charging station database includes:

[0012] Cluster analysis is performed on the charging behavior statistics to obtain multiple data clusters, and the cluster confidence of each data cluster is calculated; each data cluster corresponds to a charging station.

[0013] Candidate heavy-duty truck dedicated charging stations are determined based on multiple data clusters and the clustering confidence of each data cluster;

[0014] Determine the target charging facility parameters of the candidate heavy-duty truck dedicated charging stations from the preset charging station database;

[0015] Based on the target charging facility parameters and the clustering confidence of the corresponding data clusters of the candidate heavy-duty truck dedicated charging stations, the candidate heavy-duty truck dedicated charging stations are evaluated to obtain the evaluation parameters of the candidate heavy-duty truck dedicated charging stations.

[0016] The target charging station is determined based on the evaluation parameters of each candidate heavy-duty truck dedicated charging station.

[0017] In an optional implementation, calculating the clustering confidence of each of the data clusters includes:

[0018] The charging behavior data within each data cluster is analyzed to obtain the number of effective charging events, the stability of charging power, and the continuity of battery state rise within each data cluster. The stability of charging power is obtained by calculating the inverse of the variance of the power data, and the continuity of battery state rise is obtained by analyzing the integrity of the time series data.

[0019] The clustering confidence of each data cluster is calculated based on the number of valid charging events, the stability of the charging power, and the continuity of the battery state rise.

[0020] In an optional implementation, the step of evaluating the candidate heavy-duty truck dedicated charging stations based on the target charging facility parameters and the clustering confidence of the corresponding data clusters of the candidate heavy-duty truck dedicated charging stations to obtain the evaluation parameters of the candidate heavy-duty truck dedicated charging stations includes:

[0021] Based on the target charging facility parameters, the candidate heavy-duty truck dedicated charging stations are evaluated in multiple dimensions to obtain evaluation parameters in multiple dimensions.

[0022] The evaluation parameters for the candidate heavy-duty truck dedicated charging stations are obtained based on the evaluation parameters of the multiple dimensions and the clustering confidence of the corresponding data clusters.

[0023] In an optional implementation, the multiple dimensions include: site dimension, power grid dimension, cost dimension, facility utilization rate dimension, navigation path dimension, reservation dimension, and station scheduling dimension; the target charging facility parameters include: site physical parameters, station historical charging data, and station real-time status data.

[0024] The process involves evaluating the candidate heavy-duty truck-specific charging stations from multiple dimensions based on the target charging facility parameters, resulting in evaluation parameters across multiple dimensions, including:

[0025] Based on the site physical parameters, the candidate heavy-duty truck dedicated charging stations are evaluated to obtain the evaluation parameters for the site dimension.

[0026] Based on the historical charging data of the charging stations, the candidate heavy-duty truck dedicated charging stations are evaluated to obtain evaluation parameters in the power grid dimension, the cost dimension, and the station scheduling dimension.

[0027] Based on the real-time status data of the charging stations, the candidate heavy-duty truck-specific charging stations are evaluated to obtain evaluation parameters for the facility utilization rate dimension, the navigation route dimension, and the reservation dimension.

[0028] In an optional implementation, the real-time vehicle operation data further includes: real-time vehicle location and real-time environmental data;

[0029] The step of creating a pre-booked charging order for the current electric heavy-duty truck at the target charging station based on the charging behavior statistics includes:

[0030] The charging physical characteristic parameters of the current electric heavy truck are obtained from a preset electric heavy truck database, wherein the preset electric heavy truck database stores multiple charging physical characteristic parameters of electric heavy trucks in advance.

[0031] Based on the charging physical characteristic parameters of the current electric heavy truck and the charging facility parameters of the target charging station, determine whether the preset matching conditions are met.

[0032] If the preset matching conditions are met, the reservation time window for the target charging station is determined based on the real-time vehicle location and the real-time environmental data.

[0033] Based on the real-time status data of each charging pile in the target charging station, the target available charging pile within the reservation time window is determined.

[0034] The scheduled charging order is created based on the scheduled time window and the target available charging station.

[0035] In an optional implementation, determining the reservation time window for the target charging station based on the real-time vehicle location and the real-time environmental data includes:

[0036] Based on the real-time vehicle location and the location of the target charging station, multiple road segments are determined between the current electric heavy truck and the target charging station;

[0037] Based on the real-time environmental data, the road condition impedance coefficients of multiple road segments are determined;

[0038] The estimated arrival time of the current electric heavy truck is calculated based on the current time, the road condition impedance coefficient of multiple road segments, the heavy truck dynamics correction coefficient, the road segment length, the preset road segment speed limit, the historical prediction deviation rate, and the time taken to enter the target charging station.

[0039] Based on the estimated arrival time, a reservation time window is determined for the target charging station.

[0040] In an optional implementation, the real-time vehicle operation data further includes: current state of charge data, remaining driving range, and type of transported goods; the method further includes:

[0041] When the reservation time windows of multiple electric heavy trucks overlap, the current state of charge data, the remaining driving range, and the type of transported goods of each electric heavy truck are weighted and calculated to determine the priority of each electric heavy truck.

[0042] The step of creating the reserved charging order based on the reserved time window and the target available charging station includes:

[0043] Based on the current priority of the electric heavy truck, the corresponding reservation time window, and the target available charging pile, a reservation charging order is created.

[0044] In an optional implementation, the method further includes:

[0045] Based on the charging physical characteristic parameters of the current electric heavy truck and the channel status data of the target charging station, a guidance path is generated for the current electric heavy truck to enter the target charging station. The guidance path is used to guide the current electric heavy truck to the parking space of the target charging pile.

[0046] In an optional implementation, the charging behavior statistics are charging behavior statistics obtained by the on-board intelligent terminal on the current electric heavy truck in the following manner:

[0047] The original vehicle operation data is compared with the unique feature template of the current electric heavy truck model.

[0048] If the deviation between the original vehicle operation data and the feature template is within a preset range, then the charging behavior statistics are extracted.

[0049] Secondly, embodiments of this application also provide a charging scheduling device for electric heavy-duty trucks, the device comprising:

[0050] The acquisition module is used to acquire the real-time vehicle operation data of the current electric heavy truck; the real-time vehicle operation data includes: charging behavior statistics for a preset historical time period before the current time;

[0051] The determination module is used to determine the target charging station based on the charging behavior statistics and the preset charging station database; the preset charging station database stores the charging facility parameters of multiple charging stations.

[0052] A creation module is used to create a pre-order charging order for the current electric heavy truck at the target charging station based on the charging behavior statistics.

[0053] The locking module is used to lock the parking space of the target charging pile corresponding to the reserved charging order when it detects that the current electric heavy truck has entered a preset distance range around the target charging station.

[0054] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores program instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the charging scheduling method for electric heavy trucks as described in any of the first aspects.

[0055] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the charging scheduling method for electric heavy-duty trucks as described in any of the first aspects.

[0056] The beneficial effects of this application are:

[0057] This application provides a charging scheduling method for electric heavy-duty trucks. The method includes: acquiring real-time vehicle operation data of the current electric heavy-duty truck; the real-time vehicle operation data includes charging behavior statistics for a preset historical time period prior to the current time; determining a target charging station based on the charging behavior statistics and a preset charging station database; the preset charging station database stores charging facility parameters for multiple charging stations; creating a pre-reserved charging order for the current electric heavy-duty truck at the target charging station based on the charging behavior statistics; and locking the parking space of the target charging pile corresponding to the pre-reserved charging order when the current electric heavy-duty truck is detected to have entered a preset distance range around the target charging station.

