Electric vehicle multi-stage charging optimization scheduling method and device

Abstract

The invention is suitable for the technical field of electric vehicle scheduling, and provides an electric vehicle multi-stage charging optimization scheduling method and device, and the method comprises the steps: obtaining multi-stage constant-voltage charging data of an electric vehicle; based on the multi-stage constant-voltage charging data, constructing an electric vehicle charging model; based on the electric vehicle charging model and the personalized charging demand of the user, constructing a multi-stage modular package model, and based on the multi-stage modular package model, determining a charging package module of the user; constructing a charging scheduling model based on the charging package module of the user, and determining a preliminary charging strategy based on the charging scheduling model; optimizing the preliminary charging strategy based on a multivariate hybrid priority algorithm, and determining an optimized charging scheduling strategy of the electric vehicle; and the optimized charging scheduling strategy is used for scheduling multi-stage charging of the electric vehicle. According to the invention, the charging scheduling of the load aggregator can be realized, the optimization of the personalized charging demand of the user can be satisfied, the orderly charging of the electric vehicle is realized, and the power grid pressure is reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of electric vehicle scheduling, and in particular relates to a method and device for optimizing multi-stage charging scheduling of electric vehicles. Background Art

[0002] Promoting the use of electric vehicles (EVs) is one of the key approaches to achieving electric energy substitution. This approach contributes to a revolution in energy consumption and reduces environmental pollution. However, the uncontrolled charging of large numbers of EVs can increase peak-to-valley load variations, degrade power quality, reduce the economic efficiency of grid operation, and even threaten the safe and stable operation of the power system. EV batteries are capable of rapidly responding to signals, while most private EVs spend much of the day idle. If the charging and discharging behavior of EVs is regulated and controlled, EVs can participate in demand response as active loads. This not only prevents sudden load increases caused by uncontrolled large-scale vehicle charging but also achieves the goal of "peak shaving and valley filling."

[0003] At present, during the charging process of electric vehicles, the charging power changes, and the charging efficiency decreases rapidly after reaching the threshold. Electric vehicle users have few electricity price packages to choose from after connecting to the power grid, and their willingness to actively participate in power grid regulation is low. In addition, a large number of electric vehicle users passively connect to the power grid, resulting in a large number of electric vehicles being connected to the power grid in a disorderly manner, which in turn has a negative impact on the power grid. Summary of the Invention

[0004] To overcome the problems existing in the related technologies, the embodiments of the present application provide a method and device for optimizing the multi-stage charging scheduling of electric vehicles, which can realize the optimization of charging scheduling for load aggregators and meet the personalized charging needs of users, realize orderly charging of electric vehicles and reduce the pressure on the power grid.

[0005] This application is achieved through the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a multi-stage charging optimization scheduling method for electric vehicles, comprising:

[0007] Obtain multi-stage constant voltage charging data for electric vehicles;

[0008] Build an electric vehicle charging model based on multi-stage constant voltage charging data;

[0009] Based on the electric vehicle charging model and the user's personalized charging needs, a multi-stage modular package model is constructed, and based on the multi-stage modular package model, the user's charging package module is determined;

[0010] Based on the user's charging package module, a charging scheduling model is constructed, and a preliminary charging strategy is determined based on the charging scheduling model;

[0011] The preliminary charging strategy is optimized based on a multi-hybrid priority algorithm to determine the optimal charging scheduling strategy for electric vehicles; the optimized charging scheduling strategy is used to schedule multi-stage charging of electric vehicles.

[0012] In one embodiment, an electric vehicle charging model is constructed based on multi-stage constant voltage charging data, including:

[0013] Based on multi-stage constant voltage charging data, the charging process of electric vehicle batteries is divided into multiple stages; the multiple stages include the initial stage, the constant voltage stage and the end stage;

[0014] Determine the constant voltage value for each stage according to the battery status range;

[0015] Determine the charging current for each stage based on the initial current of the stage and the battery characteristics;

[0016] Determine the charging capacity of each stage based on the charging current of each stage;

[0017] An electric vehicle charging model is constructed based on the constant voltage value in each stage, the charging current in each stage, and the charging capacity in each stage.

[0018] In one embodiment, the constraints of the electric vehicle charging model include voltage range constraints, current range limitations, and state of charge constraints.

[0019] In one embodiment, a multi-stage modular package model is constructed based on the electric vehicle charging model and the user's personalized charging needs, including:

[0020] Determine the load status of the power grid at multiple stages based on the electric vehicle charging model;

[0021] Determining a plurality of first charging package modules based on load conditions of the power grid at multiple stages;

[0022] Determining multiple second charging package modules based on the user's personalized charging needs;

[0023] Determine multiple third-party charging package modules based on the aggregator's own needs;

[0024] The first charging package module, the second charging package module and the third charging package module are combined to form a multi-stage electricity price package configuration module set; the multi-stage electricity price package configuration module set forms a multi-stage modular package model for the user.

[0025] In one embodiment, determining a user's charging package module based on a multi-stage modular package model includes:

[0026] Calculate the correlation between the user's personalized charging needs and each charging package module in the multi-stage electricity price package configuration module set;

[0027] Based on the relevance, determine the charging package module that meets the user's personalized charging needs.

[0028] In one embodiment, a charging scheduling model is constructed based on the user's charging package module, including:

[0029] Determine user demand response based on the user's charging package module;

[0030] Determine user satisfaction based on responses to user needs;

[0031] A charging scheduling model is constructed based on user satisfaction and load aggregator's revenue.

