Ordered charging method, device and equipment for electric vehicle and storage medium

Abstract

The invention discloses an orderly charging method, device and equipment for electric vehicles and a storage medium, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining charging demand parameters corresponding to all to-be-charged vehicles and power constraint parameters of a target charging station; target weights corresponding to all types of normalized demand parameters are calculated, a vehicle scoring matrix is constructed according to all the target weights, and charging priorities corresponding to all the to-be-charged vehicles are determined based on the vehicle scoring matrix; and according to the power constraint parameter, the charging demand parameter corresponding to each to-be-charged automobile and the charging priority, determining a real-time power distribution value corresponding to each to-be-charged automobile in each rolling time window, and charging each to-be-charged automobile. The real-time power distribution value is determined by combining the power constraint, the charging requirements of different vehicles and the priorities, and vehicle charging in a multi-vehicle concurrent scene is achieved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to an orderly charging method, apparatus, device, and storage medium for electric vehicles. Background Technology

[0002] The continuous increase in the number of electric vehicles has led to a sharp rise in charging power demand during peak periods, and the contradiction between power coordination and service quality at charging stations with limited power supply capacity is becoming increasingly prominent.

[0003] Current vehicle charging methods primarily operate on a single vehicle basis, which presents a challenge in simultaneously addressing diverse needs and ensuring fair service in multi-vehicle concurrent scenarios. Therefore, how to effectively manage vehicle charging in multi-vehicle concurrent environments remains a pressing technical problem to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an orderly charging method, apparatus, device, and storage medium for electric vehicles, which can determine real-time power allocation values ​​by combining power constraints, the charging needs of different vehicles, and priorities, thereby enabling vehicle charging in multi-vehicle concurrent scenarios. The specific solution is as follows:

[0005] Firstly, this application provides an orderly charging method for electric vehicles, applied to the Internet of Things, including:

[0006] Obtain the charging demand parameters corresponding to each vehicle to be charged and the power constraint parameters of the target charging station, and normalize the charging demand parameters of each type to obtain the corresponding normalized demand parameters.

[0007] The target weights corresponding to the normalized demand parameters of each type are calculated using a preset weight evaluation model. A vehicle scoring matrix is ​​constructed based on the target weights, and the charging priority of each vehicle to be charged is determined based on the vehicle scoring matrix.

[0008] Based on the power constraint parameters, the charging demand parameters corresponding to each of the vehicles to be charged, and the charging priority, the real-time power allocation value corresponding to each vehicle to be charged in each rolling time window is determined, and the vehicles to be charged are charged in an orderly manner according to the real-time power allocation values.

[0009] Optionally, the step of calculating the target weights corresponding to each type of normalized demand parameter using a preset weight evaluation model includes:

[0010] The subjective weights corresponding to the normalized demand parameters of each type are determined by using the preset weight evaluation model and the analytic hierarchy process, and the objective weights corresponding to the normalized demand parameters of each type are determined by using the preset weight evaluation model and the entropy weight method.

[0011] The subjective weights and the corresponding objective weights are fused to obtain the target fusion weights corresponding to each type of normalized requirement parameter.

[0012] Optionally, determining the real-time power allocation value for each vehicle to be charged within each rolling time window based on the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the charging priority includes:

[0013] The total available power of the target charging station is determined based on the power constraint parameters, and power is allocated to each of the vehicles to be charged based on the minimum power requirements corresponding to each vehicle.

[0014] The remaining available power of the target charging station is determined based on the total available power and the minimum power requirement corresponding to each of the vehicles to be charged, and the remaining available power is allocated to each vehicle to be charged in sequence according to the charging priority of each vehicle.

[0015] Optionally, the orderly charging method for electric vehicles further includes:

[0016] The vehicle charging events in the target charging station are monitored in real time. If the vehicle charging events in the target charging station change, the power rolling scheduling model is controlled to redetermine the real-time power allocation value of each vehicle to be charged within a future rolling time window, based on the charging demand parameters and charging priority of each vehicle to be charged.

[0017] Optionally, the orderly charging method for electric vehicles further includes:

[0018] The characteristic information corresponding to each charging pile in the target charging station is periodically obtained using the target communication protocol; wherein, the characteristic information includes the instantaneous power and equipment status code information of the charging pile;

[0019] Based on the aforementioned feature information, it is determined whether any charging pile in the target charging station is abnormal. If any charging pile is abnormal, then that charging pile is disabled, and the power rolling scheduling model is controlled to re-determine the real-time power allocation value corresponding to each of the vehicles to be charged within a future rolling time window, based on the charging demand parameters and charging priorities corresponding to each vehicle to be charged.

[0020] Optionally, the orderly charging method for electric vehicles further includes:

[0021] The power constraint parameters and the length of the rolling time window are adjusted using a preset human-machine interface so that when the vehicle charging time at the target charging station changes, power allocation is performed using the adjusted power constraint parameters and the adjusted rolling time window.

[0022] Secondly, this application provides an orderly charging device for electric vehicles, applied to the Internet of Things, comprising:

[0023] The parameter normalization module is used to obtain the charging demand parameters corresponding to each vehicle to be charged and the power constraint parameters of the target charging station, and to normalize the charging demand parameters of each type to obtain the corresponding normalized demand parameters.

[0024] The priority determination module is used to calculate the target weights corresponding to the normalized demand parameters of each type using a preset weight evaluation model, construct a vehicle scoring matrix based on the target weights, and determine the charging priority corresponding to each vehicle to be charged based on the vehicle scoring matrix.

[0025] The vehicle charging module is used to determine the real-time power allocation value of each vehicle to be charged in each rolling time window according to the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the charging priority, and to charge each vehicle to be charged in an orderly manner according to the real-time power allocation value.

