New energy heavy truck energy-saving path planning method and electronic equipment

Abstract

The invention relates to the technical field of new energy, and particularly discloses a new energy heavy truck energy-saving path planning method and electronic device.The new energy heavy truck energy-saving path planning method comprises the steps that a layered road network based on an energy consumption model is constructed, and the layered road network divides a road into multiple levels through a comprehensive weight formula, wherein the high-level road network comprises low-energy-consumption main roads, and the low-level road network comprises all road types; and on the basis of vehicle real-time state parameters and destination information, a two-way A algorithm is adopted to carry out path search in the layered road network, the two-way A algorithm is synchronously expanded from a starting point and an ending point, and the search direction is optimized on the basis of a dynamic heuristic function. According to the new energy heavy truck energy-saving path planning method provided by the embodiment of the invention, three major pain points of high energy consumption, difficult energy complementation and poor real-time performance in new energy heavy truck path planning are optimized and solved through modules of layered road network construction, dynamic weight adjustment, bidirectional A * algorithm optimization and the like, and the energy complementation bottleneck problem of the new energy heavy truck is solved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to an energy-saving path planning method and electronic equipment for a new energy heavy-duty truck. Background Art

[0002] With the continuous development of intelligent transportation, especially electronic maps and navigation technology, it has become possible to provide new energy vehicles with real-time road network and operating conditions information. It has also provided new ideas for the dynamic planning of energy-saving paths and economical speeds of new energy vehicles, as well as the formulation of optimal management strategies that are adapted to real-time operating conditions.

[0003] Traditional path planning methods have high computational complexity when dealing with large-scale, multi-level traffic networks and are difficult to meet real-time requirements. This is especially true for vehicles such as new energy heavy trucks that require frequent refueling. Traditional methods cannot efficiently integrate road network levels and energy consumption constraints. In addition, current new energy heavy trucks are limited by battery capacity and the distribution of refueling facilities. Traditional path planning does not fully consider energy minimization and refueling needs, which can easily lead to insufficient energy or infeasible paths. Summary of the invention

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

[0005] To achieve the above-mentioned purpose, a first embodiment of the present invention proposes a method for energy-saving path planning for a new energy heavy truck, the method comprising: Constructing a hierarchical road network based on an energy consumption model, wherein the hierarchical road network divides roads into multiple levels through a comprehensive weight formula, wherein a high-level road network includes low-energy consumption trunk roads, and a low-level road network includes all road types; Based on the real-time status parameters of the vehicle and the destination information, a bidirectional A* algorithm is used to search for a path in the hierarchical road network, wherein the bidirectional A* algorithm is expanded synchronously from the starting point and the end point, and the search direction is optimized based on a dynamic heuristic function; During the route search process, the search level and range are dynamically adjusted according to the remaining energy of the vehicle. If the remaining energy is lower than the safety threshold, the energy replenishment decision is triggered and the route is replanned; The energy consumption model is updated in real time based on changes in vehicle load or external environment, the predicted energy consumption of untraveled sections is dynamically corrected, and path replanning is initiated to generate an optimized energy-saving path.

[0006] In some embodiments of the present invention, the comprehensive weight formula includes: in, and It is a nonlinear index used to enhance or weaken the influence of node attributes and edge attributes; Indicates the grade evaluation value of the road network where the road section is located. The larger the value, the lower the road network grade, and the lower the priority selected in path planning; conversely, the smaller the value, the higher the road network grade, and the higher the priority; Represents the node attribute weight, specifically the weight of the node's traffic capacity and speed limit; Represents the weight of the 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.

[0007] In some embodiments of the present invention, the dynamic heuristic function of the bidirectional A* algorithm is: ( )= ( ) in, It is the product of Euclidean distance and energy consumption coefficient; , , are dynamic weight coefficients, including road network level coefficient, energy consumption coefficient and 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, For real-time traffic time, The times are based on historical average speeds or ideal traffic conditions.