[0032] In one embodiment, the objective function of the charging scheduling model is expressed as:

[0033]

[0034] Among them, I is the daily operating income of the power sales company; Q θ (t) is the charging amount of user θ in period t; Q0(t) is the contract charging amount of all users of the power sales company in period t; Q0′(t) is the unplanned charging amount of the user in period t; P θ (t) is the electricity price of user θ in period t; C0′ is the average contract purchase cost per kilowatt-hour of the electricity sales company after using the electricity price package to sell electricity services; C T The average electricity purchase cost per kilowatt-hour for the electricity sales company to replenish electricity in the day-ahead market; C v is the daily fixed operating cost of the electricity price package; C0 is the original electricity purchase cost; C x 、C B are negotiation cost and compensation cost respectively; τ is the market share of the power sales company; γ is the average change rate of the total load of the power sales company's users; Q is the total contracted electricity purchase volume.

[0035] In one embodiment, the preliminary charging strategy is optimized based on a multivariate hybrid priority algorithm to determine an optimized charging scheduling strategy for electric vehicles, including:

[0036] Based on the charging characteristics of the charging package modules selected by electric vehicle users at different stages in the preliminary charging strategy, the highest response ratio algorithm is used to divide different users and identify users with different levels of urgency in charging needs;

[0037] Using the particle swarm optimization algorithm, target electric vehicles are selected from users with different degrees of urgency in charging needs for charging; the target electric vehicles will not occupy the charging needs of other users.

[0038] In one embodiment, the urgency is represented by a response ratio; the response ratio R p The calculation formula is expressed as:

[0039]

[0040] Among them, t i,s is the entry time of the user who chooses the i-th charging package module, t i,e is the departure time of the user who chooses the i-th charging package module, t i,chrg Charging time for users who choose the i-th charging package module.

[0041] In a second aspect, the present application provides a multi-stage charging optimization scheduling device for electric vehicles, which applies the multi-stage charging optimization scheduling method for electric vehicles of the first aspect, including:

[0042] A data acquisition module, used to acquire multi-stage constant voltage charging data of electric vehicles;

[0043] A charging model building module is used to build an electric vehicle charging model based on multi-stage constant voltage charging data;

[0044] A charging package determination module is used to build a multi-stage modular package model based on the electric vehicle charging model and the user's personalized charging needs, and determine the user's charging package module based on the multi-stage modular package model;

[0045] A preliminary charging strategy determination module is used to build a charging scheduling model based on the user's charging package module and determine a preliminary charging strategy based on the charging scheduling model;

[0046] The scheduling optimization module is used to optimize the preliminary charging strategy based on the multi-hybrid priority algorithm and determine the optimized charging scheduling strategy for electric vehicles; the optimized charging scheduling strategy is used to schedule the multi-stage charging of electric vehicles.

[0047] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0048] In an embodiment of the present application, under a multi-stage constant voltage charging mode (MS-CV) of electric vehicles, multi-stage constant voltage charging data of electric vehicles is obtained, and an electric vehicle user charging model is constructed. By performing autonomous and selective optimization scheduling on electric vehicle users based on consideration of multi-stage electric vehicle charging power attenuation, an optional multi-stage modular package model is provided to users. Then, users participate in demand response through the selected charging package module, a charging scheduling evaluation model is constructed, and finally, an electric vehicle optimized charging scheduling strategy using a multi-hybrid priority algorithm is constructed to realize the optimization of load aggregator charging scheduling and meet the personalized charging needs of users, thereby achieving orderly charging of electric vehicles and alleviating the pressure on the power grid.

[0049] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 This is a schematic diagram of an application scenario of the multi-stage charging optimization scheduling method for electric vehicles provided by an embodiment of the present application;

[0052] Figure 2 This is a flow chart of a multi-stage charging optimization scheduling method for electric vehicles provided in one embodiment of the present application;

[0053] Figure 3 It is a structural diagram of a multi-stage charging optimization scheduling device for electric vehicles provided in one embodiment of the present application. DETAILED DESCRIPTION

[0054] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0055] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0056] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0057] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0058] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0059] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0060] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] Reference Figure 1 Figure 2 shows an application scenario diagram for the multi-stage charging optimization scheduling method for electric vehicles of the present invention. This method is applied to scenarios where community electric vehicle users have varying time requirements and proposes a strategy for users to independently select packages and participate in grid scheduling optimization. Due to the dispersed locations, small capacity, and high randomness of electric vehicles, directly integrating them into the grid control system is difficult. This requires electric vehicle aggregators to play the role of intermediary and bridge. Electric vehicle aggregators 102 aggregate dispersed electric vehicle resources to form electric vehicle clusters 103 with a certain scale and regulatory capabilities, making them more easily dispatchable and controllable by the grid. Furthermore, electric vehicle aggregators 102 can also formulate reasonable charging strategies and power control schemes based on market demand and the status of the grid control center 101 to achieve optimized regulation of electric vehicle charging behavior.

[0062] Figure 2 This is a flow chart of a multi-stage charging optimization scheduling method for electric vehicles provided by an embodiment of the present application, with reference to Figure 2 , the detailed description of the multi-stage charging optimization scheduling method for electric vehicles is as follows:

[0063] The present application provides a multi-stage charging optimization scheduling method for electric vehicles, including:

[0064] Step 201: Acquire multi-stage constant voltage charging data of an electric vehicle.

[0065] The multi-stage constant voltage charging data may include the battery charging state, battery voltage, charging current and power, etc.

[0066] Step 202: construct an electric vehicle charging model based on the multi-stage constant voltage charging data.