[0026] Optionally, the priority determination module includes:

[0027] The weight determination unit is used to determine the subjective weights corresponding to each type of normalized demand parameter by using the preset weight evaluation model and the analytic hierarchy process, and to determine the objective weights corresponding to each type of normalized demand parameter by using the preset weight evaluation model and the entropy weight method.

[0028] The weight fusion unit is used to fuse each of the subjective weights and the corresponding objective weights to obtain the target fusion weights corresponding to each type of normalization requirement parameter.

[0029] Thirdly, this application provides an electronic device, comprising:

[0030] Memory, used to store computer programs;

[0031] A processor is used to execute the computer program to implement the aforementioned orderly charging method for electric vehicles.

[0032] Fourthly, this application provides a computer-readable storage medium for storing a computer program that, when executed by a processor, implements the aforementioned orderly charging method for electric vehicles.

[0033] This application first obtains the charging demand parameters corresponding to each vehicle to be charged and the power constraint parameters of the target charging station, and normalizes the charging demand parameters of each type to obtain the corresponding normalized demand parameters. Then, it uses a preset weight evaluation model to calculate the target weights corresponding to each type of normalized demand parameters, constructs a vehicle scoring matrix based on the target weights, and determines the charging priority corresponding to each vehicle to be charged based on the vehicle scoring matrix. Finally, it determines the real-time power allocation value corresponding to each vehicle to be charged in each rolling time window based on the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the charging priority, and charges each corresponding vehicle to be charged according to the real-time power allocation value. Therefore, this application achieves unified quantitative representation of multi-source heterogeneous data by normalizing diverse charging demand parameters, overcoming the limitations of traditional single-indicator evaluation. By calculating target weights and constructing a vehicle scoring matrix through a preset weight evaluation model, it realizes comprehensive quantitative evaluation and priority ranking of differentiated vehicle charging demands, replacing the fixed proportion allocation rule. By combining power constraints, charging demands, and priorities to determine real-time power allocation values ​​within a rolling time window, it achieves dynamic and on-demand scheduling of multi-vehicle charging power under total power constraints, thereby improving overall resource utilization efficiency and allocation fairness while ensuring basic charging services. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0035] Figure 1 This is a flowchart of an orderly charging method for electric vehicles disclosed in this application;

[0036] Figure 2 This is a schematic diagram of an electric vehicle charging system disclosed in this application;

[0037] Figure 3 This application discloses a flowchart for a pre-charge guidance and assessment process;

[0038] Figure 4 This is a schematic diagram of a vehicle scoring evaluation process disclosed in this application;

[0039] Figure 5 This is a schematic diagram of a two-level indicator system disclosed in this application;

[0040] Figure 6 This is a schematic diagram of an orderly charging device for electric vehicles disclosed in this application.

[0041] Figure 7 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Current electric vehicle charging methods struggle to simultaneously address diverse needs and ensure fair service in multi-vehicle concurrent scenarios. To address this, this application provides an orderly charging method for electric vehicles. By combining power constraints, the charging needs of different vehicles, and their priorities to determine real-time power allocation values, this method enables vehicle charging in multi-vehicle concurrent scenarios.

[0044] See Figure 1 As shown, this embodiment of the invention discloses an orderly charging method for electric vehicles, applied to the Internet of Things, including:

[0045] Step S11: Obtain the charging demand parameters corresponding to each vehicle to be charged and the power constraint parameters of the target charging station, and normalize the charging demand parameters of each type to obtain the corresponding normalized demand parameters.

[0046] The electric vehicle orderly charging method in this embodiment achieves power weighted allocation, real-time optimization, and dynamic parameter configuration under multi-vehicle access conditions without replacing existing station hardware and communication interfaces. The system adopts a "cloud-edge-pile" collaborative architecture, with the charging station power allocation backend edge side as the core control unit, combined with multi-path selection and power control circuits, charging pile interfaces 1...N, Modbus / OCPP (Open charge point protocol) data communication circuits, metering and detection units, and local storage; and a data processing and command issuance platform composed of the charging station power allocation backend cloud and time-series database. The software modules include: a vehicle and acquisition channel mapping table, an AHP (Analytic Hierarchy Process) – EWM (Entropy Weight Method) – GCE (Grey Correlation Evaluation) comprehensive evaluation engine, a weighted maximum-min fairness (WMMF) power allocator, and a policy-based publishing and feedback acquisition module. The method uses an edge controller as its core, connecting to each charging station via data and control lines. Based on vehicle power demand, available charging time, and business weight, a comprehensive evaluation engine first generates vehicle priorities and adjusts weights. Then, the WMMF allocator calculates and distributes the instantaneous power of each vehicle under the total power constraint of the station. It also supports local configuration of policy parameters via a local human-machine interface / mobile terminal, and cloud-based data storage and remote policy updates. When charging demand or station resources change, the terminal can reconstruct interfaces and weights via software commands to quickly adapt to different scales and types of charging scenarios without requiring additional hardware development.

[0047] The charging station power allocation backend adopts an open energy management system, forming a "cloud-edge-pile" collaborative architecture. The edge controller serves as the core, enabling local data acquisition and closed-loop control. It uniformly connects to charging interfaces, metering and detection units, and communication channels such as Ethernet / 4G / 5G / RS-485 / CAN, compatible with OCPP, Modbus, CAN, or equivalent protocols. The cloud platform handles functions such as policy templates, historical data and reports, parameter versions, and canary releases, and distributes policies and configurations to the edge side. During operation, the edge side continuously gathers multi-dimensional information from vehicles and stations, including arrival / departure times, current and target SOC (State of Charge), available charging time, vehicle-side power limits, reservation / tier and price sensitivity, as well as station-level available total power, transformer capacity and maximum demand, loop current limit, single-gun power limits, time-of-use pricing and demand response, temperature derating, and equipment alarms.

