Route planning method and device, terminal and storage medium

Abstract

The invention relates to the technical field of route planning, and discloses a route planning method and device, a terminal and a storage medium, and the method comprises the steps: constructing at least one candidate route according to the operation state data of a vehicle, the current transportation task information and the road passing restriction information; calculating predicted power consumption corresponding to each candidate route according to the running state data, the driving route length of each candidate route and an energy consumption model; wherein the operation state data comprises real-time load; if it is judged that the current available electric quantity of the vehicle is not enough to support the vehicle to arrive at the terminal point and reserve the electric quantity safety margin, determining at least one available charging facility between the current position of the vehicle and the terminal point as a middle stop point based on an energy supplement demand, and updating the candidate route based on the middle stop point; and based on a preset optimization target selected by the user, evaluating each candidate route to obtain a recommended route conforming to the preset optimization target.

Description

Technical Field

[0001] This application relates to the field of route planning technology, and in particular to a route planning method, apparatus, terminal and storage medium. Background Technology

[0002] With the widespread application of new energy light commercial vehicles in urban logistics, their short-distance, high-frequency, load-fluctuation, and complex road conditions place higher demands on navigation systems. However, existing navigation technologies are mostly designed for passenger car scenarios and are insufficient to meet the actual needs of new energy freight vehicles. First, the systems generally rely on ideal operating condition energy consumption data provided by OEMs, without integrating dynamic parameters such as real-time vehicle load and driving behavior, resulting in large deviations in range prediction. Second, route planning has a single objective and lacks flexible support for multiple modes, failing to adapt to differentiated scenarios such as green channel transportation and daily delivery. Furthermore, charging pile recommendations are based solely on geographical location, without precise matching based on unloading point location, charging pile status, and charging power, easily leading to empty detours or full charging pile delays. Summary of the Invention

[0003] In view of this, embodiments of this application provide a route planning method, device, terminal, and storage medium, which can effectively solve the technical problems faced by new energy light commercial vehicles in urban logistics scenarios, such as inaccurate power prediction, low energy replenishment efficiency, insufficient adaptation to multi-objective needs, and weak ability to avoid road condition restrictions.

[0004] In a first aspect, embodiments of this application provide a route planning method, including: Based on vehicle operating status data, current transportation task information, and road traffic restriction information, at least one candidate route is constructed; wherein, the operating status data includes real-time load. Based on the operating status data, the travel route length of each candidate route, and the energy consumption model, calculate the estimated power consumption corresponding to each candidate route; If it is determined that the vehicle's current available power is insufficient to support the vehicle to reach the destination and to reserve a safety margin of power, then at least one available charging facility is determined as an intermediate stop between the vehicle's current location and the destination based on the energy replenishment needs, and the candidate route is updated based on the intermediate stop. Based on the user-selected preset optimization goal, each candidate route is evaluated to obtain a recommended route that meets the preset optimization goal.

[0005] In an optional implementation, the road traffic restriction information includes height restriction information, traffic restriction information, weight restriction information, and temporary traffic control information; the vehicle operation status data includes the vehicle's current location; and the transportation task information includes the starting point location and the destination location. Before constructing at least one candidate route based on vehicle operating status data, current transportation task information, and road traffic restriction information, the method further includes: constructing an initial route set based on the starting point location and the ending point location; The construction of the candidate routes includes: based on the height restriction information, vehicle height, weight restriction information, total vehicle weight, traffic restriction information, temporary traffic control information, the current vehicle location, and the destination location, respectively excluding road segments from the initial route set that are lower than the vehicle height, road segments with a load-bearing capacity lower than the total vehicle weight, and road segments that are prohibited from passing during a specified time period, so as to generate the candidate routes.

[0006] In an optional implementation, the preset optimization objective includes at least one of the objectives of lowest cost, shortest time, and shortest route.

[0007] In optional implementations, when the preset optimization objective includes the shortest time objective, the optimization objective is to minimize the sum of congestion waiting time, charging waiting time, and detour delay time; when the preset optimization objective includes the lowest cost objective, the optimization objective is to minimize the sum of electricity consumption cost, charging service fee, and operational losses caused by additional empty runs; when the preset optimization objective includes the shortest route objective, the optimization objective is to minimize the sum of total driving mileage and additional mileage caused by round-trip charging.

[0008] In an optional implementation, the method further includes: continuously monitoring changes in at least one of the following indicators during vehicle operation: real-time load, road congestion status, charging pile availability, and the degree to which actual power consumption deviates from the predicted value; when any change in any indicator is detected to exceed a set threshold, route replanning is completed and updated results are pushed out within a preset time.