[0008] In some embodiments of the present invention, the dynamically adjusting the search level and scope specifically includes the following steps: Assume the remaining energy of the vehicle is , the estimated energy consumption of the vehicle to reach the destination is , the safety threshold of the vehicle is ; If the remaining energy , prioritize searching in high-level road networks; If the remaining energy , force switching to low-level road network search.

[0009] In some embodiments of the present invention, the dynamic weight coefficient , , The adjustment rules are: When the vehicle is moving, = (1- / )renew; in, is the total energy of the vehicle, for The baseline value of When the vehicle load increases, according to = (1+ΔW / )renew; in, is the load change, is the reference load; When traffic congestion is detected, according to = (1+ / )renew; in, for , It is the real-time traffic congestion time or congestion index.

[0010] In some embodiments of the present invention, the energy replenishment decision comprises the following steps: (a) Generate a set of candidate refueling points based on the vehicle's current position { }; (b) Calculate the comprehensive cost of each charging point ,in, is the current energy consumption rate, is the energy consumption rate after energy replenishment, To replenish the time cost, is the time weight coefficient, for Distance to the end point; (c) Select the energy replenishment point with the minimum total cost , and plan from the current location to and A hierarchical path to the destination.

[0011] In some embodiments of the present invention, the candidate energy replenishment point set is generated in the following manner: Based on the remaining energy and safety threshold , filter to meet Energy replenishment site.

[0012] In some embodiments of the present invention, during the path replanning process, if it is detected that the vehicle load has been reduced and the originally planned recharging points are no longer necessary, the path is recalculated and the redundant recharging points are deleted.

[0013] In some embodiments of the present invention, during the path execution phase, every preset time interval Re-evaluate the remaining energy and the energy consumption of the untraveled section. If the -∑ , immediately triggers local path replanning, where ∑ is the total estimated energy consumption of the untraveled section.

[0014] In some embodiments of the present invention, the connection rules between the various levels in the hierarchical road network are: 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 trunk road hubs.

[0015] To achieve the above-mentioned purpose, the second aspect of the present invention proposes an electronic device, including a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, the above-mentioned new energy heavy-duty truck energy-saving path planning method is implemented.

[0016] The energy-saving path planning method and electronic device for new energy heavy trucks in the embodiments of the present invention systematically solve the energy consumption constraints, computational complexity and dynamic adaptability problems in the path planning of new energy heavy trucks through mechanisms such as layered road network construction, dynamic weight adjustment, and bidirectional A* algorithm optimization, helping drivers to choose the most energy-efficient driving route, reducing the overall energy consumption level of new energy vehicles, solving the bottleneck problem of energy replenishment for new energy heavy trucks, and promoting the development of the new energy vehicle industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a method for planning an energy-saving path for a new energy heavy truck according to an embodiment of the present invention; Figure 2 is a schematic diagram of using a bidirectional A* algorithm to perform path search in a hierarchical road network in one embodiment of the present invention; Figure 3 It is a schematic structural diagram of an electronic device according to another embodiment of the present invention. DETAILED DESCRIPTION

[0018] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0019] The energy-saving path planning method and electronic device for a new energy heavy truck according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

[0020] Figure 1 It is a flow chart of a method for energy-saving path planning for a new energy heavy truck according to an embodiment of the present invention.

[0021] like Figure 1 , Figure 2 As shown, the energy-saving path planning method for new energy heavy trucks includes the following steps: 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 contains low-energy trunk roads, and the low-level road network contains all road types; due to the topological connectivity, the high-level road network will also contain a small number of other types of road sections. By dividing the road network into different levels according to energy consumption and priority, the search space can be significantly reduced and the computational efficiency can be improved. For example, in a global road network, it is only necessary to quickly screen long-distance low-energy paths in the high-level road network to avoid traversing all possible complex road sections.

[0022] As an example, the road network is divided into 3-5 levels according to the road grade. The high-level road network includes low-energy trunk roads such as expressways and national highways, the middle-level also includes urban trunk roads, and the low-level includes all roads such as secondary roads and branch roads. The capacity and speed limit are marked for each road node, and the length, slope, and historical average traffic flow are marked for each edge.

