A collaborative planning method for charging and swapping facilities for electric heavy-duty trucks in port collection and distribution.

Abstract

This invention belongs to the field of transportation technology and is a collaborative planning method for charging and swapping facilities for electric heavy-duty trucks in port collection and distribution scenarios. The method includes: establishing a deployment plan for charging and swapping facilities under the constraint of the system's total budget; establishing a transportation model and an energy consumption model based on container status and flow direction; establishing a macroscopic distribution model of charging demand to characterize the generation of potential charging demand and its spatial allocation among facilities; establishing a queuing time model and setting constraints based on fleet size; establishing an optimization model with the goal of maximizing the system's total profit; relaxing the optimization model and directly controlling the allocation of charging demand at each node through operational scheduling; decomposing the relaxed optimization model into independent node subproblems using Lagrange decomposition; and updating the Lagrange multipliers using a subgradient algorithm, iteratively coordinating each subproblem to converge to the optimal solution. This invention solves the problem of collaborative planning for charging and swapping facilities for electric container truck fleets in port collection and distribution scenarios.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, and in particular to a collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution. Background Technology

[0002] Existing technologies face an inherent contradiction in planning refueling facilities for electric heavy-duty trucks used in port operations, particularly regarding single refueling modes. On one hand, while pure charging has low construction costs, it takes a long time, even fast charging, requiring 0.5 to 1 hour. During peak port hours, this can easily lead to vehicle congestion and significant downtime for trucks, failing to meet the high-intensity, time-sensitive demands of port operations. On the other hand, while pure battery swapping is efficient (requiring only a few minutes) and aligns with port needs, its construction costs are high (approximately 3-5 times that of charging stations) and require a large reserve of spare batteries, making large-scale deployment difficult. Therefore, a single mode cannot simultaneously achieve both economic efficiency and timeliness.

[0003] Secondly, existing planning methods fail to fully consider the unique characteristics of port scenarios, leading to model distortion. Firstly, existing methods largely focus on passenger vehicles, neglecting the energy consumption differences caused by the varying loads of heavy trucks (the energy consumption of a loaded container is approximately 1.7 times that of an empty container), which severely misjudges refueling needs. Secondly, existing methods often simplify or ignore the "queueing congestion" in the refueling process, which is a key bottleneck leading to reduced operational efficiency and economic losses.

[0004] Therefore, a collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution is needed. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a collaborative planning method for charging and battery swapping facilities for electric heavy-duty trucks used in port operations. This invention aims to overcome the limitations of existing technologies that rely on a single charging mode, and the shortcomings of planning models that are detached from actual port operations (i.e., ignoring load variations, queuing congestion, and spatiotemporal unevenness). The objective of this invention is to achieve an optimal balance between overall system benefits and operational efficiency by scientifically and collaboratively deploying charging and battery swapping facilities, while meeting total budget constraints.

[0006] The technical means employed in this invention are as follows:

[0007] A collaborative planning method for charging and battery swapping facilities for electric heavy-duty trucks in port collection and distribution includes: S1, organizing container transportation operations in a multi-level transportation network composed of operational nodes, and establishing the deployment of energy replenishment facilities under the constraint of the total system budget; S2, establishing a transportation model and an energy consumption model based on the container status and flow direction; S3, establishing a macro-distribution model of energy replenishment demand to characterize the generation of potential energy replenishment demand and its spatial allocation among facilities; S4, establishing queuing time models for charging stations and battery swapping stations respectively, and establishing constraints based on the fleet size; S5, establishing an optimization model with maximizing the total system profit as the optimization objective; S6, relaxing the optimization model and directly controlling the allocation of energy replenishment demand for each node through operational scheduling; S7, decomposing the relaxed optimization model into independent node subproblems using Lagrange decomposition; and updating the Lagrange multipliers using a subgradient algorithm, and iteratively coordinating each subproblem to converge to the optimal solution.

[0008] Further, step S1 specifically includes:

[0009] The operational nodes include: port areas, near-port areas, inland distribution nodes, railway hub nodes, and remote hinterland nodes;

[0010] The set of nodes in the network is represented as The index is The destination node set is represented as The index is The decision-making problem encompasses both strategic and operational levels. The strategic level requires determining the location and scale of charging stations and battery swapping stations within budget constraints; and defining a set of energy replenishment modes. ,in Indicates charging. Indicates battery swapping, decision variables Indicates at node Construction model Number of facilities; the operation layer determines the vehicle transport flow allocation and refueling behavior within the existing infrastructure network;

[0011] Two types of energy replenishment facilities, charging stations and battery swapping stations, are deployed at network nodes. The deployment of these energy replenishment facilities must meet the overall budget constraint.

