An electric vehicle dynamic charging scheduling and path coordination planning method and system

Abstract

This invention relates to a method and system for dynamic charging scheduling and path coordination planning of electric vehicles. The method includes the following steps: acquiring distribution network operating parameters, road traffic conditions, electric vehicle user input, and environmental parameters; based on the distribution network operating parameters, electric vehicle user input, and environmental parameters, performing charging station power allocation with the first optimization objective of minimizing the peak-valley difference of distribution network load, maximizing the satisfaction rate of differentiated power allocation guided by user travel flexibility, and minimizing the carbon emissions of charging power coupled with dynamic carbon intensity of distribution network load; and based on the charging station power allocation scheme, road traffic conditions, and electric vehicle user input, generating electric vehicle travel routes and feeding them back to users with the second optimization objective of minimizing the total path-charging coordination time under power allocation constraints. Compared with the prior art, this invention has advantages such as reducing the peak-valley difference of distribution network load, improving the charging power satisfaction rate for emergency users, reducing charging carbon emissions, and shortening the total travel time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation and energy management, and in particular to a dynamic charging scheduling and path coordination planning method and system for electric vehicles. BACKGROUND

[0002] With the rapid growth of the number of electric vehicles (EVs), the coordinated optimization of charging scheduling and path planning has become a key technology direction for solving charging difficulties, travel delays, and distribution network overload problems. In the prior art, dynamic charging and path planning schemes for electric vehicles are mainly divided into two categories: one is to first plan the path and then match the charging station, and the other is to directly optimize the charging power and the path as a whole. However, both of them have the following key technical defects, which limit the actual application effect: 1. The prior art mostly adopts path planning priority or integrated general optimization mode. For example, Chinese patent CN110458332A discloses a method for scheduling the charging demand of electric vehicles based on load space transfer. The method dynamically selects a city road network path, establishes a charging navigation model and a charging station service capacity optimization model, combines game strategies, formulates interactive strategies for charging stations and electric vehicles, and realizes the orderly transfer of load space. This kind of method may result in the fact that the real-time available power of the charging station is not considered during path planning, and invalid decisions may frequently occur that the planned charging station has no available power, or the charging power allocation does not consider the accessibility of the road network, resulting in waste of charging resources.

[0003] 2. The existing charging power allocation mostly adopts average allocation, first-come-first-served, or proportional allocation strategies according to the charging capacity, without distinguishing the differences in the urgency of users' travel, resulting in the fact that commuting, medical treatment, and other urgent users are delayed due to insufficient power, while the time redundancy of shopping, leisure, and other flexible users is not effectively utilized, which not only reduces the user experience, but also cannot effectively alleviate the load of the distribution network through flexible peak shifting.

[0004] 3. The existing low-carbon charging scheme mostly uses static carbon emission coefficients (such as annual average carbon intensity) for optimization, without coupling the real-time load status of the distribution network and the line operation constraints, which may result in conflicts such as forced charging during high-carbon periods leading to distribution network overload or sacrificing line safety for emission reduction, and the emission reduction target deviates from the actual operation scenario, resulting in low actual emission reduction efficiency.

[0005] The above defects result in the fact that the existing scheme cannot simultaneously meet the coordinated needs of distribution network safety, user experience, and low-carbon emission reduction, which restricts the scheduling efficiency and system stability after the popularization of electric vehicles, and there is an urgent need for a coordinated optimization technology that can achieve strong binding of power allocation and path planning, and precise adaptation to multi-dimensional constraints and needs. SUMMARY

[0006] The present application is to overcome the defects of the prior art and provide a dynamic charging scheduling and path coordination planning method and system for electric vehicles.

[0007] The object of the present application can be achieved by the following technical solutions: According to a first aspect of the present application, a dynamic charging scheduling and path coordination planning method for electric vehicles is provided, which comprises the following steps: obtaining distribution network operation parameters, road traffic conditions, electric vehicle user inputs and environmental parameters; Based on the distribution network operation parameters, the electric vehicle user inputs and the environmental parameters, the first optimization objective is to minimize the peak-valley difference of the distribution network load, maximize the power differentiated allocation satisfaction rate of the user trip flexibility, and minimize the charging power carbon emission of the distribution network load coupled with the dynamic carbon intensity, and the charging station power allocation is performed; Based on the charging station power allocation scheme, the road traffic conditions and the electric vehicle user inputs, the second optimization objective is to minimize the path-charging coordination total time under the power allocation constraint, and the electric vehicle driving route is generated and fed back to the user.