[0058] The method described in this application accurately obtains real-time vehicle operation data of the current electric heavy-duty truck, matches the target charging station with a preset charging station database, creates a targeted reservation charging order, and locks the parking space when the current electric heavy-duty truck approaches. This effectively solves the problems of unclear facility compatibility, easy occupation of parking spaces, and blind charging planning when the current electric heavy-duty truck is charging. It significantly improves the certainty of charging reservation and the smoothness of the charging process, reduces the risk of drivers making wasted trips and waiting in line, and ensures transportation operation efficiency. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 One of the flowcharts for a charging scheduling method for an electric heavy truck provided in an embodiment of this application;

[0061] Figure 2 A second schematic flowchart illustrating a charging scheduling method for an electric heavy-duty truck provided in an embodiment of this application;

[0062] Figure 3 The third flowchart illustrates a charging scheduling method for an electric heavy-duty truck provided in this application embodiment;

[0063] Figure 4 The fourth flowchart illustrates a charging scheduling method for an electric heavy-duty truck provided in this application embodiment;

[0064] Figure 5 The fifth flowchart illustrates a charging scheduling method for an electric heavy-duty truck provided in this application embodiment;

[0065] Figure 6 A flowchart illustrating a charging scheduling method for an electric heavy-duty truck provided in this application embodiment is shown in Figure 6.

[0066] Figure 7 The seventh flowchart illustrates a charging scheduling method for an electric heavy-duty truck provided in this application embodiment;

[0067] Figure 8 This is the eighth flowchart illustrating a charging scheduling method for an electric heavy-duty truck provided in an embodiment of this application.

[0068] Figure 9 A functional module diagram of a charging scheduling device for an electric heavy truck provided in an embodiment of this application;

[0069] Figure 10 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments.

[0071] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0072] In the description of this application, it should be noted that if the terms "upper", "lower", etc. appear to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this application is usually placed in, it is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0073] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0074] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0075] To address the technical pain points faced by existing electric heavy-duty trucks (especially long-haul logistics trucks) during charging, such as facility mismatch, information lag, unreasonable navigation routes, and difficulties in accessing charging stations, this application provides a charging scheduling method for electric heavy-duty trucks. By creating a closed-loop system encompassing data collection, behavior recognition, charging assessment, and the generation of pre-booked charging orders, this method achieves intelligent and efficient charging scheduling for electric heavy-duty trucks. The method relies on a distributed system architecture comprising an onboard intelligent terminal (T-BOX), a cloud management platform, and a user mobile terminal. Specifically, the cloud management platform obtains real-time vehicle operation data from the onboard intelligent terminal. Based on charging behavior statistics from the real-time vehicle operation data and a pre-set charging station database, the target charging station is determined. Then, based on the charging behavior statistics, a pre-booked charging order for the current electric heavy-duty truck is created for the target charging station. When the current electric heavy-duty truck is detected to have entered a pre-set distance range around the target charging station, the parking space corresponding to the pre-booked charging order is locked, reserving the parking space for the target charging station for the current electric heavy-duty truck to facilitate subsequent charging.

[0076] The charging scheduling method for electric heavy-duty trucks provided in this application will be explained in detail below with reference to the accompanying drawings and specific examples. Figure 1This is one of the flowcharts illustrating a charging scheduling method for electric heavy-duty trucks provided in an embodiment of this application; as shown below. Figure 1 As shown, the method includes:

[0077] S101. Obtain the real-time vehicle operation data of the current electric heavy truck.

[0078] The real-time vehicle operation data includes: charging behavior statistics for a preset historical time period prior to the current time. The charging behavior statistics are determined by the on-board intelligent terminal on the current electric heavy-duty truck based on the exclusive feature template of the current electric heavy-duty truck model and the original vehicle operation data of the current electric heavy-duty truck. The exclusive feature template includes the power change curve, battery state rise rate and voltage-current matching law of the current electric heavy-duty truck model under standard conditions.

[0079] In this embodiment, real-time vehicle operation data of the current electric heavy truck is acquired. The vehicle operation data is collected via the vehicle CAN bus at a frequency of 10Hz to 100Hz. It includes real-time vehicle speed, battery state of charge (SOC), total battery pack voltage, total current, maximum / minimum temperature of individual cells, BMS (Battery Management System) status code, etc., and also includes charging behavior statistics for the preset historical time period before the current time.

[0080] Optionally, the charging behavior statistics are the charging behavior statistics obtained by the on-board intelligent terminal on the current electric heavy-duty truck using the following method:

[0081] The original vehicle operation data is compared with the unique feature template of the current electric heavy truck model.

[0082] If the deviation between the original vehicle operation data and the features of the exclusive feature template is within a preset range, then charging behavior statistics will be extracted.

[0083] Specifically, the exclusive feature template of the current electric heavy-duty truck model is determined from the preset heavy-duty truck charging exclusive feature library. The preset heavy-duty truck charging exclusive feature library stores the ideal models of electric heavy-duty trucks of different brands and tonnages under standard charging conditions, i.e., exclusive feature templates.

[0084] The exclusive feature template includes the power change curve of the current electric heavy truck model under standard conditions (e.g., the constant current charging stage in the SOC range of 20%-80%, and the power decay slope of the constant voltage charging stage after 80%), the battery state rise rate (i.e., the theoretical range of dSOC / dt), and the voltage and current matching law (i.e., the maximum requested current allowed by the BMS under a specific voltage U).

[0085] Multiple deviation functions are used to determine whether the feature deviation between the original vehicle operating data and the dedicated feature template is within a preset range. For example, the first deviation function corresponds to the power change curve. Represented as:

[0086]

[0087] in, This is represented as the raw power in the original vehicle operating data. This is represented as the standard power in the proprietary feature template. The deviation between the original power and the standard power... Within the preset range, for example If the deviation is less than 0.05, or less than 5%, the current original power is considered to conform to normal characteristics.

[0088] Similarly, the second deviation function corresponding to the battery state rise rate is used to determine whether the deviation between the battery state rise rate in the original vehicle operation data and the standard battery state rise rate in the dedicated feature template is within a preset range; the third deviation function corresponding to the voltage and current matching law is used to determine whether the deviation between the voltage and current matching law in the original vehicle operation data and the standard voltage and current matching law in the dedicated feature template is within a preset range; if both are within the preset range, key feature parameters, namely charging behavior statistics, are extracted from the original vehicle operation data as dimensionality reduction data. The charging behavior statistics include: charging start time, current average power, average SOC change rate, etc.

[0089] It should be noted that the purpose of extracting charging behavior statistics is to compress the amount of raw vehicle operation data. Since electric heavy trucks often travel on remote sections of highways or in mountainous areas, network signals are often unstable, resulting in limited transmission bandwidth. In order to enable the cloud dispatch center to always keep track of the core dynamics of each electric heavy truck, a bandwidth adaptation transmission strategy is implemented based on data priority. The bandwidth adaptation transmission strategy means dividing the data into different priorities. The highest priority is the core feature data (such as current SOC, remaining range, and vehicle fault alarms) and status data (such as vehicle location coordinates) that have the greatest impact on real-time dispatch decisions; the medium priority is the charging behavior statistics; and the lowest priority is the raw vehicle operation data.