[0012]

[0013] in, For a single mode The annualized construction cost of the facility. For the total budget, the construction cost of a battery swapping station is significantly higher than that of a charging station, that is... This reflects the additional investment in battery storage, automation equipment, and land use for battery swapping stations; each facility unit is equipped with individual service resources and There are 1 waiting slot.

[0014] Further, step S2 specifically includes:

[0015] Define a set of transportation modes based on container status and flow direction. , This represents the delivery of loaded containers, indicating the import flow of loaded container transport from ports to inland areas. This indicates the retrieval of empty containers, and the import flow of empty containers returning to the port from inland areas. This indicates the delivery of empty containers, with the export flow directed towards empty container distribution from the port to inland areas. This indicates the retrieval of loaded containers, with the export flow directed towards the consolidation of loaded containers from inland areas to ports; [The remaining text appears to be a fragmented and incomplete sentence, possibly due to OCR errors. A more coherent translation would require the full context.] For a predetermined starting point To the finish line In transportation mode The following needs, For the actual traffic flow served, For unmet needs, the three elements maintain a balance:

[0016]

[0017]

[0018] Operators are allowed to choose not to fully meet certain requirements, but must bear the corresponding penalty costs in the objective function. , The unit penalty cost for unmet demand. It indicates the priority of demand under limited resources, reflecting the economic trade-offs in actual operation;

[0019] Based on the Integrated Modal Emission Model (CMEM) framework, an energy consumption model suitable for the operating characteristics of electric trucks is established; transportation tasks are included. The total energy consumption is expressed as:

[0020]

[0021] in, For nodes To the node Driving distance, energy consumption rate per unit mileage It consists of three parts:

[0022]

[0023] in, For road section average driving speed To improve the power coefficient of the auxiliary system, The air drag coefficient, This is a combined coefficient of rolling resistance and road surface slope. For the vehicle's own weight. For transportation mode The load capacity of the cargo below This represents the basic energy consumption of the auxiliary system, which is inversely proportional to speed. This is the energy consumption due to air resistance, which is proportional to the square of the vehicle speed and reflects the additional energy demand when driving at high speeds. This indicates that rolling resistance and climbing energy consumption are linearly related to the total load.

[0024] Furthermore, step S3 specifically includes:

[0025] Establish a macroscopic distribution model of the energy replenishment demand, and the total energy replenishment demand of the system. Based on the vehicle's steady-state dwell probability in space To allocate, i.e., nodes Potential energy replenishment demand for:

[0026]

[0027] Among them, the steady-state probability is solved based on Markov chain theory. ,set up For nodes To the node The travel time is calculated using Little's law from node To the node Average number of vehicles on the road :

[0028]

[0029] in, To represent the actual vehicle traffic flow, define the state transition probability matrix. :

[0030]

[0031] steady-state distribution vector The steady-state equations satisfying the Markov chain are as follows:

[0032]

[0033] node The resulting energy replenishment demand Various facility points in the network Allocation between, let For the node Go to node Select mode The flow of vehicles replenishing energy satisfies the conservation condition:

[0034]

[0035] Define nodes pattern Overall arrival rate of facilities :

[0036]

[0037] Drivers choose refueling locations and modes to minimize their personal generalized costs. It consists of three parts:

[0038] in, For nodes model Average waiting time at the facility Service time, charging time Greater than battery swapping time The complementary relaxation condition is expressed as:

[0039]

[0040] According to the Wardrop equilibrium principle, in an equilibrium state, all used energy replenishment paths have equal minimum costs. That is, nodes The cost of balanced energy replenishment is such that the cost of unused paths is no less than the minimum cost.

[0041] Furthermore, step S4 specifically includes:

[0042] Waiting time during the energy replenishment process Queuing models are established for charging stations and battery swapping stations, respectively, as they are key factors affecting system efficiency.

[0043] For the charging station, assume that vehicle arrival follows a Poisson process with an arrival rate of [missing information]. The charging time follows an exponential distribution with a mean of 1 / 2. Each station has One charging station and The waiting positions are modeled as an M / M / C / K queuing system; the utilization rate is defined as... ,when When the system is stable, assume For the system has The steady-state probability of a vehicle is distributed as follows:

[0044]

[0045] Wherein, the normalization constant The expected queue length is calculated using the standard formula. The average waiting time, calculated using Little's Law, is:

[0046]

[0047] in, The blocking probability represents the probability that a vehicle cannot enter because the queue is full.

