Vehicle group energy collaborative sharing path planning method based on space-time energy situation matrix

Abstract

The invention discloses a vehicle group energy collaborative sharing path planning method based on a space-time energy situation matrix, and belongs to the field of vehicle path planning. The method comprises the following steps: performing path planning on each single vehicle in a vehicle group based on a time window constraint of a distribution point, a vehicle dynamics constraint and an energy supply and demand balance condition to obtain a global optimal path of all vehicles in the vehicle group; constructing an energy situation four-dimensional matrix based on the optimal paths of all vehicles; calculating vehicle trajectory similarity based on the energy situation four-dimensional matrix to obtain a vehicle combination with high trajectory similarity; and performing energy transmission and path speed collaborative optimization on the vehicle combination with high trajectory similarity to obtain an optimized final planning path.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle path planning, and in particular relates to a vehicle group energy collaborative sharing path planning method based on a spatio-temporal energy situation matrix. Background Art

[0002] With the wide application of electric vehicles, the problems of their driving range and charging convenience have become increasingly prominent. The V2V (vehicle-to-vehicle) energy sharing technology has emerged as an important way to solve this problem by performing energy exchange and distribution between the energy-supplying vehicle and the energy-demanding vehicle through in-vehicle bidirectional chargers and controllers. Domestic and foreign scholars have conducted extensive research on aspects such as the matching between the energy-supplying vehicle and the energy-demanding vehicle, the path planning of paired vehicles, and the energy transmission equipment between vehicles.

[0003] In terms of vehicle matching and transmission structure, the research mainly focuses on multi-objective optimization and multi-type transmission problems of pure electric vehicles. The existing technology transforms the vehicle matching problem into a maximum weighted bipartite matching problem and realizes the optimal matching of multiple objectives through an optimization algorithm; the existing technology proposes a paired matching algorithm based on path proximity, measures path similarity through a dynamic time warping algorithm, and collaboratively optimizes the charging service in path planning. However, most of the existing research focuses on single-energy-type vehicles, and the research on the complementarity of the transmission of multiple heterogeneous energy vehicles, the energy remaining estimation of vehicles at different speeds and under different traffic flows still needs to be deepened.

[0004] In terms of collaborative path planning, scholars have conducted research on the integration of multidisciplinary methods and intelligent algorithms for problems such as energy demand, vehicle driving state, and time window constraints. The existing technology proposes an ecological routing selection model dependent on fuel consumption and introduces the fuel consumption factor into path planning; the existing technology studies a double-layer optimization model of the driving path of electric logistics vehicles and V2V energy sharing to ensure that the logistics vehicles meet the charging needs of small electric vehicles while completing the distribution task. However, most of the existing path planning models simplify the impact of vehicle speed on energy consumption, and there is a large space for model optimization.

[0005] The energy efficiency optimization of urban logistics distribution systems is an important research direction in the field of intelligent transportation. Traditional path planning methods mostly focus on the shortest path search of single vehicles, ignoring the comprehensive influence of vehicle group collaborative scheduling, energy spatio-temporal matching, and dynamic traffic constraints. Therefore, the present invention proposes a vehicle group energy collaborative sharing path planning method based on a spatio-temporal energy situation matrix. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes a vehicle group energy collaborative sharing path planning method based on a spatio-temporal energy situation matrix to solve the problems existing in the above prior art.

[0007] To achieve the above object, the present invention provides a vehicle group energy collaborative sharing path planning method based on a spatio-temporal energy situation matrix, including:

[0008] Performing path planning on each single vehicle in the vehicle group based on the time window constraint of the distribution point, vehicle dynamics constraint, and energy supply and demand balance condition to obtain the global optimal path of all vehicles in the vehicle group;

[0009] Constructing an energy situation four-dimensional matrix based on the optimal paths of all the vehicles;

[0010] Calculating the vehicle trajectory similarity based on the energy situation four-dimensional matrix to obtain vehicle combinations with high trajectory similarity;

[0011] Performing energy transfer and path speed collaborative optimization on the vehicle combinations with high trajectory similarity to obtain the optimized final planned path.

[0012] Optionally, using the Floyd-Warshall algorithm to obtain the global optimal path of all vehicles in the vehicle group;

[0013] The process of obtaining the global optimal path includes:

[0014] Modeling the urban road network as a weighted graph and calculating the spherical distance between nodes based on the weighted graph using the Haversine formula;

[0015] Constructing a multi-objective optimization model based on the spherical distance between nodes, the time window constraint of the distribution point, vehicle dynamics constraint, and energy supply and demand balance condition;

[0016] Solving the multi-objective optimization model based on the Floyd-Warshall algorithm to obtain the global shortest path matrix.

[0017] Optionally, the process of calculating the vehicle trajectory similarity based on the energy situation four-dimensional matrix to obtain vehicle combinations with high trajectory similarity includes:

[0018] Classifying the vehicles based on the energy situation four-dimensional matrix to obtain energy supply vehicles and energy demand vehicles;

[0019] Evaluating the trajectory similarity between the energy supply vehicles and energy demand vehicles based on the spatio-temporal longest common subsequence to obtain vehicle combinations with high trajectory similarity.