[0023] The high-level road network ensures the high efficiency of long-distance transportation by selecting "low-energy trunk roads". These roads have the characteristics of strong traffic capacity, low energy consumption and continuous paths, which are suitable for fast search of long-distance, global optimal paths. For example, the energy consumption per unit distance of expressways is significantly lower than that of urban branches due to their small slope and high speed limit; while 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 energy replenishment needs and traffic control).

[0024] S2. Based on the real-time status parameters of the vehicle and the destination information, the bidirectional A* algorithm is used to search for paths in the hierarchical road network. The bidirectional A* algorithm is expanded synchronously from the starting point and the end point, and the search direction is optimized based on the dynamic heuristic function. Synchronous search from the starting point and the end point, combined with the dynamic heuristic function, can reduce redundant calculations and improve real-time performance. The heuristic function incorporates the road network level, energy consumption and traffic conditions, which can improve the accuracy of the search direction.

[0025] Existing methods (such as A*) cannot meet real-time requirements when searching in the entire road network due to the large number of nodes and edges. However, this solution implements a bidirectional search on a hierarchical road network to quickly locate a global low-energy path.

[0026] S3. During the path search process, the search level and range are dynamically adjusted according to the remaining energy of the vehicle. If the remaining energy is lower than the safety threshold, the energy replenishment decision is triggered and the path is replanned. That is, when the energy is sufficient, the high level is prioritized to ensure efficient energy saving; when the energy is insufficient, the low level is forced to find a recharge point or shortcut.

[0027] Traditional path planning (such as Dijkstra and A*) is mostly based on distance or time optimization, and does not take into account the energy limitations of new energy vehicles. For example, a fully loaded heavy truck may break down during long-distance transportation due to the lack of planned refueling points.

[0028] S4. Update the energy consumption model in real time based on changes in vehicle load or external environment, dynamically correct the predicted energy consumption of untraveled sections, and initiate path replanning to generate an optimized energy-saving path.

[0029] Changes in vehicle load or external environment (such as congestion, weather) trigger real-time energy consumption model updates to ensure that the path is always optimized based on the latest status. For example, when the load increases by 20%, the system will automatically avoid high-slope sections, and the energy consumption prediction error will be reduced to less than 5%. In traditional methods, technical path planning is static and cannot cope with sudden changes in vehicle status or road conditions.

[0030] This method systematically solves the energy consumption constraints, computational complexity and dynamic adaptability problems in the path planning of new energy heavy trucks through a hierarchical road network structure, efficient search algorithm, dynamic adjustment strategy and real-time optimization mechanism. It deeply couples the energy consumption model with the road network level, and achieves a balance between global energy saving and local feasibility through the coordinated optimization of the bidirectional A* algorithm and dynamic weights. Compared with the existing technology, this solution has significant improvements in energy saving effect, path reliability and computational efficiency.

[0031] In some embodiments of the present invention, the comprehensive weight formula includes: in, and is a nonlinear index 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 road sections is enhanced); Indicates the grade evaluation value of the road network where the road section is located. This value is used to classify the road network grade. The larger the value, the lower the road network grade, and the lower the priority selected in path planning. Conversely, the smaller the value, the higher the road network grade, and the higher the priority. represents the node attribute weight, specifically the weight of the node's traffic capacity and speed limit; is the traffic capacity of the node (which can be quantified as the maximum number of vehicles passing through per unit time), is the maximum capacity among all nodes; is the speed limit of the node, is the highest speed limit among all nodes; then ; The lower the traffic capacity and the lower the speed limit, the corresponding The larger the value, the more the road network level assessment value is improved, which means the road network level may be lower; Represents the weight of the road section length and energy consumption coefficient. Here, the length of the edge is set to L and the edge weight is determined based on factors such as energy consumption and time. Affects the edge attribute weight. is the maximum length of all edges, The larger the value, the higher the “cost” of the edge. The longer the edge, the higher the "cost". The larger it is, the greater the impact on the road network grade assessment value; Represents the distance-related weight; consider the impact of the distance between the current section and the starting point or end point on the road network level. Let D be the distance from the midpoint of the current section to the destination (the distance to the starting point or a combination of the two can also be selected according to actual conditions), is the maximum distance from all road segments in the road network to the destination. The farther from the destination, The larger it is, the higher the road network level assessment value will be to a certain extent; represents the traffic condition weight; calculated through real-time traffic flow data, is the actual traffic flow of the road section, is the design capacity of the road section. The greater the traffic volume, the closer or greater the road section capacity. The larger the value is, the more congested the road section is, which will increase the road network level assessment value and may reduce the road network level. It is the maximum traffic flow or maximum capacity of all road sections in the road network; Represents the energy consumption weight; combined with the vehicle load M and the unit distance energy consumption E of the road section, is the maximum load of the vehicle, is the maximum energy consumption per unit distance under specific conditions. The greater the load, the higher the energy consumption per unit distance. The larger it is, the more obvious the improvement effect on the road network grade evaluation value is, which means that the road network may be downgraded due to energy consumption factors; , , : are the maximum values ​​of each factor, which are used for normalization to eliminate dimensional differences; : are the weight coefficients of each factor, 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, vehicle remaining energy) .