[0048] The main controller periodically sends "power / current / voltage" setpoints to each charging interface and performs error correction and strategy fine-tuning based on the status codes, instantaneous power, metering, and SOC estimates returned from the charging pile. It performs limiting, safety net, or bypass operations on objects with communication anomalies or equipment failures, and records audit logs to support maintenance traceability. To improve deployment and maintenance efficiency, the system provides station-level and station group-level parameter templates, covering total power limits, loop and equipment thresholds, time-of-use pricing tables, evaluation indicators and weights, minimum service power, rolling window and step size, bypass thresholds, etc. These can be uniformly managed, versioned, and released in a canary manner through local HMI / Bluetooth / Ethernet and cloud platforms. At sites with energy storage or photovoltaics, the energy storage SOC and charging / discharging power, and the available photovoltaic power and grid connection limits are incorporated into the same optimization domain, working in conjunction with electricity price signals to achieve peak shaving and valley filling and cost optimization. In terms of engineering implementation, edge terminals are deployed in industrial IoT gateways or general embedded platforms, employing NTP (Network Time Protocol) / GPS (Global Positioning System) time synchronization and communication / electrical isolation design. Key parameters and logs are encrypted and persistently stored, supporting self-recovery after abnormal resets. When edge computing power is insufficient or the solution timeout reaches a threshold, cloud-edge collaboration is automatically activated, or a proportional allocation / minimum power guarantee or other safety strategies are implemented to ensure power supply security and service continuity. This technical solution, while ensuring safety and compliance, achieves weighted, fair, real-time controllable, and economically coordinated charging scheduling under multi-vehicle concurrent conditions, and possesses rapid replication and large-scale operation and maintenance capabilities across devices and sites.

[0049] To support the engineering implementation of the above scheduling scheme, the system selects an open-source energy management platform and completes unified access and policy orchestration on it. The platform adopts a modular architecture and provides monitoring and control capabilities for various objects such as energy storage, renewable energy, electric vehicle charging piles, heat pumps, electrolyzers, and time-of-use electricity pricing. It forms a complete IoT energy management stack consisting of field edge, cloud backend, and Web / UI. It supports fast local control similar to PLC (Programmable Logic Controller) and achieves high reusability and easy expansion through clear device abstraction and plug-in control algorithms. It also takes into account community collaboration and commercial deployment with dual license modes of AGPL-3.0 and EPL-2.0. Among them, Edge, as a lightweight runtime deployed on industrial IoT gateways or embedded devices such as Raspberry Pi, is responsible for abstracting and controlling on-site hardware such as distributed energy storage, photovoltaics, and charging piles; Backend, as the core service layer of cloud / local servers, aggregates the running data, topology, and metadata of each Edge, and provides real-time / historical data access to the upper-layer UI and third-party systems through a unified REST API (Application Programming Interface) and WebSocket. It also supports multi-tenant site management, access control, component lifecycle management, and simulation testing through modular subsystems (such as Alerting, Application, Core, Metadata, TimeData, Simulator, etc.). At the same time, it combines time-series storage such as InfluxDB and OCPP services to realize data collection and control command issuance for charging piles. In a typical deployment, the Edge runs on edge nodes such as Raspberry Pi at the site to handle local communication and execution loops, while the Backend and UI are deployed on small-scale instances to complete data aggregation, analysis, and scheduling, forming a real-time control and operation and maintenance system that is "cloud-edge" collaborative, thus naturally connecting with the aforementioned weighted fairness and rolling optimization scheduling method.

[0050] The software architecture in this embodiment is as follows: Figure 2 As shown, it includes a controller, scheduler, and various software components. Additionally, the electric vehicle charging guidance and evaluation framework in this embodiment is as follows: Figure 3 As shown, the process includes: setting the overall optimization objective, determining AHP weights, calculating grey evaluation results, and outputting charging guidance results.

[0051] In this embodiment, it is first necessary to obtain multi-dimensional parameter information (i.e., charging demand parameters) of all vehicles waiting to be charged in the station, including arrival time, expected departure time, current state of charge (SOC), target SOC, minimum and maximum available charging power, charging duration, service priority, time-of-use electricity price and user category, etc. At the same time, the station-end equipment operation constraint parameters (i.e., power constraint parameters) are collected, including the upper limit of total power supply, transformer capacity, branch current limit, upper and lower limits of single gun power, power ramp-up rate and safety protection threshold, and the parameters are normalized to eliminate the difference in dimensions between different indicators.

[0052] Step S12: Calculate the target weights corresponding to the normalized demand parameters of each type using a preset weight evaluation model, construct a vehicle scoring matrix based on the target weights, and determine the charging priority of each vehicle to be charged based on the vehicle scoring matrix.

[0053] In this implementation, the target weights corresponding to each type of normalized demand parameter are calculated using a preset weight evaluation model, including: determining the subjective weights corresponding to each type of normalized demand parameter using the preset weight evaluation model and the analytic hierarchy process, and determining the objective weights corresponding to each type of normalized demand parameter using the preset weight evaluation model and the entropy weight method; and merging each subjective weight and the corresponding objective weight to obtain the target fusion weights corresponding to each type of normalized demand parameter.

