Vehicle, control method thereof, storage medium, and program product

Abstract

The invention discloses a vehicle, a control method thereof, a storage medium and a program product. The method comprises the following steps: acquiring current position information and vehicle state information of a vehicle under the condition that an early warning signal aiming at the endurance mileage of the vehicle is received; on the basis of the current position information and the vehicle state information, at least one energy supply station which the vehicle can reach in the current state and energy supply station data corresponding to the at least one energy supply station are determined, and the energy supply station data are used for representing a set of multiple measurable parameters which quantify the current energy supply capacity of each energy supply station in the at least one energy supply station; on the basis of the current position information and the energy supply station data, a target energy complementing path of the vehicle is generated, and the target energy complementing path is used for representing a traveling track for guiding the vehicle to travel from the current position to a target energy supply station; and controlling the vehicle to run based on the target energy complementing path. According to the invention, the technical problem of low vehicle control accuracy in related technologies is solved.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more specifically, to a vehicle and its control method, storage medium, and program product. Background Technology

[0002] In related technologies, vehicle refueling route planning is mostly based on static map data and fixed rules to make a single route recommendation, and lacks a dynamic response mechanism after the route recommendation. Therefore, it is difficult to guarantee the accuracy of the recommended refueling route, which in turn leads to low vehicle control accuracy in related technologies.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a vehicle and its control method, storage medium, and program product to at least solve the technical problem of low vehicle control accuracy in related technologies.

[0005] According to one aspect of the embodiments of this application, a vehicle control method is provided, comprising: upon receiving a warning signal regarding the vehicle's remaining driving range, acquiring the vehicle's current location information and vehicle status information; based on the current location information and vehicle status information, determining at least one power supply station that the vehicle can reach in the current state, and power supply station data corresponding to the at least one power supply station, wherein the power supply station data is used to represent a set of multiple measurable parameters quantifying the current power supply capacity of each of the at least one power supply station; generating a target refueling path for the vehicle based on the current location information and the power supply station data, wherein the target refueling path is used to represent a driving trajectory guiding the vehicle from the current location to the target power supply station; and controlling the vehicle's driving based on the target refueling path.

[0006] Furthermore, based on the current location information and vehicle status information, at least one power station that the vehicle can reach in the current state, and the power station data corresponding to at least one power station, are determined, including: determining the vehicle's range coverage area based on the remaining driving range in the current location information and current status information, wherein the range coverage area is used to represent the boundary of the connected area formed by all road nodes that the vehicle can actually reach under the current operating conditions using the remaining electric power, starting from the current location information; determining at least one power station on the vehicle's planned driving route based on the vehicle's planned driving route and range coverage area in the current status information, wherein the vehicle's planned driving route is used to represent the recommended driving path followed by the vehicle under the current operating conditions; and obtaining the power station data corresponding to at least one power station.

[0007] Further, based on the current location information and energy supply station data, a target refueling route for the vehicle is generated, including: determining at least one candidate energy supply station from at least one energy supply station based on the energy supply station data and preset energy supply conditions, wherein the energy supply station data corresponding to at least one candidate energy supply station satisfies the preset energy supply conditions; generating at least one candidate refueling route based on the current location information and the energy supply station location information in the energy supply station data corresponding to at least one candidate energy supply station; scoring the at least one candidate refueling route to obtain at least one comprehensive score value corresponding to the at least one candidate refueling route; and determining the target refueling route from the at least one candidate refueling route based on the at least one comprehensive score value.

[0008] Further, determining the target energy replenishment path from at least one candidate energy replenishment path based on at least one comprehensive score value includes: sorting at least one comprehensive score value in descending order to obtain a sorting result; determining the first score value in the sorting result as the target score value; and determining the candidate energy replenishment path corresponding to the target score value as the target energy replenishment path.

[0009] Furthermore, at least one candidate energy replenishment path is scored to obtain at least one comprehensive score value corresponding to the at least one candidate energy replenishment path, including: determining at least one energy replenishment parameter corresponding to the at least one candidate energy replenishment path; and performing a weighted score based on the at least one energy replenishment parameter and the weight parameter corresponding to the at least one energy replenishment parameter to obtain at least one comprehensive score value corresponding to the at least one candidate energy replenishment path.

[0010] Further, a weighted score is performed based on at least one energy replenishment parameter and the corresponding weight parameter to obtain at least one comprehensive score value for at least one candidate energy replenishment path. This includes: when a user-defined preference weight exists, a weighted score is performed based on at least one energy replenishment parameter and the corresponding preference weight to obtain at least one comprehensive score value for at least one candidate energy replenishment path, wherein the preference weight represents a user-defined weight that conforms to the user's energy replenishment habits; when no preference weight exists, a weighted score is performed based on at least one energy replenishment parameter and its corresponding preset weight to obtain at least one comprehensive score value for at least one candidate energy replenishment path.

[0011] Furthermore, the method also includes: acquiring the vehicle's updated location information and updated status information based on a preset interval or when the current location information meets a preset update condition; determining the updated power supply station of the vehicle and the updated power supply station data corresponding to the updated power supply station based on the updated location information and updated status information; generating at least one updated power supply path based on the updated location information and updated power supply station data; and comparing and analyzing the at least one updated power supply path with the target power supply path to obtain the updated target power supply path.

[0012] Furthermore, a comparative analysis is performed on at least one updated power supply path and the target power supply path to obtain the updated target power supply path, including: scoring at least one updated power supply path to obtain at least one updated score value; comparing the at least one updated score value with the target score value corresponding to the target power supply path to determine the higher score value; and determining the path corresponding to the higher score value as the updated target power supply path.

[0013] Furthermore, the method also includes: when the vehicle travels to the target energy station based on the target energy replenishment path and completes the vehicle energy replenishment, acquiring the user's energy replenishment behavior data during the vehicle energy replenishment process, wherein the energy replenishment behavior data is used to represent the user's actual energy replenishment behavior during the vehicle energy replenishment process; constructing a user energy replenishment preference model based on the energy replenishment behavior data; and adjusting the preference weights based on the user energy replenishment preference model to obtain adjusted preference weights.

[0014] According to another aspect of the embodiments of this application, a vehicle control device is also provided, comprising: an acquisition module, configured to acquire the vehicle's current location information and vehicle status information upon receiving a warning signal regarding the vehicle's remaining range; a determination module, configured to determine, based on the current location information and vehicle status information, at least one power supply station that the vehicle can reach in the current state, and power supply station data corresponding to the at least one power supply station, wherein the power supply station data is used to represent a set of multiple measurable parameters quantifying the current power supply capacity of each of the at least one power supply station; a generation module, configured to generate a target refueling path for the vehicle based on the current location information and the power supply station data, wherein the target refueling path is used to represent a driving trajectory guiding the vehicle from its current location to the target power supply station; and a control module, configured to control the vehicle's driving based on the target refueling path.