[0032] In the above technical solution, node attributes are used to evaluate the capacity and efficiency of key nodes in the road network (such as intersections, highway entrances and exits, transportation hubs, etc.), mainly including the following parameters: Capacity and speed limits , and this method tends to avoid high nodes, reducing the increase in energy consumption caused by traffic bottlenecks or low-speed restrictions; Edge attributes are used to describe the physical characteristics and energy consumption characteristics of road segments, mainly including the following parameters: segment length L and energy consumption coefficient , this method prefers The road sections with small values ​​can reduce the overall energy consumption; In the comprehensive weight formula, θ and As a nonlinear index, it acts on the node attribute and edge attribute weights respectively. Its design intention is as follows: θ node attribute index: Enhanced influence: If θ>1, the weight of the node attribute will be amplified (for example, when θ=1.2, > ), further strengthen the avoidance of low-capacity nodes; Weakening influence: If θ<1, the weight of the node attribute will be weakened (for example, when θ=0.8, < ), reduce its impact on the classification of road network; For example, in a congested urban area, we set θ=1.5. =0.8, strengthen the avoidance of low-capacity nodes (θ>1), and weaken the influence of edge attributes ( <1), allowing the choice of a shorter but slightly more energy-intensive route.

[0033] Edge attribute index: Enhanced Impact: If >1, the weight of the edge attribute will be amplified (e.g. =1.5, > ), give priority to avoiding long-distance or high-energy consumption sections; Weakening impact: If <1, the weight of the edge attribute will be weakened (e.g. =0.7, < ), allowing the system to more flexibly select some high-energy-consuming but short-distance paths; For example, in long-distance high-speed transportation, setting θ = 0.9, =1.3, weakening the influence of node attributes (θ<1), but strengthening the avoidance of high energy consumption sections ( >1), give priority to long-distance but low-energy highways.

[0034] By dynamically adjusting the weights of node and edge attributes through nonlinear exponents, this method can flexibly balance the path efficiency and energy consumption cost in different scenarios to achieve the global energy saving goal; θ and The introduction of enhances the model's adaptability to different road network characteristics (such as dense urban road network vs. sparse inter-city road network), avoiding one-size-fits-all static parameter settings.

[0035] As an example, a highway section: =2000 vehicles / hour, =120km / h, L=50km, unit energy consumption E=0.8kWh / km; A rural road section: =500 vehicles / hour, =40km / h, L=10km, unit energy consumption E=1.5kWh / km; Then the comprehensive weight formula is used to calculate, =0.2;θ=1.2, =0.8 (enhance the influence of node attributes and weaken the influence of edge attributes), =300km (maximum distance of the road network), =2000 vehicles / hour (maximum traffic flow), =2.0kWh / km (maximum energy consumption under full load extreme road conditions); After calculation, we can get: =1.42, =0.05, =0.65; in conclusion: =0.65, belonging to the low-level road network (threshold setting: >0.5 is low level).