[0067] To construct a theoretical model for electric vehicle charging under the multi-stage constant voltage (MS-CV) charging method, it is necessary to establish a complete and rigorous theoretical framework, starting from the division of charging stages, the variation patterns of current and power, and the calculation of charging capacity. The core concept of the multi-stage constant voltage charging method (MS-CV) is to divide the charging process of electric vehicle batteries into multiple stages, maintain a constant voltage value in each stage, and dynamically adjust the charging current based on the battery's charge state and voltage characteristics, thereby optimizing charging efficiency, reducing thermal effects, and extending battery life.

[0068] Step 203: construct a multi-stage modular package model based on the electric vehicle charging model and the user's personalized charging needs, and determine the user's charging package module based on the multi-stage modular package model.

[0069] To meet the charging needs of diverse electric vehicle users while optimizing charging station resource utilization and economic efficiency, a modular user selection architecture based on multi-stage electricity price packages is proposed. This architecture focuses on diverse user needs and power load balancing. By using a multi-stage constant voltage charging method, users are offered different electricity price packages at different charging stages. This provides users with flexible charging options, taking into account their charging preferences, time constraints, and cost sensitivity.

[0070] Step 204: construct a charging scheduling model based on the user's charging package module, and determine a preliminary charging strategy based on the charging scheduling model.

[0071] To secure market share and attract users to demand response, load aggregators need to appropriately lower charging prices when offering tariff packages. However, to protect their own profits, load aggregators can enter into bilateral contracts with the power grid. By leveraging user participation in demand response, load aggregators can help alleviate peak load pressure on the grid and reduce generation costs, thereby negotiating lower electricity purchase prices with the grid. By creating attractive tariff packages, load aggregators can guide users to adjust their charging times and establish a preliminary charging demand profile. Furthermore, based on factors such as grid load conditions and price fluctuations, load aggregators can dynamically adjust charging scheduling to ensure a balanced supply and demand.

[0072] Step 205 : Optimizing the preliminary charging strategy based on the multi-element hybrid priority algorithm to determine the optimal charging scheduling strategy for the electric vehicle.

[0073] Among them, the optimized charging scheduling strategy is used to schedule multi-stage charging of electric vehicles.

[0074] While satisfying the interests of load aggregators, users are guided to actively participate in demand response through pricing, reducing the pressure on power grid production and scheduling, and achieving a win-win situation for the power grid, load aggregators and users.

[0075] In this embodiment, multi-stage constant voltage charging data of electric vehicles is obtained under the multi-stage constant voltage charging mode (MS-CV) of electric vehicles, and an electric vehicle user charging model is constructed. By performing autonomous and selective optimization scheduling for electric vehicle users based on the consideration of multi-stage electric vehicle charging power attenuation, an optional multi-stage modular package model is provided to users. Users then participate in demand response through the selected charging package module, and a charging scheduling evaluation model is constructed. Finally, an electric vehicle optimized charging scheduling strategy using a multi-hybrid priority algorithm is constructed to achieve optimization of load aggregator charging scheduling and meet the personalized charging needs of users, thereby achieving orderly charging of electric vehicles and alleviating power grid pressure.

[0076] In one embodiment, the specific steps of constructing an electric vehicle charging model based on multi-stage constant voltage charging data are introduced. Step 202 includes:

[0077] Step 2021: Based on the multi-stage constant voltage charging data, the charging process of the electric vehicle battery is divided into multiple stages.

[0078] The multiple stages include an initial stage, a constant pressure stage and an end stage.

[0079] For example, in the initial stage, the battery voltage is low, the charging current is large, and the charging power is high; in the constant voltage stage, the voltage remains constant, the charging current gradually decreases, and the battery charging process tends to be stable; finally, in the final stage, the charging current gradually approaches zero, and the battery is close to a fully charged state. The changing pattern of current and power is key to ensuring charging efficiency and battery safety. By adjusting the current at different stages, battery overheating can be effectively avoided, the impact of thermal effects can be reduced, and the battery life can be extended. In terms of charging capacity calculation, by monitoring the battery's charging status in real time, the amount of electricity required for each stage can be accurately calculated to avoid overcharging or undercharging the battery, thereby improving the system's charging efficiency and battery health.

[0080] Step 2022: Determine a constant voltage value for each stage according to the battery status range.

[0081] In the multi-stage constant voltage charging method (MS-CV), the charging process is divided into several stages, each stage corresponds to a constant voltage value V const,i The specific value is determined by the battery state (SOC) range and battery characteristics. When the SOC is 0%-50%, a higher voltage V const,1 Fast charging; when SOC reaches 50%-80%, the voltage is adjusted to the medium level V const,2 To reduce charging heat loss; when SOC exceeds 80%, the voltage is reduced to V const,3 and protects the battery by precisely controlling the current.

[0082] Step 2023: Determine the charging current for each stage based on the initial current of the stage and the battery characteristics.

[0083] During the constant voltage stage, the charging current I(t) gradually decays over time due to changes in the chemical reactions within the battery. Its variation can be described by an exponential decay model or a power law decay model, expressed as:

[0084]

[0085] Among them, I init,k is the initial current of stage k, α k This dynamic current adjustment not only optimizes battery charging efficiency but also reduces the risk of overcharging and overheating.

[0086] In step 2024 , the charging capacity of each stage is determined based on the charging current of each stage.

[0087] The charging capacity Q in each stage i By integrating the current over time, we can calculate:

[0088]

[0089] Among them, T k is the duration of the kth stage. The total charging capacity is the accumulation of the capacity of each stage, that is, In addition, through the power formula P k (t) = V const,k I(t) calculates the instantaneous charging power, which can further analyze the energy conversion and distribution during the charging process.