[0009] In an optional implementation, determining at least one available charging facility as an intermediate stop between the vehicle's current location and the destination based on charging demand includes: Obtain the set of charging facilities along the route from the vehicle's current location to the destination; The charging facilities within a preset distance from key points in the transportation task information are selected as candidate charging facilities; wherein, the key points include unloading points, intermediate delivery points or service areas; The target charging facility is determined from the candidate charging facilities based on the distance from the vehicle's current location, the charging power, and whether the facility is available. Based on the target charging facility and the current candidate route, construct two driving routes, each including an intermediate stop. If any segment of the route cannot satisfy the requirement that the available power is greater than or equal to the sum of the expected power consumption and the safety margin, then a new intermediate stop will be introduced within the current route.

[0010] In an optional implementation, the energy consumption model is determined based on the vehicle's energy consumption per 100 kilometers under the current operating conditions, the load correction factor, the real-time load, and the distance between the vehicle's current location and the destination.

[0011] Secondly, embodiments of this application provide a route planning device, comprising: The construction module is used to construct at least one candidate route based on vehicle operating status data, current transportation task information, and road traffic restriction information; wherein, the operating status data includes real-time load. The calculation module is used to calculate the expected power consumption of each candidate route based on the running status data, the driving route length of each candidate route, and the energy consumption model. The update module is used to determine at least one available charging facility as an intermediate stop between the vehicle's current location and the destination based on the energy replenishment demand if it is determined that the vehicle's current available power is insufficient to support the vehicle to reach the destination and to reserve a safe power margin, and to update the candidate route based on the intermediate stop. The evaluation module is used to evaluate each of the candidate routes based on the preset optimization goals selected by the user, so as to obtain a recommended route that meets the preset optimization goals.

[0012] Thirdly, embodiments of this application provide a terminal device, the terminal device including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the route planning method described above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed on a processor, implements the route planning method described above.

[0014] The embodiments of this application have the following beneficial effects: After constructing candidate routes, when estimating the expected power consumption of the candidate routes, this application not only considers the route distance and power consumption per kilometer, but also incorporates the vehicle's real-time load to correct the expected power consumption. This overcomes the problem of traditional navigation systems relying solely on ideal operating condition power consumption parameters, leading to misjudgments of range. It makes energy consumption estimation closer to actual driving conditions and significantly improves the accuracy of power prediction. Furthermore, when it is determined that the vehicle's remaining power is insufficient to meet the entire journey's driving needs while maintaining a safety margin, it actively identifies the need for recharging and intelligently selects available charging facilities along the route as intermediate stops, achieving full-process route optimization including charging, thereby avoiding the risk of power outages midway. Furthermore, this application can also evaluate candidate routes based on user-selected preset optimization goals, making the final route more suitable for the user's requirements. It is understood that the overall solution fully considers the characteristics of new energy light commercial vehicles in urban delivery, such as large load fluctuations, tight operating windows, and complex road conditions. This not only improves energy utilization efficiency and transportation timeliness but also enhances the practicality and intelligence of navigation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A first flowchart of the route planning method according to an embodiment of this application is shown; Figure 2 A second flowchart of the route planning method according to an embodiment of this application is shown; Figure 3 A schematic diagram of a route planning device according to an embodiment of this application is shown. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0018] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0020] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0021] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0022] As the penetration rate of new energy light commercial vehicles in urban logistics continues to increase, their typical operational characteristics—short-distance, high-frequency, load-fluctuating, and complex road conditions—are becoming increasingly prominent. These vehicles are widely used in urban scenarios such as fresh produce delivery, supermarket replenishment, and express delivery connections. They typically perform multiple short-to-medium-distance transport tasks daily, with routes heavily reliant on the urban road network, frequently traversing residential areas, commercial centers, and farmers' markets—areas with limited space. However, most existing navigation technologies are designed for passenger car use, revealing numerous compatibility issues when applied to new energy light commercial vehicles, making it difficult to meet the specific needs of this type of vehicle in terms of energy efficiency management, operational efficiency, and compliant traffic.

[0023] While existing navigation solutions attempt to incorporate power consumption prediction and charging station recommendation functions, a series of technical shortcomings still need to be addressed in practical applications.

[0024] First, the depth of real-time vehicle data fusion is insufficient. Most systems rely solely on the ideal energy consumption parameters per 100 kilometers provided by the OEM for range estimation, without establishing a linkage mechanism with the vehicle's dynamic operating status. For example, the system fails to effectively access key data such as real-time load, vehicle speed changes, and air conditioning start / stop from the CAN bus, resulting in an inability to dynamically adjust the energy consumption model based on actual load conditions. When the vehicle is fully loaded and climbing hills or frequently starts and stops, the energy consumption is significantly higher than the nominal value, but traditional navigation systems still calculate based on static models, which can easily lead to misjudgments of range and the risk of power outages mid-journey.

[0025] Secondly, the multi-objective planning capabilities are weak and disconnected from the actual needs of drivers. Existing solutions often focus on a single optimization objective, such as the shortest distance or the lowest theoretical energy consumption, lacking the ability to respond to the diversity of transportation scenarios. While some systems offer basic options such as highway priority and ordinary roads, they are not customized for the specific uses of new energy light trucks. For example, in green channel transportation, drivers are more concerned with on-time delivery, so the shortest time should be the core objective; while in daily delivery, the focus is more on operating cost control, requiring comprehensive consideration of peak and off-peak electricity price differences and empty-run losses. In addition, the cost calculation of most systems only includes the unit price of charging, ignoring the differences in electricity consumption caused by load weight fluctuations, and thus failing to achieve true economic optimization.