[0090] The system monitors network throughput in real time. When network throughput falls below a preset threshold, it automatically discards or caches the lowest priority raw vehicle operation data, prioritizing the transmission of the highest and medium priority data to ensure that the cloud dispatch center always has the current real-time vehicle operation data of electric heavy trucks.

[0091] S102. Based on charging behavior statistics and a pre-set charging station database, determine the target charging station.

[0092] The preset charging station database stores charging facility parameters for multiple charging stations.

[0093] Specifically, the cloud management platform has pre-created a database of preset charging stations, a database of preset electric heavy trucks, and a database of preset reservation orders, and has established a real-time linkage and update mechanism among the three.

[0094] The creation of the pre-set charging station database employs a hierarchical architecture to store the charging facility parameters for each station. The basic layer stores the geographical latitude and longitude coordinates and operator information for each station; the physical layer stores the entrance width, internal passage curvature radius, canopy height limit, and parking space dimensions (length × width) for each station; and the electrical layer stores the rated power, maximum output voltage / current, and charging cable length (a key parameter, as heavy trucks typically require longer cables) for each station's charging piles. Furthermore, the database dynamically maintains a status table for each charging station, recording the current number of operational charging piles, the number of faulty charging piles, and queue lengths in real time.

[0095] The creation of the pre-set electric heavy-duty truck database involves more than just storing the static IDs of each truck; it also focuses on recording detailed parameters related to the physical characteristics of charging. Specific fields include, but are not limited to: the physical dimensions (length, width, and height) of each truck, the minimum turning radius (for route planning), the rated battery voltage (e.g., 600V or 800V platform), the maximum charging current capacity (e.g., 400A, 600A), the charging interface type (dual-gun / single-gun), and the operational scheduling priority level of the fleet to which each truck belongs. This database is automatically initialized using the factory configuration table of each truck or the initial registration information from the vehicle's T-BOX.

[0096] Regarding the creation of the preset reservation order database, this database is used to store charging reservation requests from across the entire network. Each order record includes: a unique order ID, the initiating vehicle ID, the target charging station ID, the reservation time period (start time to end time), the order status (pending payment, locked, in progress, completed, canceled, defaulted), and the corresponding benefit deduction record.

[0097] The real-time linkage update mechanism automatically updates the status of related databases when a database undergoes a status change, via a trigger mechanism. For example, when a new locked order is added to the preset reservation order database, the corresponding number of available parking spaces in the preset charging station database immediately decreases by one, and this change is synchronized to the cloud within milliseconds for subsequent route planning algorithms to use. This mechanism ensures data consistency and timeliness in high-concurrency, complex scheduling scenarios involving heavy-duty trucks. However, changes to the preset electric heavy-duty truck database, such as changes in truck size or charging interface, can affect the preset charging station database's determination of the truck's suitability.

[0098] After obtaining the charging behavior statistics, the system combines them with a pre-set charging station database to determine the target charging station for charging the current electric heavy truck.

[0099] S103. Based on charging behavior statistics, create a pre-order charging order for the current electric heavy truck at the target charging station.

[0100] Specifically, based on charging behavior statistics, the parameters of the charging piles at the target charging station are verified to meet the matching conditions. Then, a reservation charging order for the current electric heavy truck at the target charging station is created. The reservation charging order includes: order ID, current electric heavy truck ID, target charging station ID, reservation time period, parking space at the target charging pile, and order status (locked).

[0101] S104. When the current electric heavy truck is detected to have entered a preset distance range around the target charging station, the parking space of the target charging pile corresponding to the reserved charging order is locked.

[0102] Specifically, the status data of the target charging station is obtained through the IoT module embedded in the charging pile or the monitoring camera on the station side, including parking space occupancy sensor signals, ground lock status, etc.

[0103] If the preset distance range is set to 500 meters, when the real-time location of the current electric heavy truck is detected to enter the 500-meter range around the target charging station, the availability of the parking space of the target charging pile is determined by verifying the station status data. If the parking space of the target charging pile is determined to be available, a locking command is automatically sent to the parking space of the target charging pile at the target charging station. The ground lock rises and sends a signal of successful locking, and performs soft locking, meaning that other electric heavy trucks cannot reserve the target charging pile.

[0104] If it is determined that the parking space at the target charging station is unavailable, the system will automatically assign the best alternative parking space within the same charging station and lock it as well.

[0105] It should be noted that if the parking space of the target charging station is determined to be locked based on the station status data, it can be determined that it is occupied by other electric heavy trucks, thus determining that the parking space of the target charging station is unavailable. Alternatively, if the target charging station reports an over-temperature fault code from its module, it can also be determined that the parking space of the target charging station is unavailable.

[0106] In another instance, if the current electric heavy truck fails to physically park after exceeding the dynamic entry time limit while in a soft-locked parking space state, or if it is detected that the estimated arrival time of the current electric heavy truck is significantly extended due to congestion inside the target charging station, the original solution is deemed invalid, triggering the adaptive reallocation process for parking spaces.

[0107] In the reallocation process, the system combines the real-time trajectory of the electric heavy truck within the target charging station with the map of the target charging station to detect path obstacles, search for other suitable parking spaces within the target charging station, recalculate the optimal allocation scheme, and relock the parking spaces in the optimal allocation scheme.

[0108] In summary, this application provides a charging scheduling method for electric heavy-duty trucks. The method includes: acquiring real-time vehicle operation data of the current electric heavy-duty truck; the real-time vehicle operation data includes: charging behavior statistics for a preset historical time period prior to the current time, wherein the charging behavior statistics are determined by the onboard intelligent terminal of the current electric heavy-duty truck based on a unique feature template of the current electric heavy-duty truck's model and the original vehicle operation data of the current electric heavy-duty truck; the unique feature template includes the power change curve, battery state rise rate, and voltage-current matching law of the current electric heavy-duty truck's model under standard conditions; determining a target charging station based on the charging behavior statistics and a preset charging station database; the preset charging station database stores: charging facility parameters of multiple charging stations; creating a pre-reserved charging order for the current electric heavy-duty truck at the target charging station based on the charging behavior statistics; and locking the parking space of the target charging pile corresponding to the pre-reserved charging order when the current electric heavy-duty truck is detected to have entered a preset distance range around the target charging station.

[0109] The method described in this application accurately obtains real-time vehicle operation data of the current electric heavy-duty truck, matches the target charging station with a preset charging station database, creates a targeted reservation charging order, and locks the parking space when the current electric heavy-duty truck approaches. This effectively solves the problems of unclear facility compatibility, easy occupation of parking spaces, and blind charging planning when the current electric heavy-duty truck is charging. It significantly improves the certainty of charging reservation and the smoothness of the charging process, reduces the risk of drivers making wasted trips and waiting in line, and ensures transportation operation efficiency.

[0110] This application also provides another possible implementation of the charging scheduling method for electric heavy trucks. Figure 2 This is a second flowchart illustrating a charging scheduling method for electric heavy-duty trucks provided in an embodiment of this application, as shown below. Figure 2 As shown, based on charging behavior statistics and a pre-set charging station database, target charging stations are determined, including:

[0111] S201. Perform cluster analysis on the charging behavior statistics to obtain multiple data clusters, and calculate the cluster confidence of each data cluster.

[0112] Each data cluster corresponds to one charging station.

