A method for generating an electric vehicle charging and discharging scheduling strategy

Abstract

This invention relates to the field of electric vehicle charging and discharging scheduling technology, and discloses a method for generating electric vehicle charging and discharging scheduling strategies. Under grid unavailability conditions, regional edge computing nodes act as anchor nodes, establishing a hierarchical self-organizing network with each electric vehicle. These nodes receive locally assessed and anonymized health level tags and available power from each electric vehicle. When an emergency charging request is received, the anchor node generates a pairing scheduling instruction based on the health level tag and available power, prioritizing electric vehicles that meet the power supply requirements as the power supplier to perform point-to-point charging and recording the transaction in the local ledger. After charging is completed, the node obtains health level tag change information and updates the pairing rules. After the grid is restored, the ledger transaction information is uploaded to the cloud for settlement, and the electric vehicle is controlled to exit emergency scheduling and switch to normal mode. This method achieves orderly offline emergency charging and discharging scheduling based on battery health status awareness without relying on the grid or centralized cloud scheduling.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging and discharging scheduling technology, and specifically to a method for generating electric vehicle charging and discharging scheduling strategies. Background Technology

[0002] Vehicle-to-Grid (V2G) technology enables electric vehicles to participate in grid interaction as distributed energy storage units. Charging during off-peak hours and feeding back energy to the grid during peak hours can achieve multiple benefits, including peak shaving and valley filling, and promote the consumption of renewable energy. With the rapid growth of electric vehicle ownership and the gradual popularization of V2G charging infrastructure, charging and discharging scheduling strategies have become a core research topic in the field of vehicle-to-grid interaction.

[0003] Current charging and discharging dispatching technologies generally assume that the power grid is in normal operation, relying on the power grid as an intermediary channel for energy transmission and cloud servers as a centralized computing and dispatching decision-making platform. However, when the distribution network becomes isolated from the main grid due to equipment maintenance, line faults, or extreme weather, forming a microgrid island, the aforementioned dispatching mechanisms relying on the power grid and cloud become ineffective, and electric vehicles within the area cannot engage in orderly energy sharing. Existing energy management research for isolated microgrids mainly focuses on fixed energy storage devices, lacking emergency dispatching solutions tailored to the mobility and battery health status of electric vehicles.

[0004] Therefore, how to establish a localized communication and collaboration mechanism between electric vehicles without grid support and cloud services; and how to protect users' sensitive battery data privacy while rationally allocating power supply tasks according to battery health status to avoid batteries in poor health bearing excessive power supply loads have become urgent technical challenges in this field. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a method for generating electric vehicle charging and discharging scheduling strategies. This method aims to improve the problems of existing technologies where the scheduling mechanism completely fails under grid unavailability conditions, and where differentiated emergency charging and discharging scheduling based on battery health status cannot be achieved while protecting user privacy.

[0006] A method for generating electric vehicle charging and discharging scheduling strategies, applied to a preset area including regional edge computing nodes and multiple electric vehicles with bidirectional charging and discharging capabilities, characterized in that, under the condition of grid unavailability, it includes: S1. After the edge computing node detects the power grid unavailability condition, it acts as an anchor node to establish a hierarchical self-organizing network with each electric vehicle in the preset area and establishes a communication connection. S2. Each electric vehicle locally assesses the battery health status and generates a health level label. The desensitized health level label and the current available power are uploaded to the anchor node through the communication connection. The health level label at least distinguishes between meeting the power supply requirements and not meeting the power supply requirements. S3. When an emergency charging request initiated by any electric vehicle is received, the anchor node generates a scheduling instruction based on the received health level tags and available power according to a preset pairing rule. S4. The electric vehicle identified as the power supplier by the scheduling instruction and the depleted electric vehicle that initiated the emergency charging request perform point-to-point charging operations according to the scheduling instruction, and the anchor node records the electricity transaction information of this charging to the local ledger. S5. After the charging operation is completed, obtain the real-time health level tag change information of the power supply party and the power-deficient party, and update the preset pairing rules accordingly. S6. When the edge computing node of the region detects that the power grid has been restored, it uploads the electricity transaction information in the local ledger to the cloud system for settlement, and controls each of the electric vehicles to exit the emergency dispatch and switch to the charging and discharging dispatch strategy in the normal power grid mode.

[0007] Furthermore, in S1, a hierarchical self-organizing network is established, and communication connections are established, including: After detecting a power grid interruption, the regional edge computing node switches to emergency dispatch mode and broadcasts a network beacon to the preset area. After receiving the network construction beacon, each of the electric vehicles sends a network access request to the regional edge computing node, and the network access request carries the node identification information of the vehicle. The edge computing node of the region registers the electric vehicle that sent the network access request as a mobile node according to the received network access requests, and registers itself as an anchor node, thereby establishing a hierarchical self-organizing network with the anchor node as the upper-layer node and each of the mobile nodes as the lower-layer node; The anchor node and each of the mobile nodes establish a communication connection through a preset short-range wireless communication protocol.

[0008] Furthermore, in S2, each of the electric vehicles locally assesses its battery health status and generates a health level label, including: Each of the electric vehicles collects voltage data of each cell in the battery pack through its own battery management system, and obtains a cell consistency evaluation index that characterizes the voltage consistency between each cell based on the voltage data; Each electric vehicle compares the cell consistency evaluation index with multiple preset consistency level thresholds, and generates the health level label based on the comparison results; The health level label includes at least a first level and a second level. The cell consistency evaluation index corresponding to the first level meets the preset power supply safety requirements, while the cell consistency evaluation index corresponding to the second level does not meet the preset power supply safety requirements.

[0009] Furthermore, in step S2, uploading the de-identified health level tag and the current available power to the anchor node via the communication connection includes: Each of the electric vehicles locally discretizes the generated health level label to remove the specific numerical information in the cell consistency evaluation index, and obtains the desensitized health level label. Each electric vehicle uploads the desensitized health level label and the current available power to the anchor node via the communication connection.

[0010] Furthermore, the step of obtaining the cell consistency evaluation index characterizing the voltage consistency among individual cells based on the voltage data includes: Calculate the voltage deviation between the voltage value of each individual cell and the average voltage value of the battery pack, and use the statistical value of the voltage deviation as the cell consistency evaluation index. Calculate the voltage ratio between the voltage value of each individual cell and the average voltage value of the battery pack, and determine the cell consistency evaluation index based on the statistical distribution characteristics of the voltage ratio; Alternatively, the voltage correlation coefficient between each individual cell can be calculated, and the changing trend of the voltage correlation coefficient can be used as an evaluation index for cell consistency.

[0011] Furthermore, in step S3, a scheduling instruction is generated according to a preset pairing rule, including: The anchor node receives the request information from the electric vehicle that initiated the emergency charging request and marks the electric vehicle that initiated the emergency charging request as a low-power electric vehicle. The anchor node traverses all the summarized health level labels and filters out each candidate electric vehicle whose health level label meets the power supply requirements. The anchor node determines at least one electric vehicle from the candidate electric vehicles based on the available power of each candidate electric vehicle and the power demand of the depleted electric vehicle. The anchor node generates the scheduling instruction, which carries the node identifier of the powered electric vehicle, the node identifier of the depleted electric vehicle, and the power supply allocated to each powered electric vehicle. The preset pairing rules record the power supply priority weights of each electric vehicle. These power supply priority weights are used to prioritize the electric vehicle when determining the power supply electric vehicle from the candidate power supply electric vehicles.

