New Energy Heavy Truck Energy Saving Path Planning Method and Electronic Equipment

Abstract

The present invention relates to the field of new energy technologies, and specifically discloses a new energy heavy truck energy-saving path planning method and an electronic device. The new energy heavy truck energy-saving path planning method includes: constructing a hierarchical road network based on an energy consumption model, where the hierarchical road network divides roads into multiple levels through a comprehensive weight formula, and the high-level road network includes low-energy consumption main roads, and the low-level road network includes all road types; based on vehicle real-time state parameters and destination information, using the bidirectional A* algorithm to perform path search in the hierarchical road network, and the bidirectional A* algorithm expands from the starting point and the end point synchronously and optimizes the search direction based on a dynamic heuristic function. The new energy heavy truck energy-saving path planning method of the embodiments of the present invention optimizes and solves the three major pain points of high energy consumption, difficult energy replenishment, and poor real-time performance in the path planning of new energy heavy trucks through modules such as hierarchical road network construction, dynamic weight adjustment, and bidirectional A* algorithm optimization, and solves the energy replenishment bottleneck problem of new energy heavy trucks.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy, and particularly relates to a method for planning an energy-saving path for a new energy heavy truck and an electronic device. Background Art

[0002] With the continuous development of intelligent transportation, especially electronic maps and navigation technologies, it has become possible to provide real-time road network and driving condition information for new energy vehicles, and it has also provided new ideas for the dynamic planning of energy-saving paths and economic speeds for new energy vehicles, as well as for formulating optimal management strategies adapted to real-time driving conditions.

[0003] When dealing with large-scale and multi-level traffic networks, traditional path planning methods have high computational complexity and are difficult to meet real-time requirements. Especially for vehicles such as new energy heavy trucks that require frequent energy replenishment, traditional methods cannot efficiently integrate road network levels and energy consumption constraints. Moreover, current new energy heavy trucks are limited by battery capacity and the distribution of energy replenishment facilities. Traditional path planning does not fully consider energy consumption minimization and energy replenishment requirements, which easily leads to insufficient energy or infeasible paths. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to some extent. To this end, an object of the present invention is to propose a method for planning an energy-saving path for a new energy heavy truck and an electronic device to reduce vehicle energy consumption.

[0005] To achieve the above object, a first aspect embodiment of the present invention proposes a method for planning an energy-saving path for a new energy heavy truck, the method comprising:

[0006] Construct a hierarchical road network based on an energy consumption model, the hierarchical road network divides roads into multiple levels through a comprehensive weight formula, where the high-level road network includes low-energy main roads, and the low-level road network includes all road types;

[0007] Based on vehicle real-time state parameters and destination information, use the bidirectional A* algorithm to search for a path in the hierarchical road network, the bidirectional A* algorithm expands from the starting point and the end point synchronously, and optimizes the search direction based on a dynamic heuristic function;

[0008] During the path search process, dynamically adjust the search level and range according to the remaining energy of the vehicle. If the remaining energy is lower than the safety threshold, trigger an energy replenishment decision and re-plan the path;

[0009] Update the energy consumption model in real time based on vehicle load changes or external environment changes, dynamically correct the predicted energy consumption of un-traveled road segments, and initiate path re-planning to generate an optimized energy-saving path.

[0010] In some embodiments of the present invention, the comprehensive weight formula includes:

[0011]

[0012] Among them, and are non-linear exponents used to enhance or weaken the influence of node attributes and edge attributes;

[0013] represents the level evaluation value of the road network where the road section is located. The larger the value, the lower the road network level, and the relatively lower the selection priority in path planning; conversely, the smaller the value, the higher the road network level and the higher the priority;

[0014] represents the node attribute weight, specifically the weight of node passing capacity and speed limit; represents the weight of road section length and energy consumption coefficient; represents the distance-related weight; represents the traffic condition weight, represents the energy consumption weight; : are the weight coefficients of each factor, and ; 、 、 : are the maximum values of each factor.

[0015] In some embodiments of the present invention, the dynamic heuristic function of the bidirectional A* algorithm is:

[0016] ( ) = ( )

[0017] Among them, is the product of the Euclidean distance and the energy consumption coefficient; 、 、 are dynamic weight coefficients, which are the road network layer coefficient, the energy consumption coefficient, and the real-time traffic coefficient respectively;

[0018] is the current road network layer, is the total number of layers, is the average energy consumption of the road section, is the real-time traffic time, is the time based on the historical average speed or ideal traffic conditions.

