Electric vehicle dynamic charging path planning method based on multi-dimensional space grid

Abstract

The invention relates to an electric vehicle dynamic charging path planning method based on a multi-dimensional space grid, which comprises the following steps of: discretizing a complex road network into uniform grid units with multi-dimensional attributes through a space rasterization modeling technology, and then obtaining a dynamic charging path of an electric vehicle through a multi-source data real-time access and processing mechanism and the fusion of dynamic data. And synchronous updating of the path planning result and the current environment is realized. And then, on the basis of a weight adjustment mechanism driven by real-time data, constructing a dynamic four-dimensional cost function, including energy cost, time cost, comfort cost and safety cost, and dynamically adjusting a weight coefficient according to multiple factors to realize adaptive optimization of a multi-target cost function. And finally, generating a global optimal path by adopting a wave propulsion algorithm. And when the SOC is lower than the threshold value, forcing the path to pass through the charging grid, and selecting the optimal charging grid according to the remaining path proportion. And the deviation between the current position of the vehicle and the planned path is verified through GPS positioning, and if the deviation exceeds a threshold value, local path re-planning is triggered.

Description

Technical Field

[0001] This invention relates to the field of energy management and route planning, and in particular to a method for dynamic charging route planning of electric vehicles based on a multi-dimensional spatial grid. Background Technology

[0002] With the acceleration of the global energy transition, the penetration rate of electric vehicles (EVs) has increased significantly. According to statistics from the International Energy Agency (IEA), global EV sales exceeded 10 million units in 2022, leading to a surge in demand for charging infrastructure. Against this backdrop, intelligent charging route planning technology has become a core means to improve user experience and alleviate range anxiety. Traditional route planning technologies are mainly geared towards gasoline-powered vehicles, aiming at finding the shortest path (such as Dijkstra's algorithm) or the fastest path (such as the A* algorithm), relying on graph theory to abstract roads into a node-edge topological structure.

[0003] Chinese patent application publication number CN114485702A discloses a method and system for electric vehicle charging route planning. By constructing a time-of-use electricity pricing model for the power grid and road network, and a comprehensive charging route planning model, it solves the problem of the comprehensive benefits of charging stations and user costs in the fast charging mode of electric vehicles, realizing the optimal charging route and charging station selection for electric vehicles, and optimizing charging load and traffic flow. However, this application still has shortcomings in how to comprehensively and fully consider multi-dimensional factors and how to quickly achieve route search.

[0004] In summary, traditional electric vehicle charging path planning methods rely on graph theory algorithms, which have the following problems:

[0005] (1) Unable to handle unstructured roads: such as rural roads, temporary access roads, etc., which are not included in the graph theory node-edge structure;

[0006] (2) Insufficient dynamic data integration: It is difficult to integrate dynamic information such as traffic flow, charging pile status, and weather conditions in real time;

[0007] (3) Single optimization objective: focusing only on the shortest distance, ignoring the multi-dimensional balance of energy consumption, time, comfort and safety. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a dynamic charging path planning method for electric vehicles based on a multi-dimensional spatial grid, so as to solve or partially solve the problems of incompatibility with unstructured roads and path planning results lagging behind actual environmental changes.

[0009] The objective of this invention can be achieved through the following technical solutions:

[0010] One aspect of the present invention provides a method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid, comprising the following steps:

[0011] Acquire multi-source heterogeneous data and vehicle status monitoring data, wherein the multi-source heterogeneous data includes static road network data, charging facility data and terrain data, as well as dynamic traffic flow data, charging pile status data and meteorological data;

[0012] Based on the road network data, multiple grid cells are formed through discretization.

[0013] Energy cost is calculated based on the vehicle status monitoring data, time cost is calculated based on the traffic flow data, comfort cost is calculated based on the terrain data, safety cost is calculated based on the meteorological data and historical accident risk density, and comprehensive cost is calculated through scene-based weight adaptation.

[0014] Starting from a preset starting grid cell, the path spreads to adjacent grid cells through wave propagation, calculates the cumulative cost, and obtains the globally optimal path via at least one charging facility under real-time power constraints based on the charging facility data.

[0015] In response to the fulfillment of the recalculation trigger condition, local path planning is re-performed.

[0016] As a preferred technical solution, the overall cost is calculated using the following formula:

[0017] Total_Cost=α·Energy+β·Time+γ·Comfort+δ·Safety

[0018] Where Total_Cost is the overall cost, Energy is the energy cost, Time is the time cost, Comfort is the comfort cost, Safety is the safety cost, and α, β, γ, and δ are the weights.