[0113] In this embodiment, before performing cluster analysis on the charging behavior statistics, a multi-level filtering mechanism is used to verify and clean the extracted charging behavior statistics, in order to remove invalid data and identify valid potential charging behaviors. The multi-level filtering mechanism includes sequentially executed vehicle-side primary filtering, edge-side secondary filtering, and cloud-based differential verification.

[0114] Specifically, the vehicle-side primary filter operates on the embedded processor of the in-vehicle intelligent terminal. This filter layer is used to initially eliminate data that is not in a charging state based on the vehicle's speed and battery status change trends. The logic is as follows: if the detected vehicle speed V>0, it is directly determined to be in a non-charging state; if the vehicle speed V=0, but dSOC / dt<0 (i.e., the battery level is not increasing or is decreasing), it is determined to be in a stationary power consumption state and is eliminated. Only when V=0 and dSOC / dt>the positive threshold is it marked as suspected charging data and uploaded.

[0115] Spatial consistency means that the GPS coordinates reported by the vehicle must fall within the geofence of a known charging station. Physical compatibility verification means that if the vehicle is an 18-meter-long heavy truck, but its coordinates are located in a narrow charging station that can only accommodate passenger cars, the edge will determine that the data is location drift or mismatch and discard it.

[0116] Cloud-based differential verification runs on a central server cluster and serves as the last line of defense for data purification. This layer is used to perform cluster analysis on uploaded charging behavior statistics, identify hot charging areas (i.e., multiple data clusters), and calculate the cluster confidence of each data cluster.

[0117] S202. Based on multiple data clusters and the clustering confidence of each data cluster, determine the candidate heavy-duty truck dedicated charging stations.

[0118] Specifically, for example, a clustering confidence threshold of 0.8 is set to filter out data clusters with a clustering confidence score ≥ 0.8, and the charging stations corresponding to these data clusters are identified as candidate dedicated charging stations for heavy-duty trucks. For data clusters with lower confidence scores, such as those with a clustering confidence score < 0.8, which may be due to GPS multipath interference or temporary charging facilities, a density-based spatial clustering algorithm (DBSCAN) is used for secondary identification. The clustering radius and minimum number of points are adjusted to attempt to separate noise from the real signal for secondary clustering. Then, based on the multiple data clusters obtained after secondary clustering and the clustering confidence scores of each data cluster, candidate dedicated charging stations for heavy-duty trucks are determined.

[0119] S203. Determine the target charging facility parameters of candidate heavy-duty truck dedicated charging stations from the preset charging station database.

[0120] S204. Based on the target charging facility parameters and the clustering confidence of the corresponding data clusters of the candidate heavy-duty truck dedicated charging stations, evaluate the candidate heavy-duty truck dedicated charging stations to obtain the evaluation parameters of the candidate heavy-duty truck dedicated charging stations.

[0121] S205. Based on the evaluation parameters of each candidate heavy-duty truck dedicated charging station, determine the target charging station.

[0122] Specifically, the target charging facility parameters of candidate heavy-duty truck dedicated charging stations are obtained, and the candidate heavy-duty truck dedicated charging stations are evaluated by combining the clustering confidence of the corresponding data clusters of the candidate heavy-duty truck dedicated charging stations to obtain evaluation parameters of the candidate heavy-duty truck dedicated charging stations. Then, the candidate heavy-duty truck dedicated charging station with the highest evaluation parameters is selected as the target charging station from the evaluation parameters of each candidate heavy-duty truck dedicated charging station.

[0123] The method provided in this application accurately identifies candidate dedicated charging stations truly suitable for electric heavy-duty trucks by clustering analysis and confidence calculation of charging behavior statistics. Then, it comprehensively evaluates the candidate dedicated charging stations by combining the evaluation parameters of the candidate heavy-duty truck charging stations. This achieves the goal of efficiently screening high-quality candidate stations from massive amounts of data, avoiding problems such as distorted station information and poor adaptability caused by traditional reliance on general maps or manual reporting. It ensures that the target charging stations meet the needs of electric heavy-duty trucks in terms of physical space, power matching, and operational stability, thereby improving the reliability and accuracy of charging station recommendations.

[0124] This application also provides another possible implementation of the charging scheduling method for electric heavy trucks. Figure 3 This is the third flowchart illustrating a charging scheduling method for electric heavy-duty trucks provided in an embodiment of this application. Figure 3 As shown, the clustering confidence of each data cluster is calculated, including:

[0125] S301. Analyze the charging behavior data within each data cluster to obtain the number of effective charging events, the stability of charging power, and the continuity of battery state rise within each data cluster.

[0126] S302. Calculate the clustering confidence of each data cluster based on the number of valid charging events, the stability of charging power, and the continuity of battery state rise.

[0127] In this embodiment, the charging behavior data within each data cluster is analyzed to obtain the number of valid charging events, the stability of charging power, and the continuity of battery state rise within each data cluster. The number of valid charging events refers to the total number of valid charging events identified within a specific cluster (location). The stability of charging power is obtained by calculating the reciprocal of the variance of the power data. The smaller the variance, the smaller the power fluctuation (the more stable), and the larger the reciprocal. The continuity of battery state rise is obtained by analyzing the integrity of the time series data.

[0128] The formula for calculating cluster confidence is as follows:

[0129]

[0130] in, These are the weighting coefficients. This is the normalization factor. This is expressed as the stability of charging power; This represents the continuity of the battery state progression. This represents the number of valid charging events. This allows us to obtain the clustering confidence score for each data cluster.

[0131] The method provided in this application embodiment, by deeply analyzing the number of effective charging events, charging power stability and battery state continuity within the data cluster, scientifically quantifies the credibility of the clustering results, effectively distinguishes stable dedicated charging stations from temporary and faulty charging facilities, eliminates interference from low-confidence data clusters, provides rigorous data support for subsequent candidate site selection, avoids incorrect site recommendations due to data noise, and further improves the accuracy of charging resource identification.

[0132] This application also provides another possible implementation of the charging scheduling method for electric heavy trucks. Figure 4 This is the fourth flowchart illustrating a charging scheduling method for electric heavy-duty trucks provided in this application embodiment. Figure 4 As shown, based on the target charging facility parameters and the clustering confidence of the corresponding data clusters of candidate heavy-duty truck dedicated charging stations, the candidate heavy-duty truck dedicated charging stations are evaluated to obtain the evaluation parameters of the candidate heavy-duty truck dedicated charging stations, including:

[0133] S401. Based on the target charging facility parameters, conduct a multi-dimensional evaluation of the candidate heavy-duty truck dedicated charging stations to obtain evaluation parameters in multiple dimensions.

[0134] In this embodiment, a multi-objective dynamic evaluation model is used to evaluate candidate heavy-duty truck dedicated charging stations from multiple dimensions based on the target charging facility parameters, thereby obtaining evaluation parameters from multiple dimensions.

[0135] Optionally, multiple dimensions include: site dimension, power grid dimension, cost dimension, facility utilization rate dimension, navigation route dimension, reservation dimension, and station scheduling dimension; target charging facility parameters include: site physical parameters, station historical charging data, and station real-time status data.

[0136] Figure 5 This is the fifth flowchart illustrating a charging scheduling method for electric heavy-duty trucks provided in this application embodiment, as shown below. Figure 5 As shown, step S401 specifically includes:

[0137] S501. Based on the site's physical parameters, evaluate the candidate heavy-duty truck-specific charging stations to obtain site-specific evaluation parameters.

