Methods, devices, vehicles, and media for optimizing on-the-go charging of heterogeneous electric truck platoons

Abstract

This application relates to the field of intelligent transportation systems, and particularly to a method, apparatus, vehicle, and medium for optimizing on-the-go charging of heterogeneous electric truck platoons. The method includes: acquiring charging plan data for each electric truck in the heterogeneous electric truck platoon at multiple stations; identifying the number of electric trucks requiring charging at the current station based on the charging plan data; if the number of electric trucks is not empty, determining the charging duration of the heterogeneous electric truck platoon based on the charging duration of each electric truck; updating the charging plan data for each electric truck based on the charging duration of the heterogeneous electric truck platoon; generating a charging timetable for the heterogeneous electric truck platoon based on the updated charging plan data; and optimizing the order of electric trucks in the heterogeneous electric truck platoon based on the charging timetable. This solves the problems of poor charging scheduling rationality and low platoon transportation efficiency in heterogeneous electric truck platoons.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation systems technology, and in particular to a method, apparatus, vehicle and medium for optimizing on-the-go charging of heterogeneous electric truck platoons. Background Technology

[0002] In recent years, driven by the demands for economic, environmental, and social benefits, the electric vehicle industry has developed rapidly. Among them, electric trucks have significant advantages such as zero emissions and low operating costs. Vehicle platooning technology maintains a small safe driving distance between vehicles through communication and other technologies, which can significantly reduce the aerodynamic drag experienced by vehicles at high speeds. Vehicle platooning can not only greatly reduce the overall energy consumption of the fleet, but also effectively improve driving safety and road traffic efficiency.

[0003] In related technologies, based on idealized assumptions, it is believed that all vehicles in a convoy are completely homogeneous in terms of battery capacity, energy consumption rate, maximum charging power, and initial state of charge. Using a fixed convoy order results in the lead vehicle always being in a high energy consumption state, while the following vehicles are always in a low energy consumption state. Summary of the Invention

[0004] This application provides a method, apparatus, vehicle, and medium for optimizing the charging of heterogeneous electric truck platoons en route, in order to solve the problems of poor rationality of charging scheduling and low platoon transportation efficiency in related technologies.

[0005] The first aspect of this application provides a method for optimizing the on-the-go charging of a heterogeneous electric truck queue, comprising the following steps: obtaining charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations; identifying the number of electric trucks that need to be charged at the current station based on the charging plan data; if the number of electric trucks is not empty, extracting the planned charging amount of the electric trucks at the current station from the charging plan data, calculating the charging time of the corresponding electric truck based on the planned charging amount, determining the charging time of the heterogeneous electric truck queue based on the charging time of each electric truck; updating the charging plan data of each electric truck based on the charging time of the heterogeneous electric truck queue, generating a charging timetable for the heterogeneous electric truck queue based on the updated charging plan data, and optimizing the sorting of electric trucks in the heterogeneous electric truck queue based on the charging timetable.

[0006] Optionally, the charging plan data of each electric truck in the heterogeneous electric truck queue at multiple stations is obtained, including: establishing a nonlinear charging model and a location-dependent energy consumption model for the electric truck; establishing a state transition equation based on the location-dependent energy consumption model, wherein the state transition equation represents the update of the electric truck's state of charge from the current station to the next station; and calculating the charging plan data of each electric truck in the heterogeneous electric truck queue at multiple stations based on the nonlinear charging model and the state transition equation.

[0007] Optionally, the expression for the nonlinear charging model:

[0008] in, It is a nonlinear charging model. For charging time, It is the overall efficiency reduction factor. This represents the initial charge level at the start of the second phase. These represent the initial charge levels at the start of each of the three phases. for At the end of the second phase, for The end of the third phase; The expression for the location-dependent energy consumption model:

[0009] in, It refers to the energy consumption of vehicles in platoons on a road segment. It's a vehicle. yes Section This refers to the energy consumption of a vehicle when it is driving alone. It's a car The vehicle's ranking position on this section of the road. This represents the energy-saving ratio at the corresponding location; State transition equation:

[0010] in, For the j-th vehicle to arrive at the station The amount of electricity at that time, For the j-th vehicle to arrive at the station The amount of electricity at that time, For vehicle j at station The amount of charge, For battery capacity, For vehicles Energy consumption of platooning on a road segment.

[0011] Optionally, based on the nonlinear charging model and state transition equation, the charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations is calculated, including: establishing a formula relating actual charging amount to dwell time based on the nonlinear charging model and state transition equation; establishing an objective function with charging time as the objective based on the formula; generating a planning problem function based on the objective function and preset constraints; and calculating the charging plan data for each electric truck at multiple stations based on the planning problem function.

[0012] Optionally, the expression for the relational formula is:

[0013] in, This represents the actual amount of electricity charged. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, To standardize the dwell time of the queue; The expression for the objective function is:

[0014] in, This is a collection of all available charging stations. For binary decision variables, For bicycles at the station Charging time, The time for entering and exiting the station is fixed.

[0015] The expression for the planning problem function is:

[0016] in, For relational formulas, As a safety threshold, This represents the cumulative energy consumption from the current station to the next station. To start from the current station To the next station Cumulative energy consumption The battery level upon arrival at the station.

[0017] The expression for the objective function is: Optionally, the formula for calculating the charging time of an electric truck is:

[0018] in, This represents the charging time at the i-th station. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, This represents the actual amount of electricity charged. The formula for updating the charging schedule data for each electric truck is:

[0019] in, This is the updated charging schedule data for each electric truck. It is a nonlinear charging function. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, For bicycles at the station Charging time, Let j be the j-th electric truck in the heterogeneous electric truck queue.

[0020] Optionally, optimizing the electric truck sorting of the heterogeneous electric truck queue based on the charging schedule includes: obtaining the departure driving order of the heterogeneous electric truck queue; using the departure driving order group as the root node of a Monte Carlo tree, calculating the number of visits to the optimal leaf node; if the number of visits is greater than the visit threshold, expanding the new leaf node of the optimal leaf node until the final node of the Monte Carlo tree is generated, where the new leaf node represents the vehicle driving order of the heterogeneous electric truck queue; integrating the vehicle driving order from the final node to the root node to generate a vehicle driving order scheme; calculating the reward score of the vehicle driving order scheme; and selecting the vehicle driving order with the highest reward score as the optimized driving order of the heterogeneous electric truck queue.

[0021] A second aspect of this application provides an in-transit charging optimization device for a heterogeneous electric truck queue, comprising: an acquisition module for acquiring charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations; an identification module for identifying the number of electric trucks requiring charging at the current station based on the charging plan data; an extraction module for extracting the planned charging amount of the electric trucks at the current station from the charging plan data if the number of electric trucks is not empty, calculating the charging duration of the corresponding electric trucks based on the planned charging amount, and determining the charging duration of the heterogeneous electric truck queue based on the charging duration of each electric truck; and an update module for updating the charging plan data of each electric truck based on the charging duration of the heterogeneous electric truck queue, generating a charging timetable for the heterogeneous electric truck queue based on the updated charging plan data, and optimizing the electric truck sorting of the heterogeneous electric truck queue based on the charging timetable.

[0022] Optionally, the acquisition module is further used to: establish a nonlinear charging model and a location-dependent energy consumption model for electric trucks; establish a state transition equation based on the location-dependent energy consumption model, wherein the state transition equation represents the update of the state of charge of the electric truck as it moves from the current station to the next station; and calculate the charging plan data of each electric truck in the heterogeneous electric truck queue at multiple stations based on the nonlinear charging model and the state transition equation.