[0020] Optionally, the expression for calculating the vehicle trajectory similarity is:

[0021] Similarity(T1,T2)=L(n,m) / min(n,m)

[0022] Wherein, L(n,m) is the length of the final STLCSS, n and m are the number of points of trajectories T1 and T2 respectively, and Similarity(T1,T2) is the similarity of vehicle trajectories.

[0023] Optionally, the matching conditions for the matching vehicles obtained based on the vehicle trajectory similarity include:

[0024] Spatial matching satisfies: d s (i,j) = sqrt(x 1i -x 2j ) 2 +(y 1i -y 2j ) 2 ≤∈

[0025] Temporal matching satisfies: d t (i,j) = |t 1i -t 2j | ≤ δ

[0026] Wherein, d s (i,j) is the spatial distance, d t (i,j) is the time difference, where x and y are the abscissa and ordinate of the point, ∈ is the spatial distance threshold, δ is the time difference threshold, i and j respectively represent two different nodes, x 1i represents the abscissa of the first vehicle at node i, x 2j represents the abscissa of the second vehicle at node j, y 1i represents the ordinate of the first vehicle at node i, y 2j represents the ordinate of the second vehicle at node j, t 1i represents the time when the first vehicle arrives at node i, t 2j represents the time when the second vehicle arrives at node j.

[0027] Optionally, the process of performing energy transfer and path speed collaborative optimization on the vehicle combinations with high trajectory similarity includes:

[0028] Performing mutual assistance meeting point optimization on the vehicle combinations with high trajectory similarity to obtain the optimal meeting point;

[0029] Performing energy transfer calculation on the energy supply vehicle and the demand vehicle based on the optimal meeting point to obtain an energy transfer result;

[0030] Performing path speed collaborative optimization on the remaining path of the demand vehicle based on the energy transfer result to obtain the optimized final planned path.

[0031] Optionally, the expression for calculating the optimal meeting point is:

[0032] s * = argmaxs∈C [αM(s) - β|t s (s) - t d (s)| - γE transfer (s)]

[0033] In the formula, s * represents the optimal meeting point, α, β, and γ represent weight coefficients, M(s) represents the spatio - temporal matching score, t s (s), t d (s) respectively represent the arrival times of the energy - supplying vehicle and the demand vehicle at the candidate point s, and E transfer represents the energy to be transmitted.

[0034] Optionally, the expression for calculating the energy transmission result is:

[0035] E transfer = min(E need (d), E available (s * ))

[0036] In the formula, E need (d) represents the energy gap required for the demand vehicle to complete the remaining path, and E available (s * ) represents the maximum energy that the energy - supplying vehicle can provide at the meeting point s * .

[0037] Compared with the prior art, the present invention has the following advantages and technical effects:

[0038] A vehicle group energy collaborative sharing path planning method based on a spatio - temporal energy situation matrix proposed by the present invention has significant technical advantages and practical application value. First, by comprehensively considering the energy consumption relationship of vehicles, the location and time window of logistics distribution tasks for path planning, it can generate optimal paths for each single vehicle in the vehicle group, effectively reducing vehicle energy consumption and improving distribution efficiency. Second, the constructed four - dimensional energy situation matrix can comprehensively reflect the energy state and spatio - temporal distribution of vehicles, providing an accurate basis for calculating the vehicle trajectory similarity. Based on the vehicle trajectory similarity for matching, vehicle combinations with great energy sharing potential can be accurately identified to achieve efficient collaborative sharing of energy. In addition, merging and re - planning the paths of the matched vehicles further optimizes the paths, reduces the vehicle driving mileage and energy consumption, and improves the overall energy efficiency of the vehicle group. This method breaks through the limitations of traditional path planning, realizes the comprehensive optimization of vehicle group collaborative scheduling, spatio - temporal energy matching, and dynamic traffic constraints, provides strong support for improving the energy efficiency of urban logistics distribution systems, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0040] Figure 1 It is the three-layer intelligent decision-making mechanism of the embodiment of the present invention;

[0041] Figure 2 It is the road network map of 20 universities in Beijing of the embodiment of the present invention;

[0042] Figure 3 It is an example of the code running result of the embodiment of the present invention;

[0043] Figure 4 It is the first visualization example of vehicle path matching and path generation results of the embodiment of the present invention;

[0044] Figure 5 It is the second visualization example of vehicle path matching and path generation results of the embodiment of the present invention. Detailed implementation manners

[0045] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The following will describe this application in detail with reference to the accompanying drawings and in combination with the embodiments.