[0036] This method switches from high-level to low-level road networks step by step (or searches in parallel) during the path planning process, which has the following core advantages: Through the high-level road network, low-energy main roads are quickly locked, and the low-level road network is supplemented with detailed optimization. If the search is directly conducted in the full 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 sections (such as urban roads with frequent starts and stops). Direct search may lead to local optimal but global high-energy consumption paths; and hierarchical search greatly compresses the search space to meet real-time requirements. In summary, when there is sufficient energy: global optimization, choose long-distance low-energy consumption paths; and when there is insufficient energy: local optimization, choose short-distance feasible paths, even if the energy consumption is high, to complete the corresponding transportation tasks.

[0037] In some embodiments of the present invention, the dynamic heuristic function of the bidirectional A* algorithm is: ( )= ( ) in, It is the product of Euclidean distance and energy consumption coefficient; , , are dynamic weight coefficients, including the network level coefficient (the higher the level, the smaller the value, which encourages long-distance search), the energy consumption coefficient (the penalty value of high-energy consumption road sections increases), and the real-time traffic coefficient (the penalty value of congested road sections increases); is the current road network level, is the total number of layers, is the average energy consumption of the road section, For real-time traffic time, The times are based on historical average speeds or ideal traffic conditions.

[0038] During the path retrieval process, when searching a high-level road network, the heuristic value decreases and the algorithm tends to search quickly and over long distances; while when searching a low-level road network, the heuristic value increases and the algorithm refines the local search.

[0039] In some embodiments of the present invention, dynamically adjusting the search level and scope specifically includes the following steps: Assume the remaining energy of the vehicle is , the estimated energy consumption of the vehicle to reach the destination is , the safety threshold of the vehicle is ; If the remaining energy , prioritize searching in high-level road networks; If the remaining energy , force switching to low-level road network search.

[0040] As an example, when (For example, the remaining energy is 200kWh, the estimated energy consumption is 150kWh, and the safety threshold is 50kWh): At this time, the search is prioritized in the high-level road network, the weight of the heuristic function is reduced, and long-distance jumps are encouraged; when the remaining energy is insufficient (such as the remaining energy is 120kWh and the estimated energy consumption is 150kWh), it is forced to switch to the low-level road network to increase the local search accuracy and facilitate finding energy replenishment points for energy replenishment.

[0041] In some embodiments of the present invention, the dynamic weight coefficient , , The adjustment rules are: When the vehicle is moving, = (1- / ); Adjust the search priority of high-level and low-level road networks, The smaller the value, the more inclined to long-distance search of high-level road networks. On the contrary, when the remaining energy is sufficient, Increased, allowing for refined searches in lower-level networks; in, is the total energy of the vehicle, for The baseline value of When the vehicle load increases, according to = (1+ΔW / )renew; Reflects the impact of load changes on energy consumption. It increases when the load increases to strengthen the avoidance of high-energy consumption sections. in, is the load change, is the reference load; When traffic congestion is detected, according to = (1+ / )renew; Reflects the impact of traffic congestion on route selection, increasing in congestion to give priority to unblocked sections; in, for , It is the real-time traffic congestion time (unit: minute) or congestion index (such as a normalized value of 0-1).

[0042] Dynamic weight coefficient , , The parameter definitions are summarized in the following table:

[0043] In some embodiments of the present invention, if the remaining energy of the vehicle minus the predicted energy consumption of the untraveled section falls below the set safety energy threshold, the system will start the 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 demand. The energy replenishment decision includes the following steps: (a) Generate a set of candidate refueling points based on the vehicle's current position { }; (b) Calculate the comprehensive cost of each charging point ,in, is the current energy consumption rate, is the energy consumption rate after energy replenishment, To replenish the time cost, is the time weight coefficient, for Distance to the end point; (c) Select the energy replenishment point with the minimum total cost , and plan from the current location to and A hierarchical path to the destination.

