Battery replacement truck freight planning method based on battery cycle sharing strategy

Abstract

A battery replacement truck freight planning method based on a battery cycle sharing strategy relates to the technical field of battery replacement truck application and comprises three steps of setting a service network, determining a space-time moving track of a battery in the service network and generating a required battery path. The step of setting the service network comprises two sub-steps of determining space-time nodes and determining four different types of arcs; generating the required battery path includes determining a service arc, a charging arc, and an idle arc, and model parameters, decision variables, objective functions, and dominating rules of the service arc, the charging arc, and the idle arc. According to the method, the problems of low energy complementing efficiency, high battery vacancy rate, resource scheduling splitting and the like in the current electric freight network can be solved, the use efficiency and the circulation frequency of battery resources are improved to the maximum extent, integrated optimization of battery replacement scheduling and transportation scheduling can be realized, and the method has a great significance in promoting application of a battery replacement mode in an electric truck logistics system. And optimal configuration of energy resources is realized, and technical support is provided for promoting green transportation development.

Description

Technical Field

[0001] This invention relates to the field of battery swapping truck application technology, and in particular to a battery swapping truck freight planning method based on a battery cycle sharing strategy. Background Technology

[0002] With the advancement of new energy strategies and the development of urban logistics towards green and intelligent directions, the application of electric trucks in long-haul transportation is gradually accelerating. Especially in medium- and long-haul scenarios, battery swapping technology has become an important alternative to traditional charging due to its fast and efficient energy replenishment characteristics. Compared with slow and fast charging modes, battery swapping can not only significantly shorten the waiting time for energy replenishment, but also decouple the battery from the vehicle, providing a realistic and feasible energy replenishment path for building a high-frequency, high-load electric freight system.

[0003] However, due to technological limitations, most battery swapping systems still face a series of key challenges. First, the battery swapping process lacks tracking and management of its entire lifecycle, often leaving swapped batteries idle and unable to quickly transition to the next service cycle, resulting in low resource utilization. Second, in traditional models, a single battery typically serves only a specific vehicle, creating a high degree of battery-vehicle dependency and hindering cross-vehicle sharing and flexible reuse, leading to resource redundancy. Third, existing logistics scheduling models primarily focus on vehicle route optimization, lacking a modeling mechanism for batteries as independent mobile resources, thus failing to support scheduling coordination in large-scale battery swapping networks. In trunk logistics service networks, batteries are core operational assets, and their utilization efficiency directly impacts the overall system's operating costs and sustainability. Therefore, there is an urgent need for an intelligent scheduling system for battery swapping scenarios, capable of achieving refined route management and reuse scheduling of batteries, overcoming pain points such as dispersed battery resources, difficulty in sharing, and chaotic scheduling, and supporting the deep integration and large-scale promotion of battery swapping technology in electric truck transportation systems. Summary of the Invention

[0004] To overcome the shortcomings of existing battery-swapping trucks due to technological limitations, as described in the background section, this invention provides a battery-swapping truck freight planning method based on a battery recycling strategy. This method addresses issues such as low energy replenishment efficiency, high battery idle rates, and fragmented resource scheduling in current electric freight networks. It breaks the one-to-one binding relationship between batteries and vehicles, enabling the cyclical sharing of batteries among multiple electric trucks. While meeting a series of physical constraints such as remaining battery power, battery swapping station capacity, and battery charging cycles, it maximizes the utilization efficiency and turnover frequency of battery resources. Furthermore, by constructing a mixed-integer programming model, it achieves integrated optimization of battery swapping scheduling and transportation scheduling. This provides effective technical support for promoting the efficient application of battery swapping in electric truck logistics systems, optimizing energy resource allocation, and promoting green transportation development.