[0046] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0047] Embodiment 1

[0048] As Figure 1 shown, in this embodiment, a vehicle group energy collaborative sharing path planning method based on a spatio-temporal energy situation matrix is provided. The present invention includes: multi-constraint coupling modeling: integrating time window and energy consumption characteristic dynamic constraints to improve the physical feasibility of path planning; energy situation matrix construction: quantifying the spatio-temporal interaction characteristics of energy through a four-dimensional matrix (position-time-supply-demand); hybrid optimization framework design: integrating heuristic algorithms and exact mathematical programming to balance computational efficiency and solution quality.

[0049] Specifically, it includes the following steps: Based on the time window constraints of the distribution points, vehicle dynamics constraints, and energy supply-demand balance conditions, path planning is performed for each single vehicle in the vehicle group to obtain the global optimal paths of all vehicles in the vehicle group; an energy situation four-dimensional matrix is constructed based on the optimal paths of all vehicles; vehicle trajectory similarity is calculated based on the energy situation four-dimensional matrix to obtain vehicle combinations with high trajectory similarity; and energy transfer and path speed co-optimization are performed on the vehicle combinations with high trajectory similarity to obtain the optimized final planned paths.

[0050] 1. Co-optimization of single vehicle path and speed

[0051] Aiming at the complexity problems of path planning and vehicle operation efficiency in urban logistics distribution, the present invention proposes a method for co-optimizing single vehicle path and speed. By integrating multiple algorithms and dynamic constraint mechanisms, comprehensive optimization of the total energy consumption and travel time of the vehicle group is achieved. Specifically, first, the urban road network is modeled as a weighted graph (G=(V,E)), integrating the time window constraints of the distribution points, vehicle dynamics constraints (speed, acceleration, jerk limits), and energy supply-demand balance conditions, constructing a multi-objective optimization model (Equation 1), and accurately calculating the spherical distance between nodes based on the Haversine formula (Equations 2 - 6), and generating a global shortest path matrix using the Floyd-Warshall algorithm (Equation 7) to provide a basic support for optimization. On this basis, the vehicle speed is dynamically adjusted (Equations 8 - 9) to ensure that the vehicle strictly meets the time window requirements at the distribution points (wait if arriving early, abandon the path if overtime), and the energy consumption is calculated in real time in combination with the road section length, travel time, and vehicle dynamics parameters to ensure the spatio-temporal matching of energy supply and demand. Further, an improved ant colony algorithm (ACO) is introduced. Through the pheromone self-adaptive evaporation factor and heuristic function enhancement strategy, parameters such as path length and real-time traffic congestion coefficient (δ_v) are dynamically integrated to optimize the path selection probability (Equation 10), and the pheromone concentration is iteratively updated (Equations 11 - 12) to screen high-quality paths with low energy consumption and short time, finally realizing the global co-optimization of path and speed. The innovation of this solution lies in: dynamically integrating time window, dynamics, and energy constraints to avoid traditional fragmented modeling; synergistically improving the calculation accuracy and optimization efficiency through the Haversine formula, Floyd-Warshall algorithm, and improved ant colony algorithm; introducing the traffic congestion coefficient and self-adaptive evaporation mechanism to enhance the adaptability to complex road networks, significantly reducing the energy consumption and time cost of logistics distribution, and providing an efficient framework for intelligent logistics systems.

[0052] 1.1 Problem definition and modeling

[0053] In the urban logistics distribution system, path planning needs to comprehensively consider multiple factors such as road network topology, vehicle dynamics constraints, and energy supply and demand balance. Define the urban road network as a weighted graph G=(V, E), where the node set V contains n distribution points (such as universities, warehouses, etc.), and the edge set E represents the road section connectivity. Each vehicle starts from the starting point and visits several distribution points in sequence, and needs to meet the following constraints:

[0054] Time window constraint: Each distribution point has a strict time window [t earliest , t latest , and the vehicle needs to arrive within this time;

[0055] Dynamics constraint: The speed v∈[v min , v max , the acceleration a∈[a min , a max , and the jerk j≤jmax;

[0056] Energy supply and demand balance: The vehicle energy consumption needs to match the spatio-temporal distribution of charging / energy supply facilities.

[0057] The objective function aims to minimize the weighted sum of the total energy consumption and travel time of the vehicle fleet:

[0058]

[0059] In the formula, E p is the energy consumption of the p-th vehicle, T p is its travel time, and w1, w2 are weight coefficients.

[0060] The constraint conditions include:

[0061] 1.2 Calculation of the distance between nodes (Haversine formula)

[0062] The basis of path planning is to accurately calculate the spatial distance between nodes. For the geographical coordinate points (latitude and longitude) in the urban road network, the Haversine formula is used to calculate the spherical distance, and its mathematical expression is:

[0063]

[0064] d = R·cd (4)

[0065] Where: a is the haversine value of half of the spherical distance between two points, c is the central angle corresponding to the spherical distance between two points (expressed in radians), d is the spherical distance between two points, R is the radius of the earth, φ1 and φ2 are the latitudes of the starting point and the end point (in radians), and λ1 and λ2 are the longitudes of the starting point and the end point (in radians).