[0015] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0020] In this embodiment of the application, upon receiving a warning signal regarding the vehicle's remaining range, the vehicle's current location information and vehicle status information are obtained; then, based on the current location information and vehicle status information, at least one power supply station that the vehicle can reach in the current state is determined, as well as the power supply station data corresponding to at least one power supply station; furthermore, based on the current location information and power supply station data, a target refueling path for the vehicle is generated; finally, the vehicle's driving is controlled based on the target refueling path. This application employs a real-time dynamic data-driven path generation method. Upon receiving a warning signal, it combines the vehicle's current location and status information to accurately calculate at least one energy station that the vehicle can reach under the current operating conditions. Simultaneously, it acquires the measurable energy supply capacity data of each energy station, i.e., energy station data. Based on the matching relationship between the current location information and the energy station data, it generates a feasible target energy replenishment path from the current location to the target energy station. Finally, it controls the vehicle according to the target energy replenishment path. This avoids the invalid path situation caused by traditional solutions that rely solely on static maps or fixed rules and recommend energy stations that exceed the vehicle's range or have no available equipment. It achieves dynamic alignment between path generation and the vehicle's actual operating capabilities, while ensuring the availability of energy stations, thereby improving the feasibility of energy replenishment paths and solving the technical problem of low vehicle control accuracy in related technologies. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of this application;

[0023] Figure 2 This is a flowchart of an optional vehicle control method according to an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of a power replenishment path planning system according to an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of a vehicle control device according to an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover 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.

[0028] According to an embodiment of this application, an embodiment of a vehicle control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Upon receiving a warning signal regarding the vehicle's remaining range, obtain the vehicle's current location information and vehicle status information.

[0031] The aforementioned warning signal can refer to a digital control signal or status flag used to trigger the energy replenishment path planning process. The warning signal can be used as the start condition for the control method proposed in this application: only when the signal is activated (that is, when the warning signal is received) will the subsequent high-power calculation process such as energy station search and path generation be executed, thereby avoiding invalid calculations and ensuring the real-time performance and energy efficiency of the system.

[0032] The aforementioned warning signals may be issued in the following ways, including but not limited to:

[0033] The first method: When the remaining driving range is detected to be lower than a preset threshold, a specific message is sent through the Controller Area Network (CAN) bus.

[0034] The second approach involves the in-vehicle navigation system proactively issuing a path risk warning when it determines that the vehicle is unlikely to reach the next refueling point on the current path, based on real-time traffic conditions, road gradient, and energy consumption prediction models.

[0035] The third method involves a cloud service platform sending remote warning commands to vehicles based on big data information such as regional charging station congestion, weather changes, or electricity price fluctuations.

[0036] The fourth method: Users can actively set the battery life warning threshold through the in-vehicle human-machine interface or mobile terminal software, and trigger the system to start the charging planning process.

[0037] The method of issuing the above warning signals needs to be determined based on the actual situation, and is not limited here.

[0038] The aforementioned vehicle can refer to the physical implementation carrier and data execution terminal of the technical solution of this application. The type of vehicle may include, but is not limited to, pure electric vehicles, plug-in hybrid electric vehicles, and gasoline vehicles. The specific vehicle type needs to be determined according to the actual control objectives. The vehicle needs to integrate at least the following key components: a Global Navigation Satellite System (GNSS) module for acquiring location information, a Battery Management System (BMS) for monitoring battery status, a Vehicle Control Unit (VCU) for coordinating the logic of various subsystems, a high-precision electronic map and navigation engine for path calculation, a 4G / 5G or Vehicle-to-Everything (V2X) communication module for accessing cloud services, and non-volatile memory for storing user preferences, energy consumption models, and historical data, thereby supporting the dynamic perception, intelligent decision-making, and closed-loop improvement capabilities required by this application.

[0039] The aforementioned current location information can refer to the vehicle's current spatial location data. The types of current location information may include, but are not limited to: raw coordinate data directly obtained from satellite positioning; dead reckoning calculated by wheel speed sensors, gyroscopes, and road topology constraints when satellite signals are interrupted (e.g., in tunnels or underground parking lots); and precise road coordinates mapped to the actual drivable lanes after high-precision map matching. The specific current location information needs to be determined based on the actual situation. Current location information can be used to provide a unique spatial starting point for refueling route planning, to calculate the actual driving distance between the vehicle and surrounding refueling stations, to determine the boundaries of the range coverage area, and to determine whether a new planned area has been entered during dynamic driving. It serves as input for achieving accurate route navigation and real-time updates.

[0040] The aforementioned vehicle status information refers to a structured set of parameters reflecting the current vehicle's operating status and energy consumption characteristics. Vehicle status information may include, but is not limited to, real-time vehicle speed, remaining driving range, current energy consumption, driving route, current driving mode, and the battery system's state of charge (SOC). Specific vehicle status information needs to be determined based on actual needs. Vehicle status information can be used to provide dynamic, realistic, and multi-dimensional operating condition input for route planning, transforming recommended solutions from rough estimations based on static theoretical values ​​to precise decisions that align with actual driving behavior, thereby improving the success rate of refueling and enhancing user experience.

[0041] In one optional embodiment, upon receiving a warning signal regarding the vehicle's remaining range, an operation to acquire the vehicle's current location information is triggered to determine the vehicle's specific coordinates in geographic space. Simultaneously, vehicle status information is collected, covering operating parameters directly related to range, such as remaining battery power, current energy consumption rate, real-time vehicle speed, and mileage traveled. This information collectively constitutes the basic data set for assessing refueling needs. This process does not rely on any external route planning or charging station information; it uses only the warning signal as a trigger to selectively extract the vehicle's own real-time location and operating status data, ensuring that subsequent decisions are based on accurate and unfiltered vehicle status.

[0042] Step S104: Based on the current location information and vehicle status information, determine at least one energy station that the vehicle can reach in the current state, and the energy station data corresponding to the at least one energy station, wherein the energy station data is used to represent a set of multiple measurable parameters that quantify the current energy supply capacity of each energy station in the at least one energy station.

[0043] The aforementioned at least one energy supply station can refer to a physical facility point within the vehicle's current driving range that can provide energy replenishment services for the vehicle. The types of functional stations may include, but are not limited to, public charging stations, highway service area charging piles, private charging pile clusters, battery swapping stations, emergency charging points, gas stations, or repair centers capable of charging, etc. The specific energy supply station needs to be determined based on the actual situation. At least one energy supply station can serve as a candidate endpoint set for the route planning in this application; its existence and accessibility directly determine the feasibility of the energy replenishment strategy. By identifying and filtering at least one energy supply station reachable within the vehicle's current driving range, the system constructs a spatial coverage map of energy supply services, providing basic geographical and functional entity support for subsequent route generation, cost assessment, and preference matching. This is a key spatial anchor point for realizing "dynamic energy replenishment recommendation" from theory to practice.

[0044] The aforementioned energy supply station data refers to a set of multiple measurable parameters that quantify the current energy service availability of any energy supply station and can be collected, analyzed, and structurally expressed by the vehicle system. Energy supply station data may include, but is not limited to, the number of available charging piles within the energy supply station, the type of charging piles, the charging power of the charging piles, the estimated refueling time, the estimated refueling cost, the real-time operating status of the energy supply station, operator identification, and historical ratings or user reviews. Specific energy supply station data needs to be determined based on the specific circumstances of each energy supply station. Energy supply station data is the core basis for this application's path weight calculation, cost assessment, and user preference matching. By quantifying and inputting multiple measurable parameters, a comprehensive score can be calculated for the "availability, economy, efficiency, and compatibility" of different energy supply stations, thereby achieving a decision-making leap from "nearest" to "better suitable," avoiding the recommendation of fully loaded, incompatible, or high-cost energy supply stations, thus improving the practical value and user satisfaction of the recommended solution.