[0023] Optionally, the expression for the nonlinear charging model:

[0024] in, It is a nonlinear charging model. For charging time, It is the overall efficiency reduction factor. This represents the initial charge level at the start of the second phase. These represent the initial charge levels at the start of each of the three phases. for At the end of the second phase, for The end of the third phase; The expression for the location-dependent energy consumption model:

[0025] in, It refers to the energy consumption of vehicles in platoons on a road segment. It's a vehicle. yes Section This refers to the energy consumption of a vehicle when it is driving alone. It's a car The vehicle's ranking position on this section of the road. This represents the energy-saving ratio at the corresponding location; State transition equation:

[0026] in, For the j-th vehicle to arrive at the station The amount of electricity at that time, For the j-th vehicle to arrive at the station The amount of electricity at that time, For vehicle j at station The amount of charge, For battery capacity, For vehicles Energy consumption of platooning on a road segment.

[0027] Optionally, the acquisition module is further used to: establish a formula relating actual charging amount and dwell time based on a nonlinear charging model and state transition equation; establish an objective function with charging time as the objective based on the formula; generate a planning problem function based on the objective function and preset constraints; and calculate charging plan data for each electric truck at multiple stations based on the planning problem function.

[0028] Optionally, the expression for the relational formula is:

[0029] in, This represents the actual amount of electricity charged. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, To standardize the dwell time of the queue; The expression for the objective function is:

[0030] in, This is a collection of all available charging stations. For binary decision variables, For bicycles at the station Charging time, The time for entering and exiting the station is fixed.

[0031] The expression for the planning problem function is:

[0032] in, For relational formulas, As a safety threshold, This represents the cumulative energy consumption from the current station to the next station. To start from the current station To the next station Cumulative energy consumption The battery level upon arrival at the station.

[0033] Optionally, the formula for calculating the charging time of an electric truck is:

[0034] in, This represents the charging time at the i-th station. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, This represents the actual amount of electricity charged. The formula for updating the charging schedule data for each electric truck is:

[0035] in, This is the updated charging schedule data for each electric truck. It is a nonlinear charging function. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, For bicycles at the station Charging time, Let j be the j-th electric truck in the heterogeneous electric truck queue.

[0036] Optionally, the update module is further used to: obtain the departure driving order of the heterogeneous electric truck queue; take the departure driving order group as the root node of the Monte Carlo tree, calculate the number of visits to the optimal leaf node, and if the number of visits is greater than the visit threshold, expand the new leaf node of the optimal leaf node until the final node of the Monte Carlo tree is generated, where the new leaf node represents the vehicle driving order of the heterogeneous electric truck queue; integrate the vehicle driving order from the final node to the root node to generate a vehicle driving order scheme, calculate the reward score of the vehicle driving order scheme, and select the vehicle driving order with the highest reward score as the driving optimization order of the heterogeneous electric truck queue.

[0037] A third aspect of this application provides a vehicle including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to perform the heterogeneous electric truck platoon charging optimization method as described above.

[0038] A fourth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which are executed by a processor to perform the heterogeneous electric truck platoon charging optimization method as described above.

[0039] Therefore, this application has at least the following beneficial effects: This application embodiment acquires charging plan data for each truck in a heterogeneous electric truck queue at multiple stations. Based on this data, it identifies the number of electric trucks requiring charging at the current station. If any vehicles require charging, it extracts the planned charging amount for each vehicle at the current station and calculates the corresponding charging time, thereby determining the total charging time for the entire queue. Subsequently, it updates the charging plan data for each truck based on the queue's charging time, generates a queue charging timetable based on the updated plan, and finally optimizes the sorting of electric trucks in the queue according to the timetable. This dynamically adapts to the charging needs of each station, taking into account the individual differences of each heterogeneous electric truck, achieving a match between the charging plan and actual charging demand. This ensures the orderly operation of charging at the current station, improves charging and scheduling efficiency, reduces vehicle waiting time, and enhances charging resource utilization and fleet operation efficiency. Therefore, it solves the problems of poor rationality in charging scheduling and low queue transportation efficiency in related technologies for heterogeneous electric truck queues.

[0040] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0041] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1This is a flowchart illustrating an in-transit charging optimization method for a heterogeneous electric truck platoon, according to an embodiment of this application. Figure 2 This is a flowchart of a Monte Carlo tree search algorithm provided according to an embodiment of this application; Figure 3 This is a schematic diagram of a multi-step joint optimization mechanism for on-the-go charging of heterogeneous electric truck platoons according to an embodiment of this application; Figure 4 This is an example diagram of an on-the-go charging optimization device for a heterogeneous electric truck queue according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a vehicle according to an embodiment of this application. Detailed Implementation

[0042] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0043] The following description, with reference to the accompanying drawings, outlines a method, apparatus, vehicle, and medium for optimizing the on-the-go charging of heterogeneous electric truck queues according to embodiments of this application. Addressing the issues of poor scheduling rationality and low transportation efficiency in heterogeneous electric truck queues mentioned in the background, this application provides a method for optimizing the on-the-go charging of heterogeneous electric truck queues. This method acquires charging plan data for each truck in the heterogeneous electric truck queue at multiple stations, identifies the number of electric trucks requiring charging at the current station based on this data, extracts the planned charging amount for each vehicle at the current station, calculates the corresponding charging time, and determines the total charging time for the entire queue. Subsequently, the charging plan data for each truck is updated based on the queue charging time, and a queue charging timetable is generated based on the updated plan. Finally, the order of electric trucks in the queue is optimized according to the timetable. This method dynamically adapts to the charging needs of each station, takes into account the individual differences of each heterogeneous electric truck, achieves matching between the charging plan and actual charging demand, ensures the orderly operation of charging at the current station, improves charging and scheduling efficiency, reduces vehicle waiting time, and enhances the utilization rate of charging resources and fleet operation efficiency. This solves the problems of poor rationality in charging scheduling and low efficiency in platoon transportation for heterogeneous electric truck platoons.

[0044] Specifically, Figure 1 This is a flowchart illustrating an in-transit charging optimization method for a heterogeneous electric truck platoon, as provided in an embodiment of this application.

[0045] like Figure 1As shown, the method for optimizing the on-the-go charging of heterogeneous electric truck platoons includes the following steps: In step S101, charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations is obtained.

[0046] Among them, the heterogeneous electric truck convoy is a collaborative driving fleet composed of multiple electric trucks with different performance parameters such as battery capacity, energy consumption, charging power, and load capacity; the charging plan data is the scheduling information of each electric truck in the convoy at multiple stations along the route, including the planned charging stations, charging time periods, charging power, charging duration and power.

[0047] Understandably, by acquiring charging plan data for each vehicle in a heterogeneous electric truck convoy at multiple stations, it is possible to coordinate the convoy's driving and charging behavior, allowing electric trucks with different performance parameters to be rationally matched with charging resources during trunk line transportation. This ensures that the convoy moves in an orderly manner, has sufficient power, and maintains stable transportation efficiency, thereby optimizing the allocation of charging resources and reducing queuing and energy waste.

[0048] Specifically, in this embodiment, the distribution and service capacity of multiple charging stations along the route are first determined based on the heterogeneous parameters such as battery capacity, energy consumption, and load of each electric truck and the driving route information. Then, combined with the vehicle driving sequence and power consumption, the appropriate charging station, charging time and power demand are calculated for each truck in the queue. A complete and feasible charging plan is formed through overall scheduling and constraint verification, and data extraction and organization are completed.

[0049] Furthermore, in the embodiments of this application, obtaining the charging plan data of each electric truck in the heterogeneous electric truck queue at multiple stations includes: establishing a nonlinear charging model and a location-dependent energy consumption model for the electric truck; establishing a state transition equation based on the location-dependent energy consumption model, wherein the state transition equation represents the update of the state of charge of the electric truck from the current station to the next station; and calculating the charging plan data of each electric truck in the heterogeneous electric truck queue at multiple stations based on the nonlinear charging model and the state transition equation.