[0048] System parameters include the number of battery swapping stations. Total battery inventory Number of battery charging stations Single battery swap time and battery charging time Define the system state as ,in For the number of vehicles, The system steady-state distribution is the number of fully charged batteries. Satisfy the global equilibrium equations; assume The number of vehicles that can complete battery swapping, from the status Transferred to The combined effect of two stochastic processes, vehicle arrival and battery charging, needs to be considered. This is achieved by solving the state transition equations and applying normalization conditions.

[0049]

[0050] Expected queue length is The blocking probability is According to Little's Law, the average waiting time is:

[0051]

[0052] in, This indicates the probability of blocking.

[0053] Total fleet size required by the system It equals the sum of the average number of vehicles in each state, which, according to Little's Law, can be expressed as:

[0054]

[0055] in, Corresponding vehicles in transit Corresponding to vehicles heading for refueling, For vehicles undergoing recharging, the system energy balance requirement is that total energy consumption equals total charging amount:

[0056]

[0057] in, Energy consumption for transportation tasks The energy consumed to travel to the recharge station This represents the average energy consumption per unit under no-load conditions. To replenish total energy, For battery capacity, To trigger the power threshold for recharging, This indicates the amount of electricity charged from the threshold to full capacity each time.

[0058] Further, step S5 specifically includes:

[0059] The decision objective is to maximize the total system profit, and the completion of the transportation task is defined as... The unit revenue is The unit time value coefficient is The vehicle operating cost coefficient is The objective function is expressed as:

[0060]

[0061] Among them, transportation revenue Reflects market prices and shipping distance; demand penalty Characterizing market share loss; energy replenishment costs Considering the time value of drivers; construction costs Annualized cost of facility investment; fleet costs Includes annualized costs for vehicle purchase and maintenance.

[0062] Further, step S6 specifically includes:

[0063] Arrival rate Alternative energy replenishment flow As a decision variable, the equilibrium constraint is removed, and the allocation of energy replenishment demand for each node is directly controlled through operation scheduling. The relaxed energy replenishment cost is simplified as follows:

[0064]

[0065] in, For nodes Given the average travel time, the fleet conservation constraint simplifies to:

[0066]

[0067] The energy balance constraint becomes:

[0068]

[0069] in, For nodes The average refueling distance is given by the objective function and constraints of the relaxation model as follows:

[0070] max

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] , ,

[0077] in, The maximum service rate for a single facility.

[0078] Further, step S7 specifically includes:

[0079] Budget constraints and energy balance constraints couple the decisions of all nodes. Lagrange multipliers are introduced for both the budget constraints and the energy balance constraints. and Construct the Lagrange function, and decompose it into independent nodal subproblems by rearranging related terms:

[0080]

[0081] Each node The subproblem is further decomposed into a transportation decision subproblem and a facility planning subproblem, wherein the transportation decision subproblem is as follows:

[0082]

[0083] ,

[0084] Define marginal profit ,when Execute the task immediately, otherwise abandon it;

[0085] The facility planning subproblem is defined using simplified notation. and The expression is as follows:

[0086]

[0087] , ,

[0088] The facility planning sub-problem for each pattern In two-dimensional space The above enumeration is used to solve the problem, and the solution is fixed. Afterwards, Perform univariate optimization and compare all combinations of choices to maximize the objective.

[0089] The subgradient algorithm defines a dual function. ,in For nodes The optimal value of the subproblem, the dual problem is In the In the next iteration, the subgradient is calculated based on the current solution; the subgradient of energy balance. for:

[0090]

[0091] Subgradient of budget constraints for:

[0092]

[0093] Update the Lagrange multipliers, where the step size is... , Set according to the decreasing rule:

[0094]

[0095] when Or reach the maximum number of iterations The process will end at that time.

[0096] Compared with the prior art, the present invention has the following advantages:

[0097] This invention optimizes queuing and congestion time during the charging process by deploying battery swapping stations at key nodes. This effectively diverts peak-hour charging demand, significantly shortening the average charging time for vehicles and resolving the congestion bottleneck common in pure charging networks. Furthermore, this invention overcomes the inherent contradiction between the high cost of battery swapping and the low efficiency of charging by scientifically and collaboratively deploying charging and battery swapping facilities, achieving superior overall system economic benefits compared to any single charging mode.