[0090] In order to evaluate the safety and efficiency of multi-stage constant voltage charging, the model needs to analyze the battery heating situation. int The existence of heating power P heat,k (t) can be expressed as:

[0091] P heat,k (t) = R int I 2 (t) (3)

[0092] In actual applications, the heat generation decreases as the current decays. This thermal management mechanism helps prevent the battery from overheating.

[0093] Step 2025: construct an electric vehicle charging model based on the constant voltage value of each stage, the charging current of each stage, and the charging capacity of each stage.

[0094] Exemplarily, the constraints of the electric vehicle charging model include voltage range constraints, current range constraints, and state of charge constraints.

[0095] The voltage range constraint is: V min ≤V const,i ≤V max ; The current range is constrained to: 0≤I(t)≤I max The state of charge constraint is: 0 ≤ SOC(t) ≤ 1. These constraints ensure the safety and efficiency of the charging process.

[0096] This embodiment divides the charging process into multiple stages and adjusts the charging voltage and current according to the characteristics of each stage, ensuring that the battery can be charged in the most optimized manner under different conditions, thereby improving charging efficiency. Dynamic current adjustment methods, such as exponential decay models or power law decay models, not only optimize charging efficiency but also significantly reduce the risks of overcharging and overheating, further improving the safety of the charging process.

[0097] Using different voltages and currents during charging at different stages can effectively prevent battery overheating and reduce thermal damage, thereby extending the battery life. Precisely controlling current and voltage can also reduce battery cycle losses and improve long-term battery performance.

[0098] The constructed electric vehicle charging model can also automatically adjust the charging strategy based on the actual battery conditions and constraints, ensuring the safety and efficiency of the charging process. By calculating the charging capacity and instantaneous charging power at each stage, it can further analyze the energy conversion and distribution during the charging process, providing data support for optimizing charging strategies.

[0099] In one embodiment, in step 203, a multi-stage modular package model is constructed based on the electric vehicle charging model and the user's personalized charging needs, including:

[0100] Step 2031: Determine the load status of the power grid in multiple stages based on the electric vehicle charging model.

[0101] Based on the characteristics of the grid load and real-time electricity price fluctuations in the power market, the charging process is divided into multiple time periods, with different pricing strategies designed for each time period. During periods of low grid load (such as late at night), low-priced packages are offered to encourage users to charge during off-peak hours. During periods of high grid load (such as the evening peak), high-priced packages are set up to use price leverage to guide users to avoid peak times. Each price package is modularly designed, including parameters such as charging time period, unit price, and power limit, allowing users to choose the package that suits their needs.

[0102] Step 2032: Determine multiple first charging package modules based on the load conditions of the power grid in multiple stages.

[0103] Step 2033: Determine multiple second charging package modules based on the user's personalized charging needs.

[0104] Step 2034: Determine multiple third charging package modules based on the aggregator's own needs.

[0105] For example, in steps 2032-2034, existing electricity price packages are typically designed based on the overall electricity usage habits and needs of traditional electricity users, primarily considering the daily electricity usage patterns of households or businesses, such as peak and off-peak demand. However, the charging behavior of electric vehicle (EV) users differs significantly from that of traditional electricity users. EV users' charging needs are more personalized, influenced by factors such as charging time, charging speed, and power quality. When selecting a multi-stage charging module, load aggregators need to comprehensively consider grid demand, their own needs, and the individual needs of users to customize different packages suitable for EV charging. Specifically, aggregators select appropriate charging stages and packages based on the grid's load conditions at different time periods to balance grid supply and demand fluctuations, reduce peak load, and avoid resource waste during off-peak periods. Aggregators also need to consider their own economic interests, including electricity procurement costs and the revenue model of electricity prices, to optimize grid operations while ensuring profits. Furthermore, aggregators need to consider users' individual charging needs, such as charging duration, cost, and speed, to ensure that the selected packages best meet their charging habits and needs. Therefore, in this process, aggregators not only need to dynamically adjust package configurations according to the needs of the power grid and the market, but also need to formulate flexible electricity price strategies at different stages to adapt to changing load demand and user preferences.

[0106] Step 2035: Combine the first charging package module, the second charging package module, and the third charging package module to form a multi-stage electricity price package configuration module set; the multi-stage electricity price package configuration module set forms a multi-stage modular package model for the user.

[0107] The package module is represented by a set as U = {M1, M2, ..., M n}, where n represents the number of package modules, M i represents the i-th package module, 1≤i≤n, where each module represents a charging demand scenario or service function, aiming to meet the diverse needs of EV users through flexible combination.

[0108] Exemplarily, in step 203, based on the multi-stage modular package model, determining the user's charging package module includes:

[0109] Step 2036 , calculating the correlation between the user's personalized charging demand and each charging package module in the multi-stage electricity price package configuration module set.

[0110] For example, the user's frequently used charging information is collected to obtain different user needs R = {R1, R2, ..., R m}, m represents the number of user demands. EV charging services are designed to meet user demands and demand response requirements. Load aggregators represent the service functions of different packages in the form of a set F = {F1, F2, ..., F s}, s represents the number of package service functions.