[0066]

[0067] Δλ=λ2 - λ1 (6)

[0068] 1.3 Global Shortest Path Calculation (Floyd - Warshall Algorithm)

[0069] To obtain the shortest path matrix for all node pairs in the road network, the Floyd - Warshall algorithm is adopted. This algorithm is applicable to weighted graphs and can handle negative - weighted edges (it is necessary to ensure that there are no negative - weight cycles).

[0070] The process of obtaining the global optimal path includes: modeling the urban road network as a weighted graph, calculating the spherical distance between nodes based on the weighted graph using the Haversine formula; constructing a multi - objective optimization model based on the spherical distance between nodes, the time - window constraints of distribution points, vehicle dynamics constraints, and energy supply - demand balance conditions; and solving the multi - objective optimization model based on the Floyd - Warshall algorithm to obtain the global shortest path matrix.

[0071] Its core idea is to gradually update the shortest path matrix D through dynamic programming. The specific steps are as follows:

[0072] Initialization: Adjacency matrix D (0) (i,j) stores the initial edge weight (i.e., the direct distance between nodes);

[0073] Iterative update: For each intermediate node k ∈ [1,n], update the shortest path for all node pairs (i,j):

[0074] D k (i,j)=min(D k-1 (i,j),D k-1 (i,k)+D k-1 (k,j)) (7)

[0075] Termination condition: When k = n, D (n) (i,j) is the global shortest path.

[0076] 1.4 Speed and Energy Consumption Calculation

[0077] (1) Speed calculation is based on distance and time. The basic formula:

[0078]

[0079] where d is the length of the road segment and t is the available driving time (constrained by the time window)

[0080] Since the time window is involved in path planning, the speed needs to be adjusted to ensure that the vehicle arrives at the target node within the allowed time. The adjustment formula is as follows:

[0081]

[0082] v max and v min are the maximum and minimum speeds of the vehicle, and t is the maximum time allowed in the time window to ensure that the vehicle does not exceed the speed limit or go too slow to meet the time window constraint. The delivery points use random speeds to ensure the simulation of various situations, and the non-delivery points directly use the maximum speed to optimize the time.

[0083] (2) Vehicle energy consumption calculation

[0084] During the path planning process, the vehicle needs to adjust its speed to meet the time window limit. In the code, it checks whether the current time exceeds the time window. If it times out, the path is abandoned. If it arrives early, it waits until the time window opens. The non-delivery points are not affected by the time window.

[0085] 1.5 Path-speed collaborative optimization

[0086] This section collaboratively optimizes the path and speed through the ant colony algorithm. The ant colony algorithm (ACO) searches for the optimal path by simulating the foraging behavior of ants and using the positive feedback mechanism of pheromone. For the urban delivery scenario, a dynamic parameter adjustment strategy is introduced:

[0087] Adaptive pheromone evaporation factor: Dynamically adjust ρ according to the path quality to avoid premature convergence;

[0088] Enhanced heuristic function: Update η by combining real-time traffic flow data ij = 1 / (d ij + δ v ), where δ v is the road section congestion coefficient.

[0089] The probability formula for an ant to select a path is:

[0090]

[0091] where: pij is the probability that an ant moves from node i to node j, τ ij is the pheromone concentration of the path (i,j)(i,j), ηij = 1 / dij is the heuristic function, the tendency to select a short path, and α and β control the importance of pheromone and the heuristic function.

[0092] After an ant completes a path, update the pheromone concentration on the path according to the path quality (such as length, cost). The pheromone update includes two processes: evaporation and increase. The formula is:

[0093] τij = (1 - ρ)τ ij + Σ ants Δτij (11)

[0094]

[0095] Among them, ρ is the pheromone evaporation factor, Q is the pheromone constant, and L is the total path length.

[0096] 2. Energy-Time-Space Matching and Dynamic Path Planning

[0097] 2.1 Four-Dimensional Matrix of Energy Situation: Define the energy situation matrix M(x, y, t, q), where: x and y are geographical coordinates; t is the timestamp; q is the energy supply-demand quantity (a positive value represents demand, and a negative value represents supply). Fill the matrix according to the time window, vehicle speed, path, and vehicle energy supply-demand situation defined in Section 1, "Cooperative Optimization of Bicycle Path Speed", to quantify the energy interaction demands of each space-time unit and provide a basis for path merging.

[0098] The process of calculating the vehicle trajectory similarity based on the four-dimensional matrix of energy situation to obtain vehicle combinations with high trajectory similarity includes: classifying vehicles into energy supply vehicles and energy demand vehicles based on the four-dimensional matrix of energy situation; evaluating the trajectory similarity between energy supply vehicles and energy demand vehicles based on the longest common subsequence in space-time to obtain vehicle combinations with high trajectory similarity.