[0012] Furthermore, in step S4, a point-to-point charging operation is performed according to the scheduling instruction, and the anchor node records the electricity transaction information for this charging operation to its local ledger, including: The powered electric vehicle and the depleted electric vehicle establish a point-to-point charging connection based on the node identification information carried in the dispatch instruction. The electric vehicle supplying power transmits electrical energy to the depleted electric vehicle through the point-to-point charging connection, and collects metering data of the transmitted power in real time during the transmission process. When the transmitted power reaches the power allocated in the scheduling instruction or when a stop instruction is received from the anchor node, the powered electric vehicle disconnects the point-to-point charging connection from the depleted electric vehicle. The anchor node generates a power transaction record for this charging operation based on the node identifier of the powered electric vehicle, the node identifier of the depleted electric vehicle, the actual transmitted power, and the timestamp of the charging operation, and writes the power transaction record into the local ledger.

[0013] Furthermore, in S5, the preset pairing rules are updated, including: After the charging operation is completed, the anchor node sends a health level tag update request to both the powered electric vehicle and the depleted electric vehicle. In response to the update request, the powered electric vehicle and the depleted electric vehicle reassess the battery health status locally and generate an updated health level label, and upload the updated health level label to the anchor node; The anchor node will compare the received updated health level label with the health level labels uploaded by the powered electric vehicle and the depleted electric vehicle before this charging operation to determine the direction of change of the health level label of each electric vehicle. When the health level label of any of the electric vehicles changes in the direction of a decrease in level, the anchor node adjusts the power supply priority weight corresponding to the electric vehicle in the preset pairing rules by reducing the weight. When the health level label of any electric vehicle changes in the direction of level increase and its current power supply priority weight is lower than the standard weight value, the anchor node restores the power supply priority weight corresponding to the electric vehicle to the standard weight value.

[0014] Furthermore, in step S6, the electricity transaction information in the local ledger is uploaded to the cloud system for settlement, including: After the regional edge computing node detects that the grid voltage and frequency have returned to a preset normal range and remain stable for a preset time, it determines that the grid has recovered. The regional edge computing node performs a hash operation on each electricity transaction record in the local ledger to generate an integrity check code for the transaction records during the grid unavailability period. The regional edge computing node summarizes all electricity transaction information recorded in the local ledger during the period of grid unavailability and generates an emergency dispatch transaction summary record. The regional edge computing node uploads the emergency dispatch transaction summary record and the integrity verification code to the cloud system, so that the cloud system can settle fees or provide incentive points for each electric vehicle according to preset settlement rules.

[0015] Furthermore, in S6, controlling each of the electric vehicles to exit emergency dispatch and switch to a charging and discharging dispatch strategy under normal grid mode includes: After the regional edge computing node successfully uploads the electricity transaction information in the local ledger to the cloud system, it broadcasts an emergency dispatch exit notification to each electric vehicle in the hierarchical self-organizing network. After receiving the emergency dispatch exit notification, each of the electric vehicles disconnects its communication connection with the anchor node, exits the hierarchical self-organizing network, and restores its communication connection with the cloud system. The regional edge computing node switches its role from the anchor node back to the regional edge computing node, releases the network and computing resources occupied during the emergency dispatch, and returns the dispatch control of each electric vehicle to the cloud system; During the exit process, the regional edge computing node obtains the real-time state of charge (SOC) of each electric vehicle at the exit time, and uploads the real-time SOC as the initial state parameter of the grid normal mode scheduling strategy to the cloud system. Each of the electric vehicles receives the charging and discharging scheduling strategy under normal grid mode issued by the cloud system and performs routine charging and discharging operations.

[0016] The invention employing the above technical solution has the following advantages: This invention establishes a hierarchical self-organizing network between regional edge computing nodes as anchor nodes and various electric vehicles. Each electric vehicle locally assesses its battery health status and generates a health level label, which is then uploaded to the anchor node after being de-identified. The anchor node generates differentiated pairing and scheduling instructions based on the health level label and available power, prioritizing electric vehicles whose health level meets the power supply requirements as power suppliers and prohibiting the selection of electric vehicles that do not meet the requirements as power suppliers. Thus, without relying on the power grid and centralized cloud scheduling, it realizes orderly scheduling of off-grid emergency charging and discharging based on battery health status perception.

[0017] This invention collects voltage data of each cell in the battery pack locally from each electric vehicle, calculates a cell consistency evaluation index that characterizes the voltage consistency between cells, and generates a health level label based on this index. Compared with the existing technology that uses the overall health status of the battery pack as the evaluation basis, this invention refines the granularity of health assessment from the whole pack level to the consistency level between cells. It can more accurately identify potential safety risks inside the battery, and avoid vehicles that are subjected to heavy power supply tasks due to local overheating or accelerated failure of individual cells caused by cell inconsistency. This improves the safety and reliability of emergency power supply.

[0018] This invention enables each electric vehicle to perform coarse-grained processing on its health level label locally to remove specific numerical information, obtaining a desensitized health level label before uploading it to the anchor node. This effectively protects the privacy of the vehicle owner's sensitive battery data while realizing battery health status perception and scheduling, thereby avoiding the risk of privacy leakage due to the lack of a trusted third party in offline emergency scenarios. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0020] Figure 1 This is a flowchart illustrating a method for generating charging and discharging scheduling strategies for electric vehicles according to the present invention. Detailed Implementation

[0021] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0022] like Figure 1 As shown, the present invention provides a method for generating electric vehicle charging and discharging scheduling strategies, applied to a preset area including regional edge computing nodes and multiple electric vehicles with bidirectional charging and discharging capabilities. The method is characterized by including the following steps when the power grid is unavailable: S1. After detecting that the power grid is unavailable, the regional edge computing node acts as an anchor node to establish a hierarchical self-organizing network with each electric vehicle in the preset area and establish a communication connection.

[0023] In this embodiment, in S1, a hierarchical self-organizing network is established, and a communication connection is established, including: After detecting a power grid interruption, the regional edge computing node switches to emergency dispatch mode and broadcasts a network beacon to the preset area. After each electric vehicle receives the network and establishes a beacon, it sends a network access request to the regional edge computing node. The network access request carries the node identification information of the vehicle itself. The regional edge computing node registers the electric vehicle that sent the network access request as a mobile node based on the received network access requests, and registers itself as an anchor node, thus establishing a hierarchical self-organizing network with the anchor node as the upper-layer node and each mobile node as the lower-layer node. The anchor node establishes a communication connection with each mobile node through a preset short-range wireless communication protocol.

[0024] Specifically, when performing step S1, the regional edge computing node continuously monitors the voltage and frequency parameters of the power grid.

[0025] Let the preset voltage normal operating range be [ , The preset normal operating frequency range is [ , At any sampling time t, obtain the effective value of the voltage at that time. With frequency value When detected or When the normal operating range is exceeded, record that moment as the start of the anomaly. and begin accumulating the abnormal duration Δ Abnormal duration Δ The calculation formula is: ; Where t is the current sampling time, This is the starting point at which the effective voltage value or frequency value is first detected to exceed the corresponding normal operating range.