[0111] The purpose of modularizing the selection of multi-stage packages is to meet the charging needs of users at different stages of electric vehicle charging. User needs are quantified to achieve the selection criteria of one or several packages at different charging stages of electric vehicles. This can help us understand the relationship between user needs and package modules at different stages. The correlation calculation formula between user charging needs and a service function of a package is:

[0112]

[0113] Among them, λ R,Fd For users' total charging needs and any package service function F d The correlation between (1≤d≤s), For users' arbitrary charging needs R b (1≤b≤m) and the service function F of the package d The correlation between Rb For demand R b The corresponding weight.

[0114] The correlation calculation formula between different package modules and package service functions is:

[0115]

[0116] in, For all package service functions and package module M i The correlation between For any package service function F d With package module M i The correlation between ω Fd Package service function F in the module when designing a package d The corresponding weight.

[0117] In summary, we can obtain the comprehensive correlation vector between all package modules and user charging needs, taking into account user needs and package design weight distribution:

[0118]

[0119] Among them, D is the comprehensive correlation vector between all package modules and the charging needs of a certain user.

[0120] Step 2037: Based on the relevance, determine a charging package module that meets the user's personalized charging needs.

[0121] Users have different charging needs, and the comprehensive correlation vectors between them and the package modules are also different. According to the comprehensive correlation, we can find a package module that better meets the user's needs to reasonably configure the user's personalized electricity price package.

[0122] In one embodiment, in step 204, a charging scheduling model is constructed based on the user's charging package module, including:

[0123] Step 2041: Determine the user demand response based on the user's charging package module.

[0124] For example, based on the design and configuration of the above-mentioned charging package module, with the goal of satisfying the benefits of all parties involved in electricity trading, while satisfying the interests of load aggregators, users are guided to actively participate in demand response through prices, thereby reducing the pressure on power grid production and scheduling.

[0125] After users choose to participate in demand response through electricity price packages, their load curves can be obtained.

[0126]

[0127] in, represents the load value of the θth user who chooses the jth package at time t, represents the original load value of the θth type of user at time t, and j is the package number (j = 1, 2, ...). Select the electricity price of the jth package in period t for the θth category user, and P0(t) is the original electricity price in period t.

[0128] Step 2042: Determine user satisfaction based on the user demand response.

[0129] For example, to secure market share and attract users to participate in demand response, load aggregators need to appropriately lower charging prices when offering electricity packages. However, to protect their own profits, load aggregators can sign bilateral contracts with the power grid. By leveraging user participation in demand response, load aggregators can help alleviate peak load pressure on the power grid and reduce generation costs, thereby negotiating with the power grid to lower the purchase price. Furthermore, the load change rate γ can serve as a key indicator for evaluating the effectiveness of user demand response.

[0130]

[0131] Where T is the number of daily load data samples.

[0132] Step 2043: construct a charging scheduling model based on user satisfaction and load aggregation benefits.

[0133] For example, after the introduction of the demand response strategy, the source of revenue for load aggregators changes. The revenue directly obtained from the electricity bills paid by users has decreased, but because demand response effectively alleviates the peak operation pressure of the power grid, aggregators can obtain more indirect revenue by reducing the cost of purchasing electricity, thereby optimizing the overall profit goal. The objective function of the charging scheduling model that meets the load aggregator's revenue is expressed as:

[0134]

[0135] Among them, I is the daily operating income of the power sales company; Q θ (t) is the charging amount of user θ in period t; Q0(t) is the contract charging amount of all users of the power sales company in period t; Q0′(t) is the unplanned charging amount of the user in period t; P θ (t) is the electricity price of user θ in period t; C0′ is the average contract purchase cost per kilowatt-hour of the electricity sales company after using the electricity price package to sell electricity services; C T The average electricity purchase cost per kilowatt-hour for the electricity sales company to replenish electricity in the day-ahead market; C v is the daily fixed operating cost of the electricity price package; C0 is the original electricity purchase cost; C x 、C B Where τ represents the market share of the electricity retailer; γ represents the average load change rate of the electricity retailer's customers; and Q represents the total contracted electricity purchase volume. The cost of supplementary electricity purchases is determined by the day-ahead market. The load aggregator's electricity purchase cost is determined in the bilaterally negotiated contract between the company and the power grid and is generally related to the average load change rate and market share of the customers it serves.

[0136] Illustratively, the objective function of the charging scheduling model that satisfies the load aggregator's profit determines the preliminary charging strategy.

[0137] This embodiment takes the diversification of user needs and the balance of power load as the core, and provides users with different electricity price packages at different charging stages through a charging method based on multi-stage constant voltage. It provides users with flexible charging options based on the user's charging preferences, time constraints, and cost sensitivity. According to the load characteristics of the power grid and the real-time electricity price fluctuations in the power market, the charging process is divided into multiple time periods, and different electricity price strategies are designed for each time period. During the period of low load on the power grid (such as late at night), low-price packages are provided to encourage users to charge off-peak; during the period of high load on the power grid (such as the evening peak), high-price packages are set to guide users to avoid peak periods through price leverage. Each electricity price package is modularly designed, including parameters such as charging period, unit electricity price, power upper limit, etc. Users can choose a suitable package according to their own needs.

[0138] In one embodiment, a process of optimizing a preliminary charging strategy based on a multivariate hybrid priority algorithm to determine an optimized charging scheduling strategy for electric vehicles is described. Step 205 includes:

[0139] Step 2051 : Based on the charging characteristics of the charging package modules selected by electric vehicle users at different stages in the preliminary charging strategy, the highest corresponding ratio algorithm is used to divide different users and determine users with different degrees of urgency in charging needs.

[0140] For example, when providing charging services for electric vehicles (EVs), load aggregators must consider both the fairness of charging and the urgency of user needs. Given that charging needs vary among users, some may have more urgent charging needs, and load aggregators must prioritize these users accordingly.