[0005] The technical solution adopted by this invention to solve its technical problem is:

[0006] A battery-swapping truck freight planning method based on a battery cycle sharing strategy includes three steps: setting up a service network, determining the spatiotemporal movement trajectory of batteries within the service network, and generating the required battery paths. Setting up the service network includes two sub-steps: determining spatiotemporal nodes and determining four different types of arcs. In determining the spatiotemporal movement trajectory of batteries within the service network, the concept of a battery path is introduced to represent the model of the battery's spatiotemporal movement trajectory. The model requires asset balance, meaning that the number of electric vehicles and batteries at each station must remain consistent at the beginning and end of each service cycle. In practical applications, the distribution carrier equips the stations with charging and battery-swapping facilities. The model uses hourly time periods, and the battery replacement time is negligible. Generating the required battery paths includes determining the service arcs, charging arcs, and idle arcs, as well as the model parameters, decision variables, objective functions, and dominating rules for the service arcs, charging arcs, and idle arcs.

[0007] Furthermore, in the aforementioned service network, the service network is a spatiotemporal network that can be represented as a directed multigraph, wherein... Represents a set of nodes. The arc represents a set of arcs, which can represent a transition in spacetime. For each cargo, its starting point and ending point are both spacetime nodes. The corresponding time components represent the available time of the cargo at the starting point and the latest allowed arrival time at the ending point, respectively. Transportation is achieved through multiple paths composed of spacetime arcs, thereby ensuring the connectivity between the starting point and the ending point.

[0008] Furthermore, the service network includes four different types of arcs: (1) charging arcs, which connect consecutive time nodes in the same station, indicating that the battery is being charged within a certain time when the arc is selected. These arcs are subject to capacity limitations; (2) idle arcs, which also connect consecutive time nodes in the same station, indicating that the electric vehicle is in a stationary state with the battery but not being charged; (3) service arcs, which define the spatial and temporal coordination of the electric vehicle transportation service, clarifying the departure and arrival information of the service. When a service arc appears in the battery path, it indicates that the battery is powering the electric vehicle in motion; (4) shift arcs, which are used to adjust the position of the electric vehicle and the battery to prepare for the next round of service.

[0009] Furthermore, in the aforementioned service network, the cost of each arc includes power consumption, asset depreciation, and operating expenses. The cost of a service arc is distance-dependent, involving power consumption and asset depreciation; while the shift change arc mainly reflects operating costs. To ensure feasibility, the duration of a service arc must be greater than the actual transportation time required.

[0010] Furthermore, in determining the spatiotemporal movement trajectory of the battery in the service network, the concept of a battery path is introduced to represent the spatiotemporal movement trajectory of the battery in the service network.

[0011] Furthermore, in the battery path required for generation, the battery is fully charged at the start of a service cycle; after completing one service arc, its charge drops to 20%; then the battery remains idle for one cycle with its charge level unchanged; subsequently, the battery is removed from the electric vehicle and enters the charging system, undergoing two consecutive charging cycles; at this point, the battery is fully charged again and ready to enter the next service cycle; in the battery path, transitioning from a non-charging arc to a charging arc indicates that the battery has been removed from the electric vehicle, while transitioning from a charging arc to other types of arcs indicates that the battery has been reinstalled. The battery swapping node divides the battery path into several segments, in which the battery state remains unchanged.

[0012] Furthermore, the model parameters for generating the required battery paths include a set of stations, a set of discrete time points, a set of all spatiotemporal nodes, a set of goods flows, a set of battery paths, a set of all arcs, a set of service arcs, a set of charging arcs, a set of cyclone arcs, a set of idle arcs, an index of arcs included in the battery path, an index of battery swapping at corresponding points in the battery path, the starting point of the goods, the ending point of the goods, the total amount of goods, the cost incurred by electric trucks passing through arcs, the cost incurred by a unit of goods passing through, the maximum load capacity of swapping trucks, the fixed cost of each battery path, the cost of each swap, the maximum vehicle capacity on the charging arc, the swapping capacity at the nodes, and the total number of swapping trucks.

[0013] Furthermore, in generating the required battery path, the decision variables include the number of batteries on the selected battery path loop, the number of battery swapping trucks on the selected path arc, and continuous variables; the objective function is to minimize the total cost.

[0014] Furthermore, in generating the required battery path, the dominance rule can be linearly relaxed using the model, the battery path can be calculated using a labeling algorithm and then added to the model for repeated iterations until there are no more battery paths to add, and then the integer programming model is solved.