[0026] when Greater than the first preset duration When the regional edge computing node determines that the power grid is unavailable, it automatically switches to emergency dispatch mode. and These are the lower and upper limits of the preset normal voltage range, respectively. and These are the lower and upper limits of the preset normal frequency range, respectively. The above parameters are pre-configured in the regional edge computing nodes and can also be remotely distributed by the cloud system according to the power grid operation standards of the preset region, which is the first preset duration.

[0027] After switching to emergency dispatch mode, the regional edge computing node activates its built-in short-range wireless communication module to generate a network establishment beacon. The beacon carries the regional edge computing node's node identifier, network identifier, and emergency dispatch mode indicator. The regional edge computing node periodically broadcasts the network establishment beacon within a preset area, with a broadcast period of 1 to 5 seconds. Each electric vehicle within the preset area is equipped with a short-range wireless communication module matching the regional edge computing node and is in listening mode. When an electric vehicle receives the network establishment beacon, it parses the emergency dispatch mode indicator in the beacon to confirm that it has entered emergency dispatch mode. The electric vehicle generates a network access request, carrying its own node identifier information. The node identifier information is the vehicle identification number, the MAC address of the onboard communication module, or a pre-assigned charging pile number. Each electric vehicle sends its network access request to the regional edge computing node via its short-range wireless communication module.

[0028] Regional edge computing nodes in the preset network access time window It receives network access requests from various electric vehicles. The duration is 30 to 60 seconds. The calculation begins when the edge computing node first broadcasts the network beacon setup.

[0029] The regional edge computing node assigns a mobile node identifier to each electric vehicle sending a network access request based on the received network access requests, registers each electric vehicle as a mobile node, and simultaneously registers itself as an anchor node. A hierarchical self-organizing network structure is formed between the anchor node and each mobile node, with the anchor node as the upper-layer node and the mobile nodes as the lower-layer nodes. Communication connections between the anchor node and each mobile node are established through a pre-defined short-range wireless communication protocol. This short-range wireless communication protocol is a local direct-connection communication protocol that does not rely on external cellular network infrastructure.

[0030] The short-range wireless communication protocol is at least one of Wi-Fi Direct protocol or Bluetooth Mesh protocol of Bluetooth 5.0 and above.

[0031] Preferably, the Wi-Fi Direct protocol is used, leveraging its ability to support one-to-many networking and its independence from external access points to quickly establish communication connections between the anchor node and each mobile node in emergency scenarios. After the communication connection is established, the anchor node maintains a mapping table between the node identifier and the communication address of each mobile node. This mapping table is used to support the exchange of scheduling information between the anchor node and each mobile node in subsequent steps.

[0032] After completing step S1, a localized communication network is formed between each electric vehicle and the regional edge computing node within the preset area, which does not rely on external cellular networks and cloud systems, providing a communication foundation for the uploading of health level labels and the issuance of scheduling instructions in subsequent steps.

[0033] In one embodiment of the present invention, the determination of anchor nodes in a hierarchical self-organizing network is not limited to regional edge computing nodes alone. When a regional edge computing node fails to initiate network formation beacon due to its own failure or depletion of backup power, some electric vehicles that have entered emergency dispatch mode can also actively initiate network formation.

[0034] Specifically, when an electric vehicle (EV) does not receive a network formation beacon broadcast by an edge computing node within a preset listening time (e.g., 10 to 30 seconds), but confirms a power grid outage through its own sensors or communication module, the EV can proactively broadcast a network formation beacon, inviting other EVs in the area to form a decentralized, hierarchical, self-organizing network. At this time, each participating EV negotiates and determines one node as the anchor node from among the online nodes according to preset anchor node election rules, with the rest acting as mobile nodes. The anchor node election rules comprehensively consider at least one of the following factors: the node's current remaining battery power, the node's computing unit processing power, the estimated dwell time of the node in the preset area, and whether the node possesses the preset complete anchor node functional software. After the anchor node is elected, it broadcasts a confirmation message to each mobile node and updates its own node identifier to the anchor node identifier, establishing a hierarchical self-organizing network with the anchor node as the upper-level node and each mobile node as the lower-level node. The subsequent steps are executed by the elected anchor node.

[0035] S2. Each electric vehicle locally assesses the battery health status and generates a health level label. The desensitized health level label and the current available power are uploaded to the anchor node via a communication connection. The health level label must at least distinguish between meeting and not meeting the power supply requirements.

[0036] In this embodiment, in S2, each electric vehicle locally assesses its battery health status and generates a health level label, including: Each electric vehicle collects voltage data of each cell in the battery pack through its own battery management system, and obtains a cell consistency evaluation index that characterizes the voltage consistency between each cell based on the voltage data. Each electric vehicle compares its battery cell consistency assessment indicators with multiple preset consistency level thresholds, and generates a health level label based on the comparison results. The health level label includes at least a first level and a second level. The cell consistency assessment index corresponding to the first level meets the preset power supply safety requirements, while the cell consistency assessment index corresponding to the second level does not meet the preset power supply safety requirements.

[0037] In this embodiment, in S2, the desensitized health level tag and the current available power are uploaded to the anchor node via a communication connection, including: Each electric vehicle locally discretizes the generated health level label to remove specific numerical information from the cell consistency evaluation index and obtains a desensitized health level label. Each electric vehicle will upload its desensitized health level label and current available battery power to the anchor node via a communication connection.

[0038] In this embodiment, a cell consistency evaluation index characterizing the voltage consistency among individual cells is obtained based on voltage data, including: Calculate the voltage deviation between the voltage value of each individual cell and the average voltage value of the battery pack, and use the statistical value of the voltage deviation as the cell consistency evaluation index. Calculate the voltage ratio between the voltage value of each individual cell and the average voltage value of the battery pack, and determine the cell consistency evaluation index based on the statistical distribution characteristics of the voltage ratio; Alternatively, the voltage correlation coefficient between individual cells can be calculated, and the trend of the voltage correlation coefficient can be used as an indicator for evaluating cell consistency.

[0039] Specifically, each electric vehicle collects voltage data of each individual cell within the battery pack through its own battery management system. Assuming the battery pack consists of N cells connected in series, the voltage value of each cell obtained at the sampling time is denoted as... Where i = 1, 2, ..., N, represents the cell number. Average voltage of the battery pack. The calculation formula is: ; Each electric vehicle calculates a cell consistency evaluation index, characterizing the voltage consistency among individual battery cells, based on the collected voltage data of each battery cell. The calculation method for the cell consistency evaluation index includes at least one of the following three methods: Method 1: Calculate the voltage value of each individual cell. With the average voltage value of the battery pack Voltage deviation between , The statistical measure of the voltage deviation value corresponding to each individual cell is used as the cell consistency evaluation index. This statistical measure is the standard deviation of each voltage deviation value. The calculation formula is: ; in, This is the average of the voltage deviation values. .

[0040] Method 2: Calculate the voltage value of each individual battery cell. With the average voltage value of the battery pack voltage ratio between , The consistency evaluation index of the cells is determined based on the statistical distribution characteristics of the voltage ratio corresponding to each individual cell.