[0044] As an example, at charging point A: the vehicle has 120 kWh of remaining energy, a safety threshold of 50 kWh, a current energy consumption rate of 2 kWh / km, a distance of 20 km, an energy consumption rate of 1.8 kWh / km after charging, and a charging time of 15 minutes (μ=0.5), at this time ≈45 Recharge point B: distance 30km, energy consumption rate after recharging 1.6kWh / km, recharging time 20 minutes, at this time =50 At this time, select the energy replenishment point A and plan the path: current position → A → end point.

[0045] As an example, the recharging time cost ( ) Due to the dynamic changes in charging pile types and queue time, the traditional static model has large prediction errors, resulting in inaccurate path planning. Therefore, real-time data of charging piles (such as idle status, queue length, charging power) can be accessed to dynamically update .For example: = + in, is the number of vehicles currently in the queue; The average charging time for a bicycle; is the number of charging piles.

[0046] In addition, the Q-learning model can be trained to predict the optimal charging period based on historical charging station congestion patterns to reduce waiting time.

[0047] In some embodiments of the present invention, the candidate energy replenishment point set is generated in the following manner: Based on the remaining energy and safety threshold , filter to meet Energy replenishment site.

[0048] As an example, if the vehicle has 120 kWh of remaining energy, a safety threshold of 50 kWh, and a current energy consumption rate of 2 kWh / km, we can get =35km, that is, select the energy replenishment points within 35km.

[0049] It should be noted that in remote areas or during peak traffic hours, the number of charging points that meet energy constraints is insufficient, which may lead to planning failure. In this case, mobile charging resource scheduling can be carried out, that is, docking with the mobile charging vehicle service platform, and dispatching mobile charging vehicles to the predetermined location when there are no fixed charging points.

[0050] The above scheme is further refined. When key parameters (such as vehicle load weight) change during the vehicle's movement, the path replanning process is triggered. This mechanism ensures that the path selection is always optimized based on the latest operating conditions and constraints, thereby maintaining the timeliness, effectiveness and optimality of the path planning scheme. Specifically, when the increase in vehicle load leads to an increase in energy demand, the system will evaluate whether it is necessary to replenish energy or adjust the energy replenishment strategy; conversely, if the vehicle load is reduced, the energy consumption is reduced, and the originally planned energy replenishment may no longer be necessary. At this time, the system will recalculate the optimal energy consumption route under the road section based on the changed conditions to ensure that the path planning is both efficient and energy-saving. By implementing this dynamic adjustment mechanism, the system can continuously provide optimized path planning solutions in complex and changing environments to adapt to changes in energy demand under different circumstances.

[0051] It can be summarized as follows: 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, the path is recalculated and the redundant charging points are deleted. For example, if the vehicle load is reduced (such as from 30 tons to 20 tons), the original charging point A is no longer necessary, then the energy consumption is recalculated, charging point A is deleted, and the low-level road network path is directly planned.

[0052] As an example, after the load is reduced, if there is a sudden congestion or energy consumption increases on the subsequent road sections, deleting the recharging points may lead to energy shortage. Therefore, during the long-term driving of the vehicle, a dynamic priority marking method can be used to classify the recharging points according to the risk of the subsequent road sections, and retain more backup points on high-risk sections.

[0053] In some embodiments of the present invention, during the path execution phase, every preset time interval Re-evaluate the remaining energy and the energy consumption of the untraveled section. If the -∑ , immediately triggers local path replanning, where ∑ is the total estimated energy consumption of the untraveled section.

[0054] It should be noted that frequent path switching caused by frequent triggering of local replanning may affect driving stability. Therefore, sliding window optimization can be used to optimize only the most recent The road sections that have not been driven within the time period are replanned to reduce the overall impact.

[0055] In some embodiments of the present invention, the connection rules between each level in the hierarchical road network are as follows: the low-level road network is connected to the middle-level road network through key nodes (such as main road entrances), and the middle-level road network is connected to the high-level road network through main road hubs (such as highway ramps).

[0056] Since key nodes may become bottlenecks and cause path interruptions, virtual connection nodes are temporarily created during traffic congestion or construction to allow cross-level jumps. For example, drones can be 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 the weight in the comprehensive weight formula). ), lower priority during congestion.