[0008] The distribution network operation parameters include node voltage, line load rate and basic load, the road traffic conditions include traffic flow and queue length information, the electric vehicle user inputs include the user's trip plan, charging demand and preference settings, and the environmental parameters include carbon emission intensity.

[0009] The first optimization objective is expressed as: , , , wherein, is the total load of the distribution network at time t, , is the basic load of the distribution network at time t, is the total allocated power of the charging station j at time t, is the power allocated by the charging station j to the vehicle i at time t, is the number of charging stations, is the scheduling time length; is the set of vehicles selecting the charging station j at time t, is the trip emergency coefficient of the vehicle i, which is determined according to the user trip flexibility, is the charging demand power of the vehicle i at time t; is the real-time carbon intensity of the power grid at time t, which is determined by the environmental parameters.

[0010] The constraint conditions of the charging station power allocation include distribution network safety constraints, charging station power constraints, vehicle power allocation constraints, emergency user guarantee constraints and line load rate constraints.

[0011] The second optimization target is represented as: , wherein, is a path of the vehicle i, is a certain section in the path, is a length of the section , is a free flow speed of the section at time t, is a congestion coefficient of the section at time t, is a set of all charging stations selected by the vehicle i in path planning, is a charging amount of the vehicle i at time t at the charging station j, is an available power of the charging station j allocated to the vehicle i at time t, is a charging efficiency.

[0012] The constraint conditions satisfied by the generation process of the electric vehicle driving route include a power distribution connection constraint, a path connectivity constraint, an SOC constraint, a charging amount constraint, and an accessibility constraint.

[0013] The generation of the electric vehicle driving route is solved by using a power-time double-weighted A* algorithm, wherein the heuristic function is: , wherein, is an estimated time, is an available power of the node n , , is a corresponding weight.

[0014] The method further includes: monitoring the running state in real time, and re-performing the charging station power distribution and the generation of the electric vehicle driving route when it is detected that the power grid load rate exceeds a preset threshold or the traffic flow variation degree exceeds a preset threshold.

[0015] The environmental parameters further include environmental temperature and humidity data, and the measurement error of the collected data is corrected by a preset compensation algorithm based on the environmental temperature and humidity data.

[0016] According to a second aspect of the present application, a dynamic charging scheduling and path coordination planning system for electric vehicles is provided for implementing the method, and the system comprises: a data acquisition module for acquiring distribution network operation parameters, road traffic conditions, electric vehicle user inputs, and environmental parameters; a scheduling decision module including a load balancing optimization unit and a path planning unit: The load balancing optimization unit is used to allocate power to charging stations based on distribution network operation parameters, electric vehicle user input, and environmental parameters, with the primary optimization objectives being minimizing the peak-valley difference of distribution network load, maximizing the satisfaction rate of power differentiation allocation guided by user travel flexibility, and minimizing the carbon emissions of charging power coupled with dynamic carbon intensity of distribution network load. The path planning unit is used to generate electric vehicle driving routes and feed them back to the user, based on the charging station power allocation scheme, road traffic conditions and electric vehicle user input, with the second optimization objective being to minimize the total path-charging coordination time under power allocation constraints.

[0017] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0018] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0019] Compared with the prior art, the present invention has the following beneficial effects: (1) Existing power allocation methods mostly adopt average allocation or first-come-first-served, ignoring the differences in the urgency of users' travel. This invention considers the differentiated power allocation based on the flexibility of users' travel when allocating power at charging stations, thereby increasing the charging power satisfaction rate for emergency users to over 95%, avoiding travel delays caused by uneven power allocation, and guiding flexible users to actively stagger their travel times, indirectly smoothing out the peak-valley difference in the distribution network and reducing load fluctuations.

[0020] (2) Existing carbon emission optimization mostly uses static coefficients, while the environmental parameters obtained in this invention include real-time carbon intensity. By introducing it into the objective function of power allocation of charging stations, carbon emission reduction can be bound to the distribution network load, avoiding the emission reduction target from deviating from the actual operating scenario, reducing carbon emissions during the charging process, and without breaking the distribution network safety constraints.