[0019] As a preferred technical solution, the energy cost is negatively correlated with the real-time electricity consumption, and the time cost is positively correlated with the congestion level of the real-time traffic flow. The real-time electricity consumption is obtained based on vehicle status monitoring data, and the congestion level of the real-time traffic flow is calculated from the traffic flow data.

[0020] As a preferred technical solution, the comfort cost is calculated using the following formula:

[0021] Comfort=0.6·Iroughness+0.4·max(0,κ-30)

[0022] Wherein, Comfort is the comfort cost, Iroughness is the road smoothness, max() is the maximum value, κ is the curvature of the curve, and the road smoothness and the road smoothness are obtained from the terrain data.

[0023] As a preferred technical solution, the scenario-based weight adaptation is implemented using the following formula:

[0024] Low battery scenario:

[0025] Low battery scenario:

[0026] anew = αdefault + 0.2 × (1 - SOC)

[0027] Traffic rush hour scenario:

[0028] βnew=βdefault×(1+0.5×Congestion_Index)

[0029] Severe weather scenarios:

[0030] δnew = δdefault + 0.4

[0031] Where αnew, βnew, and δnew are the updated weights of α, β, and δ, αdefault, βdefault, and δdefault are the preset weights of α, β, and δ, SOC is the real-time battery level, and Congestion_Index is the congestion index.

[0032] As a preferred technical solution, in the process of global optimal path planning, when the SOC is lower than the threshold, the path is forced to pass through the charging grid, and the optimal charging grid is selected according to the proportion of the remaining distance.

[0033] As a preferred technical solution, the recalculation triggering conditions include:

[0034] Local path planning is triggered when the deviation between the vehicle's current position and the planned path exceeds a threshold.

[0035] In response to charging station occupancy and / or traffic accidents, switch to nearby charging stations or mark accident grids as impassable, triggering local path planning.

[0036] As a preferred technical solution, after acquiring multi-source heterogeneous data and vehicle condition monitoring data, the following is also included:

[0037] The road network data is subjected to topology repair and attribute assignment processing;

[0038] For the charging facility data, spatial distribution features are extracted and stored, and a spatial index is constructed;

[0039] Based on the vehicle status monitoring data, battery management system data and driving modes are extracted.

[0040] Another aspect of the present invention provides a dynamic charging path planning system for electric vehicles based on a multi-dimensional spatial grid, used to implement the aforementioned dynamic charging path planning method for electric vehicles based on a multi-dimensional spatial grid, the system comprising:

[0041] The data input layer is used to acquire multi-source heterogeneous data and vehicle status monitoring data;

[0042] Spatial analysis layer, used to form multiple raster cells through discretization;

[0043] A dynamic calculation layer is used to adaptively adjust the fusion weights based on the scenario.

[0044] The path generation layer is used to start from a preset starting grid cell, spread to adjacent grid cells through wave propagation, calculate the cumulative cost, and obtain the globally optimal path via at least one charging facility under real-time power constraints based on the charging facility data.

[0045] The dynamic correction layer is used to re-plan the local path in response to the recalculation trigger condition being met.

[0046] In another aspect, an electronic device is provided, characterized in that it includes one or more processors, a memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the aforementioned method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid.

[0047] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0048] (1) Compatible with complex road networks: This invention supports unstructured roads and dynamic road network changes by using spatial grid modeling instead of the traditional node-edge structure, thereby improving the adaptability and robustness of path planning.

[0049] (2) Real-time dynamic integration: This invention constructs a real-time access and processing mechanism for multi-source data such as traffic flow, charging pile status, and weather, ensuring that the path planning results are updated synchronously with the current environment.

[0050] (3) Comprehensive and full consideration of multi-dimensional factors: This aspect constructs a dynamic weight model to balance the four-dimensional goals of energy consumption, time cost, driving comfort and safety risk, and respond to users' personalized needs, such as requirements for economic mode and comfort mode.

[0051] (4) Dynamic adjustment: The present invention dynamically adjusts the route strategy based on real-time power (SOC) and weather conditions (such as rain and snow). For example, it prioritizes charging stations when the power is low and avoids high-risk road sections when the weather is bad. Attached Figure Description

[0052] Figure 1 This is an architecture diagram of the electric vehicle dynamic charging path planning system based on a multi-dimensional spatial grid in the embodiment.