[0098] This invention optimizes site selection based on actual port operation needs, resulting in a clear functional differentiation. High-efficiency battery swapping stations are deployed at core port nodes with high demand to ensure operational efficiency; simultaneously, a network of charging stations is deployed near the port to meet basic energy replenishment needs. This invention innovatively considers "load-weight difference energy consumption" in the model, distinguishing the energy consumption differences between loaded and empty container transport. This makes the model's characterization of energy replenishment needs far more accurate than existing technologies, thus making the planning scheme more realistically feasible. Attached Figure Description

[0099] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0100] Figure 1 This is a flowchart of the collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution in this invention. Detailed Implementation

[0101] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0102] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0103] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0104] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0105] like Figure 1 As shown, this invention provides a collaborative planning method for charging and swapping facilities of electric heavy-duty trucks in port collection and distribution, solving the problem of collaborative planning of charging and swapping facilities for electric truck fleets in port collection and distribution scenarios.

[0106] S1. Organize container transportation operations in a multi-level transportation network composed of operational nodes, and establish energy replenishment facilities under the constraints of the total system budget; In specific implementation, the operational nodes, as a preferred embodiment of the present invention, include: port areas, near-port areas, inland distribution nodes, railway hub nodes, and remote hinterland nodes;

[0107] The set of nodes in the network is represented as The index is The destination node set is represented as The index is The decision-making problem encompasses both strategic and operational levels. The strategic level requires determining the location and scale of charging stations and battery swapping stations within budget constraints; and defining a set of energy replenishment modes. ,in Indicates charging. Indicates battery swapping, decision variables Indicates at node Construction model The number of facilities; the operational layer, within the existing infrastructure network, determines the allocation of vehicle transport flow and refueling behavior; considering that the time scale of infrastructure deployment (several years) is much longer than that of daily operational decisions (hours), this invention adopts a steady-state equilibrium assumption, that is, using the steady-state traffic flow distribution and refueling demand of a typical operating day as the basis for planning. On the one hand, the long-term benefits of infrastructure depend mainly on the average operating state rather than instantaneous fluctuations. On the other hand, port collection and distribution operations themselves exhibit strong periodicity and regularity, making the steady-state assumption have a good practical basis.

[0108] Two types of energy replenishment facilities, charging stations and battery swapping stations, are deployed at network nodes. The deployment of these energy replenishment facilities must meet the overall budget constraint.

[0109]

[0110] in, For a single mode The annualized construction cost of the facility. For the total budget, the construction cost of a battery swapping station is significantly higher than that of a charging station, that is... This reflects the additional investment in battery storage, automation equipment, and land use for battery swapping stations; each facility unit is equipped with Each service resource (charging pile or battery swapping station) and There are 1 waiting slot.

[0111] S2. Port cargo handling operations have clear flow directions and load characteristics. Based on container status and flow direction, a transportation model and an energy consumption model are established. In specific implementation, as a preferred embodiment of this invention, a set of transportation modes is defined based on container status and flow direction. , This represents the delivery of loaded containers, indicating the import flow of loaded container transport from ports to inland areas. This indicates the retrieval of empty containers, and the import flow of empty containers returning to the port from inland areas. This indicates the delivery of empty containers, with the export flow directed towards empty container distribution from the port to inland areas. This indicates the retrieval of loaded containers, with the export flow directed towards the consolidation of loaded containers from inland areas to ports; [The remaining text appears to be a fragmented and incomplete sentence, possibly due to OCR errors. A more coherent translation would require the full context.] For a predetermined starting point To the finish line In transportation mode The following needs, For the actual traffic flow served, For unmet needs, the three elements maintain a balance:

[0112]

[0113]

[0114] Operators are allowed to choose lines that do not fully meet certain needs, especially low-profit lines, but must bear the corresponding penalty costs in the objective function. , The unit penalty cost for unmet demand. It indicates the priority of demand under limited resources, reflecting the economic trade-offs in actual operation;

[0115] The energy consumption of electric container trucks is significantly dependent on their load condition; the energy consumption difference between loaded and empty container transport can reach over 70%, which is a major driving factor in the spatial distribution of replenishment demand. Based on the Integrated Modal Emission Model (CMEM) framework, an energy consumption model suitable for the operating characteristics of electric container trucks is established; the transport task... The total energy consumption is expressed as:

[0116]

[0117] in, For nodes To the node Driving distance (km), energy consumption rate per unit mileage It consists of three parts:

[0118]

[0119] in, For road section Average driving speed (km / h) For auxiliary systems such as air conditioning and lighting, the typical power factor is approximately 5kW. The drag coefficient is related to the vehicle's drag surface area and air density. This is a combined coefficient of rolling resistance and road surface slope. This refers to the vehicle's own weight (approximately 8 tons). For transportation mode Loading capacity of the cargo, when the container is full tons, empty container ton. This represents the basic energy consumption of the auxiliary system, which is inversely proportional to speed. This is the energy consumption due to air resistance, which is proportional to the square of the vehicle speed and reflects the additional energy demand when driving at high speeds. This indicates that rolling resistance and climbing energy consumption are linearly related to the total load.