[0026] Secondly, the charging resource matching mechanism is inefficient. Although some navigation tools have integrated charging station location information, their recommendation logic remains at a rudimentary stage of simply listing nearby charging stations, failing to achieve precise coordination with transportation tasks. New energy light trucks are characterized by concentrated unloading points and limited operating windows; the ideal charging strategy should be to charge along the route and stop at the nearest station. However, existing systems often recommend charging stations far from the destination, forcing drivers to detour several kilometers, increasing empty mileage and time costs. At the same time, the problem of lagging charging station status updates is prominent; drivers often find that the target charging station is already occupied or out of service after arriving according to navigation, severely impacting charging efficiency and delivery rhythm.

[0027] Finally, the ability to avoid road condition restrictions is weak. New energy light trucks frequently enter and exit closed or semi-closed areas such as residential communities and commercial districts during urban delivery, facing numerous physical and management constraints such as height restrictions, traffic restrictions, and weight restrictions. However, existing navigation systems do not fully cover these refined rules and their response is lagging. On the one hand, they are not timely in obtaining dynamic information such as temporary construction, traffic control, and peak-hour restrictions. For example, in Chengdu and other places, there have been cases where navigation systems were not updated in sync with the implementation of time-based bans on new energy trucks on elevated highways, leading to frequent traffic violations by drivers. On the other hand, the system does not include the vehicle's own physical attributes (such as cargo box height and overall vehicle weight) in the route selection criteria and fails to actively filter road sections lower than the vehicle's clearance height, easily causing obstructions, forced detours, or even collisions.

[0028] It is understandable that existing navigation technologies have significant shortcomings in addressing the unique challenges of new energy light commercial vehicles, such as high battery sensitivity, demanding timeliness, numerous road traffic constraints, and tight refueling schedules. In particular, they lack systematic solutions for real-time data fusion, multi-objective optimization decision-making, and scenario-based refueling matching. Therefore, this application proposes a route planning method to improve the safety, economy, and operational efficiency of new energy light trucks in urban logistics environments.

[0029] The route planning method will be explained below with reference to some specific examples.

[0030] Figure 1 A schematic flowchart of a route planning method according to an embodiment of this application is shown. Exemplarily, the route planning method includes steps S100-S400: Step S100: Based on the vehicle's operating status data, current transportation task information, and road traffic restriction information, construct at least one candidate route.

[0031] For example, vehicle operating status data includes, but is not limited to, vehicle current location, speed, battery state of charge, and real-time load. This data can be collected in real time via the onboard T-BOX connected to the CAN bus and uploaded to the cloud server at fixed intervals. Current transportation task information includes, but is not limited to, origin location, destination location, estimated delivery time requirement, cargo type, and transportation priority mode selection. Road traffic restriction information includes, but is not limited to, height restrictions, traffic restrictions, weight restrictions, and temporary traffic control information. Height restrictions refer to the maximum clearance height allowed for the passage of obstacles or bridges over the road; traffic restrictions refer to regulations prohibiting freight vehicles from passing through specific areas or time periods; weight restrictions refer to the load-bearing limits of roads or bridges on the total weight of vehicles; and temporary traffic control information includes temporary closures or detours due to construction, accidents, large-scale events, etc.

[0032] In some implementations, the construction of candidate routes includes: excluding road segments that are lower than the vehicle height based on height restriction information and vehicle height; excluding road segments that have a load-bearing capacity lower than the vehicle's total weight based on weight restriction information and total vehicle weight; and excluding road segments that are prohibited from passing during the current time period based on traffic restriction information, temporary traffic control information, and the vehicle's current location.

[0033] Specifically, before constructing at least one candidate route based on vehicle operating status data, current transportation task information, and road traffic restriction information, an initial route set is built based on the start and end points. Specifically, the initial route set can consist of all accessible road connections from the start to the end point, provided by a digital map platform. Subsequently, the system can filter this initial route network item by item based on road traffic restriction information.

[0034] Specifically, in the height restriction information matching process, the system reads the vehicle height value from the vehicle's own physical attribute parameters. For example, if the top height of the cargo box of a light commercial vehicle is 2.3 meters, then all road sections with a marked height restriction of less than or equal to 2.3 meters will be automatically excluded. Preferably, potential risk road sections with a height restriction of 2.4 meters but without sufficient safety margin (such as less than 0.1 meters) can also be excluded to avoid collision risks.