[0057] As an example, if the entrance to a main road is congested, the algorithm automatically selects the suboptimal connection node.

[0058] In some embodiments of the present invention, the specific method of updating the energy consumption model in real time includes the following steps: Step 1: Real-time load monitoring and dynamic adjustment of rolling resistance The vehicle load data is collected in real time through on-board sensors. When the load change exceeds a threshold (such as ΔM>10% rated load), the energy consumption model is updated, the rolling resistance is recalculated according to the new load, and the energy consumption parameters per unit distance are adjusted.

[0059] Step 2: Multi-sensor data fusion and environmental parameter correction Integrate GPS, IMU and other sensor data to obtain environmental parameters such as road slope and wind speed in real time; The energy consumption model is dynamically modified according to environmental parameters (such as slope and headwind) to adjust the energy consumption per unit distance.

[0060] Step 3: Machine learning driven model parameter optimization Automatically optimize energy consumption model parameters (such as rolling resistance coefficient and air resistance coefficient) using historical path database and reinforcement learning algorithm; The model is continuously iterated through reward functions (such as the deviation between actual energy consumption and predicted energy consumption) to improve long-term prediction accuracy.

[0061] Step 4: Dynamic weight adjustment and path planning linkage When the load increases, the energy consumption weight coefficient is dynamically increased (such as ), guide path planning to avoid high energy consumption sections; When the external environment changes (such as congestion), the search weight is adjusted to give priority to low-energy or low-congestion paths.

[0062] Step 5: Real-time data-driven local path replanning Real-time monitoring of remaining energy and energy consumption of untraveled sections. -∑ When the local re-planning is triggered, redundant energy replenishment points are dynamically deleted and the path is re-planned to ensure energy safety.

[0063] By integrating vehicle status, road attributes, environmental factors and historical data, this update mechanism breaks through the limitations of the single data source of existing technologies, and 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. Existing technologies mostly rely on global calculations and respond slowly. This mechanism deeply couples the energy consumption model with path planning to achieve a dynamic balance of energy consumption, time and safety. These contents give this solution a significant advantage in the field of energy consumption management of new energy heavy trucks, and provide key technical support for achieving energy saving in all scenarios.

[0064] Corresponding to the above embodiment, the present invention further provides an electronic device.

[0065] like Figure 3 The figure shows a schematic diagram of the structure of an electronic device in the present invention, where the electronic device 200 includes: a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, such as through a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in actual applications, the transceiver 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation on the embodiments of the present invention.

[0066] 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 may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. The processor 201 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0067] The bus 202 may include a path to transmit information between the above components. The bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 202 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0068] The memory 203 is used to store a computer program corresponding to the energy-saving path planning method for new energy heavy trucks of the above embodiment of the present invention, and the 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 contents shown in the above method embodiment.

[0069] The electronic device 200 includes, but is not limited to, mobile terminals such as laptop computers, PADs (tablet computers), and fixed terminals such as desktop computers. Figure 3 The electronic device 200 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0070] The electronic device 200 of the embodiment of the present invention collects vehicle status (load, remaining energy) and road condition data (traffic flow, slope) in real time, updates the weight of the hierarchical road network every 5 seconds, triggers the two-way A* algorithm to re-search, and when the remaining energy is lower than the threshold, starts the energy replenishment decision and generates a new path within 10 seconds. Through mechanisms such as hierarchical road network construction, dynamic weight adjustment, and two-way A* algorithm optimization, the energy consumption constraints, computational complexity, and dynamic adaptability problems in the path planning of new energy heavy trucks are systematically solved, helping drivers choose the most energy-efficient driving route and reducing the overall energy consumption level of new energy vehicles.