[0053] Figure 2 This is a flowchart of static data processing in the embodiment;

[0054] Figure 3 This is a schematic diagram of dynamic data fusion in the embodiment;

[0055] Figure 4 This is a flowchart of the raster attribute calculation process in the embodiment;

[0056] Figure 5 This is a flowchart of the dynamic weight adjustment process in the embodiment;

[0057] Figure 6 This is a flowchart of the wave propulsion algorithm in the embodiment;

[0058] Figure 7 This is a schematic diagram of the dynamic correction process in the embodiment;

[0059] Figure 8 This is a diagram illustrating the status of emergency response in the embodiment.

[0060] Figure 9 This is a schematic diagram of the electronic device in the embodiment. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0062] Example 1

[0063] To address the shortcomings of existing technologies, such as insufficient dynamic data fusion capabilities, singular optimization objectives, and lack of dynamic weight adjustment mechanisms, this embodiment provides a dynamic charging path planning method for electric vehicles based on multi-dimensional spatial grids. First, spatial grid modeling technology is used to discretize complex road networks into uniform grid cells with multi-dimensional attributes, achieving compatibility with unstructured roads and dynamic road network changes. Then, through a multi-source data real-time access and processing mechanism, including the fusion of dynamic data such as traffic flow, charging pile status, and meteorological data, the path planning results are updated synchronously with the current environment. Next, based on a real-time data-driven weight adjustment mechanism, a dynamic four-dimensional cost function is constructed, including energy cost, time cost, comfort cost, and safety cost. The weight coefficients are dynamically adjusted according to factors such as real-time state of charge (SOC), traffic flow, and meteorological conditions, achieving adaptive optimization of the multi-objective cost function. Finally, the Wavefront Propagation Algorithm is employed, starting from the starting grid and spreading to adjacent grids, calculating the cumulative cost and generating the globally optimal path. When the SOC is below a threshold, the path is forced to pass through a charging grid, and the optimal charging grid is selected based on the remaining distance percentage. Meanwhile, GPS positioning is used to check the deviation between the vehicle's current location and the planned route. If the deviation exceeds a threshold, local route replanning is triggered. In response to emergencies such as charging station occupancy and traffic accidents, the system automatically switches to a nearby charging station or marks the accident grid as impassable, and recalculates the local route.

[0064] Specifically, the method includes the following steps:

[0065] Step S1: Obtain multi-source heterogeneous data and vehicle status monitoring data.

[0066] See Figure 1 It integrates multi-source heterogeneous data, including static geographic information, dynamic environmental parameters, and vehicle status data.

[0067] See Figure 2 Static data processing includes topology restoration and attribute assignment for road network data, storage of spatial distribution characteristics and construction of spatial indexes for charging facility data, and generation of elevation models for terrain data. Specifically, GIS data is first acquired, and topology restoration and slope calculation are performed. Finally, attribute assignment is performed and spatial database storage is completed.

[0068] See Figure 3 Dynamic data fusion utilizes multi-protocol interfaces to access traffic flow data, charging pile status data, and meteorological data in real time, and performs corresponding data processing. Specifically, it first uses traffic API, charging pile MQTT, and meteorological API to parse data and map attributes, enabling dynamic updates of the fencing.

[0069] Vehicle condition monitoring includes acquiring data from the battery management system and selecting driving modes.

[0070] Step S2: Based on the road network data, multiple grid cells are formed through discretization processing.

[0071] See Figure 4 The road network is discretized into 50m×50m grid cells, and multi-dimensional attributes such as basic toll cost, charging radiation intensity, and risk density are assigned. The basic toll cost is calculated based on factors such as road speed limits and gradients, the charging radiation intensity is calculated using an improved inverse distance weighted interpolation algorithm, and the risk density is estimated based on kernel density estimation of historical accident data.

[0072] Step S3: Calculate energy cost based on vehicle status monitoring data, time cost based on traffic flow data, comfort cost based on terrain data, safety cost based on meteorological data and historical accident risk density, and calculate comprehensive cost through scenario-based weight adaptation.

[0073] See Figure 4 Based on a real-time data-driven weight adjustment mechanism, a dynamic four-dimensional cost function is constructed, including energy cost, time cost, comfort cost, and safety cost. The weight coefficients are dynamically adjusted according to factors such as real-time power consumption (SOC), traffic flow, and weather conditions to achieve adaptive optimization of the multi-objective cost function.