[0050] Among them, the nonlinear charging model is a model that represents the nonlinear relationship between the charging power and the amount of electricity of an electric truck; the location-dependent energy consumption model is a model that reflects the energy consumption law of the vehicle by combining location factors such as road conditions and distance of the driving segment; and the state transition equation is an equation that describes the update of the vehicle's electricity state between adjacent stations based on the changes in energy consumption.

[0051] It is understood that the embodiments of this application construct a nonlinear charging model and a location-related energy consumption model for electric trucks, and construct a state transition equation representing the update of the power status between stations based on the energy consumption model. This allows for the calculation of charging plan data for each vehicle in a heterogeneous electric truck queue at multiple stations, which can better reflect the energy consumption and charging characteristics of vehicles in accordance with actual working conditions. This enables scientific planning and precise scheduling of fleet charging behavior, effectively improving the rationality and feasibility of charging arrangements, avoiding transportation delays or resource waste caused by power estimation errors, and providing reliable data support and decision-making assurance for the efficient and safe operation of electric truck queues.

[0052] Furthermore, in the embodiments of this application, the expression of the nonlinear charging model is:

[0053] in, It is a nonlinear charging model. For charging time, It is the overall efficiency reduction factor. This represents the initial charge level at the start of the second phase. These represent the initial charge levels at the start of each of the three phases. for At the end of the second phase, for The end of the third phase.

[0054] The expression for the location-dependent energy consumption model:

[0055] in, It refers to the energy consumption of vehicles in platoons on a road segment. It's a vehicle. yes Section This refers to the energy consumption of a vehicle when it is driving alone. It's a car The vehicle's ranking position on this section of the road. It represents the energy-saving ratio for the corresponding location.

[0056] State transition equation:

[0057] in, To reach the station The amount of electricity at that time, To reach the station The amount of electricity at that time, For the site The amount of charge, For battery capacity, For vehicles Energy consumption of platooning on a road segment.

[0058] It is understood that the embodiments of this application construct a nonlinear charging model that considers the influence of temperature and health status, and combine it with a platooning energy consumption model related to platoon position to establish a state transition equation that characterizes the update of the state of charge between stations. This calculates the charging plan data of each vehicle in the heterogeneous electric truck platoon at multiple stations, which can better fit the actual charging characteristics of electric trucks and the energy consumption law of platooning. This significantly improves the accuracy of power estimation and charging planning, effectively avoids unreasonable charging arrangements, transportation delays or energy waste caused by model deviations, and realizes intelligent scheduling of the charging behavior of heterogeneous fleets.

[0059] Specifically, addressing the nonlinear characteristics of the lithium-ion battery charging process, this application employs a piecewise linear function to approximate the charging curve, and further introduces ambient temperature fluctuations and battery health degradation as dynamic penalty terms, thereby enhancing physical accuracy under complex long-distance operating conditions. The charging process is divided into three stages, and the mapping function between charging time and state of charge is as follows:

[0060] in, It is a nonlinear charging model. For charging time, It is the overall efficiency reduction factor. This represents the initial charge level at the start of the second phase. These represent the initial charge levels at the start of each of the three phases. for At the end of the second phase, for The end of the third phase.

[0061] Considering the aerodynamic effects of convoy driving, a vehicle's energy consumption depends significantly on its specific position within the convoy. The low-pressure wake created by the vehicle in front significantly reduces air resistance for following vehicles within this area, thus optimizing energy consumption. On the road section Formation energy consumption The calculation formula is:

[0062] in, It refers to the energy consumption of vehicles in platoons on a road segment. It's a vehicle. yes Section This refers to the energy consumption of a vehicle when it is driving alone. It's a car The vehicle's ranking position on this section of the road. It represents the energy-saving ratio for the corresponding location.

[0063] The formula for updating the charge state of a vehicle as it moves from one station to the next is:

[0064] in, To reach the station The amount of electricity at that time, To reach the station The amount of electricity at that time, For the site The amount of charge, For battery capacity, For vehicles Energy consumption of platooning on a road segment.

[0065] Furthermore, in the embodiments of this application, the charging plan data of each electric truck in the heterogeneous electric truck queue at multiple stations is calculated based on the nonlinear charging model and state transition equation, including: establishing a relationship formula between actual charging amount and dwell time based on the nonlinear charging model and state transition equation; establishing an objective function with charging time as the objective based on the relationship formula; generating a planning problem function based on the objective function and preset constraints; and calculating the charging plan data of each electric truck at multiple stations based on the planning problem function.

[0066] Among them, the actual charging amount is the actual increase in the state of charge of the electric truck after it has completed charging at each station according to the nonlinear charging model; the dwell time is the total dwell time of the vehicle at the station; the objective function is the optimization objective constructed to minimize the overall charging time of the fleet; and the planning problem function is the optimization function formed by integrating the objective function with constraints such as vehicle battery power, station capacity, and transportation timeliness.

[0067] It is understood that the embodiments of this application rely on a nonlinear charging model and state transition equation to construct a formula relating actual charging amount and dwell time, establish an objective function with charging time as the core, and then generate a complete planning problem function by combining conditions such as power constraints, station capacity, and transportation timeliness. Finally, the accurate charging plan data of heterogeneous electric truck queues at multiple stations is obtained by solving the problem. This realizes the transformation of charging scheduling from theoretical modeling to practical application, accurately matches the charging needs and dwell restrictions of trucks with different performance, improves fleet operation efficiency, ensures transportation punctuality, and optimizes energy use efficiency.

[0068] Furthermore, in the embodiments of this application, the expression of the relational formula is:

[0069] in, This represents the actual amount of electricity charged. It is the inverse function of the charging function. To reach the station The amount of electricity at that time, To standardize the dwell time of the queue; The expression for the objective function is:

[0070] in, This is a collection of all available charging stations. For binary decision variables, For bicycles at the station Charging time, Fixed entry and exit time; The expression for the planning problem function is:

[0071] in, For relational formulas, As a safety threshold, This represents the cumulative energy consumption from the current station to the next station. To start from the current station To the next station Cumulative energy consumption The battery level upon arrival at the station.

[0072] It is understood that the embodiments of this application realize the global optimization planning of electric vehicle charging and driving by constructing the actual charging amount calculation formula, objective function and planning problem function, quantifying the vehicle charging demand and energy consumption constraints, minimizing the total charging and operation time of the vehicle under the premise of ensuring battery safety threshold, improving vehicle operation efficiency and energy utilization rationality, and providing quantitative support and scientific basis for the optimization decision of electric vehicle route and charging scheduling.

[0073] Specifically, this application embodiment calculates the optimal charging plan independently for each electric truck in the platoon, based on the fixed driving route of each electric truck, to meet requirements such as energy consumption constraints and battery safety thresholds throughout the vehicle's journey. The optimization objective is to minimize the total charging time of a single vehicle, thus completing the pre-planning of charging decisions at the single-vehicle level. The objective function is defined as follows:

[0074] in, This is a collection of all available charging stations. For binary decision variables, For bicycles at the station Charging time, The time for entering and exiting the station is fixed.

[0075] The binary decision variable is used to characterize whether a vehicle stops at a station for charging. A value of 1 indicates that the vehicle stops at the station for charging, and a value of 0 indicates that the vehicle does not stop at the station.

[0076] Single-vehicle charging scheduling needs to meet minimum battery level constraints, overcharge prevention constraints, and battery level evolution constraints. The minimum battery level constraint means that the vehicle's System Unit (SoC) must not fall below a safe threshold when it arrives at any station. Overcharge protection constraint means that the SoC (System-on-Chips) of the vehicle must not exceed the battery capacity limit when leaving the station, i.e. The energy evolution constraint indicates that the energy changes of a vehicle between adjacent stations follow the energy consumption formula: ,in Energy consumption of road sections.