[0019] In some embodiments of the present invention, the dynamic adjustment of the search level and range specifically includes the following steps:

[0020] Let the remaining energy of the vehicle be , and the estimated energy consumption of the vehicle to reach the destination be , the safety threshold of the vehicle is ;

[0021] If the remaining energy , preferentially search in the high-level road network;

[0022] If the remaining energy , force to switch to the low-level road network for searching.

[0023] In some embodiments of the present invention, the adjustment rules of the dynamic weight coefficients , , are as follows:

[0024] When the vehicle is in motion, = (1 - / ) is updated;

[0025] Wherein, is the total energy of the vehicle, is 's reference value;

[0026] When the vehicle load increases, is updated according to = (1 + ΔW / );

[0027] Wherein, is the load change amount, is the reference load;

[0028] When traffic congestion is detected, is updated according to = (1 + / );

[0029] Wherein, is , is the real-time traffic congestion time or congestion index.

[0030] In some embodiments of the present invention, the charging decision includes the following steps:

[0031] (a) Generate a set of candidate charging points { } according to the current position of the vehicle;

[0032] (b) Calculate the comprehensive cost of each charging point, wherein, is the current energy consumption rate, is the energy consumption rate after charging, For the charging time cost, is the time weight coefficient, is the distance to the end point;

[0033] (c) Select the charging point with the minimum total cost , and plan the hierarchical paths from the current position to and to the end point.

[0034] In some embodiments of the present invention, the generation method of the set of candidate charging points is as follows:

[0035] Based on the remaining energy and the safety threshold , filter the charging stations that meet .

[0036] In some embodiments of the present invention, during the path replanning process, if it is detected that the vehicle load is reduced and the originally planned charging point is no longer necessary, recalculate the path and delete the redundant charging points.

[0037] In some embodiments of the present invention, during the path execution stage, at every preset time interval re-evaluate the remaining energy and the energy consumption of the un-traveled sections. If it meets -∑ , immediately trigger local path replanning, where ∑ is the total estimated energy consumption of the un-traveled sections.

[0038] In some embodiments of the present invention, the connection rule between each level in the hierarchical road network is as follows: The low-level road network is connected to the middle-level road network through key nodes, and the middle-level road network is connected to the high-level road network through main road hubs.

[0039] To achieve the above object, an embodiment of the second aspect of the present invention proposes an electronic device, including a memory, a processor, and a computer program stored on the memory. When the computer program is executed by the processor, the above new energy heavy truck energy-saving path planning method is implemented.

[0040] The new energy heavy truck energy-saving path planning method and electronic device of the embodiments of the present invention, through mechanisms such as hierarchical road network construction, dynamic weight adjustment, and bidirectional A* algorithm optimization, systematically solve the problems of energy consumption constraint, computational complexity, and dynamic adaptability in the path planning of new energy heavy trucks, help drivers select the most energy-saving driving routes, reduce the overall energy consumption level of new energy vehicles, solve the charging bottleneck problem of new energy heavy trucks, and promote the development of the new energy vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1It is a schematic flow chart of an energy-saving path planning method for a new energy heavy truck according to an embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of using the bidirectional A* algorithm to perform path search in a hierarchical road network according to an embodiment of the present invention;

[0043] Figure 3 It is a schematic structural diagram of an electronic device according to another embodiment of the present invention. Detailed implementation manners

[0044] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0045] The energy-saving path planning method and electronic device of the embodiment of the present invention will be described below with reference to the accompanying drawings.

[0046] Figure 1 It is a schematic flow chart of an energy-saving path planning method for a new energy heavy truck according to an embodiment of the present invention.

[0047] As shown in Figure 1 , Figure 2 , the energy-saving path planning method for a new energy heavy truck includes the following steps:

[0048] S1. Construct a hierarchical road network based on an energy consumption model. The hierarchical road network divides roads into multiple levels through a comprehensive weight formula, where the high-level road network includes low-energy main roads, and the low-level road network includes all road types; due to topological connectivity, there will also be a small number of other types of road segments in the high-level road network. By dividing the road network into different levels according to energy consumption and priority, the search space can be significantly reduced and the calculation efficiency can be improved. For example, in the global road network, only long-distance low-energy paths need to be quickly screened in the high-level road network, avoiding traversing all possible complex road segments.

[0049] As an example, the road network is divided into 3-5 levels according to road grades. The high-level road network includes low-energy main roads such as highways and national roads, the middle level also includes urban main roads, and the low level includes all roads such as secondary roads and branch roads. The traffic capacity and speed limit are marked for each road node, and the length, slope, and historical average traffic flow are marked for each edge.