[0120] S3. Establish a macroscopic distribution model of energy replenishment demand to characterize the generation of potential energy replenishment demand and its spatial allocation among facilities; in specific implementation, as a preferred embodiment of the present invention, establish the macroscopic distribution model of energy replenishment demand, and the total energy replenishment demand of the system. Based on the vehicle's steady-state dwell probability in space To allocate, i.e., nodes Potential energy replenishment demand for:

[0121]

[0122] Among them, the steady-state probability is solved based on Markov chain theory. ,set up For nodes To the node The travel time is calculated using Little's law from node To the node Average number of vehicles on the road :

[0123]

[0124] in, To represent the actual vehicle traffic flow, define the state transition probability matrix. :

[0125]

[0126] steady-state distribution vector The steady-state equations satisfying the Markov chain are as follows:

[0127]

[0128] The above equation has a unique positive solution. (Node) The resulting energy replenishment demand Various facility points in the network Allocation between, let For the node Go to node Select mode The flow of vehicles replenishing energy satisfies the conservation condition:

[0129]

[0130] Define nodes pattern Overall arrival rate of facilities :

[0131]

[0132] Drivers choose refueling locations and modes to minimize their personal generalized costs. It consists of three parts:

[0133] in, For nodes model Average waiting time at the facility Service time, charging time Greater than battery swapping time The complementary relaxation condition is expressed as:

[0134]

[0135] According to the Wardrop equilibrium principle, in an equilibrium state, all used energy replenishment paths have equal minimum costs. That is, nodes The equilibrium cost of refueling is such that the cost of unused routes is no less than the minimum cost. This equilibrium characterizes a steady state where "no vehicle is willing to unilaterally change its refueling decision," reflecting the rational choice behavior of drivers given the infrastructure layout.

[0136] S4. Establish queuing time models for charging stations and battery swapping stations respectively, and establish constraints based on fleet size; in specific implementation, as a preferred embodiment of the present invention, the waiting time for the refueling process is... Queuing is a key factor affecting system efficiency. In the highly time-sensitive scenario of port cargo handling, congestion at energy replenishment facilities will directly reduce fleet utilization and increase operating costs. Queuing models are established for charging stations and battery swapping stations respectively.

[0137] For the charging station, assume that vehicle arrival follows a Poisson process with an arrival rate of [missing information]. The charging time follows an exponential distribution with a mean of 1 / 2. Each station has One charging station and The waiting positions are modeled as an M / M / C / K queuing system; the utilization rate is defined as... ,when When the system is stable, assume For the system has The steady-state probability of a vehicle is distributed as follows:

[0138]

[0139] Wherein, the normalization constant The expected queue length is calculated using the standard formula. The average waiting time, calculated using Little's Law, is:

[0140]

[0141] in, The blocking probability represents the probability that a vehicle cannot enter because the queue is full.

[0142] The complexity of battery swapping stations lies in the cyclical flow of batteries, requiring consideration of the coupling between vehicle queues (open system) and battery queues (closed system), which can be modeled as a hybrid queuing network. System parameters include the number of swapping stations. Total battery inventory Number of battery charging stations Single battery swap time and battery charging time Define the system state as ,in For the number of vehicles, The system steady-state distribution is the number of fully charged batteries. Satisfy the global equilibrium equations; assume The number of vehicles capable of battery swapping (constrained by the number of vehicles, batteries, and workstations) is determined from the state. Transferred to We need to consider the combined effects of two stochastic processes: vehicle arrival (Poisson distribution) and battery charging (binomial distribution). This is achieved by solving the state transition equations and applying normalization conditions.

[0143]

[0144] Expected queue length is The blocking probability is According to Little's Law, the average waiting time is:

[0145]

[0146] in, This indicates the probability of blocking.

[0147] Total fleet size required by the system It equals the sum of the average number of vehicles in each state, which, according to Little's Law, can be expressed as:

[0148]

[0149] in, Corresponding vehicles in transit Corresponding to vehicles heading for refueling, For vehicles undergoing refueling, this constraint will influence planning decisions (through...). Impact on fleet size) and operational decisions ( , The coupling reflects the impact of infrastructure investment on operational efficiency. System energy balance requires total energy consumption to equal total charging capacity.