[0077] To address the mixed-integer nonlinear programming problem, this embodiment transforms continuous variables into functions dependent on binary variables. If charging is chosen at a station, the amount of charge should be just sufficient to support the vehicle's journey to the next selected station, while retaining a safety margin. This simplifies the problem to a pure integer programming problem, with the expression for the programming problem function being:

[0078] in, For relational formulas, As a safety threshold, This represents the cumulative energy consumption from the current station to the next station. To start from the current station To the next station Cumulative energy consumption The battery level upon arrival at the station.

[0079] According to the embodiments of this application, by acquiring the charging plan data of each vehicle in a heterogeneous electric truck convoy at multiple stations, the overall planning of the convoy's driving and charging behavior can be realized. This allows electric trucks with different performance parameters to be reasonably matched with charging resources during trunk transportation, ensuring that the convoy moves in an orderly manner, has sufficient power, and stable transportation efficiency. It can optimize the allocation of charging resources and reduce queuing and energy waste.

[0080] In step S102, the number of electric trucks that need to be charged at the current station is identified based on the charging plan data.

[0081] It is understood that the embodiments of this application identify the number of electric trucks that need to be charged at the current station based on the charging plan data. This allows for the identification of the charging vehicle sets at each station within the platoon, quantification of the scale of vehicles requiring charging and individual charging time requirements, thereby determining the unified charging time for the platoon, updating the actual charging amount and vehicle departure status, and replanning subsequent journeys. This achieves matching and efficient coordination of platoon charging, eliminates vehicle waiting redundancy, ensures the consistency of charging time, improves platoon operation efficiency and energy utilization rationality, and ensures the orderliness and reliability of electric truck platoon charging scheduling on fixed routes.

[0082] Specifically, in this embodiment, the initial optimal charging plan calculated based on a single-vehicle model for each electric truck in the platoon is used as input data. The current station index is set to 1, and the current station index is used as the starting station reference for collaborative charging scheduling to perform initialization operations. Subsequently, at the dimension of the current station, the charging set identification stage is entered. All numbered electric trucks in the platoon are traversed, and the planned charging amount value corresponding to each vehicle at this station is read and verified one by one. Vehicles that meet the charging conditions are classified and integrated to construct a set of vehicles to be charged at the current station. The expression for the set of vehicles to be charged is:

[0083] in, Gathering vehicles waiting to be charged This represents the actual amount of electricity charged. This refers to the current site.

[0084] During the identification process, the system simultaneously records the identification of each vehicle in the set, the planned charging amount, and the charging amount upon arrival at the station, and finally completes the accurate statistics of the number of electric trucks that need to be charged at the current station and the determination of the set of charging vehicles.

[0085] According to the embodiments of this application, the number of electric trucks that need to be charged at the current station can be identified based on the charging plan data. This allows for the locking of the charging vehicle sets at each station within the platoon, quantification of the scale of vehicles requiring charging and individual charging time requirements, thereby determining the unified charging time for the platoon, updating the actual charging amount and vehicle departure status, and replanning subsequent journeys. This achieves matching and efficient coordination of platoon charging, eliminates vehicle waiting redundancy, ensures the consistency of charging time, improves platoon operation efficiency and energy utilization rationality, and ensures the orderliness and reliability of electric truck platoon charging scheduling on fixed routes.

[0086] In step S103, if the number of electric trucks is not empty, the planned charging amount of the electric trucks at the current station is extracted from the charging plan data, the charging time of the corresponding electric truck is calculated based on the planned charging amount, and the charging time of the heterogeneous electric truck queue is determined based on the charging time of each electric truck.

[0087] Understandably, if the number of electric trucks needing charging at the current station is not empty, the planned charging amount for each vehicle at the station is extracted. The individual charging time for each vehicle is calculated by combining the inverse function of the charging function and the arriving power. Then, the unified charging time for the heterogeneous electric truck queue is determined based on the longest charging time. The above process can quantify the charging needs of each vehicle in the queue, constrain the unified dwell time of the queue with the longest demand time, ensure that all vehicles can complete the planned charging, effectively eliminate waiting redundancy between vehicles, realize the collaborative adaptation of the charging needs of heterogeneous fleets, ensure the synchronization and continuity of platooning, and improve the overall energy replenishment efficiency and operational economy of the platoon.

[0088] Specifically, the system checks if the set of vehicles to be charged is empty. If the set is empty, it determines that no vehicle needs to be charged at the current station, sets the uniform charging time for the queue at that station to 0, updates the station index, and returns to the charging set identification step to continue traversing the next station. If the set is not empty, it extracts the planned charging amount for each electric truck at the current station, combines it with the battery state of charge of each electric truck when it arrives at the station, calculates the individual charging time required to complete the planned charging amount using the inverse function of the charging function, compares it with the charging time of all vehicles in the set, selects the maximum value as the uniform charging time for the heterogeneous electric truck queue at the current station, and after determining the charging time for that station, it proceeds to the subsequent actual charging amount update and departure status update steps.

[0089] Furthermore, in the embodiments of this application, the formula for calculating the charging time of the electric truck is as follows:

[0090] in, For the charging time at the i-th station, It is the inverse function of the charging function. To reach the station The amount of electricity at that time, This represents the actual amount of electricity charged. The formula for updating the charging schedule data for each electric truck is:

[0091] in, This is the updated charging schedule data for each electric truck. It is a nonlinear charging function. It is the inverse function of the charging function. To reach the station The amount of electricity at that time, For bicycles at the station Charging time, Electric trucks in a heterogeneous electric truck fleet.

[0092] It is understood that the embodiments of this application construct a formula for calculating the charging time of electric truck stations based on the inverse function of the charging function, and combine it with the charging plan data update method that takes the maximum value to accurately quantify the actual charging time of vehicles at each station. Under the premise of meeting the vehicle's power demand, the optimal charging time for a single vehicle is determined, providing accurate time parameter support for platoon collaborative charging scheduling.

[0093] Specifically, this application embodiment calculates the individual charging time required to complete the planned charging amount for each vehicle in the set of vehicles to be charged, taking into account its battery state of charge when it arrives at the station, using an inverse function of the charging function. The calculation formula is as follows:

[0094] in, For the charging time at the i-th station, It is the inverse function of the charging function. To reach the station The amount of electricity at that time, This represents the actual amount of electricity charged.

[0095] Then, the required time of all vehicles in the set is compared, and the maximum value is taken as the unified charging time for the queue at the current station, ensuring that all vehicles can complete the planned charging. The unified charging time is:

[0096] in, For bicycles at the station Charging time, Let i be the charging time at the i-th station.

[0097] Next, based on the determined uniform duration, the actual charging amount of each vehicle at the station is calculated and updated in reverse. The update formula for the charging plan data of each electric truck is as follows:

[0098] in, This is the updated charging schedule data for each electric truck. It is a nonlinear charging function. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, For bicycles at the station Charging time, Electric trucks in a heterogeneous electric truck fleet.

[0099] Synchronously update the departure charge status of each vehicle at the station:

[0100] in, It is the state of charge of each vehicle at the station upon departure. For the j-th vehicle to arrive at the station Battery level at that time.

