Emergency regulation and control strategy for electric vehicle

Abstract

The invention discloses an emergency regulation and control strategy for an electric vehicle. The emergency regulation and control strategy comprises the following steps: (1) dividing into a plurality of areas according to requirements and the position of a charging pile; (2) arranging an information network layer capable of executing strategies and cloud computing, wherein the information network layer comprises a central cloud layer and an edge cloud layer; (3) the central cloud layer receives the power grid data and the traffic data, and obtains a load loss amount and an economic loss according to the power grid data and a load power loss assessment method; (4) the edge cloud layer receives the driving EV data, the user travel plan data and the charging pile data and sends the data to the central cloud layer; (5) the central cloud layer intelligently distributes a charging and discharging task of each charging pile through a charging and discharging general task and an intelligent task distribution strategy, decomposes the charging and discharging tasks into regulation and control instructions and sends the regulation and control instructions to the edge cloud layer; and (6) the edge cloud layer sends corresponding regulation and control instructions to the electric vehicle and the charging pile at the same time according to the regulation and control instruction of the central cloud layer. Normal operation of the whole electric automobile parking system can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency control of electric vehicles, and in particular to an emergency control strategy for electric vehicles. Background Art

[0002] The V2G emergency response control strategy is an intelligent management solution that rapidly balances power supply and demand and ensures stable grid operation by real-time scheduling of electric vehicle charging and discharging during sudden grid load surges, fluctuations in renewable energy generation, or other emergency situations. The core mechanisms of this emergency control strategy include: 1. A dynamic response mechanism: Based on real-time grid data (such as frequency fluctuations and load gaps), the aggregation platform issues charging and discharging instructions to connected electric vehicles, prioritizing high-capacity vehicles for reverse discharge to provide emergency power support to specific areas or equipment. 2. A tiered dispatching system, comprising a global layer: the grid dispatch center issues regional charging and discharging demand signals based on grid-wide load gap forecasts; an aggregation layer: third-party operators use intelligent algorithms to match available vehicle resources and generate charging and discharging schedules; and a terminal layer: the onboard battery management system (BMS) and charging piles coordinate charging and discharging operations to ensure battery safety thresholds are not breached. 3. An economic incentive model: This utilizes mechanisms such as peak-valley electricity price amplification and emergency response subsidies. For example, pilot projects in Beijing have shown that vehicle owners can earn 10-15 yuan per emergency discharge, with annual cumulative benefits exceeding 4,000 yuan. This strategy has been applied in demonstration stations in Shanghai, Henan and other places, achieving seamless control through the Internet of Vehicles platform, allowing a single vehicle to complete the mode switch from charging to reverse discharge within 30 seconds.

[0003] However, in the current field of electric vehicle emergency control, the use of V2G emergency response control strategy still has the following problems:

[0004] Users use electric vehicles mainly for commuting between work and home. They have their own special driving habits and plans, and of course they also have new driving intentions. The existing electric vehicle emergency control cannot take into account the possibility of insufficient charging or excessive charging fees. If the power grid fails, is under maintenance, or is hit by a disaster, the existing electric vehicle emergency control is not timely and cannot guarantee the normal operation of the entire electric vehicle parking system. Summary of the Invention

[0005] In order to overcome the above-mentioned shortcomings of the prior art, the present invention proposes an electric vehicle emergency control strategy. By setting up an execution strategy and an information network layer of cloud computing, receiving power grid data and traffic data, it is possible to evaluate power outage losses. The electric vehicle emergency control of the present invention takes into account the possibility of insufficient charging or excessive charging fees; if the power grid fails, is under maintenance or is hit by a disaster, the existing electric vehicle emergency control is timely and can ensure the normal operation of the entire electric vehicle parking system.