[0054] Specifically, in the aforementioned steps, a multi-index comprehensive evaluation system for electric vehicle fleet charging is established. This system covers indicators such as energy gap, available time, waiting time, user level / reservation type, demand rigidity, and historical service quality. After standardization and dimensionless processing of each indicator, this embodiment uses the Analytic Hierarchy Process (AHP) to determine subjective weights and the Entropy Weight Method (EWM) to determine objective weights. Then, the Grey Relational Evaluation Method (GCE) is used to comprehensively evaluate different vehicles, ultimately obtaining a comprehensive vehicle score and priority sequence. The evaluation process is as follows: Figure 4 As shown, the process includes: inputting the evaluation index system, establishing judgment matrices at each level, calculating the weights of the judgment indicators and completing consistency verification, determining whether the CR is less than a preset threshold; if so, standardizing the judgment matrices at each level, calculating the objective weights of each indicator, calculating the adjusted weights of each indicator, calculating the evaluation weights and the gray evaluation matrix, and finally calculating the comprehensive evaluation value. The specific steps of the above AHP-EWM-GCE evaluation process are as follows:

[0055] (1) Subjective weights were determined using the Analytic Hierarchy Process (AHP):

[0056] Step 1: Establish a multi-level network structure according to the evaluation objectives;

[0057] Step 2: Construct a pairwise comparison judgment matrix A using formula (1), where A is the element in the i-th row and j-th column of an n-dimensional matrix, and satisfies three conditions: (i) (ii) (iii) .

[0058] (1);

[0059] Step 3: Calculate the relative weights of the judgment matrix using equation (2). And the consistency check is completed through equations (3)-(5).

[0060] (2);

[0061] (3);

[0062] (4);

[0063] (5);

[0064] Wherein, CI stands for Consistency Index. is the largest eigenvalue, and RI is the average random consistency index.

[0065] If CR is less than the preset threshold, the judgment matrix is ​​readjusted (the index weights of the judgment matrix are calculated using Formula 2, and consistency verification is completed using Formulas 3-5).

[0066] Step 4: Calculate the score (i.e., subjective weights) by combining the weights and evaluation vectors:

[0067] (6);

[0068] in, Let l be the weight vector of the l-th layer. Let be the evaluation vector for the l-th layer.

[0069] (2) Determining objective weights using the Entropy Weight Method (EWM):

[0070] Step 1: Normalization. To eliminate the impact of differences in different indicator dimensions and data ranges, the original data should be standardized, as shown in formula (7).

[0071] (7);

[0072] The above This represents the minimum value of this indicator across all vehicles. This is the minimum value of this indicator among all vehicles.

[0073] Step 2: Calculate the information entropy of each indicator The information entropy of index i is used to reflect the amount of information provided by the index. Therefore, the information entropy of index i can be calculated by equation (8).

[0074] (8);

[0075] Step 3: Calculate objective weights based on entropy values :

[0076] (9);

[0077] (3) Grey relational evaluation method (GCE) is used to comprehensively evaluate different vehicles:

[0078] Step 1: Set up the evaluation comment set according to formula (10) and determine the gray category according to formula (11).

[0079] (10);

[0080] (11);

[0081] For example, K = {Excellent (90-100 points), Good (80-89), Average (70-79), Poor (60-69)}; C = [95, 85, 75, 65] (the center value of each gray class).

[0082] Step 2: There are m experts who score each indicator, and then the evaluation sample matrix D is obtained as shown in equation (12).

[0083] (12);

[0084] For example, for a certain SOC indicator, Expert 1: 70 points (average); Expert 2: 75 points (average); Expert 3: 80 points (good).

[0085] Step 3: Determine the whitening weight function. The whitening function shown in equation (13) is as follows:

[0086] (13);

[0087] For example, vehicle A has a SOC score of 75, which is considered a "medium" gray category ( =75), then we have: (75) = 75 / 75 = 1.0;

[0088] For "good" gray category ( =85): (75) = max((2×85-75) / 85, 1) = 1.12;

[0089] For the "excellent" gray category ( =95): (75) = max((2×95-75) / 95, 1) = 1.21;

[0090] For the "poor" gray class ( =65): (75) = 0 (not in the interval [0,130]);

[0091] Step 4: Calculate the evaluation weights and matrix of the grey evaluation according to equations (14)-(16).

[0092] (14);

[0093] (15);

[0094] (16);

[0095] in, Let be a vector of this matrix. For the q-th evaluation index, the corresponding to the th The gray evaluation weights for each gray category. For the q-th evaluation index to belong to the th Gray statistics for each gray category For the first Whitening weight function for each gray category.

[0096] According to formulas (17)-(18), the gray evaluation matrix Q (i.e., the vehicle rating matrix) and the comprehensive evaluation value are calculated using weights. That is, the above calculation is repeated for all 8 indicators, and then the weighted sum is obtained to finally obtain the total evaluation value Z for each vehicle (reflecting the charging priority of the vehicle).

[0097] (17);

[0098] (18);

[0099] in, It is the transpose of the gray category vector C.

[0100] In addition, this embodiment uses the AHP-EWM-GCE comprehensive evaluation method through a two-level evaluation index system, and the steps are as follows:

[0101] (1) Use the analytic hierarchy process (AHP) to calculate the weights of the electric vehicle charging response evaluation index system.

[0102] For the two-level electric vehicle charging guidance evaluation index system, the weights of the primary and secondary indicators are shown in (19)-(20):

[0103] (19);

[0104] (20);

[0105] These weights were calculated using equations (1)-(5) and their consistency was verified.

[0106] in, It is the subjective weight of the primary indicator; It is the subjective weight of the secondary indicator.

[0107] It should be noted that the two levels of weights in this embodiment are as follows: Figure 5 As shown, vehicle charging indicators can be divided into four primary indicators: rechargeable battery characteristics, charging time and space characteristics, charging response characteristics, and charging economy characteristics. Each primary indicator includes corresponding secondary indicators.

[0108] (2) Use the EWM method to determine the objective weights of the indicator system.

[0109] Similarly, the entropy weight of each layer index is calculated using equations (7)-(9), where equations (21)-(22) represent the objective weight expressions of the first-level and second-level indicators. Then, the subjective weights are adjusted using equation (23), where equations (24)-(25) represent the adjusted weights of the first-level and second-level indicators.