[0041] Method 3: Calculate the voltage correlation coefficient between individual battery cells, and use the trend of the voltage correlation coefficient as a battery cell consistency evaluation index. Specifically, collect the voltage time-series data of each individual battery cell within a preset time window, calculate the Pearson correlation coefficient between the voltage sequences of any two individual battery cells, and use the mean or minimum value of each correlation coefficient as a battery cell consistency evaluation index. Each electric vehicle compares the calculated battery cell consistency evaluation index with multiple preset consistency level thresholds, and generates a health level label based on the comparison results. Assume that the preset consistency level thresholds include at least the first threshold. Second threshold ,and < .

[0042] Preset consistency level threshold and The voltage rating can be determined based on the battery type and battery pack's rated voltage level through offline calibration or statistical analysis of historical operating data. In offline calibration, voltage data for each individual cell in the same type of battery pack with different aging levels is collected under standard operating conditions. The distribution range of the standard deviation of the voltage deviation is statistically analyzed, and the first pre-set proportional quantile of the distribution range is set as... The preset proportional quantile of the latter part of the distribution interval is set to .

[0043] In the statistical analysis method based on historical operating data, historical cell voltage data of each electric vehicle under normal use conditions are collected over a long period of time. The standard deviation of the voltage deviation at each moment is calculated, and the preset quantile of the historical statistical values ​​is used as the basis for analysis. and The value of .

[0044] For example, for a ternary lithium battery pack with a rated voltage of 400V, It can be set to a value in the range of 5mV to 10mV. It can be set to a value within the range of 15mV to 25mV. When the cell consistency evaluation index is based on the standard deviation of the voltage deviation value... When indicating, The smaller the value, the better the voltage consistency between the cells.

[0045] In this case, if If so, a first-level health level label is generated, corresponding to meeting the preset power supply safety requirements; if If the condition is not met, a second-level health level label will be generated. This second level corresponds to a failure to meet the preset power supply safety requirements. More thresholds can be set as needed to generate more granular health level labels.

[0046] After each electric vehicle generates a health level label locally, the health level label undergoes coarse-grained processing to remove specific numerical information from the cell consistency assessment indicators. Coarse-grained processing means retaining only the health level category information of the health level label, without including the specific calculated values ​​of the cell consistency assessment indicators.

[0047] The specific implementation of coarse-grained processing is as follows: After generating a health level label locally, the electric vehicle encodes the health level label in the form of an enumeration value, with the first level encoded as the first enumeration value and the second level encoded as the second enumeration value. When constructing the data packet uploaded to the anchor node, this data packet only contains the enumeration value field of the health level label and the current available power field, and does not contain the original calculated value of the cell consistency evaluation index. After receiving the data packet, the anchor node can only parse and obtain the level category and available power of the health level label, and cannot reverse-engineer the specific value of the cell consistency evaluation index. Each electric vehicle uploads the desensitized health level label after coarse-grained processing and the current available power to the anchor node through the communication connection established in step S1. The available power is the remaining available power value of the electric vehicle's current battery.

[0048] By generating health level labels through local assessment and uploading them in anonymized form, sensitive battery data for each electric vehicle is kept locally, with only the anonymized level labels uploaded. This approach achieves battery health status awareness and scheduling while protecting the privacy of vehicle owners.

[0049] S3. When an emergency charging request is received from any electric vehicle, the anchor node generates a scheduling instruction based on the received health level tags and available power according to the preset pairing rules.

[0050] In this embodiment, in step S3, a scheduling instruction is generated according to a preset pairing rule, including: The anchor node receives the request information from the electric vehicle that initiates the emergency charging request and marks the electric vehicle that initiates the emergency charging request as a low-power electric vehicle. The anchor node iterates through all the health level labels that have been collected and filters out the candidate electric vehicles whose health level labels meet the power supply requirements. Anchor nodes determine at least one electric vehicle to be supplied from among the candidate electric vehicles based on the available power of each candidate electric vehicle and the power demand of the depleted electric vehicle. Anchor nodes generate scheduling instructions, which carry the node identifiers of powered electric vehicles, the node identifiers of depleted electric vehicles, and the power supply allocated to each powered electric vehicle. The preset pairing rules record the power supply priority weights of each electric vehicle. These power supply priority weights are used to prioritize electric vehicles when determining which electric vehicle to supply power from the candidate electric vehicles.

[0051] Specifically, in step S2, after the anchor node receives the desensitized health level tags and available power uploaded by each electric vehicle, the anchor node maintains a scheduling information table for each networked electric vehicle within its jurisdiction. The scheduling information table includes the node identifier, health level tag, and current available power of each electric vehicle.

[0052] When an anchor node receives an emergency charging request from any electric vehicle, it extracts the node identifier of the requesting electric vehicle from the received request, marks the electric vehicle as a low-power electric vehicle, and records the node identifier and required electricity amount of the low-power electric vehicle in the scheduling information table. The required electricity amount is the amount of electricity needed for the low-power electric vehicle to reach the target state of charge at the current moment.

[0053] The anchor node iterates through the health level labels of each electric vehicle in the scheduling information table, filtering out candidate electric vehicles whose health level labels meet the power supply requirements. Let the total number of networked electric vehicles in the scheduling information table be M, and the health level label of the j-th electric vehicle be denoted as... Where j = 1, 2, ..., M. If If the first level is selected, then the j-th electric vehicle is included in the candidate set of powered electric vehicles.

[0054] The anchor node retrieves the available power of each candidate electric vehicle from the scheduling information table. Let the number of candidate electric vehicles be K, and the available power of the k-th candidate electric vehicle be denoted as [K]. Where k = 1, 2, ..., K. The electricity demand of a depleted electric vehicle is denoted as... Anchor nodes are determined based on the available power of each candidate electric vehicle. The electricity demand of electric vehicles with low power At least one electric vehicle is selected from the candidate electric vehicles for power supply.

[0055] The method for determining the electric vehicle to be supplied with electricity is as follows: If K=1, meaning there is only one candidate electric vehicle, then that candidate electric vehicle is directly determined as the electric vehicle to be supplied with electricity. If K>1, then the available power of each candidate electric vehicle is... The electric vehicles are sorted in descending order, starting with the candidate electric vehicles with the largest available power, and selected sequentially until the sum of the available power of all selected electric vehicles meets the power demand of the depleted electric vehicle. The selected candidate electric vehicles are designated as power supply electric vehicles, while the unselected candidate electric vehicles are reserved as backup power supply electric vehicles.

[0056] If the sum of the available electricity of all K candidate electric vehicles is less than the electricity demand of the depleted electric vehicle. Then all K candidate electric vehicles are determined as electric vehicles, and the power supply is allocated according to the proportion of the available power of each candidate electric vehicle to the total available power of the K candidate electric vehicles. The power supply allocated to the kth electric vehicle is denoted as . The calculation formula is: ; in, The amount of electricity required for a low-power electric vehicle. Let K be the available electricity of the k-th candidate electric vehicle, and let K be the sum of the available electricity of the K candidate electric vehicles. The available power supply for the m-th candidate electric vehicle.