[0138] Among them, the site dimension is used to measure whether the parking space size and turning radius meet the requirements of heavy trucks. Therefore, the evaluation parameters of the site dimension of each candidate heavy truck dedicated charging station are determined based on the site physical parameters.

[0139] S502. Based on the historical charging data of the charging stations, evaluate the candidate heavy-duty truck dedicated charging stations to obtain evaluation parameters in the power grid dimension, cost dimension, and station scheduling dimension.

[0140] Among them, the power grid dimension is used to measure the voltage stability of the charging station and whether it has the ability to charge in an orderly manner; the cost dimension is used to measure the comprehensive cost of electricity price, service fee and parking fee; the charging station scheduling dimension is used to measure the average charging turnover rate of the charging station. The historical charging data of the charging station is analyzed to determine the evaluation parameters of the power grid dimension, the cost dimension and the charging station scheduling dimension for each candidate heavy truck dedicated charging station.

[0141] S503. Based on the real-time status data of the charging stations, evaluate the candidate heavy-duty truck-specific charging stations to obtain evaluation parameters for facility utilization, navigation path, and reservation dimensions.

[0142] Among them, the facility utilization rate dimension is used to measure the current congestion level of the charging station, aiming to avoid congestion; the navigation route dimension is used to measure the height and weight restrictions and congestion of the roads leading to the charging station; and the reservation dimension is used to measure whether the charging station supports pre-reservation of parking spaces and the fairness of the default handling mechanism. Therefore, by analyzing the real-time status data of the charging stations, the evaluation parameters for the facility utilization rate dimension, navigation route dimension, and reservation dimension of each candidate heavy-duty truck dedicated charging station are determined.

[0143] S402. Based on the evaluation parameters of multiple dimensions and the clustering confidence of the corresponding data clusters of the candidate heavy-duty truck dedicated charging stations, the evaluation parameters of the candidate heavy-duty truck dedicated charging stations are obtained.

[0144] Specifically, the multi-objective dynamic evaluation model is defined as:

[0145]

[0146] in, The normalized scores representing the above dimensions, For the corresponding weighting coefficients, It is a correction factor function based on cluster confidence, typically a monotonically increasing function, used to reduce the scoring weight of low-confidence data sources and prevent misleading results. This yields the evaluation parameters for each candidate heavy-duty truck-dedicated charging station.

[0147] The method provided in this application embodiment evaluates multiple dimensions such as site, power grid, and cost based on different attributes of site physical parameters, historical charging data of the station, and real-time status data of the station. This achieves a precise correspondence between the evaluation basis and the evaluation target. Furthermore, by combining clustering confidence to correct the score, it comprehensively considers multiple core needs, breaks through the limitations of single-dimensional evaluation, ensures the comprehensiveness and objectivity of the evaluation results, and enables the recommended target charging stations to meet the basic charging needs of electric heavy trucks.

[0148] This application embodiment also provides another possible implementation of the charging scheduling method for electric heavy trucks, and the real-time vehicle operation data also includes: real-time vehicle location and real-time environmental data; Figure 6 This is the sixth flowchart illustrating a charging scheduling method for electric heavy-duty trucks provided in this application embodiment, as shown below. Figure 6 As shown, based on charging behavior statistics, a pre-booked charging order is created for the current electric heavy-duty truck at the target charging station, including:

[0149] S601. Obtain the charging physical characteristic parameters of the current electric heavy-duty truck from the preset electric heavy-duty truck database.

[0150] Among them, the preset electric heavy truck database stores multiple charging physical characteristic parameters of electric heavy trucks in advance;

[0151] S602. Based on the current charging physical characteristic parameters of the electric heavy truck and the charging facility parameters of the target charging station, determine whether the preset matching conditions are met.

[0152] S603. If the preset matching conditions are met, determine the reservation time window for the target charging station based on the real-time vehicle location and real-time environmental data.

[0153] In this embodiment, the preset matching conditions include: Condition 1: The physical dimensions of the vehicle are less than or equal to the physical dimensions of the target parking space. (Compare vehicle length and width) Length and width of parking space ,Require and Condition 2: The vehicle's turning radius is adapted to the width and curvature of the navigation path within the target charging station. Simulated vehicle trajectory ensures no collision risk. Condition 3: The vehicle's maximum supporting charging power matches the power level of the target charging station. This avoids large vehicles using small charging stations (resulting in slow charging) or small vehicles using large charging stations (wasting resources). Condition 4: The vehicle and its fleet's current reservation count does not exceed the preset limit to prevent malicious order placement and station hogging.

[0154] If the charging physical characteristics parameters of the current electric heavy-duty truck and the charging facility parameters of the target charging station are both determined to meet the above-mentioned preset matching conditions, then the reservation time window for the target charging station is determined based on the real-time vehicle location and real-time environmental data.

[0155] S604. Based on the real-time status data of each charging pile in the target charging station, determine the target available charging pile within the reservation time window.

[0156] S605. Create a scheduled charging order based on the scheduled time window and the target available charging station.

[0157] Specifically, the system obtains real-time status data of each charging pile in the target charging station, determines the working status of each charging pile within the reservation time window, identifies the idle charging piles as target idle charging piles, and then creates a reservation charging order based on the reservation time window.

[0158] It should be noted that if the electric heavy truck has not arrived by the preset timeout threshold after the reservation window has started, the reservation charging order will be automatically cancelled and the breach of contract will be recorded, and the parking space will be released to subsequent vehicles.

[0159] The method provided in this application embodiment matches and verifies the charging physical characteristic parameters with the station facility parameters, and determines the reservation window and available charging piles by combining real-time vehicle location and environmental data. It creates a precisely matched reservation charging order, strictly controls the compatibility threshold between the vehicle and the station, and avoids charging failure or inefficiency caused by parameter mismatch. At the same time, the flexible reservation window setting takes into account the uncertainty during the driving process, improves the flexibility and feasibility of the reservation service, and ensures the efficient use of charging resources.

[0160] This application also provides another possible implementation of the charging scheduling method for electric heavy trucks. Figure 7 This is the seventh flowchart illustrating a charging scheduling method for electric heavy-duty trucks provided in this application embodiment, as shown below. Figure 7 As shown, based on real-time vehicle location and real-time environmental data, the reservation time window for the target charging station is determined, including:

[0161] S701. Based on the real-time vehicle location and the location of the target charging station, determine multiple road segments between the current electric heavy truck and the target charging station.

[0162] S702. Determine the road condition impedance coefficients of multiple road segments based on real-time environmental data.

[0163] S703. Based on the current time, the road condition impedance coefficient of multiple road segments, the heavy truck dynamics correction coefficient, the road segment length, the preset road segment speed limit, the historical prediction deviation rate, and the time to enter the target charging station, calculate the estimated arrival time of the current electric heavy truck.

[0164] In this embodiment, the real-time environmental data comes from the meteorological service interface, including current temperature, rainfall, road surface slippage coefficient, etc.

