A highway charging online energy management method and related device

Abstract

This invention belongs to the field of transportation-energy integration and intelligent scheduling control technology, and discloses an online energy management method and related device for highway charging. The method acquires the perceived travel time, current state of charge, charging price, and charging waiting time; calculates the selection probability of each charging station based on a preset utility function and sends it to the charging station, triggering the charging station to complete demand aggregation, queue updating, energy storage adjustment, and power optimization, and then receives the updated queue of waiting demands. This invention enables the rational selection of electric vehicle charging stations and the dynamic optimization scheduling of energy at the charging station, reducing electric vehicle charging waiting time and travel costs, improving charging station operating efficiency, reducing operating costs, and ensuring the stability and economy of highway charging services.

Description

Technical Field

[0001] This invention belongs to the field of transportation-energy integration and intelligent dispatch control technology, and relates to an online energy management method and related devices for highway charging. Background Technology

[0002] As the penetration rate of electric vehicles continues to increase, the bottlenecks in the operation of electric vehicles on highways are becoming increasingly prominent, the most prominent of which is the insufficient energy supply during peak charging periods. Especially during peak charging demand periods such as holidays, long queues often occur at some highway charging stations, severely restricting the efficient operation of electric vehicles on highways and reducing the user's travel experience.

[0003] To alleviate the aforementioned charging supply and demand imbalance, highway service areas are gradually promoting the integrated "photovoltaic-storage-charging" station model, which integrates photovoltaic power generation, energy storage, and charging services to achieve on-site energy production, storage, and consumption. However, this model faces core challenges in energy coordination management during actual operation. The root cause lies in the spatiotemporal mismatch and randomness of multiple factors: On the one hand, there is a significant mismatch between the power generation of renewable energy (mainly photovoltaic) in service areas and the charging demand of electric vehicles in terms of time and space, resulting in insufficient renewable energy absorption and energy waste; on the other hand, photovoltaic power generation itself is intermittent (affected by natural factors such as sunlight intensity and weather, resulting in unstable output), and the charging demand of electric vehicles is random (the user's travel time, charging opportunity, and power are uncertain). In addition, the grid electricity price is volatile. These three factors interact and further exacerbate the difficulty of energy scheduling and coordinated management of the "photovoltaic-storage-charging" system.

[0004] From the user's perspective, the choice of charging station for electric vehicles on highways is influenced by multiple factors, including travel time (time spent traveling to the charging station), charging congestion (queue time at the station), charging costs (total electricity and service fees), and the vehicle's state of charge (remaining battery power). A systematic analysis of these influencing factors can help users develop more scientific and reasonable charging strategies, thereby improving charging efficiency, shortening travel time, and reducing overall travel costs. This is a crucial dimension that needs to be considered when optimizing highway charging services.

[0005] Furthermore, existing energy management methods for integrated photovoltaic-storage-charging stations often rely on deterministic assumptions about future information, assuming that key parameters such as renewable energy output, charging load, and electricity prices can be accurately predicted. However, in actual system operation, these parameters are all random variables, affected by various uncertainties, and their predicted values ​​inevitably deviate from actual values, exhibiting inherent prediction uncertainty. If such prediction uncertainty is not fully considered and incorporated into the optimization design, it will directly lead to the degradation of the actual performance of the energy management model, making it impossible to achieve optimal energy scheduling and effectively solve core problems such as highway charging congestion and insufficient renewable energy absorption. Summary of the Invention

[0006] The purpose of this invention is to provide an online energy management method and related device for highway charging, which solves the problems of highway charging congestion and insufficient renewable energy consumption.

[0007] This invention is achieved through the following technical solution: A highway charging online energy management method, applied to electric vehicles, includes the following processes: Obtain the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Get and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station terminal. Based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, the probability of the electric vehicle choosing each charging station is calculated based on the preset utility function of the electric vehicle choosing a charging station. The selection probability of electric vehicles choosing each charging station is sent to each charging station. The selection probability triggers each charging station to: aggregate new charging demands at each station based on the selection probability to obtain the new charging demand volume for each station; update the status of the electric vehicle waiting-to-be-serviced charging demand queue at each charging station based on the new charging demand volume and the actual charging service volume provided by each station; simultaneously update the energy storage energy status based on the current energy status, energy storage charging power, and energy storage discharging power, and update the energy storage virtual queue accordingly; based on the updated electric vehicle waiting-to-be-serviced charging demand queue and the energy storage virtual queue, solve the single-time-slot online optimization problem using a preset charging station operating cost model to obtain the optimization result, which includes grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power; update the electric vehicle waiting-to-be-serviced charging demand queue at each charging station based on the electric vehicle charging service power as the queue status for the next time slot. Receive the queue of electric vehicle charging needs sent by each charging station.

[0008] Furthermore, obtaining the perceived travel time of the electric vehicle from its current location to each charging station specifically involves: calculating the perceived travel time of the electric vehicle from its current location to each charging station based on the real-time distance between the electric vehicle and the charging station, and highway traffic flow information; the expression is: ; in, For road section At the current moment Traffic flow; The length of the road segment between adjacent charging stations; For the first n electric vehicles at the current moment The instantaneous position; This refers to the free-flow speed on the highway. For road section Traffic capacity; a and b are BPR coefficients; The index of the nearest charging station is given by the expression: ; For the first i The location of each charging station; For the first n electric vehicles from the current location to the 1st i Perceived travel time at each charging station; For the first The location of the charging station on the highway; For the first The section of road Real-time traffic flow at any given moment; For road section Traffic capacity; This is a collection of all charging stations.

[0009] Furthermore, the charging waiting time of electric vehicles at each charging station is calculated based on the electric vehicle charging demand queue and the maximum service capacity of each station, as shown in the expression: ; in, For the first i The number of charging piles configured at each charging station; The charging power of a single charging station; For the first i The charging station at the current time The queue of electric vehicle charging demand waiting for service; For electric vehicles in the first i Charging wait time at each charging station.