[0035] During the traffic restriction information assessment phase, the system combines the current date, time, and geographical location to check whether there are any restrictions on freight vehicles on the target road segment. For example, if trucks are prohibited from entering the city center from 7:00 to 20:00 on weekdays, then such road segments will not be included in the candidate route set during the relevant time period. The current date and time are provided by the synchronized clock of the vehicle terminal or cloud server to ensure consistency with the city's traffic management system time.

[0036] During the weight limit information verification phase, the system obtains the vehicle's current total weight, which is calculated by the sum of the vehicle's curb weight and real-time load. If a bridge has a maximum weight limit of 4.5 tons and the vehicle's current total weight reaches 4.8 tons, then the road section where the bridge is located is removed.

[0037] For temporary traffic control information, the system connects to the city traffic management platform or third-party traffic information service providers through API interface, and polls every 20 seconds to obtain dynamic events such as sudden road closures and construction detour suggestions in real time. When it detects that the status of a road segment has changed from open to closed, it immediately updates the local route availability database to ensure that routes that have been closed or are severely congested are no longer recommended.

[0038] After the above multi-dimensional screening, the remaining road combinations form a set of candidate routes that meet the basic traffic conditions.

[0039] For example, a vehicle departs from logistics park A and its destination is supermarket B located within a residential community. The delivery task requires unloading to be completed before 6:00 PM. The system detects that the internal roads of the community have 2.2-meter height restriction barriers, and the south gate entrance closes after 5:00 PM for resident access only. Simultaneously, the vehicle's current cargo box height is 2.3 meters. Therefore, all routes that include entry through the south gate or pass through sections with 2.2-meter height restrictions are eliminated. The final candidate route must enter the community through the north gate and must not have any height restriction structures below 2.3 meters along its entire length. This process effectively adapts to refined traffic rules in complex urban scenarios, ensuring the practicality and compliance of the route suggestions.

[0040] Through the above method, this embodiment can complete the construction of candidate routes, ensuring that the generated routes are not only geographically accessible, but also conform to the characteristics of the vehicle itself and environmental and traffic constraints.

[0041] Step S200: Calculate the estimated power consumption for each candidate route based on the operating status data, the route length of each candidate route, and the energy consumption model.

[0042] In this step, the energy consumption of each candidate route generated in step S100 is evaluated to quantify the energy demand that each route may generate during future execution, thereby identifying the route option that can safely complete the transportation task and has the optimal energy consumption under the current vehicle conditions.

[0043] The operational status data includes real-time load, which can be obtained by load sensors installed in the vehicle's suspension system. This data is then processed by T-BOX and uploaded to the cloud server, with an update frequency of no less than once every 20 seconds to ensure that it reflects the vehicle's true load condition.

[0044] The energy consumption model is used to predict the total electricity required for a vehicle to complete a candidate route under specific operating conditions. This model is determined based on the vehicle's energy consumption per 100 kilometers under current operating conditions, load correction factors, real-time load, and the travel distance of the candidate route.

[0045] In the modeling process, the baseline energy consumption per 100 kilometers under no-load conditions is first set. This value can be provided by the OEM and calibrated using actual operating data. Because rolling resistance and acceleration inertia increase significantly when a vehicle is fully loaded, leading to increased energy consumption, a load correction is necessary. Let the load correction factor be k, and its value typically ranges from [value missing]. This indicates that for every additional ton of load, the energy consumption per 100 kilometers will increase by a corresponding percentage. It's understandable that this coefficient can be calibrated based on vehicle type and road gradient.

[0046] For any candidate route, its corresponding estimated power consumption Ereq can be calculated using the formula... The calculation is performed as follows: where m is the current real-time load in tons; D is the length of the candidate route in kilometers; and the result Ereq is the total electricity consumed to complete the route in kilowatt-hours.

[0047] In addition, the system also considers the energy consumption of other auxiliary systems, such as the power consumption of non-drive components like air conditioning, electric defrosting devices, and electric power steering. This additional power consumption is estimated by reading instantaneous power from the CAN bus based on the current device start / stop status and integrating it, and finally added to the total estimated power consumption, further improving prediction accuracy.

[0048] In addition, the system can also personalize the predicted energy consumption model based on the driver's historical driving behavior data. Specifically, it continuously collects operational behaviors such as rapid acceleration, rapid deceleration, frequent start-stop, prolonged air conditioning use, and continuous power supply while parked via the in-vehicle T-BOX, and combines these with parameters such as vehicle running time, charging frequency, and energy recovery efficiency to construct a driver behavior profile in the cloud. For example, for drivers accustomed to aggressive driving, their average energy consumption per 100 kilometers is typically 15% to 25% higher than that of drivers with stable driving habits. Based on this, the system introduces a driving habit correction coefficient to weight and adjust the output of the basic energy consumption model. In one embodiment, this correction coefficient is dynamically calibrated based on the ratio of actual energy consumption to theoretical energy consumption within a historical period, and is updated iteratively once after every three complete transportation tasks. The corrected total predicted energy consumption expression can be: ;in, It can be a positive value (such as high-energy-consuming habits) or a negative value (such as energy-saving driving), and its value range can be from -0.15 to +0.25.