[0021] (3) The objective function of the electric vehicle driving path planning in this invention integrates the calculation of road segment driving time and charging time, avoiding the deviation in total time optimization caused by the separation of driving and charging time in traditional methods, thus greatly improving the accuracy of total trip time calculation and ultimately shortening the total trip time compared to traditional methods. Furthermore, by using the charging station power allocation obtained from the first stage planning as the core parameter for charging time calculation, the path planning is forced to select only charging stations with available power, completely solving the problem of invalid decision-making in existing technologies where no charging station power is available.

[0022] (4) In the electric vehicle driving path planning, the present invention adopts a dual-weight heuristic function to evaluate the timeliness of the path and the availability of charging station power at the same time, and prioritizes the selection of the solution with sufficient power and shorter path, thereby improving the efficiency of candidate charging station screening and reducing charging waiting time. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system structure diagram of the present invention; Figure 3 This is a structural diagram of the data acquisition module of the present invention; Figure 4 This is a structural diagram of the execution feedback module of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0026] Example 1 This embodiment provides a method for dynamic charging scheduling and path cooperative planning of electric vehicles, such as... Figure 1 As shown, the method includes the following steps: S1 acquires power distribution network operation parameters, road traffic conditions, electric vehicle user inputs, and environmental parameters.

[0027] In this embodiment, the acquired distribution network operation parameters include node voltage, line load rate, and base load; the road traffic conditions include traffic flow and queue length information; electric vehicle user input includes the user's travel plan, charging needs, and preference settings; and environmental parameters include carbon emission intensity. In a preferred embodiment, ambient temperature and humidity data can also be acquired to correct measurement errors in the collected data using a preset compensation algorithm.

[0028] S2, based on distribution network operation parameters, electric vehicle user input and environmental parameters, takes minimizing the peak-valley difference of distribution network load, maximizing the satisfaction rate of power differentiation allocation guided by user travel flexibility, and minimizing the carbon emissions of charging power coupled with dynamic carbon intensity of distribution network load as the primary optimization objectives to allocate power to charging stations.

[0029] S21, Define the optimization objective.

[0030] The first optimization objective is expressed as: , , , in, Let t be the total load of the distribution network. , Let t be the basic load of the distribution network. Let the total power allocated to charging station j at time t be denoted as . Let J be the power that charging station j allocates to vehicle i at time t. For the number of charging stations, The scheduling time length; Let t be the set of vehicles selected at charging station j. The travel urgency coefficient for vehicle i is determined based on the user's travel flexibility. The charging power required by vehicle i at time t; The real-time carbon intensity of the power grid at time t is determined by environmental parameters.

[0031] S22, Determine the constraints.

[0032] 1) Distribution network security constraints: , in, This represents the maximum capacity of the distribution network.

[0033] 2) Power constraints of charging stations: , in, This represents the maximum power supply capacity of charging station j.

[0034] 3) Vehicle power distribution constraints: , Ensure that the sum of the power allocated to all vehicles at each charging station equals the power allocated to that charging station.

[0035] 4) Emergency User Protection Constraints: , 5) Line load factor constraint: , , in, Let be the overall load rate of the k-th line at time t. Let be the base load rate of the k-th line at time t. Charging station-line association matrix ( To indicate that charging station j is connected to line k). Let k be the rated apparent power of the k-th line. This is the charging power factor.

[0036] S23 is solved using the improved NSGA-Ⅲ multi-objective optimization algorithm.

[0037] Traditional NSGA-II suffers from uneven solution distribution and slow convergence speed in multi-objective scenarios with three or more objectives. This embodiment adds constraint handling and adaptive adjustment of reference points, which can accurately match constraint requirements and dynamic scenario needs. Specifically, it includes the following steps: Step 1: Input Parameters and Initialization Settings Input parameters: Distribution network operation parameters, user input, environmental parameters, charging station parameters; Algorithm parameter settings: Population size is set to 200, maximum number of iterations is set to 100, crossover probability is set to 0.8, mutation probability is set to 0.05; Reference point initialization: Based on the value range of the three targets, generate a uniformly distributed set of reference points (number = population size × 0.5).

[0038] Step 2: Population Encoding and Initialization: Real-number encoding is used, with each individual corresponding to a complete power allocation scheme. The encoding length is M×T, where M is the number of charging stations and T is the time length. The encoded individual vector is represented as follows: The initial population is generated by constrained random sampling to ensure that each individual meets the constraints of power distribution network safety and charging station power limit, thus avoiding invalid solutions.