[0074] See Figure 5 Dynamic four-dimensional cost function:

[0075] Total_Cost=α·Energy+β·Time+γ·Comfort+δ·Safety

[0076] Energy cost: negatively correlated with SOC, as shown in the formula:

[0077] Energy = 1 - SOC;

[0078] ε = 1 - SOC

[0079] Time cost: Positively correlated with real-time traffic flow

[0080] Comfort cost: based on road smoothness (Iroughness) (value range 0-1) and curve curvature (κ);

[0081] C=0.6·Iroughness+0.4·max(0,κ-30)

[0082] Safety costs are determined by weather conditions and risk density.

[0083] Weight adaptive strategy:

[0084] Low power scenario (SOC < 20%):

[0085] αnew=αdefault+0.2×(1-SOC)

[0086] Traffic peak (congestion index > 70%):

[0087] βnew=βdefault×(1+0.5×Congestion_Index)

[0088] Severe weather (such as heavy rain):

[0089] δnew = δdefault + 0.4

[0090] Step S4: Starting from the preset starting grid cell, the path spreads to adjacent grid cells through wave propagation, calculates the cumulative cost, and obtains the globally optimal path via at least one charging facility under real-time power constraints based on charging facility data.

[0091] See Figure 6 The system employs a wavefront propagation algorithm, starting from the initial grid and spreading outwards to adjacent grids, calculating cumulative costs to generate a globally optimal path. When the State of Charge (SOC) is below a threshold, the path is forced to pass through charging grids, and the optimal charging grid is selected based on the remaining distance. Specifically, path planning prioritizes passing through charging grids. The charging radiation intensity calculated in step S2 reflects the availability and service range of charging stations in different areas, thus influencing the vehicle's decision when selecting a charging station.

[0092] Step S5: In response to the recalculation trigger condition being met, local path planning is performed again.

[0093] See Figure 7 and Figure 8 The system uses GPS positioning to verify the deviation between the vehicle's current location and the planned route. If the deviation exceeds a threshold, it triggers a local route replanning. Simultaneously, in response to emergencies such as charging station occupancy and traffic accidents, it automatically switches to a nearby charging station or marks accident grids as impassable, and recalculates the local route.

[0094] In summary, this method achieves real-time multi-objective path planning in complex road networks through spatial rasterization modeling and dynamic weight overlay technology. It integrates toll costs, charging radiation, and risk density through multi-dimensional calculation of raster attributes; it responds to multi-source data such as electricity consumption, traffic, and weather through adaptive dynamic weight adjustment; it efficiently calculates the path with the lowest cumulative cost through wave-driven path generation; and it ensures the robustness and real-time performance of path planning through a real-time correction mechanism.

[0095] This method, through spatial rasterization cost analysis and dynamic weight overlay, can quickly propagate within the grid and find the optimal path, making it suitable for large-scale path planning problems. In contrast, while reinforcement learning-based charging path planning methods perform well in terms of dynamic adaptability, they have higher computational complexity and require more computing resources. Furthermore, it combines different cost models and constraints to adapt to various complex environments and needs. For example, it can dynamically adjust weight coefficients based on real-time traffic flow, charging station status, and weather data to optimize the cost model, thereby providing more personalized path planning solutions. Through a multi-path output mechanism, it provides users with multiple path options, labeled with total cost, estimated time, and charging times, enabling users to choose the optimal path according to their needs.

[0096] Example 2

[0097] Based on Example 1, this example provides a dynamic charging path planning system for electric vehicles based on a multi-dimensional spatial grid, used to implement the charging path planning method as described in Example 1. (See also...) Figure 1 The system includes:

[0098] The data input layer is used to acquire multi-source heterogeneous data and vehicle status monitoring data.

[0099] Spatial analysis layer, used to form multiple raster cells through discretization.

[0100] The dynamic calculation layer is used to adaptively adjust the fusion weights based on the scenario.

[0101] The path generation layer is used to start from a preset starting grid cell, spread to adjacent grid cells through wave propagation, calculate the cumulative cost, and obtain the globally optimal path via at least one charging facility under real-time power constraints based on the charging facility data.

[0102] The dynamic correction layer is used to re-plan the local path in response to the recalculation trigger condition being met.

[0103] Example 3

[0104] This embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the electric vehicle dynamic charging path planning method based on a multi-dimensional spatial grid as described in Embodiment 1.