[0101] According to the embodiments of this application, if the number of electric trucks requiring charging at the current station is not empty, the planned charging amount of each vehicle at the station is extracted, and the individual charging time of each vehicle is calculated by combining the inverse function of the charging function and the charging amount at the station. Then, the unified charging time of the heterogeneous electric truck queue is determined based on the longest charging time. The above process can quantify the charging needs of each vehicle in the queue, constrain the unified dwell time of the queue with the longest demand time, ensure that all vehicles can complete the planned charging, effectively eliminate waiting redundancy between vehicles, realize the collaborative adaptation of the charging needs of heterogeneous fleets, ensure the synchronization and continuity of platooning, and improve the overall energy replenishment efficiency and operational economy of the platoon.

[0102] In step S104, the charging plan data of each electric truck is updated according to the charging duration of the heterogeneous electric truck queue, the charging schedule of the heterogeneous electric truck queue is generated according to the updated charging plan data, and the electric truck sorting of the heterogeneous electric truck queue is optimized according to the charging schedule.

[0103] Among them, the charging timetable is a time-sequential charging arrangement formed by dynamically updating the charging plan based on the actual charging time of each vehicle in the heterogeneous electric truck queue; the electric truck sorting is a sorting of the heterogeneous electric truck queue that is rearranged and optimized.

[0104] It is understood that the embodiments of this application dynamically update the charging plan data of each vehicle based on the actual charging time of the heterogeneous electric truck queue, generate a charging timetable that adapts to the overall needs of the queue, and further optimize the queue sorting of electric trucks based on the timetable. This enables the charging arrangement to be highly matched with the actual situation of the vehicles, improves the rationality and timeliness of the overall charging scheduling of the heterogeneous electric truck queue, reduces vehicle waiting time and resource waste, makes the operation of the heterogeneous electric truck queue smoother, and improves the coordination efficiency of logistics transportation and energy replenishment.

[0105] Specifically, this embodiment of the application collects the actual charging time data of each truck in the heterogeneous electric truck queue, and dynamically updates the original charging plan data by combining heterogeneous parameters such as the battery capacity, remaining power, energy consumption characteristics, and charging equipment power of each vehicle. Subsequently, based on the updated charging plan data of each vehicle, the charging resources are reasonably allocated by taking into account factors such as the number of charging equipment, charging priority, and queue task arrangement. The charging start time, end time, and occupied charging station of each truck are clearly defined to generate an orderly charging timetable for the heterogeneous electric truck queue. Finally, based on the charging timetable and combined with indicators such as the difference in charging time of each truck, the urgency of the task, and energy consumption efficiency, the queue order is optimized through a reasonable sorting algorithm to avoid problems such as congestion and excessive waiting time during the charging process, thereby achieving standardization and efficiency of the entire heterogeneous electric truck queue charging scheduling.

[0106] Furthermore, in the embodiments of this application, optimizing the electric truck sorting of the heterogeneous electric truck queue according to the charging schedule includes: obtaining the departure driving order of the heterogeneous electric truck queue; taking the departure driving order group as the root node of the Monte Carlo tree, calculating the number of visits to the optimal leaf node, and if the number of visits is greater than the visit threshold, expanding the new leaf node of the optimal leaf node until the final node of the Monte Carlo tree is generated, wherein the new leaf node represents the vehicle driving order of the heterogeneous electric truck queue; integrating the vehicle driving order from the final node to the root node to generate a vehicle driving order scheme, calculating the reward score of the vehicle driving order scheme, and selecting the vehicle driving order with the highest reward score as the driving optimization order of the heterogeneous electric truck queue.

[0107] The system includes the following components: departure order (initial setting of the order in which vehicles depart and travel in the heterogeneous electric truck queue); Monte Carlo tree (search structure for intelligent optimization); root node (starting node of the tree structure based on the initial departure order); optimal leaf node (the best performing branch node in the current tree); visit count (frequency of the optimal leaf node being searched and evaluated); visit threshold (preset criterion for deciding whether to continue expanding the node branch); new leaf node (new vehicle travel generated from the optimal leaf node); final node (the end node when the Monte Carlo tree expansion terminates); vehicle travel sequence scheme (complete electric truck queue arrangement formed by integrating the root node to the final node); reward score (quantitative evaluation score of the sorting scheme); and driving optimization order (selecting the optimal queue travel order with the highest reward score and best fit for the charging schedule from all schemes).

[0108] It is understood that this application embodiment obtains the initial departure order of the heterogeneous electric truck queue based on the charging schedule, uses the departure order as the root node of the Monte Carlo tree, calculates the number of visits to the optimal leaf node and compares it with the visit threshold, continuously expands the optimal leaf node to generate new vehicle driving order nodes until the final node of the Monte Carlo tree is obtained, and then integrates the driving order from the root node to the final node to form multiple vehicle driving order schemes. By calculating the reward score of each scheme, the scheme with the highest score is selected as the queue driving optimization order. It can select the optimal solution from multiple sorting schemes, realize the adaptation of the parameter differences of heterogeneous electric trucks to charging needs, effectively avoid the connection conflict between charging and driving, reduce vehicle charging waiting and driving delays, and improve the overall operation efficiency of the queue.

[0109] Specifically, the goal of dynamic vehicle sequence optimization is to maximize the effective charging power of the queue by dynamically adjusting the driving order of vehicles on the road segment between two charging stations, given a unified charging plan generated by the preceding steps, and balancing the energy consumption of each vehicle.

[0110] Because the combination space for vehicle sorting is enormous, this embodiment employs the Monte Carlo tree search algorithm for efficient solution, such as... Figure 2 As shown, the platooning and sorting problem is modeled as a tree search structure. Each level of the tree corresponds to a segment of the journey, and the depth of the tree is equal to the total number of segments. The nodes of the tree represent a vehicle arrangement combination on that segment. A complete path from the root node to the leaf node represents a complete vehicle sequence scheme for all segments of the platoon during the entire journey.

[0111] The algorithm searches for the optimal complete path within the tree. The Monte Carlo tree search algorithm dynamically evaluates the value of nodes iteratively, with each iteration consisting of four steps: selection, expansion, simulation, and backtracking. Starting from the root node, the most promising child node is selected using the UCB1 (Upper Confidence Bound) criterion until a leaf node is reached. The selection formula is as follows: The formula for the average node score is: .in, σ is the average rating of the child node, uσ is the number of visits to the child node, u is the total number of visits to the parent node, and G is the weighting parameter. It is the reward value when node σ is visited for the dth time.

[0112] If the currently selected leaf node has been visited (visited ≥ 1 time), then the node is expanded to generate child nodes representing all possible train sequences for the next road segment, and these child nodes are added to the search tree. If the leaf node has never been visited, then the expansion is skipped and the simulation phase begins directly.

[0113] Starting from the expanded node (or an unvisited leaf node), a random strategy is used to generate the vehicle order for all subsequent road segments until the destination is reached, forming a complete platooning itinerary. The reward score after the simulation is completed is defined as the total charging power of the queue, and the calculation formula is:

[0114] in, It is a set of charging stations determined by the preliminary steps. Indicates vehicle On the site The amount of charge, A uniform charging time determined for the preceding steps.

[0115] The calculated reward is backpropagated to update the total number of visits and average score of all nodes on the path from the current leaf node back to the root node.

[0116] Finally, after reaching the preset number of iterations or the time limit, the algorithm terminates and outputs the vehicle ranking scheme corresponding to the highest-scoring complete path. This information is then fed back to the preceding step model for the next round of iterative updates.

[0117] This application embodiment obtains the initial departure order of a heterogeneous electric truck queue based on a charging schedule. The departure order is used as the root node of a Monte Carlo tree. By calculating the number of visits to the optimal leaf node and comparing it with the visit threshold, the optimal leaf node is continuously expanded to generate new vehicle driving order nodes until the final node of the Monte Carlo tree is obtained. Then, the driving orders from the root node to the final node are integrated to form multiple vehicle driving order schemes. By calculating the reward score of each scheme, the scheme with the highest score is selected as the queue driving optimization order. This can select the optimal solution from multiple sorting schemes, realize the adaptation of the parameter differences of heterogeneous electric trucks to charging needs, effectively avoid the connection conflict between charging and driving, reduce vehicle charging waiting and driving delays, and improve the overall operating efficiency of the queue.