[0110] Equations (21)-(22) represent the objective weight expressions for the primary and secondary indicators. Then, the subjective weights are adjusted using equation (23), where equations (24)-(25) represent the adjusted weights for the primary and secondary indicators.

[0111] (twenty one);

[0112] (twenty two);

[0113] (twenty three);

[0114] (twenty four);

[0115] (25);

[0116] in, It is the objective weight of the primary indicator; It is the objective weight of the secondary indicator; It is the first Adjustment weights for each indicator; It is the adjustment weight of the primary indicator; It is the adjustment weight of the secondary indicator.

[0117] (3) Use the GCE method to comprehensively evaluate different types of electric vehicles. The adjusted weights of each indicator in the GCE method are derived from steps 1 and 2.

[0118] The following is a specific example to illustrate the above evaluation method:

[0119] Determine the evaluation set (grey categories): Assume there are 4 gray categories, namely "Excellent", "Good", "Average", and "Poor", and the corresponding center values ​​(thresholds) can be: C1=1.0 (Excellent), C2=0.8 (Good), C3=0.6 (Average), and C4=0.4 (Poor). These center values ​​can be set according to the actual situation.

[0120] For each secondary indicator, there are expert scores (or scores mapped from data). Suppose that for a certain secondary indicator of vehicle A (e.g., current SOC), there are m expert scores, resulting in scores d1, d2, ..., dm. Then, the weight of this indicator belonging to each gray class is calculated based on the whitening weight function.

[0121] Whitening weight function (based on the center value) (For example)

[0122] =d / When d is in [0, ]between;

[0123] = (2 -d) / When d is in [ , 2 ]between;

[0124] 0, Other;

[0125] Note: Here, d is the average of the expert scores (or the scores from individual experts, which are then combined).

[0126] Assume that for indicator 11, the average expert score for vehicle A is 0.8. Then calculate the weight belonging to each gray class:

[0127] For the gray class "excellent" (C1=1.0): 0.8 is within [0,1.0], so f1=0.8 / 1.0=0.8;

[0128] For the gray class "Good" (C2=0.8): 0.8 equals C2, which belongs to [C2,2C2]=[0.8,1.6], so f2 = max((2×0.8-0.8) / 0.8, 1) = max(1,1)=1;

[0129] For the gray class "Medium" (C3=0.6): 0.8 is within [C3,2C3]=[0.6,1.2], so f3 = (2×0.6-0.8) / 0.6 = (1.2-0.8) / 0.6 = 0.4 / 0.6≈0.667;

[0130] For the gray class "difference" (C4=0.4): 0.8 is neither in [0,0.8] nor in [0.4,0.8] (because 0.8>0.8), so f4=0;

[0131] Then, these f values ​​are normalized to obtain the weights of index 11 belonging to each gray class (i.e., the gray evaluation weight vector):

[0132] The total is 0.8 + 1 + 0.667 + 0 = 2.467.

[0133] r11 = (0.8 / 2.467, 1 / 2.467, 0.667 / 2.467, 0 / 2.467) = (0.324, 0.405,0.271, 0);

[0134] Note: There are actually multiple experts involved, so each expert's score should be calculated separately for whitening weight, and then the results should be combined. For simplicity, the average value is used here.

[0135] Perform the above calculations for each secondary indicator to obtain the gray evaluation weight vector for each secondary indicator (the length of which is the number of gray classes, which is 4 in this case).

[0136] Then, for each primary indicator, the gray evaluation weight vectors of its secondary indicators are weighted and combined (using the weights of the secondary indicators) to obtain the gray evaluation weight vector of that primary indicator.

[0137] For example, for primary indicator 1, there are two secondary indicators. Assumption:

[0138] The grey evaluation weight vector for indicator 11 is: r11 = (0.324, 0.405, 0.271, 0);

[0139] The grey evaluation weight vector for index 12 is: r12 = (0.2, 0.3, 0.4, 0.1);

[0140] Secondary indicator weights: w11=0.6, w12=0.4;

[0141] The grey evaluation weight vector for Level 1 indicator 1 is:

[0142] R1 = w11×r11 + w12 ×r12;

[0143] = 0.6×(0.324,0.405,0.271,0) + 0.4×(0.2,0.3,0.4,0.1);

[0144] = (0.1944,0.243,0.1626,0) + (0.08,0.12,0.16,0.04);

[0145] = (0.2744, 0.363, 0.3226, 0.04);

[0146] Note: The weighted summation here is performed by weighting the weights of each gray class.

[0147] Similarly, the grey evaluation weight vectors R1, R2, and R3 for all primary indicators are obtained.

[0148] Then, the gray evaluation weight vector of the first-level indicator is weighted according to the weight of the first-level indicator to obtain the comprehensive gray evaluation weight vector of vehicle A.

[0149] Assumption:

[0150] R1 = (0.2744, 0.363, 0.3226, 0.04);

[0151] R² = (0.3, 0.4, 0.2, 0.1);

[0152] R3 = (0.2, 0.3, 0.4, 0.1);

[0153] Primary indicator weights: W1=0.5, W2=0.3, W3=0.2;

[0154] The comprehensive grey evaluation weight vector for vehicle A is:

[0155] R = W1×R1 + W2×R2 + W3×R3= 0.5×(0.2744,0.363,0.3226,0.04) + 0.3×(0.3,0.4,0.2,0.1) + 0.2×(0.2,0.3,0.4,0.1)= (0.1372,0.1815,0.1613,0.02) +(0.09,0.12,0.06,0.03) + (0.04,0.06,0.08,0.02)= (0.2672, 0.3615, 0.3013,0.07);

[0156] This vector represents the degree to which vehicle A belongs to the four gray classes "excellent, good, average, and poor", with values ​​of approximately 0.2672, 0.3615, 0.3013, and 0.07, respectively.