[0050] The high-level road network ensures the efficiency of long-distance transportation by screening "low-energy main roads". These roads have strong traffic capacity, low energy consumption, and continuous paths, making them suitable for rapid search of long-distance and globally optimal paths. For example, highways have a small slope and a high speed limit, and the energy consumption per unit distance is significantly lower than that of urban branch roads. The low-level road network includes all roads, including rural roads and urban roads, covering sections with short distances, high energy consumption, and complex traffic environments, and is used for refined local path planning to ensure that feasible paths can still be found under special circumstances (such as charging requirements and traffic control).

[0051] S2. Based on the vehicle's real-time state parameters and destination information, the bidirectional A* algorithm is used to search for paths in the hierarchical road network. The bidirectional A* algorithm expands synchronously from the starting point and the end point, and optimizes the search direction based on a dynamic heuristic function. Searching synchronously from the starting point and the end point and combining with the dynamic heuristic function can reduce redundant calculations and improve real-time performance. Incorporating the road network hierarchy, energy consumption, and traffic conditions into the heuristic function can improve the accuracy of the search direction.

[0052] When existing methods (such as A*) search in the full-scale road network, it is difficult to meet the real-time requirements due to the large number of nodes and edges. In this solution, a bidirectional search is implemented on the hierarchical road network to quickly locate the globally low-energy path.

[0053] S3. During the path search process, the search hierarchy and range are dynamically adjusted according to the remaining energy of the vehicle. If the remaining energy is lower than the safety threshold, a charging decision is triggered and the path is re-planned. That is, when the energy is sufficient, the high-level is preferred to ensure high efficiency and energy conservation; when the energy is insufficient, the low-level is forced to find a charging point or a shortcut.

[0054] Traditional path planning (such as Dijkstra and A*) is mostly optimized based on distance or time, without considering the energy limitation of new energy vehicles. For example, a fully loaded heavy truck may break down halfway during long-distance transportation due to the lack of planned charging points.

[0055] S4. Based on the changes in vehicle load or external environment, the energy consumption model is updated in real time, the predicted energy consumption of the un-traveled section is dynamically corrected, and path re-planning is initiated to generate an optimized energy-saving path.

[0056] Changes in vehicle load or external environment (such as congestion and weather) trigger real-time updates of the energy consumption model to ensure that the path is always optimized based on the latest state. For example, when the load increases by 20%, the system will automatically avoid high-slope sections, and the energy consumption prediction error is reduced to less than 5%. In traditional methods, the technical path planning is static and cannot cope with sudden changes in vehicle status or road conditions.

[0057] Through a hierarchical road network structure, an efficient search algorithm, a dynamic adjustment strategy, and a real-time optimization mechanism, this method systematically solves the problems of energy consumption constraints, computational complexity, and dynamic adaptability in the path planning of new energy heavy trucks. It deeply couples the energy consumption model with the road network hierarchy and achieves a balance between global energy conservation and local feasibility through the collaborative optimization of the bidirectional A* algorithm and dynamic weights. Compared with the existing technologies, this solution has made significant progress in terms of energy-saving effect, path reliability, and computational efficiency.

[0058] In some embodiments of the present invention, the comprehensive weight formula includes:

[0059]

[0060] Wherein, and are non-linear exponents used to enhance or weaken the influence of node attributes and edge attributes (for example, when θ>1, the negative impact of high-energy consumption sections is strengthened);

[0061] represents the grade evaluation value of the road network where the road section is located. This value is used to divide the road network grades. The larger the value, the lower the road network grade, and the relatively lower the priority selected in path planning; conversely, the smaller the value, the higher the road network grade and the higher the priority;

[0062] represents the node attribute weight, specifically the weight of the node passing capacity and speed limit; Let be the passing capacity of the node (which can be quantified as the maximum number of vehicles passing through per unit time), be the maximum passing capacity among all nodes; be the speed limit of the node, be the highest speed limit among all nodes; then ; For nodes with lower passing capacity and lower speed limit, the corresponding value is larger, and the more it improves the road network grade evaluation value, which means the possible lower road network grade where it is located;

[0063] represents the weight of the road section length and energy consumption coefficient. Here, the length of the edge is set as L and the edge weight determined based on factors such as energy consumption and time affects the edge attribute weight. Let be the maximum length among all edges, the larger it is, the higher the "cost" of this edge; then . The longer the edge and the higher the "cost", the larger it is, and the greater the impact on the road network grade evaluation value;

[0064] Represents the distance - related weight; considering the impact of the distance between the current road segment and the starting point or the ending point on the road network level. Let D be the distance from the mid - point of the current road segment to the destination (or the distance to the starting point or a combination of both can be selected according to the actual situation), and is the maximum distance from all road segments in the road network to the destination. Then . The farther away from the destination, is larger, which to a certain extent increases the evaluation value of the road network level;