[0039] Step 3: Non-dominated sorting: For each individual in the population, calculate 3 objective function values; Non-dominated hierarchy division: The first level is the Pareto optimal level, the second level is only dominated by individuals in the first level, and so on; Constraint violation penalty: For individuals that violate the emergency user guarantee constraint, their dominance level is downgraded by 2 levels, and solutions that seriously violate the constraint are forcibly eliminated.

[0040] Step 4: Reference Point Adaptive Adjustment: Calculate the number of associated individuals corresponding to each reference point (i.e., the number of individuals dominated by that reference point); Dynamically adjust the reference point weights: During peak distribution network load periods ( ):Increase Corresponding reference point density; Low-carbon priority period ( , (mean carbon strength): increase Corresponding reference point density; during peak commuting hours (emergency users account for >50%): increase The corresponding reference point density.

[0041] Step 5: Elite Preservation and Population Renewal: Elite pool construction: The individual with the most associated reference points among the non-dominated solutions at each level is included in the elite pool to ensure the diversity of solutions and optimization effect; Crossover operation: Simulated binary crossover (SBX) is used to crossover individuals in the elite pool to generate offspring individuals. After crossover, the power constraints need to be re-verified, and if they are violated, they are adjusted to the boundary values. Mutation operation: Use polynomial mutation to perform small mutations on 10% of the genes randomly selected in an individual. After mutation, the distribution network security constraints need to be checked. If they are violated, the mutation is repeated. Generation of the new generation population: Merge individuals from the elite pool with their offspring, sort them by non-dominant level and reference point correlation, and retain the top N individuals as the new generation population.

[0042] Step 6: Convergence Judgment and Result Output: The change in the Pareto front of the population over 10 consecutive generations is <10. -3 When the time is right, the Pareto optimal solution set is output, and each solution corresponds to a complete power allocation scheme. Then, a scenario-based selection interface is provided, and users can select the corresponding Pareto solution as the final power allocation result according to actual needs (such as peak-hour power distribution network protection and off-peak carbon protection).

[0043] S3, based on the charging station power allocation scheme, road traffic conditions and electric vehicle user input, takes minimizing the total path-charging coordination time under power allocation constraints as the second optimization objective, generates the electric vehicle driving route and feeds it back to the user.

[0044] S31, Set optimization goals.

[0045] The second optimization objective is expressed as: , in, Let i be the path of vehicle i. For a certain segment of the path, For road section Length, For road section The free-flow velocity at time t, For road section The congestion coefficient at time t Let be the set of all charging stations selected by vehicle i during route planning. The amount of charge given to vehicle i at time t and charging station j. Let J be the available power that charging station j allocates to vehicle i at time t. For charging efficiency.

[0046] S32, set constraints.

[0047] 1) Power distribution connection constraints: , This means that the vehicle's charging power does not exceed the total power allocated to the charging station.

[0048] 2) Path connectivity constraints: It is a set of connecting road segments from the origin to the destination.

[0049] 3) SOC constraints: and , in, Let i be the state of charge (SOC) of vehicle i at time t. , These are the upper and lower limits of SOC, respectively. Let i be the energy consumption of vehicle i at time t and on road segment e. Let be the total battery capacity of vehicle i.

[0050] 4) Charging capacity constraint: , in, The charging time of vehicle i at charging station j.

[0051] 5) Reachability constraints: S33 is solved using the power-time dual-weight A* algorithm to generate the electric vehicle driving route.

[0052] Step 1: Input parameters and road network preprocessing Obtain the power allocation results, road traffic condition parameters, and user parameters output in step S2.

[0053] Road network modeling: Road intersections, charging stations, starting points, and destinations are all set as road network nodes, and the driving segments between adjacent nodes are set as edges. The weight of each edge is a combination of driving time and power accessibility. Based on the power allocation results of step S2, charging stations that have allocated available power to vehicle i and are accessible in the road network are selected and included in the candidate charging station set.

[0054] Step 2: Design the heuristic function.

[0055] The core of the improved A* algorithm is the heuristic function, which is set to: , in, To estimate the time, For nodes n Available power, , For the corresponding weights.

[0056] If node n is a charging station, then If node n is a normal intersection, then It serves only as a path node and has no charging function.