[0099] 2.2 Energy Mutual Aid

[0100] 2.2.1 Space-Time Matching Based on Energy State Classification

[0101] According to the final remaining battery level (SOC) of the vehicle without charging, the vehicle is divided into an energy supply vehicle (which can provide energy) and an energy demand vehicle (which needs to receive energy). The specific process is as follows:

[0102] Energy State Classification: Execute a non-charging delivery plan for each vehicle, and simulate the change in battery level after its delivery along a fixed path. Optimize the speed of each section of the path through a multi-objective ant colony algorithm to minimize the total cost (energy consumption + time window penalty) until all delivery tasks are completed, and calculate the final remaining SOC.

[0103]

[0104] Among them, v k represents the speed of the k-th section, d k represents the distance of the k-th section, SOC final represents the final SOC, and SOC init represents the initial SOC.

[0105] By judging the state of the final SOC, if the battery level is insufficient, the vehicle is marked as a demand vehicle. If the battery level is sufficient, the vehicle is marked as an energy supply vehicle.

[0106]

[0107] Supply Vehicle: The final SOC > 0, indicating that there is remaining energy available for sharing.

[0108] Demand vehicle: The final SOC ≤ 0, indicating that external energy supplementation is required to complete the mission.

[0109] Spatiotemporal Matching (STLCSS) Trajectory Extraction: In multi-vehicle path optimization, evaluating the similarity between different vehicle paths is crucial for optimizing scheduling and resource allocation. The Spatiotemporal Longest Common Subsequence (STLCSS) is an effective trajectory similarity metric that comprehensively considers spatial location and time information. STLCSS is based on the classical Longest Common Subsequence (LCSS) algorithm, with an additional consideration of the time dimension. It determines the similarity degree of two trajectories by defining thresholds in both space and time.

[0110] Given two trajectories T1 and T2, where each trajectory consists of points arranged in chronological order, and each point contains spatial coordinates and a timestamp, i.e., T1 = {(x 1i , y 1i , t 1i )} and T2 = {(x 2j , y 2j , t 2j )}. The goal of STLCSS is to find the longest common subsequence between the two trajectories such that the differences between corresponding points in both space and time are within the preset thresholds.

[0111] Matching Conditions:

[0112] Spatial Matching: The Euclidean distance between two points satisfies the following condition:

[0113] d s (i, j) = sqrt((x 1i - x 2j ) 2 + (y 1i - y 2j ) 2 ≤ ∈ (15)

[0114] where ∈ is the spatial distance threshold.

[0115] Time Matching: The time difference between two points satisfies the following condition:

[0116] d t (i, j) = |t 1i - t 2j | ≤ δ (16)

[0117] where δ is the time difference threshold.

[0118] The recurrence formula of this algorithm is:

[0119]

[0120] Among them, L(i - 1, j - 1)+1 indicates that the current point is matched, so the LCSS length is incremented by 1; max(L(i - 1, j), L(i, j - 1)) indicates skipping a point and continuing to find the longest match d s (i, j) is the spatial distance, d t (i, j) is the time difference.

[0121] Similarity calculation:

[0122] Through normalization, the similarity of the two trajectories is obtained:

[0123] Similarity(T1, T2) = L(n, m) / min(n, m) (18)

[0124] L(n, m) is the final STLCSS length, and n and m are the number of points of trajectories T1 and T2 respectively.

[0125] 0 ≤ L(n, m) ≤ 1 (19)

[0126] The larger the value of Similarity(T1, T2), the higher the similarity of the trajectories.

[0127] Allocating the optimal energy - supplying vehicle: For each demand vehicle d ∈ D, calculate its STLCSS score with all supply vehicles s ∈ S to form a matrix M∣S∣×∣D∣.

[0128] For each demand vehicle d j Select the supply vehicle s with the highest score i :

[0129]

[0130] where S = {s1, s2, s3,.., s i} represents the set of supply vehicles, j represents the demand vehicle, and M(i, j) represents the matching score between supply vehicle i and demand vehicle j.

[0131] 2.2.2 Mutual - assistance meeting point optimization

[0132] The process of energy transfer and path - speed collaborative optimization for vehicle combinations with high trajectory similarity includes: performing mutual - assistance meeting point optimization on vehicle combinations with high trajectory similarity to obtain the optimal meeting point; calculating the energy transfer based on the optimal meeting point for the energy - supplying vehicle and the demand vehicle to obtain the energy transfer result; performing path - speed collaborative optimization on the remaining path of the demand vehicle based on the energy transfer result to obtain the optimized final planned path.

[0133] In vehicle route planning, the goal of optimizing the mutual assistance meeting point is to find an optimal meeting point s for the supply vehicle and the demand vehicle * , so that the two vehicles can efficiently converge in the spatio-temporal dimension and complete energy mutual assistance. This process needs to meet the following conditions:

[0134] Spatio-temporal synchronization: The arrival times of the two vehicles at the meeting point are as close as possible.

[0135] Energy transfer efficiency: The energy consumption of the transfer is minimized and the power shortage of the demand vehicle is met.

[0136] Route feasibility: The routes of the two vehicles to the meeting point need to meet topological constraints (such as road network connectivity).

[0137] Optimization goal: Maximize the spatio-temporal matching score, while minimizing the arrival time difference and transfer energy consumption. The optimization goal is shown in Equation (21).