[0165] The formula for calculating the estimated arrival time is as follows:

[0166]

[0167] in, The estimated arrival time refers to the specific system time at which the electric heavy truck arrives at the target charging station and completes its docking. This indicates the current time, referring to the cloud-based standard time at which the calculation was triggered; This represents a road segment index, which guides the navigation route planning process by dividing the entire journey from the current electric heavy truck to the target charging station into segments based on the real-time vehicle location and the location of the target charging station. Each road segment; This indicates the total number of road segments, specifically the total number of independent road segments included in the current planned route; Indicates the first The length of each road segment is expressed in kilometers (km). Indicates the first Speed ​​limits are displayed on the road segment map, in kilometers per hour (km / h). This represents the road condition impedance coefficient, which is calculated based on real-time environmental data (such as rainfall, road surface slippage coefficient) and congestion index; when the road surface is slippery or congested, this coefficient... ; This represents the dynamic correction coefficient for heavy-duty trucks, calculated based on the vehicle's real-time total current, voltage, and SOC. This coefficient is adjusted when the vehicle is under low battery or high load conditions. This is used to correct the problem that heavy trucks accelerate slower than standard map algorithms; : Represents the historical prediction deviation rate, which is a value derived from the historical ETA accuracy statistics of this vehicle on similar road sections; if historical predictions are generally slow, this value is negative, and vice versa, and is used for algorithm self-calibration; This indicates the time taken to enter the target charging station, calculated based on the station's entrance width, internal passage curvature radius, and current congestion or queuing conditions. This determines the estimated arrival time of the electric heavy-duty truck.

[0168] S704. Based on the estimated arrival time, determine the reservation time window for the target charging station.

[0169] Specifically, calculate the confidence score for the estimated time of arrival ( This rating takes into account the complexity of real-time traffic conditions. (e.g., whether there is congestion), historical prediction deviation rate (Accuracy of past predictions for this road section), current vehicle driving status (Whether the speed is constant) and queuing situation at the station The confidence score is expressed as follows:

[0170]

[0171] Secondly, the duration of the appointment time window is calculated based on the confidence score. The logic is inversely related: the lower the confidence score, the longer the calculated appointment time window, to tolerate more uncertainty.

[0172] The formula for calculating the appointment time window is expressed as follows:

[0173] ;

[0174] in, Set the base window duration (e.g., 15 minutes). This is the adjustment coefficient. Then, the reservation time window for the target charging station is determined.

[0175] The method provided in this application embodiment accurately calculates the estimated arrival time based on real-time vehicle location and environmental data, and dynamically adjusts the elastic window duration according to the confidence level. This ensures the accuracy of the reservation time while tolerating deviations caused by unforeseen factors such as road conditions and vehicle conditions. It effectively reduces the risk of order default or resource waste caused by fluctuations in arrival time, balances the locking and flexible allocation of charging resources, and improves the fault tolerance rate and user experience of the reservation service.

[0176] This application embodiment also provides another possible implementation of the charging scheduling method for electric heavy trucks. The real-time vehicle operation data also includes: current state of charge data, remaining driving range, and type of transported goods. Figure 8 This is the eighth flowchart illustrating a charging scheduling method for electric heavy-duty trucks provided in this application embodiment. Figure 8 As shown, the method also includes:

[0177] S801. When the reservation time windows of multiple electric heavy-duty trucks overlap, the current state of charge data, remaining driving range, and type of transported goods of each electric heavy-duty truck are weighted and calculated to determine the priority of each electric heavy-duty truck.

[0178] The above process involves creating a pre-booked charging order based on the pre-booked time window and the target available charging station, including:

[0179] S802. Based on the current priority of the electric heavy truck, the corresponding reservation time window, and the target available charging pile, create a reservation charging order.

[0180] In this embodiment, when the reservation windows of multiple electric heavy-duty trucks overlap, dynamic priority reordering is performed. The estimated arrival time, confidence score of the estimated arrival time, current state of charge data, remaining driving range, type of transported goods, and reservation time flexibility of the multiple electric heavy-duty trucks are weighted and calculated to determine the priority of each electric heavy-duty truck. Based on the current priority of the electric heavy-duty truck, the corresponding reservation time window, and the target available charging station, a reservation charging order is created.

[0181] If the confidence score of the estimated arrival time is lower than the preset threshold (indicating that the road conditions are extremely uncontrollable), the reservation time slot will no longer be forcibly locked. Instead, to avoid idle resources, the vehicles will be switched to the real-time queuing mode and resources will be allocated according to the actual arrival order.

[0182] In the method provided in this application embodiment, when multiple vehicle reservation windows overlap, the priority is calculated by combining the vehicle's current charge status, remaining range, and cargo timeliness. This prioritizes the charging rights of vehicles with urgent needs, avoiding resource contention or charging difficulties caused by disordered reservations. It achieves fair and efficient scheduling of charging resources, and is particularly suitable for the timeliness requirements of different goods in logistics transportation scenarios, ensuring the smoothness of the overall transportation chain.

[0183] This application also provides another possible implementation of the charging scheduling method for electric heavy-duty trucks, which further includes:

[0184] Based on the current charging physical characteristic parameters of the electric heavy truck and the channel status data of the target charging station, a guidance path is generated for the current electric heavy truck to enter the target charging station. The guidance path is used to guide the current electric heavy truck to the parking space of the target charging pile.

[0185] In this embodiment, electric heavy trucks are prone to getting stuck when entering the site. Therefore, the entry guidance also includes a channel guidance path and compatibility prompts to guide the current electric heavy truck to the parking space of the target charging station. Based on the correspondence between the turning radius of the current electric heavy truck and the width of the internal channel of the target charging station, dynamic driving operation prompts are provided during navigation. For example, a prompt may indicate that the right-turn lane ahead is narrow and ask the driver to use the left lane, informing the driver whether the current lane meets the passage conditions and the turning space required.

[0186] Furthermore, this function has been enhanced into a dynamic real-world perception and intelligent driving assistance guidance mechanism. By integrating surrounding environmental data collected by vehicle-mounted sensors (such as lidar and ultrasonic radar) with channel status data uploaded by fixed monitoring equipment at the depot, a real-time digital twin model of the depot's interior is created, enabling real-time identification of dynamic obstacles within the channels (such as pedestrians, temporarily stacked goods, and haphazardly parked vehicles).

[0187] Using artificial intelligence models (such as CNN-based deep learning networks), based on obstacle type Obstacle distance Vehicle dimensions and remaining channel width Real-time calculation of the risk level of the navigation path Specifically, it is expressed as:

[0188]

[0189] When the risk level exceeds the preset threshold, a multimodal warning is issued to the driver via voice commands and an augmented reality display interface.

[0190] The augmented reality interface overlays and displays the location of obstacles and recommended avoidance trajectories in real time, and provides specific steering and passage suggestions based on real-time perception results (such as displaying green safety trajectory lines and red collision areas on the HUD), which greatly reduces the blind spot risks for heavy truck drivers.

[0191] It should be noted that actual operation data is continuously collected throughout the navigation and charging process, and this data is used to iteratively optimize the exclusive feature template, station operation rules, and evaluation algorithm.

[0192] End-to-end iterative optimization specifically includes three dimensions:

[0193] The first dimension (feature library optimization): compare the deviation between the actual charging time and the estimated arrival time. When the statistical mean of the deviation exceeds the threshold, it indicates that the original dedicated feature template is inaccurate (possibly due to battery aging), and the system automatically updates the basic parameters in the dedicated feature library (such as correcting the charging power curve).

[0194] The second dimension (operational rule optimization): Statistics on the reservation conflict rate and congestion frequency of parking lots. If a parking lot experiences frequent conflicts exceeding a threshold, optimization suggestions for the parking lot's operation rules are generated (e.g., suggesting extending the reservation interval or redrawing lanes to increase the number of parking spaces).