[0057] Step 3: Path Search and Constraint Verification Initialize an open list: add the starting point, record the starting point status and cumulative time, and set the parent node to empty; initialize a closed list to place explored nodes, which is initially empty.

[0058] Select from the open list smallest node , , The cumulative time from the starting point to n; if To find the destination, terminate the search and backtrack to the parent node to obtain the optimal path; traverse... All neighboring nodes Calculate from to adjacent nodes The driving time and the vehicle's SOC after driving; if If it is a candidate charging station, then verify the power constraint and confirm. If the value is >0, after successful verification, calculate the required charging amount and charging time, and update the cumulative time and SOC; if... If it is not in the closed list, add it to the open list and record its parent node and state; Move to the closed list; repeat the above steps until the path search is complete.

[0059] Step 4: Optimal Path Output and Post-processing: When the destination D is found, backtrack the parent node chain to obtain the complete path and the set of planned charging stations, and output it to the user.

[0060] Example 2 This embodiment provides a dynamic charging scheduling and path cooperative planning system for electric vehicles, used to implement the method of Embodiment 1, such as... Figure 2 As shown, the system includes: Data acquisition module: used to acquire power distribution network operation parameters, road traffic conditions, electric vehicle user input, and environmental parameters; The scheduling decision module includes a load balancing optimization unit and a path planning unit: The load balancing optimization unit is used to allocate power to charging stations based on distribution network operation parameters, electric vehicle user input, and environmental parameters, with the primary optimization objectives being minimizing the peak-valley difference of distribution network load, maximizing the satisfaction rate of power differentiation allocation guided by user travel flexibility, and minimizing the carbon emissions of charging power coupled with dynamic carbon intensity of distribution network load. The path planning unit is used to generate electric vehicle driving routes and feed them back to the user, based on the charging station power allocation scheme, road traffic conditions and electric vehicle user input, with the second optimization objective being to minimize the total path-charging coordination time under power allocation constraints.

[0061] like Figure 3As shown, the data acquisition module includes a power grid status monitoring unit, a traffic flow sensing unit, a user demand analysis unit, and an environmental adaptation unit. The power grid status monitoring unit collects real-time operating parameters such as node voltage and line load rate through current transformers and voltage sensors installed on power distribution equipment. These sensors are directly connected to the central processing unit of the data acquisition module and transmit data to the dispatch decision module via a wired communication interface. The traffic flow sensing unit uses geomagnetic sensors and cameras deployed at key road nodes to acquire traffic flow and queue length information. The geomagnetic sensors are connected to the data acquisition module via a wireless communication module, while the cameras convert image data into structured data using a video decoder before transmitting it to the storage unit of the data acquisition module. The user demand analysis unit receives users' travel plans, charging needs, and preference settings through in-vehicle terminals or mobile applications. These terminal devices establish connections with the data acquisition module via mobile communication networks. After protocol parsing, the data is standardized into a unified format and stored in the database of the data acquisition module. The environmental adaptation unit further enhances the functionality of the data acquisition module. The meteorological data acquisition subunit collects information on changes in meteorological conditions using anemometers, rain gauges, and barometers. These instruments are connected to the main control board of the environmental adaptation unit via an RS485 bus, and the main control board then forwards the data to the central processing unit of the data acquisition module. The temperature and humidity compensation subunit collects environmental temperature and humidity data using temperature and humidity sensors distributed at key nodes and transmits the data to the compensation calculation module of the environmental adaptation unit via an I2C bus. The compensation calculation module corrects the measurement errors of the power grid status monitoring unit according to a preset algorithm. The light intensity monitoring subunit records changes in light intensity using a photoresistor sensor. The analog signal output by the sensor is converted from analog to digital and transmitted to the signal processing module of the environmental adaptation unit. Finally, the signal processing module sends the data to the storage unit of the data acquisition module. Through the collaborative work of the meteorological data acquisition subunit, the temperature and humidity compensation subunit, and the light intensity monitoring subunit, the environmental adaptation unit enhances the system's adaptability to environmental changes and improves the accuracy and reliability of data acquisition.

[0062] The specific implementation methods of the load balancing optimization unit and the path planning unit in the scheduling decision module are described in Example 1, and will not be repeated here.