[0138] s * = argmax s∈C [αM(s) - β|t s (s) - t d (s)| - γE transfer (s)] (21)

[0139]

[0140] E transfer (s) = max(0, E need (d) - E available (s)) (23)

[0141] where α, β, γ represent weight coefficients, M(s) represents the spatio-temporal matching score (based on the STLCSS algorithm), L s , L d represent the trajectory lengths of the supply vehicle and the demand vehicle, t s (s), t d (s) represent the arrival times of the supply vehicle and the demand vehicle at the candidate point s respectively, E transfer represents the energy to be transferred (unit: kWh), E need (d) represents the power shortage of the demand vehicle. E available (s) represents the energy that the supply vehicle can provide at the meeting point.

[0142] Optimization process: Construct a set of candidate points, extract nodes from the remaining path of the demand vehicle, or generate neighboring nodes (such as charging stations, intersections) based on the road network topology.

[0143] C = {s|s ∈ Path d} ∪ {s | dist(s, Path d ) ≤ δ} (24)

[0144] where δ represents the spatial neighborhood radius (e.g., 1 km).

[0145] Extract the trajectory segments Traj s , Traj d . Call Equation (22) to calculate the similarity score M(s), as shown in Equation (18).

[0146] Optimize the speeds v s , v d of the energy supply vehicle and the demand vehicle on the path segment to minimize the arrival time difference:

[0147]

[0148] According to the law of conservation of energy, the energy consumption E return of the energy supply vehicle when returning from the meeting point to the distribution center satisfies the equation:

[0149] E available (s) = SOC s (s) - E return (s) (26)

[0150] 2.2.3 Energy transfer

[0151] Energy transfer calculation model: The goal of energy transfer calculation is to determine the energy transfer amount E * at the meeting point s transfer between the energy supply vehicle (Supply Vehicle) and the demand vehicle (Demand Vehicle) to meet the gap of the demand vehicle while avoiding excessive discharge of the energy supply vehicle.

[0152] The core formula is as follows:

[0153] E transfer = min(E need (d), E available (s * )) (27)

[0154] E need (d) represents the energy gap (unit: kWh) required for the demand vehicle to complete the remaining path, and E available (s * ) is the maximum energy (unit: kWh) that the energy supply vehicle can provide at the meeting point s * .

[0155] E need (d) = Capacity · (1 - SOC d (s* )) (28)

[0156] E available (s * ) = Capacity·(SOC s (s * ) - SOC min ) (29)

[0157] where Capacity represents the battery capacity (kWh), SOC d (s * ) represents the remaining power percentage of the demand vehicle when it arrives at s * , SOC s (s * ) represents the remaining power percentage of the energy supply vehicle when it arrives at s * , and SOC min represents the lower limit of safe discharge of the energy supply vehicle (e.g., 20%).

[0158] The transmission process needs to satisfy the energy conservation constraint:

[0159]

[0160] After the demand vehicle receives energy, its SOC needs to meet the subsequent path requirements:

[0161]

[0162] represents the updated battery state, and SOC d (s * ) represents the current battery state, that is, the remaining power of the demand vehicle at the meeting point s * .

[0163] Energy transmission time and distance:

[0164] Transmission time:

[0165] Target distance: D target = v transfer · T transfer (33)

[0166] Energy consumption model during movement: The energy consumption model during movement is used to calculate the energy consumption of the vehicle on the path segment. Its core is to establish the relationship between speed v, distance d, and energy consumption E consumed .

[0167] The core formula is as follows:

[0168]

[0169] Remaining battery level after the vehicle completes a path segment:

[0170]

[0171] 2.2.4 Subsequent path speed collaborative optimization

[0172] Remaining path planning: After energy transfer, the demand vehicle needs to continue to complete the remaining delivery tasks. This requires calculating the path from the meeting point s * to the end point and ensuring that the SOC new is sufficient to support this journey and meets the time window constraints of the delivery points.

[0173]

[0174] where E consumed ,k is the energy consumption of the k-th path segment, and P penalty ,k is the time window penalty of the k-th path segment.

[0175] SOC dynamic update model: During each segment of the remaining path, the SOC is dynamically updated according to the driving distance, speed, and energy consumption model.

[0176]

[0177] where SOC j represents the SOC of the next node, SOC i represents the SOC of the current node, v ij represents the speed from node i to node j, and d ij represents the distance from node i to node j.

[0178] Energy consumption model: The energy consumption of the vehicle on each path segment is calculated based on parameters such as speed and distance.

[0179]

[0180] where E consumed,i,j represents the energy consumption from node i to node j.