[0141] To this end, using the Highest Response Ratio Optimization (HRPT) algorithm as a scheduling strategy is an effective approach. This algorithm combines users' charging needs with their selected charging packages, assessing each user's urgency and thereby prioritizing charging needs. Load aggregators first categorize users' needs based on their charging packages and prioritize users with higher urgency, ensuring they can complete their charging tasks as quickly as possible. Users with less urgent needs are then scheduled according to their package's charging plan, thus avoiding wasted resources.

[0142] In practice, for users with higher urgency, load aggregators will tailor charging schedules based on their power requirements and charging schedules. Users who choose fast-charging plans will be prioritized, while those who choose standard plans may have their charging scheduled later. This flexible scheduling mechanism allows load aggregators to effectively balance user fairness and charging urgency, while improving the overall efficiency of the charging system.

[0143] The urgency is characterized by the response ratio; the response ratio R p The calculation formula is expressed as:

[0144]

[0145] Among them, t i,s is the entry time of the user who chooses the i-th charging package module, t i,e is the departure time of the user who chooses the i-th charging package module, t i,chrg Charging time for users who choose the i-th charging package module.

[0146] In step 2052, a particle swarm optimization algorithm is used to select target electric vehicles for charging from users with different urgency levels of charging needs.

[0147] At the same time, it is ensured that the target electric vehicle will not occupy the charging needs of other users.

[0148] Particle swarm optimization (PSO), as a powerful optimization technology, has been widely used to solve complex problems in various engineering fields. Compared with other evolutionary random algorithms, PSO is more efficient in dealing with certain types of optimization problems, especially when the solution space is large or there are many constraints, it can effectively find the global optimal solution. In the charging scheduling of electric vehicles, in order to ensure that high-urgency users can complete charging as soon as possible, the particle swarm optimization algorithm is introduced into the charging scheduling of electric vehicles. At this stage, the algorithm makes full use of the fact that the adjustable capacity of electric vehicles among high-urgency users is relatively small, that is, for these users, the flexibility of charging amount and time is relatively low, so more refined and rapid charging scheduling is required. Based on the particle swarm optimization algorithm, the system selects electric vehicles from high-urgency users for charging in turn, ensuring that they can complete charging in the shortest time without having too much impact on the charging needs of other users. The calculation formula of the particle swarm optimization algorithm is as follows:

[0149] ν h,l =round(wν h,l-1 +c1r1(p best -x h,l-1 )+c2r2(g best -x h,l-1 )) (11)

[0150] x h,l =x h,l-1 +ν h,l (12)

[0151] Among them, round represents a random number between 0 and 1; ν h,l and x h,l are the velocity and position of the hth particle at the lth iteration respectively; x h,l-1 is the particle position of the hth particle at the l-1th iteration; p best , g best They represent the individual optimal solution and the global optimal solution in each iteration process, respectively. w is the inertia factor; c1 is the individual learning factor, c2 is the population learning factor; r1 and r2 are both random parameters.

[0152] This embodiment fully considers the charging characteristics of electric vehicles at different scheduling stages, combines it with a maximum response ratio optimization algorithm, and determines the charging priority of electric vehicles based on the different plans selected by users. For users with a high charging urgency, a particle swarm optimization algorithm is used for further scheduling optimization, thereby improving overall scheduling efficiency.

[0153] By utilizing a multi-hybrid priority algorithm, the system not only prioritizes those users who urgently need charging, taking into account EV charging needs, but also balances the economic benefits for load aggregators and the load balance of the power grid. This process prioritizes users with urgent charging needs, ensuring their charging needs are promptly addressed, thereby improving user satisfaction. At the same time, through targeted optimized scheduling, load aggregators can achieve more accurate and efficient resource scheduling, thereby improving overall efficiency. Ultimately, this optimization strategy achieves a balance between user demand and grid load during EV charging scheduling, maximizing user satisfaction and generating higher economic benefits for load aggregators.

[0154] The electric vehicle multi-stage charging optimization scheduling method of the embodiment of the present application analyzes the charging characteristics of the electric vehicle multi-stage constant voltage charging mode (MS-CV), and then combines the MS-CV charging mode to obtain the multi-stage constant voltage charging data of the electric vehicle under the electric vehicle multi-stage constant voltage charging mode, construct an electric vehicle user charging model, and provide users with an optional multi-stage modular package model by performing autonomous and selective optimization scheduling on the basis of considering the multi-stage electric vehicle charging power attenuation. Then, the user participates in demand response through the selected charging package module, and a charging scheduling evaluation model is constructed based on user satisfaction requirements and load aggregator economic benefit requirements. Finally, an electric vehicle optimized charging scheduling strategy using a multi-hybrid priority algorithm is constructed to achieve optimization of load aggregator charging scheduling and meet the personalized charging needs of users, thereby achieving orderly charging of electric vehicles and alleviating power grid pressure.

[0155] See also Figure 3 An embodiment of the present application provides an electric vehicle multi-stage charging optimization scheduling device, which applies the electric vehicle multi-stage charging optimization scheduling method as described in the above embodiment, including a data acquisition module 301, a charging model construction module 302, a charging package determination module 303, a preliminary charging strategy determination module 304 and a scheduling optimization module 305.