[0150]

[0151] in, Energy consumption for transportation tasks The energy consumed to travel to the recharge station This represents the average energy consumption per unit under no-load conditions. To replenish total energy, For battery capacity, To trigger the power threshold for recharging, This indicates the amount of electricity charged from the threshold to full capacity each time.

[0152] S5. Establish an optimization model with the goal of maximizing the total system profit; in specific implementation, as a preferred embodiment of the present invention, the decision objective is to maximize the total system profit, and the completion of the transportation task is defined as... The unit revenue is The unit time value coefficient is The vehicle operating cost coefficient is The objective function is expressed as:

[0153]

[0154] Among them, transportation revenue Reflects market prices and shipping distance; demand penalty Characterizing market share loss; energy replenishment costs Considering the time value of drivers; construction costs Annualized cost of facility investment; fleet costs Includes annualized costs for vehicle purchase and maintenance.

[0155] The computational challenges of the objective function are mainly reflected in three aspects: complementary constraints transform the problem into mathematical programming with equilibrium constraints (MPEC), and the queuing model introduces highly nonlinear implicit functions. Integer variables This introduces combinatorial complexity. Directly applying commercial solvers struggles to handle medium-sized instances. To address the coordinated planning problem, a Lagrange decomposition algorithm is proposed, which solves the problem through three steps: relaxing user equilibrium, dual decomposition, and subgradient iteration. First, the energy replenishment flow is... Replace with arrival rate First, the platform can directly control the allocation of energy replenishment needs for each node. Second, the global coupling constraints are relaxed in a dual manner, decomposing the problem into independent node subproblems. Finally, the coordination subproblems are updated through dual multipliers, gradually converging to a feasible solution.

[0156] S6. Relax the optimized model and directly control the energy replenishment demand allocation of each node through operation scheduling; in specific implementation, as a preferred embodiment of the present invention, the arrival rate is used. Alternative energy replenishment flow As a decision variable, the equilibrium constraint is removed, and the allocation of energy replenishment demand for each node is directly controlled through operation scheduling. The relaxed energy replenishment cost is simplified as follows:

[0157]

[0158] in, For nodes Given the average travel time, the fleet conservation constraint simplifies to:

[0159]

[0160] The energy balance constraint becomes:

[0161]

[0162] in, For nodes The average refueling distance is given by the objective function and constraints of the relaxation model as follows:

[0163] max

[0164]

[0165]

[0166]

[0167]

[0168]

[0169] , ,

[0170] in, The maximum service rate for a single facility.

[0171] S7. The relaxed optimization model is decomposed into independent node subproblems using Lagrange decomposition; and the Lagrange multipliers are updated using the subgradient algorithm. The subproblems are then iteratively coordinated to converge to the optimal solution. In a preferred embodiment of this invention, budget constraints and energy balance constraints are coupled to the decisions of all nodes. Lagrange multipliers are introduced into both the budget constraints and the energy balance constraints. and Construct the Lagrange function, and decompose it into independent nodal subproblems by rearranging related terms:

[0172]

[0173] Each node The subproblem is further decomposed into a transportation decision subproblem and a facility planning subproblem, wherein the transportation decision subproblem is as follows:

[0174]

[0175] ,

[0176] This problem has a closed-ended solution. Define marginal profit. ,when The task is executed on time, or abandoned; this reflects the trade-off between the benefits minus energy and time costs and the penalty of abandonment.

[0177] The facility planning subproblem is defined using simplified notation. and The expression is as follows:

[0178]

[0179] , ,

[0180] The facility planning sub-problem for each pattern In two-dimensional space The above enumeration is used to solve the problem, and the solution is fixed. Afterwards, Perform univariate optimization and compare all combinations of choices to maximize the objective.

[0181] The subgradient algorithm defines a dual function. ,in For nodes The optimal value of the subproblem, the dual problem is In the In the next iteration, the subgradient is calculated based on the current solution; the subgradient of energy balance. for:

[0182]

[0183] Subgradient of budget constraints for:

[0184]

[0185] Update the Lagrange multipliers, where the step size is... , Set according to the decreasing rule:

[0186]

[0187] when Or reach the maximum number of iterations The process will end at that time.

[0188] Example

[0189] The complete optimization model in this invention is as follows:

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204] ,

[0205] This problem combines the complementary constraints of MPEC, the implicit nonlinearity of the queuing model, and the combinatorial complexity of integer variables, and belongs to the category of mixed integer nonlinear programming problems.

[0206] The complete algorithm flow of this invention is shown in Table 1.