[0006] The technical solution adopted by the present invention to solve the technical problem is: an electric vehicle emergency control strategy, comprising the following steps:

[0007] (1) Divide into several areas based on demand and charging pile locations;

[0008] (2) Arrange an information network layer capable of executing strategies and cloud computing, the information network layer including a central cloud layer and an edge cloud layer; the central cloud layer is used to receive power grid data and traffic network data; the power grid data includes disaster-stricken nodes and power supply requirements; the central cloud layer is also used to exchange data with the edge cloud layer and send control instructions to the edge cloud layer; the edge cloud layer is used to extract the spatiotemporal distribution characteristics of EV users, and the edge cloud layer is also used to receive driving EV data, user travel plan data, and charging pile data, and send control instructions to electric vehicles and charging piles respectively;

[0009] (3) The central cloud layer receives power grid data and traffic data, and obtains the load loss amount and economic loss based on the power grid data and the load loss loss assessment method; and obtains the total charging and discharging task based on the traffic data and the global optimization strategy for power supply restoration;

[0010] (4) The edge cloud layer receives EV driving data, user travel plan data, and charging pile data and sends the above data to the central cloud layer; the edge cloud layer ensures that the charging cost is minimized under the constraints of the owner's electricity demand through the user market price incentive-charge and discharge response mechanism: calculates the regional dispatchability; and executes the regional dispatch optimization strategy;

[0011] (5) The central cloud layer receives traffic data in real time. The central cloud layer intelligently allocates the charging and discharging tasks of each charging pile through the total charging and discharging tasks and the intelligent task allocation strategy, and decomposes the charging and discharging tasks into control instructions and sends them to the edge cloud layer;

[0012] (6) The edge cloud layer sends corresponding control instructions to electric vehicles and charging piles at the same time according to the control instructions of the central cloud layer.

[0013] Furthermore, the load power loss assessment method includes the following steps:

[0014] Calculate power outage loss L oc =SP×p×α; where SP is the power shortage, p is the average electricity price, and α is the electricity price multiplier reflecting the specified loss level;

[0015] Based on the power outage losses, the probability of damage to power facilities in the affected nodes and the probability of damage to other power facilities due to cascading effects are assessed, and a neural network model with pre-trained power facilities and probabilities is used to predict the load loss and economic losses after the power outage. The neural network model is trained using a large number of data sets, which include power facilities and probabilities and the correctly calculated load loss and economic losses. The input of the neural network model is the load loss and economic losses.

[0016] Furthermore, the global optimization strategy for power restoration includes the following steps: key areas are set from the region; a theoretically optimal charging plan is formulated based on global information, and local scheduling dynamically adjusts charging and discharging power according to real-time data to prioritize power restoration in key areas;

[0017] The optimal charging plan specifically involves constructing a mathematical model with the goal of minimizing power outage losses and charging costs, and includes the following steps:

[0018] (1) Establishing an electric vehicle charging, discharging and cost model:

[0019]

[0020] Where: t0 and t1 represent the arrival and departure times of the electric vehicle, and both arrival and departure occur at the beginning of the time; E0 is the remaining power of the electric vehicle at the beginning of charging and discharging, E t With E t+1 are the SOC values ​​at time t and t+1 respectively; η a and η b The energy conversion efficiency of electric vehicle batteries during charging and discharging; and They are the maximum power of charge and discharge respectively; Δt is the duration of each charging action; μ t is the charging and discharging power of electric vehicles, when μ t When P > 0, it means the electric vehicle is charging, otherwise it means discharging; t is the electricity price at time t, v t is the total cost;

[0021] (2) Calculate the total charging cost W and power outage loss F, then the objective function is set as:

[0022] min(α·W+β·F), where α is the weight of the total charging cost and β is the weight of the power outage loss.

[0023] Furthermore, the extraction of EV user spatiotemporal distribution characteristics specifically includes the following steps:

[0024] Data preprocessing: EV charging record data is filled with missing values ​​and corrected for outliers using the mean imputation method to ensure consistency between timestamps and geographic coordinates; the GPS coordinates of EV user trajectories are converted and matched with the road network GIS data;

[0025] Edge feature extraction: Cloud and ground cover are segmented based on the normalized water index to generate a binary mask. The BEV model is deployed to generate a bird's-eye view of the road environment by fusing multi-camera data obtained from the traffic network.