[0157] Finally, the comprehensive gray evaluation weight vector is weighted by the center value (or score) of each gray class to obtain the comprehensive score of vehicle A.

[0158] Assuming the center values ​​(scores) of each gray class are: C1=95 (Excellent), C2=85 (Good), C3=75 (Average), C4=65 (Poor), then the overall score of vehicle A is:

[0159] Z = 0.2672×95 + 0.3615×85 + 0.3013×75 + 0.07×65= 83.259;

[0160] Therefore, vehicle A's overall score is 83.259.

[0161] Furthermore, this embodiment can combine time-of-use pricing and peak-valley pricing strategies during operation, prioritizing energy replenishment for high-demand vehicles during low-price periods and suppressing load peaks and achieving peak shifting and valley filling during high-price periods; for demand-based pricing scenarios, electricity cost optimization is achieved by setting peak penalty terms and maximum demand constraints.

[0162] In typical application scenarios, this embodiment can be used in bus charging stations, logistics park charging areas, park microgrids, and distributed charging networks. Taking a park-type charging station as an example, the system first establishes dynamic load constraints based on the station's real-time available power and time-of-use electricity price information. Upon vehicle access, its overall priority is immediately calculated. When electricity prices are low, the system tends to increase power allocation to vehicles with lower SOC or higher weights; during peak electricity price periods, it automatically limits the charging rate of non-critical vehicles to achieve peak shaving and valley filling. For sites with energy storage systems, the system simultaneously optimizes energy storage charging and discharging with vehicle power allocation to achieve energy self-balancing and minimize electricity costs at the station.

[0163] Step S13: Determine the real-time power allocation value of each vehicle to be charged in each rolling time window according to the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the charging priority, and charge each vehicle to be charged in an orderly manner according to the real-time power allocation value.

[0164] In this embodiment, within each rolling time-domain window, the instantaneous power setting value of each vehicle is obtained by performing time-segment optimization based on the vehicle's comprehensive score and weight ratio, combined with the total power constraint. The power setting command is then sent to the corresponding charging pile through the edge controller to realize dynamic power scheduling of each charging channel.

[0165] In this embodiment, the real-time power allocation value of each vehicle to be charged in each rolling time window is determined according to the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the priority. This includes: determining the total available power of the target charging station based on the power constraint parameters, and allocating power to each vehicle to be charged based on the minimum power demand corresponding to each vehicle to be charged; determining the remaining available power of the target charging station based on the total available power and the minimum power demand corresponding to each vehicle to be charged, and allocating the remaining available power to each vehicle to be charged in sequence according to the charging priority of each vehicle to be charged.

[0166] Specifically, this embodiment constructs a rolling optimization model for charging scheduling with weighted fairness as the objective, under constraints of total station power, equipment capacity, and safe operation boundary. The optimization objective adopts weighted maximum-min fairness or... – The fairness model maximizes overall power utilization and service fairness while ensuring that all charging vehicles receive at least the minimum service power.

[0167] Based on weighted maximum-minimum fairness (WMMF) or – The fairness model constructs a power allocation optimization objective function; when the total power at the station is insufficient, priority is given to ensuring that each vehicle receives an allocation value no less than the minimum service power, and within the remaining power range, it is allocated proportionally according to the comprehensive weight to achieve a balance between fairness and utilization. The power allocation method is as follows:

[0168] There are M electric vehicles that need to be charged, and each electric vehicle... The charging demand is The weight is The instantaneous total power capacity of the charging station is (i.e., total available power). This embodiment aims to maximize the minimum charging amount to achieve a trade-off between fairness and efficiency. The objective function can be expressed as:

[0169] ;

[0170] Input: Charging demand for electric vehicles Weight Total power capacity .

[0171] Output: Power allocated to each electric vehicle .

[0172] Define minimum power allocation as The problem of maximizing minimum power allocation can be represented by the following optimization problem: The conditions are: .

[0173] Initialize the weight w_i of each car, and initialize the power requirement of each car. Calculate the weighted power requirement for each vehicle. ,in The charging time.

[0174] Following the principle of weighted maximum and minimum fairness, priority is given to vehicles with higher power demands, and available power p is gradually allocated until the power demands of all vehicles are met or the maximum power P is reached. The specific steps are as follows:

[0175] Cars are ranked according to their power requirements. Sort the cars from largest to smallest, and let the sorted car indices be {1, 2, ..., m}. Initialize minimum power allocation. The power is p. Power is allocated starting with the first car in the sorted order. For the i-th car, the allocated power is: The allocated power must not exceed the vehicle's power requirements, while ensuring that the total power does not exceed [a certain limit]. Last update: minimum power allocation power. Update available power (i.e., remaining available power).

[0176] Repeat the above steps until the power requirements of all cars are met or the maximum power P is reached, at which point the result converges.

[0177] It should be noted that during execution, the communication interface is compatible with OCPP, Modbus, CAN or their equivalent protocols, and the time synchronization module is based on NTP or GPS timing mechanism; when the edge computing power is insufficient or the optimization solution timeout exceeds the preset threshold, cloud-edge collaborative solution is adopted or a safe allocation strategy is adopted to ensure real-time performance.

[0178] In addition, in this embodiment, the vehicle charging events in the target charging station can be monitored in real time. If the vehicle charging events in the target charging station change, the power rolling scheduling model is controlled to redetermine the real-time power allocation value of each vehicle to be charged within a future rolling time window, based on the charging demand parameters and charging priority of each vehicle to be charged.

[0179] In other words, the rolling optimization model adopts a time-domain sliding window mechanism. The optimization trigger conditions include new vehicle access, vehicle disconnection, SOC reaching the threshold, time-of-use pricing switching, equipment alarms, and changes in available power at the station. The optimization window length and step size are adaptively adjusted according to load fluctuations and calculation time limits, and support hot start to inherit the solution of the previous cycle to improve the solution speed.