[0207] Table 1 Algorithm Flow

[0208]

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collaborative planning of port freight collection and distribution electric heavy truck charging and swapping facilities, characterized in that, include: S1. Organize container transportation operations in a multi-level transportation network composed of operational nodes, and establish energy replenishment facilities under the constraints of the total system budget. S2. Based on the container status and flow direction, establish a transportation model and an energy consumption model; Define a set of transportation modes based on container status and flow direction. , This represents the delivery of loaded containers, indicating the import flow of loaded container transport from ports to inland areas. This indicates the retrieval of empty containers, and the import flow of empty containers returning to the port from inland areas. This indicates the delivery of empty containers, with the export flow directed towards empty container distribution from the port to inland areas. This indicates the retrieval of loaded containers, with the export flow directed towards the consolidation of loaded containers from inland areas to ports; [The remaining text appears to be a fragmented and incomplete sentence, possibly due to OCR errors. A more coherent translation would require the full context.] For a predetermined starting point To the finish line In transportation mode The following needs, For the actual traffic flow served, For unmet needs, the three elements maintain a balance: Operators are allowed to choose not to fully meet certain requirements, but must bear the corresponding penalty costs in the objective function. , The unit penalty cost for unmet demand. It indicates the priority of demand under limited resources, reflecting the economic trade-offs in actual operation; Based on the Integrated Modal Emission Model (CMEM) framework, an energy consumption model suitable for the operating characteristics of electric trucks is established; transportation tasks are included. The total energy consumption is expressed as: in, For nodes To the node Driving distance, energy consumption rate per unit mileage It consists of three parts: in, For road section average driving speed To improve the power coefficient of the auxiliary system, The air drag coefficient, This is a combined coefficient of rolling resistance and road surface slope. For the vehicle's own weight. For transportation mode The load capacity of the cargo below This represents the basic energy consumption of the auxiliary system, which is inversely proportional to speed. This is the energy consumption due to air resistance, which is proportional to the square of the vehicle speed and reflects the additional energy demand when driving at high speeds. This indicates that rolling resistance and climbing energy consumption are linearly related to the total load. S3. Establish a macroscopic distribution model of energy replenishment demand to characterize the generation of potential energy replenishment demand and its spatial allocation among facilities. S4. Establish queuing time models for charging stations and battery swapping stations respectively, and establish constraints based on fleet size; S5. Establish an optimization model with the goal of maximizing the total profit of the system. S6. Relax the optimized model and directly control the energy replenishment demand allocation of each node through operation scheduling; S7. Use Lagrange decomposition to decompose the relaxed optimization model into independent nodal subproblems; and use the subgradient algorithm to update the Lagrange multipliers, and iteratively coordinate the subproblems to converge to the optimal solution. Budget constraints and energy balance constraints couple the decisions of all nodes, and Lagrange multipliers are introduced for both budget constraints and energy balance constraints. and Construct the Lagrange function, and decompose it into independent nodal subproblems by rearranging related terms: Each node The subproblem is further decomposed into a transportation decision subproblem and a facility planning subproblem, wherein the transportation decision subproblem is as follows: , Define marginal profit ,when Execute the task immediately, otherwise abandon it; The facility planning subproblem is defined using simplified notation. and The expression is as follows: , , The facility planning sub-problem for each pattern In two-dimensional space The above enumeration is used to solve the problem, and the solution is fixed. Afterwards, Perform univariate optimization and compare all combinations of choices to maximize the objective. The subgradient algorithm defines a dual function. ,in For nodes The optimal value of the subproblem, the dual problem is In the In the next iteration, the subgradient is calculated based on the current solution; the subgradient of energy balance. for: Subgradient of budget constraints for: Update the Lagrange multipliers, where the step size is... , Set according to the decreasing rule: when Or reach the maximum number of iterations The process will end at that time.

2. The collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution as described in claim 1, characterized in that, Step S1 specifically includes: The operational nodes include: port areas, near-port areas, inland distribution nodes, railway hub nodes, and remote hinterland nodes; The set of nodes in the network is represented as The index is The destination node set is represented as The index is The decision-making problem encompasses both strategic and operational levels. The strategic level requires determining the location and scale of charging stations and battery swapping stations within budget constraints; and defining a set of energy replenishment modes. ,in Indicates charging. Indicates battery swapping, decision variables Indicates at node Construction model Number of facilities; the operation layer determines the vehicle transport flow allocation and refueling behavior within the existing infrastructure network; Two types of energy replenishment facilities, charging stations and battery swapping stations, are deployed at network nodes. The deployment of these energy replenishment facilities must meet the overall budget constraint. in, For a single mode The annualized construction cost of the facility. For the total budget, the construction cost of a battery swapping station is significantly higher than that of a charging station, that is... This reflects the additional investment in battery storage, automation equipment, and land use for battery swapping stations; each facility unit is equipped with individual service resources and There are 1 waiting slot.