[0026] Spatiotemporal coupling analysis phase: The Monte Carlo method is used to randomly generate EV driving path and charging strategy combination scenarios to simulate user travel patterns; a road network-distribution network coupling model is constructed to quantify the impact of traffic congestion on the spatiotemporal migration of charging load; a spatiotemporal cube model is used to correlate cloud movement trajectories with charging station load fluctuation trends; and an edge service grid architecture is introduced to achieve real-time data feedback and dynamically update feature extraction parameters.

[0027] Furthermore, the method for formulating the excitation-charge-discharge response mechanism includes the following steps:

[0028] Establish a tiered incentive mechanism based on local incentive policies for supplying power to the grid;

[0029] Establish a charging and discharging model based on the peak, valley, and normal periods of real-time electricity prices; this charging and discharging model integrates time-of-use electricity prices and SOC status for collaborative decision-making;

[0030] Implement a staggered sharing system for charging parking spaces;

[0031] Formulate V2G grid connection technical specifications.

[0032] Furthermore, the calculation of the regional schedulability includes the following steps:

[0033] The regions are divided into active energy storage regions and passive response regions according to the real-time SOC mean and regional distribution network capacity margin;

[0034] Establish a charge and discharge model, specifically:

[0035]

[0036] in, and are the total charging power and total discharging power of charging pile j in the region during period t, respectively; and are respectively the charging scheduling power and discharging scheduling power of electric vehicle n in period t, is the set of electric vehicles in charging pile j in the region; T is the scheduling time set;

[0037] Quantify the dispatchability indicators and calculate the equivalent energy storage capacity, load regulation rate and cross-regional support capability index.

[0038] Furthermore, the execution regional scheduling optimization strategy establishes a regional scheduling optimization strategy table, which is provided with the corresponding range of all data obtained in the step of calculating the regional dispatchable capacity and the associated scheduling optimization strategy, and can obtain the scheduling optimization strategy by comparing the calculated regional dispatchable capacity.

[0039] Furthermore, the intelligent task allocation strategy is specifically to decompose tasks according to the scheduling optimization strategy, which can allocate charging and discharging tasks to each charging pile and decompose the charging and discharging tasks into control instructions and send them to the edge cloud layer.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] This invention proposes an emergency control strategy for electric vehicles. This strategy divides demand and charging station locations into several zones, deploying an information network layer capable of executing strategies and cloud computing. The central cloud layer receives power grid and traffic network data. Load loss and economic losses are determined based on power grid data and a load loss loss assessment method. The overall charging and discharging tasks are determined based on traffic data and a global power restoration optimization strategy, and corresponding control instructions are sent to electric vehicles and charging stations. This emergency control strategy for electric vehicles takes into account the possibility of insufficient charging or excessive charging fees. If the power grid fails, undergoes maintenance, or is struck by a disaster, the existing emergency control strategy for electric vehicles is timely, ensuring the normal operation of the entire electric vehicle parking system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0043] Figure 1 This is a flow chart of an electric vehicle emergency control strategy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the present invention.

[0046] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be internal communication between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0047] like Figure 1 As shown, an electric vehicle emergency control strategy according to an embodiment of the present invention includes the following steps:

[0048] (1) Divide into several areas based on demand and charging pile locations;

[0049] (2) Arrange an information network layer capable of executing strategies and cloud computing, the information network layer including a central cloud layer and an edge cloud layer; the central cloud layer is used to receive power grid data and traffic network data; the power grid data includes disaster-stricken nodes and power supply requirements; the central cloud layer is also used to exchange data with the edge cloud layer and send control instructions to the edge cloud layer; the edge cloud layer is used to extract the spatiotemporal distribution characteristics of EV users, and the edge cloud layer is also used to receive driving EV data, user travel plan data, and charging pile data, and send control instructions to electric vehicles and charging piles respectively;

[0050] (3) The central cloud layer receives power grid data and traffic data, and obtains the load loss amount and economic loss based on the power grid data and the load loss loss assessment method; and obtains the total charging and discharging task based on the traffic data and the global optimization strategy for power supply restoration;