[0195] The third dimension (algorithm weight optimization): Based on user feedback (such as post-charging ratings and complaints) and operational data, the weights of each dimension in the multi-objective evaluation model are dynamically adjusted. For example, if users generally complain that a certain station is difficult to find, the system will automatically increase the weight of the reliability of the navigation path for that station, thereby reducing the overall rating of that station in the next recommendation.

[0196] The method provided in this application generates a customized guidance path based on the vehicle's physical characteristics and the station's access status data. Combined with a digital twin model and a multimodal early warning mechanism, it accurately avoids access obstacles and traffic risks, solving the pain points of heavy trucks being large, difficult to enter the site, and having many blind spots. It helps drivers quickly and safely drive into the target parking space, shortens the entry time, reduces safety risks such as scratches, and further improves the entire service experience from reservation to charging.

[0197] The following will continue to explain the charging scheduling device and electronic device for electric heavy trucks provided in any of the above embodiments of this application. The specific implementation process and the resulting technical effects are the same as those in the corresponding method embodiments. For the sake of brevity, the parts not mentioned in this embodiment can be referred to the corresponding content in the method embodiment.

[0198] Figure 9 This is a functional module diagram of a charging scheduling device for an electric heavy-duty truck provided in an embodiment of this application. Figure 9 As shown, the charging scheduling 100 of the electric heavy truck includes:

[0199] The acquisition module 110 is used to acquire the real-time vehicle operation data of the current electric heavy truck. The real-time vehicle operation data includes: charging behavior statistics for a preset historical time period before the current time. The charging behavior statistics are obtained by comparing the original vehicle operation data of the current electric heavy truck with the on-board intelligent terminal of the current electric heavy truck using the exclusive feature template of the model to which the current electric heavy truck belongs.

[0200] The determination module 120 is used to determine the target charging station based on charging behavior statistics and a preset charging station database; the preset charging station database stores charging facility parameters of multiple charging stations.

[0201] Create module 130 to create a pre-order charging order for the current electric heavy truck at the target charging station based on charging behavior statistics;

[0202] The locking module 140 is used to lock the parking space of the target charging pile corresponding to the reservation charging order when it detects that the current electric heavy truck has entered a preset distance range around the target charging station.

[0203] Optionally, the determining module 120 is further configured to perform cluster analysis on charging behavior statistics to obtain multiple data clusters and calculate the cluster confidence of each data cluster; each data cluster corresponds to a charging station; based on the multiple data clusters and the cluster confidence of each data cluster, candidate heavy-duty truck-specific charging stations are determined; target charging facility parameters of the candidate heavy-duty truck-specific charging stations are determined from a preset charging station database; the candidate heavy-duty truck-specific charging stations are evaluated based on the target charging facility parameters and the cluster confidence of the data clusters corresponding to the candidate heavy-duty truck-specific charging stations to obtain evaluation parameters of the candidate heavy-duty truck-specific charging stations; and target charging stations are determined based on the evaluation parameters of each candidate heavy-duty truck-specific charging station.

[0204] Optionally, the determining module 120 is further configured to analyze the charging behavior data within each data cluster to obtain the number of effective charging events, the stability of charging power, and the continuity of battery state rise within each data cluster; wherein, the stability of charging power is obtained by calculating the inverse variance of the power data, and the continuity of battery state rise is obtained by analyzing the integrity of the time series data; and the clustering confidence of each data cluster is calculated based on the number of effective charging events, the stability of charging power, and the continuity of battery state rise.

[0205] Optionally, the determining module 120 is further configured to perform multi-dimensional evaluation of candidate heavy-duty truck dedicated charging stations based on the target charging facility parameters, and obtain evaluation parameters of multiple dimensions; and obtain evaluation parameters of candidate heavy-duty truck dedicated charging stations based on the evaluation parameters of multiple dimensions and the clustering confidence of the data clusters corresponding to the candidate heavy-duty truck dedicated charging stations.

[0206] Optionally, multiple dimensions include: site dimension, power grid dimension, cost dimension, facility utilization rate dimension, navigation route dimension, reservation dimension, and station scheduling dimension; target charging facility parameters include: site physical parameters, station historical charging data, and station real-time status data;

[0207] Optionally, the determining module 120 is also used to evaluate candidate heavy-duty truck dedicated charging stations based on site physical parameters to obtain site-level evaluation parameters; to evaluate candidate heavy-duty truck dedicated charging stations based on historical charging data of the stations to obtain power grid-level evaluation parameters, cost-level evaluation parameters, and station scheduling-level evaluation parameters; and to evaluate candidate heavy-duty truck dedicated charging stations based on real-time station status data to obtain facility utilization-level evaluation parameters, navigation path-level evaluation parameters, and reservation-level evaluation parameters.

[0208] Optionally, the real-time vehicle operation data also includes: real-time vehicle location and real-time environmental data; the creation module 130 is also used to obtain the charging physical characteristic parameters of the current electric heavy-duty truck from a preset electric heavy-duty truck database, wherein the preset electric heavy-duty truck database stores multiple charging physical characteristic parameters of electric heavy-duty trucks in advance; based on the charging physical characteristic parameters of the current electric heavy-duty truck and the charging facility parameters of the target charging station, it is determined whether the preset matching conditions are met; if the preset matching conditions are met, a reservation time window for the target charging station is determined based on the real-time vehicle location and real-time environmental data; based on the real-time status data of each charging pile in the target charging station, the target available charging pile within the reservation time window is determined; and a reservation charging order is created based on the reservation time window and the target available charging pile.

[0209] Optionally, the determining module 120 is further configured to: determine multiple road segments between the current electric heavy truck and the target charging station based on the real-time vehicle location and the location of the target charging station; determine the road condition impedance coefficients of the multiple road segments based on real-time environmental data; calculate the estimated arrival time of the current electric heavy truck based on the current time, the road condition impedance coefficients of the multiple road segments, the heavy truck dynamics correction coefficient, the road segment length, the preset road segment speed limit, the historical prediction deviation rate, and the time taken to enter the target charging station; and determine the reservation time window for the target charging station based on the estimated arrival time.

[0210] Optionally, the real-time vehicle operation data also includes: current state of charge data, remaining driving range, and type of transported goods; the calculation module is used to perform weighted calculations on the current state of charge data, remaining driving range, and type of transported goods of each electric heavy truck when the reservation time windows of multiple electric heavy trucks overlap, in order to determine the priority of each electric heavy truck.

[0211] The creation module 130 is also used to create a pre-booked charging order based on the current priority of the electric heavy truck, the corresponding reservation time window, and the target available charging pile.

[0212] Optionally, the device further includes:

[0213] The generation module is used to generate a guidance path for the current electric heavy truck to enter the target charging station based on the charging physical characteristic parameters of the current electric heavy truck and the channel status data of the target charging station. The guidance path is used to guide the current electric heavy truck to the parking space of the target charging pile.

[0214] Optionally, the charging behavior statistics are obtained by the on-board intelligent terminal on the current electric heavy-duty truck in the following manner: the original vehicle operation data is compared with the exclusive feature template of the current electric heavy-duty truck model; the exclusive feature template includes the power change curve, battery state rise rate and voltage-current matching law of the current electric heavy-duty truck model under standard conditions; if the feature deviation between the original vehicle operation data and the exclusive feature template is within a preset range, the charging behavior statistics are extracted.

[0215] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0216] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0217] Figure 10 This is a schematic diagram of an electronic device provided in an embodiment of this application. This electronic device can be used for charging scheduling of electric heavy-duty trucks. Figure 10 As shown, the electronic device includes: a processor 210, a storage medium 220, and a bus 230.