[0015] Compared with existing technologies, the advantages of this invention are as follows: This invention designs an intelligent scheduling method for battery path tracking and shared reuse around the battery swapping mechanism, solving problems such as low energy replenishment efficiency, high battery idle rate, and fragmented resource scheduling in current electric freight networks. Specifically, it independently models the "life path" of each battery in a time-space network, dynamically tracks its state changes in multiple stages such as service, battery swapping, charging, and idle, and constructs reusable battery circulation paths accordingly. This breaks the one-to-one binding relationship between batteries and vehicles, enabling the cyclical sharing of batteries among multiple electric trucks. The system combines column generation methods and label path algorithms in its optimization algorithm, maximizing the utilization efficiency and circulation frequency of battery resources while satisfying a series of physical constraints such as remaining battery power, battery swapping station capacity, and battery charging cycle. In addition, the system jointly considers vehicle paths, battery paths, and transportation task allocation, and achieves integrated optimization of battery swapping scheduling and transportation scheduling by constructing a mixed integer programming model. This invention is particularly suitable for high-frequency, fast-paced logistics scenarios such as trunk transportation, significantly reducing the amount of spare batteries required, reducing vehicle waiting time for energy replenishment, and improving the scheduling elasticity and scalability of the overall network. The deployment of this system is expected to promote the efficient application of battery swapping in electric truck logistics systems and provide key technological support for optimizing energy resource allocation and promoting green transportation development. In summary, this invention has promising application prospects. Attached Figure Description

[0016] Figure 1 This is the network structure diagram of the present invention.

[0017] Figure 2 This is the battery path diagram of the present invention.

[0018] Figure 3 This is a diagram of the layered structure of a network. Detailed Implementation

[0019] Figure 1 As shown, a battery-swapping truck freight planning method based on a battery cycle sharing strategy includes three steps: setting up a service network, determining the spatiotemporal movement trajectory of the battery in the service network, and generating the required battery path.

[0020] Figure 1 As shown, the service network is a spatiotemporal network that can be represented as a directed multigraph, where... Represents a set of nodes. An arc represents a set of arcs. Each node is a spatiotemporal point, embodying both spatial and temporal dimensions. Therefore, an arc can represent a spatiotemporal transfer. For each cargo, its starting point and ending point are both spatiotemporal nodes, and the corresponding time components represent the available time of the cargo at the starting point and the latest allowed arrival time at the ending point. Transportation is achieved through multiple paths composed of spatiotemporal arcs, thus ensuring connectivity between the starting point and the ending point. In this invention, the system includes four different types of arcs. Charging arcs connect consecutive time nodes within the same station, indicating that the battery is charged within a certain time when the arc is selected; these arcs are limited by capacity. Idle arcs also connect consecutive time nodes within the same station, representing the stationary state of the electric vehicle carrying a battery but not charging. Service arcs define the spatial and temporal coordination of the electric vehicle transportation service, clarifying the departure and arrival information of the service. Service arcs appear in the battery path, indicating that the battery is powering the moving electric vehicle. Shift arcs are used to adjust the position of the electric vehicle and battery to prepare for the next round of service. The cost of each arc may include energy consumption, asset depreciation, and operating expenses. The cost of a service arc is distance-dependent, involving electricity consumption and asset depreciation; shift arcs primarily reflect operating costs, such as driver wages. To ensure feasibility, the duration of a service arc must be longer than the actual transport time required.

[0021] Figure 2 As shown, this invention introduces the concept of a battery path to represent the spatiotemporal movement trajectory of a battery within a service network. For example, when a battery powers a moving electric vehicle, its path traverses space and consumes power. This model requires asset balancing, meaning that the number of electric vehicles and batteries at each station must remain consistent at the beginning and end of each service cycle. In practice, distribution carriers (such as courier companies) equip their stations with charging and battery swapping facilities. The model in this invention uses hourly time periods, assuming that battery replacement time is negligible. Figure 2 A simplified battery path is illustrated, comprising service arcs (black), charging arcs (gray), and idle arcs (blue). Dark nodes represent swapping nodes, located at the junctions of charging arcs and other types of arcs. At the start of a service cycle, the battery is fully charged. After completing one service arc, its charge drops to 20%. The battery then idles for one cycle, maintaining its charge level. Subsequently, the battery is removed from the electric vehicle and enters the charging system, undergoing two consecutive charging cycles. At this point, the battery is fully charged again, ready for the next service cycle. In the battery path, transitioning from a non-charging arc to a charging arc indicates that the battery has been removed from the electric vehicle, while transitioning from a charging arc to another type of arc indicates that the battery has been reinstalled. Swapping nodes divide the battery path into segments, within which the battery state remains unchanged.