[0181] Time window penalty and constraint handling: Ensure that in the remaining path, the SOC does not fall below the minimum requirement (such as SOCrequired), otherwise, the path or speed strategy needs to be adjusted, as shown in Equation (30), and use the multi-objective ant colony algorithm for speed planning, set the time window penalty, and ensure that the delivery still meets the time window after mutual assistance, as shown in Equation (31)

[0182]

[0183] Penalty j = P e·max(0, t earliest,j - t arrival,j ) + P l ·max(0, t arrival,j - t latest,j ) (40)

[0184] 2.5 Dynamic Path Planning and Cooperative Control

[0185] Set the similarity threshold, and perform path merging and replanning for the matching vehicles. Construct a mixed-integer programming model with the goal of minimizing the total energy consumption:

[0186]

[0187] The constraint conditions include vehicle capacity, time window, path continuity, and energy balance, and the branch and bound method is used to solve it.

[0188] 3. Simulation Analysis

[0189] The present invention designs an intelligent decision-making mechanism of "single-vehicle path optimization - energy-time-space matching - dynamic path planning". First, perform multi-constraint path planning: combine the energy consumption relationship of each arc with the location and time window of the vehicle task, and use the Floyd-Warshall algorithm to plan the single-vehicle path; after obtaining the optimal paths of all vehicles in the vehicle group, calculate the energy required by the vehicles, construct an energy situation four-dimensional matrix (location - time - supply - demand), apply the dynamic time warping (DTW) algorithm to calculate the vehicle trajectory similarity, and design a mixed-integer programming model to solve the maximum-weight bipartite matching to ensure the effectiveness of the energy-time-space interaction of the vehicle group. The aim is to achieve the optimal allocation of the energy efficiency and time-space resources of the vehicle group through the cooperation of multiple algorithms. Specifically, taking the university distribution network in Beijing as an example, combining the Floyd-Warshall global path planning, ant colony optimization (ACO) dynamic optimization, and spatio-temporal longest common subsequence (STLCSS) similarity analysis, construct a mixed-integer programming model, and finally realize the cooperative replanning of the vehicle group path and vehicle speed.

[0190] The parameter settings are shown in Table 1 and Table 2: For the road network of 20 universities in Beijing, as Figure 2 shown, calculate the global shortest path matrix, and the result is as Figure 3 shown

[0191] Table 1

[0192]

[0193] Table 2

[0194]

[0195]

[0196] Time window parameter setting:

[0197] The time window data is stored in the time_windows variable, and the specific settings are as follows:

[0198] time_windows = [0, 10; 1, 12; 2, 14; 3, 16; 4, 18; 5, 20; 6, 22;

[0199] 7, 24; 8, 26; 9, 28; 10, 30; 11, 32; 12, 34; 13, 36; 14, 38; 15, 40; 16, 42; 17, 44; 18, 46; 19, 48, 20, 50; 21, 52];

[0200] time_windows is a two-dimensional matrix, and each row represents a node and its corresponding time window:

[0201] [Node number, latest arrival time].

[0202] Node coordinate setting: The coordinate information of 20 universities in Beijing is selected, and the road network map is as Figure 2 shown. Figure 3 It is an example of the code running result.

[0203] The delivery nodes of vehicle 4 are set as: S9, S16, the time windows of the delivery points are 28h and 42h, and the total energy consumption is 1.07 kwh. At the same driving time, with an average speed of 20 km / h throughout the whole section, the total energy consumption of this vehicle is 1.14 kwh. It can be seen that using speed planning can reduce the energy consumption by 6%.

[0204] If the STLCSS similarity > 56%, then merge the paths and re-plan the paths using the ant colony algorithm:

[0205] Path matching result:

[0206] Matching vehicle of vehicle 1 (similarity > 55%): None.

[0207] Matching vehicle of vehicle 2 (similarity > 55%): None.

[0208] Matching vehicle of vehicle 3 (similarity > 55%): Vehicle 4 (5.0%) 6 (58.3%) 5.833333e+01 (100.0%).

[0209] Matching vehicle of vehicle 4 (similarity > 55%): Vehicle 3 (5.0%) 6 (7.0%) 8 (58.3%) 9.166667e+01 (58.3%) 8.571429e+01 (94.3%).

[0210] Matched vehicles of vehicle 5 (similarity > 55%): vehicle 3 (4.0%), 6 (7.0%), 8 (58.3%), 9.166667e+01 (58.3%), 7.857143e+01 (94.3%).

[0211] Matched vehicles of vehicle 6 (similarity > 55%): vehicle 3 (4.0%), 5 (100.0%), 5.833333e+01 (58.3%).

[0212] Matched vehicles of vehicle 7 (similarity > 55%): vehicle 4 (5.0%), 8 (85.7%), 7.857143e+01 (85.7%).

[0213] Matched vehicles of vehicle 8 (similarity > 55%): vehicle 4 (5.0%), 7 (94.3%), 9.230769e+01 (85.7%).

[0214] Matched vehicles of vehicle 9 (similarity > 55%): none.

[0215] Figures 4 - 5 This is an example of vehicle path matching and visualization of path generation results.

[0216] This embodiment combines the ant colony algorithm (ACO) + STLCSS for multi-vehicle path optimization, which can effectively reduce energy consumption and travel time, and supports the merging of similar paths to optimize the overall efficiency.