[0065] Represents the traffic condition weight; calculated through real - time traffic flow data. Let be the actual traffic flow of the road segment, be the designed traffic capacity of the road segment. Then . When the traffic flow is larger and approaches or exceeds the traffic capacity of the road segment, is larger, reflecting the traffic congestion of this road segment, which will increase the evaluation value of the road network level and may reduce the level of the road network where it is located, is the maximum traffic flow or maximum traffic capacity of all road segments in the road network;

[0066] Represents the energy consumption weight; calculated by combining the load M of the vehicle and the energy consumption per unit distance E of the road segment. Let be the maximum load of the vehicle, be the maximum energy consumption per unit distance under specific conditions. Then . The larger the load and the higher the energy consumption per unit distance, is larger, and the more obvious the promotion effect on the evaluation value of the road network level, which means that the road network where it is located may have its level reduced due to energy consumption factors;

[0067] , , : Are the maximum values of each factor respectively, used for normalization processing to eliminate the dimension difference;

[0068] : Are the weight coefficients of each factor respectively, and ; and these coefficients can be set according to the importance of each factor in the actual application scenario; updated through real - time data (such as current traffic flow, remaining energy of the vehicle) .

[0069] In the above - mentioned technical solution, the node attributes are used to evaluate the traffic capacity and efficiency of key nodes (such as intersections, highway entrances and exits, transportation hubs, etc.) in the road network, mainly including the following parameters: traffic capacity and speed limit , and this method tends to avoid nodes with high to reduce the increase in energy consumption caused by traffic bottlenecks or low - speed restrictions;

[0070] Edge attributes are used to describe the physical characteristics and energy consumption characteristics of road segments (road sections), mainly including the following parameters: road segment length L and energy consumption coefficient , this method preferentially selects road segments with small values to reduce overall energy consumption;

[0071] In the comprehensive weight formula, θ and as non-linear exponents, act on the weights of node attributes and edge attributes respectively, and their design intentions are as follows:

[0072] θ node attribute exponent:

[0073] Enhanced influence: If θ > 1, the weight of node attributes will be amplified (for example, when θ = 1.2, >[[]] ), further strengthening the avoidance of nodes with low traffic capacity;

[0074] Weakened influence: If θ < 1, the weight of node attributes will be weakened (for example, when θ = 0.8, <[[[]] ), reducing its impact on the road network level division;

[0075] For example, in a congested urban area, set θ = 1.5, = 0.8, strengthen the avoidance of nodes with low traffic capacity (θ > 1), and at the same time weaken the influence of edge attributes ( < 1), allowing the selection of a shorter but slightly higher energy consumption detour path.

[0076] Edge attribute exponent:

[0077] Enhanced influence: If > 1, the weight of edge attributes will be amplified (for example, when = 1.5, >[[[]] ), preferentially avoiding long-distance or high-energy consumption road segments;

[0078] Weakened influence: If < 1, the weight of edge attributes will be weakened (for example, when = 0.7, <[[[]] ), allowing the system to more flexibly select some high-energy consumption but short-distance paths;

[0079] For example, during long-distance highway transportation, set θ = 0.9, = 1.3, weaken the influence of node attributes (θ < 1), but strengthen the avoidance of high-energy consumption road segments ( > 1), and preferentially select long-distance but low-energy consumption highways.

[0080] By dynamically adjusting the weights of node and edge attributes through a non-linear exponent, this method can flexibly balance the traffic efficiency and energy consumption cost of paths under different scenarios, achieving the global energy-saving goal; the introduction of θ and enhances the adaptability of the model to different road network characteristics (such as urban dense road networks vs. intercity sparse road networks), avoiding a one-size-fits-all static parameter setting.

[0081] As an example, for a certain highway section: = 2000 vehicles / hour, = 120 km / h, L = 50 km, unit energy consumption E = 0.8 kWh / km;

[0082] For a certain rural road section: = 500 vehicles / hour, = 40 km / h, L = 10 km, unit energy consumption E = 1.5 kWh / km;

[0083] Then, the comprehensive weight formula is used for calculation. Let = 0.2; θ = 1.2, = 0.8 (enhancing the influence of node attributes and weakening the influence of edge attributes), = 300 km (the maximum distance of the road network), = 2000 vehicles / hour (the maximum traffic flow), = 2.0 kWh / km (the maximum energy consumption under full-load extreme road conditions);

[0084] After calculation, it can be obtained that:

[0085] = 1.42, = 0.05, = 0.65;

[0086] Conclusion: = 0.65, belonging to the low-level road network (threshold setting: > 0.5 is the low level).