[0180] Furthermore, this embodiment can periodically acquire the characteristic information corresponding to each charging pile in the target charging station using the target communication protocol; wherein, the characteristic information includes the instantaneous power and equipment status code information of the charging pile; based on each characteristic information, it is determined whether there is any abnormality in each charging pile in the target charging station; if any charging pile is abnormal, then that charging pile is disabled, and the power rolling scheduling model is controlled to redetermine the real-time power allocation value corresponding to each vehicle to be charged within a future rolling time window, based on the charging demand parameters and charging priority corresponding to each vehicle to be charged.

[0181] In other words, the execution layer communicates with the charging pile through the edge controller, and issues power, current and voltage settings according to a fixed period or event triggering period; and collects the pile-side status code, instantaneous power, SOC estimation and metering data in real time. When a communication abnormality or equipment failure is detected, a fast redistribution and fault bypass strategy is triggered to ensure continuous power supply to the remaining vehicles.

[0182] In addition, the power constraint parameters and the length of the rolling time window are adjusted using a preset human-machine interface so that when the vehicle charging time at the target charging station changes, the power allocation is performed using the adjusted power constraint parameters and the adjusted rolling time window.

[0183] In other words, this embodiment supports dynamically configurable parameters, including the station-level total power limit, transformer and circuit thresholds, time-of-use pricing tables, evaluation index sets and weights, minimum service power, optimization window and step size, bypass strategy thresholds, etc.; parameters can be added, deleted, modified, and distributed in a templated manner through local human-machine interface, Bluetooth, Ethernet, or cloud platform.

[0184] It should be noted that this embodiment can also set multi-level constraints, including upper limits for transformer capacity, upper limits for feeder and branch current, upper and lower limits for gun / module power, power ramp-up and ramp-down rates, temperature derating curves, power factor requirements, demand response commands, and coordination constraints with energy storage / PV grid-connected power, ensuring system operational safety and equipment protection. At sites containing energy storage systems or distributed power sources, the system incorporates energy storage SOC and charging / discharging power into unified optimization decision variables, coupling them with electric vehicle power allocation to achieve coordinated scheduling of energy storage and vehicles, improving service fairness and energy utilization.

[0185] In summary, the charging scheduling terminal corresponding to this embodiment includes a processor, a memory, a communication interface, a time synchronization module, and a power control interface; the memory contains program modules that can run on the processor, including a data acquisition module, an index normalization and weight calculation module, a comprehensive evaluation module, an optimization solution module, an execution and monitoring module, and a log auditing module.

[0186] Therefore, this application achieves unified quantitative representation of multi-source heterogeneous data by normalizing diverse charging demand parameters, overcoming the limitations of traditional single-indicator evaluation. By calculating target weights and constructing a vehicle scoring matrix through a preset weight evaluation model, it realizes comprehensive quantitative evaluation and priority ranking of differentiated vehicle charging demands, replacing the fixed proportion allocation rule. By combining power constraints, charging demands, and priorities to determine real-time power allocation values ​​within a rolling time window, it achieves dynamic and on-demand scheduling of multi-vehicle charging power under total power constraints, thereby improving overall resource utilization efficiency and allocation fairness while ensuring basic charging services.

[0187] See Figure 6 As shown, this embodiment of the invention discloses an orderly charging device for electric vehicles, applied to the Internet of Things, comprising:

[0188] The parameter normalization module 11 is used to obtain the charging demand parameters corresponding to each vehicle to be charged and the power constraint parameters of the target charging station, and to normalize the charging demand parameters of each type to obtain the corresponding normalized demand parameters.

[0189] The priority determination module 12 is used to calculate the target weights corresponding to the normalized demand parameters of each type using a preset weight evaluation model, construct a vehicle scoring matrix based on the target weights, and determine the charging priority corresponding to each vehicle to be charged based on the vehicle scoring matrix.

[0190] The vehicle charging module 13 is used to determine the real-time power allocation value of each vehicle to be charged in each rolling time window according to the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the charging priority, and to charge each corresponding vehicle to be charged in an orderly manner according to the real-time power allocation value.

[0191] In some specific embodiments, the priority determination module 12 may specifically include:

[0192] The weight determination unit is used to determine the subjective weights corresponding to each type of normalized demand parameter by using the preset weight evaluation model and the analytic hierarchy process, and to determine the objective weights corresponding to each type of normalized demand parameter by using the preset weight evaluation model and the entropy weight method.

[0193] The weight fusion unit is used to fuse each of the subjective weights and the corresponding objective weights to obtain the target fusion weights corresponding to each type of normalization requirement parameter.

[0194] In some specific embodiments, the vehicle charging module 13 may specifically include:

[0195] The total power determination unit is used to determine the available total power of the target charging station based on the power constraint parameters, and to allocate power to each of the vehicles to be charged based on the minimum power requirements corresponding to each vehicle to be charged.

[0196] A power allocation unit is used to determine the remaining available power of the target charging station based on the total available power and the minimum power requirement corresponding to each of the vehicles to be charged, and to allocate the remaining available power to each vehicle to be charged in sequence according to the charging priority of each vehicle.

[0197] In some specific embodiments, the electric vehicle orderly charging device further includes:

[0198] The charging event monitoring module is used to monitor vehicle charging events in the target charging station in real time. If the vehicle charging events in the target charging station change, the power rolling scheduling model is controlled to re-determine the real-time power allocation value of each vehicle to be charged within a future rolling time window, based on the charging demand parameters and charging priority of each vehicle to be charged.