[0063] In a preferred embodiment, the scheduling decision module further includes an energy storage coordination unit, which includes an energy storage status assessment subunit, a charging and discharging strategy optimization subunit, and an energy balance control subunit.

[0064] The energy storage coordination unit further enhances the functionality of the scheduling decision module. The energy storage status assessment subunit reads data from the energy storage device's battery management system via the CAN bus to obtain key parameters such as current energy storage capacity and charge / discharge efficiency. This data is transmitted to the main control module of the energy storage coordination unit via an internal communication bus. The charge / discharge strategy optimization subunit formulates a charge / discharge plan for the energy storage device based on the input from the energy storage status assessment subunit and data from the grid status monitoring unit. This plan is then sent to the energy storage device's control system via the Modbus protocol. The energy balance control subunit adjusts the charge / discharge power of the energy storage device in real time using a closed-loop control algorithm to ensure a dynamic balance between energy supply and demand within the region. Control commands are transmitted to the energy storage device's power conversion module via PWM signals.

[0065] The energy storage coordination unit achieves efficient coordination between energy storage devices and the power grid through multi-level collaboration among the energy storage status assessment subunit, the charging and discharging strategy optimization subunit, and the energy balance control subunit. This reduces the peak-valley difference in the power grid and improves energy utilization efficiency.

[0066] In a preferred embodiment, the system may further include an execution feedback module.

[0067] like Figure 4 As shown, the execution feedback module includes a real-time adjustment unit, an anomaly handling unit, and a result output unit. The real-time adjustment unit maintains a connection with the scheduling decision module via a wireless communication interface. When it detects that the grid load rate exceeds a preset threshold or that traffic flow changes significantly (e.g., the traffic flow change rate exceeds a threshold), it sends an adjustment request to the scheduling decision module. The adjustment request is transmitted to the central controller of the scheduling decision module via the TCP / IP protocol. The anomaly handling unit classifies and handles anomalies occurring during system operation, such as equipment failures and communication interruptions, and triggers corresponding emergency plans. The emergency plans are communicated to maintenance personnel via a dedicated communication link. The result output unit provides the final charging scheduling scheme and route planning results to the user through a visual interface or voice prompts. The visual interface is connected to the vehicle's display screen via an HDMI interface, and the voice prompts are output through an audio decoder driving a speaker.

[0068] In a preferred embodiment, the execution feedback module further includes a user interaction unit, which includes a voice interaction subunit, a touch display subunit, and a remote control subunit.

[0069] The user interaction unit further enhances the functionality of the execution feedback module. The voice interaction subunit enables natural language interaction between the user and the system via an in-vehicle microphone array and speaker. The microphone array connects to the voice recognition module of the voice interaction subunit via a USB interface, and the speaker connects to the speech synthesis module via an audio interface. The touch display subunit provides an intuitive graphical interface via an in-vehicle display screen or mobile terminal. The display screen connects to the graphics processing module of the touch display subunit via an LVDS interface. The remote control subunit establishes a connection with the user terminal via a cloud server, allowing users to remotely monitor and manage the charging process from different locations. The cloud server communicates with the user terminal via the HTTPS protocol. The energy consumption optimization unit dynamically adjusts the operating status of each device in the system by monitoring its real-time power consumption. For example, it reduces the calculation frequency of the path planning unit during off-peak hours or shuts down some redundant sensors under low load conditions. Power consumption data is collected by the power monitoring module and transmitted to the control module of the energy consumption optimization unit via the SPI bus. The safety protection unit identifies potential safety hazards, such as communication link interruptions and hardware failures, by monitoring the system's operating status in real time and triggers corresponding protection mechanisms. These protection mechanisms notify maintenance personnel via a dedicated communication link.

[0070] The user interaction unit enhances the user experience and meets the operational needs of different user groups through the diverse design of voice interaction subunits, touch display subunits, and remote control subunits.

[0071] In a preferred embodiment, the execution feedback module further includes an energy consumption optimization unit and a security protection unit.

[0072] The energy optimization unit dynamically adjusts the operating status of each device in the system by monitoring its real-time power consumption. For example, it reduces the calculation frequency of the path planning unit during off-peak hours or shuts down some redundant sensors under low load conditions. The safety protection unit identifies potential safety hazards, such as communication link interruptions and hardware failures, by monitoring the system's operating status in real time and triggering corresponding protection mechanisms.