[0045] In one optional embodiment, based on the vehicle's current location information and vehicle status information, at least one energy station that can be reached within the current driving range is calculated in real time, and energy station data corresponding to each energy station is obtained. This data is a set of multiple measurable parameters that quantify the current energy supply capacity of each energy station, including the number of available charging piles, charging pile type and power, estimated charging time, charging cost, queuing time, equipment operating status, charging protocol compatibility, operator identification, etc., to objectively reflect the real-time service capacity of each energy station and provide accurate and dynamic decision-making basis for subsequent route generation and weight evaluation.

[0046] For example, an accessible range boundary can be defined with the current location in the current location information as the center and the remaining driving range in the current status information as the radius. Then, a high-precision map and energy supply facility database are called to filter at least one energy supply station with charging or battery swapping functions located within the boundary. Energy supply station data for each station is obtained in real time through V2X or cloud application programming interface (API), including the number of currently available charging piles, charging pile type and power, equipment operating status, estimated charging time, charging cost, protocol compatibility and queuing information. This determines at least one energy supply station that can be reached under the current vehicle operating conditions and its corresponding set of multi-dimensional measurable parameters used to quantify its immediate energy supply capability.

[0047] Step S106: Based on the current location information and the energy station data, generate the target energy replenishment path for the vehicle, wherein the target energy replenishment path is used to represent the driving trajectory that guides the vehicle from the current location to the target energy station.

[0048] The aforementioned target refueling path can refer to the optimal driving trajectory that guides the vehicle safely and efficiently from its current location to the target refueling station. The types of target refueling paths can include, but are not limited to, the shortest distance path, the fastest arrival path, the lowest energy consumption path, and multi-target combination paths. The specific target refueling path needs to be determined based on actual needs. The target refueling path is the execution vehicle for achieving "dynamic refueling recommendation" in this application. It can be used to transform the abstract refueling station selection result into driving instructions that can be directly executed by the driver or autonomous driving system, ensuring that the vehicle completes the refueling task with minimal energy consumption or in the shortest time without deviating from the predetermined route or adding unnecessary detours. Simultaneously, it provides spatial and temporal constraints for subsequent energy consumption adjustment suggestions (such as vehicle speed adjustment).

[0049] The aforementioned target power station can refer to the final recommended destination for the power replenishment task, i.e., the power station ultimately selected. The target power station serves as the decision output and spatial anchor point for power replenishment path planning. It determines the computational boundary of the path generation module, ensuring that the system does not generate invalid or unachievable driving trajectories. At the same time, the characteristics of the target power station (such as charging power, cost, and waiting time) are the core basis for subsequent energy consumption improvement suggestions, cost estimates, and user notifications. It is a key hub connecting the closed loop of "environmental perception - strategy decision-making - path execution".

[0050] In one optional embodiment, based on the vehicle's current location information and the data from the power supply station, a target refueling path is generated. This operation calculates and determines a driving trajectory from the current location to the target power supply station by associating and matching the vehicle's current spatial coordinates with the location and status data of surrounding power supply stations. The generation of this trajectory does not depend on path quality evaluation or user preference adjustments; it only takes location information and power supply station data as input and directly outputs a path guiding the vehicle's movement. Its function is to provide the vehicle with clear and executable driving guidance, ensuring that the vehicle can reach a target node identified as a power supply station from its current point, thus achieving a logical connection from spatial location to the refueling target.

[0051] Step S108: Control vehicle movement based on target refueling path.

[0052] In one optional embodiment, vehicle driving is controlled based on the target charging path. This means that after determining a target charging path that has been weighted and matched with preferences, the vehicle's driving control system is directly driven to navigate and control the motion along the route planned by the path. There is no need for manual intervention in path selection. This ensures that the vehicle automatically moves forward according to the charging target location and driving trajectory generated by the system, thereby realizing continuous and coherent driving behavior from the current location to the designated charging station. Its function is to directly convert the decision result of the charging path into the actual motion command of the vehicle, ensuring the physical accessibility of the charging target and the consistency of execution.

[0053] In this embodiment of the application, upon receiving a warning signal regarding the vehicle's remaining range, the vehicle's current location information and vehicle status information are obtained; then, based on the current location information and vehicle status information, at least one power supply station that the vehicle can reach in the current state is determined, as well as the power supply station data corresponding to at least one power supply station; furthermore, based on the current location information and power supply station data, a target refueling path for the vehicle is generated; finally, the vehicle's driving is controlled based on the target refueling path. This application employs a real-time dynamic data-driven path generation method. Upon receiving a warning signal, it combines the vehicle's current location and status information to accurately calculate at least one energy station that the vehicle can reach under the current operating conditions. Simultaneously, it acquires the measurable energy supply capacity data of each energy station, i.e., energy station data. Based on the matching relationship between the current location information and the energy station data, it generates a feasible target energy replenishment path from the current location to the target energy station. Finally, it controls the vehicle according to the target energy replenishment path. This avoids the invalid path situation caused by traditional solutions that rely solely on static maps or fixed rules and recommend energy stations that exceed the vehicle's range or have no available equipment. It achieves dynamic alignment between path generation and the vehicle's actual operating capabilities, while ensuring the availability of energy stations, thereby improving the feasibility of energy replenishment paths and solving the technical problem of low vehicle control accuracy in related technologies.

[0054] Optionally, based on the current location information and vehicle status information, determine at least one power supply station that the vehicle can reach in the current state, and the power supply station data corresponding to at least one power supply station, including: determining the vehicle's range coverage area based on the remaining driving range in the current location information and current status information, wherein the range coverage area is used to represent the boundary of the connected area formed by all road nodes that the vehicle can actually reach under the current operating conditions using the remaining electric power, starting from the current location information; determining at least one power supply station on the vehicle's planned driving route based on the vehicle's planned driving route and range coverage area in the current status information, wherein the vehicle's planned driving route is used to represent the recommended driving path followed by the vehicle under the current operating conditions; and obtaining the power supply station data corresponding to at least one power supply station.

[0055] The aforementioned remaining driving range refers to the maximum theoretical distance a vehicle can continue driving without recharging, calculated based on the remaining battery energy and real-time energy consumption rate, under the combined influence of the current state of charge, ambient temperature, driving mode, air conditioning load, and historical energy consumption models. The types of remaining driving range may include, but are not limited to, theoretical driving range calculated based on the battery's nominal capacity and ideal operating condition energy consumption models, and real-time driving range dynamically corrected based on the current actual energy consumption rate (such as high-speed cruising, frequent start-stop, and air conditioning power). The specific remaining driving range needs to be determined based on actual needs. Remaining driving range can serve as a core threshold parameter for judging the accessibility of power stations. It can be used to determine the physical spatial boundaries that the vehicle can reach under current operating conditions and is the sole input for constructing the "driving range coverage," ensuring that the system only evaluates power facilities that the vehicle is actually capable of reaching, avoiding the recommendation of invalid paths beyond its capabilities.

[0056] The aforementioned range coverage area can refer to the boundary of a connected region formed by all road nodes that the vehicle can actually reach under the current operating conditions, calculated by a path search algorithm using high-precision road network topology data and real-time traffic constraints (such as speed limits, gradients, traffic lights, and congestion indices), with the vehicle's current location as the center and the real-time remaining range as the radius. The type of range coverage area can include, but is not limited to, dynamic and static coverage areas, and the specific range coverage area needs to be determined based on actual needs. The range coverage area can serve as a physical constraint for spatial filtering, filtering out power stations located outside the vehicle's range and retaining only truly "reachable" candidate stations, thereby improving the feasibility and reliability of path recommendations and preventing users from running out of power due to misjudgments of geographical range.