[0057] When there are multiple candidate electric vehicles for power supply, the anchor node further obtains the cell consistency evaluation index corresponding to each candidate electric vehicle when generating the health level label in step S2, and prioritizes the electric vehicle whose cell consistency evaluation index meets the preset power supply safety requirements and has a higher health level as the power supply electric vehicle.

[0058] When there are multiple candidate electric vehicles and their available power is similar, the anchor node further obtains the power supply priority weight of each candidate electric vehicle recorded in the preset pairing rules. If the difference in available power between any two candidate electric vehicles is less than a preset threshold, the power supply priority weight will be prioritized. Higher-end electric vehicles serve as power sources.

[0059] Power supply priority weight Standard weight values As preset values, each electric vehicle's The process changes dynamically with step S5 during emergency dispatch. The anchor node generates a dispatch instruction, which carries the node identifiers of each powered electric vehicle, the node identifiers of the depleted electric vehicles, and the power supply allocated to each powered electric vehicle. The anchor node sends scheduling instructions to each powered electric vehicle and the electric vehicle with low power, and records the scheduling decision log for this emergency charging request. The scheduling decision log includes the time the emergency charging request was received, the node identifier of the electric vehicle with low power, the node identifier of each powered electric vehicle, and the allocated power supply for each powered electric vehicle.

[0060] S4. Electric vehicles identified as power suppliers by the dispatching instruction and low-power electric vehicles that initiate emergency charging requests will perform point-to-point charging operations according to the dispatching instruction, and the anchor node will record the electricity transaction information of this charging to the local ledger. In this embodiment, in S4, a point-to-point charging operation is executed according to the scheduling instruction, and the anchor node records the electricity transaction information of this charging operation to the local ledger, including: Electric vehicles with power supply and those with depleted power establish a point-to-point charging connection based on the node identification information carried in the dispatch instructions. Electric vehicles powered by electricity transmit electrical energy to depleted electric vehicles via point-to-point charging connections, and collect metering data of the transmitted electricity in real time during the transmission process. When the transmitted power reaches the power allocated in the scheduling instruction or when a stop instruction is received from the anchor node, the point-to-point charging connection between the powered electric vehicle and the depleted electric vehicle is disconnected. The anchor node generates a power transaction record for this charging operation based on the node identifier of the electric vehicle supplying power, the node identifier of the electric vehicle with depleted power, the actual amount of power transmitted, and the timestamp of the charging operation, and writes the power transaction record to the local ledger.

[0061] Specifically, after the anchor node generates the scheduling instruction in step S3, each powered electric vehicle and the depleted electric vehicle performs point-to-point charging operations according to the scheduling instruction, and the anchor node records the electricity transaction information of this charging to its local ledger. Each powered electric vehicle and the depleted electric vehicle receives the scheduling instruction sent by the anchor node and extracts the node identifier of each powered electric vehicle, the node identifier of the depleted electric vehicle, and the electricity allocated to each powered electric vehicle from the scheduling instruction.

[0062] Electric vehicles with supplied power and those with depleted power establish a point-to-point charging connection based on the node identification information carried in the dispatch instructions. The point-to-point charging connection is established as follows: the electric vehicles with supplied power and those with depleted power exchange connection parameters through their respective short-range wireless communication modules. These parameters include the physical connection port type, maximum allowable charging power, and supported charging protocol versions. Based on the exchanged connection parameters, the electric vehicles with supplied power and those with depleted power establish a physical connection through a bidirectional charging and discharging interface. The bidirectional charging and discharging interface supports both DC and AC charging and discharging modes.

[0063] When using DC charging / discharging mode, a physical connection is established between the powered electric vehicle (EV) and the depleted EV via a V2V DC charging cable. The plugs at both ends of this cable are matched to the discharge port of the powered EV and the charging port of the depleted EV, respectively. When using AC charging / discharging mode, the powered EV outputs AC power through the onboard V2L discharge function, and the depleted EV receives AC power through the onboard charger. Preferably, DC V2V mode is used in emergency scenarios to achieve higher energy transfer efficiency. When multiple powered EVs are present, the depleted EV establishes point-to-point charging connections with each powered EV sequentially, executing charging operations according to the power supply sequence allocated to each powered EV in the dispatch instructions.

[0064] The electric vehicle supplying power transfers energy to the depleted electric vehicle via a point-to-point charging connection. During the transfer, the battery management system of the electric vehicle supplying power collects real-time metering data of the transferred energy. Let the allocated power supply for the k-th electric vehicle be denoted as . The cumulative amount of electricity transmitted by the electric vehicle at time t is denoted as . Cumulative transmitted power The calculation formula is: ; in, This is the start time of charging. The real-time transmission power of the k-th powered electric vehicle at time τ is obtained by the battery management system of the powered electric vehicle through sampling.

[0065] Electric vehicles continuously judge during power transmission Has the allocated power supply been reached? .when achieve When the charging process is complete, the powered electric vehicle automatically stops power transmission and sends a charging completion notification to the anchor node. Alternatively, if the anchor node determines that the charging operation needs to be terminated early based on the emergency dispatch status, the anchor node sends a stop command to both the powered electric vehicle and the depleted electric vehicle. Upon receiving the stop command, the powered electric vehicle stops power transmission.

[0066] After the powered electric vehicle stops transmitting power, the point-to-point charging connection between the powered electric vehicle and the depleted electric vehicle is broken. Both the powered electric vehicle and the depleted electric vehicle upload their respective metering data of the actual amount of electricity transmitted during this charging operation to the anchor node. Let the actual amount of electricity transmitted by the k-th powered electric vehicle be... The actual amount of electricity received by a low-power electric vehicle is .

[0067] Anchor nodes receive the actual transmitted power from each powered electric vehicle. The actual received electricity transmitted by the depleted electric vehicle The anchor node is determined based on the node identifier of the electric vehicle supplying power, the node identifier of the electric vehicle with depleted power, and the actual amount of electricity transmitted during this charging operation. The system generates a power transaction record for this charging operation by including the start and end timestamps of the charging operation. The power transaction record includes the following fields: transaction identifier, node identifier of the powered electric vehicle, node identifier of the depleted electric vehicle, and actual power transferred. The charging start time and charging end time are specified. The anchor node writes the electricity transaction records to its local ledger, which is a database file maintained in the anchor node's local storage device that records energy transactions during emergency dispatch periods.

[0068] Each time a point-to-point charging operation is completed, the anchor node generates a corresponding electricity transaction record and writes it to its local ledger. All electricity transaction records during the period of grid unavailability constitute the complete transaction data for this emergency dispatch, which is used for uploading and clearing after the grid is restored in step S6.

[0069] S5. After this charging operation is completed, obtain the real-time health level label change information of the power supply party and the power-deficient party, and update the preset pairing rules accordingly. In this embodiment, in S5, the preset pairing rules are updated, including: After this charging operation is completed, the anchor node sends health level label update requests to both the powered electric vehicle and the depleted electric vehicle. In response to the update request, powered electric vehicles and depleted electric vehicles reassess the battery health status locally and generate updated health level labels, then upload the updated health level labels to the anchor node. The anchor node will compare the received updated health level label with the health level labels uploaded by the powered electric vehicle and the depleted electric vehicle before this charging operation to determine the direction of change of the health level label of each electric vehicle. When the health level label of any electric vehicle changes in the direction of a decrease in level, the anchor node adjusts the power supply priority weight corresponding to that electric vehicle in the preset pairing rules by reducing the weight; When the health level label of any electric vehicle changes in the direction of level increase and its current power supply priority weight is lower than the standard weight value, the anchor node restores the power supply priority weight of the electric vehicle to the standard weight value.