[0010] Furthermore, the utility function for selecting a charging station for an electric vehicle includes perceived travel time cost, charging waiting time cost, charging fee, and range anxiety cost; the expression for the utility function is: ; in, For the first n electric vehicles at the current moment t Select the i The utility function value for each charging station; These are the negative weighting coefficients of the utility function; This is the conversion factor for travel time cost; For the first n electric vehicles from the current location to the 1st i Perceived travel time at each charging station; The conversion factor for charging waiting time cost; For electric vehicles in the first i The charging station at the current time Charging wait time; For the first i The charging station at the current time The charging electricity price; For the conversion factor of range anxiety cost; For the first n electric vehicles at the current moment t The state of charge.

[0011] Furthermore, based on a pre-defined utility function for electric vehicles choosing charging stations, the probability of an electric vehicle choosing each charging station is calculated. Specifically, a Logit discrete choice model is used to calculate the probability of an electric vehicle choosing each charging station; the expression is: ; in, For the first n Electric vehicle selection i The probability of a charging station; For charging stations; For the current moment t No. n Electric vehicle selection i The utility function value for each charging station.

[0012] This invention also discloses an online energy management method for highway charging, applied to various charging stations, comprising the following processes: The new charging demand is aggregated based on the selection probability of each charging station sent by the electric vehicle terminal to obtain the new charging demand of each charging station; the status of the electric vehicle waiting charging demand queue of each charging station is updated based on the new charging demand of each charging station and the actual charging service provided by the charging station; at the same time, the energy storage energy status is updated based on the current energy status of the energy storage, as well as the energy storage charging power and energy storage discharging power, and the energy storage virtual queue is updated accordingly. Based on the updated electric vehicle charging demand queue and energy storage virtual queue, a preset charging station operating cost model is used to solve the single-slot online optimization problem and obtain the optimization results. The optimization results include the grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power. The electric vehicle charging demand queue of each charging station is updated according to the electric vehicle charging service power as the queue status for the next time slot. The updated electric vehicle charging demand queue is then sent to the electric vehicle end. The calculation process for the selection probability is as follows: obtain the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Get and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station terminal. Based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, the probability of the electric vehicle choosing each charging station is calculated based on a preset utility function for electric vehicle selection.

[0013] Furthermore, the additional charging power requirement is: ; in, For the first i The charging station at the current time The number of electric vehicles that make the charging choice; For the first n The electric vehicle arrived at the i The state of charge at each charging station upon arrival; For the first n The battery capacity of an electric vehicle; For the current moment t No. i The new charging demand of each charging station; For the first n Electric vehicle selection i The probability of a charging station.

[0014] Furthermore, the status of the electric vehicle waiting charging demand queue of each charging station is updated based on the newly added charging demand and the actual charging service provided by each charging station; the expression is: ; in, For the current moment No. i The queue of electric vehicles waiting for charging services at each charging station; For the next moment i The queue of electric vehicles waiting for charging services at each charging station; For the current moment t No. i The charging power provided by each charging station to electric vehicles; For the current moment t No. i The new charging demand of each charging station; This refers to the length of a single time slot.

[0015] Furthermore, the charging station operating cost model includes an objective model and constraints: The target model includes the grid purchase cost and the battery loss cost caused by the charging and discharging of the energy storage device, and its expression is: ; in, For the current moment t The power grid to the first i The power delivered by each charging station; The length of the current time; This refers to the battery loss coefficient. For the current moment t The grid electricity price, and the range of values ​​is ,in , This represents the highest / lowest value of the grid electricity price; The constraints include the power balance equations and inequality constraints for the charging station. The power balance equation for the charging station is: ; in, For the current moment t No. i The photovoltaic power generation capacity of each charging station; For the current moment t The power grid to the first i The power delivered by each charging station (the power purchased by the grid for the i-th charging station). For the current moment t No. i The charging service power provided by each charging station to electric vehicles; The power variables in the above power balance equation for charging stations must satisfy the following constraints: ; ; ; ; ; ; in, and The first i Minimum / maximum photovoltaic power generation of each charging station; For the power grid to the first i The maximum power output of each charging station; The maximum service power of the i-th charging station; For the current moment t No. i A queue of electric vehicle charging demand awaiting service; For the current moment t No. i The new charging demand of each charging station; These are Boolean variables used to characterize the operating mode of energy storage devices. It is in a discharge state. (In charging state); , These are the maximum charging / discharging power of the energy storage device, and they satisfy... , , , These are the energy storage charge / discharge rate coefficients; The aforementioned energy storage virtual queue is: ; in, This is the offset; For the current moment t No. i The status of the energy storage virtual queue of each charging station; For the current moment t No. i Energy status of energy storage devices at each charging station; The energy storage state is updated based on the current energy state, charging power, and discharging power; the expression is: ; in, , Each represents the current time. t No. i The charging / discharging power of the energy storage device in each charging station; , These represent the charging / discharging efficiency of the energy storage device. The length of a single time slot; For a moment t +1th i The energy storage status of each charging station; The capacity constraints of the above-mentioned energy storage device are: ; in, , The first i Upper / lower limits of the energy storage capacity of each charging station.

[0016] Furthermore, the optimization results are obtained using a pre-defined charging station operating cost model, specifically as follows: The energy management problem of charging stations is modeled as a long-run average operating cost minimization optimization problem: ; The Lyapunov drift plus penalty online optimization mechanism is introduced to transform the optimization problem of minimizing long-term average operating cost into a single-time-slot online real-time optimization problem, which is solved independently at each charging station to obtain the grid power purchase, energy storage charging and discharging power and electric vehicle charging service power. The single-slot online real-time optimization problem is as follows: ; A collection of charging stations.

[0017] This invention also discloses an online energy management system for highway charging applied to electric vehicles, comprising: The data acquisition module is used to acquire the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Obtain the queue of electric vehicle charging demand and the maximum service capacity of each charging station sent by each charging station terminal; The charging waiting time calculation module is used to obtain and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station. The probability calculation module is used to calculate the probability of an electric vehicle choosing each charging station based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, and based on a preset utility function for electric vehicles to choose charging stations. The communication module is used to send the selection probability of each charging station to each charging station and to receive the electric vehicle charging demand queue of each charging station sent by each charging station.