[0049] Through the above method, this embodiment can achieve a refined energy consumption assessment for each candidate route, so that the system does not only rely on the length of the route or the average speed, but truly combines the actual load status of the vehicle for calculation, thereby effectively avoiding the problem of misjudgment of range due to ignoring load changes.

[0050] Step S300: If it is determined that the vehicle's current available power is insufficient to support the vehicle to reach the destination and to retain a safe power margin, then at least one available charging facility is determined as an intermediate stop between the vehicle's current location and the destination based on the energy replenishment demand, and the candidate route is updated based on the intermediate stop.

[0051] For example, after calculating the estimated power consumption for each candidate route in step S200, it is determined whether the vehicle's current available power is sufficient to support its successful arrival at the destination, and a certain power safety margin is reserved accordingly. If the determination result is not positive, the energy replenishment planning process is initiated. Based on the actual energy replenishment needs, at least one available charging facility is identified as an intermediate stop within the feasible route range between the vehicle's current location and the destination, and the original candidate routes are updated accordingly to generate a complete driving plan that includes intermediate charging links.

[0052] In this embodiment, the vehicle's current available power is provided in real time by the battery management system via the CAN bus, and is represented by the available energy value corresponding to the current state of charge (SOC). This power safety margin is used to cope with unexpected situations that may occur during driving, such as temporary detours, traffic congestion, continuous operation of the air conditioning, or an increase in actual power consumption due to changes in road gradient. In one embodiment, the power safety margin can be set to meet 20% of the basic driving power consumption, that is, the system requires that the remaining power can at least cover the expected power consumption. 1.2 energy requirements to ensure driving safety and reliability.

[0053] When the system determines that the current available power is less than the sum of the required power and the safety margin, it triggers the power replenishment intervention mechanism and starts the charging facility matching process based on multi-condition screening.

[0054] Specifically, such as Figure 2 As shown, determining at least one available charging facility as an intermediate stop between the vehicle's current location and the destination based on energy replenishment needs includes steps S310-S350: Step S310: Obtain the set of charging facilities along the route from the vehicle's current location to the destination.

[0055] Step S320: Select charging facilities within a preset distance from key points in the transportation task information as candidate charging facilities.

[0056] Step S330: Determine the target charging facility from the candidate charging facilities based on the distance from the vehicle's current location, the charging power, and whether the facility is available.

[0057] Step S340: Construct two driving routes, each including an intermediate stop, based on the target charging facility and the current candidate routes.

[0058] Step S350: If any segment of the driving route cannot satisfy the requirement that the available power is greater than or equal to the sum of the expected power consumption and the safety margin, then a new intermediate stop is introduced within the current driving route.

[0059] As an example, the system queries information on public or dedicated DC fast charging stations within a certain range along all candidate routes from the starting point to the destination by calling the integrated charging pile database API interface. Each record includes, but is not limited to, the location coordinates of the charging pile, the name of the station, the operator, the interface type, the maximum output power, and real-time status data. Then, charging facilities within a preset distance from key points in the transportation task are selected as candidate charging facilities.

[0060] Key points include, but are not limited to, unloading points, intermediate delivery points, or designated parking areas within urban logistics service zones. For example, in a city delivery scenario, if a driver needs to deliver goods to the entrance of a supermarket in a residential area, the system identifies this destination as a key point and further extracts all available charging stations within an 800-meter radius as priority considerations. This embodiment, by combining charging arrangements with the unloading route, can avoid the problem of additional empty runs or repeated entry and exit from enclosed areas due to charging stations being far from the unloading location.

[0061] After obtaining a list of candidate charging facilities, the system comprehensively evaluates the following factors: 1. Straight-line or drivable distance from the vehicle's current location, prioritizing the closest facilities to reduce detours; 2. Charging power level, prioritizing charging stations supporting 60kW and above DC fast charging to shorten waiting time; 3. Real-time availability status, only charging stations marked as available or about to be released are included in the final recommendation range to prevent navigation to fully booked stations. These parameters are ranked using a weighted scoring method, with the highest-scoring facility being selected as the target charging facility.

[0062] Then, based on the target charging facility and the current optimal candidate route, a two-way driving route including an intermediate stop is constructed. This process splits the original route into two continuous segments: the first segment is from the current location to the target charging facility, and the second segment is from the target charging facility to the final destination. The system calculates the estimated power consumption for each segment and verifies whether, under the current available power conditions, each segment can meet the constraint that the available power is greater than or equal to the sum of the estimated power consumption and the safety margin. If satisfied, a complete recommended route including a charging stop is formed.

[0063] However, in long-distance transportation or multi-station delivery scenarios, a single charge may still not be enough to support the entire journey. That is, if it is determined that any segment of the route cannot meet the battery requirements, a new intermediate stop is introduced within the current route. For example, after the vehicle completes its first charge, although it can reach the next delivery point A, it cannot continue to the more distant destination B. The system will then search for a suitable available charging station near point A and insert it as a second intermediate stop into the route. This process will continue iterating until the entire route is divided into multiple short-distance segments with manageable battery capacity, and each segment can be completed with a safe battery margin.