[0087] During the path planning process, this method switches (or searches in parallel) from the high-level road network to the low-level road network step by step, with the following core advantages:

[0088] Quickly lock in the low - energy main roads through the high - level road network, and use the low - level road network to supplement details and optimize. When directly searching in the full - scale low - level road network, the number of nodes and edges is huge, the computational complexity is high, and the low - level road network contains a large number of high - energy consumption sections (such as urban roads with frequent starts and stops). Direct search may lead to a locally optimal but globally high - energy consumption path; moreover, hierarchical search greatly compresses the search space to meet the real - time requirements. Generally speaking, when the energy is sufficient: global optimization, choose a long - distance low - energy consumption path; when the energy is insufficient: local optimization, choose a short - distance feasible path, even if the energy consumption is high, to complete the corresponding transportation task.

[0089] In some embodiments of the present invention, the dynamic heuristic function of the bidirectional A* algorithm is:

[0090] ( )= ( )

[0091] Among them, is the product of the Euclidean distance and the energy consumption coefficient; 、 、 are dynamic weight coefficients, which are respectively the road network level coefficient (a small value for high - level roads, encouraging long - distance search), the energy consumption coefficient (the penalty value for high - energy consumption sections increases), and the real - time traffic coefficient (the penalty value for congested sections increases);

[0092] is the current road network level, is the total number of layers, is the average energy consumption of the section, is the real - time traffic time, is the time based on the historical average speed or ideal traffic conditions.

[0093] During the path retrieval process, when searching in the high - level road network, the heuristic value decreases, and the algorithm tends to quickly search for long distances; when searching in the low - level road network, the heuristic value increases, and the algorithm refines the local search.

[0094] In some embodiments of the present invention, dynamically adjusting the search level and range specifically includes the following steps:

[0095] Let the remaining energy of the vehicle be , the estimated energy consumption for the vehicle to reach the destination be , and the safety threshold of the vehicle be ;

[0096] If the remaining energy , give priority to searching in the high - level road network;

[0097] If the remaining energy , force to switch to low - level road network search.

[0098] As an example, when (such as remaining energy 200 kWh, estimated energy consumption 150 kWh, safety threshold 50 kWh):

[0099] At this time, give priority to searching in the high - level road network, reduce the weight of the heuristic function, and encourage long - distance jumps; while when the remaining energy is insufficient (such as remaining energy 120 kWh, estimated energy consumption 150 kWh), force to switch to the low - level road network, increase the local search accuracy, and also facilitate finding charging points for charging.

[0100] In some embodiments of the present invention, the adjustment rules of the dynamic weight coefficients , , are as follows:

[0101] When the vehicle is driving, = (1 - / ) ; Adjust the search priorities of the high - level and low - level road networks, The smaller the value, the more inclined to long - distance search in the high - level road network. On the contrary, when the remaining energy is sufficient, increases, allowing refined search in the low - level road network;

[0102] Among them, is the total energy of the vehicle, is 's reference value;

[0103] When the vehicle load increases, According to = (1 + ΔW / ) update; reflects the impact of load change on energy consumption, and increases when the load increases to strengthen the avoidance of high - energy - consumption sections;

[0104] Among them, is the load change amount, is the reference load;

[0105] When traffic congestion is detected, According to = (1 + / ) update; reflects the impact of traffic congestion on route selection, and increases during congestion to preferentially select unobstructed sections;

[0106] Among them, is , the real-time traffic congestion time (unit: minute) or congestion index (such as a normalized value between 0 and 1).

[0107] Dynamic weight coefficient 、 、 The parameter definitions of are summarized in the following table:

[0108]

[0109] In some embodiments of the present invention, if the remaining energy of the vehicle minus the predicted energy consumption of the un-traveled section drops below the set safety energy threshold, the system will initiate a route replanning process and make an intelligent energy replenishment decision. On this basis, the system will re-optimize the route according to the latest conditions to ensure that the vehicle can reach the final destination efficiently and safely while meeting the energy requirements. The energy replenishment decision includes the following steps:

[0110] (a)Generate a set of candidate energy replenishment points { } according to the current position of the vehicle;

[0111] (b)Calculate the comprehensive cost of each energy replenishment point , where is the current energy consumption rate, is the energy consumption rate after energy replenishment, is the time cost of energy replenishment, is the time weight coefficient, is the distance to the end point;

[0112] (c)Select the energy replenishment point with the minimum total cost, and plan the hierarchical route from the current position to and to the end point.