[0057] The aforementioned planned driving route refers to the recommended driving path generated by the navigation system based on the user-set destination and real-time traffic information, which the vehicle will actually follow under the current operating conditions. The planned driving route can be used to further filter out charging stations located along the user's actual driving path within the range coverage area, avoiding recommending "convenient" stations that deviate from the route, ensuring that the recommended charging points are consistent with the user's travel goals, and improving the practicality and adoption rate of the recommendations; at the same time, this route provides a spatial reference benchmark for subsequent route weighting (such as whether it is convenient or whether to increase mileage).

[0058] In one optional embodiment, based on the vehicle's current location information and remaining range in its current status information, the range coverage area formed by all road nodes that the vehicle can actually reach under the current operating conditions is calculated. This range accurately reflects the maximum reachable boundary of the vehicle without charging. Subsequently, combined with the planned driving route currently followed by the vehicle, only power stations located on the planned route and within the range coverage area are selected, thereby excluding all power stations on non-planned routes that deviate from the user's intended driving intention, ensuring that subsequent power station selections are always consistent with the user's driving goal. On this basis, multi-dimensional power station data corresponding to the selected power stations are obtained, including quantifiable parameters such as charging power, waiting time, and equipment availability. Combined with the current location and this data, an optimal charging route is dynamically generated to guide the vehicle to efficiently reach the target power station along the original route. This effectively avoids extra detours, increased energy consumption, and wasted time caused by blindly selecting power stations that are far away or off the route, achieving deep synergy between the charging strategy and the user's driving intention, improving charging efficiency and travel experience.

[0059] Optionally, based on the current location information and energy station data, a target refueling route for the vehicle is generated, including: determining at least one candidate energy station from at least one energy station based on the energy station data and preset energy supply conditions, wherein the energy station data corresponding to at least one candidate energy station satisfies the preset energy supply conditions; generating at least one candidate refueling route based on the current location information and the energy station location information in the energy station data corresponding to at least one candidate energy station; scoring the at least one candidate refueling route to obtain at least one comprehensive score value corresponding to the at least one candidate refueling route; and determining the target refueling route from the at least one candidate refueling route based on the at least one comprehensive score value.

[0060] The aforementioned preset energy supply conditions refer to a set of threshold rules, either pre-defined by the system or user-defined, used to filter candidate energy supply stations. These preset conditions may include, but are not limited to, vehicle compatibility, economic efficiency, service quality, time availability, and safety / environmental conditions. Specific preset conditions need to be determined based on user needs and the real-time status of the energy supply stations. As a first-level filtering mechanism, preset energy supply conditions are used to eliminate stations from all reachable energy supply stations that do not meet user preferences or vehicle compatibility, ensuring that only candidates with actual energy replenishment feasibility are retained. This improves the efficiency and accuracy of subsequent route generation and recommendations, avoiding invalid calculations and interference with recommendations.

[0061] For example, the vehicle compatibility condition in the preset power supply conditions can be "only supports 800V high-voltage platforms" or "only accepts a certain protocol"; the service quality condition can be "number of available charging piles ≥ 1" or "no queuing or waiting time ≤ 5 minutes"; the economic condition can be "unit electricity price ≤ 1.0 yuan / kWh" or "total charging cost ≤ 30 yuan"; the brand preference condition can be "only for a certain brand of battery swapping stations"; the time availability condition can be "operating hours cover the current moment"; and the safety / environment condition can be "avoid areas with insufficient nighttime lighting" (if enabled by the user). This application supports multi-condition combination logic, which can be set by the user or by system default. The above values ​​are only examples, and the specific values ​​need to be determined according to the actual situation, which are not limited here.

[0062] The aforementioned power station location information can refer to the precise geographic coordinates (latitude and longitude) and road node information corresponding to each candidate power station. The types of power station location information can include, but are not limited to, geographic coordinates (specific latitude and longitude), road node information (corresponding to intersections in the map road network), and address information (auxiliary description, not the primary basis for path calculation). The specific power station location information needs to be determined based on actual needs. Power station location information can serve as a key input for generating start-endpoint pairs for candidate power replenishment paths. Together with the vehicle's current location information, the power station location information constitutes the geographic endpoints for path planning, enabling the system to calculate the specific driving trajectory from the current location to each candidate station based on the road network topology. This forms the physical basis for path visualization and navigation guidance.

[0063] At least one of the above candidate energy supply stations can refer to energy supply stations that have been screened based on preset functional conditions.

[0064] The aforementioned at least one candidate refueling path can refer to the path by which a vehicle reaches at least one candidate refueling station. At least one candidate refueling path may include, but is not limited to, the shortest distance path, the fastest arrival path, the lowest energy consumption path, the most convenient path (the path with the smallest deviation from the vehicle's planned route), and the best overall path (the path with higher priority after multi-objective weighting), etc. The specific at least one candidate refueling path needs to be determined based on actual needs. At least one candidate refueling path carries the conversion function from "station selection" to "navigation," providing a quantifiable and comparable entity for subsequent scoring and optimization, and is the direct carrier for realizing dynamic and personalized path recommendations.

[0065] The aforementioned comprehensive score can refer to a quantitative evaluation score obtained by weighting multiple attributes related to the path (such as distance, time, energy consumption, refueling cost, queuing time, user preference weight, etc.) based on a preset multi-dimensional scoring model for each candidate refueling path. At least one comprehensive score can serve as the decision-making basis for path selection. The comprehensive score quantifies subjective preferences (such as prioritizing time or cost savings) with objective parameters, enabling automated path ranking and ensuring that the final recommended target path is the "most suitable for user needs" under the current conditions, avoiding recommendation bias caused by a single indicator (such as selecting only the shortest route).

[0066] In one optional embodiment, firstly, energy station data is filtered based on preset energy supply conditions, retaining only those energy stations that meet comprehensive constraints such as charging efficiency, waiting time, service type, or price as candidate energy stations, thereby effectively filtering out unsuitable energy replenishment options. Then, by combining the vehicle's current location with the power station location information of each candidate energy station, multiple drivable candidate energy replenishment paths are generated, forming a differentiated energy replenishment solution set. Next, for each candidate path, a quantitative score is obtained by comprehensively considering factors such as driving distance, estimated time, energy consumption, energy station queuing status, and charging rate, resulting in a corresponding comprehensive score value. Finally, based on the comprehensive score values ​​of each path, the optimal path is selected as the target energy replenishment path, and the vehicle is guided to automatically adjust its driving route accordingly, achieving intelligent decision-making for energy replenishment strategies. This accurately identifies the path with the best overall experience among multiple optional energy supply solutions, improving energy replenishment efficiency and user satisfaction, and solving the problems of long energy replenishment times and poor experience caused by traditional solutions that rely on a single dimension for path selection and lack systematic evaluation.

[0067] Optionally, determining a target energy replenishment path from at least one candidate energy replenishment path based on at least one comprehensive score value includes: sorting at least one comprehensive score value in descending order to obtain a sorting result; determining the first score value in the sorting result as the target score value; and determining the candidate energy replenishment path corresponding to the target score value as the target energy replenishment path.