[0018] This invention also discloses an online energy management system for highway charging applied at various charging stations, comprising: The demand aggregation module is used to aggregate new charging demands based on the selection probability of electric vehicles choosing each charging station sent by the electric vehicle terminal, and obtain the new charging demand of each charging station. The arrival queue update module is used to update the status of the electric vehicle waiting charging demand queue of each charging station based on the new charging demand and the actual charging service provided by each charging station. The energy storage virtual queue update module is used to update the energy storage energy status based on the current energy status of the energy storage, as well as the energy storage charging power and energy storage discharging power, and update the energy storage virtual queue accordingly. The online optimization solution module is used to solve the single-slot online optimization problem based on the updated electric vehicle charging demand queue and energy storage virtual queue, using a preset charging station operating cost model, and obtain the optimization results, which include grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power. The service queue update module updates the electric vehicle charging demand queue of each charging station according to the electric vehicle charging service power, which serves as the queue status for the next time slot. The communication module is used to receive the selection probability of electric vehicles choosing each charging station and send the queue of electric vehicles waiting for charging services from each charging station to the electric vehicle terminal.

[0019] Compared with the prior art, the present invention has the following beneficial technical effects: This invention discloses an online energy management method for highway charging, comprising an electric vehicle terminal and charging station terminals, and using an electric vehicle waiting-to-be-serviced charging demand queue (reflecting the congestion level of the charging station) as the connecting link between the two modules, thereby forming a closed-loop optimization mechanism for demand allocation and energy management. It includes the following processes: Regarding the allocation of electric vehicle demand, a utility function for electric vehicles to choose charging stations is constructed based on highway traffic conditions, service area charging station queuing conditions, electric vehicle charge status, and time-of-use pricing. The utility function includes perceived travel time cost, charging waiting time cost, charging fee, and range anxiety cost. Based on the utility function, a Logit discrete choice model is used to calculate the selection probability of electric vehicles choosing each charging station, and the new charging demand is aggregated by station based on the selection probability to obtain the new charging demand of each service area along the highway.

[0020] In distributed online energy management, operational constraints including energy storage devices and power balance are established, and an energy management optimization problem is constructed with the goal of minimizing the long-term average operating cost of charging stations. The operating cost includes the grid purchase cost and the battery loss cost caused by the charging and discharging of energy storage devices. A queue of electric vehicle charging demand and a virtual queue of energy storage are constructed. Based on the Lyapunov drift plus penalty online optimization framework, the long-term stochastic optimization problem is decomposed into a real-time decision-making process. At each discrete moment, each charging station independently solves the optimization sub-problem and outputs control commands for grid purchase power, energy storage charging and discharging power, and charging service power that satisfy power balance and equipment constraints. This addresses multiple uncertainties such as photovoltaic output, electricity price, and electric vehicle charging demand, and the balance between queue stability and operating cost is achieved through weighted control parameters.

[0021] In terms of closed-loop feedback, the electric vehicle charging demand queue updated by the distributed online energy management module is used to characterize the queuing and congestion status of charging stations and serves as the basis for calculating the charging waiting time in the utility function of the electric vehicle demand allocation module, thereby updating the selection probability of electric vehicles for each charging station at the next moment. At the same time, the new charging demand of each charging station obtained by the demand allocation module based on the selection probability serves as the input of the energy management module, affecting its power allocation and charging service decisions, and further changing the evolution process of the electric vehicle charging demand queue, thereby realizing the closed-loop coupling and collaborative optimization of demand allocation and energy management.

[0022] This invention constructs a closed-loop collaborative mechanism for electric vehicle charging demand allocation and online energy management in highway service area scenarios. The onboard terminal, based on dynamic information such as highway traffic conditions, service area queuing status, and charging prices released by roadside sensing devices or cloud platforms, and combined with the vehicle's own state of charge, generates charging station selection intention information and uploads it to the demand allocation module.

[0023] The demand allocation module models the charging station selection behavior of electric vehicles based on a discrete choice model, obtains the new charging demand of each service area at the current moment, and sends the demand to the corresponding charging station.

[0024] After obtaining new charging demand, each service area combines local energy storage status, photovoltaic output, grid electricity price, and charging service capacity, and uses the online energy management optimization module to solve for grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power in order to achieve the predetermined operation goals.

[0025] Furthermore, the electric vehicle waiting demand queue updated by the energy management module is used to characterize the queuing congestion level at the station and is fed back to the demand allocation module as the basis for waiting time calculation, thereby affecting the selection probability of subsequent electric vehicles and forming a closed-loop optimization mechanism of "demand generation - energy scheduling - queue feedback - demand reallocation".

[0026] This invention offers significant advantages in interpretable demand allocation, online energy optimization, and distributed deployability, with at least the following beneficial effects: By constructing a utility function incorporating factors such as perceived travel time, charging waiting time, charging costs, and range anxiety, and explicitly characterizing user preferences using a Logit discrete choice model, it enables adaptive migration of charging demand between stations when traffic conditions, electricity prices, and queuing congestion change, thereby effectively alleviating local congestion and load imbalance; based on the Lyapunov drift-penalty online optimization framework, it achieves real-time decision-making at each current moment without relying on the prior statistical distribution of charging demand, photovoltaic output, and electricity prices, and can utilize weighted... The system achieves a controllable trade-off between operating costs and charging service quality by adjusting parameters, while ensuring the stability of the charging demand queue and the energy storage virtual queue. It utilizes the electric vehicle waiting-to-be-serviced charging demand queue as a feedback link between demand allocation and energy management, allowing the queue status updated by the energy management module to participate in the waiting time calculation of the demand allocation module. This forms a closed-loop collaborative mechanism of "queue-selection-demand-scheduling-queue," which suppresses charging queue backlog, improves the local photovoltaic consumption level, and reduces the grid purchase cost. In addition, each charging station can independently solve optimization decisions locally, with low communication overhead and strong scalability, making it suitable for distributed deployment and large-scale application scenarios along highways with multiple service areas. Attached Figure Description

[0027] Figure 1This is a schematic diagram illustrating the application of the online energy management method for highway charging of the present invention to electric vehicles. Figure 2 This is a schematic diagram illustrating the application of the online energy management method for highway charging according to the present invention at a charging station. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the present invention, and not all of them.