[0105] like Figure 9 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for the business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the above method. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0106] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0107] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0108] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid, characterized in that, Includes the following steps: Acquire multi-source heterogeneous data and vehicle status monitoring data, wherein the multi-source heterogeneous data includes static road network data, charging facility data and terrain data, as well as dynamic traffic flow data, charging pile status data and meteorological data; Based on the road network data, multiple grid cells are formed through discretization. Energy cost is calculated based on the vehicle status monitoring data, time cost is calculated based on the traffic flow data, comfort cost is calculated based on the terrain data, safety cost is calculated based on the meteorological data and historical accident risk density, and comprehensive cost is calculated through scene-based weight adaptation. Starting from a preset starting grid cell, the path spreads to adjacent grid cells through wave propagation, calculates the cumulative cost, and obtains the globally optimal path via at least one charging facility under real-time power constraints based on the charging facility data. In response to the fulfillment of the recalculation trigger condition, local path planning is re-performed.

2. The method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid according to claim 1, characterized in that, The comprehensive cost is calculated using the following formula: Total_Cost=α·Energy+β·Time+γ·Comfort+δ·Safety Where Total_Cost is the overall cost, Energy is the energy cost, Time is the time cost, Comfort is the comfort cost, Safety is the safety cost, and α, β, γ, and δ are the weights.

3. The method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid according to claim 2, characterized in that, The energy cost is negatively correlated with the real-time electricity consumption, and the time cost is positively correlated with the real-time traffic flow congestion level. The real-time electricity consumption is obtained based on vehicle status monitoring data, and the real-time traffic flow congestion level is calculated from the traffic flow data.

4. The method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid according to claim 2, characterized in that, The aforementioned comfort cost is calculated using the following formula: Comfort=0.6·Iroughness+0.4·max(0,κ-30) Wherein, Comfort is the comfort cost, Iroughness is the road smoothness, max() is the maximum value, κ is the curvature of the curve, and the road smoothness and the road smoothness are obtained from the terrain data.

5. The method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid according to claim 2, characterized in that, The scenario-based weight adaptation is implemented using the following formula: Low battery scenario: αnew=αdefaulr+0.2×(1-SOC) Traffic rush hour scenario: βnew=βdefault×(1+0.5×Congestion_Index) Severe weather scenarios: δnew = δdefault + 0.4 Where αnew, βnew, and δnew are the updated weights of α, β, and δ, αdefault, βdefault, and δdefault are the preset weights of α, β, and δ, SOC is the real-time battery level, and Congestion_Index is the congestion index.

6. The method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid according to claim 1, characterized in that, In the process of global optimal path planning, when the SOC is lower than the threshold, the path is forced to pass through the charging grid, and the optimal charging grid is selected according to the proportion of the remaining distance.

7. The method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid according to claim 1, characterized in that, The recalculation triggering conditions include: Local path planning is triggered when the deviation between the vehicle's current position and the planned path exceeds a threshold. In response to charging station occupancy and / or traffic accidents, switch to nearby charging stations or mark accident grids as impassable, triggering local path planning.

8. The method for dynamic charging path planning of electric vehicles based on a multi-dimensional spatial grid according to claim 1, characterized in that, After acquiring multi-source heterogeneous data and vehicle condition monitoring data, the process also includes: The road network data is subjected to topology repair and attribute assignment processing; For the charging facility data, spatial distribution features are extracted and stored, and a spatial index is constructed; Based on the vehicle status monitoring data, battery management system data and driving modes are extracted.

9. A dynamic charging path planning system for electric vehicles based on a multi-dimensional spatial grid, characterized in that, For implementing the electric vehicle dynamic charging path planning method based on a multi-dimensional spatial grid as described in any one of claims 1-8, the system comprises: The data input layer is used to acquire multi-source heterogeneous data and vehicle status monitoring data; Spatial analysis layer, used to form multiple raster cells through discretization; A dynamic calculation layer is used to adaptively adjust the fusion weights based on the scenario. The path generation layer is used to start from a preset starting grid cell, spread to adjacent grid cells through wave propagation, calculate the cumulative cost, and obtain the globally optimal path via at least one charging facility under real-time power constraints based on the charging facility data. The dynamic correction layer is used to re-plan the local path in response to the recalculation trigger condition being met.

10. An electronic device, characterized in that, It includes one or more processors, memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the electric vehicle dynamic charging path planning method based on a multi-dimensional spatial grid as described in any one of claims 1-8.

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