[0218] Storage medium 220 stores machine-readable instructions executable by processor 210. When the electronic device is running, processor 210 communicates with storage medium 220 via bus 230, and processor 210 executes the machine-readable instructions to perform the steps of the above method embodiment. The specific implementation and technical effects are similar and will not be described again here.

[0219] Optionally, this application also provides a storage medium 220, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the above-described method embodiments. The specific implementation and technical effects are similar, and will not be repeated here.

[0220] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0221] 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; that is, 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 according to actual needs.

[0222] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0223] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0224] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A charging scheduling method for electric heavy-duty trucks, characterized in that, The method includes: Obtain real-time vehicle operation data of the current electric heavy-duty truck; the real-time vehicle operation data includes: charging behavior statistics for a preset historical time period before the current time, the charging behavior statistics are the charging behavior statistics determined by the on-board intelligent terminal on the current electric heavy-duty truck according to the exclusive feature template of the current electric heavy-duty truck model and the original vehicle operation data of the current electric heavy-duty truck, the exclusive feature template includes the power change curve, battery state rise rate and voltage-current matching law of the current electric heavy-duty truck model under standard conditions; Based on the charging behavior statistics and the preset charging station database, the target charging station is determined; the preset charging station database stores the charging facility parameters of multiple charging stations. Based on the charging behavior statistics, create a pre-order charging order for the current electric heavy truck at the target charging station; When the current electric heavy truck is detected to have entered a preset distance range around the target charging station, the parking space of the target charging pile corresponding to the reserved charging order is locked. The step of determining the target charging station based on the charging behavior statistics and the preset charging station database includes: Cluster analysis is performed on the charging behavior statistics to obtain multiple data clusters, and the cluster confidence of each data cluster is calculated; each data cluster corresponds to a charging station. Candidate heavy-duty truck dedicated charging stations are determined based on multiple data clusters and the clustering confidence of each data cluster; Determine the target charging facility parameters of the candidate heavy-duty truck dedicated charging stations from the preset charging station database; Based on the target charging facility parameters and the clustering confidence of the corresponding data clusters of the candidate heavy-duty truck dedicated charging stations, the candidate heavy-duty truck dedicated charging stations are evaluated to obtain the evaluation parameters of the candidate heavy-duty truck dedicated charging stations. The target charging station is determined based on the evaluation parameters of each of the candidate heavy-duty truck dedicated charging stations; The calculation of the clustering confidence of each of the data clusters includes: The charging behavior data within each data cluster is analyzed to obtain the number of effective charging events, the stability of charging power, and the continuity of battery state rise within each data cluster. The stability of charging power is obtained by calculating the inverse of the variance of the power data, and the continuity of battery state rise is obtained by analyzing the integrity of the time series data. The clustering confidence of each data cluster is calculated based on the number of valid charging events, the stability of the charging power, and the continuity of the battery state rise. The formula for calculating the cluster confidence score is as follows: in, These are the weighting coefficients. Normalization factor; This refers to the stability of the charging power; This is represented as the continuity of the battery state rise; This represents the number of valid charging events.

2. The method according to claim 1, characterized in that, The evaluation of the candidate heavy-duty truck dedicated charging stations, based on the target charging facility parameters and the clustering confidence of the corresponding data clusters, yields evaluation parameters for the candidate heavy-duty truck dedicated charging stations, including: Based on the target charging facility parameters, the candidate heavy-duty truck dedicated charging stations are evaluated in multiple dimensions to obtain evaluation parameters in multiple dimensions. The evaluation parameters for the candidate heavy-duty truck dedicated charging stations are obtained based on the evaluation parameters of the multiple dimensions and the clustering confidence of the corresponding data clusters.

3. The method according to claim 2, characterized in that, The multiple dimensions include: site dimension, power grid dimension, cost dimension, facility utilization rate dimension, navigation path dimension, reservation dimension, and station scheduling dimension; the target charging facility parameters include: site physical parameters, station historical charging data, and station real-time status data. The process involves evaluating the candidate heavy-duty truck-specific charging stations from multiple dimensions based on the target charging facility parameters, resulting in evaluation parameters across multiple dimensions, including: Based on the site physical parameters, the candidate heavy-duty truck dedicated charging stations are evaluated to obtain the evaluation parameters for the site dimension. Based on the historical charging data of the charging stations, the candidate heavy-duty truck dedicated charging stations are evaluated to obtain evaluation parameters in the power grid dimension, the cost dimension, and the station scheduling dimension. Based on the real-time status data of the charging stations, the candidate heavy-duty truck-specific charging stations are evaluated to obtain evaluation parameters for the facility utilization rate dimension, the navigation route dimension, and the reservation dimension.

4. The method according to claim 1, characterized in that, The real-time vehicle operation data also includes: real-time vehicle location and real-time environmental data; The step of creating a pre-booked charging order for the current electric heavy-duty truck at the target charging station based on the charging behavior statistics includes: The charging physical characteristic parameters of the current electric heavy truck are obtained from a preset electric heavy truck database, wherein the preset electric heavy truck database stores multiple charging physical characteristic parameters of electric heavy trucks in advance. Based on the charging physical characteristic parameters of the current electric heavy truck and the charging facility parameters of the target charging station, determine whether the preset matching conditions are met. If the preset matching conditions are met, the reservation time window for the target charging station is determined based on the real-time vehicle location and the real-time environmental data. Based on the real-time status data of each charging pile in the target charging station, the target available charging pile within the reservation time window is determined. The scheduled charging order is created based on the scheduled time window and the target available charging station.

5. The method according to claim 4, characterized in that, The step of determining the reservation time window for the target charging station based on the real-time vehicle location and the real-time environmental data includes: Based on the real-time vehicle location and the location of the target charging station, multiple road segments are determined between the current electric heavy truck and the target charging station; Based on the real-time environmental data, the road condition impedance coefficients of multiple road segments are determined; The estimated arrival time of the current electric heavy truck is calculated based on the current time, the road condition impedance coefficient of multiple road segments, the heavy truck dynamics correction coefficient, the road segment length, the preset road segment speed limit, the historical prediction deviation rate, and the time taken to enter the target charging station. Based on the estimated arrival time, a reservation time window is determined for the target charging station.

6. The method according to claim 5, characterized in that, The real-time vehicle operation data also includes: current state of charge data, remaining driving range, and type of transported goods; the method also includes: When the reservation time windows of multiple electric heavy trucks overlap, the current state of charge data, the remaining driving range, and the type of transported goods of each electric heavy truck are weighted and calculated to determine the priority of each electric heavy truck. The step of creating the reserved charging order based on the reserved time window and the target available charging station includes: Based on the current priority of the electric heavy truck, the corresponding reservation time window, and the target available charging pile, a reservation charging order is created.

7. The method according to claim 4, characterized in that, The method further includes: Based on the charging physical characteristic parameters of the current electric heavy truck and the channel status data of the target charging station, a guidance path is generated for the current electric heavy truck to enter the target charging station. The guidance path is used to guide the current electric heavy truck to the parking space of the target charging pile.

8. The method according to claim 1, characterized in that, The charging behavior statistics are obtained by the on-board intelligent terminal on the current electric heavy truck using the following method: The original vehicle operation data is compared with the unique feature template of the current electric heavy truck model. If the deviation between the original vehicle operation data and the feature template is within a preset range, then the charging behavior statistics are extracted.

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