[0029] The detailed description of the embodiments of the present invention provided in the following figures is not intended to limit the scope of the claimed invention, but merely to illustrate one selected embodiment of the invention. All other embodiments obtained by those skilled in the art based on the figures and embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0030] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0031] Integrated solar-energy storage-charging stations in highway service areas represent a crucial scenario for the integration of transportation and energy. Differences in charging station service quality, regional solar energy resources, electricity prices, and electric vehicle charging demands lead to a spatial-temporal mismatch between power supply and demand in service areas. The closed nature and one-way traffic characteristics of highways impose significant path constraints on users' charging decisions. During highway travel, electric vehicle users need to sequentially visit all service areas along the route to replenish sufficient power in the shortest possible time to reach the downstream charging station. Charging station selection is a dynamic decision-making process, based on real-time traffic flow, charging load, renewable energy generation status, and information from downstream charging stations for sequence optimization. Existing research struggles to adequately adapt to the unique topology of highways and the complex preferences of users. Furthermore, in the energy management of integrated solar-energy storage-charging stations, existing research relies on predicting uncertainties such as charging demand, photovoltaic output, and electricity prices. However, in practical engineering, these uncertainties are difficult to predict accurately, and prediction errors directly lead to performance degradation of the optimization model.

[0032] Based on this, the present invention provides an online energy management method for highway charging, which can effectively cope with multiple uncertainties in the charging scenario of highway service areas, improve the quality of electric vehicle charging services and reduce the operating cost of charging stations.

[0033] Example 1 This embodiment applies to electric vehicles, such as... Figure 1 As shown, the process includes the following: S1. Obtain the real-time distance between electric vehicles and charging stations, highway traffic flow information, the queue of electric vehicles waiting to be charged, the maximum service capacity of each station, and the charging price sent by each charging station. S2. Calculate the perceived travel time of the electric vehicle from its current location to each charging station based on the real-time distance between the electric vehicle and the charging station, as well as highway traffic flow information; Based on the queue of electric vehicle charging demand and the maximum service capacity of each charging station, the charging waiting time of electric vehicles at each charging station is calculated.

[0034] S3. Obtain the current state of charge of the electric vehicle; based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, calculate the selection probability of the electric vehicle choosing each charging station based on the preset utility function of the electric vehicle choosing a charging station. The selection probability of electric vehicles choosing each charging station is sent to each charging station. The selection probability triggers each charging station to: aggregate new charging demands at each station based on the selection probability to obtain the new charging demand volume for each station; update the status of the electric vehicle waiting-to-be-serviced charging demand queue at each charging station based on the new charging demand volume and the actual charging service volume provided by each station; simultaneously update the energy storage energy status based on the current energy status, energy storage charging power, and energy storage discharging power, and update the energy storage virtual queue accordingly; based on the updated electric vehicle waiting-to-be-serviced charging demand queue and the energy storage virtual queue, solve the single-time-slot online optimization problem using a preset charging station operating cost model to obtain the optimization result, which includes grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power; update the electric vehicle waiting-to-be-serviced charging demand queue at each charging station based on the electric vehicle charging service power as the queue status for the next time slot. Receive the queue of electric vehicle charging needs sent by each charging station.

[0035] Example 2 S2 is further described based on Example 1.

[0036] In S2, the perceived travel time of the electric vehicle from its current location to each charging station is calculated based on the real-time distance between the electric vehicle and the charging station, as well as highway traffic flow information. The expression is as follows: ; in, For road section k At the current moment tTraffic flow; The length of the road segment between adjacent charging stations; For the first n electric vehicles at the current moment t The instantaneous position; This refers to the free-flow speed on the highway. For road section Traffic capacity; a and b are BPR coefficients; The index of the nearest charging station is given by the expression: ; For the first i The location of a charging station.

[0037] For the first n The perceived travel time of an electric vehicle from its current location to the i-th charging station; For the first The charging station (i.e., the first charging station) n The location of the nearest charging station (in front of the vehicle) on the highway; For the first Each road segment (the section from the vehicle's current location to the nearest charging station) at the current moment t Real-time traffic flow; For road section Traffic capacity of (the section of road currently being traveled by the electric vehicle); This is a collection of all charging stations.

[0038] The expression for calculating perceived travel time is the Bureau of Public Roads (BPR) travel time function.

[0039] Example 3 S3 is further described based on Example 1.

[0040] Construct a utility function for electric vehicles to select charging stations that includes perceived travel time, charging waiting time, charging price of each charging station, and vehicle state of charge. Based on the preset utility function for electric vehicles to select charging stations, calculate the probability of electric vehicles selecting each charging station.

[0041] The utility function for the electric vehicle to select a charging station is: ; in, For the first n electric vehicles at the current moment t Select the i The utility function value for each charging station; These are the negative weighting coefficients of the utility function; This is the conversion factor for travel time cost; For the first n electric vehicles from the current location to the 1st i Perceived travel time at each charging station; The conversion factor for charging waiting time cost; For electric vehicles in the first i The charging station at the current time Charging wait time; For the first i The charging station at the current time The charging electricity price; For the conversion factor of range anxiety cost; For the first n electric vehicles at the current moment t The state of charge.

[0042] The charging wait time used in the above utility function The expression is: ; in, For the first i The number of charging piles configured at each charging station; The charging power of a single charging station; For the first i The charging station at the current time The queue of electric vehicle charging demand waiting for service; For electric vehicles in the first i Charging wait time at each charging station; For the first i The maximum service power of each charging station.

[0043] The Logit discrete choice model is used to calculate the probability of a vehicle choosing each charging station. The expression is as follows: .