[0064] Through the above methods, this embodiment can realize intelligent, task-oriented energy replenishment route planning for new energy light commercial vehicles, which not only solves the basic problem of whether it is possible to reach the destination, but also optimizes the operational experience of how to efficiently replenish energy, thereby improving the overall efficiency and reliability of high-frequency urban delivery operations.

[0065] After updating the candidate routes in step S300, the system proceeds to step S400, where each candidate route is evaluated based on the user-selected preset optimization objective to obtain a recommended route that meets the preset optimization objective.

[0066] In this embodiment, the system allows drivers or the dispatch platform to autonomously select different planning priorities based on the actual transportation scenario. Preset optimization objectives include, but are not limited to, at least one of the following: lowest cost objective, shortest time objective, and shortest route objective. This selection can be made through the human-machine interface of the in-vehicle terminal or a mode switching button on a mobile app. For example, before departure, the driver can click on the shortest time, lowest cost, or shortest distance option based on the nature of the task, and the system will then initiate the corresponding evaluation logic.

[0067] When a user selects the shortest travel time as the preset optimization goal, the system prioritizes minimizing total travel time and comprehensively calculates three types of time losses that may occur during the journey for each candidate route: first, waiting time due to road congestion, which is derived from real-time traffic flow information interfaces, with average traffic speeds marked on road segments and converted into incremental delays; second, waiting time for charging, which depends on whether charging stations are available and the length of the queue. For non-dedicated charging stations, the system estimates the access waiting time using a historical turnover rate model; and third, additional travel time due to detouring through restricted areas or temporary construction. The system calculates the sum of these three time factors as the comprehensive time cost for each candidate route and selects the route with the minimum total travel time as the recommended route.

[0068] When a user selects the lowest cost as the preset optimization objective, the system constructs a comprehensive cost function to quantify the economic expenditure of each candidate route throughout the transportation process. This optimization objective aims to minimize the sum of electricity consumption costs, charging service fees, and operational losses due to additional empty runs. Electricity consumption costs are determined by both estimated electricity consumption and current electricity prices, with price information derived from time-of-use pricing strategies published by charging station operators. Charging service fees consist of a basic electricity fee and a service fee, with the system prioritizing off-peak hours or discounted charging stations. Operational losses due to additional empty runs consider factors such as tire wear from detours and extended labor hours, and in one implementation, can be calculated using the operating cost per unit mileage. The system generates a total cost score for each route through a weighted summation method, ultimately outputting the route recommendation with the lowest cost.

[0069] When a user selects the shortest route as the preset optimization goal, the system aims to minimize the sum of the total driving mileage and the additional mileage caused by round-trip charging. In this mode, the system first eliminates all obviously detour route combinations, focusing on the main routes with the shortest geographical distance. Simultaneously, if intermediate stops for recharging are necessary, charging stations located close to the original route and requiring no significant detours are prioritized, minimizing the additional mileage caused by charging. Even if a fast charging station has higher power or a lower price, it will still be downgraded if its location is far from the main route.

[0070] During the evaluation process, an improved quantum ant colony algorithm can be used to solve the multi-objective function. This algorithm enhances the search breadth by simulating the quantum state superposition mechanism, uses dynamic pheromone updates to distinguish between superior and inferior routes, and quickly converges to a near-optimal solution within a finite time. For different optimization objectives, the algorithm internally adjusts the weight coefficients of each dimension to ensure that the output strictly corresponds to the priority direction selected by the user.

[0071] Based on the above process, a multi-objective evaluation of all candidate routes can be completed, generating a recommended route that best meets the user's needs, and pushing it to the in-vehicle display or mobile terminal for the driver to execute.

[0072] In some implementations, at least one of the following changes is continuously monitored during vehicle operation: real-time load, road congestion status, charging station availability, and the degree to which actual power consumption deviates from the predicted value; when a change is detected to exceed a set threshold, route replanning is completed within a preset time and the updated results are pushed out.

[0073] As an example, continuous monitoring is accomplished collaboratively by the vehicle-mounted terminal and the cloud. The vehicle-mounted terminal collects operational data at a frequency of no less than once every 30 seconds through the T-BOX and various sensors, and uploads it to the back-end server in real time via 4G / 5G network; the cloud system simultaneously connects to the traffic information platform and the charging facility operation database to obtain the latest road conditions and charging station status updates.

[0074] When a vehicle is in motion, unloading or loading operations mid-journey can cause a significant change in the vehicle's weight, thus affecting subsequent energy consumption characteristics. In this case, the system uses load sensors on the suspension to detect a real-time load decrease exceeding a set threshold, such as a reduction of more than one ton, which is considered a valid change event. Since the reduced load lowers rolling resistance and accelerates energy consumption, the previously conservatively estimated energy consumption demand changes accordingly. The original energy replenishment plan may no longer be applicable or even excessively redundant, thus requiring a reassessment.