[0068] The aforementioned ranking result can refer to an ordered sequence formed by linearly arranging the comprehensive scores corresponding to all candidate energy replenishment paths from highest to lowest (descending order) according to their numerical values. The ranking result can serve as a quantitative expression of the decision-making basis for path selection. By globally ranking the comprehensive scores of multiple candidate paths, the hierarchical relationship of optimal, suboptimal, etc., can be intuitively identified, providing a standardized and repeatable execution logic for subsequent "selection of the optimal path" and avoiding recommendation uncertainty caused by tied or disordered scores.

[0069] The aforementioned target score value can refer to the comprehensive score value that ranks first (i.e., the maximum value) in the ranking results. It corresponds to the candidate energy replenishment path with a high comprehensive score and is the overall optimal one. It is the numerical identifier of the "optimal solution" determined by the system through quantitative comparison among all available options. The target score value can serve as the decision threshold and output anchor point for path recommendation, marking that the system has completed the transformation from "multi-option evaluation" to "single optimal recommendation." Its value is directly used to trigger the display of the "recommended path" identifier on the user interface and serves as a benchmark reference for subsequent dynamic updates (e.g., triggering an update when the score of a new path exceeds this value). At the same time, the target score value can be used to diagnose the rationality of the recommendation (e.g., prompting "no high-quality option" when the score is too low), improving the robustness of the system.

[0070] In one optional embodiment, at least one comprehensive score value is sorted in descending order to form a clear priority sequence, i.e., a sorting result. Then, the score value ranked first in the sorting result is selected as the unique target score value. Subsequently, the candidate charging path associated with the target score value is directly determined as the final target charging path. In this way, a clear, unique and executable path selection criterion is established among multiple candidate paths with similar scores or all meeting the preset charging conditions. This avoids the problem of uncertain path decision-making or multiple candidates due to fuzzy scoring criteria, and achieves accurate, efficient and deterministic selection of charging paths. This ensures that the vehicle can quickly guide to the most suitable charging station according to the better strategy when the range is critical, thereby improving charging efficiency and driving safety.

[0071] Optionally, scoring at least one candidate energy replenishment path to obtain at least one comprehensive score value corresponding to the at least one candidate energy replenishment path includes: determining at least one energy replenishment parameter corresponding to the at least one candidate energy replenishment path; and performing a weighted score based on the at least one energy replenishment parameter and the weight parameter corresponding to the at least one energy replenishment parameter to obtain at least one comprehensive score value corresponding to the at least one candidate energy replenishment path.

[0072] The aforementioned at least one replenishment parameter can refer to a set of measurable and calculable objective indicators used to quantify the key performance of each candidate replenishment path during the replenishment process. This at least one replenishment parameter may include, but is not limited to, replenishment distance, estimated arrival time, replenishment cost, queuing time, energy consumption increment, charging pile compatibility score, and environmental comfort. The specific at least one replenishment parameter needs to be determined based on the specific candidate replenishment path and the status of the power supply station. This at least one replenishment parameter can serve as an input variable for the comprehensive scoring. The replenishment parameter transforms the abstract path selection requirement into calculable and comparable numerical characteristics, enabling the system to fairly and consistently evaluate multiple candidate paths based on objective data rather than subjective judgment. This is the core foundation for achieving automated, data-driven recommendation.

[0073] The aforementioned weighting parameters can refer to numerical coefficients assigned to each energy replenishment parameter, used to characterize the relative importance of that parameter in the overall path optimization decision. Weighting parameters may include, but are not limited to, preference weights and preset weights; the specific weighting parameters need to be determined based on the actual situation. Weighting parameters can be used to dynamically integrate user-specific needs with system strategies, enabling the same set of energy replenishment parameters to produce different scoring results for different users or scenarios. This supports diverse recommendation strategies such as "cost-saving," "time-saving," and "comfort-oriented," and is a key control variable for achieving "personalized energy replenishment recommendation" in this application.

[0074] In one optional embodiment, firstly, at least one energy replenishment parameter corresponding to at least one candidate energy replenishment path is extracted, including but not limited to quantifiable indicators such as driving time, charging waiting time, charging cost, and energy consumption. Then, a weighted comprehensive score is calculated based on the weight parameters preset by the user or system to obtain a comprehensive score value for each candidate path. The comprehensive score value comprehensively reflects the overall merits of the path under multi-dimensional energy supply needs, thus providing an objective and configurable decision-making basis for subsequent path selection. Through the above weighted scoring mechanism, dynamic adaptation to different user preferences (such as prioritizing time saving, cost reduction, or charging waiting time) can be achieved without changing the path generation logic. This effectively solves the technical deficiency of relying solely on the location and status information of basic energy supply stations, which cannot distinguish path performance differences, ultimately achieving the effect of accurately selecting better energy replenishment paths based on personalized needs, improving user experience and energy replenishment efficiency.

[0075] Optionally, a weighted score is performed based on at least one energy replenishment parameter and the weight parameter corresponding to the at least one energy replenishment parameter to obtain at least one comprehensive score value corresponding to at least one candidate energy replenishment path. This includes: if a user-defined preference weight exists, a weighted score is performed based on at least one energy replenishment parameter and the preference weight corresponding to the at least one energy replenishment parameter to obtain at least one comprehensive score value corresponding to at least one candidate energy replenishment path, wherein the preference weight is used to represent a user-defined weight that conforms to the user's energy replenishment habits; if no preference weight exists, a weighted score is performed based on at least one energy replenishment parameter and its corresponding preset weight to obtain at least one comprehensive score value corresponding to at least one candidate energy replenishment path.

[0076] The aforementioned preference weights refer to a set of numerical coefficients, either actively set by the user or automatically learned based on their historical energy replenishment behavior, used to characterize the user's personalized emphasis on different energy replenishment parameters in energy replenishment decisions. Types of preference weights may include, but are not limited to, manually set preference weights, behavior-learned preference weights, and context-related preference weights; the specific preference weights need to be determined according to actual needs. Preference weights are the core control variable for achieving personalized energy replenishment recommendations. When users have explicit or implicit energy replenishment habits, preference weights allow the system to abandon general strategies and prioritize matching the user's long-established decision-making patterns, improving recommendation acceptance and user experience. Their use ensures that when "users are willing to express preferences," the system prioritizes respecting individual differences, achieving a "personalized" intelligent service.

[0077] The aforementioned preset weights refer to a set of default weighting coefficients uniformly set by system vendors or algorithm engineers before product release, based on typical user group behavior statistics, industry practices, or the principle of maximizing energy efficiency. These preset weights are applicable to users without personalized preferences. The preset weights serve as the system's "safety baseline" and "default strategy," ensuring that even when users have not defined any preferences, reasonable, reliable, and secure energy replenishment path recommendations can still be generated based on universally applicable principles (such as prioritizing economy and balancing efficiency). This avoids scoring failures or random recommendations due to the lack of weight input, guaranteeing the product's basic usability in all usage scenarios.

[0078] In one optional embodiment, the system first determines whether user-defined preference weights exist. If so, it assigns preference weights to each charging parameter based on the user's long-term charging behavior, such as prioritizing fast charging stations, avoiding peak hours, or favoring specific brand charging piles. This weighted calculation generates at least one comprehensive score that aligns with the user's actual needs. If no preference weights are detected, the system automatically switches to a preset general weighting system. The preset weights are then used to weight the score with at least one charging parameter to obtain at least one comprehensive score. This ensures that even without personalized configuration, the system can still perform scientific scoring based on default strategies. This conditional scoring mechanism dynamically adapts to both user preferences and system default values, enabling intelligent responses to individual differences in the scoring logic. It effectively overcomes the problems of rigid scoring standards and neglect of user habits in traditional solutions, which lead to a disconnect between path recommendations and actual needs. This improves the personalization level of charging path recommendations and user satisfaction, ultimately achieving the technical effect of enhancing human-machine collaborative experience while ensuring system robustness.