[0044] in, For the first n Electric vehicle selection i The probability of a charging station; For charging stations; For the current moment t No. n Electric vehicle selection i The utility function value for each charging station.

[0045] Example 4 This embodiment is applied to various charging stations, such as Figure 2 As shown, the process includes the following: The new charging demand is aggregated based on the selection probability of each charging station sent by the electric vehicle terminal to obtain the new charging demand of each charging station; the status of the electric vehicle waiting charging demand queue of each charging station is updated based on the new charging demand of each charging station and the actual charging service provided by the charging station; at the same time, the energy storage energy status is updated based on the current energy status of the energy storage, as well as the energy storage charging power and energy storage discharging power, and the energy storage virtual queue is updated accordingly. Based on the updated electric vehicle charging demand queue and energy storage virtual queue, a preset charging station operating cost model is used to solve the single-slot online optimization problem and obtain the optimization results. The optimization results include the grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power. The electric vehicle charging demand queue of each charging station is updated according to the electric vehicle charging service power as the queue status for the next time slot. The updated electric vehicle charging demand queue is then sent to the electric vehicle end. The calculation process for the selection probability is as follows: obtain the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Get and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station terminal. Based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, the probability of the electric vehicle choosing each charging station is calculated based on a preset utility function for electric vehicle selection.

[0046] Example 5 Based on Example 4, the new charging demand is aggregated at various charging stations according to the selection probability to obtain the new charging demand for each charging station.

[0047] The additional charging demand is: ; in, For the first i The charging stations at the service area are currently at [time]. t The number of electric vehicles that make charging choices within the current time period is calculated from the traffic flow entering the corresponding road segment, the vehicle entry rate of the service area, and the electric vehicle penetration rate. For electric vehicles to reach the first i The state of charge at each charging station upon arrival; For the first n Battery capacity of an electric vehicle.

[0048] Among them, electric vehicles reached the i The formula for calculating the state of charge at each charging station is as follows:

[0049] in, For the first The state of charge of an electric vehicle upon arrival at the i-th charging station; Energy consumption per unit distance; For the first The distance traveled by an electric vehicle from its current location to the i-th charging station can be represented as: ; The starting time; The arrival time; For the first The state of charge of an electric vehicle at the moment of its inception.

[0050] Example 6 Based on Example 4, the status of the electric vehicle charging demand queue can be updated according to the new charging demand electricity obtained by each charging station in the current time slot and the actual charging service electricity provided by the charging station. This update reflects the scale of charging demand that has not yet been served by each charging station at the current moment, thereby characterizing the degree of queuing congestion at the charging station.

[0051] Furthermore, by introducing the electric vehicle charging demand queue and the energy storage virtual queue as system state variables, the long-term average operating cost minimization optimization problem, which originally included time-coupled constraints, can be reconstructed. The electric vehicle charging demand queue is used to characterize the accumulation and release of charging demand over time, while the energy storage virtual queue describes the operating state of the energy storage device under energy constraints.

[0052] Building upon this foundation, by constructing a Lyapunov function and introducing a Lyapunov drift penalty mechanism, the long-term optimization problem, which originally relied on future information, can be transformed into a single-slot online optimization problem. This allows the system to make real-time decisions based solely on the current system state. By solving this single-slot optimization problem, the optimal operating strategy for the charging station in the current slot can be obtained, including grid power purchase, energy storage charging power, energy storage discharging power, and electric vehicle charging service power. This achieves coordinated control of minimizing charging station operating costs and system queue stability.

[0053] The arrival and departure of electric vehicles at a charging station can be modeled as a queue with only a single server, and the update expression for the queue of electric vehicle charging demand is: ; in, For the current moment No. i The queue of electric vehicles waiting for charging services at each charging station; For the next moment i The queue of electric vehicles waiting for charging services at each charging station; For the current moment t No. i The charging power provided by each charging station to electric vehicles; For the current moment t No. i The new charging demand of each charging station; This refers to the length of a single time slot.

[0054] The aforementioned energy storage virtual queue is: ; in, This is the offset; For the current moment t No. i The status of the energy storage virtual queue of each charging station; For the current moment t No. i Energy status of energy storage devices at each charging station.

[0055] The above energy storage state update expression is: ; in, , Each represents the current time. t No. i The charging / discharging power of the energy storage device in each charging station; , These represent the charging / discharging efficiency of the energy storage device. The length of a single time slot; For a moment t +1th i The energy storage status of each charging station.

[0056] The electric vehicle charging demand queue and energy storage virtual queue constructed above serve as state variables of the energy management optimization module. In any discrete time slot, the energy management optimization module constructs and solves a single-time slot online real-time optimization decision model based on the queue state and in combination with the photovoltaic output of the charging station, the charging price, and the new charging demand of each charging station generated by the charging demand allocation module. The model outputs the operation control scheme of the charging station, which includes at least the grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power.

[0057] A power balance equation for the charging station is established, and operational constraints are set for the grid power purchase, energy storage charging / discharging power, photovoltaic output, and electric vehicle charging service power, as well as the capacity constraint of the energy storage device, to construct a charging station operating cost model. The charging station operating cost model includes grid power purchase costs and battery loss costs caused by energy storage charging and discharging.

[0058] The power balance equation for the charging station is: ; in, For the current moment t No. i The photovoltaic power generation capacity of each charging station; For the current moment t The power grid to the first i The power delivered by the charging station (the first) i (Power purchased by the grid for each charging station); For the current moment t No. i The charging power provided by each charging station to electric vehicles.

[0059] The power variables in the above power balance equation for charging stations must satisfy the following constraints: ; ; ; ; ; ; in, and The first i Minimum / maximum photovoltaic power generation of each charging station; For the power grid to the first i The maximum power output of each charging station; For the first i The maximum service power of each charging station; For the current moment t No. i A queue of electric vehicle charging demand awaiting service; For the current moment t No. i The new charging demand of each charging station; These are Boolean variables used to characterize the operating mode of energy storage devices. It is in a discharge state. (In charging state); , These are the maximum charging / discharging power of the energy storage device, and they satisfy... , , , These are the energy storage charge / discharge rate coefficients, respectively.