[0199] In some specific embodiments, the electric vehicle orderly charging device further includes:

[0200] The feature information acquisition module is used to periodically acquire feature information corresponding to each charging pile in the target charging station using the target communication protocol; wherein, the feature information includes the instantaneous power and device status code information of the charging pile;

[0201] The charging pile disabling module is used to determine whether there is any abnormality in each charging pile in the target charging station based on the aforementioned feature information. If any charging pile is abnormal, the module disables that charging pile and controls the power rolling scheduling model to re-determine the real-time power allocation value corresponding to each of the vehicles to be charged within a future rolling time window, based on the charging demand parameters and charging priorities of each vehicle to be charged.

[0202] In some specific embodiments, the electric vehicle orderly charging device further includes:

[0203] The parameter adjustment module is used to adjust the power constraint parameters and the length of the rolling time window using a preset human-machine interface, so that when the vehicle charging time at the target charging station changes, power allocation can be performed using the adjusted power constraint parameters and the adjusted rolling time window.

[0204] Furthermore, embodiments of this application also disclose an electronic device, Figure 7This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0205] Figure 7 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the orderly charging method for electric vehicles disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0206] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0207] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0208] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the orderly charging method for electric vehicles executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0209] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned orderly charging method for electric vehicles. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0210] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0211] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0212] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0213] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0214] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for orderly charging of electric vehicles, characterized in that, Applications in the Internet of Things (IoT) include: Obtain the charging demand parameters corresponding to each vehicle to be charged and the power constraint parameters of the target charging station, and normalize the charging demand parameters of each type to obtain the corresponding normalized demand parameters. The target weights corresponding to the normalized demand parameters of each type are calculated using a preset weight evaluation model. A vehicle scoring matrix is ​​constructed based on the target weights, and the charging priority of each vehicle to be charged is determined based on the vehicle scoring matrix. Based on the power constraint parameters, the charging demand parameters corresponding to each of the vehicles to be charged, and the charging priority, the real-time power allocation value corresponding to each vehicle to be charged in each rolling time window is determined, and the vehicles to be charged are charged in an orderly manner according to the real-time power allocation values.

2. The orderly charging method for electric vehicles according to claim 1, characterized in that, The calculation of the target weights corresponding to each type of normalized demand parameter using a preset weight evaluation model includes: The subjective weights corresponding to the normalized demand parameters of each type are determined by using the preset weight evaluation model and the analytic hierarchy process, and the objective weights corresponding to the normalized demand parameters of each type are determined by using the preset weight evaluation model and the entropy weight method. The subjective weights and the corresponding objective weights are fused to obtain the target fusion weights corresponding to each type of normalized requirement parameter.

3. The orderly charging method for electric vehicles according to claim 1, characterized in that, The step of determining the real-time power allocation value for each vehicle to be charged within each rolling time window based on the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the charging priority includes: The total available power of the target charging station is determined based on the power constraint parameters, and power is allocated to each of the vehicles to be charged based on the minimum power requirements corresponding to each vehicle. The remaining available power of the target charging station is determined based on the total available power and the minimum power requirement corresponding to each of the vehicles to be charged, and the remaining available power is allocated to each vehicle to be charged in sequence according to the charging priority of each vehicle.

4. The orderly charging method for electric vehicles according to claim 1, characterized in that, Also includes: The vehicle charging events in the target charging station are monitored in real time. If the vehicle charging events in the target charging station change, the power rolling scheduling model is controlled to redetermine the real-time power allocation value of each vehicle to be charged within a future rolling time window, based on the charging demand parameters and charging priority of each vehicle to be charged.

5. The orderly charging method for electric vehicles according to claim 1, characterized in that, Also includes: The characteristic information corresponding to each charging pile in the target charging station is periodically obtained using the target communication protocol; wherein, the characteristic information includes the instantaneous power and equipment status code information of the charging pile; Based on the aforementioned feature information, it is determined whether any charging pile in the target charging station is abnormal. If any charging pile is abnormal, then that charging pile is disabled, and the power rolling scheduling model is controlled to re-determine the real-time power allocation value corresponding to each of the vehicles to be charged within a future rolling time window, based on the charging demand parameters and charging priorities corresponding to each vehicle to be charged.

6. The orderly charging method for electric vehicles according to any one of claims 1 to 5, characterized in that, Also includes: The power constraint parameters and the length of the rolling time window are adjusted using a preset human-machine interface so that when the vehicle charging time at the target charging station changes, power allocation is performed using the adjusted power constraint parameters and the adjusted rolling time window.

7. An orderly charging device for electric vehicles, characterized in that, Applications in the Internet of Things (IoT) include: The parameter normalization module is used to obtain the charging demand parameters corresponding to each vehicle to be charged and the power constraint parameters of the target charging station, and to normalize the charging demand parameters of each type to obtain the corresponding normalized demand parameters. The priority determination module is used to calculate the target weights corresponding to the normalized demand parameters of each type using a preset weight evaluation model, construct a vehicle scoring matrix based on the target weights, and determine the charging priority corresponding to each vehicle to be charged based on the vehicle scoring matrix. The vehicle charging module is used to determine the real-time power allocation value of each vehicle to be charged in each rolling time window according to the power constraint parameters, the charging demand parameters corresponding to each vehicle to be charged, and the charging priority, and to charge each vehicle to be charged in an orderly manner according to the real-time power allocation value.

8. The electric vehicle orderly charging device according to claim 7, characterized in that, The priority determination module includes: The weight determination unit is used to determine the subjective weights corresponding to each type of normalized demand parameter by using the preset weight evaluation model and the analytic hierarchy process, and to determine the objective weights corresponding to each type of normalized demand parameter by using the preset weight evaluation model and the entropy weight method. The weight fusion unit is used to fuse each of the subjective weights and the corresponding objective weights to obtain the target fusion weights corresponding to each type of normalization requirement parameter.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the electric vehicle orderly charging method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the orderly charging method for electric vehicles as described in any one of claims 1 to 6.

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