[0118] To better understand the solution of this application, the following specific embodiment describes the method or execution flow for optimizing on-the-go charging of heterogeneous electric truck queues, as follows: Figure 3 As shown: 1. Construct a physical model that considers nonlinear charging and location energy consumption.

[0119] (1) Establishing a nonlinear charging model: Addressing the nonlinear characteristics of lithium-ion battery charging, this invention approximates the charging curve using a piecewise linear function and further introduces ambient temperature fluctuations and battery health degradation as dynamic penalty terms, thereby enhancing physical accuracy under complex long-distance operating conditions. The charging process is divided into three stages: charging time... The mapping function to the state-of-charge SoC is defined as follows:

[0120] in, It is a nonlinear charging model. For charging time, It is the overall efficiency reduction factor. This represents the initial charge level at the start of the second phase. These represent the initial charge levels at the start of each of the three phases. for At the end of the second phase, for The end of the third phase.

[0121] The range of values ​​is within The combined efficiency reduction factor is used to dynamically characterize the combined penalty effect of external environment and battery aging on charging rate. This combined function is decoupled into the product of temperature influence factor and health influence factor, i.e. . This reflects the decrease in actual charging power of lithium-ion batteries when they deviate from their optimal operating temperature due to reduced internal chemical activity. The phenomenon of increased internal resistance and decreased charging reception capacity due to long-term vehicle operation was quantified.

[0122] (2) Establish a position-dependent energy consumption model: Considering the aerodynamic effects during platooning, the energy consumption of a vehicle depends on its position within the platoon. On the road section The formula for calculating the formation energy consumption is:

[0123] in, It refers to the energy consumption of vehicles in platoons on a road segment. It's a vehicle. yes Section This refers to the energy consumption of a vehicle when it is driving alone. It's a car The vehicle's ranking position on this section of the road. It represents the energy-saving ratio for the corresponding location.

[0124] (3) Establish the state transition equation: vehicle From the site Arrival Station The SoC state update formula is:

[0125] in, For the j-th vehicle to arrive at the station The amount of electricity at that time, For the j-th vehicle to arrive at the station The amount of electricity at that time, For vehicle j at station The amount of charge, For battery capacity, For vehicles Energy consumption of platooning on a road segment.

[0126] The relationship between actual charging amount and uniform queue dwell time is given by the formula:

[0127] in, This represents the actual amount of electricity charged. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, To standardize the dwell time for the queue.

[0128] 2. Optimization of single-vehicle charging planning and scheduling.

[0129] Before coordinating charging scheduling, the optimal charging plan for each electric truck in the platoon is calculated independently on a fixed route. The goal is to minimize the total charging time of a single vehicle while meeting travel constraints.

[0130] With the goal of minimizing the total charging time, the objective function is defined as follows:

[0131] in, This is a collection of all available charging stations. For binary decision variables, For bicycles at the station Charging time, The time for entering and exiting the station is fixed.

[0132] Single-vehicle charging scheduling needs to meet three constraints: (i) Minimum power constraint: The SoC of the vehicle must not be lower than the safety threshold when it arrives at any station, i.e. .

[0133] (ii) Overcharge protection constraint: The SoC of the vehicle when leaving the station must not exceed the upper limit of the battery capacity, i.e. .

[0134] (iii) Energy Evolution Constraint: The energy changes of a vehicle between adjacent stations follow the energy consumption formula:

[0135] in, The change in vehicle battery level between stops should follow energy consumption. The change in vehicle battery power between stations follows energy consumption. For relational formulas, Energy consumption of road sections.

[0136] To solve the aforementioned mixed-integer nonlinear programming problem, this invention employs a deterministic mapping method to transform continuous variables... Transformed into a binary variable The function simplifies the problem to a pure integer programming problem. The mapping logic is: if the choice is made at the site... When docked for charging, the amount of charge is... It should be just enough to allow the vehicle to travel to the next selected station, while retaining a safety margin. The calculation formula is:

[0137] in, For relational formulas, As a safety threshold, This represents the cumulative energy consumption from the current station to the next station. To start from the current station To the next station Cumulative energy consumption The battery level upon arrival at the station.

[0138] This part of the system can obtain information for each vehicle. The optimal charging set under ideal conditions is: ,

[0139] in, For each vehicle The optimal charging set under ideal conditions. For vehicles On the site The amount of charge.

[0140] 3. Integration of platoon charging solutions based on multi-vehicle collaboration.

[0141] To address the issue of inconsistent charging demands among heterogeneous vehicles at the same station, this invention proposes an integrated algorithm for platooning charging schemes based on multi-vehicle collaboration, which coordinates the optimal charging plans of each vehicle into a unified platooning charging schedule.

[0142] Input: Each vehicle in the platoon The initial optimal charging plan calculated based on the single-vehicle model:

[0143] in, For each vehicle The optimal charging set under ideal conditions. Indicates vehicle On the site The amount of charge.

[0144] Output a unified queue charging schedule:

[0145] in, To standardize the charging schedule for the queue, Indicates the queue is at the station The uniform charging time.

[0146] (1) Initialization. Set the current site index. =1.

[0147] (2) Identify charging stations. At the current station Iterate through all vehicles in the platoon and identify the set of vehicles that plan to charge at this station:

[0148] in, A collection of vehicles for charging. Indicates vehicle On the site The charging amount is denoted by j, where j is the j-th electric truck in the heterogeneous electric truck queue.

[0149] (3) Determine whether to skip the station. (Set of decisions) Is it empty? If it is empty, it means there are no vehicles that need to charge here, then... Simultaneously update the site index , and return to step 2.

[0150] (4) Calculate individual demand time. For a set Every car in Calculate the time required to complete the planned charging amount:

[0151] in, This represents the charging time at the i-th station. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, This represents the actual amount of electricity charged.

[0152] (5) Determine the uniform dwell time for the queue. Compare the required times of all vehicles in the set, and take the maximum value as the queue's dwell time at the current station. Uniform charging time:

[0153] in, To ensure a uniform charging duration at the i-th station, This represents the charging time at the i-th station.

[0154] (6) Update the actual charging amount. Based on the determined uniform duration. Update the actual charging amount for each vehicle at this station:

[0155] in, This is the updated charging schedule data for each electric truck. It is a nonlinear charging function. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, For bicycles at the station Charging time, Let j be the j-th electric truck in the heterogeneous electric truck queue.

[0156] (7) Update departure status. Update the departure charge status of each car at this station:

[0157] in, It is the state of charge of each vehicle at the station upon departure. For the j-th vehicle to arrive at the station Battery level at that time.

[0158] (8) Re-plan the remaining journey. Use the updated departure status as the new initial SoC and set the station... As a new starting point, the single-vehicle optimization model is invoked to re-plan the subsequent charging schedule for each vehicle.

[0159] (9) Cycle Control and Termination Check. Check the updated charging plan. If all vehicles do not need to be charged at the remaining stations, then charge all remaining stations. Charging time Set to 0; otherwise, update the site index. , and return to step 2.

[0160] (10) Output the final generated unified charging schedule for the formation:

[0161] in, To standardize the charging schedule for the formation For bicycles at the station Charging time.

[0162] 4. Dynamic vehicle order optimization based on Monte Carlo tree search.