[0051] (4) The edge cloud layer receives EV driving data, user travel plan data, and charging pile data and sends the above data to the central cloud layer; the edge cloud layer ensures that the charging cost is minimized under the constraints of the owner's electricity demand through the user market price incentive-charge and discharge response mechanism: calculates the regional dispatchability; and executes the regional dispatch optimization strategy;

[0052] (5) The central cloud layer receives traffic data in real time. The central cloud layer intelligently allocates the charging and discharging tasks of each charging pile through the total charging and discharging tasks and the intelligent task allocation strategy, and decomposes the charging and discharging tasks into control instructions and sends them to the edge cloud layer;

[0053] (6) The edge cloud layer sends corresponding control instructions to electric vehicles and charging piles at the same time according to the control instructions of the central cloud layer.

[0054] The load power loss assessment method includes the following steps:

[0055] Calculate power outage loss L oc =SP×p×α; where SP is the power shortage, p is the average electricity price, and α is the electricity price multiplier reflecting the specified loss level;

[0056] Based on the power outage losses, the probability of damage to power facilities in the affected nodes and the probability of damage to other power facilities due to cascading effects are assessed, and a neural network model with pre-trained power facilities and probabilities is used to predict the load loss and economic losses after the power outage. The neural network model is trained using a large number of data sets, which include power facilities and probabilities and the correctly calculated load loss and economic losses. The input of the neural network model is the load loss and economic losses.

[0057] The global optimization strategy for power restoration includes the following steps: Key areas are identified within the region; a theoretically optimal charging plan is developed based on global information; and local scheduling dynamically adjusts charging and discharging power based on real-time data, prioritizing power restoration in key areas.

[0058] The optimal charging plan is to build a mathematical model with the goal of minimizing power outage losses and charging costs, including the following steps:

[0059] (1) Establishing an electric vehicle charging, discharging and cost model:

[0060]

[0061] Where: t0 and t1 represent the arrival and departure times of the electric vehicle, and both arrival and departure occur at the beginning of the time; E0 is the remaining power of the electric vehicle at the beginning of charging and discharging, E t With E t+1 are the SOC values ​​at time t and t+1 respectively; η a and η b The energy conversion efficiency of electric vehicle batteries during charging and discharging; and They are the maximum power of charge and discharge respectively; Δt is the duration of each charging action; μ t is the charging and discharging power of electric vehicles, when μ t When P > 0, it means the electric vehicle is charging, otherwise it means discharging;t is the electricity price at time t, v t is the total cost;

[0062] (2) Calculate the total charging cost W and power outage loss F, then the objective function is set as:

[0063] min(α·W+β·F), where α is the weight of the total charging cost and β is the weight of the power outage loss.

[0064] Extracting the spatiotemporal distribution characteristics of EV users specifically includes the following steps:

[0065] Data preprocessing: EV charging record data is filled with missing values ​​and corrected for outliers using the mean imputation method to ensure consistency between timestamps and geographic coordinates; the GPS coordinates of EV user trajectories are converted and matched with the road network GIS data;

[0066] Edge feature extraction: Cloud and ground cover are segmented based on the normalized water index to generate a binary mask. The BEV model is deployed to generate a bird's-eye view of the road environment by fusing multi-camera data obtained from the traffic network.

[0067] Spatiotemporal coupling analysis phase: The Monte Carlo method is used to randomly generate EV driving path and charging strategy combination scenarios to simulate user travel patterns; a road network-distribution network coupling model is constructed to quantify the impact of traffic congestion on the spatiotemporal migration of charging load; a spatiotemporal cube model is used to correlate cloud movement trajectories with charging station load fluctuation trends; and an edge service grid architecture is introduced to achieve real-time data feedback and dynamically update feature extraction parameters.