[0022] The model parameters are as follows:

[0023] H site collection,

[0024] Set of discrete time points

[0025] The set of all spatiotemporal nodes

[0026] Commodity flow collection,

[0027] Battery path set,

[0028] The set of all arcs,

[0029] Service arc set,

[0030] Charging arc collection,

[0031] Cyclone arc set,

[0032] Set of free arcs

[0033] Θ s，(i，j) The battery path s contains arc (i,j), and this metric is 1; otherwise, it is 0.

[0034] Φ s,i The battery swapping index for battery path s is 1 at point i, and 0 in other cases.

[0035] o k The starting point of product k,

[0036] d k The endpoint of product k,

[0037] w k The total quantity of commodity k

[0038] c i，j The cost incurred by the electric truck through arc (i, j)

[0039] f k(i，j) The cost incurred per unit of product k through (i,j)

[0040] u Maximum load capacity of battery swapping trucks

[0041] π Fixed cost per battery path,

[0042] σ The cost of each battery swap

[0043] m i,jThe maximum vehicle capacity on the charging arc (i,j)

[0044] l i Battery swapping capacity at node i

[0045] τ Total number of battery swapping trucks

[0046] The decision variables are as follows:

[0047] θ s Select the number of batteries on the battery path ring s.

[0048] y i，j Select the number of battery swapping trucks for the path arc (i, j).

[0049] x k，(i，j) A continuous variable, representing the flow of commodity k along arc (i, j).

[0050] The objective function is as follows:

[0051]

[0052] The constraints are as follows:

[0053]

[0054]

[0055] X≥0. (1m)

[0056] The objective function (1a) minimizes the total cost. Constraints (1b)-(1d) ensure the balance of traffic flow for all goods. Constraint (1e) guarantees the conservation of traffic flow for vehicles in the network. Constraint (1f) requires that each path must have sufficient electric truck capacity to carry goods during transportation. Constraint (1g) stipulates that electric trucks performing transportation services must be equipped with batteries. Constraint (1h) is a charging arc capacity constraint, constraint (1i) is a battery swapping capacity constraint at each node, and constraint (1j) is a vehicle quantity constraint. Constraints (1k)-(1m) limit the range of values ​​for the decision variables. Through the synergistic effect of constraints (1e) and (1g), batteries and vehicles can be reset at the end of each service cycle.

[0057] Figure 3 As shown, a one-way labeling algorithm is used to generate the required battery path. In dynamic programming, the constraint dual of linear relaxation of model (1) is used for modeling. Specifically, β, δ and ψ represent the dual of linear programming (1g)-(1i) respectively. First, the nodes in the figure are divided into layers according to time. The node with the earliest time of each station is taken as the starting point, and the node with the latest time of each station is taken as the ending point. In this way, the nodes are divided into layers according to time as follows: Figure 3As shown. Using p i，j Let $\frac{i}{j}$ represent the change in electricity as the truck crosses arc (i, j). Then, we define a multidimensional index for dynamic programming and the governing rule. For the spatiotemporal nodes... The tag format is defined as Used to record path information. γ n A value of 1 indicates that the charging arc has been transmitted to the tag; otherwise, a value of 0 indicates the tag has been reached. Refers to the label of the previous node passed in. ac n and AP n The cumulative cost reduction and energy state for reaching node n are marked, a n Let the arc represent the distance to point n. During the labeling algorithm, each node may generate multiple labels. These labels need to be compared according to the domination rule. Each node only retains labels that have not been dominated. Domination rule: Assume that a spatiotemporal node n has two labels, i.e. and If the condition is met at the intermediate node and Or satisfy at the endpoint We call Dominate And remove from the label set of spatiotemporal node n

[0058]

[0059] The model (1) can be linearly relaxed, the battery path can be calculated using the labeling algorithm and then added to the model (1) for repeated iterations until there are no battery paths that can be added, and then the integer programming model (1) can be solved.