[0073] The introduction of energy consumption optimization unit and safety protection unit improves the system's operating efficiency and safety, and extends the service life of the equipment.

[0074] Example 3 The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0075] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0076] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0077] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0078] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0079] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0080] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic charging scheduling and path cooperative planning of electric vehicles, characterized in that, The method includes the following steps: Acquire power distribution network operation parameters, road traffic conditions, electric vehicle user input, and environmental parameters; Based on distribution network operation parameters, electric vehicle user inputs, and environmental parameters, the power allocation of charging stations is carried out with the primary optimization objectives of minimizing the peak-valley difference of distribution network load, maximizing the satisfaction rate of power differentiation allocation guided by user travel flexibility, and minimizing the carbon emissions of charging power coupled with dynamic carbon intensity of distribution network load. Based on the charging station power allocation scheme, road traffic conditions, and electric vehicle user input, the second optimization objective is to minimize the total path-charging coordination time under power allocation constraints, generate electric vehicle driving routes, and feed them back to the users.

2. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 1, characterized in that, The power distribution network operation parameters include node voltage, line load rate, and base load; the road traffic conditions include traffic flow and queue length information; the electric vehicle user input includes the user's travel plan, charging needs, and preference settings; and the environmental parameters include carbon emission intensity.

3. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 1, characterized in that, The first optimization objective is expressed as: ， ， ， in, Let t be the total load of the distribution network. , Let t be the basic load of the distribution network. Let the total power allocated to charging station j at time t be denoted as . Let J be the power that charging station j allocates to vehicle i at time t. For the number of charging stations, The scheduling time length; Let t be the set of vehicles selected at charging station j. The travel urgency coefficient for vehicle i is determined based on the user's travel flexibility. The charging power required by vehicle i at time t; The real-time carbon intensity of the power grid at time t is determined by environmental parameters.

4. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 1, characterized in that, The constraints on power allocation at charging stations include distribution network security constraints, charging station power constraints, vehicle power allocation constraints, emergency user protection constraints, and line load rate constraints.

5. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 1, characterized in that, The second optimization objective is expressed as: ， in, Let i be the path of vehicle i. For a certain segment of the path, For road section Length, For road section The free-flow velocity at time t, For road section The congestion coefficient at time t Let be the set of all charging stations selected by vehicle i during route planning. The amount of charge given to vehicle i at time t and charging station j. Let J be the available power that charging station j allocates to vehicle i at time t. For charging efficiency.

6. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 1, characterized in that, The constraints satisfied by the process of generating the electric vehicle driving route include: power allocation connection constraints, path connectivity constraints, SOC constraints, charging quantity constraints, and reachability constraints.

7. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 1, characterized in that, The electric vehicle's driving route is generated using a power-time dual-weight A* algorithm, where the heuristic function is: ， in, To estimate the time, For nodes n Available power, , For the corresponding weights.

8. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 1, characterized in that, The method further includes: real-time monitoring of the operating status, and re-allocating the power of the charging station and generating the driving route of the electric vehicle when the power grid load rate exceeds a preset threshold or the traffic flow changes exceed a preset threshold.

9. The method for dynamic charging scheduling and path cooperative planning of electric vehicles according to claim 2, characterized in that, The environmental parameters also include environmental temperature and humidity data. Based on the environmental temperature and humidity data, a preset compensation algorithm is used to correct the measurement error of the collected data.

10. A dynamic charging scheduling and path cooperative planning system for electric vehicles, characterized in that, For implementing the method as described in claims 1-9, the system comprises: Data acquisition module: used to acquire power distribution network operation parameters, road traffic conditions, electric vehicle user input, and environmental parameters; The scheduling decision module includes a load balancing optimization unit and a path planning unit: The load balancing optimization unit is used to allocate power to charging stations based on distribution network operation parameters, electric vehicle user input, and environmental parameters, with the primary optimization objectives being minimizing the peak-valley difference of distribution network load, maximizing the satisfaction rate of power differentiation allocation guided by user travel flexibility, and minimizing the carbon emissions of charging power coupled with dynamic carbon intensity of distribution network load. The path planning unit is used to generate electric vehicle driving routes and feed them back to the user, based on the charging station power allocation scheme, road traffic conditions and electric vehicle user input, with the second optimization objective being to minimize the total path-charging coordination time under power allocation constraints.

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