[0068] The method for formulating the excitation-charge-discharge response mechanism includes the following steps:

[0069] Establish a tiered incentive mechanism based on local incentive policies for supplying power to the grid;

[0070] Establish a charging and discharging model based on the peak, valley, and normal periods of real-time electricity prices; this charging and discharging model integrates time-of-use electricity prices and SOC status for collaborative decision-making;

[0071] Implement a staggered sharing system for charging parking spaces;

[0072] Formulate V2G grid connection technical specifications.

[0073] Calculating the regional schedulability includes the following steps:

[0074] The regions are divided into active energy storage regions and passive response regions according to the real-time SOC mean and regional distribution network capacity margin;

[0075] Establish a charge and discharge model, specifically:

[0076]

[0077] in, and are the total charging power and total discharging power of charging pile j in the region during period t, respectively; and are respectively the charging scheduling power and discharging scheduling power of electric vehicle n in period t, is the set of electric vehicles in charging pile j in the region; T is the scheduling time set;

[0078] Quantify the dispatchability indicators and calculate the equivalent energy storage capacity, load regulation rate and cross-regional support capability index.

[0079] Execute the regional scheduling optimization strategy and establish a regional scheduling optimization strategy table. The regional scheduling optimization strategy table is provided with the corresponding range of all data obtained in the step of calculating the regional dispatchable capacity and the associated scheduling optimization strategy, and the scheduling optimization strategy can be obtained by comparing the calculated regional dispatchable capacity.

[0080] The intelligent task allocation strategy specifically decomposes tasks according to the scheduling optimization strategy. This task can allocate the charging and discharging tasks to each charging pile and decompose the charging and discharging tasks into control instructions and send them to the edge cloud layer.

[0081] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An electric vehicle emergency control strategy, characterized in that: The following steps are involved: (1) Divide into several areas based on demand and charging pile locations; (2) Arrange an information network layer capable of executing strategies and cloud computing, the information network layer including a central cloud layer and an edge cloud layer; the central cloud layer is used to receive power grid data and traffic network data; the power grid data includes disaster-stricken nodes and power supply requirements; the central cloud layer is also used to exchange data with the edge cloud layer and send control instructions to the edge cloud layer; the edge cloud layer is used to extract the spatiotemporal distribution characteristics of EV users, and the edge cloud layer is also used to receive driving EV data, user travel plan data, and charging pile data, and send control instructions to electric vehicles and charging piles respectively; (3) The central cloud layer receives power grid data and traffic data, and obtains the load loss amount and economic loss based on the power grid data and the load loss loss assessment method; and obtains the total charging and discharging task based on the traffic data and the global optimization strategy for power supply restoration; (4) The edge cloud layer receives EV driving data, user travel plan data, and charging pile data and sends the above data to the central cloud layer; The edge cloud layer ensures that the charging cost is minimized under the constraints of the owner's electricity demand through the user market price incentive-charge and discharge response mechanism: calculate the regional dispatchability; implement the regional dispatch optimization strategy; (5) The central cloud layer receives traffic data in real time. The central cloud layer intelligently allocates the charging and discharging tasks of each charging pile through the total charging and discharging tasks and the intelligent task allocation strategy, and decomposes the charging and discharging tasks into control instructions and sends them to the edge cloud layer; (6) The edge cloud layer sends corresponding control instructions to electric vehicles and charging piles at the same time according to the control instructions of the central cloud layer.

2. An electric vehicle emergency control strategy according to claim 1, characterized in that: The load power loss assessment method comprises the following steps: Calculate power outage loss L oc =SP×p×α; where SP is the power shortage, p is the average electricity price, and α is the electricity price multiplier reflecting the specified loss level; Based on the power outage losses, the probability of damage to power facilities in the affected nodes and the probability of damage to other power facilities due to cascading effects are assessed, and a neural network model with pre-trained power facilities and probabilities is used to predict the load loss and economic losses after the power outage. The neural network model is trained using a large number of data sets, which include power facilities and probabilities and the correctly calculated load loss and economic losses. The input of the neural network model is the load loss and economic losses.