[0079] Optionally, the method further includes: acquiring updated location information and updated status information of the vehicle based on a preset interval or when the current location information meets preset update conditions; determining the updated power supply station of the vehicle and the updated power supply station data corresponding to the updated power supply station based on the updated location information and updated status information; generating at least one updated power supply path based on the updated location information and updated power supply station data; and comparing and analyzing the at least one updated power supply path with the target power supply path to obtain the updated target power supply path.

[0080] The aforementioned preset interval duration refers to a pre-set time period threshold used to trigger automatic recalculation of the charging path. The preset interval duration may include, but is not limited to, 5 minutes, 2 minutes, 1 minute, etc., and the specific preset interval duration needs to be determined based on actual update requirements and the vehicle's real-time speed. The preset interval duration serves as the time benchmark for the "periodic refresh mechanism," ensuring that even when the user has not moved or their operating conditions have changed, it can still respond to dynamically changing charging station statuses (such as charging pile occupancy, electricity price adjustments, and queue length increases), avoiding recommendation failure due to information lag and improving the real-time performance and reliability of recommendations.

[0081] The aforementioned preset update conditions refer to a set of non-time-based event triggering rules pre-set by the system to determine whether immediate recalculation of the charging path is necessary. These preset update conditions may include, but are not limited to, location information change conditions and charging station status change conditions; the specific preset update conditions need to be determined based on actual update requirements. Preset update conditions can be used to achieve "event-driven" real-time response, ensuring rapid correction of recommendations in the event of critical status changes (such as entering a new area or a sudden change in charging station status), preventing users from missing optimal charging points due to information delays, and improving the initiative and safety of recommendations.

[0082] The updated location information mentioned above can refer to the vehicle's actual spatial location at the time of the update. This updated location information serves as the spatial starting point for path recalculation. It ensures that the system generates paths based on the "latest real location" rather than historical cached locations, making it a core input for achieving "dynamic tracking" capabilities and preventing the recommended path from deviating from actual requirements due to positioning drift.

[0083] The aforementioned updated status information refers to an updated set of vehicle operating status parameters. This updated status information is used to recalculate range coverage and energy consumption models, serving as the core basis for determining "which charging stations are still accessible" and "which routes have higher energy consumption." Updating the status information directly impacts the feasibility and economic assessment of charging routes, ensuring that recommendations are always based on current real-world operating conditions.

[0084] The updated charging stations mentioned above refer to the set of newly identified charging stations within the current vehicle's capabilities that meet preset charging conditions, after recalculating the range coverage based on updated location and status information. This includes previously recommended stations (if still available) and newly added or removed stations. The updated charging stations reflect dynamic changes in charging facility availability and form the basis of the "candidate pool" for route updates. By updating the charging stations, the system no longer relies on historical recommendation lists but instead re-filters each time, ensuring that recommended stations are always "currently truly available" and preventing the recommendation of closed, fully stocked, or faulty stations.

[0085] The aforementioned updated power supply station data refers to the latest set of status parameters that corresponds one-to-one with the updated power supply station. This updated data can provide the latest and most accurate assessment basis for path scoring, especially in scenarios such as electricity price fluctuations, sudden increases in queues, and equipment failures, ensuring the timeliness and reliability of the comprehensive score.

[0086] The aforementioned at least one updated refueling path can refer to the set of all feasible driving paths from the current vehicle location to each updated refueling station, which are regenerated using a path planning algorithm based on updated location information and updated refueling station data.

[0087] The aforementioned updated target power replenishment path refers to the currently optimal power replenishment path selected after comparing and analyzing at least one updated power replenishment path with the original target power replenishment path, based on changes in the comprehensive score. The updated target power replenishment path can be used to dynamically adjust path recommendations, automatically evolving the optimal path without interfering with user operations. This ensures that users always receive the power replenishment solution that best suits their preferences and needs under current conditions, improving the system's intelligence level and the continuity of user experience.

[0088] In one optional embodiment, when a preset interval is reached or the vehicle's current location information meets preset update conditions, the vehicle's updated location information and update status information are acquired in real time. Then, based on the updated location information and update status information, the available updated charging stations within the current range and their corresponding updated charging station data (including the number, type, electricity price, queuing status, etc.) are recalculated. Further, using the updated location information as the starting point and the updated charging station data as the ending point, at least one new updated charging path is generated. Finally, the at least one updated charging path is compared with the currently recommended target charging path through a comprehensive score to obtain the updated target charging path. This update process enables the vehicle to continuously adapt to dynamic changes in road conditions, charging station status, and its own status during driving, achieving real-time adjustment of the charging strategy without manual intervention. This effectively solves the problem of increased range risk or decreased charging efficiency caused by statically fixed paths in traditional solutions, achieving a synergistic effect of ensuring the vehicle's safe arrival at the charging station, improving overall charging efficiency, and enhancing user experience.

[0089] Optionally, a comparative analysis is performed on at least one updated power supply path and a target power supply path to obtain an updated target power supply path, including: scoring at least one updated power supply path to obtain at least one updated score value; comparing the at least one updated score value with the target score value corresponding to the target power supply path to determine the higher score value; and determining the path corresponding to the higher score value as the updated target power supply path.

[0090] The aforementioned update score value can refer to a single numerical result used to quantify the overall merits of each update and replenishment path after weighted calculation based on its corresponding replenishment parameters (such as distance, time, cost, energy consumption, waiting time, compatibility, etc.) and weight parameters (preference weight or preset weight). Each update and replenishment path corresponds to an update score value.

[0091] The aforementioned higher score value can refer to the score with the larger value selected after comparing at least one updated score value with the target score value corresponding to the target energy replenishment path.

[0092] In one optional embodiment, at least one updated refueling path is quantitatively scored to obtain at least one updated score value. These updated score values ​​are then compared with the target score value corresponding to the original target refueling path. The path with the higher score is selected and established as the new target refueling path. This ensures that each path update is based on the current better score result without human intervention. This solves the problem that the refueling path cannot maintain its optimality due to the lack of quantitative comparison and selection mechanism in the original scheme. It realizes adaptive improvement of refueling paths in dynamic driving environment, improving vehicle refueling efficiency and range safety.

[0093] Optionally, the method further includes: when the vehicle travels to the target energy supply station based on the target energy replenishment path and completes the vehicle energy replenishment, acquiring user energy replenishment behavior data during the vehicle energy replenishment process, wherein the energy replenishment behavior data is used to represent the user's actual energy replenishment behavior during the vehicle energy replenishment process; constructing a user energy replenishment preference model based on the energy replenishment behavior data; and adjusting the preference weights based on the user energy replenishment preference model to obtain adjusted preference weights.