3. The collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution as described in claim 1, characterized in that, Step S3 specifically includes: Establish a macroscopic distribution model of the energy replenishment demand, and the total energy replenishment demand of the system. Based on the vehicle's steady-state dwell probability in space To allocate, i.e., nodes Potential energy replenishment demand for: Among them, the steady-state probability is solved based on Markov chain theory. ,set up For nodes To the node The travel time is calculated using Little's law from node To the node Average number of vehicles on the road : in, To represent the actual vehicle traffic flow, define the state transition probability matrix. : steady-state distribution vector The steady-state equations satisfying the Markov chain are as follows: node The resulting energy replenishment demand Various facility points in the network Allocation between, let For the node Go to node Select mode The flow of vehicles replenishing energy satisfies the conservation condition: Define nodes pattern Overall arrival rate of facilities : Drivers choose refueling locations and modes to minimize their personal generalized costs. It consists of three parts: in, For nodes model Average waiting time at the facility Service time, charging time Greater than battery swapping time The complementary relaxation condition is expressed as: According to the Wardrop equilibrium principle, in an equilibrium state, all used energy replenishment paths have equal minimum costs. That is, nodes The cost of balanced energy replenishment is such that the cost of unused paths is no less than the minimum cost.

4. The collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution as described in claim 1, characterized in that, Step S4 specifically includes: Waiting time during the energy replenishment process Queuing models are established for charging stations and battery swapping stations, respectively, as they are key factors affecting system efficiency. For the charging station, assume that vehicle arrival follows a Poisson process with an arrival rate of [missing information]. The charging time follows an exponential distribution with a mean of 1 / 2. Each station has One charging station and The waiting positions are modeled as an M / M / C / K queuing system; the utilization rate is defined as... ,when When the system is stable, assume For the system has The steady-state probability of a vehicle is distributed as follows: Wherein, the normalization constant The expected queue length is calculated using the standard formula. The average waiting time, calculated using Little's Law, is: in, The blocking probability represents the probability that a vehicle cannot enter because the queue is full. System parameters include the number of battery swapping stations. Total battery inventory Number of battery charging stations Single battery swap time and battery charging time Define the system state as ,in For the number of vehicles, The system steady-state distribution is the number of fully charged batteries. Satisfy the global equilibrium equations; assume The number of vehicles that can complete battery swapping, from the status Transferred to The combined effect of two stochastic processes, vehicle arrival and battery charging, needs to be considered. This is achieved by solving the state transition equations and applying normalization conditions. Expected queue length is The blocking probability is According to Little's Law, the average waiting time is: in, Indicates the probability of blocking; Total fleet size required by the system It equals the sum of the average number of vehicles in each state, which, according to Little's Law, can be expressed as: in, Corresponding vehicles in transit Corresponding to vehicles heading for refueling, For vehicles undergoing recharging, the system energy balance requirement is that total energy consumption equals total charging amount: in, Energy consumption for transportation tasks The energy consumed to travel to the recharge station This represents the average energy consumption per unit under no-load conditions. To replenish total energy, For battery capacity, To trigger the power threshold for recharging, This indicates the amount of electricity charged from the threshold to full capacity each time.

5. The collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution as described in claim 1, characterized in that, Step S5 specifically includes: The decision objective is to maximize the total system profit, and the completion of the transportation task is defined as... The unit revenue is The unit time value coefficient is The vehicle operating cost coefficient is The objective function is expressed as: Among them, transportation revenue Reflects market prices and shipping distance; demand penalty Characterizing market share loss; energy replenishment costs Considering the time value of drivers; construction costs Annualized cost of facility investment; fleet costs Includes annualized costs for vehicle purchase and maintenance.

6. The collaborative planning method for charging and swapping facilities for electric heavy-duty trucks used in port collection and distribution as described in claim 1, characterized in that, Step S6 specifically includes: Arrival rate Alternative energy replenishment flow As a decision variable, the equilibrium constraint is removed, and the allocation of energy replenishment demand for each node is directly controlled through operation scheduling. The relaxed energy replenishment cost is simplified as follows: in, For nodes Given the average travel time, the fleet conservation constraint simplifies to: The energy balance constraint becomes: in, For nodes The average refueling distance is given by the objective function and constraints of the relaxation model as follows: max , , in, The maximum service rate for a single facility.

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