[0070] Specifically, after the point-to-point charging operation is completed in step S4, the anchor node initiates the update process of the preset pairing rules. Based on the node identifiers of the powered electric vehicle and the depleted electric vehicle recorded in this charging operation, the anchor node sends health level tag update requests to both the powered electric vehicle and the depleted electric vehicle.

[0071] The health level label update request carries the node identifier of the anchor node and the transaction identifier of this charging operation. After receiving the health level label update request, the powered electric vehicle and the depleted electric vehicle re-execute the health level label generation process in step S2 locally.

[0072] Specifically, each electric vehicle re-collects the voltage data of each cell in the battery pack through its own battery management system, calculates the cell consistency evaluation index at the current moment, compares the recalculated cell consistency evaluation index with multiple preset consistency level thresholds, and generates an updated health level label. Electric vehicles with power and those with depleted power upload the updated health level label to the anchor node via a communication connection.

[0073] The anchor node receives updated health level labels uploaded by both powered and depleted electric vehicles. Let the health level label uploaded to the anchor node by the powered electric vehicle before this charging operation be... After this charging operation is completed, the updated and uploaded health level label will be... Let the health level label uploaded to the anchor node by the depleted electric vehicle before this charging operation be... After this charging operation is completed, the updated and uploaded health level label will be... .

[0074] The anchor node compares the updated health level label with the health level label uploaded before this charging operation to determine the direction of change for each electric vehicle's health level label. The direction of change includes three scenarios: level increase, level decrease, and no change. For electric vehicles being charged, the comparison... and .like The corresponding battery health level is higher than The corresponding battery health level determines the direction of the health rating label change for the powered electric vehicle, indicating an upward trend. If... The corresponding battery health level is lower than If the corresponding battery health level is determined, the health rating label of the powered electric vehicle will be determined to be decreasing. If the corresponding battery health levels are the same, the rating will be determined to remain unchanged.

[0075] For electric vehicles with depleted power, the same comparison method as for electrically powered electric vehicles is used. and To determine the direction of change in the health rating labels of electric vehicles with low battery levels.

[0076] When the health level label of any electric vehicle changes in a downward direction, the anchor node adjusts the power supply priority weight corresponding to that electric vehicle in the preset pairing rules. The power supply priority weight represents the priority of that electric vehicle in being selected as the power supplier in subsequent emergency charging requests.

[0077] Let the power supply priority weight of the i-th electric vehicle in the preset pairing rules be . , The initial value is preset to the standard weight value. When the health level label of the i-th electric vehicle is detected to be decreasing, the anchor node reduces the power supply priority weight corresponding to that electric vehicle. The adjusted power supply priority weight is shown below. The calculation formula is: ; in, The power supply priority weight before adjustment The attenuation coefficient is... The value of is between zero and one. The specific value is set according to the security preferences of the emergency dispatch scenario; the higher the security preference, the lower the threshold. The smaller the value, the greater the decrease in the probability that an electric vehicle with a declining health level will be selected as the power supplier.

[0078] Preferably, A constant value between 0.5 and 0.8 is used. After the power supply priority weight is reduced, the probability that the electric vehicle will be preferentially selected as the power supplier by the anchor node in subsequent emergency charging requests decreases accordingly.

[0079] When the health level label of the i-th electric vehicle is detected to be changing in the direction of level increase, if the current power supply priority weight Less than the standard weight value The anchor node restores the power supply priority weight of the electric vehicle to [the specified value]. .

[0080] The anchor node records the updated power supply priority weights into the preset pairing rules, which are used in step S3 to determine the priority order of electric vehicles when an emergency charging request is received. Through the above feedback loop, the preset pairing rules can adaptively adjust according to the actual changes in the battery health status of each electric vehicle after providing charging services, continuously optimizing the rationality and safety of emergency dispatch.

[0081] In one embodiment of the present invention, to ensure the reliability of the emergency dispatch process, the hierarchical self-organizing network supports a dynamic detection and handover recovery mechanism for anchor node failures. This mechanism includes the following steps: Heartbeat detection: During normal operation, the anchor node periodically broadcasts heartbeat messages to each mobile node at a preset heartbeat cycle. These heartbeat messages carry the anchor node's node identifier and current timestamp. Each mobile node maintains a heartbeat timeout timer; if it does not receive any message from the anchor node within Q consecutive heartbeat cycles, it determines that the current anchor node has failed.

[0082] Where Q is a preset positive integer, and the typical value of Q is between 3 and 5.

[0083] Re-election: When a mobile node detects an anchor node failure, it broadcasts a failure declaration to the network, initiating a new anchor node election process. Upon receiving the failure declaration, each online mobile node reassesses its capabilities, including remaining battery power, computing power, and dwell time, according to pre-defined anchor node election rules. The mobile node with the highest capability value is then selected as the new anchor node through negotiation. After the election, the new anchor node broadcasts an anchor node handover notification to all mobile nodes, and each mobile node updates its maintained anchor node identifier and communication address mapping.

[0084] State Backup and Recovery: To ensure a smooth takeover by the new anchor node, the original anchor node periodically backs up critical emergency dispatch status information to at least one designated backup node during normal operation. This critical status information includes: a list of currently online mobile nodes, health level labels for each mobile node, available power, the latest copy of the local ledger, and logs of incomplete dispatch transactions.

[0085] The selection criteria for designated backup nodes include: sufficient remaining battery power, adequate computing and storage capacity, and stable network status. When the original anchor node fails and a new anchor node is generated, the new anchor node first checks whether each mobile node stores the critical status information of the most recent backup of the original anchor node. If the backup information exists, the new anchor node restores the emergency dispatch status and local ledger data from the backup information. If the backup information does not exist, the new anchor node re-initiates a request to each mobile node to report health level tags and available power, and reconstructs the dispatch status based on the latest reported data. After the status is restored, the new anchor node takes over from the original anchor node and continues to execute the emergency dispatch tasks in steps S2 to S5.

[0086] S6. When the regional edge computing node detects that the power grid has been restored, it uploads the electricity transaction information in the local ledger to the cloud system for settlement, and controls each electric vehicle to exit the emergency dispatch and switch to the charging and discharging dispatch strategy in the normal power grid mode.

[0087] In this embodiment, in step S6, the electricity transaction information in the local ledger is uploaded to the cloud system for settlement, including: Once the regional edge computing node detects that the grid voltage and frequency have returned to a preset normal range and remained stable for a preset duration, it determines that the grid has recovered. The regional edge computing node performs hash operations on each electricity transaction record in the local ledger to generate an integrity check code for the transaction records during the grid unavailability period; The regional edge computing node will aggregate all electricity transaction information recorded in the local ledger during the period of grid unavailability and generate an emergency dispatch transaction summary record; The regional edge computing nodes upload the emergency dispatch transaction summary record and integrity verification code to the cloud system, so that the cloud system can settle fees or provide incentive points for each electric vehicle according to the preset settlement rules.