[0094] The aforementioned charging behavior data refers to a structured data set automatically collected by the system during the process of a vehicle traveling along a target charging route to a target charging station and completing the actual charging operation. This data reflects the user's actual behavioral choices during the charging process. Charging behavior data may include, but is not limited to, actual charging time, actual charging cost, selected charging station type, charging route mileage, and whether a reservation service was used. Specific charging behavior data needs to be determined based on actual circumstances. Charging behavior data can be used as objective evidence of users' true preferences, replacing or correcting subjective preference weights set by users. This enables automatic learning and evolution from user-declared preferences to user behavioral preferences, making system recommendations closer to real usage habits and improving the accuracy and credibility of personalized services.

[0095] The aforementioned user energy replenishment preference model can refer to a mathematical model established based on historical energy replenishment behavior data, using machine learning or statistical analysis methods, to characterize user behavioral tendencies in different energy replenishment scenarios. This model can transform scattered, unstructured energy replenishment behavior data into computable and generalizable preference expressions, enabling behavior-driven weight self-learning. This allows the system to dynamically understand users' deep habits without requiring manual settings, thereby improving the intelligence and long-term adaptability of recommendations.

[0096] The aforementioned adjustment of preference weights can refer to a new set of weights obtained by quantitatively correcting the original user-set or system-default preference weights based on the output of the user's replenishment preference model, which is used for the next replenishment path scoring. The changes in its values ​​reflect the deviation and evolution trend between the user's actual behavior and the original preference settings.

[0097] In one optional embodiment, after the vehicle travels to the target energy station based on the target energy replenishment path and completes energy replenishment, real-time data on the user's energy replenishment behavior during the process is collected. This includes behavioral characteristics such as charging duration selection, charging power preference, whether the air conditioning is turned on, whether the cabin is preheated, and whether the energy replenishment is interrupted and the user leaves early. Based on this real energy replenishment behavior data, a dynamically evolving user energy replenishment preference model is constructed. This model is then used to adaptively correct the original statically set preference weights, thereby obtaining adjusted preference weights. This ensures that the weight parameters in subsequent path planning can truly reflect the changes in individual user habits and needs. This overcomes the technical defects of traditional methods where preference weights are fixed and cannot continuously improve with usage behavior, leading to recommended paths deviating from the user's true intentions. This achieves personalized, dynamic, and accurate energy replenishment path recommendations, improving the user's energy replenishment experience and the system's intelligence level.

[0098] In one alternative embodiment, Figure 2 This is a flowchart of an optional vehicle control method according to an embodiment of this application, such as... Figure 2As shown, this method begins by determining that the vehicle's range is insufficient. When the vehicle's range is insufficient, it obtains the current vehicle status, location, and driving route to determine the operational status of available charging stations within the range radius. Next, it generates multiple charging route options. Furthermore, it determines weight values ​​based on user preferences, compares the weights of multiple charging routes, and recommends the most economical, convenient, and fastest charging route to the user, while also providing energy consumption adjustment suggestions. Finally, the charging route is dynamically updated based on the user's location and vehicle status.

[0099] This application embodiment automatically and accurately identifies available charging stations and their operational status within the driving radius when the vehicle's range is insufficient, by combining real-time vehicle status, location, and driving route. Based on user-personalized preference weights, it comprehensively scores and compares multiple charging routes, intelligently recommending the most economical, convenient, and fastest solution, while dynamically providing energy consumption optimization suggestions, effectively alleviating user range anxiety. Furthermore, the system updates charging routes and recommendation strategies in real time based on continuous changes in vehicle location and status, achieving a closed loop of "perception-decision-feedback," improving the accuracy, timeliness, and personalization of charging recommendations, reducing ineffective driving and energy waste, and enhancing user experience and system intelligence.

[0100] In one alternative embodiment, Figure 3 This is a schematic diagram of a power replenishment path planning system according to an embodiment of this application, such as... Figure 3 As shown, the system includes a range calculation module, a vehicle status monitoring module, a refueling path generation module, and a weight calculation module. The vehicle status monitoring module is connected to the range calculation module, the range calculation module is connected to the refueling path generation module, and the refueling path generation module is connected to the weight calculation module.

[0101] The vehicle status monitoring module collects the vehicle's current location, real-time speed, remaining battery power, energy consumption per unit mile, and auxiliary load status in real time. This information is then transmitted unidirectionally to the range calculation module via the vehicle data bus, which calculates the theoretical remaining range under the current operating conditions. When the remaining range falls below a preset threshold, the range calculation module triggers the charging path generation module. This module, combining a high-precision map with a real-time charging station database (including the number, type, brand, availability, electricity price, and queue prediction of charging piles), searches for and generates all reachable candidate charging paths, starting from the current location and using the remaining range as the radius. Subsequently, the waiting... The list of selected routes is sent to the weight calculation module, which integrates the user's preset personalized preference weights (such as time sensitivity, cost sensitivity, and preference for charging station type) to give each route a weighted score, obtain a comprehensive ranking, and feed back the Top-N recommended routes to the human-computer interaction interface. At the same time, when the user selects and completes charging, the system automatically records the charging behavior data (time, location, cost, and selected route) and sends it back to the weight calculation module to dynamically update the user preference model, realize continuous improvement of the recommendation strategy, and thus form a complete closed-loop control architecture of "perception → calculation → generation → scoring → feedback → learning".

[0102] According to an embodiment of this application, a vehicle control device is provided. It should be noted that the device can be used to execute the above-described vehicle control method.

[0103] Figure 4 This is a schematic diagram of a vehicle control device according to an embodiment of this application, such as... Figure 4 As shown, the device includes: an acquisition module 402, a determination module 404, a generation module 406, and a control module 408.

[0104] The acquisition module 402 is used to acquire the vehicle's current location information and vehicle status information upon receiving a warning signal regarding the vehicle's remaining range. The determination module 404 is used to determine, based on the current location information and vehicle status information, at least one energy station that the vehicle can reach in the current state, and the corresponding energy station data for at least one energy station, wherein the energy station data represents a set of multiple measurable parameters that quantify the current energy supply capacity of each of the at least one energy station. The generation module 406 is used to generate a target energy replenishment path for the vehicle based on the current location information and energy station data, wherein the target energy replenishment path represents the driving trajectory that guides the vehicle from its current location to the target energy station. The control module 408 is used to control the vehicle's driving based on the target energy replenishment path.

[0105] Optionally, the determining module is used to determine the vehicle's range coverage area based on the remaining driving range in the current location information and the current status information, wherein the range coverage area represents the boundary of the connected area formed by all road nodes that the vehicle can actually reach under the current operating conditions using the remaining electric power, starting from the current location information; based on the vehicle's planned driving route and the range coverage area in the current status information, determine at least one power supply station on the vehicle's planned driving route, wherein the vehicle's planned driving route represents the recommended driving path followed by the vehicle under the current operating conditions; and obtain power supply station data corresponding to at least one power supply station.

[0106] Optionally, the generation module is used to determine at least one candidate energy supply station from at least one energy supply station based on energy supply station data and preset energy supply conditions, wherein the energy supply station data corresponding to at least one candidate energy supply station satisfies the preset energy supply conditions; generate at least one candidate energy replenishment path based on the current location information and the energy supply station location information in the energy supply station data corresponding to at least one candidate energy supply station; score the at least one candidate energy replenishment path to obtain at least one comprehensive score value corresponding to the at least one candidate energy replenishment path; and determine the target energy replenishment path from the at least one candidate energy replenishment path based on the at least one comprehensive score value.

[0107] Optionally, the generation module is also used to sort at least one comprehensive score value in descending order to obtain a sorting result; determine the first score value in the sorting result as the target score value; and determine the candidate energy replenishment path corresponding to the target score value as the target energy replenishment path.