[0156] The data acquisition module 301 is used to acquire multi-stage constant voltage charging data of the electric vehicle;

[0157] A charging model building module 302 is used to build an electric vehicle charging model based on multi-stage constant voltage charging data;

[0158] The charging package determination module 303 is used to build a multi-stage modular package model based on the electric vehicle charging model and the user's personalized charging needs, and determine the user's charging package module based on the multi-stage modular package model;

[0159] A preliminary charging strategy determination module 304 is configured to construct a charging scheduling model based on the user's charging package module and determine a preliminary charging strategy based on the charging scheduling model;

[0160] The scheduling optimization module 305 is used to optimize the preliminary charging strategy based on the multi-element hybrid priority algorithm and determine the optimized charging scheduling strategy for the electric vehicle; the optimized charging scheduling strategy is used to schedule the multi-stage charging of the electric vehicle.

[0161] In one embodiment, the charging model building module 302 is specifically configured to:

[0162] Based on multi-stage constant voltage charging data, the charging process of electric vehicle batteries is divided into multiple stages; the multiple stages include the initial stage, the constant voltage stage and the end stage;

[0163] Determine the constant voltage value for each stage according to the battery status range;

[0164] Determine the charging current for each stage based on the initial current of the stage and the battery characteristics;

[0165] Determine the charging capacity of each stage based on the charging current of each stage;

[0166] An electric vehicle charging model is constructed based on the constant voltage value in each stage, the charging current in each stage, and the charging capacity in each stage.

[0167] Exemplarily, the constraints of the electric vehicle charging model include voltage range constraints, current range constraints, and state of charge constraints.

[0168] In one embodiment, in the charging package determination module 303, a multi-stage modular package model is constructed based on the electric vehicle charging model and the user's personalized charging needs, including:

[0169] Determine the load status of the power grid at multiple stages based on the electric vehicle charging model;

[0170] Determining a plurality of first charging package modules based on load conditions of the power grid at multiple stages;

[0171] Determining multiple second charging package modules based on the user's personalized charging needs;

[0172] Determine multiple third-party charging package modules based on the aggregator's own needs;

[0173] The first charging package module, the second charging package module and the third charging package module are combined to form a multi-stage electricity price package configuration module set; the multi-stage electricity price package configuration module set forms a multi-stage modular package model for the user.

[0174] In one embodiment, in the charging package determination module 303, the user's charging package module is determined based on the multi-stage modular package model, including:

[0175] Calculate the correlation between the user's personalized charging needs and each charging package module in the multi-stage electricity price package configuration module set;

[0176] Based on the relevance, determine the charging package module that meets the user's personalized charging needs.

[0177] In one embodiment, in the preliminary charging strategy determination module 304, a charging scheduling model is constructed based on the user's charging package module, including:

[0178] Determine user demand response based on the user's charging package module;

[0179] Determine user satisfaction based on responses to user needs;

[0180] A charging scheduling model is constructed based on user satisfaction and load aggregator's revenue.

[0181] For example, the objective function of the charging scheduling model is expressed as:

[0182]

[0183] Among them, I is the daily operating income of the power sales company; Q θ (t) is the charging amount of user θ in period t; Q0(t) is the contract charging amount of all users of the power sales company in period t; Q0′(t) is the unplanned charging amount of the user in period t; P θ (t) is the electricity price of user θ in period t; C0′ is the average contract purchase cost per kilowatt-hour of the electricity sales company after using the electricity price package to sell electricity services; C T The average electricity purchase cost per kilowatt-hour for the electricity sales company to replenish electricity in the day-ahead market; C v is the daily fixed operating cost of the electricity price package; C0 is the original electricity purchase cost; C x 、C B are negotiation cost and compensation cost respectively; τ is the market share of the power sales company; γ is the average change rate of the total load of the power sales company's users; Q is the total contracted electricity purchase volume.

[0184] In one embodiment, in the scheduling optimization module 305, the preliminary charging strategy is optimized based on the multivariate hybrid priority algorithm to determine the optimal charging scheduling strategy for the electric vehicle, including:

[0185] Based on the charging characteristics of the charging package modules selected by electric vehicle users at different stages in the preliminary charging strategy, the highest response ratio algorithm is used to divide different users and identify users with different levels of urgency in charging needs;

[0186] Using the particle swarm optimization algorithm, target electric vehicles are selected from users with different degrees of urgency in charging needs for charging; the target electric vehicles will not occupy the charging needs of other users.

[0187] Exemplarily, the urgency is characterized by a response ratio; the response ratio R p The calculation formula is expressed as:

[0188]

[0189] Among them, t i,s is the entry time of the user who chooses the i-th charging package module, t i,e is the departure time of the user who chooses the i-th charging package module, t i,chrg Charging time for users who choose the i-th charging package module.

[0190] For the beneficial effects of a multi-stage charging optimization scheduling device for electric vehicles, please refer to the beneficial effects of a multi-stage charging optimization scheduling method for electric vehicles.

[0191] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0192] It should be noted that although the detailed description above mentions several units / modules or sub-units / modules of the electric vehicle multi-stage charging optimization scheduling device, this division is merely exemplary and not mandatory. In fact, depending on the implementation of the present application, the features and functions of two or more units / modules described above can be embodied in a single unit / module. Conversely, the features and functions of a single unit / module described above can be further divided and embodied by multiple units / modules.

[0193] Furthermore, although the operations of the method of the present application are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0194] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When a processor executes the computer-executable instructions, the electric vehicle multi-stage charging optimization scheduling method provided in the above embodiment of the present application is implemented.