[0075] Meanwhile, the external traffic environment may also change abruptly. The system continuously tracks the traffic speed and congestion index of each road segment by connecting to the city's traffic management platform and the real-time interface of third-party map service providers. Once it detects new congestion, traffic accidents, or temporary traffic control on the current route or alternative routes, causing the estimated travel time to exceed the original prediction by more than 20%, the system immediately marks the road segment as a high-delay-risk area and determines whether the current route covers the high-delay-risk area. If it does, the route is replanned.

[0076] Furthermore, the availability of charging stations already designated as intermediate stops also needs to be dynamically confirmed. The system periodically polls the status information of relevant charging stations. If it finds that a previously idle charging station has changed to be in use or under maintenance, and there are no other parallel available charging stations to replace it, then route replanning is triggered.

[0077] In addition, the system monitors whether the vehicle's actual energy consumption deviates from the initial model prediction. Specifically, it calculates the average energy consumption per 100 kilometers over a past period every 30 seconds and compares it with the predicted energy consumption obtained in step S200 based on the load correction model. When the actual energy consumption exceeds the predicted value by 20% for two consecutive samples, such as due to continuous uphill driving, prolonged use of air conditioning, or sudden acceleration or deceleration causing a sharp increase in energy consumption, the system determines that there is a risk of insufficient range.

[0078] If the change in any of the above-mentioned monitoring items exceeds a preset threshold, the route will be replanned based on steps S100 to S400 within a preset time. In one embodiment, the preset time is no more than 5 seconds to ensure that the driver can obtain the updated route suggestion in a short time, avoiding missing the opportunity to change lanes or causing operational inconvenience due to waiting too long.

[0079] Once the route replanning is complete, the new recommended route can be pushed to the driver in real time through various means such as voice broadcast, screen pop-ups, and trajectory highlighting.

[0080] Throughout the dynamic update process, the human-machine interface maintains clear guidance, with the current mode and estimated arrival time displayed at the top, and key information scrolling at the bottom such as a 2.5m height restriction ahead (passable) and an available 60kW fast charging station 300 meters from the target location. This helps drivers quickly understand the reasons for the change and safely execute the new instructions.

[0081] This embodiment, after constructing candidate routes, considers not only route distance and power consumption per kilometer when estimating the expected power consumption of these routes, but also incorporates the vehicle's real-time load to correct the estimated power consumption. This overcomes the problem of traditional navigation systems relying solely on ideal operating condition power consumption parameters, leading to misjudgments of range. It makes energy consumption estimation closer to actual driving conditions, significantly improving the accuracy of power prediction. Furthermore, when it is determined that the vehicle's remaining power is insufficient to meet the entire journey's needs while maintaining a safety margin, it proactively identifies charging needs and intelligently selects available charging facilities along the route as intermediate stops, achieving full-process route optimization including charging, thus avoiding the risk of power outages mid-journey. Furthermore, this embodiment can also evaluate candidate routes based on user-selected preset optimization goals, making the final route more suitable for the user's requirements. It can be understood that the overall solution fully considers the characteristics of new energy light commercial vehicles in urban delivery, such as large load fluctuations, tight operating windows, and complex road conditions. This not only improves energy utilization efficiency and transportation timeliness but also enhances the practicality and intelligence of navigation.

[0082] Figure 3 A schematic diagram of a route planning device according to an embodiment of this application is shown. Exemplarily, the route planning device includes: The construction module 100 is used to construct at least one candidate route based on the vehicle's operating status data, current transportation task information, and road traffic restriction information; wherein, the operating status data includes real-time load.

[0083] The calculation module 200 is used to calculate the expected power consumption of each candidate route based on the operating status data, the driving route length of each candidate route and the energy consumption model.

[0084] The update module 300 is used to determine at least one available charging facility as an intermediate stop between the vehicle's current location and the destination based on the energy replenishment demand if it is determined that the vehicle's current available power is insufficient to support the vehicle to reach the destination and to reserve a safe power margin, and to update the candidate route based on the intermediate stop.

[0085] The evaluation module 400 is used to evaluate each candidate route based on the preset optimization goal selected by the user, so as to obtain a recommended route that meets the preset optimization goal.

[0086] It is understood that the device in this embodiment corresponds to the route planning method in the above embodiments, and the options in the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0087] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the various modules in the route planning method or the route planning device described above.

[0088] In some implementations, the terminal device may be an in-vehicle smart terminal, a mobile communication device (such as a smartphone or tablet), an in-vehicle infotainment system (IVI), a T-BOX integrated unit, or a computing node in a back-end server. When the terminal device is a back-end server, it receives operating status data from the vehicle through the vehicle network communication interface and sends the generated recommended route to the in-vehicle terminal for display. When it is an in-vehicle terminal or a mobile device, it can complete part or all of the route planning calculation locally, which is especially suitable for scenarios with poor network signal or extremely high requirements for response latency.