[0163] The goal of dynamic vehicle sequencing optimization is to maximize the effective charging power of the queue by dynamically adjusting the vehicle travel order on the road segment between two charging stations, given a unified charging plan generated by previous steps, and balancing the energy consumption of each vehicle. Since the combination space of vehicle sequencing is enormous... This invention employs the Monte Carlo tree search algorithm for efficient solution.

[0164] The platooning problem is modeled as a tree search structure. Each level of the tree corresponds to a segment of the journey, and the depth of the tree equals the total number of segments. Each node in the tree represents a possible arrangement of vehicles on that segment (e.g., car 1-car 3-car 2). A complete path from the root node to a leaf node represents a complete sequence of vehicles across all segments during the entire journey. The algorithm searches the tree to find the optimal complete path.

[0165] The Monte Carlo tree search algorithm dynamically evaluates the value of nodes through iteration. Each iteration includes the following four core steps: (1) Selection: Starting from the root node, the most promising child node is selected using the UCB1 (Upper Confidence Bound) criterion until a leaf node is reached. The selection formula is:

[0166] The formula for the average node score is as follows: .in, σ is the average rating of the child node, uσ is the number of visits to the child node, u is the total number of visits to the parent node, and G is the weighting parameter. It is the reward value when node σ is visited for the dth time.

[0167] (2) Expansion: If the currently selected leaf node has been visited (visited ≥ 1 time), then the node is expanded to generate child nodes representing all possible vehicle sequences of the next road segment, and these child nodes are added to the search tree. If the leaf node has never been visited, then the expansion is skipped and the simulation phase is entered directly.

[0168] (3) Simulation: Starting from the extended node (or unvisited leaf node), a random strategy is used to generate the vehicle sequence for all subsequent road segments until the destination is reached, forming a complete platooning trip plan. The reward score after the simulation is completed is defined as the total charging power of the queue, calculated using the following formula:

[0169] in, It is the total charging power. It is a set of charging stations determined by the preliminary steps. Indicates vehicle On the site The amount of charge, A uniform charging time determined for the preceding steps.

[0170] (4) Backtracking: Backtrack the reward calculated by simulation to update the total number of visits and average score of all nodes on the path from the current leaf node backtracking to the root node.

[0171] Finally, after reaching the preset number of iterations or the time limit, the algorithm terminates and outputs the vehicle ranking scheme corresponding to the highest-scoring complete path. This information is then fed back to the preceding step model for the next round of iterative updates.

[0172] In summary, the heterogeneous electric truck queue charging optimization method proposed in this application obtains charging plan data for each truck in the heterogeneous electric truck queue at multiple stations, identifies the number of electric trucks that need charging at the current station based on this data, extracts the planned charging amount for each vehicle at the current station and calculates the corresponding charging time accordingly, thereby determining the charging time of the entire queue, and then updates the charging plan data for each truck according to the queue charging time. Based on the updated plan, a queue charging timetable is generated, and finally the sorting of electric trucks in the queue is optimized according to the timetable. This method can dynamically adapt to the charging needs of each station, take into account the individual differences of each heterogeneous electric truck, achieve the matching of charging plan and actual charging needs, ensure the orderly operation of charging work at the current station, improve charging and scheduling efficiency, reduce vehicle waiting time, and improve the utilization rate of charging resources and fleet operation efficiency.

[0173] Next, referring to the accompanying drawings, a heterogeneous electric truck platoon charging optimization device according to an embodiment of this application is described.

[0174] Figure 4 This is a block diagram of a heterogeneous electric truck platoon charging optimization device according to an embodiment of this application.

[0175] like Figure 4 As shown, the heterogeneous electric truck queue on-the-go charging optimization device includes: an acquisition module 401, an identification module 402, an extraction module 403, and an update module 404.

[0176] The system includes: an acquisition module 401 for acquiring charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations; an identification module 402 for identifying the number of electric trucks requiring charging at the current station based on the charging plan data; an extraction module 403 for extracting the planned charging amount of the electric trucks at the current station from the charging plan data if the number of electric trucks is not empty, calculating the charging time of the corresponding electric trucks based on the planned charging amount, and determining the charging time of the heterogeneous electric truck queue based on the charging time of each electric truck; and an update module 404 for updating the charging plan data of each electric truck based on the charging time of the heterogeneous electric truck queue, generating a charging timetable for the heterogeneous electric truck queue based on the updated charging plan data, and optimizing the electric truck sorting of the heterogeneous electric truck queue based on the charging timetable.

[0177] Furthermore, the acquisition module 401 is used to: establish a nonlinear charging model and a location-dependent energy consumption model for electric trucks; establish a state transition equation based on the location-dependent energy consumption model, the state transition equation representing the update of the state of charge of the electric truck from the current station to the next station; and calculate the charging plan data of each electric truck in the heterogeneous electric truck queue at multiple stations based on the nonlinear charging model and the state transition equation.

[0178] Furthermore, the expression for the nonlinear charging model is:

[0179] in, It is a nonlinear charging model. For charging time, It is the overall efficiency reduction factor. This represents the initial charge level at the start of the second phase. These represent the initial charge levels at the start of each of the three phases. for At the end of the second phase, for The end of the third phase; The expression for the location-dependent energy consumption model:

[0180] in, It refers to the energy consumption of vehicles in platoons on a road segment. It's a vehicle. yes Section This refers to the energy consumption of a vehicle when it is driving alone. It's a car The vehicle's ranking position on this section of the road. This represents the energy-saving ratio at the corresponding location; State transition equation:

[0181] in, For the j-th vehicle to arrive at the station The amount of electricity at that time, For the j-th vehicle to arrive at the station The amount of electricity at that time, For vehicle j at station The amount of charge, For battery capacity, For vehicles Energy consumption of platooning on a road segment.

[0182] Furthermore, the acquisition module 401 is used to: establish a relationship formula between actual charging amount and dwell time based on the nonlinear charging model and state transition equation; establish an objective function with charging time as the objective based on the relationship formula; generate a planning problem function based on the objective function and preset constraints; and calculate the charging plan data of each electric truck at multiple stations based on the planning problem function.

[0183] Furthermore, the expression for the relational formula is:

[0184] in, This represents the actual amount of electricity charged. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, To standardize the dwell time of the queue; The expression for the objective function is:

[0185] in, This is a collection of all available charging stations. For binary decision variables, For bicycles at the station Charging time, The time for entering and exiting the station is fixed.

[0186] The expression for the planning problem function is:

[0187] in, For relational formulas, As a safety threshold, This represents the cumulative energy consumption from the current station to the next station. To start from the current station To the next station Cumulative energy consumption The battery level upon arrival at the station.

[0188] Furthermore, the formula for calculating the charging time of electric trucks is as follows:

[0189] in, This represents the charging time at the i-th station. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, This represents the actual amount of electricity charged. The formula for updating the charging schedule data for each electric truck is:

[0190] in, This is the updated charging schedule data for each electric truck. It is a nonlinear charging function. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, For bicycles at the station Charging time, Let j be the j-th electric truck in the heterogeneous electric truck queue.

[0191] Furthermore, the update module 404 is used to: obtain the departure driving order of the heterogeneous electric truck queue; take the departure driving order group as the root node of the Monte Carlo tree, calculate the number of visits to the optimal leaf node, and if the number of visits is greater than the visit threshold, expand the new leaf node of the optimal leaf node until the final node of the Monte Carlo tree is generated, where the new leaf node represents the vehicle driving order of the heterogeneous electric truck queue; integrate the vehicle driving order from the final node to the root node to generate a vehicle driving order scheme, calculate the reward score of the vehicle driving order scheme, and select the vehicle driving order with the highest reward score as the driving optimization order of the heterogeneous electric truck queue.