[0060] The capacity constraint of the energy storage device is: ; in, , The first i Upper / lower limits of the energy storage capacity of each charging station.

[0061] The charging station operating cost model includes the cost of purchasing electricity from the grid and the cost of battery losses caused by charging and discharging of the energy storage device, and its expression is as follows: ; in, For the current moment t The power grid to the first i The power delivered by each charging station; The length of the current time; This refers to the battery loss coefficient. For the current moment t The grid electricity price, and the range of values ​​is ,in , This represents the highest / lowest value of the grid electricity price.

[0062] Based on this, the energy management of the charging station system can be modeled as a long-run average operating cost minimization optimization problem: .

[0063] By introducing the Lyapunov drift plus penalty online optimization mechanism, the long-term average operating cost minimization optimization problem is transformed into a single-slot online real-time optimization problem, which is solved independently at each charging station to obtain the grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power.

[0064] To reduce the reliance of traditional energy management methods on future information and improve the model's adaptability in stochastic operating environments, this invention constructs an online decision-making mechanism based on Lyapunov optimization. This online decision-making mechanism includes a Lyapunov function, a Lyapunov drift function, and a Lyapunov drift plus penalty function. By applying an upper bound to the drift plus penalty function, the long-term optimization problem with time coupling characteristics is transformed into a single-slot online real-time optimization problem, enabling the system to make energy scheduling decisions based on the current state.

[0065] The Lyapunov function is: ; in, For Lyapunov functions, used to measure the overall congestion level or stable state of the system queue; The state vector of the virtual queue; For energy storage virtual queue vectors; A vector representing the queue of electric vehicles waiting to be charged at a charging station.

[0066] The stability of the queue is ensured by a Lyapunov drift function, which is: ; To simultaneously consider queue stability and operational cost optimization, a Lyapunov drift plus penalty function is defined as follows: ; in, This is the Lyapunov drift function, used to reflect the changing trend of the system queue state between adjacent time points; V It is a non-negative weight control parameter used to balance cost and queue stability; For expectation operators.

[0067] Find the upper bound for the drift penalty function: ; in, .

[0068] Real-time control decisions can be obtained by minimizing the upper bound of the drift plus penalty function. The single-slot online real-time optimization problem is as follows: .

[0069] The electric vehicle charging demand queue is updated again based on the optimization results as the queue state for the next time slot; and the updated queue state is fed back to the charging station's utility function to correct the charging waiting time, thereby forming a closed-loop collaborative control mechanism for demand allocation and energy management.

[0070] This invention addresses operational challenges in highway service areas, including random fluctuations in electric vehicle charging load, uneven spatial and temporal distribution of demand, and supply uncertainties caused by changes in photovoltaic output and electricity prices. It proposes an online energy management method for highway charging. This method, through the coordinated operation of a charging demand allocation module and an energy management optimization module, achieves dynamic guidance of charging demand and real-time optimization of charging station operation strategies, forming an intelligent operational decision-making mechanism suitable for engineering deployment.

[0071] Example 7 Based on embodiments 1-3, the present invention also discloses an online energy management system for highway charging applied to electric vehicles, comprising: The data acquisition module is used to acquire the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Obtain the queue of electric vehicle charging demand and the maximum service capacity of each charging station sent by each charging station terminal; The charging waiting time calculation module is used to obtain and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station. The probability calculation module is used to calculate the probability of an electric vehicle choosing each charging station based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, and based on a preset utility function for electric vehicles to choose charging stations. The communication module is used to send the selection probability of each charging station to each charging station and to receive the electric vehicle charging demand queue of each charging station sent by each charging station.

[0072] Example 8 Based on embodiments 4-6, the present invention also discloses an online energy management system for highway charging applied at various charging stations, comprising: The demand aggregation module is used to aggregate new charging demands based on the selection probability information of electric vehicles choosing each charging station sent by the electric vehicle terminal, so as to obtain the new charging demand of each charging station. The arrival queue update module is used to update the status of the electric vehicle waiting charging demand queue of each charging station based on the new charging demand and the actual charging service provided by each charging station. The energy storage virtual queue update module is used to update the energy storage energy status based on the current energy status of the energy storage, as well as the energy storage charging power and energy storage discharging power, and update the energy storage virtual queue accordingly. The online optimization solution module is used to solve the single-slot online optimization problem based on the updated electric vehicle charging demand queue and energy storage virtual queue, using a preset charging station operating cost model, and obtain the optimization results, which include grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power. The service queue update module updates the electric vehicle charging demand queue of each charging station according to the electric vehicle charging service power, which serves as the queue status for the next time slot. The communication module interacts with the electric vehicle terminal, receiving the selection probability of each charging station chosen by the electric vehicle and sending the queue of electric vehicle charging demand waiting for service from each charging station to the electric vehicle terminal.

[0073] This invention comprehensively considers multiple influencing factors such as perceived travel time, charging waiting time, charging price, and vehicle state of charge to calculate the probability of electric vehicles selecting each charging station and output the new charging demand for each charging station. By modeling user selection behavior, it can effectively prevent vehicles from excessively clustering at a single charging station in actual operation, alleviate queuing congestion, improve overall charging service efficiency, and achieve proactive load regulation driven by the demand side, rather than the passive load of the traditional model. After obtaining new charging demand, the distributed online energy management module combines photovoltaic output, grid price, and energy storage operation status to construct a queue of electric vehicle charging demand and a virtual queue of energy storage, and generates charging station operation decisions in real time based on the Lyapunov online optimization method. The decision variables include grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power, which can be directly used to guide charging stations in determining power purchase scale, adjusting energy storage system operation strategies, and allocating charging pile output power, thereby achieving load smoothing, reducing peak-hour power purchase costs, and improving the local consumption capacity of photovoltaic power generation. Furthermore, the electric vehicle charging demand queue serves as a key state variable connecting demand allocation and energy management, enabling a closed-loop feedback mechanism between demand guidance and energy scheduling: energy management decisions affect the queue backlog level, the queue status reflects the degree of congestion at charging stations and influences the site selection behavior of electric vehicles, thereby changing the spatial distribution of charging demand, and the updated demand participates in scheduling optimization again, thus forming a dynamic collaborative operation process of "demand-scheduling-feedback-redistribution".