[0071] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced 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, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, regenerate energy, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0072] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned 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 by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0073] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0074] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0075] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for energy-saving path planning for a new energy heavy truck, characterized in that: The method comprises: Constructing a hierarchical road network based on an energy consumption model, wherein the hierarchical road network divides roads into multiple levels through a comprehensive weight formula, wherein a high-level road network includes low-energy consumption trunk roads, and a low-level road network includes all road types; Based on the real-time status parameters of the vehicle and the destination information, a bidirectional A* algorithm is used to search for a path in the hierarchical road network, wherein the bidirectional A* algorithm is expanded synchronously from the starting point and the end point, and the search direction is optimized based on a dynamic heuristic function; During the route search process, the search level and range are dynamically adjusted according to the remaining energy of the vehicle. If the remaining energy is lower than the safety threshold, the energy replenishment decision is triggered and the route is replanned; The energy consumption model is updated in real time based on changes in vehicle load or external environment, the predicted energy consumption of untraveled sections is dynamically corrected, and path replanning is initiated to generate an optimized energy-saving path.

2. The energy-saving path planning method for new energy heavy trucks according to claim 1 is characterized in that: The comprehensive weight formula includes: in, and It is a nonlinear index used to enhance or weaken the influence of node attributes and edge attributes; Indicates the grade evaluation value of the road network where the road section is located. The larger the value, the lower the road network grade, and the lower the priority selected in path planning; conversely, the smaller the value, the higher the road network grade, and the higher the priority; Represents the node attribute weight, specifically the weight of the node's traffic capacity and speed limit; Represents the weight of the 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.

3. The energy-saving path planning method for new energy heavy trucks according to claim 1 is characterized in that: The dynamic heuristic function of the bidirectional A* algorithm is: ( )= （ ） in, It is the product of Euclidean distance and energy consumption coefficient; , , are dynamic weight coefficients, including road network level coefficient, energy consumption coefficient and 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, For real-time traffic time, The times are based on historical average speeds or ideal traffic conditions.

4. The energy-saving path planning method for new energy heavy trucks according to claim 1 is characterized in that: The dynamic adjustment of the search level and scope specifically includes the following steps: Assume the remaining energy of the vehicle is , the estimated energy consumption of the vehicle to reach the destination is , the safety threshold of the vehicle is ; If the remaining energy , prioritize searching in high-level road networks; If the remaining energy , force switching to low-level road network search.

5. The energy-saving path planning method for new energy heavy trucks according to claim 3 is characterized in that: The dynamic weight coefficient , , The adjustment rules are: When the vehicle is moving, = (1- / )renew; in, is the total energy of the vehicle, for The baseline value of When the vehicle load increases, according to = (1+ΔW / )renew; in, is the load change, is the reference load; When traffic congestion is detected, according to = (1+ / )renew; in, for , It is the real-time traffic congestion time or congestion index.

6. The energy-saving path planning method for new energy heavy trucks according to claim 1 is characterized in that: The energy replenishment decision-making includes the following steps: (a) Generate a set of candidate refueling points based on the vehicle's current position { }; (b) Calculate the comprehensive cost of each charging point ; in, is the current energy consumption rate, is the energy consumption rate after energy replenishment, To replenish the time cost, is the time weight coefficient, for Distance to the end point; (c) Select the energy replenishment point with the minimum total cost , and plan from the current location to and Hierarchical path to the destination.

7. The energy-saving path planning method for new energy heavy trucks according to claim 6 is characterized in that: The candidate energy replenishment point set is generated in the following manner: Based on the remaining energy and safety threshold , filter to meet Energy replenishment site.

8. The energy-saving path planning method for new energy heavy trucks according to claim 1 is characterized in that: During the path replanning process, if it is detected that the vehicle load is reduced and the originally planned charging points are no longer necessary, the path is recalculated and the redundant charging points are deleted.

9. The energy-saving path planning method for new energy heavy trucks according to claim 1 is characterized in that: During the path execution phase, every preset time interval Re-evaluate the remaining energy and the energy consumption of the untraveled section. If the -∑ , immediately triggers local path replanning, where ∑ is the total estimated energy consumption of the untraveled section.

10. The energy-saving path planning method for new energy heavy trucks according to claim 1 is characterized in that: The connection rules between the various levels in the hierarchical road network are 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 trunk road hubs.

11. An electronic device, characterized in that: It includes a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, it implements the energy-saving path planning method for new energy heavy trucks as described in any one of claims 1-10.

Images