[0113] As an example, for energy replenishment point A: the remaining energy of the vehicle is 120 kWh, the safety threshold is 50 kWh, the current energy consumption rate is 2 kWh / km, the distance is 20 km, the energy consumption rate after energy replenishment is 1.8 kWh / km, and the energy replenishment time is 15 minutes (μ = 0.5). At this time

[0114] ≈45

[0115] For energy replenishment point B: the distance is 30 km, the energy consumption rate after energy replenishment is 1.6 kWh / km, and the energy replenishment time is 20 minutes. At this time

[0116] =50

[0117] Then select the charging point A at this time and plan the route: current location → A → destination.

[0118] As an example, the charging time cost ( ) is affected by the type of charging pile and the dynamic change of queuing time. The prediction error of the traditional static model is large, resulting in inaccurate route planning. Therefore, the real-time data of the charging pile (such as idle state, queuing length, charging power) can be accessed to dynamically update . For example:

[0119] = +

[0120] Where, is the current number of queuing vehicles; is the average charging time per vehicle; is the number of charging piles.

[0121] In addition, the Q-learning model can also be trained to predict the optimal charging time period according to the historical congestion pattern of the charging station, reducing the waiting time.

[0122] In some embodiments of the present invention, the generation method of the set of candidate charging points is:

[0123] Based on the remaining energy and the safety threshold , screen the charging stations that meet .

[0124] As an example, if the remaining energy of the vehicle is 120 kWh, the safety threshold is 50 kWh, and the current energy consumption rate is 2 kWh / km, then = 35 km, that is, screen the charging points within 35 km.

[0125] It should be noted that in remote areas or during peak traffic periods, the number of charging points that meet the energy constraints is insufficient, which may lead to the failure of the plan. At this time, mobile charging resource scheduling can be carried out, that is, docking with the mobile charging vehicle service platform, and scheduling the mobile charging vehicle to a predetermined position when there is no fixed charging point.

[0126] To further refine the above solution, during the vehicle's movement, when key parameters (such as vehicle load weight) change, the process of re-planning the route is triggered. This mechanism ensures that the route selection is always optimized based on the latest operating conditions and constraints, thus maintaining the timeliness, effectiveness, and optimality of the route planning scheme. Specifically, when the vehicle load increases, leading to an increase in energy demand, the system will evaluate whether to replenish energy or adjust the energy replenishment strategy; conversely, if the vehicle load decreases, resulting in a reduction in energy consumption, the originally planned energy replenishment may no longer be necessary. At this time, the system will recalculate the optimal energy consumption route for the section under the changed conditions to ensure that the route planning is both efficient and energy-saving. By implementing this dynamic adjustment mechanism, the system can continuously provide optimized route planning solutions in a complex and changing environment, adapting to changes in energy demand under different circumstances.

[0127] It can be summarized as follows: During the route re-planning process, if it is detected that the vehicle load has decreased and the originally planned energy replenishment point is no longer necessary, the route is recalculated and the redundant energy replenishment points are deleted. For example, if the vehicle load decreases (such as from 30 tons to 20 tons) and the original energy replenishment point A is no longer necessary, the energy consumption is recalculated, the energy replenishment point A is deleted, and the route of the lower-level road network is directly planned.

[0128] As an example, after the load is reduced, if there is a sudden traffic jam or an increase in energy consumption on the subsequent section, deleting the energy replenishment point may lead to insufficient energy. Therefore, during the long-term driving process of the vehicle, the dynamic priority marking method can be used to classify the energy replenishment points according to the risks of the subsequent sections, and more backup points are reserved for high-risk sections.

[0129] In some embodiments of the present invention, during the route execution phase, at every preset time interval the remaining energy and the energy consumption of the un-traveled sections are re-evaluated. If it meets -∑ , local route re-planning is immediately triggered, where ∑ is the total estimated energy consumption of the un-traveled sections.

[0130] It should be noted that since frequent triggering of local re-planning may lead to frequent route switching, which may affect driving stability, sliding window optimization can be used to re-plan only the un-traveled sections within the most recent time, reducing the global impact.

[0131] In some embodiments of the present invention, the connection rules between the levels in the hierarchical road network are as follows: The lower-level road network is connected to the middle-level road network through key nodes (such as the entrance of the main road), and the middle-level road network is connected to the high-level road network through main road hubs (such as highway ramps).