[0074] Compared with existing technologies, this invention solves at least the following engineering operation problems: 1) Suppressing disorderly demand aggregation and avoiding the problem of severe queuing in some service areas and underutilization of adjacent sites through a demand guidance mechanism; 2) Enhancing dispatching capabilities under uncertain environments, enabling real-time dispatching without relying on accurate forecasting of photovoltaic output or load; 3) Reducing the proportion of grid power purchases during periods of high electricity prices through online optimization; 4) Improving the utilization level of renewable energy, enhancing the local absorption capacity of photovoltaic power, and reducing curtailment; 5) Enhancing system scalability by adopting a distributed architecture to support large-scale deployment and collaborative operation of multiple service areas along highways.

[0075] Based on the same inventive concept, this invention also provides a computer-readable storage medium storing a computer program or instruction set thereon. The storage medium includes, but is not limited to, a USB flash drive, a portable hard drive, a solid-state drive, an optical disc, server storage, a cloud image repository, or an embedded storage chip. When the computer program is executed by a processor, the aforementioned online energy management method for highway charging is implemented. This invention also proposes a computer-readable storage medium storing a computer program or instruction set thereon. The storage medium includes, but is not limited to, a USB flash drive, a portable hard drive, a solid-state drive, an optical disc, server storage, a cloud image repository, or an embedded storage chip. When the computer program is executed by a processor, the aforementioned online energy management method for highway charging is implemented.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for online energy management of highway charging, characterized in that, When applied to electric vehicles, the following processes are included: Obtain the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Get and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station terminal. Based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, the probability of the electric vehicle choosing each charging station is calculated based on the preset utility function of the electric vehicle choosing a charging station. The selection probability of electric vehicles choosing each charging station is sent to each charging station. The selection probability triggers each charging station to: aggregate new charging demands at each station based on the selection probability to obtain the new charging demand volume for each station; update the status of the electric vehicle waiting-to-be-serviced charging demand queue at each charging station based on the new charging demand volume and the actual charging service volume provided by each station; simultaneously update the energy storage energy status based on the current energy status, energy storage charging power, and energy storage discharging power, and update the energy storage virtual queue accordingly; based on the updated electric vehicle waiting-to-be-serviced charging demand queue and the energy storage virtual queue, solve the single-time-slot online optimization problem using a preset charging station operating cost model to obtain the optimization result, which includes grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power; update the electric vehicle waiting-to-be-serviced charging demand queue at each charging station based on the electric vehicle charging service power as the queue status for the next time slot. Receive the queue of electric vehicle charging needs sent by each charging station.

2. The method for online energy management of highway charging according to claim 1, characterized in that, The process of obtaining the perceived travel time of the electric vehicle from its current location to each charging station specifically involves: calculating the perceived travel time of the electric vehicle from its current location to each charging station based on the real-time distance between the electric vehicle and the charging station, as well as highway traffic flow information; the expression is: ； in, For road section At the current moment Traffic flow; The length of the road segment between adjacent charging stations; For the first n electric vehicles at the current moment The instantaneous position; This refers to the free-flow speed on the highway. For road section Traffic capacity; a and b are BPR coefficients; The index of the nearest charging station is given by the expression: ; For the first i The location of each charging station; For the first n electric vehicles from the current location to the i Perceived travel time at each charging station; For the first The location of a charging station on the highway; For the first The section of road Real-time traffic flow at any given moment; For road section Traffic capacity; A collection of all charging stations; The charging wait time of electric vehicles at each charging station is calculated based on the electric vehicle charging demand queue and the maximum service capacity of each station, as sent by each station. The expression is as follows: ； in, For the first i The number of charging piles configured at each charging station; The charging power of a single charging station; For the first i The charging station at the current time The queue of electric vehicle charging demand waiting for service; For electric vehicles in the first i Charging wait time at each charging station.

3. The method for online energy management of highway charging according to claim 1, characterized in that, The utility function for selecting a charging station for an electric vehicle includes perceived travel time cost, charging waiting time cost, charging fee, and range anxiety cost; the expression for the utility function is: ； in, For the first n electric vehicles at the current moment t Select the i The utility function value for each charging station; These are the negative weighting coefficients of the utility function; This is a conversion factor for travel time cost; For the first n electric vehicles from the current location to the i Perceived travel time at each charging station; The conversion factor for charging waiting time cost; For electric vehicles in the first i The charging station at the current time Charging wait time; For the first i The charging station at the current time The charging electricity price; For the conversion factor of range anxiety cost; For the first n electric vehicles at the current moment t The state of charge.

4. The method for online energy management of highway charging according to claim 1, characterized in that, Based on a pre-defined utility function for electric vehicles choosing charging stations, the probability of an electric vehicle choosing each charging station is calculated. Specifically, a Logit discrete choice model is used to calculate the probability of an electric vehicle choosing each charging station; the expression is: ； in, For the first n Electric vehicle selection i The probability of a charging station; For charging stations; For the current moment t No. n Electric vehicle selection i The utility function value for each charging station.

5. A method for online energy management of highway charging, characterized in that, Applied to various charging stations, it includes the following processes: The new charging demand is aggregated based on the selection probability of each charging station sent by the electric vehicle terminal to obtain the new charging demand of each charging station; the status of the electric vehicle waiting charging demand queue of each charging station is updated based on the new charging demand of each charging station and the actual charging service provided by the charging station; at the same time, the energy storage energy status is updated based on the current energy status of the energy storage, as well as the energy storage charging power and energy storage discharging power, and the energy storage virtual queue is updated accordingly. Based on the updated electric vehicle charging demand queue and energy storage virtual queue, a preset charging station operating cost model is used to solve the single-slot online optimization problem and obtain the optimization results. The optimization results include the grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power. The electric vehicle charging demand queue of each charging station is updated according to the electric vehicle charging service power as the queue status for the next time slot. The updated electric vehicle charging demand queue is then sent to the electric vehicle end. The calculation process for the selection probability is as follows: obtain the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Get and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station terminal. Based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, the probability of the electric vehicle choosing each charging station is calculated based on a preset utility function for electric vehicle selection.