3. The electric vehicle emergency control strategy according to claim 1, characterized in that: The global optimization strategy for power restoration includes the following steps: Key areas are identified within the region; a theoretically optimal charging plan is developed based on global information; and local scheduling dynamically adjusts charging and discharging power based on real-time data, prioritizing power restoration in key areas. The optimal charging plan specifically involves constructing a mathematical model with the goal of minimizing power outage losses and charging costs, and includes the following steps: (1) Establishing an electric vehicle charging, discharging and cost model: Where: t0 and t1 represent the arrival and departure times of the electric vehicle, and both arrival and departure occur at the beginning of the time; E0 is the remaining power of the electric vehicle at the beginning of charging and discharging, E t With E t+1 are the SOC values ​​at time t and t+1 respectively; η a and η b The energy conversion efficiency of electric vehicle batteries during charging and discharging; and They are the maximum power of charge and discharge respectively; Δt is the duration of each charging action; μ t is the charging and discharging power of electric vehicles, when μ t When P > 0, it means the electric vehicle is charging, otherwise it means discharging; t is the electricity price at time t, v t is the total cost; (2) Calculate the total charging cost W and power outage loss F, then the objective function is set as: min(α·W+β·F), where α is the weight of the total charging cost and β is the weight of the power outage loss.

4. The electric vehicle emergency control strategy according to claim 1, characterized in that: The extraction of EV user spatiotemporal distribution characteristics specifically includes the following steps: Data preprocessing: EV charging record data is filled with missing values ​​and corrected for outliers using the mean imputation method to ensure consistency between timestamps and geographic coordinates; the GPS coordinates of EV user trajectories are converted and matched with the road network GIS data; Edge feature extraction: Cloud and ground cover are segmented based on the normalized water index to generate a binary mask. The BEV model is deployed to generate a bird's-eye view of the road environment by fusing multi-camera data obtained from the traffic network. Spatiotemporal coupling analysis phase: The Monte Carlo method is used to randomly generate EV driving path and charging strategy combination scenarios to simulate user travel patterns; a road network-distribution network coupling model is constructed to quantify the impact of traffic congestion on the spatiotemporal migration of charging load; a spatiotemporal cube model is used to correlate cloud movement trajectories with charging station load fluctuation trends; and an edge service grid architecture is introduced to achieve real-time data feedback and dynamically update feature extraction parameters.

5. The electric vehicle emergency control strategy according to claim 1, characterized in that: The method for formulating the excitation-charge-discharge response mechanism comprises the following steps: Establish a tiered incentive mechanism based on local incentive policies for supplying power to the grid; Establish a charging and discharging model based on the peak, valley, and normal periods of real-time electricity prices; this charging and discharging model integrates time-of-use electricity prices and SOC status for collaborative decision-making; Implement a staggered sharing system for charging parking spaces; Formulate V2G grid connection technical specifications.

6. The electric vehicle emergency control strategy according to claim 1, characterized in that: The calculation of regional schedulability includes the following steps: The regions are divided into active energy storage regions and passive response regions according to the real-time SOC mean and regional distribution network capacity margin; Establish a charge and discharge model, specifically: in, and are the total charging power and total discharging power of charging pile j in the region during period t, respectively; and are respectively the charging scheduling power and discharging scheduling power of electric vehicle n in period t, is the set of electric vehicles in charging pile j in the region; T is the scheduling time set; Quantify the dispatchability indicators and calculate the equivalent energy storage capacity, load regulation rate and cross-regional support capability index.

7. The electric vehicle emergency control strategy according to claim 1, characterized in that: The execution area scheduling optimization strategy establishes a regional scheduling optimization strategy table, which is provided with the corresponding range of all data obtained in the step of calculating the regional dispatchable capacity and the associated scheduling optimization strategy, and can obtain the scheduling optimization strategy by comparing the calculated regional dispatchable capacity.

8. The electric vehicle emergency control strategy according to claim 1, characterized in that: The intelligent task allocation strategy specifically decomposes tasks according to the scheduling optimization strategy, which can allocate charging and discharging tasks to each charging pile and decompose the charging and discharging tasks into control instructions and send them to the edge cloud layer.

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