[0060] Figure 1 , 2As shown in Figure 3, the present invention has the following advantages. (1) Improved battery resource utilization efficiency. In this invention, by introducing the concept of "battery path", the traditional one-to-one binding between battery and vehicle is broken, and the battery can be cyclically shared and flexibly reused among multiple electric trucks. The system dynamically tracks and manages the entire life cycle path of the battery in the service, swap, charging and idle stages to ensure that the battery status is controllable and avoids resource idleness. By optimizing the battery circulation path, the system significantly improves the battery utilization efficiency and circulation frequency, and reduces the waste of battery resources. This refined battery management method not only optimizes resource allocation, but also provides efficient support for the operation of large-scale battery swapping networks. It is particularly suitable for trunk logistics scenarios that require high-frequency battery allocation, saving logistics companies battery procurement and maintenance costs. (2) Improved energy replenishment efficiency and shortened vehicle waiting time. Compared with the traditional slow charging or fast charging mode, the battery swapping technology of this system significantly shortens the energy replenishment time through rapid battery replacement. The system ensures that the battery can quickly enter the service state when needed through spatiotemporal network modeling and intelligent management of battery swapping nodes, minimizing the stagnation time of vehicles waiting for energy replenishment. The optimized scheduling of battery swapping nodes further improves the accuracy of battery allocation, enabling vehicles to quickly resume transportation tasks. This efficient energy replenishment mechanism is particularly suitable for high-frequency, fast-paced trunk logistics scenarios, which can significantly improve vehicle operating efficiency, reduce transportation interruptions, and thus improve the throughput and service quality of the overall logistics network. (3) Integrated optimized scheduling improves the overall efficiency of the system. This invention uses a mixed integer programming model to jointly optimize vehicle paths, battery paths, and transportation task allocation, achieving a high degree of synergy between battery swapping scheduling and transportation scheduling. Traditional logistics scheduling only focuses on vehicle path optimization, while this system innovatively incorporates batteries as an independent resource into the scheduling framework. Through column generation methods and label path algorithms, it generates a globally optimal scheduling scheme under the premise of satisfying various physical constraints (such as battery power and battery swapping station capacity). This integrated optimization method breaks through the limitations of the traditional model, improves the scheduling flexibility and operating efficiency of the logistics network, and ensures maximum resource utilization. It is particularly suitable for complex and ever-changing large-scale logistics networks. (4) Reduce operating costs. Through battery sharing and efficient circulation mechanisms, the system significantly reduces the reserve requirements of spare batteries, reducing the initial investment and battery maintenance costs of logistics companies. Its objective function comprehensively considers energy consumption, asset depreciation, operating expenses, and battery swapping costs, minimizing overall operating costs by optimizing the scheduling paths of batteries and vehicles. The cost models for service arcs, charging arcs, idle arcs, and shift swapping arcs are meticulously designed to ensure cost control under different operating scenarios. This cost optimization strategy not only improves the company's economic efficiency but also reduces long-term operating costs by minimizing resource redundancy, providing economic assurance for the large-scale application of battery swapping trucks in long-haul logistics.