[0132] Since critical nodes may become congestion bottlenecks, leading to path interruptions, virtual connection nodes are temporarily created during traffic congestion or construction to allow cross-level jumps. For example, drones are used to monitor road conditions in real time and dynamically generate temporary paths. In addition, the weights of connection nodes can be adjusted according to real-time traffic flow (such as ), and the priority is reduced during congestion.

[0133] As an example, if the entrance of a main road is congested, the algorithm automatically selects a sub-optimal connection node.

[0134] In some embodiments of the present invention, the specific method for real-time updating of the energy consumption model includes the following steps:

[0135] Step 1: Real-time load monitoring and dynamic adjustment of rolling resistance

[0136] Vehicle load data is collected in real time through on-vehicle sensors. When the load change exceeds a threshold (such as ΔM > 10% of the rated load), the energy consumption model is triggered for update; the rolling resistance is recalculated based on the new load, and the energy consumption parameter per unit distance is adjusted.

[0137] Step 2: Multi-sensor data fusion and environmental parameter correction

[0138] Fuse the data of sensors such as GPS and IMU to obtain environmental parameters such as road slope and wind speed in real time;

[0139] Dynamically correct the energy consumption model according to environmental parameters (such as slope and headwind), and adjust the energy consumption per unit distance.

[0140] Step 3: Machine learning-driven model parameter optimization

[0141] Utilize the historical path database and reinforcement learning algorithm to automatically optimize the energy consumption model parameters (such as rolling resistance coefficient and air resistance coefficient);

[0142] Continuously iterate the model through a reward function (such as the deviation between the actual energy consumption and the predicted energy consumption) to improve the long-term prediction accuracy.

[0143] Step 4: Linkage between dynamic weight adjustment and path planning

[0144] When the load increases, the energy consumption weight coefficient is dynamically increased (such as ), guiding the path planning to avoid high-energy consumption sections;

[0145] When the external environment changes (such as congestion), adjust the search weight and preferentially select low-energy consumption or low-congestion paths.

[0146] Step 5: Real-time data-driven local path replanning

[0147] Real-time monitor the remaining energy and the energy consumption of the un-traveled road segments. When -∑ is satisfied, trigger local replanning; and dynamically delete redundant charging points and replan the route to ensure energy safety.

[0148] By integrating vehicle status, road attributes, environmental factors and historical data, this update mechanism breaks through the limitations of single data source in the existing technology. Then, through real-time feedback and machine learning, it realizes the continuous evolution of the energy consumption model, local replanning and incremental update to ensure real-time performance. While the existing technology mostly relies on global computing and has slow response, this mechanism deeply couples the energy consumption model with route planning to achieve dynamic balance among energy consumption, time and safety. These features enable this solution to have significant advantages in the field of energy consumption management of new energy heavy trucks and provide key technical support for achieving full-scenario energy conservation.

[0149] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0150] As Figure 3 shown in the structural schematic diagram of an electronic device in the present invention, the electronic device 200 includes: a processor 201 and a memory 203. Among them, the processor 201 and the memory 203 are connected, such as connected through a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation to the embodiments of the present invention.

[0151] The processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in combination with the disclosure of the present invention. The processor 201 may also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0152] The bus 202 may include a path for transmitting information between the above components. The bus 202 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 202 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 it is only represented by a thick line in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0153] The memory 203 is used to store a computer program corresponding to the new energy heavy truck energy-saving path planning method of the foregoing embodiments of the present invention, and this computer program is controlled and executed by the processor 201. The processor 201 is used to execute the computer program stored in the memory 203 to implement the content shown in the foregoing method embodiments.

[0154] Among them, the electronic device 200 includes but is not limited to: mobile terminals such as laptop computers, PADs (tablet computers), etc. and fixed terminals such as desktop computers, etc. Figure 3 The shown electronic device 200 is only an example and should not bring any restrictions to the functions and usage scope of the embodiments of the present invention.

[0155] The electronic device 200 of the embodiments of the present invention collects vehicle status (load, remaining energy) and road condition data (traffic flow, slope) in real time, updates the hierarchical road network weights every 5 seconds, triggers the bidirectional A* algorithm to re-search, and when the remaining energy is lower than the threshold, starts the energy replenishment decision-making and generates a new path within 10 seconds. Through mechanisms such as hierarchical road network construction, dynamic weight adjustment, and bidirectional A* algorithm optimization, it systematically solves the problems of energy consumption constraints, computational complexity, and dynamic adaptability in the path planning of new energy heavy trucks, helps drivers select the most energy-saving driving routes, and reduces the overall energy consumption level of new energy vehicles.