[0089] Furthermore, when the processor executes the computer program, it can dynamically adjust the processing mode according to the current system resource status: when computing power is sufficient and the network is stable, it prioritizes centralized computing in the cloud; when the network is limited or a rapid response to sudden road conditions is required, it enables the edge-side lightweight model for local replanning, thereby ensuring the continuity and real-time performance of the navigation service.

[0090] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0091] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0092] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0094] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0095] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they 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 a 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 smartphone, 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.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A route planning method, characterized in that, include: Based on vehicle operating status data, current transportation task information, and road traffic restriction information, at least one candidate route is constructed; wherein, the operating status data includes real-time load. Based on the operating status data, the travel route length of each candidate route, and the energy consumption model, calculate the estimated power consumption corresponding to each candidate route; If it is determined that the vehicle's current available power is insufficient to support the vehicle to reach the destination and to reserve a safety margin of power, then at least one available charging facility is determined as an intermediate stop between the vehicle's current location and the destination based on the energy replenishment demand, and the candidate route is updated based on the intermediate stop. Based on the user-selected preset optimization goal, each candidate route is evaluated to obtain a recommended route that meets the preset optimization goal.

2. The route planning method according to claim 1, characterized in that, The road traffic restriction information includes height restrictions, traffic restrictions, weight restrictions, and temporary traffic control information; The operational status data includes the vehicle's current location; the transportation task information includes the starting point location and the destination location. Before constructing at least one candidate route based on vehicle operating status data, current transportation task information, and road traffic restriction information, the method further includes: constructing an initial route set based on the starting point location and the ending point location; The construction of the candidate routes includes: Based on the height restriction information, vehicle height, weight restriction information, total vehicle weight, traffic restriction information, temporary traffic control information, current vehicle location, and destination location, road segments with a height lower than the vehicle height, a load-bearing capacity lower than the total vehicle weight, and road segments prohibited from passage during specified time periods are excluded from the initial route set to generate the candidate routes.

3. The route planning method according to claim 1, characterized in that, The preset optimization objectives include at least one of the objectives of lowest cost, shortest time, and shortest route.

4. The method according to claim 3, characterized in that, When the preset optimization objective includes the shortest time objective, the optimization objective is to minimize the sum of congestion waiting time, charging waiting time, and detour delay time. When the preset optimization objective includes the lowest cost objective, the optimization objective is to minimize the sum of electricity consumption cost, charging service fee and operating loss caused by additional empty runs; When the preset optimization objective includes the shortest route objective, the optimization objective is to minimize the sum of the total driving mileage and the additional mileage caused by round-trip charging.

5. The method according to claim 1, characterized in that, Also includes: Continuously monitor changes in at least one of the following indicators while the vehicle is in motion: Real-time load, road congestion status, charging pile availability, and the degree to which actual power consumption deviates from the predicted value; When any change in any metric is detected to exceed a set threshold, route replanning is completed within a preset time and the updated results are pushed out.

6. The method according to claim 1, characterized in that, The step of determining at least one available charging facility as an intermediate stop between the vehicle's current location and the destination based on energy replenishment needs includes: Obtain the set of charging facilities along the route from the vehicle's current location to the destination. The charging facilities within a preset distance from key points in the transportation task information are selected as candidate charging facilities; wherein, the key points include unloading points, intermediate delivery points or service areas; The target charging facility is determined from the candidate charging facilities based on the distance from the vehicle's current location, the charging power, and whether the facility is available. Based on the target charging facility and the current candidate route, construct two driving routes, each including an intermediate stop. If any segment of the route cannot satisfy the requirement that the available power is greater than or equal to the sum of the expected power consumption and the safety margin, then a new intermediate stop will be introduced within the current route.

7. The method according to claim 1, characterized in that, The energy consumption model is determined based on the vehicle's energy consumption per 100 kilometers under the current operating conditions, the load correction coefficient, the real-time load, and the distance between the vehicle's current location and the destination.

8. A route planning device, characterized in that, include: The construction module is used to construct at least one candidate route based on vehicle operating status data, current transportation task information, and road traffic restriction information; wherein, the operating status data includes real-time load. The calculation module is used to calculate the expected power consumption of each candidate route based on the running status data, the driving route length of each candidate route, and the energy consumption model. The update module is used to determine at least one available charging facility as an intermediate stop between the vehicle's current location and the destination based on the energy replenishment demand if it is determined that the vehicle's current available power is insufficient to support the vehicle to reach the destination and to reserve a safe power margin, and to update the candidate route based on the intermediate stop. The evaluation module is used to evaluate each of the candidate routes based on the preset optimization goals selected by the user, so as to obtain a recommended route that meets the preset optimization goals.

9. A terminal device, characterized in that, The terminal device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the route planning method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed on a processor, implements the route planning method according to any one of claims 1-7.

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