[0217] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A vehicle group energy collaborative sharing path planning method based on a spatio-temporal energy situation matrix, characterized in that, It includes the following steps: Based on the time window constraints of the distribution points, vehicle dynamics constraints, and energy supply-demand balance conditions, path planning is performed for each individual vehicle in the vehicle group to obtain the global optimal paths of all vehicles in the vehicle group; Based on the optimal paths of all the vehicles, an energy situation four-dimensional matrix is constructed; Based on the energy situation four-dimensional matrix, the vehicle trajectory similarity is calculated to obtain vehicle combinations with high trajectory similarity; For the vehicle combinations with high trajectory similarity, energy transfer and path speed collaborative optimization are performed to obtain the optimized final planned paths.

2. The vehicle group energy collaborative sharing path planning method based on the spatio-temporal energy situation matrix according to claim 1, wherein, The Floyd-Warshall algorithm is used to obtain the global optimal paths of all vehicles in the vehicle group; The process of obtaining the global optimal paths includes: Model the urban road network as a weighted graph, and based on the weighted graph, use the Haversine formula to calculate the spherical distance between nodes; Based on the spherical distance between nodes, time window constraints of the distribution points, vehicle dynamics constraints, and energy supply-demand balance conditions, a multi-objective optimization model is constructed; Based on the Floyd-Warshall algorithm, the multi-objective optimization model is solved to obtain the global shortest path matrix.

3. The vehicle group energy collaborative sharing path planning method based on the spatio-temporal energy situation matrix according to claim 1, characterized in that, The process of calculating the vehicle trajectory similarity based on the energy situation four-dimensional matrix to obtain vehicle combinations with high trajectory similarity includes: Based on the energy situation four-dimensional matrix, the vehicles are classified into energy supply vehicles and energy demand vehicles; Based on the spatio-temporal longest common subsequence, the trajectory similarity between the energy supply vehicles and energy demand vehicles is evaluated to obtain vehicle combinations with high trajectory similarity.

4. The vehicle group energy collaborative sharing path planning method based on the spatio-temporal energy situation matrix according to claim 3, wherein, The expression for calculating the vehicle trajectory similarity is: Similarity(T1,T2)=L(n,m) / min(n,m) Where L(n,m) is the final STLCSS length, n and m are the number of points of trajectories T1 and T2 respectively, and Similarity(T1,T2) is the vehicle trajectory similarity.

5. The method for planning the vehicle group energy collaborative sharing path based on the spatio-temporal energy situation matrix according to claim 4, wherein The matching conditions for obtaining the matching vehicles based on the vehicle trajectory similarity include: Spatial matching satisfies: d s (i,j) = sqrt(x 1i - x 2j ) 2 + (y 1i - y 2j ) 2 ≤ ∈ The time matching satisfies: d t (i,j) = |t 1i - t 2j | ≤ δ where d s (i,j) is the spatial distance, and d t (i,j) is the time difference, where x and y are the abscissa and ordinate of the point, ∈ is the spatial distance threshold, δ is the time difference threshold, i and j respectively represent two different nodes, and x 1i represents the abscissa of the first vehicle at node i, and x 2j represents the abscissa of the second vehicle at node j, y 1i represents the ordinate of the first vehicle at node i, and y 2j represents the ordinate of the second vehicle at node j, t 1i represents the time when the first vehicle arrives at node i, and t 2j represents the time when the second vehicle arrives at node j.

6. The method for planning a vehicle group energy collaborative sharing path based on a spatio-temporal energy situation matrix according to claim 1, wherein, The process of performing energy transfer and path speed collaborative optimization for the vehicle combinations with high trajectory similarity includes: For the vehicle combinations with high trajectory similarity, mutual assistance meeting point optimization is performed to obtain the optimal meeting point; Based on the optimal meeting point, energy transfer calculation is performed for the energy supply vehicles and energy demand vehicles to obtain the energy transfer result; Based on the energy transfer result, path speed collaborative optimization is performed for the remaining paths of the energy demand vehicles to obtain the optimized final planned paths.

7. The vehicle group energy collaborative sharing path planning method based on the spatio-temporal energy situation matrix according to claim 6, wherein The expression for calculating the optimal meeting point is: s * = argmax s∈C [αM(s) - β|t s (s) - t d (s)| - γE transfer (s)] Where s * represents the excellent encounter point, α, β, γ represent the weight coefficients, M(s) represents the spatio-temporal matching score, and t s (s), t d (s) respectively represent the times when the energy supply vehicle and the demand vehicle arrive at the candidate point s, and E transfer represents the energy to be transmitted.

8. The method for planning the vehicle group energy collaborative sharing path based on the spatio-temporal energy situation matrix according to claim 7, wherein The expression for calculating the energy transfer result is: E transfer = min(E need (d), E available (s * )) where, E need (d) represents the energy gap required for the demand vehicle to complete the remaining path, E available (s * ) is the maximum energy that the energy supply vehicle can provide at the meeting point s * ​

Images