[0156] Note that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, new energy, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0157] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0158] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0159] Furthermore, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0160] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A new energy heavy truck energy-saving path planning method, characterized in that The method includes: Construct a hierarchical road network based on an energy consumption model. The hierarchical road network divides roads into multiple levels through a comprehensive weight formula, where the high-level road network includes low-energy consumption main roads, and the low-level road network includes all road types; Based on the real-time state parameters of the vehicle and the destination information, use the bidirectional A* algorithm to search for paths in the hierarchical road network. The bidirectional A* algorithm expands synchronously from the starting point and the ending point, and optimizes the search direction based on a dynamic heuristic function; During the path search process, dynamically adjust the search level and range according to the remaining energy of the vehicle. If the remaining energy is lower than the safety threshold, trigger an energy replenishment decision and re-plan the path; Based on the changes in vehicle load or external environment, update the energy consumption model in real time, dynamically correct the predicted energy consumption of the un-traveled road sections, and initiate path re-planning to generate an optimized energy-saving path; The dynamic heuristic function of the bidirectional A* algorithm is: ( )= （ ） Among them, is the product of the Euclidean distance and the energy consumption coefficient; , , are dynamic weight coefficients, which are respectively the road network hierarchy coefficient, the energy consumption coefficient, and the real-time traffic coefficient; is the current road network level, is the total number of layers, is the average energy consumption of the road section, is the real-time traffic time, is the time based on the historical average speed or under ideal traffic conditions.

2. The new energy heavy truck energy-saving path planning method according to claim 1, wherein The comprehensive weight formula includes: Among them, and are non-linear exponents used to enhance or weaken the influence of node attributes and edge attributes; Represents the level evaluation value of the road network where the road section is located. The larger the value, the lower the road network level, and the relatively lower the priority selected in route planning; conversely, the smaller the value, the higher the road network level and the higher the priority. Represents the weight of node attributes, specifically the weight of node passing capacity and speed limit; Represents the weight of road segment length and energy consumption coefficient; Represents the distance-related weight; Represents the traffic condition weight, Represents the energy consumption weight; : are the weight coefficients of each factor, and ; 、 、 : are the maximum values of each factor.

3. The new energy heavy truck energy-saving path planning method according to claim 1, wherein The specific steps of dynamically adjusting the search level and range include the following: Let the remaining energy of the vehicle be , the estimated energy consumption for the vehicle to reach the destination be , and the safety threshold of the vehicle be ; If there is remaining energy , search preferentially in the high-level road network; If the remaining energy , force a switch to the lower-level road network search.

4. The new energy heavy truck energy-saving path planning method according to claim 1, characterized in that The dynamic weight coefficient , , is adjusted according to the following rules: When the vehicle is in motion, = (1 - / ) Update; Among them, is the total energy of the vehicle, is 's reference value; When the vehicle load increases, According to = (1 + ΔW / ) Update; Among them, is the load change amount, is the reference load; When traffic congestion is detected, Press = (1 + / ) Update; Among them, is , the real-time traffic congestion time or congestion index.

5. The new energy heavy truck energy-saving path planning method according to claim 1, wherein The energy replenishment decision includes the following steps: (a) Generate a candidate charging point set { } based on the current position of the vehicle; (b)Calculate the comprehensive cost of each energy replenishment point ; Among them, is the current energy consumption rate, is the energy consumption rate after energy replenishment, is the time cost of energy replenishment, is the time weight coefficient, is the distance to the end point; (c) Select the charging point with the minimum total cost , and plan the hierarchical path from the current location to and to the end point.

6. The energy-saving path planning method for new energy heavy trucks according to claim 5, characterized in that The generation method of the candidate energy replenishment point set is: Based on the remaining energy and the safety threshold , screen the charging stations that meet .

7. The energy-saving path planning method for new energy heavy trucks according to claim 1, wherein During the path re-planning process, if it is detected that the vehicle load is reduced and the originally planned energy replenishment point is no longer necessary, re-calculate the path and delete the redundant energy replenishment points.

8. The new energy heavy truck energy-saving path planning method according to claim 1, characterized in that During the path execution phase, at every preset time interval re-evaluate the remaining energy and the energy consumption of the un-traveled road segments. If it satisfies -∑ , immediately trigger local path replanning, where ∑ is the total estimated energy consumption of the un-traveled road segments.

9. The new energy heavy truck energy-saving path planning method according to claim 1, wherein The connection rule between each level in the hierarchical road network is: the low-level road network is connected to the middle-level road network through key nodes, and the middle-level road network is connected to the high-level road network through the main road hub.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory. When the computer program is executed by the processor, it implements the new energy heavy truck energy-saving path planning method according to any one of claims 1-9.

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