6. The method for online energy management of highway charging according to claim 5, characterized in that, The additional charging demand is: ； in, For the first i The charging station at the current time The number of electric vehicles that make the charging choice; For the first n The electric vehicle arrived at the i The state of charge at each charging station upon arrival; For the first n The battery capacity of an electric vehicle; For the current moment t No. i The new charging demand of each charging station; For the first n Electric vehicle selection i The probability of a charging station; The status of the electric vehicle charging demand queue of each charging station is updated based on the newly added charging demand and the actual charging service provided by each charging station; the expression is: ； in, For the current moment No. i The queue of electric vehicles waiting for charging services at each charging station; For the next moment i The queue of electric vehicles waiting for charging services at each charging station; For the current moment t No. i The charging power provided by each charging station to electric vehicles; For the current moment t No. i The new charging demand of each charging station; This refers to the length of a single time slot.

7. The method for online energy management of highway charging according to claim 5, characterized in that, The charging station operating cost model includes an objective model and constraints: The target model includes the grid purchase cost and the battery loss cost caused by the charging and discharging of the energy storage device, and its expression is: ； in, For the current moment t The power grid to the first i The power delivered by each charging station; The length of the current time; This refers to the battery loss coefficient. For the current moment t The grid electricity price, and the range of values ​​is ,in , This represents the highest / lowest value of the grid electricity price; The constraints include the power balance equations and inequality constraints for the charging station. The power balance equation for the charging station is: ； in, For the current moment t No. i The photovoltaic power generation capacity of each charging station; For the current moment t The power grid to the first i The power delivered by each charging station (the power purchased by the grid for the i-th charging station). For the current moment t No. i The charging service power provided by each charging station to electric vehicles; The power variables in the above power balance equation for charging stations must satisfy the following constraints: ； ； ； ； ； ； in, and The first i Minimum / maximum photovoltaic power generation of each charging station; For the power grid to the first i The maximum power output of each charging station; The maximum service power of the i-th charging station; For the current moment t No. i A queue of electric vehicle charging demand awaiting service; For the current moment t No. i The new charging demand of each charging station; These are Boolean variables used to characterize the operating mode of energy storage devices. It is in a discharge state. (In charging state); , These are the maximum charging / discharging power of the energy storage device, and they satisfy... , , , These are the energy storage charge / discharge rate coefficients; The aforementioned energy storage virtual queue is: ； in, This is the offset; For the current moment t No. i The status of the energy storage virtual queue of each charging station; For the current moment t No. i Energy status of energy storage devices at each charging station; The energy storage state is updated based on the current energy state, charging power, and discharging power; the expression is: ； in, , Each represents the current time. t No. i The charging / discharging power of the energy storage device in each charging station; , These represent the charging / discharging efficiency of the energy storage device. The length of a single time slot; For a moment t +1th i The energy storage status of each charging station; The capacity constraints of the above-mentioned energy storage device are: ； in, , The first i Upper / lower limits of the energy storage capacity of each charging station.

8. The method for online energy management of highway charging according to claim 7, characterized in that, The method employs a pre-defined charging station operating cost model to solve the single-slot online optimization problem and obtain the optimization results, specifically: The energy management problem of charging stations is modeled as a long-run average operating cost minimization optimization problem: ； The Lyapunov drift plus penalty online optimization mechanism is introduced to transform the optimization problem of minimizing long-term average operating cost into a single-time-slot online real-time optimization problem, which is solved independently at each charging station to obtain the grid power purchase, energy storage charging and discharging power and electric vehicle charging service power. The single-slot online real-time optimization problem is as follows: ； A collection of charging stations.

9. A highway charging online energy management system for electric vehicles, characterized in that, include: The data acquisition module is used to acquire the perceived travel time of the electric vehicle from its current location to each charging station; Obtain the current state of charge of electric vehicles and the charging price published by each charging station; Obtain the queue of electric vehicle charging demand and the maximum service capacity of each charging station sent by each charging station terminal; The charging waiting time calculation module is used to obtain and calculate the charging waiting time of electric vehicles at each charging station based on the electric vehicle charging demand queue and the maximum service capacity of each station sent by each charging station. The probability calculation module is used to calculate the probability of an electric vehicle choosing each charging station based on the charging price of each charging station, the perceived travel time of the electric vehicle from its current location to each charging station, the current state of charge of the electric vehicle, and the charging waiting time of the electric vehicle at each charging station, and based on a preset utility function for electric vehicles to choose charging stations. The communication module is used to send the selection probability of each charging station to each charging station and to receive the electric vehicle charging demand queue of each charging station sent by each charging station.

10. A highway charging online energy management system applied to various charging stations, characterized in that, include: The demand aggregation module is used to aggregate new charging demands based on the selection probability of electric vehicles choosing each charging station sent by the electric vehicle terminal, and obtain the new charging demand of each charging station. The arrival queue update module is used to update the status of the electric vehicle waiting charging demand queue of each charging station based on the new charging demand and the actual charging service provided by each charging station. The energy storage virtual queue update module is used to update the energy storage energy status based on the current energy status of the energy storage, as well as the energy storage charging power and energy storage discharging power, and update the energy storage virtual queue accordingly. The online optimization solution module is used to solve the single-slot online optimization problem based on the updated electric vehicle charging demand queue and energy storage virtual queue, using a preset charging station operating cost model, and obtain the optimization results, which include grid power purchase, energy storage charging and discharging power, and electric vehicle charging service power. The service queue update module updates the electric vehicle charging demand queue of each charging station according to the electric vehicle charging service power, which serves as the queue status for the next time slot. The communication module is used to receive the selection probability of electric vehicles choosing each charging station and send the queue of electric vehicles waiting for charging services from each charging station to the electric vehicle terminal.

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