[0088] In this embodiment, in S6, controlling each electric vehicle to exit emergency dispatch and switch to the charging and discharging dispatch strategy under normal grid mode includes: After the electricity transaction information of the regional edge computing node in the local ledger is successfully uploaded to the cloud system, an emergency dispatch exit notification is broadcast to each electric vehicle in the hierarchical self-organizing network. After receiving the emergency dispatch exit notification, each electric vehicle disconnects its communication connection with the anchor node, exits the hierarchical self-organizing network, and restores its communication connection with the cloud system. The regional edge computing node switches its role from anchor node back to regional edge computing node, releasing the network and computing resources occupied during emergency dispatch, and returning the dispatch control of each electric vehicle to the cloud system; During the exit process, the regional edge computing node obtains the real-time state of charge (SOC) of each electric vehicle at the exit time and uploads the real-time SOC as the initial state parameter of the grid normal mode scheduling strategy to the cloud system. Each electric vehicle receives the charging and discharging scheduling strategy under normal grid conditions from the cloud system and executes routine charging and discharging operations.

[0089] Specifically, the regional edge computing nodes continuously monitor the voltage and frequency parameters of the power grid. At any sampling time t, they obtain the effective voltage value at that time. With frequency value Let the preset voltage operating range be []. , The preset normal operating frequency range is... .

[0090] When detected and Record this moment as the recovery start moment. and began to accumulate the duration of recovery stability. Duration of recovery to stable condition The calculation formula is: ; when At that time, the regional edge computing node determines that the power grid has recovered. Among them, For the second preset duration, The value is greater than the first preset duration used in step S1 to determine the unavailability of the power grid. This is to avoid frequent switching of dispatch modes caused by frequent changes in power grid status.

[0091] After the regional edge computing node determines that the power grid has been restored, it performs a hash operation on each electricity transaction record in the local ledger to generate an integrity check code for the transaction records during the period of power grid unavailability.

[0092] Suppose there are P electricity transaction records in the local ledger, arranged in chronological order of charging operation completion time. .

[0093] The regional edge computing node concatenates P electricity transaction records into a transaction data string. The splicing method involves sequentially connecting the field values ​​of each electricity transaction record in chronological order.

[0094] The edge computing nodes in the region use a preset hash function H to process the transaction data string. Perform hash operations to generate integrity check codes. The calculation formula is: ; The preset hash function H is the SHA-256 hash algorithm. Integrity check code. This is used by the cloud system to verify the integrity of transaction records during emergency dispatch after receiving them, preventing transaction data from being tampered with during storage or transmission.

[0095] The regional edge computing node summarizes all electricity transaction information recorded in the local ledger during the period when the power grid is unavailable, and generates an emergency dispatch transaction summary record.

[0096] The emergency dispatch transaction summary record includes the following information: the start and end times of the grid unavailability period, the node identifier of each electric vehicle, the cumulative power supply and power reception of each electric vehicle as a power supplier and power deficit provider during this emergency dispatch, and detailed data of each power transaction record. The regional edge computing node will then link the emergency dispatch transaction summary record with an integrity check code. Upload them to the cloud system together.

[0097] After receiving the data, the cloud system first recalculates the checksum for each electricity transaction record in the emergency dispatch transaction summary record using the same preset hash function H. Then, it compares the recalculated checksum with the integrity checksum uploaded by the regional edge computing node. Compare them.

[0098] If the two match, it is determined that the transaction record has not been tampered with, and the cloud system will settle the fees or provide incentive points for each electric vehicle according to the preset settlement rules.

[0099] The pre-set settlement rules include: for electric vehicles that act as power suppliers during emergency dispatch, electricity fees will be deducted or charging points will be awarded based on their cumulative electricity supply at a pre-set unit electricity compensation price; for electric vehicles that act as power-deficient parties, fees will be settled based on their cumulative electricity received at a pre-set unit electricity billing price.

[0100] After successfully uploading the emergency dispatch transaction summary record and integrity verification code to the cloud system, the regional edge computing node initiates the exit and switching process. The regional edge computing node broadcasts an emergency dispatch exit notification to all electric vehicles within the hierarchical self-organizing network.

[0101] The emergency dispatch exit notification carries the node identifier of the regional edge computing node and a power grid restoration indicator. After receiving the emergency dispatch exit notification, each electric vehicle parses the power grid restoration indicator to confirm that the power grid has returned to normal.

[0102] Each electric vehicle disconnects from the anchor node, exits the hierarchical self-organizing network, and restores its communication connection with the cloud system. The communication connection with the cloud system is established via a cellular mobile communication module. The regional edge computing node switches its role from anchor node back to regional edge computing node, releasing the network and computing resources occupied during emergency dispatch. Network resources include the communication bandwidth and connection permissions of the short-range wireless communication module, while computing resources include processor computing power and memory storage space. The regional edge computing node returns the dispatch control of each electric vehicle to the cloud system, which then takes over the subsequent charging and discharging dispatch management.

[0103] During the exit process, the edge computing nodes of the region acquire the real-time State of Charge (SOC) of each electric vehicle at the exit time. Let the real-time SOC of the i-th electric vehicle at the exit time be... , After the regional edge computing nodes send SOC query requests to each electric vehicle, the battery management system of each electric vehicle reads and uploads the information. The regional edge computing nodes then display the real-time state of charge (SOC) of each electric vehicle. The initial state parameters for the power grid's normal mode dispatch strategy are uploaded to the cloud system. The cloud system uses... Given the initial state of charge (SOC) values ​​of each electric vehicle, generate a charging and discharging scheduling strategy under normal grid conditions.

[0104] Each electric vehicle receives the charging and discharging scheduling strategy under normal grid conditions from the cloud system and executes routine charging and discharging operations. These routine operations include orderly charging based on time-of-use pricing signals, participating in grid demand response, or performing V2G reverse discharging. At this point, the entire emergency dispatch process is complete.

[0105] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for generating charging and discharging scheduling strategies for electric vehicles, applied to a preset region including regional edge computing nodes and multiple electric vehicles with bidirectional charging and discharging capabilities, characterized in that, Under conditions where the power grid is unavailable, including: S1. After the edge computing node detects the power grid unavailability condition, it acts as an anchor node to establish a hierarchical self-organizing network with each electric vehicle in the preset area and establishes a communication connection. S2. Each electric vehicle locally assesses the battery health status and generates a health level label. The desensitized health level label and the current available power are uploaded to the anchor node through the communication connection. The health level label at least distinguishes between meeting the power supply requirements and not meeting the power supply requirements. S3. When an emergency charging request initiated by any electric vehicle is received, the anchor node generates a scheduling instruction based on the received health level tags and available power according to a preset pairing rule. S4. The electric vehicle identified as the power supplier by the scheduling instruction and the depleted electric vehicle that initiated the emergency charging request perform point-to-point charging operations according to the scheduling instruction, and the anchor node records the electricity transaction information of this charging to the local ledger. S5. After the charging operation is completed, obtain the real-time health level tag change information of the power supply party and the power-deficient party, and update the preset pairing rules accordingly. S6. When the edge computing node of the region detects that the power grid has been restored, it uploads the electricity transaction information in the local ledger to the cloud system for settlement, and controls each of the electric vehicles to exit the emergency dispatch and switch to the charging and discharging dispatch strategy in the normal power grid mode.

2. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 1, characterized in that, In S1, a hierarchical self-organizing network is established, and communication connections are established, including: Upon detecting a power grid interruption, the regional edge computing node switches to emergency dispatch mode and broadcasts a network beacon to the preset area. After receiving the network construction beacon, each of the electric vehicles sends a network access request to the regional edge computing node, and the network access request carries the node identification information of the vehicle. The edge computing node of the region registers the electric vehicle that sent the network access request as a mobile node according to the received network access requests, and registers itself as an anchor node, thereby establishing a hierarchical self-organizing network with the anchor node as the upper-layer node and each of the mobile nodes as the lower-layer node; The anchor node and each of the mobile nodes establish a communication connection through a preset short-range wireless communication protocol.

3. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 1, characterized in that, In S2, each of the electric vehicles locally assesses its battery health status and generates a health level label, including: Each of the electric vehicles collects voltage data of each cell in the battery pack through its own battery management system, and obtains a cell consistency evaluation index that characterizes the voltage consistency between each cell based on the voltage data; Each electric vehicle compares the cell consistency evaluation index with multiple preset consistency level thresholds, and generates the health level label based on the comparison results; The health level label includes at least a first level and a second level. The cell consistency evaluation index corresponding to the first level meets the preset power supply safety requirements, while the cell consistency evaluation index corresponding to the second level does not meet the preset power supply safety requirements.

4. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 3, characterized in that, In step S2, the anonymized health level tag and the current available power are uploaded to the anchor node via the communication connection, including: Each of the electric vehicles locally discretizes the generated health level label to remove the specific numerical information in the cell consistency evaluation index, and obtains the desensitized health level label. Each electric vehicle uploads the desensitized health level label and the current available power to the anchor node via the communication connection.

5. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 3, characterized in that, The process of obtaining cell consistency evaluation indicators characterizing the voltage consistency among individual cells based on the voltage data includes: Calculate the voltage deviation between the voltage value of each individual cell and the average voltage value of the battery pack, and use the statistical value of the voltage deviation as the cell consistency evaluation index. Calculate the voltage ratio between the voltage value of each individual cell and the average voltage value of the battery pack, and determine the cell consistency evaluation index based on the statistical distribution characteristics of the voltage ratio; Alternatively, the voltage correlation coefficient between each individual cell can be calculated, and the changing trend of the voltage correlation coefficient can be used as an evaluation index for cell consistency.

6. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 1, characterized in that, In step S3, a scheduling instruction is generated according to a preset pairing rule, including: The anchor node receives the request information from the electric vehicle that initiated the emergency charging request and marks the electric vehicle that initiated the emergency charging request as a low-power electric vehicle. The anchor node traverses all the summarized health level labels and filters out each candidate electric vehicle whose health level label meets the power supply requirements. The anchor node determines at least one electric vehicle from the candidate electric vehicles based on the available power of each candidate electric vehicle and the power demand of the depleted electric vehicle. The anchor node generates the scheduling instruction, which carries the node identifier of the powered electric vehicle, the node identifier of the depleted electric vehicle, and the power supply allocated to each powered electric vehicle. The preset pairing rules record the power supply priority weights of each electric vehicle. These power supply priority weights are used to prioritize the electric vehicle when determining the power supply electric vehicle from the candidate power supply electric vehicles.

7. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 6, characterized in that, In step S4, a point-to-point charging operation is performed according to the scheduling instruction, and the anchor node records the electricity transaction information for this charging operation to its local ledger, including: The powered electric vehicle and the depleted electric vehicle establish a point-to-point charging connection based on the node identification information carried in the dispatch instruction. The electric vehicle supplying power transmits electrical energy to the depleted electric vehicle through the point-to-point charging connection, and collects metering data of the transmitted power in real time during the transmission process. When the transmitted power reaches the power allocated in the scheduling instruction or when a stop instruction is received from the anchor node, the powered electric vehicle disconnects the point-to-point charging connection from the depleted electric vehicle. The anchor node generates a power transaction record for this charging operation based on the node identifier of the powered electric vehicle, the node identifier of the depleted electric vehicle, the actual transmitted power, and the timestamp of the charging operation, and writes the power transaction record into the local ledger.

8. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 6, characterized in that, In step S5, the preset pairing rules are updated, including: After the charging operation is completed, the anchor node sends a health level tag update request to both the powered electric vehicle and the depleted electric vehicle. In response to the update request, the powered electric vehicle and the depleted electric vehicle reassess the battery health status locally and generate an updated health level label, and upload the updated health level label to the anchor node; The anchor node will compare the received updated health level label with the health level labels uploaded by the powered electric vehicle and the depleted electric vehicle before this charging operation to determine the direction of change of the health level label of each electric vehicle. When the health level label of any of the electric vehicles changes in the direction of a decrease in level, the anchor node adjusts the power supply priority weight corresponding to the electric vehicle in the preset pairing rules by reducing the weight. When the health level label of any electric vehicle changes in the direction of level increase and its current power supply priority weight is lower than the standard weight value, the anchor node restores the power supply priority weight corresponding to the electric vehicle to the standard weight value.

9. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 1, characterized in that, In step S6, the electricity transaction information in the local ledger is uploaded to the cloud system for settlement, including: After the regional edge computing node detects that the grid voltage and frequency have returned to a preset normal range and remain stable for a preset time, it determines that the grid has recovered. The regional edge computing node performs a hash operation on each electricity transaction record in the local ledger to generate an integrity check code for the transaction records during the grid unavailability period. The regional edge computing node summarizes all electricity transaction information recorded in the local ledger during the period of grid unavailability and generates an emergency dispatch transaction summary record. The regional edge computing node uploads the emergency dispatch transaction summary record and the integrity verification code to the cloud system, so that the cloud system can settle fees or provide incentive points for each electric vehicle according to preset settlement rules.

10. The method for generating a charging and discharging scheduling strategy for an electric vehicle according to claim 1, characterized in that, In step S6, controlling each electric vehicle to exit emergency dispatch and switch to a charging and discharging dispatch strategy under normal grid mode includes: After the regional edge computing node successfully uploads the electricity transaction information in the local ledger to the cloud system, it broadcasts an emergency dispatch exit notification to each electric vehicle in the hierarchical self-organizing network. After receiving the emergency dispatch exit notification, each of the electric vehicles disconnects its communication connection with the anchor node, exits the hierarchical self-organizing network, and restores its communication connection with the cloud system. The regional edge computing node switches its role from the anchor node back to the regional edge computing node, releases the network and computing resources occupied during the emergency dispatch, and returns the dispatch control of each electric vehicle to the cloud system; During the exit process, the regional edge computing node obtains the real-time state of charge (SOC) of each electric vehicle at the exit time, and uploads the real-time SOC as the initial state parameter of the grid normal mode scheduling strategy to the cloud system. Each of the electric vehicles receives the charging and discharging scheduling strategy under normal grid mode issued by the cloud system and performs routine charging and discharging operations.

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