[0061] The foregoing has shown and described the basic principles and main features of the present invention, as well as its advantages. It will be apparent to those skilled in the art that the present invention is limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0062] Furthermore, it should be understood that although this specification describes the embodiments, the embodiments do not necessarily contain only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in the embodiments can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A battery-swapping truck freight planning method based on a battery cycle-sharing strategy, characterized in that, The process includes three steps: setting up a service network, determining the spatiotemporal movement trajectory of the battery within the service network, and generating the required battery path. Setting up the service network includes two sub-steps: determining spatiotemporal nodes and determining four different types of arcs. In determining the spatiotemporal movement trajectory of the battery within the service network, the concept of a battery path is introduced to represent the spatiotemporal movement trajectory model of the battery within the service network. The model requires asset balance, meaning that the number of electric vehicles and batteries at each station must remain consistent at the beginning and end of each service cycle. In practical applications, the distribution carrier equips the stations with charging and battery swapping facilities. The model uses a time cycle divided by hours, and the battery replacement time is negligible. The generation of the required battery path includes determining the service arc, charging arc, and idle arc, as well as the model parameters, decision variables, objective function, and dominance rules for the service arc, charging arc, and idle arc.

2. The battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, In setting up a service network, the service network is a spatiotemporal network that can be represented as a directed multigraph, where... Represents a set of nodes. The arc represents a set of arcs, which can represent a transition in spacetime. For each cargo, its starting point and ending point are both spacetime nodes. The corresponding time components represent the available time of the cargo at the starting point and the latest allowed arrival time at the ending point, respectively. Transportation is achieved through multiple paths composed of spacetime arcs, thereby ensuring the connectivity between the starting point and the ending point.

3. The battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, The service network is configured with four different types of arcs: (1) Charging arcs, which connect consecutive time nodes at the same station, indicating that the battery is being charged within a certain time period when the arc is selected. These arcs are subject to capacity limitations; (2) Idle arcs, which also connect consecutive time nodes at the same station, indicating that the electric vehicle is in a stationary state with the battery but not being charged. (3) Service arc: Defines the spatial and temporal coordination of electric vehicle transportation services, and clarifies the departure and arrival information of the service. When the service arc appears in the battery path, it means that the battery is powering the electric vehicle in motion. (4) Shift arc: Used to adjust the position of the electric vehicle and the battery to prepare for the next round of service.

4. The battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, In setting up a service network, the cost of each arc includes power consumption, asset depreciation, and operating expenses. The cost of a service arc is related to distance and involves power consumption and asset depreciation; while shift arcs mainly reflect operating costs. To ensure feasibility, the duration of a service arc must be longer than the actual transportation time required.

5. The battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, In determining the spatiotemporal movement trajectory of a battery within a service network, the concept of a battery path is introduced to represent the spatiotemporal movement trajectory of a battery within the service network.

6. The battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, In the battery path required for generation, the battery is fully charged at the beginning of the service cycle; after completing one service arc, its charge drops to 20%; then the battery is idle for one cycle with its charge remaining unchanged. Subsequently, the battery is removed from the electric vehicle and enters the charging system, undergoing two consecutive charging cycles. At this point, the battery is fully charged again and ready for the next service cycle. In the battery path, a transition from a non-charging arc to a charging arc indicates that the battery has been removed from the electric vehicle, while a transition from a charging arc to another type of arc indicates that the battery has been reinstalled. The battery swapping node divides the battery path into several segments, within which the battery state remains unchanged.

7. The battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, The model parameters for generating the required battery paths include the following: station set, discrete time point set, all spatiotemporal node set, commodity flow set, battery path set, all arc set, service arc set, charging arc set, cyclone arc set, idle arc set, battery path arc inclusion index, battery path battery swapping index at corresponding points, commodity origin, commodity destination, total commodity quantity, cost incurred by electric trucks passing through arcs, cost incurred by a unit commodity passing through, maximum load capacity of swapping trucks, fixed cost of each battery path, cost of each battery swap, maximum vehicle capacity on charging arcs, swapping capacity at nodes, and total number of swapping trucks.

8. The battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, In generating the required battery path, the decision variables include the number of batteries on the selected battery path loop, the number of battery swapping trucks on the selected path arc, and continuous variables. The objective function is to minimize the total cost.

9. A battery-swapping truck freight planning method based on a battery cycle sharing strategy according to claim 1, characterized in that, In generating the required battery paths, the dominance rule can be linearly relaxed using the model. The battery paths are calculated using a labeling algorithm and then added to the model for iterative iteration until there are no more battery paths to add. Finally, an integer programming model is solved.

Images