[0192] It should be noted that the foregoing explanation of the embodiment of the method for optimizing the charging of heterogeneous electric truck queues in transit also applies to the heterogeneous electric truck queue charging optimization device of this embodiment, and will not be repeated here.

[0193] The heterogeneous electric truck queue in-transit charging optimization device proposed in this application obtains charging plan data for each truck in the heterogeneous electric truck queue at multiple stations. Based on this data, it identifies the number of electric trucks that need charging at the current station. If there are vehicles that need charging, it extracts the planned charging amount for each vehicle at the current station and calculates the corresponding charging time accordingly, thereby determining the charging time of the entire queue. Then, it updates the charging plan data of each truck according to the queue charging time, generates a queue charging timetable based on the updated plan, and finally optimizes the sorting of electric trucks in the queue according to the timetable. This device can dynamically adapt to the charging needs of each station, take into account the individual differences of each heterogeneous electric truck, achieve the matching of charging plan and actual charging needs, ensure the orderly operation of charging work at the current station, improve charging and scheduling efficiency, reduce vehicle waiting time, and improve the utilization rate of charging resources and fleet operation efficiency.

[0194] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0195] When processor 502 executes the program, it implements the heterogeneous electric truck queue in-transit charging optimization method provided in the above embodiments.

[0196] Furthermore, the vehicle also includes: Communication interface 503 is used for communication between memory 501 and processor 502.

[0197] The memory 501 is used to store computer programs that can run on the processor 502.

[0198] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0199] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a unified line to complete communication between them. The unified line can be an Industry Standard Architecture (ISA) unified line, a Peripheral Component Interconnect (PCI) unified line, or an Extended Industry Standard Architecture (EISA) unified line, etc. Unified lines can be categorized into address unified lines, data unified lines, and control unified lines, etc. For ease of representation, Figure 5 The text uses only one thick line to represent a uniform line, but this does not mean that there is only one uniform line or one type of uniform line.

[0200] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0201] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0202] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the above-described method for optimizing the charging of heterogeneous electric truck platoons en route.

[0203] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0204] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0205] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0206] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0207] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for optimizing the on-route charging of heterogeneous electric truck platoons, characterized in that, Includes the following steps: Obtain charging schedule data for each electric truck in a heterogeneous electric truck queue at multiple stations; The number of electric trucks that need to be charged at the current station is identified based on the charging plan data. If the number of electric trucks is not empty, then extract the planned charging amount of the electric trucks at the current station from the charging plan data, calculate the charging time of the corresponding electric trucks based on the planned charging amount, and determine the charging time of the heterogeneous electric truck queue based on the charging time of each electric truck. The charging plan data of each electric truck is updated according to the charging duration of the heterogeneous electric truck queue. A charging schedule for the heterogeneous electric truck queue is generated according to the updated charging plan data. The electric truck sorting of the heterogeneous electric truck queue is optimized according to the charging schedule.

2. The method for optimizing the on-the-go charging of heterogeneous electric truck platoons according to claim 1, characterized in that, The acquisition of charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations includes: Establish a nonlinear charging model and a location-dependent energy consumption model for the electric truck; A state transition equation is established based on the location-related energy consumption model. The state transition equation represents the update of the electric truck's state of charge as it travels from the current station to the next station. Based on the nonlinear charging model and the state transition equation, the charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations is calculated.

3. The method for optimizing the on-route charging of heterogeneous electric truck platoons according to claim 2, characterized in that, The expression for the nonlinear charging model is as follows: in, It is a nonlinear charging model. For charging time, It is the overall efficiency reduction factor. This represents the initial charge level at the start of the second phase. These represent the initial charge levels at the start of each of the three phases. for At the end of the second phase, for The end of the third phase; The expression for the location-dependent energy consumption model is as follows: in, It refers to the energy consumption of vehicles platooning on a road segment. It's a vehicle. yes Section This refers to the energy consumption of a vehicle when it is driving alone. It's a car The vehicle's ranking position on this section of the road. This represents the energy-saving ratio at the corresponding location; The state transition equation is: in, For the j-th vehicle to arrive at the station The amount of electricity at that time, For the j-th vehicle to arrive at the station The amount of electricity at that time, For vehicle j at station The amount of charge, For battery capacity, For vehicles Energy consumption of platooning on a road segment.

4. The method for optimizing the on-the-go charging of heterogeneous electric truck platoons according to claim 2, characterized in that, The step of calculating the charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations based on the nonlinear charging model and the state transition equation includes: Based on the nonlinear charging model and the state transition equation, a formula is established to determine the relationship between the actual charging amount and the dwell time. Based on the formula, an objective function with charging time as the objective is established. Generate a planning problem function based on the objective function and preset constraints; The charging schedule data for each electric truck at multiple stations is calculated based on the aforementioned planning problem function.

5. The method for optimizing the on-route charging of heterogeneous electric truck platoons according to claim 4, characterized in that, The expression for the relational formula is: in, This represents the actual amount of electricity charged. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, To standardize the dwell time of the queue; The expression for the objective function is: in, This is a collection of all available charging stations. For binary decision variables, For bicycles at the station Charging time, The time for entering and exiting the station is fixed. The expression for the planning problem function is: in, For relational formulas, As a safety threshold, This represents the cumulative energy consumption from the current station to the next station. To start from the current station To the next station Cumulative energy consumption The battery level upon arrival at the station.

6. The method for optimizing on-the-go charging of heterogeneous electric truck platoons according to claim 1, characterized in that, The formula for calculating the charging time of the electric truck is as follows: in, This represents the charging time at the i-th station. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, This represents the actual amount of electricity charged. The update formula for the charging plan data of each electric truck is as follows: in, This is the updated charging schedule data for each electric truck. It is a nonlinear charging function. It is the inverse function of the charging function. For the j-th vehicle to arrive at the station The amount of electricity at that time, For bicycles at the station Charging time, Let j be the j-th electric truck in the heterogeneous electric truck queue.

7. The method for optimizing the on-the-go charging of heterogeneous electric truck platoons according to claim 1, characterized in that, The step of optimizing the electric truck sorting of the heterogeneous electric truck queue according to the charging schedule includes: Obtain the departure order of the heterogeneous electric truck queue; The departure driving sequence group is used as the root node of the Monte Carlo tree. The number of visits to the optimal leaf node is calculated. If the number of visits is greater than the visit threshold, the new leaf node of the optimal leaf node is expanded until the final node of the Monte Carlo tree is generated. The new leaf node represents the vehicle driving sequence of the heterogeneous electric truck queue. The vehicle driving order from the final node to the root node is integrated to generate a vehicle driving order scheme. The reward score of the vehicle driving order scheme is calculated, and the vehicle driving order with the highest reward score is selected as the driving optimization order of the heterogeneous electric truck queue.

8. A heterogeneous electric truck platoon charging optimization device, characterized in that, include: The acquisition module is used to acquire charging plan data for each electric truck in the heterogeneous electric truck queue at multiple stations; The identification module is used to identify the number of electric trucks that need to be charged at the current station based on the charging plan data; The extraction module is used to extract the planned charging amount of the electric trucks at the current station from the charging plan data if the number of electric trucks is not empty, calculate the charging time of the corresponding electric trucks based on the planned charging amount, and determine the charging time of the heterogeneous electric truck queue based on the charging time of each electric truck. The update module is used to update the charging plan data of each electric truck according to the charging duration of the heterogeneous electric truck queue, generate the charging timetable of the heterogeneous electric truck queue according to the updated charging plan data, and optimize the electric truck sorting of the heterogeneous electric truck queue according to the charging timetable.

9. A vehicle, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the heterogeneous electric truck platoon in-transit charging optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instructions are executed by a processor to implement the in-transit charging optimization method for heterogeneous electric truck platoons as described in any one of claims 1-7.

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