[0108] Optionally, the generation module is further configured to determine at least one energy replenishment parameter corresponding to at least one candidate energy replenishment path; and to perform a weighted score based on at least one energy replenishment parameter and the weight parameter corresponding to at least one energy replenishment parameter to obtain at least one comprehensive score value corresponding to at least one candidate energy replenishment path.

[0109] Optionally, the generation module is further configured to, in the presence of user-defined preference weights, perform a weighted scoring based on at least one energy replenishment parameter and the preference weights corresponding to at least one energy replenishment parameter to obtain at least one comprehensive score value corresponding to at least one candidate energy replenishment path, wherein the preference weights are used to represent user-defined weights that conform to the user's energy replenishment habits; in the absence of preference weights, perform a weighted scoring based on at least one energy replenishment parameter and its corresponding preset weights to obtain at least one comprehensive score value corresponding to at least one candidate energy replenishment path.

[0110] Optionally, the device is further configured to acquire updated location information and updated status information of the vehicle based on a preset interval or when the current location information meets preset update conditions; determine the updated power supply station of the vehicle and the updated power supply station data corresponding to the updated power supply station based on the updated location information and updated power supply station data; generate at least one updated power supply path based on the updated location information and updated power supply station data; and compare and analyze at least one updated power supply path with a target power supply path to obtain the updated target power supply path.

[0111] Optionally, the device is further configured to score at least one updated power replenishment path to obtain at least one updated score value; compare the at least one updated score value with the target score value corresponding to the target power replenishment path to determine the higher score value; and determine the path corresponding to the higher score value as the updated target power replenishment path.

[0112] Optionally, the device is also used to acquire user refueling behavior data during the vehicle refueling process when the vehicle is driven to the target energy station based on the target refueling path and the vehicle refueling is completed. The refueling behavior data is used to represent the user's actual refueling behavior during the vehicle refueling process. A user refueling preference model is constructed based on the refueling behavior data. The preference weights are adjusted based on the user refueling preference model to obtain the adjusted preference weights.

[0113] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0114] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0115] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0116] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0117] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0118] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0120] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of this application 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 as a software functional unit.

[0122] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0123] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling a vehicle, characterized in that, include: Upon receiving a warning signal regarding the vehicle's remaining range, the system obtains the vehicle's current location and status information. Based on the current location information and the vehicle status information, at least one power supply station that the vehicle can reach in the current state is determined, as well as the power supply station data corresponding to the at least one power supply station, wherein the power supply station data is used to represent a set of multiple measurable parameters that quantify the current power supply capacity of each power supply station in the at least one power supply station. Based on the current location information and the energy supply station data, a target energy replenishment path for the vehicle is generated, wherein the target energy replenishment path is used to represent the driving trajectory that guides the vehicle from the current location to the target energy supply station; The vehicle's movement is controlled based on the target refueling path.

2. The method according to claim 1, characterized in that, Based on the current location information and the vehicle status information, determine at least one power supply station that the vehicle can reach in the current state, and the power supply station data corresponding to the at least one power supply station, including: Based on the current location information and the remaining driving range in the current status information, the driving range coverage area of ​​the vehicle is determined, wherein the driving range coverage area is used to represent the boundary of the connected area formed by all road nodes that the vehicle can actually reach under the current operating conditions using the remaining electric power, starting from the current location information. Based on the vehicle's planned driving route and the range coverage in the current status information, at least one power station on the vehicle's planned driving route is determined, wherein the vehicle's planned driving route is used to represent the recommended driving path followed by the vehicle under the current operating conditions; Obtain the energy supply station data corresponding to the at least one energy supply station.

3. The method according to claim 1, characterized in that, Based on the current location information and the energy supply station data, a target refueling path for the vehicle is generated, including: Based on the energy supply station data and preset energy supply conditions, at least one candidate energy supply station is determined from the at least one energy supply station, wherein the energy supply station data corresponding to the at least one candidate energy supply station satisfies the preset energy supply conditions; Based on the current location information and the energy station location information in the energy station data corresponding to the at least one candidate energy station, at least one candidate energy replenishment path is generated. The at least one candidate energy replenishment path is scored to obtain at least one comprehensive score value corresponding to the at least one candidate energy replenishment path; The target energy replenishment path is determined from the at least one candidate energy replenishment path based on the at least one comprehensive score value.

4. The method according to claim 3, characterized in that, Determining the target energy replenishment path from the at least one candidate energy replenishment path based on the at least one comprehensive score value includes: Sort the at least one comprehensive score value in descending order to obtain the sorting result; The first score value in the sorting results is determined as the target score value; The candidate energy replenishment path corresponding to the target score value is determined as the target energy replenishment path.

5. The method according to claim 3, characterized in that, The at least one candidate energy replenishment path is scored to obtain at least one comprehensive score value corresponding to the at least one candidate energy replenishment path, including: Determine at least one energy replenishment parameter corresponding to the at least one candidate energy replenishment path; A weighted score is performed based on the at least one energy replenishment parameter and the weight parameter corresponding to the at least one energy replenishment parameter to obtain the at least one comprehensive score value corresponding to the at least one candidate energy replenishment path.

6. The method according to claim 5, characterized in that, A weighted score is performed based on the at least one energy replenishment parameter and the corresponding weight parameter to obtain the at least one comprehensive score value corresponding to the at least one candidate energy replenishment path, including: In the presence of user-defined preference weights, a weighted score is performed based on the at least one energy replenishment parameter and the preference weights corresponding to the at least one energy replenishment parameter to obtain the at least one comprehensive score value corresponding to the at least one candidate energy replenishment path, wherein the preference weights are used to represent the user-defined weights that conform to the user's energy replenishment habits; In the absence of the preference weight, a weighted score is performed based on the at least one energy replenishment parameter and its corresponding preset weight to obtain the at least one comprehensive score value corresponding to the at least one candidate energy replenishment path.

7. The method according to claim 4, characterized in that, The method further includes: Based on a preset interval or when the current location information meets a preset update condition, the updated location information and update status information of the vehicle are obtained. Based on the updated location information and the updated status information, the updated power supply station of the vehicle and the updated power supply station data corresponding to the updated power supply station are determined. At least one updated energy replenishment path is generated based on the updated location information and the updated energy supply station data; The updated target power supply path is obtained by comparing and analyzing the at least one updated power supply path with the target power supply path.

8. The method according to claim 7, characterized in that, The at least one updated power replenishment path is compared and analyzed with the target power replenishment path to obtain the updated target power replenishment path, including: The at least one updated energy replenishment path is scored to obtain at least one updated score value; The at least one updated score value is compared with the target score value corresponding to the target energy replenishment path to determine the higher score value; The path corresponding to the higher score value is determined as the updated target energy replenishment path.

9. The method according to claim 6, characterized in that, The method further includes: When the vehicle is driven to the target energy station based on the target energy replenishment path and the vehicle energy replenishment is completed, the user's energy replenishment behavior data during the vehicle energy replenishment process is obtained, wherein the energy replenishment behavior data is used to represent the user's actual energy replenishment behavior during the vehicle energy replenishment process; A user energy replenishment preference model is constructed based on the aforementioned energy replenishment behavior data; The preference weights are adjusted based on the user energy replenishment preference model to obtain the adjusted preference weights.

10. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 9.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 9.

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