[0195] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0196] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A multi-stage charging optimization scheduling method for electric vehicles, characterized in that: include: Obtain multi-stage constant voltage charging data for electric vehicles; Building an electric vehicle charging model based on the multi-stage constant voltage charging data; Building a multi-stage modular package model based on the electric vehicle charging model and the user's personalized charging needs, and determining the user's charging package module based on the multi-stage modular package model; Building a charging scheduling model based on the user's charging package module, and determining a preliminary charging strategy based on the charging scheduling model; The preliminary charging strategy is optimized based on a multi-element hybrid priority algorithm to determine an optimized charging scheduling strategy for the electric vehicle; the optimized charging scheduling strategy is used to schedule multi-stage charging of the electric vehicle.

2. The electric vehicle multi-stage charging optimization scheduling method according to claim 1, characterized in that: The method of constructing an electric vehicle charging model based on the multi-stage constant voltage charging data includes: Based on the multi-stage constant voltage charging data, the charging process of the electric vehicle battery is divided into multiple stages; the multiple stages include an initial stage, a constant voltage stage and an end stage; Determine the constant voltage value for each stage according to the battery status range; Determine the charging current for each stage based on the initial current of the stage and the battery characteristics; Determining the charging capacity of each stage based on the charging current of each stage; The electric vehicle charging model is constructed based on the constant voltage value of each stage, the charging current of each stage, and the charging capacity of each stage.

3. The multi-stage charging optimization scheduling method for electric vehicles according to claim 2, characterized in that: The constraints of the electric vehicle charging model include voltage range constraints, current range limitations, and state of charge constraints.

4. The electric vehicle multi-stage charging optimization scheduling method according to claim 1, characterized in that: The multi-stage modular package model is constructed based on the electric vehicle charging model and the user's personalized charging needs, including: Determining the load status of the power grid in multiple stages based on the electric vehicle charging model; Determining a plurality of first charging package modules based on the load conditions of the power grid in multiple stages; Determining multiple second charging package modules based on the user's personalized charging needs; Determine multiple third-party charging package modules based on the aggregator's own needs; The first charging package module, the second charging package module and the third charging package module are combined to form a multi-stage electricity price package configuration module set; the multi-stage electricity price package configuration module set forms a multi-stage modular package model for the user.

5. The electric vehicle multi-stage charging optimization scheduling method according to claim 4, characterized in that: Determining the user's charging package module based on the multi-stage modular package model includes: Calculating the correlation between the user's personalized charging demand and each charging package module in the multi-stage electricity price package configuration module set; Based on the correlation, a charging package module that meets the personalized charging needs of the user is determined.

6. The electric vehicle multi-stage charging optimization scheduling method according to claim 1, characterized in that: The user-based charging package module constructs a charging scheduling model, including: Determining a user demand response based on the user's charging package module; determining user satisfaction based on the user demand responses; Based on the user satisfaction and the revenue of the load aggregator, a charging scheduling model is constructed.

7. The electric vehicle multi-stage charging optimization scheduling method according to claim 1, characterized in that: The objective function of the charging scheduling model is expressed as: Among them, I is the daily operating income of the power sales company; Q θ (t) is the charging amount of user θ in period t; Q0(t) is the contract charging amount of all users of the power sales company in period t; Q0′(t) is the unplanned charging amount of the user in period t; P θ (t) is the electricity price of user θ in period t; C0′ is the average contract purchase cost per kilowatt-hour of the electricity sales company after using the electricity price package to sell electricity services; C T The average electricity purchase cost per kilowatt-hour for the electricity sales company to replenish electricity in the day-ahead market; C v is the daily fixed operating cost of the electricity price package; C0 is the original electricity purchase cost; C x 、C B are negotiation cost and compensation cost respectively; τ is the market share of the power sales company; γ is the average change rate of the total load of the power sales company's users; Q is the total contracted electricity purchase volume.

8. The electric vehicle multi-stage charging optimization scheduling method according to claim 1, characterized in that: The method of optimizing the preliminary charging strategy based on the multivariate hybrid priority algorithm and determining the optimized charging scheduling strategy for the electric vehicle includes: Based on the charging characteristics of the charging package modules selected by electric vehicle users at different stages in the preliminary charging strategy, different users are divided using the highest corresponding ratio algorithm to determine users with different degrees of urgency in charging needs; By using a particle swarm optimization algorithm, target electric vehicles are selected from users with different degrees of urgency in charging needs for charging; the target electric vehicles will not occupy the charging needs of other users.

9. The electric vehicle multi-stage charging optimization scheduling method according to claim 8, characterized in that: The urgency is characterized by a response ratio; the response ratio R p The calculation formula is expressed as: Among them, t i,s is the entry time of the user who chooses the i-th charging package module, t i,e is the departure time of the user who chooses the i-th charging package module, t i,chrg Charging time for users who choose the i-th charging package module.

10. A multi-stage charging optimization scheduling device for electric vehicles, characterized in that: The method for optimizing the multi-stage charging of an electric vehicle according to any one of claims 1 to 9 is applied, comprising: A data acquisition module, used to acquire multi-stage constant voltage charging data of electric vehicles; A charging model building module, configured to build an electric vehicle charging model based on the multi-stage constant voltage charging data; A charging package determination module is used to build a multi-stage modular package model based on the electric vehicle charging model and the user's personalized charging needs, and determine the user's charging package module based on the multi-stage modular package model; A preliminary charging strategy determination module is used to build a charging scheduling model based on the user's charging package module and determine a preliminary charging strategy based on the charging scheduling model; The scheduling optimization module is used to optimize the preliminary charging strategy based on a multi-element hybrid priority algorithm to determine the optimized charging scheduling strategy for the electric vehicle; the optimized charging scheduling strategy is used to schedule multi-stage charging of the electric vehicle.

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